“You don’t need to buy software anymore. Just build it yourself.”

If you spend any time on X or LinkedIn, you’ve seen this pitch. The founders of the hottest AI companies are telling us that coding is dead, natural language is the new programming, and anyone can build a custom CRM or support system over the weekend.

It’s a compelling narrative. It’s also completely contradicted by their own actions.

I analyzed the internal tech stacks of the biggest names in AI—OpenAI, Anthropic, Replit, Lovable, and Bolt.new—using BuiltWith data. What I found is a massive gap between what they sell and what they do. These com

panies, armed with the smartest engineers on the planet and the most advanced AI models ever created, are not building their own internal tools.

They are buying them. Just like everyone else.

Here is the data-backed truth about the “build anything” myth, and the pragmatic lessons every founder needs to learn from it.

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The Data: What AI Companies Actually Use

When you look under the hood of these AI juggernauts, you don’t find custom-built, AI-generated internal tools. You find standard SaaS subscriptions.

About the data: BuiltWith is a Sydney-based web intelligence platform founded in 2007 that crawls millions of websites to detect the technologies they run — used by sales teams, analysts, and researchers worldwide as a standard source for tech stack data. It does quarterly crawls across its database, making it a reliable but point-in-time snapshot rather than a live feed.

Let’s break this down by category to see exactly where these companies choose to spend their money instead of their engineering time.

1. Customer Support: The Intercom Dominance

If there’s one area where you’d expect AI companies to build their own solutions, it’s customer support. After all, answering questions is what LLMs do best. Yet, the data shows a complete reliance on established vendors.

Anthropic didn’t build a custom support bot from scratch. They bought Intercom’s service and implemented FinAI

In a recent case study by FinAI, Emily Lampert, Head of Product Support at Anthropic, noted that Fin automatically resolved tens of thousands of customer queries, achieving a 50.8% resolution rate in just over a month [1].

Why didn’t Anthropic build this themselves? This is what their Product Support Operations said

They chose speed, trust, and Intercom’s deep expertise in customer service workflows over the vanity of building it in-house.

Make a note of this — “Deep Expertise in Customer Service”

Check out the case study

2. Project Management: The Linear Standard

Managing complex engineering projects requires robust tools. You might think companies building the future of software would create their own issue trackers. They don’t.

OpenAI uses Linear. They scaled it from a small trial to over 3,000 users across the organization [2]. Before Linear, cross-team collaboration at OpenAI was chaotic, with different teams using different tools.

Gabriel Peal, an engineer at OpenAI, explained the appeal:

They chose Linear because it remained fast and performant at scale, reducing the friction of creating and modifying tickets. They bought a tool that set a standard for quality craftsmanship, rather than wasting time building a mediocre internal clone.

Most of the Top AI companies you know are their customers - Cursor, Vercel, Perplexity, Lovable, Replit etc..

3. Documentation: The Mintlify Takeover

Developer documentation is critical for AI platforms. Surely, companies like Anthropic and OpenAI build their own custom documentation sites?

Wrong again.

Anthropic uses Mintlify to serve documentation to over 1.5 million developers every month [3]. When launching the Claude Sonnet 3.5 model, they needed a platform that could scale instantly without adding engineering overhead.

Instead of building infrastructure, Anthropic partnered with Mintlify to co-develop AI-native features like LLMs.txt, improving how their docs are ingested by other AI tools.

4. CRM and HR: The Enterprise Staples

The pattern continues across every major business function.

OpenAI, Anthropic, and Replit all use Salesforce. Bolt.new uses HubSpot. For hiring, they rely on Greenhouse and Rippling.

These are complex, heavily regulated, and feature-dense domains.

Building a CRM is easy. Maintaining a CRM that handles enterprise sales pipelines, integrates with marketing automation, and provides reliable forecasting is a nightmare.

The Pragmatism Behind the Hypocrisy

The irony is thick. The companies selling the dream of “code your own software” are making the exact opposite choice internally. But when you look past the marketing, their decisions make perfect business sense.

Here is why the smartest engineers in the world choose to buy:

1. Maintenance is the Killer
Building version 1.0 of a tool is easy, especially with AI. Maintaining it is brutal. Internal tools require constant updates, security patches, bug fixes, and feature additions. Every hour an engineer spends fixing an internal CRM is an hour they aren’t spending improving Claude or ChatGPT.

2. It’s Not Their Moat
Anthropic’s competitive advantage is their AI models, not their customer support software. Salesforce has 25 years of expertise in CRM workflows. Intercom has spent a decade perfecting customer communication. Why would an AI company try to rebuild that expertise from scratch?

3. Speed to Market
As the Anthropic/Mintlify case study shows, buying allows companies to move incredibly fast. You can deploy an enterprise-grade documentation site in days. Building it takes months.

The Build vs. Buy Framework for Founders

This data provides a wake-up call for founders and builders. Just because you can build it doesn’t mean you should.

Here is a pragmatic framework for making the Build vs. Buy decision:

When to BUILD:

• It is your core differentiator. If the software directly impacts your unique value proposition, build it. (e.g., OpenAI building their own model training infrastructure).

• Standard tools cannot handle your specific workflow. Your operations are so unique that modifying a SaaS tool would be harder than building from scratch.

• You need absolute control over data security. Regulatory or compliance reasons demand that the data never touches a third-party server.

When to BUY:

• It is a commodity function. CRM, HR, customer support, and project management are solved problems. Buy the solution.

• The vendor has deeper domain expertise. You cannot out-build Salesforce on CRM features or Intercom on support routing.

• You need to move fast. If time-to-market matters, buying is always faster than building.

• You want to avoid maintenance debt. Let the vendor worry about uptime, security patches, and feature requests.

The Verdict

The narrative that AI will replace all SaaS companies because “everyone will just build their own software” is a myth. The very companies creating this AI are proving it false every day.

They understand a fundamental truth of software engineering: the cost of software isn’t in the building; it’s in the maintaining.

Use AI to build your core product faster. Use AI to prototype new ideas. But when it comes to running your business, follow the lead of OpenAI and Anthropic. Buy the best tools available, and focus your engineering talent on the problems only you can solve.

References

[1] Intercom. “Anthropic Customer Story.” https://fin.ai/customers/anthropic
[2] Linear. “OpenAI Customer Story.” https://linear.app/customers/openai
[3] Mintlify. “Anthropic Customer Story.” https://www.mintlify.com/customers/anthropic

If you read this all the way through, you will end up with a working blueprint for a second brain that compounds over time. The goal is to replace scattered PDFs, browser tabs, notes, and chat threads with a system that keeps getting better as you add material.

That is what makes Andrej Karpathy’s llm-wiki gist worth studying. GitHub shows the gist was first published on April 4, 2026.1 The idea is simple and strong: instead of asking an LLM to rediscover your knowledge from raw files every time, you let it maintain a persistent wiki that keeps improving as new sources come in.1

This guide turns that pattern into a practical setup for TheToolNerd readers. The stack is straightforward.

Use Obsidian as the local knowledge base, use an agentic coding assistant such as Claude Code to maintain the wiki, and use a simple ingest workflow so every useful article, note, or transcript has a home.

If you already work with AI coding tools, this also fits well with my earlier write-ups on Claude Code and Windsurf. For adjacent tools, browse the TheToolNerd directory.

What you will get from this guide

Before you start building, it helps to know what this setup is supposed to give you.

Why this pattern matters

Most people still use LLMs like temporary search layers. They upload documents, ask a question, get an answer, and move on. That works for one-off retrieval, but it does not build memory. Karpathy’s argument is different. He describes a persistent, LLM-maintained wiki that sits between you and your raw documents, so synthesis happens once and then gets updated over time instead of being recomputed from scratch on every question.

“The wiki is a persistent, compounding artifact. The cross-references are already there. The contradictions have already been flagged. The synthesis already reflects everything you’ve read.” - Andrej Karpathy

That is the real shift. You stop treating the model like a temporary answer machine and start using it as a maintainer for a knowledge system.

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The core idea in plain English

Keep your raw material raw, and let the model build the layer you actually read.

Your articles, notes, screenshots, transcripts, and PDFs live in a raw/ folder. Those files remain the source of truth. The model does not rewrite them. It reads them and updates a separate wiki made of Markdown pages. That wiki becomes the place where tools, concepts, companies, ideas, and cross-references live.

When you ask a question like “What AI editors have I covered?” the model no longer has to sift through untouched files every single time. It can check the wiki, read the index, follow the relevant pages, and answer from a body of knowledge that has already been organized.

The architecture without the fluff

Karpathy keeps the pattern intentionally abstract, but the practical version is easy to follow once you break it into layers.1

What matters most is the separation of responsibilities. raw/ is where source material lives. wiki/ is where the model writes pages. CLAUDE.md or AGENTS.md is where the operating rules live. index.md tells the model what pages already exist, and log.md tells it what happened recently.

Without that structure, you have a chatbot browsing a folder. With it, you have a repeatable maintenance workflow. How the system works

What makes the LLM Wiki pattern practical is that the operating model stays small. Karpathy reduces it to three operations: ingest, query, and lint.1 That is the right level of complexity for a first version.

Ingest means adding a new source to the raw collection and asking the model to process it. The model reads the source, updates existing pages, creates new ones where needed, refreshes the index, and appends an entry to the log.1

Query means asking questions against the wiki, not against a pile of disconnected files. This improves answer quality because the model is working from pages that already contain structure, cross-links, and synthesis.1

Lint means running a health check on the wiki. The model looks for stale claims, contradictions, missing links, orphan pages, and concepts that deserve their own page.1

Why index.md and log.md matter more than they look

These two files sound administrative, but they are the reason the system stays usable.

index.md is the content map. It tells the model what pages exist and what each one is about. Karpathy notes that this can work surprisingly well at moderate scale, which means you can postpone heavier retrieval infrastructure until you actually need it.1

log.md is the timeline. It records what was ingested, what was queried, and when maintenance passes happened. That gives both you and the model a clear sense of recency and change.1

Once a few sources are ingested, the Obsidian graph stops looking decorative and starts acting like a real map. You can see hubs, weak links, and isolated pages quickly.

Step 1: Create a local Obsidian vault

Start with a fresh vault in Obsidian. A literal name such as toolnerd-secondbrain works well because it is easy to reference in your terminal, your coding tool, and your Web Clipper settings.

Keep the vault local. That gives you ordinary folders and files underneath, which is exactly what you want when an agent is reading from raw/ and writing to wiki/.

Step 2: Open the same folder in your coding assistant

Open that vault folder in Claude Code, Cursor, Windsurf, or whichever agentic coding environment you prefer. Then give the agent the llm-wiki gist and ask it to instantiate the pattern inside that vault.

The point is to turn the abstract idea file into a working local structure.

Step 3: Let the agent scaffold the wiki

At this point, the agent should create the core folders and files for you. You want a clear raw-source layer, a wiki layer, a schema file, and the two support files that make the system navigable.

Do not overcomplicate this first version. A clean folder structure matters more than clever automation.

Step 4: Clip an article into raw/articles

Now move from setup to actual use.

Open an article you want to save, then use Obsidian Web Clipper to send it into the vault. In the clipper settings, make sure the vault name matches exactly. Set the destination folder to raw/articles so the article lands in the source layer instead of being mixed into the generated wiki.

• Add the Obsidian Web Clipper Extension

• Configure it if you want to store the pages in a specific vault

• Click Add to Obsidian

Step 5: Confirm the article landed in the vault

After clipping, the article should appear inside raw/articles as a Markdown file with metadata and body content. That file is now part of the raw layer. Leave it alone and let the model read from it.

Step 6: Run ingest on that file

Go back to your coding assistant and ask it to ingest the article. A simple command pattern works well here: put the file into raw/articles/ and run ingest [filename].

This is where the pattern starts to pay off. One source can update many pages. The model can revise tool pages, create concept pages, refresh summaries, and update the index in the same pass.

Step 7: Query the wiki, not the raw file

Once a few sources are ingested, shift your questions to the wiki layer.

Those questions work because they require synthesis, not simple retrieval. The wiki gives the model a place to reason from.

Step 8: Run lint when the wiki gets messy

Most people skip this part. That is a mistake.

Ask the model to lint the wiki for broken links, duplicated concepts, stale claims, missing pages, and weak cross-references. This is the maintenance loop that keeps the system usable after the novelty wears off.

Tips and tricks worth stealing from the original article

Karpathy’s gist includes several practical suggestions that are easy to miss on a quick read, and they are worth adopting early.1

Obsidian Web Clipper is the fastest way to convert a web article into clean Markdown. If you collect a lot of articles, it removes a lot of friction. Karpathy also suggests downloading images locally. That is more useful than it sounds. If an article includes diagrams, screenshots, or charts, local assets keep them accessible even after the original URLs break.

Graph view is useful because it shows whether your wiki is growing in clusters, whether some topics are becoming hubs, and whether you have orphan pages that need linking. Dataview is another good upgrade once your wiki pages have frontmatter. It can generate dynamic lists and filtered views from metadata, which is useful when you want better navigation across tool pages, concepts, or recent updates.

There is also a useful mental shift here: the wiki is the artifact, and the chat is only the interface. Once that clicks, you start saving useful answers back into the wiki instead of losing them in conversation history.

Where this setup is strong, and where it breaks

This pattern is strong when you are reading, collecting, and synthesizing over time. It works well for competitive analysis, article research, course notes, tool tracking, learning sprints, and personal knowledge management. It is especially good in domains where the links between ideas matter as much as the ideas themselves.

It is weaker if you expect perfect automation on day one. The first few ingests need supervision. Naming conventions will evolve. Some pages will be messy early on. That is normal. Karpathy is explicit that the gist describes a pattern, not a rigid implementation.

That is why the first version should stay small. Do not overbuild search. Do not overdesign the schema. Do not import your entire digital life in one weekend. Start with ten sources. Make sure ingest, query, and lint feel natural. Then expand.

My take

What I like about this pattern is that it fixes the part of knowledge systems that humans usually avoid: maintenance. Reading is interesting. Thinking is interesting. Updating fifty interlinked notes is not. LLMs are much better at that kind of repetitive editorial work, which is why this setup feels useful instead of theoretical.

So if you want a practical starting point, do this.

• Create a vault. Hand your agent the llm-wiki gist.

• Let it generate the schema and the first structure.

• Clip one article into raw/articles.

• Run ingest.

• Open the graph.

• Read the index.

• Ask a question that requires synthesis. Then save the useful answer back into the wiki.

That is enough to feel the difference.

If you build this, pair it with the tools you already use every day.

When I first read Sequoia’s latest thesis, “Services: The New Software,” I was skeptical. Another prediction about AI replacing jobs? But as I read, I realized this was different. The viewpoint they present isn’t just about automation; it’s a fundamental shift in how we value human capability.

The core premise is undeniable: the next trillion-dollar company won’t be a SaaS giant selling you a better dashboard. It will be a software company masquerading as a services firm, selling you the completed work itself.

“A company might spend $10K a year for QuickBooks and $120K on an accountant to close the books. The next legendary company will just close the books.” [1]

This shift from selling tools (Copilots) to selling outcomes (Autopilots) is going to rewrite the rules for every agency, consultancy, and professional services firm on earth. Here is why this matters, and how it changes the game for builders and buyers alike.

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The Intelligence vs. Judgment Divide

The most profound insight from the report is the distinction between intelligence and judgment.

“Writing code is mostly intelligence. Knowing what to build next is judgement. Translating a spec into code, testing, debugging: the rules are complex but they are rules. Judgement is different. It requires experience and taste, instinct built on years of practice. Deciding which feature to build next, whether to take on tech debt, when to ship before it’s ready.” [1]

Writing code, translating a spec, testing, and debugging - these are acts of intelligence. The rules are complex, but they are still rules. AI is exceptionally good at learning rules.

Judgment is entirely different. Judgment is deciding which feature to build next. It’s knowing when to take on technical debt to hit a market window, or having the taste to know when a product is ready to ship. Judgment is instinct built on years of practice.

As AI commoditizes intelligence (the how), the market value is rapidly shifting to judgment (the what and why). Sequoia puts it plainly: “Today’s judgement will become tomorrow’s intelligence.” [1]

This creates a fascinating paradox for the workforce. If AI handles the “grunt work” of intelligence, how do junior professionals ever develop the experience required for high-level judgment?

We are entering an era where companies will place a massive premium on seasoned professionals who possess this taste. However, freshers still hold a unique advantage: they approach problems from first principles, unburdened by “how it’s always been done,” allowing them to innovate in ways experienced veterans might miss entirely.

Copilots vs. Autopilots: The Architecture Shift

The transition from intelligence to judgment is driving a massive shift in product architecture.

“A copilot sells the tool. An autopilot sells the work.” [1]

Until recently, AI models were still developing intelligence and judgment, so the right approach was to build a copilot first: put AI in the hands of a professional and let them decide what to do with it. Companies like Harvey sell to law firms, and Rogo sells to investment banks. The professional is the customer, the tool makes them more productive, and they take responsibility for the output.

Today, the models are intelligent enough that in some categories the best place to start is as an autopilot. Crosby sells to the company that needs an NDA drafted, not to outside counsel. WithCoverage sells to the CFO who needs insurance, not to the broker. The customer is buying the outcome directly.

The higher the intelligence ratio in any field, the sooner autopilots will win.

The 6:1 Ratio: Why Services Are the Real Prize

If you want to understand why AI companies are pivoting to services, look at the math.

The work budget in any profession dwarfs the tool budget. Traditional SaaS companies fight viciously over that single software dollar. AI-native service providers (Autopilots) are bypassing that fight entirely and capturing the six-dollar work budget from day one.

The customer doesn’t want to buy a tool and learn how to use it; they want to buy the outcome directly.

This is the fundamental reason why the next $1T company won’t be a SaaS platform - it will be an AI-native services firm.

The Outsourcing Wedge: Vendor Swaps vs. Reorgs

So, where does this revolution start? It starts with outsourced work.

The playbook is clear: companies should start with the outsourced, intelligence-heavy task. Nail distribution. Expand toward the insourced, judgment-heavy work as the AI compounds. The outsourced task is the wedge. The insourced work is the long-term TAM.

