The whole point of Mato is to make human-in-the-loop supervision easier, not to encourage autonomous loops.
In my daily workflow I constantly run multiple coding agents at the same time. The annoying part isn’t the AI itself — it’s switching between tabs, terminals, and different tools just to check what each agent is doing.
I built Mato mainly because I wanted a faster way to jump between agents, review their outputs, and approve or intervene when needed. Think of it more like tmux for AI workers, where a human manager can oversee multiple agents at once.
Personally I’m also skeptical of fully self-driving loops. In practice the plan → execute → review cycle with a human in the loop is still the most reliable way to work with AI today.
I built this because most existing agent frameworks felt either too academic (great papers, few real-world tools) or too demo-ish (cool examples, but brittle in production).
We needed something that could actually run GAIA-style tasks end-to-end: reasoning → tool use → verification → retry loops → success.
So GAIA Agent is basically the stack I wished existed:
- Zero-config agent (createGaiaAgent())
- 18+ built-in tools: browser, search, sandbox, memory, filesystem
- Fully TypeScript, modular, and swappable
- Built to run GAIA Benchmark without custom wiring
- Simple enough for side projects, but reliable enough for production
This is very early, but feedback from other agent-devs would help a ton.
If you try it and something feels off, missing, or over-engineered — please tell me.
Would love to hear what kinds of agents you’re building too.
It’s not the AI company’s fault. Think of it like a recruiter: they recommend a cashier, the cashier miscalculates expenses — you don’t sue the recruiter.
Responsibility moves inward, not upward. At some point, it’s about who decided to trust the tool.
Introducing AITable.ai <https://aitable.ai>.
From now on, everyone can build AI apps with Table in 1-click.
With AITable.ai, anyone can update, edit, add, or delete data in real-time a database-spreadsheet interface and effortlessly harness their own data to craft their ChatGPT.
AITable can be used for:
* Build AI Apps with Table in 1-click: Utilize your tabular data to train a custom ChatGPT / Chatbot / AI Form.
* Website Copilot Widget: Embed interactive AI chat widgets or copilot directly for your website.
* Data Copilot: Chat to your data and obtain valuable insights, analytics, and visualizations.
* Enterprise ChatGPT: Leverage AITable's robust permission features to ensure secure and efficient management of ChatGPT usage across your entire organization.
* Third-party Platform Integration: Connect easily with platforms like Discord, Slack, Whatsapp. OpenAI compatible APIs provided.
* Data Management: Base upon APITable <https://github.com/apitable/apitable>, AITable can be also used as a project management, sales leads management, and so on, such as an alternative to Trello, Monday, Asana, Salesforce.
AITable.ai<https://aitable.ai> is now beta! Sincerely invite you to join AITable.
Over the past two weeks I ran a small experiment.
What if a company had no employees — only AI agents?
My agents worked ~20,000 minutes building a product: writing code, fixing bugs, running marketing and sales tasks.
I started joking that they were like a “Claws company”.
This project is called Buda.
It’s an experiment in what an AI-native company could look like — where the organization itself is made of agents.
The name Buda comes from a story.
In the anime "Record of Ragnarok", when gods decide to destroy humanity, Buddha chooses to stand on the human side.
We liked that idea — AI getting stronger every year, but hopefully standing with humans.
In some ways it's similar in spirit to OpenClaw, but focused on the idea of an entire company made of agents.