Crosby started with NDAs: a well-defined task, primarily intelligence, that most companies already outsource to external counsel. The budget exists, the scope is clear, the ROI is immediate, and the substitution is frictionless. [1]

This is why the initial wave of AI disruption isn’t targeting internal teams; it’s targeting the BPOs, the external agencies, and the managed service providers.

The Opportunity Map: Where Autopilots Are Winning Now

Sequoia maps out the massive TAM across various service verticals. Here is a look at where the autopilot revolution is already taking hold:

Source: Sequoia Capital, “Services: The New Software” (2026)

The agent deployment picture is equally revealing:

Source: Sequoia Capital, “Services: The New Software” (2026)

Software engineering got to autopilot first because it’s primarily intelligence work. Every other profession is following the same path, just a few years behind.

The Boutique Revolution: Small Teams, Massive Output

What does this mean for the future of the services sector? We are about to witness the rise of the “Boutique Agency” on steroids.

The traditional model relied on massive headcount to scale revenue. Giants like Accenture or TCS built empires on this premise. But in the AI-native era, team size is no longer correlated with output.

A 10-person specialised agency, armed with the right AI agents, can now deliver the volume and quality of a 500-person firm. The team size shrinks, but the capacity to deliver massive projects expands. Unless the legacy giants radically change how they operate, these hyper-efficient boutique firms will start winning their contracts.

This is also correlated with the wave of layoffs we’ve seen at large IT services companies. It isn’t just a market correction - it’s a structural shift. The companies that adapt fastest will survive. The ones that don’t will find their margins eroded by 10-person startups operating at 10x their efficiency.

The Rise of the Architect-PM

This shift also redefines the most valuable roles in tech. For the first time, we might need more Product Managers than traditional Software Engineers.

If AI is writing the code, the “Engineering” role evolves. The new elite builder is an “Architect-PM” - someone who possesses the technical IQ to architect complex systems, combined with the deep user empathy required to solve real human pain points. They aren’t just translating specs into code; they are solutioning what the user actually needs.

The best professionals in the next decade won’t be the ones who can write the most code. They will be the ones who can ask the right questions, understand the user’s pain at a deep level, and architect a system that solves it elegantly.

Who Wins, Who Gets Disrupted

The services sector is about to be split into two camps:

Winners: AI-native autopilot companies in high-intelligence verticals (insurance, accounting, legal, healthcare billing, IT support). Boutique agencies with deep domain expertise and AI fluency. Senior professionals with strong judgment and taste. Freshers who embrace first-principles thinking over conventional wisdom.

Under Pressure: Large BPOs and IT services firms that rely on headcount arbitrage. Mid-level professionals in purely intelligence-heavy roles (basic coding, data entry, form processing, standard contract drafting). Traditional SaaS tools that sell to professionals rather than directly to outcome-buyers.

The Verdict

Sequoia’s thesis is a wake-up call. The era of selling software as a tool is peaking. The era of selling software as a service - literally doing the work - is just beginning.

Whether you are building an AI startup or running a services firm, the mandate is clear: stop selling the hammer, and start selling the house.

The companies that understand this shift early will capture the six-dollar budget. The ones that don’t will keep fighting over the one-dollar budget - and wonder why growth is slowing.

References

[1] Sequoia Capital. “Services: The New Software” by Julien Bek. Published March 5, 2026. https://sequoiacap.com/article/services-the-new-software/

We spent the last decade moving everything to the cloud. We traded local files for Google Docs and native apps for browser tabs. The browser became the operating system. It made collaboration easy, but it created a massive problem for AI: the cloud is a sandbox.

Cloud apps cannot see your local files. They cannot run your terminal. They cannot click your native apps. To do real work, an AI agent needs to break out of the browser tab and live in your /Users/folder.

This is the desktop renaissance. We are moving from “Chat-in-a-Box” to “Agent-on-the-Metal.” And it all started with a viral script.

The OpenClaw Catalyst

OpenClaw went viral for a reason. It proved that users want an agent that can take over their mouse and keyboard to execute multi-step workflows. Before OpenClaw, we asked AI questions. After OpenClaw, we assign AI jobs.

I documented my own journey with OpenClaw in “How am I using OpenClaw to run TheToolNerd,” where I built a “Mission Control” system with multiple specialized agents—Scout finds new tools, SteveJobs prioritizes them, and Ralph builds features. The system works 80% of the time, but when it does work, it’s magical. Having an agent find tools while you sleep, prioritize your morning, and build features from a sentence—that’s the future.

The virality of OpenClaw also sparked an entire ecosystem of wrappers. In “The OpenClaw Wrapper Bubble,” I tracked how 10+ SaaS platforms hit $20K+ MRR in days by simply making OpenClaw easy to deploy. This shows the raw demand for desktop agents.

Now, every major AI company is scrambling to build their own, more reliable, enterprise-ready version of a “Computer-Using Agent.” They realize that whoever controls the desktop controls the workflow.

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Top Desktop AI Agents in 2026

1. Claude Desktop (with Claude Code & Co-work)

The Enterprise-Ready Leader

Anthropic didn’t just build a desktop app—they built the foundation for the entire “Computer Use” revolution. Claude Desktop features direct “Computer Use” API integration, which means Claude can literally take over your mouse to click, type, and scroll through your screen like a human would. But the real power lies in two specific products: Claude Code and Claude Co-work.

Claude Code runs in your local terminal and understands your entire codebase without uploading it to a server. This is the terminal-based product that started the revolution.

Anthropic had this vision a long time ago: if you can control the whole computer from the terminal—every file, every folder, every structure—then you have a true general-purpose agent. Claude Code proved this works. It can read your project structure, understand your architecture, and write code that fits seamlessly into your existing system.

For non-techies, Claude Co-work is the game-changer. It’s Claude’s way of saying, “I’ll handle the boring stuff.” It manages your calendar, drafts emails, schedule tasks, organizes local files, and handles repetitive tasks without requiring you to understand how it works under the hood. You just describe what you want, and it does it. This is transforming work for people who’ve never touched a terminal.

Claude is currently winning the enterprise trust game because they prioritize safety alongside capability. They understand that giving an agent OS-level access is a massive responsibility.

Download: claude.ai/download

Read more: Claude CoWork: The AI Agent That Automates Your Mac

2. OpenAI Codex App

The Multi-Agent Command Center

This is a dedicated macOS and Windows app, separate from ChatGPT. OpenAI designed it specifically for managing multiple AI agents working in parallel on complex coding and system tasks. It features built-in worktrees and parallel execution. Think of it as a “command center” where you can spawn multiple agents, each with its own workspace, each working on different parts of your codebase simultaneously. It treats your OS as a canvas for agentic swarms.

Download: openai.com/codex

3. Manus Desktop (My Computer)

The Polished General Worker

Manus brought the OpenClaw vision to a polished, consumer app. The “My Computer” feature gives it direct access to local folders. It executes CLI commands, manages files, and automates workflows across your entire PC seamlessly.

Unlike the raw power of OpenClaw, Manus feels like a finished product. It handles the messy reality of local file management—moving files, organizing folders, executing batch operations—without the debugging headaches. It’s the tool you can actually trust with your important files.

Download: manus.im/desktop

Read more: Manus is better than OpenClaw for you

4. MiniMax Agent

The Expert Agent Workbench

China’s answer to the OpenClaw virality. MiniMax built a dedicated desktop app that batch-processes local files, executes terminal commands, and maintains persistent memory of your OS activities.

But MiniMax went further—they launched MaxClaw, their own version of OpenClaw, built directly into their agent ecosystem.

It includes 5000+ pre-built skills, allowing you to chain complex workflows without writing a single line of code.

MiniMax integrated its latest proprietary models (MiniMax 2.7), optimized for lower cost and high-throughput workflows. The platform focuses on reliability and structured task execution, making it well-suited for batch processing and repeatable agent workflows.

Download: agent.minimax.io/download

5. Perplexity Computer

The Long-Horizon Researcher (Recently Launched)

Perplexity just made a serious move with Perplexity Computer in Feb 2026 - a system designed to go beyond chat and actually execute work. Perplexity AI positions it as a general-purpose “digital co-worker” that breaks goals into tasks, spins up sub-agents, and runs them in parallel across tools and environments.

Under the hood, it orchestrates multiple AI models, routing each step to the best one for reasoning, research, or execution. Each task runs in an isolated environment with access to a real browser, filesystem, and integrations—making it capable of handling long-running workflows with minimal supervision

Download: https://www.perplexity.ai/computer/new

6. GenSpark Browser

The Agentic Workspace

GenSpark positions its desktop app as a “Super Browser” for macOS. It features an “Autopilot Mode” that autonomously handles phone calls, creates slides, and manages docs locally. While others focus on code and research, GenSpark is building the “creative agent”—one that can handle the full workflow from brainstorming to presentation.

Download: genspark.ai/browser

7. Skywork Desktop

The Silent OS Operator

A full desktop agent that runs quietly in the background. Skywork executes multi-step workflows across Mac and Windows, acting as a reliable digital twin for repetitive OS-level tasks. It’s the agent you set and forget.

Download: skywork.ai/desktop

8. ChatGPT Desktop App

The Gateway to Local Context

While not a full OS-controller yet, the ChatGPT desktop app is the trojan horse for OpenAI’s broader agentic ambitions. With deep OS integration (like Option+Space on Mac), voice mode, and the ability to read local screen context directly from your IDE, it bridges the gap between chat and local context. It’s the entry point to OpenAI’s agentic vision.

Download: chatgpt.com/download

The “Claw” Wars: Everyone is Building Their Own Version

The OpenClaw effect didn’t just inspire desktop apps—it sparked a full ecosystem of “Claw” variants. Every major AI company is now building their own version, optimized for their strengths.

Kimi Claw (Moonshot AI) launched a browser-native platform that runs OpenClaw agents 24/7 without local hardware. It’s designed for users who want the power of OpenClaw but prefer a cloud-hosted solution. Kimi Claw includes pre-built skills and integrates Moonshot’s K2.5 model, making it a cost-effective alternative for developers.

MaxClaw (MiniMax) is MiniMax’s proprietary version of OpenClaw, built directly into their agent ecosystem. It focuses on reliability and includes expert skills for batch processing, terminal execution, and persistent memory management.

KiloClaw (Kilo Code) takes a different approach—it’s a hosted OpenClaw service that deploys in seconds with 500+ models, enterprise security, and zero DevOps required. You can spin up a full OpenClaw instance in less than 20 seconds without touching the terminal.

For enterprise readiness, NVIDIA NemoClaw is a game-changer. NVIDIA announced NemoClaw at GTC 2026 as an enterprise-focused software stack that adds security and privacy controls to OpenClaw. NemoClaw installs in a single command and provides sandbox isolation, a “privacy router,” and compliance features that make OpenClaw suitable for production environments. It’s NVIDIA’s way of saying, “OpenClaw is powerful, but here’s how to run it safely in your enterprise.”

The message is clear: OpenClaw proved the concept. Now, everyone is racing to build the most reliable, secure, and user-friendly version.

The China Play: Localization and Control

While Western companies are building more reliable versions of OpenClaw, China’s tech giants are doing something different. They’re not just adopting OpenClaw—they’re localizing it, monetizing it, and integrating it into their super-apps to maintain control over the AI agent ecosystem.

Tencent, ByteDance, and Zhipu AI are taking a different approach. Instead of focusing purely on standalone desktop agents, they are integrating agent capabilities directly into their existing ecosystems—messaging platforms, cloud infrastructure, and model APIs.

The strategy is not just to build better agents, but to control distribution. By embedding agent functionality into platforms users already rely on, they create tightly integrated environments where workflows, data, and models are all connected.The

Agentic OS Layer

We are building a new layer in the technology stack.

At the bottom, you have your Operating System—your files, your terminal, your native apps. At the top, you have the Human User, assigning high-level goals. In the middle sits the Agentic OS Layer. Tools like Manus, Claude, Codex, and Perplexity act as the bridge. They translate intent into clicks, keystrokes, and terminal commands.

Cloud AI is blocked by a firewall from your local files. Desktop AI has direct access. That access changes everything.

The Million Dollar Question: Safety vs. Utility

Giving an agent “Screen Vision” and terminal access is like giving a stranger the keys to your house. Is it safe?

We are trading privacy for extreme productivity. The winners in this space won’t just be the smartest models. They will be the ones that build the most trusted sandboxes and permission layers. This is why Claude is currently winning the enterprise trust game. They prioritize safety alongside capability. NVIDIA’s NemoClaw is another signal—enterprises are demanding security wrappers around these powerful agents.

You have to decide if the speed is worth the access. For most developers and builders, the answer is yes. But for sensitive data or mission-critical systems, you need to choose carefully.

The Verdict

The browser isn’t dead. But it is no longer the only place work happens.

Mastering one of these desktop agents will be the defining productivity skill of 2026. You need to learn how to manage a digital worker that lives on your machine. Whether you choose Claude for enterprise trust, Manus for polish, Perplexity for long-horizon research, or one of the “Claw” variants for cost-effectiveness, the choice is yours.

Pick one. Download Claude Code, install Manus, or set up the Codex app. Give it a local folder and a messy task. Watch it work. You will understand why the desktop era is back.

The future isn’t in the cloud. It’s on your desktop, working while you sleep.

The research is solid - it’s available on Amazon if you want to dive deep - but I wanted to turn that methodology into something functional. Something more than just a generic dashboard

.

This is the story of how I used a combination of AI tools to transform doctoral thesis into a full-scale enterprise application called The Bridge OS - by Ainapur.

The Before and After

Before we dive into the build, here’s a look at the transformation. We went from a static consulting methodology to a dynamic, AI-powered operating system.

Visit : Ainapur

Why did I select Lovable?

I’ve spent sometime testing AI builders for building this app. I’ve started with Anything, but my initial prototype wasn’t that good. Then I tried with Lovable, and the first proto it built was very simple and interesting, and then I decided to build on it.

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I also had some credits to use accumulated by non-usage, and honestly, I was impressed by how much the platform has evolved. I am a fan of Claude Code & Replit , but Replit can consume credits fast if you aren’t careful and Replit doesnt have credits rollover, even if I don’t use them. Lovable’s initial design felt right for an enterprise-grade tool.

Step 1: The Vision and the Prompt

I started with a basic prompt to scaffold the skeleton:

“I want to build a Balanced ScoreCard application, where any company can build, design their balanced scorecard with strategy map and an implementation plan.

Output should be like the image attached. I should be able to connect the arrows, etc..

Read a lot about Balanced Score Card. It should be an AI Chatbot, with which users can chat and build their scorecard.

Think about the idea, and the design .. how it will work in HTML CSS style before coding.”

Lovable - created a plan for me and once approved, within minutes, Lovable gave me a working React + TypeScript + Tailwind CSS stack. I asked it to work only on Design and not the backend.

It was a good start, but it was generic. It didn’t have the “soul” of my dad’s methodology yet.

Step 2: Injecting Domain Expertise

This is where the magic happened. I didn’t just want a BSC tool; I wanted a tool that understood the domain.

I uploaded my dad’s PhD thesis as a reference document. Lovable parsed the content and used it to shape the data model. Suddenly, the AI assistant wasn’t just giving generic business advice - it was referencing Kaplan & Norton’s framework and understanding the specific cause-effect linkages.

I also brainstormed with Google Gemini to nail down the product name and positioning. We landed on The Bridge OS - the operating system that bridges the gap between strategy and execution.

Step 3: Designing UI with Magic Patterns

A tool for enterprise clients can’t look like a weekend hackathon project. I used Magic Patterns to define the design system based on the problem statement I created with Gemini.

I had long conversations with it and once I was satisfied I asked it to write a design guide, that I pasted back in Lovable to build it

Step 4: Designing Logo with Claude

For the logo, I actually turned to Claude. While Gemini and other platforms struggled to create something I liked, Claude was incredible at generating SVG logos.

The beauty of SVG is that you can edit it directly - no more fighting with “nano banana” style pixelated generations.

Step 5: Creating the Launch Video

I have asked Lovable to create a launch video as well. You read it right — Lovable for building the launch video. It already had context about my application and I have given it the product screenshots and asked it to create it into a video.

It used the Remotion Dev skill to do it. However, I have added the music using Descript

Lovable has recently launched an updated version of their app to go beyond just building apps.

The Workflow: From Idea to Execution

To explain how everything fits together, I put together this infographic:

Feature Deep-Dive: What We Built

1. The AI Consultant

The heart of the platform is an AI chat assistant that understands the thesis. It doesn’t just list bullet points; it maps strategic objectives to the four BSC perspectives - following the guideline defined in the book.

2. Interactive Strategy Maps

Visualizing cause-effect relationships is crucial for executives. The Bridge OS generates these maps automatically based on the objectives defined in the scorecard.

3. SWOT Analysis & Matrix

AI-generated SWOT with a strategy formulation matrix. Not just a list of bullet points - actual strategic options derived from the intersection of internal capabilities and external factors.

4. Gap Analysis & Benchmarking

Compares current state against industry benchmarks and best practices. Identifies where the organization falls short and what initiatives are needed to close the gaps.

5. Full Balanced Scorecard

The complete BSC table with strategic objectives, KPIs, current values, targets, and strategic initiatives. Everything an organization needs to track strategy execution in one view.

6. Department Cascading

This was a key takeaway from the PhD research. The platform allows the CEO-level BSC to cascade down to HR, Operations, and Finance, ensuring full organizational alignment.

My Verdict

Building this taught me one major lesson: No single AI Tool is a Magic .. we need a combination of different tools with different capabilities to build something really interesting.

Anyone can prompt an AI to build a dashboard.

If you’re sitting on specialized knowledge - whether it’s a thesis, a book, or years of experience - the barrier to turning that into a product has never been lower.

“Create a skill to write test-driven development cases for any feature.”

That was the full prompt. No spec document, no example files, no instructions on how to build it. Claude took that single line and handled everything — writing the skill, testing it, comparing results, and packaging a .skill file ready to use. This is what that process looked like.

The one-prompt demo is impressive — but that’s not how I usually build skills. My workflow is at the end of the article

What My Test Driven Development Skill Does

The finished skill, called tdd-writer, gives you this whenever you describe a feature:

• A full test file in whatever framework your project uses (Jest, pytest, Go testing, RSpec...)

• Tests in the right order: happy path first, then edge cases, then error scenarios

• A one-liner to run the tests immediately

• A short list of what you need to build to make them pass

No implementation code. Only failing tests that define what the code should do — the way TDD is supposed to work.

If you would like to know more about Claude Skill 2.0 , check out this article

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How Claude Built the Skill - Skill Creator Workflow

The workflow Claude follows when using skill creator looks like this:

1. Wrote the Skill File

The first thing Claude produced was SKILL.md — a plain text file that acts as the skill’s brain. The top section (frontmatter) tells Claude when to use it:

---
name: tdd-writer
description: >
  Writes test-driven development (TDD) cases for any feature before
  implementation code exists. Trigger on phrases like "write tests for",
  "tests before code", "help me TDD this", or when a user describes a
  feature and asks how to approach it.
---

The rest of the file contains the actual instructions — things like: detect the test framework from the project files, write tests in happy-path-first order, never write implementation code.

That last rule got its own dedicated section:

## What NOT to do

- Don't write implementation code. If you find yourself writing the
  actual feature, stop.
- Don't write tests that always pass (e.g. `assert True`).
- Don't mock everything — only mock at system boundaries.

2. Created Test Prompts

Next, Claude wrote three realistic prompts to check whether the skill actually works. These aren’t hypothetical — they’re the kind of messages a developer would actually send:

1. Python password validator — check for min length, uppercase, digits, special characters

2. Jest shopping cart — an addItem function that handles duplicates and rejects bad prices

3. Go database query — FetchUserByID using testify, with proper error types

3. Ran Two Agents Per Prompt (in Parallel)

For each of those three prompts, Claude launched two subagents simultaneously — one with the skill loaded, one without.

The idea: if the skill is doing its job, the outputs should be noticeably better.

Here’s what the skill produced for the Python prompt:

"""
TDD tests for password_validator module.
All tests should currently FAIL (Red phase).

Run with:
    pytest test_password_validator.py -v
"""

import pytest
from password_validator import validate_password

# Happy path
def test_valid_password_returns_true():
    # Arrange
    password = "Abcdef1!"
    # Act
    result = validate_password(password)
    # Assert
    assert result is True

# Edge case — boundary value
def test_valid_password_exactly_eight_characters_returns_true():
    password = "A1!bcdef"  # exactly 8 chars, all rules satisfied
    assert validate_password(password) is True

# Error case
def test_none_input_raises_type_error():
    with pytest.raises(TypeError):
        validate_password(None)

And it closed with a plain-English reminder of what to do next:

Red:      Run the tests now — they should all fail. That's correct.
Green:    Implement the minimum code to make each test pass, one at a time.
Refactor: Once all green, clean up. The tests protect you.

The baseline agent (no skill) produced equally good test code. What it skipped: the run command, the “what to implement” list, and the Red-Green-Refactor reminder at the end.

4. Graded and Reviewed

Each run was scored against six assertions — things like “test names are descriptive,” “no implementation code was written,” and “run instructions are included.” Results went into an eval viewer:

The 28% gap traces back to one thing: the skill consistently added the scaffolding — run instructions, implementation roadmap, Red-Green-Refactor — that plain Claude left out.

5. Packaged

One command:

python3 -m scripts.package_skill ~/.claude/skills/tdd-writer

✅ Skill is valid!
✅ Successfully packaged to: tdd-writer.skill

The skill was already active in the Claude Code session from the moment it was created. No restart, no configuration.

How I Actually Use Skill Creator (In Practice)

In reality, I work with Claude step by step, just like I would with a developer:

• I start with a task (e.g., generate tests, extract data, structure output)

• I refine the output through multiple iterations

• I correct edge cases, improve structure, and push it toward what I actually need

• I don’t touch the implementation manually — I let Claude handle everything

Only once I’m fully satisfied with the behavior…

👉 I ask Claude to convert that workflow into a reusable skill.

So instead of:
“Create a skill from scratch”

The real pattern is:
“Refine a workflow → Lock it in as a skill”

That’s the shift.

Skills aren’t where you start —
they’re what you create after something works reliably.

The Whole Thing Took One Prompt

The user typed: “Create a skill to write test-driven development cases for any feature.”

Claude wrote the skill, designed the test cases, ran six parallel agents, graded the outputs, opened the review UI, and packaged the result. The only human input after the initial prompt was a single word: “approved.”

That’s what skill creator is for.

These are like small, reusable tools that help AI do specific tasks. By making the framework open-source, Anthropic allowed everyone to build on it. You have multiple marketplaces created to discover skills.

One of the famous one’s is - https://skills.sh

People started turning their daily work—like summarizing documents or writing code—into these automated skills.

If you want to learn more about SKILLS , MCPs, Claude Code check out these articles:

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But there was a problem. Building a skill felt more like guessing than engineering. The first version of Skill Creator helped you start, but it didn’t help you check if your skill actually worked well. You would write instructions, run the skill, and if it failed, you had to guess how to fix it.

This new update is a significant improvement.

It removes the guesswork and makes building skills much more reliable. Here is what you need to know.

From Guessing to Engineering

Before this update, Skill Creator was just a basic tool to help you set up files. Once you had the files, you were on your own. You would write your instructions and hope for the best. If the AI made a mistake, you had to manually change your prompts and try again.

Now, there is a clear, four-step process to make sure your skills are solid:

• Create

• Eval

• Improve

• Benchmark

The Four Modes: How to Build Better Claude Skills

This new way of working is a loop. It builds, tests, and fixes the skills until they are perfect.

Why This New Version of Claude Skill is Better

The new Skill Creator is much more reliable because of three main features.

1. The Evaluation Pipeline: Testing Your AI

This is the most important part. You can now create test cases. You give the AI an input and tell it what the correct output should be. For example, Anthropic had a skill for reading PDFs that sometimes struggled with forms. By using these tests, they found exactly where it failed and fixed it.

This is like a “check-up” for your AI. Without it, you never really know if your skill is reliable.

2. AI Assisted Debugging

When a test fails, the Improve mode analyzes the failure logs and suggests changes to the skill’s instructions.

Instead of manually guessing how to fix the prompt, you get targeted suggestions based on what actually failed during the evaluation runs.

It’s not fully autonomous self-repair, but it dramatically speeds up debugging.

3. Regression Testing: Keeping it Safe

As skills get more complex, they can break easily. The Benchmark mode lets you run a full set of tests every time you change something. It shows you how many tests passed, how long it took, and how much it cost.

This is a safety net. It gives you the confidence to improve your skills without worrying about breaking them.

If you found these articles useful, please share them with others who might benefit.

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Two Types of Skills You Can Build

Anthropic explains that skills usually fall into two categories.

Capability skills extend what the base model can do.
For example, a skill that extracts information from complex PDFs or fills out forms.

Preference skills encode how you want work done.
For example, formatting meeting notes in a specific structure or writing documentation in your company’s style.

The new evaluation system helps you measure both.

If a capability skill becomes unnecessary because the base model improves, the tests will reveal it. If a preference skill stops behaving the way you expect, the benchmarks will catch it.

Advanced Features: Faster Testing and Comparisons

Anthropic also added multi-agent support. This means you can run many tests at the same time using different “agents.” It makes testing much faster.

There are also comparator agents for A/B testing. You can compare two different versions of a skill to see which one is better. The AI judges the results without knowing which version is which, so the choice is fair.

Making Sure the Claude Skill Starts at the Right Time

A skill is only useful if it starts when you need it. Skill Creator now helps you write better descriptions so the AI knows exactly when to use the skill. It looks at your description and suggests ways to make it clearer.

Final Thoughts: A Must-Have for AI Builders

This update is a big step forward. It moves us away from just “guessing” with prompts and toward building real, reliable AI tools.

By giving us tools to test and fix our skills, Anthropic has made it possible to build AI that we can actually trust. If you want to build professional AI tools, you should start using this new workflow today.

In the next article you will see how claude created a skill using the updated Claude Skill Creator 2.0

Claude wrote the skill, designed the test cases, ran six parallel agents, graded the outputs, opened the review UI, and packaged the result. The only human input after the initial prompt was a single word: “approved.”

In 2026, top-tier 3D Generative AI tools like Tripo AI made it possible to go from a text prompt or a reference image to a fully textured, visually impressive 3D models in seconds. Suddenly, game developers, indie creators, animators, and product designers who had never opened Blender could generate 3D assets on demand. But for anyone who has tried to actually use one of these models in a game engine or animation pipeline, the same problem keeps surfacing.

“The model looks good on screen. But the moment you open it in Blender, Unity, or Unreal Engine, the mesh underneath is a disaster: chaotic geometry, uneven polygon distribution and broken edge flow. Before you can use it for anything real, you’re looking at hours of manual retopology.”

Retopology, the process of rebuilding a 3D model’s underlying mesh structure so it’s clean, organised, and optimised for real-time use, has been the hidden bottleneck of AI-generated 3D assets. The visuals were good. The underlying mesh structure of clean low-poly geometry didn’t keep up.

For every AI-generated asset that looked good, there was a 3D artist somewhere spending two to four hours manually retopologizing it before it could enter a real pipeline inside Blender or Unreal Engine.

Tripo Smart Mesh P1.0 is built specifically to close this gap. It generates clean, structured low-poly topology as part of the generation itself, so the model that comes out is already production-ready, no manual cleanup required in 2 seconds.

If you are looking to learn about the Best AI 3D Models & Step-by-step guides then checkout the previous blogs we have written

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Why Mesh Topology Has Always Been the Bottleneck

To understand why Tripo Smart Mesh is a significant upgrade, it helps to understand what the old workflow actually looked like for developers and creators working with AI-generated 3D assets:

The standard pipeline before Smart Mesh was:

Generate → Inspect mesh in Blender → Spend 2–4 hours manually retopologizing → Optimize polygon count for performance → Finally import into the project.

The AI handled the visual generation, but handed off 80% of the actual production work.

The core issue was that standard AI 3D generators prioritise visual output. The resulting meshes are dense and chaotic.

A 3D artist looking at the wireframe would immediately recognize it as something that needs to be rebuilt from scratch before it can be rigged, animated, or used in Games.

What Tripo Smart Mesh P1.0 Actually Does

Smart Mesh moves topology work from post-processing to the generation stage.

Instead of a visually accurate but chaotic mesh requiring cleanup, Smart Mesh immediately outputs a clean, organised, low-poly, engine-ready mesh with structured edge flow, optimised polygon distribution, and suitability for rigging/animation.

The workflow is simplified:

Generate with Smart Mesh → Export → Use directly.

This eliminates hours of per-asset cleanup for game development and XR teams, supporting scalable asset pipelines. Generating in only 2 seconds also enables rapid iteration across large libraries.

Step-by-Step: How to Use Smart Mesh in Tripo Studio

Here is the complete workflow for generating a clean, game-ready 3D asset using Smart Mesh in Tripo Studio.

Step 1: Open Tripo Studio and Set Up Your Generation

Head to Tripo Studio and log in or create a free account. You will land on the generation workspace.

You have two input options:

• Text prompt: Describe your asset directly — for example, “a wooden medieval chest” or “a sci-fi crate with metal paneling”

• Image reference: Use the Image to 3D Model feature to upload a concept image or reference photo and convert it into a 3D asset. This is especially useful when you already have visual direction for your asset.

Step 2: Select the Smart Mesh Model and upload an Image

Before generating, select Smart Mesh (P1.0) as your generation model. This is the critical step that changes the output type from a standard AI mesh to a clean, structured low-poly topology.

This model selection instructs Tripo to prioritise mesh structure and real-time usability in the output rather than generating maximum visual density at the expense of mesh quality. I noticed that using high-contrast, evenly lit reference images yielded the absolute best low-poly structure. It gives the AI a much clearer outline to work with, which translates to a cleaner mesh.For this review, I chose this animated woman’s image to generate the 3D model along with the smart mesh.

Step 3: Generate the Asset (approximately 2 seconds)

Click Generate. Smart Mesh produces the structured mesh of triangles. In the hands-on testing, the speed was incredibly impressive as it consistently delivered the mesh in just 2 seconds, which completely changes the pace of rapid iteration. The preview in the canvas will show the model you can switch to wireframe view to inspect the topology directly.

The difference from a standard AI mesh is immediately visible in the wireframe: organised edge loops, even polygon distribution, clean silhouettes. The mesh looks like something a human 3D artist would build - not an AI output that needs to be repaired.

This is the output from the Smart Mesh model.

For comparison, I have also generated a 3D model of the same woman without the mesh. This would consume significant time for 3D artists when they import this into 3D software due to the lack of a wireframe mesh.

Step 4: Review the Mesh Quality

Tripo Studio lets you inspect the model in solid and wireframe mode before exporting. For Smart Mesh outputs, the polygon distribution is already optimised for real-time performance, lightweight enough for game engines and interactive applications, while maintaining accurate shape quality.

When inspecting the wireframes side-by-side, the difference is night and day. The Smart Mesh instantly provides an even polygon distribution with clean edge loops, whereas the standard AI mesh often results in chaotic, dense geometry that is unusable out of the box. Because the topology is so well-structured from the start, I found that it handles edge flow around joints and facial features beautifully, which is absolutely essential if you plan to rig the model later. There is no need to rebuild the mesh before handing it off to an animator or importing it into an engine. You can see the clear difference between the two models generated from the same image below.

Step 5: Export in Your Preferred Format

Once you are satisfied with the mesh, export in the format that suits your pipeline. Tripo Studio supports:

• GLB / glTF: Ideal for web 3D, XR applications, and lightweight pipelines

• FBX: For Blender, Unity, Unreal Engine, and any pipeline that requires skeletal animation compatibility

• OBJ: For general 3D software import

Because the Smart Mesh output already has clean topology, the exported model can be imported directly into your engine or software without the retopology step that previously slowed production down.

Smart Mesh vs. H3.1 HD Model, Which One Do You Need?

Tripo offers two distinct generation models, each designed for a different point in the pipeline. Understanding which to use depends entirely on what you are building.

• Smart Mesh (P1.0): Clean low-poly topology, optimised for real-time pipelines, game engines, XR, and web 3D. Use this when the mesh needs to perform.

• H3.1 HD Model: High-density geometry with maximum visual detail. Designed for hero assets, marketing visuals, cinematic renders, and close-up shots where polygon count is not a constraint.

For most game development and interactive application workflows, Smart Mesh is the right starting point. If you need a showcase asset for a storefront render or a cinematic cutscene, H3.1 is the better choice.

Who Should Use Smart Mesh

• Game developers building prop and environment asset libraries at scale

• Indie developers who need to move fast without dedicated 3D artists on the team

• XR and web 3D creators where performance constraints and clean geometry are critical

• Production teams running rapid iteration cycles generate, test, replace, and iterate without bottlenecks

Final Verdict

The problem with AI-generated 3D assets was never visual quality. It was that the outputs were too structurally messy to use in a real pipeline without extensive manual work. Tripo Smart Mesh P1.0 directly addresses this, generating clean, organised, production-ready topology as part of the generation process itself.

For developers and creators working at scale, this is more than a minor quality-of-life improvement. It removes what has historically been the most time-consuming step between AI generation and actual production use. The 2-second generation speed makes it practical to build out large asset libraries without the bottleneck that previously made AI-generated 3D impractical for serious pipelines.

You can try Smart Mesh now directly in Tripo Studio. For full technical details on the feature, visit the Smart Mesh P1.0 feature page.

Check out their official video :

Disclosure: This post is an advertorial.

In this article, we will see how AI can generate a complete Standard Operating Procedure using PFMEA and Control Plan as inputs, while ensuring alignment with international manufacturing and safety standards.

SOP: The Execution Backbone of Manufacturing Quality

A Standard Operating Procedure (SOP) is a documented instruction that defines how a manufacturing process must be executed to ensure:

• Product quality
• Process consistency
• Operator safety
• Regulatory compliance
• Standardized training

An SOP converts engineering requirements into practical shop-floor actions. In modern manufacturing systems, SOPs are essential for compliance with global standards such as:

• ISO 9001 – Quality Management Systems
• IATF 16949 – Automotive Quality Management
• ISO 45001 / OHSAS 18001 – Occupational Health & Safety
• ISO 14001 – Environmental Management
• OSHA Safety Regulations
• AIAG Manufacturing Standards

These standards require organizations to define controlled work instructions and documented procedures to ensure consistent process execution.

Relationship Between PFMEA, Control Plan and SOP: The Manufacturing Quality “Golden Thread”

A properly designed manufacturing documentation system follows a clear hierarchy. This interconnected system forms what we call the “Manufacturing Quality Golden Thread.”

PFMEA (Process Failure Mode & Effects Analysis) – Identifies potential process failures.
Control Plan – Defines the monitoring and control methods for the process.
SOP (Standard Operating Procedure) – Defines how operators execute the process on the shop floor.

Example:

• PFMEA identifies a failure mode: Diameter exceeds tolerance

• Control Plan defines the control: Measure diameter every 10 parts using micrometre

• SOP converts this into operator instruction: After machining every 10th component, measure the diameter using a calibrated micrometer and record the value in the inspection log.

Thus, the SOP ensures the Control Plan is actually executed on the shop floor.

If you would like to learn about Creating FMEA && Control Plan with AI checkout these previous articles

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Step-by-Step Method Using AI

Step 1: Login to ChatGPT or any advanced AI tool.

Step 2: Upload the following documents

• PFMEA

• Control Plan

• Engineering Drawing (optional but recommended)

Step 3: Write a structured prompt

Just like PFMEA and Control Plan, prompt quality determines output quality. A vague prompt gives generic instructions. A detailed prompt generates industry-ready SOPs.

Example Instruction for AI SOP Generation

You are a manufacturing process expert in making SOPs. Create a Manufacturing Standard Operating Procedure (SOP) for a rough turning operation using the attached PFMEA and Control Plan as reference inputs.

The SOP must align with internationally accepted manufacturing and safety standards including:


• ISO 9001:2015 – Quality Management System documentation requirements

• IATF 16949 – Automotive manufacturing process control

• ISO 45001 / OHSAS 18001 – Occupational Health & Safety

• OSHA machine safety guidelines

• AIAG manufacturing documentation practices

# Process Details

• Operation: Rough Turning

• Objective: Achieve diameter Ø50 mm over 80 mm length

• Raw Material: Steel rod Ø60 mm × 111 mm (+1 mm)

• Machine Type: CNC Lathe (Semi-automatic)

• Operator Skill Level: Skilled operator

The SOP must include the following structured sections:


1. Purpose

2. Scope

3. Applicable Standards and References

4. Roles and Responsibilities

5. Safety Requirements

6. Required Tools and Equipment

7. Raw Material Requirements

8. Machine Setup Procedure

9. Step-by-Step Operating Instructions

10. In-Process Quality Controls aligned with Control Plan

11. Reaction Plan for Non-Conformance

12. Documentation and Record Keeping

13. Shutdown Procedure

14. Training and Competency Requirements


# Alignment Requirements

• SOP must reflect PFMEA identified risks and controls.

• Inspection steps must match the Control Plan inspection frequency.

• Critical process parameters must be highlighted.

These include:

• Cutting speed

• Feed rate

• Depth of cut

• Tool wear monitoring

• Coolant flow


# Formatting Requirements

• SOP must be written in clear shop-floor language.

• Instructions must be step-by-step and numbered.

• Safety warnings must be clearly indicated.

• The SOP should be suitable for training operators and for external audits.

If any assumptions are required due to incomplete information, list them clearly.

Example AI Generated SOP

SOP Title: Standard Operating Procedure - Rough Turning Operation – Steel Rod

1. Purpose

This procedure defines the standardized method for performing the rough turning operation on a steel rod to achieve the required diameter and length while ensuring product quality, operator safety, and compliance with international manufacturing standards.

2. Scope

This SOP applies to all CNC lathe operations performing rough turning for the specified component. Applicable departments include: Production, Quality Assurance & Maintenance.

3. Applicable Standards and References

This SOP aligns with the following standards:

• ISO 9001 – Quality Management Systems
• IATF 16949 – Automotive Manufacturing Quality
• ISO 45001 – Occupational Health and Safety
• OSHA Machine Safety Regulations
• AIAG Manufacturing Process Standards

4. Responsibilities

• Production Operator

– Execute machining operations according to SOP

– Perform in-process inspections

– Report deviations

• Quality Inspector

– Verify dimensional conformity

– Conduct process audits

• Production Supervisor

– Ensure trained operators execute the process

5. Safety Precautions

Operators must follow these safety requirements:

• Wear safety goggles and safety shoes

• Ensure machine guards are closed before operation

• Keep hands away from rotating components

• Use chip removal brush instead of hands

• Stop machine before inspecting tools

Emergency stop button location must be known before operating the machine.

6. Required Tools and Equipment

• Machine: CNC Lathe

• Cutting Tool: Carbide turning insert

• Measuring Instruments: Digital micrometer / Vernier caliper

• Other Equipment: Coolant system, Chip removal brush

7. Raw Material Requirements

• Material: Steel rod

• Dimensions:

– Diameter: Ø60 mm

– Length: 111 mm (+1 mm)

Material must be inspected for: Surface damage, Rust, Bending.

8. Machine Setup Procedure

1. Switch on the CNC machine.

2. Load the machining program.

3. Install the turning insert tool.

4. Set tool offsets.

5. Check coolant supply.

6. Mount raw material securely in chuck.

7. Perform a dry run without cutting.

9. Operating Procedure

Step 1: Load raw material into chuck.
Step 2: Secure chuck jaws.
Step 3: Start spindle rotation.
Step 4: Initiate rough turning cycle.
Step 5: Machine rod to achieve approximately Ø50 mm diameter over 80 mm length. Step 6: Ensure coolant flow during machining.
Step 7: Monitor chip formation and cutting sound.
Step 8: Unload finished component.

10. In-Process Quality Checks

• Characteristic: Diameter Ø50 mm

• Measurement Method: Digital Micrometer

• Inspection Frequency: Every 10 components

Results must be recorded in inspection sheet.

11. Reaction Plan

If dimension exceeds tolerance:

1. Stop machine immediately.

2. Inspect tool wear.

3. Adjust tool offset.

4. Re-machine next component.

5. Inform supervisor if issue persists.

12. Documentation

Operators must record:

• Production quantity

• Inspection results

• Machine downtime

• Tool changes

13. Shutdown Procedure

1. Stop the spindle.

2. Remove workpiece.

3. Clean machine bed.

4. Switch off coolant system.

5. Turn off machine.

14. Training Requirements

Operators must be trained in:

• CNC machine operation

• Measuring instruments

• Safety procedures

• Control Plan compliance

Final Thoughts

Using AI tools, a complete manufacturing SOP can now be generated within minutes. Instead of spending several days preparing documentation manually, engineers can now focus on process optimization and continuous improvement.

When integrated properly, AI can generate the entire manufacturing documentation stack:

• PFMEA

• Control Plan

• Standard Operating Procedure

• Work Instructions

• Operator Training Material

This represents a major leap in how manufacturing organizations approach quality documentation and process standardization.

For the past month and a half, I’ve been living a tale of two AI agents. On one side was OpenClaw, the powerful, open-source darling of the developer community that promises raw, untethered control.

On the other was Manus ( Now acquired by Meta), the polished, increasingly capable agent that aims for seamless integration into your daily workflow. I had tried out earlier and was blown away by its potential at that point in time (few months ago).

Check out my previous articles on Manus

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I recently got access to it via the Lenny’s Newsletter (Go and get yours to access $25K worth tools like Lovable, Bolt, Cursor, Elevenlabs and a lot more)

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By Lenny Rachitsky

Everyone is talking about OpenClaw, and for good reason - it’s a glimpse into the future of agentic AI. But after extensive hands-on testing, my conclusion is this:

OpenClaw is powerful and is right for a specific customers but for most of the professionals like PMs, Marketers, Researchers, Business Owners etc.. Manus might be the right one.

I went deep on both platforms, using them for real-world tasks, from research and coding to content creation. Here’s what I found.

OpenClaw: The All-Powerful Agent That Demands Everything

OpenClaw is incredibly powerful. It gives you direct terminal access, lets you bring your own models, and offers a level of control that feels limitless. You can chain together complex commands, give it access to your local files, and watch it execute tasks with a raw capability that no closed-source tool can match. It’s the dream for tinkerers and developers who want to build a truly bespoke AI assistant.

I have written a detailed article on what I built with OpenClaw and my experiences

The Security Tightrope and the “Black Box” Problem

My biggest reservation with OpenClaw is security. The platform’s open nature and deep system access create a massive attack surface. While the project is improving, the risk of a “400K lines of code” leading to a serious vulnerability is a constant concern. It’s difficult for anyone to audit it properly as well. Recent history backs this up, with researchers discovering critical flaws like CVE-2026-25253, a one-click remote code execution vulnerability that allowed a malicious website to hijack a user’s agent .

Beyond the security risks, there’s a significant clarity issue. More often than not, I found myself unsure of what OpenClaw was doing behind the scenes.

It can feel like a black box; you provide a prompt and wait for the result, with little insight into the intermediate steps.

This lack of transparency makes debugging difficult and erodes trust, especially when the agent has access to sensitive data and systems.

Finally, there’s the management overhead.

Maintaining an OpenClaw instance is not a trivial task. It requires constant monitoring, updates, and a deep understanding of the underlying system.

For an individual, this is a significant time investment and a constant source of friction. And let’s not forget the credit consumption; OpenClaw can be a credit hog. I personally use Kimi K2.5 to manage its usage, unlike other models like Claude, Gemini and Codex, which can get exhausted quickly.

What truly makes OpenClaw stand out is its multi-agent power. You can spin off multiple agents, each with its own context, memory, and file management. This capability allows for highly complex, parallel workflows, making it incredibly powerful for those who can harness it.

While many are launching their own versions of OpenClaw by adapting it—like Kimi Claw, Kilo Claw, and MiniMax agent Claw—these are essentially building on OpenClaw’s infrastructure.

Manus, on the other hand, operates on its own distinct design.

Manus: The Reliable Workhorse That Just Gets Things Done

After the high-stakes environment of OpenClaw, setting up Manus felt like a piece of cake. It may not have the raw, untamed power of its open-source counterpart, but it excels in reliability, ease of use, and seamless integration.

Manus is clearly designed for knowledge workers and the general public, not just developers. Its for Product managers, Marketers, Writers, Researchers, Finance Folks etc..

I had been publicly asking the Manus team on X (formerly Twitter) for a better conversational interface, since the time OpenClaw got launched and I got access to Manus.

My main gripe was that while the agent was powerful, the web-based chat felt disconnected, often losing context between sessions. A few weeks ago, they delivered.

Let’s first understand why Manus is easy for Product Managers, Researchers, Marketers, General Audience

• Easy to use & familiar - user interface via chat

• You can even reach out to manus on Slack, Email as well

• Connect to MCPs easily with ready plugins for tools like Notion, Google, Meta Ads Manager, GitHub, Linear, ElevenLabs, Supabase, Vercel and a lot more

• Skills are easy to create and install. For any repetitive work, create a skill and create it as a scheduled task.

• Scheduled Tasks for repetitive work.

Manus - Telegram Integration

The new Manus Agents feature, which integrates Manus directly into Telegram, is the first step in the direction of personal agents . It solves the context problem beautifully. Now, I can have ongoing conversations with my agent, and it remembers everything, just like a human assistant.

Integrating with Telegram is simpler than OpenClaw.

It’s the full power of Manus—research, coding, document generation—all accessible from a simple chat interface. I can send voice notes, drop in files, and get results delivered right back to my phone. It’s the seamless experience I’ve been waiting for. In the Manage Agents section, you can easily access all files and documents uploaded during your chat and conversation.

You can also define your assistant’s identity, name, tone, and other details in the personalization section, similar to what you can do with OpenClaw.

Backed by Meta AI

Another important shift: Manus has reportedly been acquired by Meta AI. If that integration fully materializes, it changes the risk profile entirely. Backing from a company like Meta means stronger infrastructure, deeper research support, and long-term stability. For businesses and professionals, that kind of ecosystem backing matters.

Where Manus Falls Short & its problems

Of course, Manus isn’t perfect.

Its biggest limitation is the lack of a terminal interface.

You can’t get under the hood in the same way you can with OpenClaw, which means you sacrifice a degree of power and flexibility. You are also locked into their ecosystem, so you can’t bring your own models to experiment with.

Manus also doesn’t currently offer the ability to spin off multiple independent agents or have separate conversations with each, a key feature of OpenClaw. While sub-agents and sub-tasks are on the roadmap, they aren’t clearly defined or available yet.

My other major issue is the cost.

The new “Agent Mode” consumes credits at an alarming rate, far more than the standard chat.

Sometimes the Manus gets into loop and consumes all the credits. You need to ask again their support team and try to get it resolved. This is a very bad experience. And has happened with me a couple of times.

This feels like a poor user experience.

While the reliability is better, the cost can be a significant deterrent for heavy users. However, Manus’s pre-built features, like its wide agents that can parallelly run research tasks, are powerful enough for many use cases, even without building custom agents.

Manus vs. OpenClaw: A Quick Comparison

My Verdict: Manus or OpenClaw - Use the one that fits your need

After a month and a half in the trenches with both platforms, my verdict is clear. While OpenClaw represents an exciting, powerful future, it’s still a tool for experts and hobbyists who are willing to accept the risks and management overhead.

It’s a fantastic project, but it’s not something I can recommend for most professionals or businesses today, who aren’t well versed with or comfortable with tech complex configurations.

Manus, on the other hand, is ready for prime time.

It’s a reliable, secure, and increasingly powerful agent that integrates seamlessly into the tools I already use. The addition of the Telegram interface has addressed my biggest pain point and transformed it into a true AI assistant that I can rely on for day-to-day tasks.

I got access to Manus thanks to Lenny’s newsletter, and if you’d like to get more such tools, do check out Lenny’s newsletter.

For developers and AI product builders like myself, the choice comes down to a first-principles analysis of what matters most: raw, untethered power or reliable, consistent execution. Right now, for the vast majority of use cases, reliability wins. Manus delivers on that promise, and with its rapid pace of development, it’s the agent I’ll be sticking with.

We are seeing a “spatial computing” revolution where every industry, from healthcare to heavy construction, is demanding three-dimensional visualization. Architects require rapid visualization props. Product designers need to instantly translate sketches into prototypes. Robotics engineers demand thousands of synthetic 3D objects to train complex navigation algorithms.

The bottleneck is that traditional 3D modeling is slow and technically demanding. Manually modeling a specific chair or a mechanical part can take days. AI-powered platforms like Tripo Studio solve this by using generative AI to produce 3D models from text prompts or 2D images. This isn’t just assisting game developers; it’s creating new efficiencies for professionals in design and engineering.

Here is how Tripo’s AI capabilities drive innovation across eight industries, including the tools to visualize them in Virtual Reality (VR).

If you want to go deeper into how AI is changing 3D workflows, check out these guides:

• 👉 From Zero to Film-Ready 3D Characters with Tripo AI — Learn how creators are generating production-quality characters using Text to 3D and advanced workflows.

• 👉 The Best AI 3D Model Generators for 2026 — Compare the top tools, including Tripo, and see how Image to 3D and generative pipelines stack up.
  

These articles complement the real-world use cases covered here and provide hands-on perspectives for builders, designers, and AI enthusiasts.

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1. Architecture and Interior Design: Instant Virtual Staging

For designers and architects, communicating a vision to a client requires “staging”, populating an empty 3D space with furniture, lighting, and decor to create a sense of place. Historically, this meant tedious hours scouring expensive asset libraries for a specific item, or modeling it from scratch.

The 3D Solution:

Using the Text to 3D model generation feature in Tripo AI, a designer can simply input a detailed description and receive a usable 3D asset in seconds.

• Workflow: Instead of generic stock assets, generate unique decor items instantly. You can fill a s

• helf with specific books, vases, or sculptures that match the client’s exact taste.

• Benefit: Provides truly custom visualizations to clients and drastically reduces the design-to-presentation time. Tripo stands out with Style Consistency. You can use prompts to ensure every object shares the same aesthetic. Tripo’s “Texture Upscale” feature will give that extra texture fidelity that makes the difference between a “game asset” and a ”photorealistic prop.” This is important because architectural renders are often viewed at 4K resolution.

VR Integration: Walking Through the Design

Architects don’t just want to see a render; they want to walk through it to understand the scale.

• Recommended Tools: Arkio or Sentio VR.

• How to Use: Export your furniture model from Tripo as a .GLB file. Drag and drop it directly into Arkio (available on Meta Quest and iPad). You can then place the furniture in a mixed-reality view of the actual room, or scale it down to “dollhouse mode” to view the layout from above.

2. Product Design and Industrial Manufacturing: Rapid Prototyping

The transition from a two-dimensional sketch to a three-dimensional prototype is often the most time-consuming phase in product development. Industrial designers may sketch dozens of iterations for a new gadget or tool before a CAD engineer even begins work.

The 3D Solution:

Designers can upload their rough concept sketches directly into Tripo’s Image to 3D model feature.

• Workflow: A concept is sketched on paper, photographed, uploaded to Tripo, and instantly converted into a 3D mesh that can be rotated and viewed from all angles.

• Benefit: A shape is useless if you don’t know what it feels like. Tripo automatically generates Physically Based Rendering (PBR) Maps (Roughness, Metallic, Normal), allowing designers to visualise whether a new headphone casing looks better in “Matte Plastic” or “Brushed Aluminum.”

This facilitates rapid iteration on volume and form in minutes, not days. It effectively bridges the gap between 2D ideation and 3D CAD, enabling faster and cheaper decision-making before costly engineering begins.

VR Integration: Ergonomic Validation

Designers need to “hold” the object to check ergonomics and scale before manufacturing.

• Recommended Tool: Gravity Sketch.

• How to Use: Import the Tripo 3D model into Gravity Sketch (Free on Meta Quest). This allows designers to sketch over the 3D model in 3D space, refining the shape or adding annotations for engineers. It turns a static model into an interactive design review.

3. Robotics: Training Data for AI Perception

Robots in logistics and warehousing must accurately perceive objects in their environment. Training a robot’s AI to recognize a tool or a package requires thousands of unique 3D examples within a virtual simulation (Sim2Real).

The 3D Solution:

Engineers use Tripo Studio to batch-generate hundreds with their Powerful API for diverse variations of common objects.

• Workflow: Generate vast and diverse datasets (e.g., 50 different types of bottles, 100 different tools) to effectively train computer vision systems.

• Benefit: It drastically lowers the cost and time required to produce synthetic training data, accelerating the development of reliable real-world robot interaction. Also Engineers can effectively use the Batch generate feature using Tripo’s API to generate many variations of 3D meshes without using the web interface or use the rich library of ready-to-use 3D model assets. This will help them to create a massive synthetic dataset for a fraction of the cost of real-world data collection.

4. Real Estate: Interactive Property Tours & Virtual Staging

Static property photos are fading. High-end listings now demand immersive virtual tours. A common issue is that professional property scans often contain “holes” or rooms that appear empty.

The 3D Solution:

Real estate marketers use generative AI to digitally stage and ‘dress’ these virtual tours.

• Workflow: Use Text to 3D model in Tripo AI to create aesthetic, specific props (e.g., “Modern fruit bowl,” “Elegant light fixture”) that match the home’s style. These are seamlessly inserted into the virtual tour.

• Benefit : Tripo’s auto-retopology ensures the models are low-poly and lightweight (small file size) so they don’t crash the viewer’s phone, while still looking high-quality. This increases viewer engagement and allows potential buyers to better visualize the space’s full potential, leading to faster sales cycles.

VR Integration: The Virtual Open House

Converting a static tour into an immersive walkthrough where clients can walk from room to room.

• Recommended Tools: Matterport or Spatial.io.

• How to Use: Upload your 3D models to a Spatial.io gallery that clients can visit via a VR headset or just a web browser. It’s as easy as uploading a photo to Instagram, creating a “Virtual Showroom” accessible from anywhere in the world.

5. Gaming and VR: The Asset Production Pipeline

Game development remains a primary driver of 3D technology. Beyond “hero” characters, the cost lies in environmental assets. A game world requires hundreds of background elements: rocks, trees, crates, and barrels

The 3D Solution:

Game studios are integrating Image to 3D model in Tripo AI to turn concept art for these props directly into textured, game-ready assets.

• Workflow: Concept artists sketch a desired prop, and AI instantly converts it into an optimized 3D model. Technical artists can then focus on final rigging.

• Benefit: Custom Texture Styles & Magic Brush. You can use the AI texture Generator to apply a “Stylized Fantasy”, creating custom texture styles, which helps keep environments cohesive.

Tripo allows you to enforce a visual style consistently across different assets. You can apply a “Hand-Painted” or “Voxel” filter to a generic barrel. Senior 3D artists are freed to concentrate on high-value, unique assets, while AI manages the production of high-volume background content that populates the game world.

6. Automotive Industry: Concept Ideation & Design Evaluation

Automotive design traditionally relies on slow processes like clay modeling. Before a car ever reaches the physical clay stage, designers go through countless digital iterations to test aesthetics and ergonomics.

The 3D Solution:

Automotive designers use Image to 3D model in Tripo AI to transform 2D sketches of specific car components, rims, steering wheels, seats, or mirrors, into instant 3D objects.

• Workflow: A designer can sketch ten variations of a steering wheel, convert them all to 3D instantly, and place them into a VR environment to check for feel and fit.

• Benefit: GLB/USDZ Export. The ability to instantly get a standard file format means designers can throw the model into a high-end renderer like VRED immediately. This allows for “Rapid 3D Sketching,” enabling instantaneous evaluation of a part’s ergonomics and aesthetics in a 3D context.

VR Integration: High-Fidelity Inspection

Checking reflections and surface quality in a photorealistic environment.

• Recommended Tool: Sketchfab (Easy/Universal) or Autodesk VRED (Pro).

• How to Use: Export the steering wheel or rim to Sketchfab. Anyone with a VR headset can hit the “View in VR” button on the website and inspect the part immediately, seeing how light interacts with the curves.

7. Healthcare: Medical Visualization and Simulation

Medical 3D libraries are often limited to idealized, “textbook” anatomy. However, real patient cases are complex, involving tumors, specific fracture types, and rare pathologies.

The 3D Solution:

Medical educators use Text to 3D model in Tripo AI to generate highly specific “Synthetic Pathologies” for training.

• Workflow: An instructor generates 3D models based on pathology descriptions: “Healthy human liver,” “Liver with early-stage cirrhosis,” “Complex tibial fracture.”

• Benefit: Tripo uses the models that have the context for Anatomical Specificity. The AI can interpret complex medical prompts.These assets facilitate a “Digital Cadaver Lab” experience, allowing students to examine and plan for rare conditions they may not encounter physically for years.

VR Integration: Collaborative Learning

Professors and students examining a model together in the same virtual room.

• Recommended Tool: Engage VR or [suspicious link removed].

• How to Use: Professors can import specific pathological models generated by Tripo into a shared virtual classroom in Engage VR. Students put on headsets and can pass the “virtual organ” around to discuss the specific defect.

8. E-commerce/Retail: Interactive Product Previews

In online retail, 2D product photos are becoming insufficient. Customers expect to see a product from every angle or use Augmented Reality (AR) to place the item in their living space.

The 3D Solution:

AI is used to rapidly create a full 3D model from as little as a single product photograph or text description.

• Workflow: An e-commerce platform feeds a new product photo into the AI, which generates an interactive 3D model. This model is then instantly usable in an AR application on the customer’s phone.

• Benefit : Automated Texture Mapping. Tripo doesn’t just make the shape, it wraps the photo onto the model as a texture, preserving the brand logos and fabric details crucial for retail. Provides a premium, high-engagement shopping experience, reduces customer friction, and significantly lowers return rates.

Viewing Your Models in VR (The Easy Way)

You don’t need expensive enterprise software to actually see these models in the real world. If you have a Meta Quest 3 or Apple Vision Pro, viewing your AI creations is incredibly accessible.

Think of Sketchfab as the “YouTube of 3D”, just upload your model, open their site in your headset’s browser, and hit “View in VR” to stand right next to it. If you need to collaborate, drag and drop your model into a free Spatial.io room, send a link to your team, and walk around the object together as avatars. Or, for the quickest fix, just use the native approach: export as .USDZ (for Apple) or .GLB (for Quest) directly from Tripo AI, and AirDrop or email it to yourself, and open the file to see it sitting on your coffee table in Mixed Reality as if it were physically there.

The Future is 3D: A Fundamental Professional Tool

The hurdle to widespread 3D adoption has been the technical complexity of the software. Tools like Tripo Studio is dismantling that barrier, placing professional-grade 3D generation within reach of every creator, designer, and engineer.

Whether you are an architect selling a vision, a product designer refining a shape, a robotics engineer training perception systems, or an e-commerce manager enhancing product pages, the ability to generate 3D assets is becoming an essential professional superpower.

With its versatile toolkit—from Text-to-3D and Image-to-3D generation to batch processing, API access, PBR material support, and style-consistent texturing—Tripo adapts to the unique demands of each industry. We are moving from a world where 3D was a specialized discipline to one where it is a fundamental tool for communication, creation, and innovation across every industry. The spatial computing revolution is here, and Tripo is your gateway to being part of it.

Disclosure: This post is an advertorial.

I tried running my blog with a team of AI agents. Here’s what actually happened.

I wanted a personal assistant who never sleeps. Someone to handle the repetitive stuff scheduled tasks, tool research, email management - while keeping my context and preferences front and center. When OpenClaw started going viral in early 2026, it seemed like exactly what I was looking for.

If you haven’t yet heard of OpenClaw - it’s basically an open-source personal AI assistant that lives where you do: in your messaging apps. Unlike cloud-only assistants, OpenClaw is a local-first agent runtime that bridges powerful LLMs (like Claude 4.5 Sonnet) with your actual system tools.

But it has a challenging setup process, broken configs, and hours of debugging before I got my first agent to respond. But once it worked, it really worked good for sometime, till you try to more complex tasks. However, I must this is still a product that you should learn and know as it has a great potential.

This is the honest story of how I built Mission Control to manage TheToolNerd.com, - what works, what breaks, and why you should still try it.

(inspired by this post by Bhanu Teja P)

If you wanted to get started with OpenClaw easily check out this article:

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What I Built - Mission Control Dashboard

Mission Control is a 7-agent system running on OpenClaw, purpose-built to handle TheToolNerd operations

• Cron Tasks

• Content Tasks

• Product Tasks

The whole workflow can work autonomously but I don’t as I still can’t rely on it.

The backend: Convex.dev handles real-time database, task queues, and agent status. It’s the only reliable part of the stack.

The infrastructure: Started on Railway, because I had the subscription from Lenny’s Newsletter - but I would recommend Hostinger, if you are starting New.

The Problem I Was Solving

Running TheToolNerd.com involves a lot of moving parts:

• Tool research: Finding new AI tools, checking pricing, writing descriptions

• Content pipeline: Articles, newsletters, social posts

• Email management: Partnership inquiries, reader questions

• Database updates: Adding tools, updating affiliate links

• Social monitoring: Watching for trending tools, competitor moves

I wanted an AI system that could handle 70% of this on autopilot - with my preferences, my tone, my context baked in. Not a generic assistant. My assistant.

OpenClaw promised exactly that: specialized agents with persistent memory, scheduled tasks, and the ability to actually do things (not just chat). But it’s not yet there.

Tools I have connected

• Supabase — To access my ToolNerd Database

• GitHub CLI — To let agents create commits, open PRs, and manage code changes directly from workflows.

• Vercel CLI — To deploy site updates and trigger builds automatically when agents ship changes.

• Zoho Mail — To read, draft, and manage partnership and reader emails inside agent workflows.

• Agent Mail — To give agents a dedicated inbox for notifications, task updates, and automated communications.

• Make.com — To connect OpenClaw with external apps and orchestrate automations without custom code.

• Brave API — To run live web searches so agents can discover tools, news, and fresh information.

• Firecrawl API — To reliably scrape and extract structured data from websites for research tasks.

How It Works (When It Works)

1. Discovery

Every morning at 7 AM, Scout wakes up and scans YC launches, GitHub trending, and Product Hunt. When it finds something relevant, it creates a task card in the Mission Control Dashboard (Inbox column).

This it works consistenly most of the time.

2. My Approval

I get a Telegram notification:
“New AI tool found: [Name] - [One-line description].”

In this image below, I actually gave it a voice note, not even typed it and it did the job.

I can approve on the Mission Control Dashboard if I want Scout to research it further. I often approve from the telegram itself and it does the work.

3. Auto-Assignment

The system spawns Scout as a sub-agent, updates status to WORKING, and moves the task to In Progress.

4. Deep Research

Scout extracts tool info using Browser Tools/Web Fetch Tool via Browser API or Firecrawl API.

It adds findings as comments on the task card, then moves it to Review.

5. SteveJobs Review

Every 4 hours, SteveJobs checks the Review column, prioritizes tasks, and sends me a Telegram brief:

📋 DAILY PRIORITY LIST — Feb 17
NEEDS YOUR APPROVAL
🔥 AI image generator [Tool X] — 50% cheaper than Midjourney
New YC launch [Tool Y] — Workflow automation, trending on HN

✅ READY TO EXECUTE
Research on [Tool Z] → Add to Supabase?

📊 SQUAD STATUS
Scout: Researching 2 tools
SocialHunter: Found 3 opportunities

6. Execution

If I approve, SteveJobs either:

• Tells Scout to add the tool to our Supabase database

• Assigns Ralph to build a new feature for the site

• Flags it for my manual review if it’s complex

Building Agent Skills is key to running OpenClaw effectively.

I’ve created 3–4 dedicated skills specifically for ToolNerd workflows. Here’s the process I followed:

• Step 1 — Break down the task: I’d go to Claude Code and ask it to outline the work step by step until the workflow felt solid. I would actually work along with Claude Code to ensure the output is as expected.

• Step 2 — Convert into a skill: At the end of the conversation, I’d ask Claude to package those steps into a reusable skill.

• Step 3 — Deploy via Telegram: Once ready, I’d update the skill in OpenClaw directly through Telegram so agents could start using it.

This simple loop — design → convert → deploy — made it much easier to standardize repetitive operations.

There are many companies building OpenClaw deployment wrappers and already hitting $20K MRR, check out the article here.

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The Reality: What Breaks

OpenClaw Updates Always breaks what I had configured

Every time I update OpenClaw to the latest version, something breaks. Config files get overwritten. Environment variables disappear. Agents stop responding. Then I’m in the terminal debugging, fixing configs, restarting services.

It’s not “set and forget” — it’s “set and maintain constantly.”

OpenClaw is Token Hungry

OpenClaw consumes a ton of tokens. Context windows fill up fast with agent spawns, sub-agents, tool calls, and memory syncs. I burned through my OpenAI credits in a day.

My solution: Switched to Kimi K2.5 paid plan of $40. Better token limits, sufficient quality for my use cases, and significantly cheaper. I use it extensively now.

Context Loss

OpenClaw gets confused. It forgets context between sessions.

The compaction/memory flush is supposed to help, but agents still lose track of what they were doing.

I have to re-explain preferences repeatedly — “No, I prefer bullet points over paragraphs” shouldn’t need to be said five times.

Not Reliable Enough for Production

The system works 80% of the time. The other 20%? Agents fail silently, tasks get stuck in limbo, heartbeats miss spawns. I can’t trust it for mission-critical work like sending newsletters or publishing articles. It’s a helper, not a replacement for human oversight.

The Honest Assessment

What Works

Specialized agents with clear roles

Scout doesn’t do SteveJobs’s job. Ralph doesn’t research. Boundaries matter — when each agent has a narrow focus, they perform better.

ConvexDB as source of truth

Real-time sync, reliable, never breaks. The dashboard shows accurate status. Even when OpenClaw glitches, I can see exactly what’s happening in the database. This is actually good, if someone uses it.

To be honest, I started realising that I am able to do a lot without even referring to the dashboard

When agents work, they save real time

2–3 hours daily on research, monitoring, and data entry. Scout finds tools faster than I could. SocialHunter catches opportunities I’d miss. But this only happens when the system is stable.

What Doesn’t Work

• Maintenance burden : Updates break things. Configs need tweaking. Agents need restarting. It’s a part-time job.

• Token costs : Expensive to run at scale. Budget planning is essential.

• Reliability : 80% success rate isn’t good enough for business-critical workflows. I still manually check everything before publishing.

• Context management: Agents forget preferences. You repeat yourself. Frustrating.

What can I build with OpenClaw

Chief of Staff agents to coordinate priorities and keep everything moving.
Marketing agents to research, draft, schedule, and optimize campaigns.
Personal assistants to manage your inbox, calendar, and daily operations.
Sales agents to qualify leads, follow up, and keep your pipeline warm.

…and countless other roles you can spin up as needed.

Build a virtual team that runs the day-to-day so you can focus on what actually grows the business.

My Verdict

OpenClaw has great potential. The architecture is good and evolving. The agent system is powerful. But it’s not ready for prime time as a “set and forget” solution.

Maintenance is hard. Updates break things. Token costs are high. Reliability is spotty.

I keep using it because when it works, it’s magical. Having Scout find tools while I sleep, having SteveJobs prioritize my morning, having Ralph build features from a sentence — that’s the future.

If you try OpenClaw, go in with eyes open. It’s a hobby project that happens to be powerful, not enterprise software. Beautiful, powerful, frustrating.

What I Would Do Differently

1. Skip Railway, use Hostinger VPS from day one — more control, cheaper, no surprise bills

2. Start with Kimi K2.5 - better token economics, don’t burn through OpenAI credits learning

3. Build smaller - 3 agents, not 5. Less coordination overhead while learning

4. Have Claude Code/Codex on standby - you will need it for debugging

5. Keep expectations low - this is experimental tech, not a productivity miracle

What Just Happened

February 14, 2026: Peter Steinberger, the Austrian developer behind OpenClaw (formerly Clawdbot, briefly Moltbot), announced he’s joining OpenAI.

February 16, 2026: OpenAI CEO Sam Altman confirmed the hire on X, stating Steinberger will “drive the next generation of personal agents.”

What is OpenClaw?

OpenClaw is an open-source AI agent framework that connects AI models to your real-world tools — email, calendars, Slack, web browsers, and more — so the AI can actually do things, not just chat.

The simple version: You give it natural language commands like "check my calendar for tomorrow and email the team the agenda," and it executes the whole workflow autonomously.

Key features:

• Multi-tool orchestration — chains email, messaging, CRMs, browsers, calendars together
• Error handling & caching — manages state across long workflows
• Self-hostable — run it on your own infrastructure

Why it blew up: Steinberger shared demos of OpenClaw running real workflows through a simple chat interface, coordinating multiple tools from a single conversation. It gave people a concrete glimpse of what “AI agents that actually do things” could look like.

It's not another chatbot wrapper. It's an orchestration layer that handles authentication, API calls, error recovery, and parallel execution across multiple services

OpenClaw Growth Numbers

By the Numbers

• GitHub Stars: > 200,000

• Forks: 36K

• Monthly Losses (personal subsidy): $10,000–$20,000

• Acquisition Offers: Interests from Meta and OpenAI

• Time to Viral: ~3 months (Nov 2025 – Feb 2026)

• Previous Names: Clawdbot → Moltbot → OpenClaw

The Timeline

• November 2025: Steinberger open-sources Clawdbot

• January 2026: Anthropic raised trademark concerns over name similarity to Claude

• Late January 2026: Rebrands to Moltbot, then quickly to OpenClaw

• February 14, 2026: Announces OpenAI move

• February 15, 2026: Altman confirms; OpenClaw to become a foundation

If you would like to setup and try it out yourself, check out my previous article and also read how people have built wrappers around it and minting money

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Why Steinberger Chose OpenAI

Steinberger had options :

• Offers from major tech companies, including Meta

• Pursuing a new venture-backed startup

• Potentially significant financial upside

What he chose instead was joining OpenAI to focus on advancing AI agents at global scale.

Four reasons why he decided to join OpenAI

Not interested in running a company

He’s done the company thing and wasn’t interested in doing that again. For him it was all about the bigger goal.

Mission match
Both sides believe AI agents are the next platform after smartphones.

Access
Direct exposure to frontier research and collaboration with leading researchers.

Speed
“Teaming up with OpenAI is the fastest way to bring this to everyone.”

“I’m a builder at heart… What I want is to change the world, not build a large company.”

What Happens to OpenClaw?

Good News for Open Source

• OpenClaw will become a foundation (non-profit)

• Will remain open source and independent

• OpenAI will continue supporting the project

• Steinberger will remain involved

Steinberger’s Words:

“It’s always been important to me that OpenClaw stays open source and given the freedom to flourish. Ultimately, I felt OpenAI was the best place to continue pushing on my vision and expand its reach.”

The Real Question

Will users trust OpenClaw with sensitive workflows now that it’s linked to OpenAI?

Historical Context

It’s not the first time an open-source project thrived alongside strong corporate involvement. Some of the most critical infrastructure in tech followed this path:

Linux: started by Linus Torvalds, heavily backed by companies like IBM, Intel, and Red Hat — became the foundation of modern computing and the backbone of the cloud.

Kubernetes: created at Google and donated to the CNCF with neutral governance is now the industry standard for container orchestration.

PostgreSQL: supported by multiple companies and a strong foundation remains one of the most trusted open-source databases in production.

VS Code: developed and open-sourced by Microsoft became the most widely used developer editor globally while maintaining a vibrant extension ecosystem.

Rust: originally incubated at Mozilla and now governed by the Rust Foundation -continues to grow as a major systems programming language adopted by companies like Microsoft, Amazon, and Google.

Strong governance and community ownership have historically allowed open-source projects to thrive even when closely tied to large organizations.

OpenClaw could become the Linux of AI agents if the foundation works.

The real story isn’t whether OpenClaw stays open source — it’s whether agent infrastructure becomes a new battleground between open ecosystems and platform companies.

Enter the SaaS wrapper wave: one-click platforms that deploy OpenClaw in minutes. In early 2026, we’ve seen a literal explosion of these services. Some hit $20K+ MRR in less than a week. Founders who built these in a weekend are now listing them for life-changing exits.

But saturation hit fast. X threads are now buzzing with “wrapper bubble” warnings and fire sales. Is this a sustainable business model, or just a high-speed arbitrage play on user laziness?

What Are OpenClaw Wrappers?

Instead of cloning GitHub repos and fighting with Docker, these wrappers offer:

• Zero-Config Deploy: One click and your bot is live.

• Managed Hosting: No need to buy a Mac Mini or manage a Hetzner VPS.

• Pre-built Integrations: Instant connection to Telegram, WhatsApp, and Discord.

The value prop: The power of OpenClaw, without the terminal.

If you are looking to deploy on own, check out this article:

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The 10 Trending SaaS Wrappers (Feb 2026)

Here are the real players currently dominating the OpenClaw ecosystem, ranked by traction and unique utility.

1. SimpleClaw — The $29K MRR Breakout

Official Link: simpleclaw.com

Key Feature: Under 1-minute deployment for non-technical users.

Traction: $29,911 Total Revenue (Verified) [1]

Status: Listed for sale at $2.25M [2]

SimpleClaw is the poster child for this movement. It proved that “dead simple” is a feature people will pay premium prices for.

2. SetupClaw – The “White Glove” Service

Official Link: setupclaw.com

Key Feature: Personal setup service; they even offer to ship you a pre-configured Mac Mini.

Traction: $14,028 Total Revenue [1]

While others focus on cloud, SetupClaw leans into the “local-first” soul of OpenClaw, helping users run it on their own hardware without the headache.

3. QuickClaw – OpenClaw on Your iPhone

Official Link: quickclaw.app

Key Feature: Mobile-first interface; get your agent running on your phone in 30 seconds.

Traction: $2,955 Total Revenue [1]

QuickClaw solves the “mobile gap,” providing a native-feeling app experience for an ecosystem that usually lives in a terminal or a Telegram chat.

Other options to Install on your own

Emergent.sh: They’ve turned OpenClaw into a “chip” that you can launch instantly. No terminal required.

Hostinger : With Hostinger’s One-Click OpenClaw VPS you get a real server with your own domain and 99.9% uptime guarantees in $5 per month.

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4. ClawWrapper – The “SaaS-in-a-Box”

Official Link: clawwrapper.com

Key Feature: A white-label platform for other founders to launch their own wrappers.

Traction: $10,280 Total Revenue [1]

The “meta” play. ClawWrapper sells the shovels for the gold rush, allowing anyone to rebrand and ship a wrapper in hours.

5. LobsterFarm – The Power User’s Cloud

Official Link: lobsterfarm.ai

Key Feature: One-click VPS deployment with “eject” capability (take your data and leave).

Traction: Growing rapidly among the “privacy-conscious” crowd.

LobsterFarm differentiates by promising no vendor lock-in, appealing to the core open-source ethos of the OpenClaw community.

6. YourClaw – WhatsApp Productivity Hub

Official Link: yourclaw.dev

Key Feature: Deep focus on WhatsApp integration for calendar and email management.

Status: Listed for sale [1]

YourClaw targets the “WhatsApp-first” demographic, turning the world’s most popular chat app into a lightweight productivity OS.

7. ClawOn.Cloud – The Enterprise-Lite Wrapper

Official Link: clawon.cloud

Key Feature: Focus on security and isolated environments for small teams.

Traction: Steady growth in the B2B segment.

ClawOn.Cloud is positioning itself as the “secure” alternative, moving away from the indie hacker vibe toward professional reliability.

8. ClawSimple – The Budget Operator

Official Link: clawsimple.com

Key Feature: Low-cost, automated deployment for multiple bots.

Traction: Popular for users running “bot farms” or multiple agents.

If you need five different agents for five different tasks, ClawSimple is the most cost-effective way to manage the fleet.

9. RunClaw – The High-Performance Host

Official Link: runclaw.sh

Key Feature: Optimized for speed and low-latency agent responses.

Status: Listed for sale [1]

RunClaw focuses on the infrastructure layer, ensuring that your agent doesn’t lag when performing complex browser-based tasks.

10. CloudClaw – The Terminal-Free Experience

Official Link: cloudclaw.dev

Key Feature: A completely GUI-based setup; you never see a single line of code.

Status: Listed for sale [1]

CloudClaw is built for the “normie” market—people who want the utility of an AI agent but have zero interest in how the engine works.

Pricing

Please find the probable pricing.

The Sustainability Question: Foundation or Fad?

The objective of these platforms is clear: make OpenClaw easy. But as we see OpenClaw evolve into a foundation for agentic AI, a critical question arises: Will these indie wrappers sustain?

The “Thin Wrapper” Trap

Most of these startups are “thin wrappers.” They add a UI and a payment gateway to Peter Steinberger’s open-source code. When the barrier to entry is this low, competition becomes a race to the bottom on price.

The Exit Wave

The fact that SimpleClaw, YourClaw, and CloudClaw are all listed for sale simultaneously on TrustMRR is a massive signal. Founders are cashing out because they know the “moat” is non-existent.

Once the official OpenClaw project releases an “Official Cloud” or a “One-Click Installer,” 90% of these wrappers could vanish overnight.

The First-Principles View

If we look at this from first principles:

1. The Problem: OpenClaw is hard to install.

2. The Solution: Wrappers make it easy.

3. The Risk: “Ease of use” is a feature, not a product.

For a wrapper to survive, it must move beyond deployment. It needs to offer proprietary skills, unique data integrations, or superior security that the base OpenClaw doesn’t provide.

My Verdict

The OpenClaw wrapper boom is a masterclass in velocity. It shows that if you can solve a friction point for a trending technology, you can generate $20K MRR in a weekend.

However, as a long-term business, most of these are “feature startups.” They are built on someone else’s foundation. Unless they pivot to adding deep, proprietary value, they will likely be absorbed by the foundation itself or crushed by the next wave of automation.

Should you use one? Yes, if you value your time. Should you build one? Only if you’re prepared to sell it in 30 days.

References

[1] TrustMRR. (2026). OpenClaw startups - Verified revenue on TrustMRR. https://trustmrr.com/special-category/openclaw

[2] santisiri. (2026). SimpleClaw $2.25M Exit Listing. X (formerly Twitter).

[3] OpenClaw Official GitHub. https://github.com/openclaw/openclaw

I can't design. But I can code my way out of it.

I don’t know difference between Neo-Brutalism and Minimalism. I can’t tell you why some websites feel premium while others feel cheap. I just know what I like when I see it.

And when I see a beautiful website? I want to build something that looks like that.

But here’s the problem: I don’t have the deep technical design knowledge. I’m a non-technical guy trying to build products that look good.

My process was painful — screenshots, inspecting elements, trying to decode the “secret sauce.”

I needed a better way. Something that could look at any website and hand me the recipe. That was the birth of “ designstyles.xyz”

What I Built (In Short)

I built designstyles.xyz — a tool that lets you:

• Paste any website URL

• Instantly extract its design style (Minimalist, Neo-Brutalist, Glassmorphism, etc.)

• Get colors, typography, and layout cues

• Copy a ready-to-use CSS / prompt to recreate the look

All without knowing design theory.

If you would like to know about the latest updates in AI & Productivity do subscribe

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How I Built It: The End-to-End Architecture

• User Input → Astro Frontend → Firecrawl API

• Firecrawl → Screenshot + Branding JSON → Mistral Vision

• Mistral → Design Classification → Supabase (JSONB)

• Database → Render Brand Card → Copy-to-Clipboard

The Discovery: Firecrawl’s Branding Format

I went looking for tools that could “see” and understand websites. I found Firecrawl.

Their /scrape endpoint with the branding format is a something that I needed. One API call extracts everything:

{
  "url": "https://example.com",
  "formats": ["branding", "screenshot"]
}

The branding format returns structured design data:

{
  "colors": {
    "primary": "#1a1a1a",
    "secondary": "#f5f5f5",
    "accent": "#1d9bf0",
    "text": "#333333"
  },
  "typography": {
    "fonts": ["Inter", "SF Pro"],
    "headingSizes": [48, 32, 24],
    "lineHeight": 1.6
  },
  "assets": {
    "logo": "https://...",
    "favicon": "https://..."
  }
}

Adding Visual Intelligence: Mistral Vision

Structured data is great, but classifying design styles is visual. That’s where Mistral comes in.

Instead of chasing giant, expensive models, Mistral focuses on high signal per parameter — models that are small, sharp, and practical for real products.

I use the Mistral’s Ministral 3B vision-capable model because it’s the perfect trade-off for this use case. At ~3B parameters, it’s cheap, fast, and surprisingly good at visual reasoning. I don’t need a massive model to write essays — I need one that can look at a screenshot, understand layout, spacing, typography, and vibe, and give me a clean, structured design judgment.

Why Ministral 3B works so well here:

• Cost-effective: Inference is cheap enough that each analysis costs cents, not dollars

• Fast: Low latency keeps the UX snappy

• Good visual intuition: Strong enough to classify design styles and hierarchy without fine-tuning

• No overkill: Bigger models wouldn’t add meaningful value for this task

In short: Mistral 3B is “just enough intelligence” — which is exactly what you want when you’re turning screenshots into product decisions at scale.

Model: https://huggingface.co/mistralai/Ministral-3-3B-Instruct-2512

Check out this powerful demo to know its capabilities : https://huggingface.co/spaces/mistralai/Ministral_3B_WebGPU

I pass the Firecrawl screenshot to Mistral’s vision model with this prompt:

Analyze this website screenshot:
- Identify the design style (Neo-Brutalism, Glassmorphism, Minimalism, etc.)
- Describe the visual hierarchy
- Generate a CSS/prompt to replicate this aesthetic

Why This Stack?

• Firecrawl handles JavaScript-heavy sites that break traditional scrapers. The branding format is purpose-built for design extraction. ( around $0.006 per credit)

• Mistral is cheap ($0.07/1K images) and fast. No fine-tuning needed — it understands design aesthetics out of the box.

• Supabase JSONB stores nested design data without rigid schemas. Query any color, font, or style instantly.

Total cost per analysis: ~$0.0067

Builder Tip: Self-Host Supabase to Avoid Extra SaaS Costs

I didn’t use Supabase Cloud.

For Designstyles, I self-hosted Supabase on my Hostinger VPS ($5 per month). My needs were simple (Postgres + JSONB). Hostinger is affordable for smaller projects like mine.

Why this worked:

• Supabase is open-source → easy to self-host

• JSONB is perfect for nested design data

• No usage-based pricing

• One less SaaS bill

I already had the server running, so the extra cost was basically zero.

Final Thoughts

This started with frustration, not a startup idea.

I didn’t need better design taste—I needed a faster way to borrow it. Most websites already expose their design decisions; Designstyles simply turns that into something usable.

If you’re a builder who struggles with design, try it once:
paste a URL, get the design system, and move on with your build.

If you’re building solo, here’s the lesson:

Don’t overbuild. Don’t overthink.
Pick cheap primitives, connect them well, and ship.

The problem? Setting it up can quickly turn into a nightmare, especially if you’re not very technical.

A local-first AI agent with deep system access isn’t something to casually install on your personal machine. One wrong move and you could end up deleting files or triggering actions you didn’t fully intend. That’s why setup matters just as much as the excitement.So it’s important we setup in a secure environment & play with it.

In this article, I have shortlisted few easy ways to set up OpenClaw and start running..before that let’s understand what is OpenClaw

Check out my other articles and Subscribe to stay updated

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What is OpenClaw and Why is Everyone Talking About It?

OpenClaw (which you might remember as Clawdbot or Moltbot) is an open-source personal AI assistant that lives where you do: in your messaging apps. Unlike cloud-only assistants, OpenClaw is a local-first agent runtime that bridges powerful LLMs (like Claude 3.5 Sonnet) with your actual system tools.

Why it’s powerful:

Multi-Channel Mastery: Chat with it via WhatsApp, Telegram, Discord, or Slack. You’re not locked into a single platform—OpenClaw goes where you are.

Non-technical users: start with Telegram
If you’re not very technical, Telegram is by far the easiest channel to set up.
It doesn’t require QR scans, browser pairing, or device-level permissions like WhatsApp.
You simply create a bot, paste the token, and you’re live in minutes.

If this is your first time using OpenClaw, Telegram is the smoothest and least error-prone starting point.

Deep System Access: It can run shell commands, browse the web, and manage files. This isn’t a chatbot that’s afraid of your filesystem; it’s an agent that actually does things.

Persistent Memory: It remembers your preferences and past conversations locally. No more explaining context every time you chat.

Proactive Intelligence: It doesn’t just wait for you; it can monitor your inbox or run cron jobs autonomously. Imagine an AI that actually gets work done while you sleep.

“OpenClaw is the most interesting project I’ve come across recently—an open-source personal AI assistant that can call almost any tool through plugins.” — @openclaw

Why I’m Excited About OpenClaw

It does feel like a step towards AGI but still a long way to go as it’s filled with bugs & security challenges.

Privacy First: Your data stays on your infrastructure. No vendor lock-in, no mysterious API calls to a third party.

Zero Latency: Because it’s built on Node.js and optimized for agentic workflows, it feels snappy. You won’t be staring at loading spinners.

The “Lobster” Ecosystem (Clawhub): The community-built skills (AgentSkills) are exploding. There’s a skill for almost everything now—from financial analysis to social media management.

Hardware Flexibility: You can run it on a $500 Mac Mini or a $5/mo cloud VPS. The choice is yours.

Website : https://openclaw.ai/
Github Repo: https://github.com/openclaw/openclaw

The “Quick & Easy” Deployment Paths

If you want to skip the 3-hour manual setup, here are your best options. I’ve ranked them by speed and ease of use.

1. Emergent.sh: The “5-Minute” Path (Fastest)

Emergent.sh is currently the gold standard for “I want it now.” They’ve turned OpenClaw into a “chip” that you can launch instantly. No terminal required.

Step-by-Step:

1. Navigate to Emergent.sh: Go to the Emergent Home Page.

   

2. Select the MoltBot Chip: Look for the red lobster icon.

3. Hit Launch: Emergent automatically provisions a VM, installs all dependencies, and sets up the agent runtime.

4. Connect LLM: By default, it uses Emergent’s optimized keys, but you can paste your own Anthropic API key for better rates.

5. Publish: Click the “Publish” button in the top right to ensure your bot stays online 24/7.

Best for: Beginners who want to test OpenClaw without touching a single line of code. This is the path I’d recommend if you’re just curious.

Checkout this article & video to know more : https://emergent.sh/tutorial/moltbot-on-emergent

Time to Live: ~5 minutes

2. Hostinger: The “Pro-Level” VPS Path

If you want full control and a dedicated IP, Hostinger’s One-Click OpenClaw VPS is the way to go. You get a real server with your own domain and 99.9% uptime guarantees in $5 per month.

Step-by-Step:

1. Pick a Plan: Head to Hostinger and select a KVM VPS plan (I recommend KVM 2 for smooth performance) or Click on this link and click on deploy https://www.hostinger.com/in/vps/docker/openclaw

   

2. Select OpenClaw Template: During checkout, choose the “OpenClaw” Docker template.

3. Get Your Token: Once deployed, check your dashboard for the OPENCLAW_GATEWAY_TOKEN.

4. Access the UI: Navigate to your VPS IP address. Paste your token to unlock the Control UI.

5. Scan the QR: Go to the “Channels” tab, select WhatsApp, and scan the QR code with your phone. You’re live. Else select Telegram.

Best for: Users who want a permanent, high-performance “AI Employee” with 99.9% uptime. This is for people who are serious about running OpenClaw long-term.

Time to Live: ~10 minutes

Pricing: Starting at $2.99/month (promotional), regular pricing $5.99/month and up.

3. Railway: The “Developer-Friendly” Cloud Path

Railway.app offers a middle ground—cloud-native scaling with a beautiful UI. If you’re a developer who loves clean dashboards and easy logs, this is your jam.

Step-by-Step:

1. Use the Template: Click the OpenClaw Railway Template.

   https://railway.com/deploy/clawdbot-railway-template

   

2. Configure Variables: Set your SETUP_PASSWORD and ensure the port is set to 8080.

3. Add a Volume: This is crucial. Attach a volume at /data so your AI doesn’t “forget” everything when the server restarts.

4. Run the Wizard: Visit your generated Railway URL + /setup to finish the configuration in your browser.

Best for: Developers who want easy logs, version control, and simple scaling. Railway’s dashboard is a joy to use.

Time to Live: ~8 minutes

Pricing: Pay-as-you-go, starting at $0 with a free tier. But if you want to make good use of it, its preferred to go with a Always deployed Server.

4. Render: The “Infrastructure as Code” Path

Render allows you to deploy OpenClaw using an Infrastructure as Code approach, defining your entire stack declaratively. If you love YAML and reproducible deployments, Render is for you.

Step-by-Step:

1. Deploy to Render: Click the Deploy to Render link.

2. Set SETUP_PASSWORD: Render will prompt you to set this environment variable.

3. Build and Deploy: Render will automatically build the Docker image and deploy your service.

4. Complete Setup Wizard: Once deployed, navigate to https://<your-service>.onrender.com/setup, enter your SETUP_PASSWORD, select a model provider, and configure channels.

Best for: Users who prefer declarative infrastructure and easy scaling options. Render’s free tier is generous.

Complete Guide : https://docs.openclaw.ai/render

Time to Live: ~12 minutes

Pricing: Free tier available, paid plans start at $7/month.

5. Northflank: The “One-Click Template” Path

Northflank provides a one-click template for OpenClaw, offering a streamlined deployment experience. It’s similar to Emergent but with more control over infrastructure.

Step-by-Step:

1. Deploy OpenClaw: Click the Deploy OpenClaw button to open the template.

2. Create Account: If you don’t have one, create an account on Northflank.

3. Deploy Stack: Click “Deploy OpenClaw now” and set the SETUP_PASSWORD environment variable.

4. Complete Setup: Once deployed, open the public OpenClaw URL and complete the setup at /setup.

Best for: Users who want a quick, no-terminal deployment with persistent storage and a balance between simplicity and control.

Complete Guide: https://northflank.com/stacks/deploy-openclaw

Time to Live: ~7 minutes

Pricing: Free tier available, paid plans start at $5/month.

6. Cloudflare MoltWorker: The “Serverless” Path

Cloudflare MoltWorker allows you to run OpenClaw as a self-hosted AI agent on Cloudflare’s serverless platform, leveraging their Sandbox SDK and Developer Platform APIs. This is the most advanced option—perfect if you’re already in the Cloudflare ecosystem.

Step-by-Step:

1. Clone Repository: Clone the MoltWorker GitHub repository locally.

2. Install Dependencies: Run npm install in the project directory.

3. Set API Key: Use npx wrangler secret put ANTHROPIC_API_KEY for direct Anthropic access, or configure Cloudflare AI Gateway for Unified Billing.

4. Generate Gateway Token: Generate and set a gateway token using export MOLTBOT_GATEWAY_TOKEN=$(openssl rand -hex 32)and echo "$MOLTBOT_GATEWAY_TOKEN" | npx wrangler secret put MOLTBOT_GATEWAY_TOKEN.

5. Deploy: Run npm run deploy to deploy your MoltWorker.

6. Access Control UI: Navigate to

https://your-worker.workers.dev/?token=YOUR_GATEWAY_TOKEN

(replace with your worker subdomain and token) and complete the setup, including Cloudflare Access for UI protection.

Pricing: Requires a Cloudflare Workers Paid plan (starting at $5 USD/month) for Sandbox containers. Additional costs may apply for AI Gateway usage depending on your model provider and billing method.

Best for: Developers seeking a serverless, highly scalable, and secure deployment with Cloudflare’s ecosystem. This option gives you the most control and the best performance.

Complete Guide: https://blog.cloudflare.com/moltworker-self-hosted-ai-agent/

Time to Live: ~15 minutes (if you’re familiar with Cloudflare)

Do You Need a Mac Mini?

You’ll often see people on X/Twitter showing off OpenClaw running on a dedicated Mac Mini. While that’s the “ultimate” local setup for maximum privacy and power, you don’t need it to start.

The cloud options above (Emergent, Hostinger, Railway, Render, Northflank, Cloudflare MoltWorker) give you the same power for a fraction of the upfront cost. Start with a VPS, and if you fall in love with the “Lobster life,” then go buy the hardware.

Pricing Comparison Table

Emergent.sh

• Starting Price: $0 (free tier)

• Setup Time: ~5 minutes

• Best For: Beginners, quick testing

• Free Tier: Yes

Hostinger

• Starting Price: $5.99 / month

• Setup Time: ~10 minutes

• Best For: Long-term usage, dedicated IP

• Free Tier: No

Railway

• Starting Price: $5 (Hobby Plan)

• Setup Time: ~8 minutes

• Best For: Developers, logs, clean DX

• Free Tier: Yes

Render

• Starting Price: $19 per month

• Setup Time: ~12 minutes

• Best For: Infrastructure as code, YAML lovers

• Free Tier: Yes

Northflank

• Starting Price: ~ $5 - $10 per month

• Setup Time: ~7 minutes

• Best For: Balance of simplicity and control

• Free Tier: Yes

Cloudflare MoltWorker

• Starting Price: $5 / month

• Setup Time: ~15 minutes

• Best For: Serverless deployments, high scalability

• Free Tier: No

My Verdict

If you’ve been sitting on the sidelines because the setup looked too hard, try Emergent.sh today. It takes 5 minutes, and you’ll finally understand why the AI community is obsessed with this red lobster.

The barrier to entry is gone. There’s no excuse not to try it.

Ready to deploy?

• 🚀 Deploy on Emergent.sh

• 🌐 Get Hostinger VPS

• 🚂 Launch on Railway

• 🚀 Deploy on Render

• ☁️ Deploy on Northflank

• ⚙️ Deploy with Cloudflare MoltWorker

What’s Next?

Once you’ve deployed OpenClaw, the real fun begins. You can:

• Connect WhatsApp/Telegram: Scan the QR code and start chatting with your AI from your phone.

• Add Skills: Browse the growing library of community-built skills to extend functionality.

• Integrate Tools: Connect your favorite apps—Gmail, Slack, GitHub, Zapier, and more.

• Automate Workflows: Set up autonomous tasks that run on a schedule or trigger.

The possibilities are endless. OpenClaw isn’t just another tool; it’s the beginning of truly autonomous AI agents in your life.

A single stylized character typically requires sculpting, retopology, UV unwrapping, texturing, rigging, and extensive cleanup before a single frame can be animated.

In 2026, this bottleneck will be dismantled by Tripo AI. This guide walks through the process of transforming a single character image into an animation-ready 3D model, ready for professional use in Blender or Unity.

If you’re still exploring options, we’ve also compared the best AI 3D model generators available today—covering strengths, pricing, and use cases—before diving deep into Tripo AI.

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Why 3D Characters Beat Pure 3D AI Video for Consistent, Scalable Content

With the rapid improvement of AI video generation, it is natural to ask:

Why bother with 3D software at all? Gemini Veo3.1 or Sora can do it easily

While AI video excels at one-off clips and visual experimentation, it often struggles with character persistence. Maintaining consistent proportions and features across multiple scenes is a significant challenge for generative video.

A 3D character, however, serves as a reusable digital actor. Once created, it offers:

• Absolute Consistency: The character remains identical across every episode or shot.

• Predictable Animation: Precise control over poses, timing, and movements.

• Full Editability: The ability to refine the model or change environments at any time.

Tripo AI, which was recently launched Tripo Studio 1.0 that bridges the gap between the ease of AI and the control of traditional 3D, making it an essential tool for YouTubers, indie animators, and small production teams.

Step-by-Step Guide to Building a 3D Character with Tripo AI

Step 1: Defining the Character Reference

The process begins at Tripo AI Studio 1.0. While the platform includes a prebuilt library of high-quality 3D character models, you can also generate fully customized characters using text or image references with built-in models like Flux Kontext and GPT-4o Image Gen. But the most accurate results for 3D modeling often come from a clear reference image. You can do this by heading over to Tripo’s Image to 3D model generation

Figure 1: Our starting point—a clean, stylized 3D cartoon boy.

For the best results, ensure your reference image is clear and defines the character’s proportions accurately.

Step 2: Generating the 3D Model

Once the image is uploaded, you can choose between Standard and Ultra modes. Standard mode is highly efficient for stylized characters, while Ultra mode is reserved for AAA-level complexity.

Figure 2: The Tripo AI interface allows for quick image-to-3D conversion.

After clicking “Generate Model,” Tripo AI functions as a full AI 3D model generator, converting a single reference image into a usable 3D asset in seconds - a task that would traditionally take days of manual sculpting.

Figure 3: The initial solid 3D model generated from the reference image.

You can further refine the model using the built-in settings, such as smoothing the surface or adjusting the polygon count through retopology to suit your project’s technical requirements.

Figure 4: Reviewing the mesh topology and vertex count.

Step 3: Texturing and PBR Optimization

Next, we move to the Textures section. Tripo AI can generate textures based on your original reference image or a specific text prompt. For stylized animation, the focus is on clean base colors and consistent shading.

Figure 5: The AI-powered texturing process.

The result is a full-colored, refined texture that responds naturally to lighting. For professional-grade results, you can use the Texture Upscale feature to add fine details and the PBR Generator to ensure the character interacts correctly with light in engines like Unity or Unreal.

Features & Benefit

Texture Upscale :Increases resolution for close-up shots and high-poly models.

PBR (Physically Based Rendering) Generator :Creates Physically Based Rendering maps for realistic lighting.

Segmentation: (Ultra Mode) Splits the character into editable parts (head, limbs).

Figure 6: The character with fully optimized PBR textures.

Explore more deep dives, hands-on guides, and experiments in AI tools from my earlier articles below.

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Step 4: One-Click Auto-Rigging

Rigging—the process of creating a skeleton for animation—is traditionally one of the most tedious steps in the 3D pipeline. Tripo AI automates this entirely. By selecting Auto Rig, the AI detects the character’s humanoid anatomy and generates a functional skeleton.

Figure 7: The automatically generated skeletal structure.

This single click replaces hours of manual bone placement and weight painting, making the character instantly ready for movement.

Step 5: Applying and Testing Animations

To verify the quality of the rig and textures, Tripo AI provides a library of pre-made animation presets, including idle, walk, and run cycles.

Figure 8: Browsing the library of built-in animation presets.

When you select a preset, the AI retargets the skeleton in real-time. This is an excellent way to test for mesh deformation issues or to quickly populate a scene with background characters.

Figure 9: Testing the character’s movement with a run cycle.

Walk & Run Cycle Preview — Testing Animation Quality and Rig Stability

This preview demonstrates how the character performs during basic locomotion cycles, helping verify natural motion, joint deformation, and overall rig accuracy before export.

Step 6: Exporting for Production

The final step is exporting your character for use in external software. Tripo AI supports industry-standard formats:

• FBX (Filmbox): Ideal for Blender, Unity, and Unreal Engine (includes animations).

• GLB (Transmission Format Binary) the binary version of glTF: Perfect for web-based applications and lightweight pipelines.

Figure 10: Exporting the finished asset for production.

Once imported into Blender or Unity or Spline, you have full creative control to modify animations, set up cinematic lighting, and build your final scenes.

Final Verdict: The New Era of 3D Creation

Tripo AI is more than just a convenience; it represents a fundamental shift in how 3D content is produced. By removing the technical hurdles of sculpting, retopology, and rigging, it allows creators to focus on what truly matters: storytelling and creative direction.

Whether you are an indie filmmaker, a YouTuber, or a small marketing team, the ability to turn a single image into a film-ready 3D character in minutes is a game-changer. AI isn’t replacing the animator; it’s finally making animation accessible to everyone.

Disclosure: This post is an advertorial.

• Product managers drowning in data consolidation.

• Finance teams manually entering expense reports.

• Content creators spending hours repurposing videos across platforms.

• Researchers manually synthesizing 50+ documents.

Most powerful AI capabilities have been locked behind technical barriers. To automate workflows, you needed to write scripts. To process data at scale, you needed programming knowledge. To use Claude's agentic power i.e Claude Code, you needed to be comfortable in the terminal.

That changed when Anthropic built Claude CoWork.

CoWork removes the terminal requirement entirely.

It brings autonomous AI agents to your Mac desktop, making powerful automation accessible to anyone. No coding required. No terminal. Just describe what you want, and Claude handles the rest.

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What Is Claude CoWork? The AI Intern for Your Mac

Claude CoWork is a desktop application for macOS that executes multi-step workflows autonomously. Unlike chat interfaces where you describe a task and receive suggestions, CoWork actually does the work.

When you describe an outcome to CoWork, it doesn't tell you how to do it. It does it. It reads files from your computer, analyzes them, creates new documents, organizes data, and delivers finished results.

The system is built on the Claude Code Agent SDK, running in the background of the Claude Desktop app. It was built in just 10 days using Claude Code itself.

How It Works: Three Simple Steps

Step 1: Describe Your Outcome

You tell Claude CoWork what you want to achieve in plain English. For example: "Analyze these 50 customer feedback emails and create a summary report with the top 10 issues and recommendations."

Step 2: Review the Plan

Claude analyzes your request and presents a step-by-step plan. It shows you exactly what it will do, what files it will access, and what it will create. This transparency is crucial for trust and safety.

Step 3: Execute and Deliver

You approve the plan, and Claude runs it autonomously. It processes files, runs scripts, generates outputs, and presents the finished work—all without requiring your input at each step.

Setting Up Claude CoWork: 5 Easy Steps

Setting up CoWork is straightforward. Here’s what you need to know.

Prerequisites

• Platform: macOS only (Desktop App). Windows support is not yet available.

• Subscription: Requires a paid Claude plan (Pro, Max, Team, or Enterprise). Free tier users cannot access CoWork.

• Software: Latest Claude Desktop App downloaded from claude.ai.

• Connection: Active internet connection is required throughout the session. CoWork depends on cloud connectivity.

• Storage: Sufficient disk space needed. Large batch operations may require 1–5 GB of temporary storage.

• Permissions: macOS will prompt for file access. Grant permissions for CoWork to read and write files.

Installation Walkthrough

Step 1: Update Your Claude App

Launch the Claude Desktop app and ensure you’re running the latest version.

• Go to Settings and check for updates.

• CoWork is available in the latest versions of the app.

Step 2: Switch to CoWork Mode

Look for the mode selector at the top of the app. You’ll see options like “Chat,” “Code,” and “CoWork.” Click on the “CoWork” tab (it might also be labeled “Tasks” depending on your version).

Step 3: Grant File Access Permissions

macOS will prompt you to allow Claude to access your files. Click “Allow” to grant permissions. This is necessary for CoWork to read files from your computer and create new ones.

Step 4: Define Your Task

Clearly describe the final outcome you want. Be specific about what you want to achieve. For example:

“I have a folder called ‘Receipts’ with 70 receipt images. Please rename them based on the store name and date of purchase, then create an Excel spreadsheet with all the transaction details and a visual chart showing my spending by store.”

Step 5: Review and Execute Claude will analyze your request and present a plan. Review it carefully. If everything looks good, click “Allow” to grant permission for the task to begin. The app must remain open while Claude is working.

Real-World Test: From 70 Messy Receipts to a Professional Dashboard

To understand what CoWork can actually do, I tested it with a practical task that usually drains hours of manual work: Expense Reporting.

I had a folder with 70 messy receipt images ( downloaded from Kaggle)—Walmart, Whole Foods, Trader Joe’s, scattered and randomly named. I asked Claude to rename them by store and date, then create a comprehensive expense report with a visual dashboard.

What CoWork Delivered

Task 1: Renaming the Files

Claude scanned each receipt - wrote a code to extract text using the Tesseract Library and extracted the store name and purchase date. Based on this information, the files were automatically renamed and neatly organized by store and date—no manual sorting required.

Task 2: Analyze the Data in Each File and Create an Expense Report

Claude then analyzed the contents of each receipt to extract transaction details like amounts and dates. It handled messy cases (missing dates, unclear store names) gracefully and clearly flagged what could and couldn’t be identified. The output included:

• Total spending of $747.18 across 12 verified transactions

• A CSV file with formulas for totals and averages (ready for Excel or Numbers)

• An interactive HTML dashboard showing peak spending months and most frequent stores (Walmart appeared 5 times)

Overall, a task that would normally take 3–4 hours of manual work was completed in minutes, with fewer errors and far more consistent results than manual data entry.

What Can Claude CoWork Actually Do?
Real Use Cases

Beyond expense reporting, here are powerful use cases being shared by users across YouTube, Reddit, and Twitter.

1. Multi-Platform Content Repurposing for Creators

Content creators spend hours repurposing content across platforms. CoWork automates this.

The Task: Take a long-form video file (30-60 minutes), identify the 3-5 most engaging segments, crop them for Instagram Reels/TikTok format, and draft unique captions optimized for each platform.

What CoWork Does: It analyzes the video, identifies high-engagement moments based on pacing and topic changes, automatically crops and resizes them, and generates platform-specific captions with relevant hashtags.

Time Saved: 4-6 hours of manual work completed in 15-20 minutes.

2. Data Synthesis to Professional Reports for Product Teams

Product managers and finance teams deal with scattered data sources every day. CoWork consolidates and presents this data in a professional format.

The Task:

Analyze a folder containing meeting transcripts, voice memos, scattered notes, and email summaries. Synthesize all the information into a formal PowerPoint presentation with key insights and an Excel spreadsheet with working formulas.

What CoWork Does:

It reads all the documents, identifies key themes and decisions, organizes them hierarchically, creates a professional presentation with proper formatting, and generates a data spreadsheet with calculations.

Time Saved: 6-8 hours of manual work completed in 30-45 minutes.

3. Deep Research and Theme Extraction for Analysts

Researchers and analysts often process large volumes of text data. CoWork handles knowledge management tasks that exceed the context limits of standard chat models.

The Task: Analyze the transcripts from 50-100 podcast episodes and extract the top 10 recurring themes, key connections between topics, and notable quotes.

What CoWork Does: It processes all transcripts, identifies patterns and themes, creates a structured document with connections, and generates a summary report with actionable insights.

Time Saved: 20+ hours of manual analysis completed in 1-2 hours.

4. Batch File Processing and Organization

CoWork excels at organizing and processing large batches of files.

The Task: You have 500 photos from a recent event. Organize them by date, create folders by time of day, generate captions for each photo based on content, and create a web gallery HTML file.

What CoWork Does: It processes all photos, extracts metadata, organizes them hierarchically, generates descriptive captions using vision AI, and creates an interactive gallery.

Time Saved: 8-10 hours of manual work completed in 20-30 minutes.

Beyond the Obvious: Additional Use Cases

CoWork can handle a wide variety of tasks across different professional domains. Here are additional workflows that can be automated:

• File Management

  • Batch Renaming: Apply consistent naming conventions to hundreds of files in a single command

  • Complexity: Low

  • Time Saved: 30 minutes – 1 hour

• Data Analysis

  • Outlier Detection: Run statistical analysis on CSV or Excel files to flag anomalies and generate a report

  • Complexity: Medium

  • Time Saved: 1–2 hours

• Document Processing

  • Expense Report Automation: Drop receipts into a folder and generate a formatted report with totals

  • Complexity: Medium

  • Time Saved: 2–3 hours

• Knowledge Management

  • Transcript Analysis: Extract action items and key decisions from meeting transcripts

  • Complexity: Medium

  • Time Saved: 1–2 hours

• Research

  • Literature Review: Summarize 20+ research papers and identify common themes

  • Complexity: High

  • Time Saved: 4–6 hours

• Marketing

  • Email Campaign Generation: Create personalized email templates based on customer data

  • Complexity: Medium

  • Time Saved: 2–3 hours

• Finance

  • Invoice Processing: Extract data from invoices and reconcile with accounting records

  • Complexity: High

  • Time Saved: 3–4 hours

• HR

  • Resume Screening: Analyze 100+ resumes and create a shortlist based on defined criteria

  • Complexity: High

  • Time Saved: 2–3 hours

• Content

  • Blog Post Generation: Create multiple blog posts from a single long-form article

  • Complexity: Medium

  • Time Saved: 1–2 hours

• Quality Assurance

  • Test Report Generation: Analyze test results and create comprehensive QA reports

  • Complexity: Medium

  • Time Saved: 1–2 hours

Pricing and Availability

CoWork is available exclusively to paid Claude plans:

Best Practices

1. Be Specific: Instead of “Analyze these files,” try “Analyze these 50 customer support tickets and create a report showing the top 5 issues.”

2. Organize First: Put input files in a dedicated folder before running tasks

3. Review the Plan: Always check the proposed plan before approving execution

4. Start Small: Begin with 10-20 files to understand how CoWork works

5. Keep App Open: Don’t close the app or sleep your computer during execution

Who Should Use CoWork?

Here are concise, high-signal use cases that are distilled from real CoWork usage patterns and product docs, written to fit naturally into your article.

Product Managers

• Combine meeting notes, transcripts, emails, and docs into structured PRDs or executive summaries

• Generate insight dashboards from CSV/Excel exports (roadmaps, feedback, metrics)

• Turn scattered notes into polished PowerPoint decks with charts and clear narratives

Finance Teams

• Convert folders of receipt images into categorized expense reports with totals

• Extract invoice data from PDFs and reconcile it with accounting spreadsheets

• Batch-rename and organize financial files for audits and month-end closing

Content Creators

• Organize large media libraries by topic, date, or campaign automatically

• Identify highlight moments in long videos and prep short-form content drafts

• Turn research notes into first-draft blogs, scripts, or newsletters

Researchers

• Summarize and synthesize dozens of papers, reports, or transcripts into key themes

• Extract structured data from PDFs and images into analyzable spreadsheets

• Perform cross-document pattern detection for literature reviews and meta-analysis

Marketers

• Sort and tag campaign assets for easy reuse across channels

• Generate email campaigns using CRM exports and audience segmentation data

• Create branded reports and proposals from raw performance data

In short

• Automates file-heavy, multi-step workflows that normally require switching between 5–6 tools

• Best suited for knowledge workers who lose hours to organization, synthesis, and formatting

This keeps the section tight, skimmable, and clearly value-driven—perfect for a ToolNerd-style deep dive.

Verdict

Claude CoWork shifts the focus from “What can AI tell me?” to “What can AI do for me?” It makes agentic AI practical for everyday knowledge work.

Should you try it? Yes, if you’re on a Mac with a paid Claude plan. Start with a simple task, see how it works, then scale up to more complex workflows.

If you haven’t heard of Ralph Loop yet, let me introduce you to what it is and why it’s powerful.

What is Ralph Loop?

Ralph Loop is essentially a simple iterative loop where an AI system keeps attempting a task, evaluates the outcome, and then tries again - until the task is done.

Instead of trying to solve everything in one shot, the loop allows the model to:

• attempt a solution

• observe what worked or failed

• adjust based on that feedback

• repeat

The model doesn’t keep the state in its own memory. The state is stored outside — in files, outputs, tests, or the environment. Every run starts fresh, but it can still use the results from the previous run.

This makes the whole system surprisingly robust and easy to reason about.

Why the name “Ralph”?

Ralph, from The Simpsons, is simple, persistent, and completely unbothered by whether he’s doing things the “right” way.

He doesn’t plan ahead, doesn’t overthink outcomes, and doesn’t stop just because something failed once.

He just keeps going. That’s the spirit behind the name.

Ralph Loop works the same way — not smart in a single shot, but effective through repetition. Try, fail, adjust, repeat. No ego, no memory baggage, just forward motion.

That’s exactly how this pattern works. The model isn’t expected to be perfect. It’s expected to try, fail, learn from the outcome, and repeat.

Ralph is a Bash loop.

Where this concept really started

The core idea of this loop-based approach was articulated much earlier by Geoffrey Huntley, way back in July 2025 long before it became popular in today’s AI tooling discussions.

He described this pattern as a deterministic loop operating in a non-deterministic environment — letting repeated attempts converge on a working solution over time.

You can read his original explanations here:

https://ghuntley.com/ralph/
https://ghuntley.com/loop/

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How it became popular again

This one post by Boris Cherny (the one who built Claude Code), made it viral!!

People are doing a lot of experiments with Ralph Loop and how to make effective use of it but in this article i will focus on the simple one that I was done by Ryan Carson (https://x.com/ryancarson), founder of Amp.

He has packaged this idea into a clean, easy-to-try implementation — which made it much more accessible for builders who wanted to experiment quickly.

That version is simple, effective, and very easy to get started with.

Check out this repo:

https://github.com/snarktank/ralph

Read this article by him:

For a More Detailed Video:

Step-by-Step: Running Ralph Loop with Claude Code

Now that we have understood Ralph Loop concept, let’s actually run it.

We’ll do this with Claude Code (primary), and I’ll mention AMP CLI wherever relevant ( as this one is created by founder of Amp ). By the way this logic would work in any CLI or Coding Agent you are using.

Step 1: Install Claude Code CLI (if you don’t have it)

Claude Code is the easiest way to run Ralph Loop right now.

Reference doc for setting up Claude Code: https://code.claude.com/docs/en/quickstart

Mac:

curl -fsSL https://claude.ai/install.sh | bash

Windows Power Shell:

irm https://claude.ai/install.ps1 | iex

Once installed, make sure this works:

claude --help

If you prefer AMP CLI, that works too — Ralph supports both.

# AMP CLI (alternative)
npm install -g @amp-labs/amp

You only need one of these.

Step 2: Get Ralph into Your Project

You have two ways to do this.

Option A: Copy Ralph into your project (recommended for first-timers)

This keeps everything local and easy to inspect.

git clone https://github.com/snarktank/ralph.git

From your project root:

mkdir -p scripts/ralph
cp ralph/ralph.sh scripts/ralph/
cp ralph/CLAUDE.md scripts/ralph/
chmod +x scripts/ralph/ralph.sh

You now have:

• the Ralph loop script

• a Claude-specific prompt file

• everything versioned with your project

Option B: Install Ralph as a Claude skill (cleaner if you use Claude a lot)

Claude Code supports skills, and Ralph ships as one.

mkdir -p ~/.claude/skills
cp -r ralph/skills/prd ~/.claude/skills/
cp -r ralph/skills/ralph ~/.claude/skills/

This makes prd and ralph available globally inside Claude Code.

If you’re using AMP instead:

mkdir -p ~/.config/amp/skills
cp -r ralph/skills/prd ~/.config/amp/skills/
cp -r ralph/skills/ralph ~/.config/amp/skills/

Step 3: Create a PRD (using the PRD skill)

Before Ralph can loop, it needs clear tasks.

You don’t write the PRD manually.

You give one sentence.

Inside Claude Code, run the PRD skill and describe your project, for example:

“Build a simple Next.js app with a login page and dashboard.”

Claude will ask a few follow-up questions and generate a structured PRD.

Save the output as something like:

prd.md

That PRD is the source of truth for everything Ralph does next.

Step 4: Convert the PRD into Ralph format (PRD → PRD JSON)

Ralph doesn’t work directly on markdown.
It works on a structured task list: prd.json.

Use the PRD converter (comes with the Ralph skill):

• Load the ralph skill

• Convert prd.md → prd.json

At the end of this step, you should see a file like:

prd.json

This file contains:

• individual user stories

• pass/fail flags

• metadata Ralph uses to decide what to work on next

Step 5: Run the Ralph Loop

Now comes the boring (and powerful) part.

From your project root in the Terminal:

./scripts/ralph/ralph.sh --tool claude

Or, with a max iteration limit:

./scripts/ralph/ralph.sh --tool claude 10

That’s it.

You’ve started the loop.

What Happens After You Run It

Each iteration, Ralph will:

1. Pick the next unfinished item from prd.json

2. Ask Claude Code to implement it

3. Run checks (tests, type checks, etc.)

4. If it passes:

   • commit the changes

   • mark the item as complete in prd.json

5. Write learnings to progress.txt

6. Update AGENTS.md / CLAUDE.md with patterns it discovers

7. Loop again

Every iteration starts with a fresh Claude context.
All memory lives in files and git — not the model.

When Does It Stop?

Ralph exits automatically when:

• all PRD items are marked as passed

• or the max iteration count is reached

Files You Should Keep an Eye On

• prd.json → what Ralph is working on

• progress.txt → what Ralph has learned

• AGENTS.md/CLAUDE.md → conventions and patterns

• git history → actual progress

If something feels off, these files tell you why.

That’s it.

If you can write one sentence describing your project, you can run Ralph Loop with Claude Code.

The rest is just a loop doing its thing.

Reference Articles & New Tools for reference

https://www.aihero.dev/tips-for-ai-coding-with-ralph-wiggum

https://www.aihero.dev/essential-ai-coding-feedback-loops-for-type-script-projects

TUI based Interface for Ralph: https://ralph-tui.com

https://github.com/iannuttall/ralph

If you have the Google AI pro license and sitting on it and using only GEMINI for chat!! You are simply wasting your productivity.

Check out the detailed plan here : Google Gemini Plans

In this post I will like cover the various tools that are available for you in your Gemini AI Pro subscription, that might make your powerful.

No Time to Read? Here’s the Scoop

• Google AI Pro is not a chatbot upgrade. It’s Google’s full AI stack—video, images, coding agents, research tools, and Workspace automation.

• Creators get Veo video, Imagen 3, Whisk, Doppl, and Google Vids.

• Developers get agentic coding via Jules, Gemini Code Assist, Stitch, and experimental agent-first IDEs.

• Researchers and students get NotebookLM Plus with multi-document analysis and AI-generated audio summaries.

• Everyone gets Gemini built directly into Gmail, Docs, Slides, Sheets, and Meet.

• Globally, this costs about $20 a month. In India, eligible Jio 5G users get it free for 18 months.

• If you only use it for chat, you’re wasting it.

I will be covering what’s in store for Creators, Developers, Non-developers/ Knowledge Workers .. as well tell you how to claim you free account in Jio with 18 months of free access.

ChatGPT also has a free version and you should try out. Covered in this article below:

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Category 1: Creative Studio & Marketing

Best for: creators, marketers, agencies

Google Flow (Video via Veo)

Unlocked: Veo video models (Labs access)

Google’s VEO models are state of the art when it comes to Video generations and the best in the market.

Flow lets you generate short cinematic videos using Google’s Veo models. The real advantage is asset grounding. You can upload logos, products, and reference images so outputs stay visually consistent.

This works best for short-form marketing clips—ads, reels, launch visuals—not long narrative films.

Who benefits most:

• Startup founders and marketers creating launch videos without a production team

• Agencies prototyping video concepts before full production

• Content creators experimenting with visual storytelling quickly

Whisk & ImageFX (Image Generation)

Unlocked: Imagen 3 and Nano Banana Pro limits

ImageFX focuses on fast iteration. You can switch styles and compositions without rewriting prompts. ( Available in select countries)

Whisk blends multiple images into cohesive mockups, handling lighting and texture better than most general-purpose image tools.

This pair is useful for mood boards, campaign concepts, and early creative direction.

Who benefits most:

• Designers exploring styles and visual directions

• Marketing teams testing creative ideas fast

• Founders who need visuals before hiring a designer

Doppl (Virtual Try-On)

Doppl lets you try generated clothing on real photos with believable fabric behavior. Lighting and drape matter here, and Doppl handles both reasonably well.

This is most useful for fashion concepts and early design visualization, not final product shots.

Who benefits most:

• Fashion designers visualizing new collections

• E-commerce brands testing concepts before samples

• Creators experimenting with digital fashion content

Google Vids

Most of them don’t know about this cool product which is released by Google last year.

Google Vids shines when you already have knowledge locked inside documents -PDFs, slide decks, training material, playbooks, or long reports. Instead of rewriting everything from scratch, you can feed this material directly into Vids and turn it into clear explainer videos with structure, visuals, and voiceover generated automatically. It feels less like “making a video” and more like repackaging what you already know.

• AI-first video drafts: Generate a complete video structure from a prompt or document.

• Document-to-video conversion: Turn Docs, PDFs, and Slides into explainer videos automatically.

• Scene-based editing: Edit videos section by section without complex timelines.

• AI voiceovers: Create synced narration without recording audio.

• Webcam and screen recording: Record walkthroughs and demos directly inside Vids.

• Built-in teleprompter: Stay on script while recording yourself.

• Auto-selected visuals: Match stock visuals and assets to your script.

• Drive asset integration: Pull images, videos, and files directly from Google Drive.

• Basic animations and transitions: Add motion for clarity without heavy editing.

• Real-time collaboration: Edit, comment, and suggest changes like Docs.

• Easy sharing and export: Share via Drive links or export in standard formats.

• Workspace-native workflow: Works seamlessly across Docs, Slides, Gmail, and Drive.

• Veo-powered video clips: Generate short AI video snippets for visual fillers.

• Automatic captions: Improve accessibility with auto-generated subtitles.

• Explain-first design: Optimized for training, onboarding, and internal communication.

This is especially useful for:

• Consultants who want to turn reports and frameworks into client-ready explainers

• Teachers and trainers converting course material into short learning videos

• Internal enablement teams creating onboarding or process walkthroughs

• Solo creators sitting on written content but short on video skills

It won’t replace a motion designer, but it’s one of the fastest ways to turn existing knowledge assets into watchable, shareable videos.

Google Pomelli

Google Pomelli is Google Labs’ new AI marketing assistant built for small and mid-sized businesses that don’t have a full creative team but still need consistent, on-brand marketing.

At its core, Pomelli tries to solve one problem: turning a business’s existing identity into usable marketing content without starting from scratch every time.

You start by giving Pomelli your website URL. It scans the site and builds what Google calls a “Business DNA” — your tone of voice, visual style, colors, typography, and overall brand personality. This becomes the foundation for everything it generates.

Once that’s set, Pomelli:

• Suggests campaign ideas tailored to your brand and goals

• Generates ready-to-use marketing assets (images + copy)

• Keeps visuals and messaging consistent across campaigns

• Lets you edit and export assets for social, web, or ads

Think of it less as a chatbot and more as an AI creative assistant that understands your brand context.

This is useful for

• Founders and solo operators

• Small marketing teams

• Agencies prototyping campaign concepts

• Businesses that struggle with visual and tone consistency

It reduces the back-and-forth between “idea → design → rewrite → redesign” into a single workflow.

Availability

Pomelli is currently in public beta, available in English in limited regions (US, Canada, Australia, New Zealand), and free while it remains a Labs experiment.

If you want to get started with AI and want to get more information about AI check out these articles:

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Category 2: Development & Prototyping

Best for: developers, founders, product teams

Jules (Async Coding Agent)

Jules works inside GitHub. It’s your virtual background agent. You assign it an issue, and it spins up an environment, writes code, runs tests, and opens a pull request.

It’s reliable for dependency updates, refactors, and documentation. It still needs review, but it saves real time.

Who benefits most:

• Solo developers maintaining side projects

• Small teams drowning in backlog cleanup

• Engineering leads delegating low-risk tasks asynchronously

Stitch (UI to Code)

Stitch bridges design and implementation. Describe a UI, and it generates both the visual layout and functional frontend components.

This is useful for validating UX ideas before handing work to engineering.

Who benefits most:

• Product managers testing flows without design handoff

• Founders prototyping ideas quickly

• Designers moving faster from concept to code

Antigravity (Agent-First IDE)

Antigravity is a VSCode fork and competition to likes of Cursor, Windsurf, and many other IDEs out there.

Anti-gravity is unique. Unlike a traditional coding environment, it focuses on directing agents rather than you writing code line-by-line. You describe the outcome, and agents handle different parts of the build.

You get access to the best models like Gemini 3 Pro, Gemini 3 Flash, Claude Opus 4.5, Claude Sonnet 4.5 etc..

They’re powerful for experiments and MVPs. Expect rough edges. This is early-stage tooling.

I have written about it in one of my previous article as well:

Who benefits most:

• Early-stage founders building proof-of-concepts

• Tinkerers exploring agent-driven development

• Teams experimenting with new workflows

There are many other tools like

Google Code Assist : Google Code Assist is Google’s AI coding companion that helps you write, explain, refactor, and debug code directly inside your IDE with Gemini-powered context awareness.

Gemini CLI : This is like your Claude Code, Codex, OpenCode, Factory AI etc.. and is open source

Category 3: Research & Knowledge Work

Best for: students, analysts, strategy leads

NotebookLM Plus

NotebookLM lets you analyze large collections of documents together. You can query them, cross-reference ideas, and generate summaries grounded in your sources.

The standout feature is Audio Overviews & Video Overview. It turns your material into a conversational podcast-style summary that’s surprisingly useful for review and revision.

Who benefits most:

• Students revising large syllabi

• Researchers and analysts reviewing dense material

• Knowledge workers who learn better by listening

Illuminate

If reading and researching research papers is the difficult part for you, I think Illuminated is the one you should go for.

Illuminate converts dense academic papers into structured explanations and audio discussions.Learn About generates adaptive learning material that changes depth based on how you interact with it.

These tools are best treated as study accelerators, not authorities.

Who benefits most:

• Students breaking down complex subjects

• Lifelong learners exploring new fields

• Professionals refreshing foundational knowledge

Category 4: Everyday Productivity (Where Most Value Hides)

Best for: anyone using Google Workspace

• Gmail drafts replies from short prompts.

• Docs expands outlines into structured documents.

• Slides generates images and fixes layouts automatically.

• Meet transcribes calls and tracks action items.

• Sheets builds formulas, charts, and summaries from raw data.

Individually, these features feel small. Used daily, they compound.

If you are in India and a Jio Subscriber - It’s free for you.

Globally, Google AI Pro is a paid subscription.

In India, Jio’s partnership turns it into a temporary arbitrage. Eligible users on ₹349+ 5G plans get 18 months free. The tools are the same. The limits are the same. Only the price changes.

If you’re outside India, this shows what Google is bundling.

If you’re in India and eligible, this shows what to activate before the window closes.

How to Claim the Offer (India)

1. Open the MyJio app.

2. Confirm an active Unlimited 5G plan (₹349+).

3. Tap the Google AI Pro banner.

4. Activate the 18-month subscription.

Make the most of this access. Lean into AI, explore Google Gemini Pro, and actually learn how to use these tools. AI isn’t optional anymore—it’s becoming a basic skill, and the gap will only widen between people who experiment early and those who don’t.