Open Source
OpenClaw Gains Popularity with 236,000 GitHub Stars
Lev Selectorhigh confidence
Why it matters
- →The rise of OpenClaw reflects the increasing importance of user-friendly AI tools in the developer community.
OpenClaw, a rapidly growing open-source AI agent platform, has garnered significant attention, achieving 236,000 stars on GitHub. While it is praised for its convenience, experts note that its capabilities can also be replicated using Claude Code with proper prompting. This highlights the ongoing trend of harness engineering, where the structure around AI models is deemed more critical than the models themselves.
Read originalThe story around this
Earlier coverage that leads up to this article, and what followed. Lines connect each piece to the closest one after it, converging here.
How we got here
- 1OpenClaw: Personal AI Assistant for Coding
Ollama Blog · February 1, 2026 · Related
- 2OpenClaw Surpasses 214,000 GitHub Stars
Lev Selector · February 20, 2026 · Same story
What happened next
- 3OpenClaw Enables AI-Run Businesses
Lev Selector · March 20, 2026 · Related
- 4NVIDIA Enhances OpenClaw for Autonomous AI Agents
NVIDIA Blog · April 30, 2026 · Same story
- 5OpenClaw AI Agents Now Available on Hostinger
Matt Wolfe · May 6, 2026 · Related
- 6AI Agents Gain Physical Control with OpenClaw
WIRED AI · May 20, 2026 · Related
- 7AI Agents Revolutionize Coding with Claude Code
WIRED AI · May 26, 2026 · Same story
Follow this story
Open the full story →OpenClaw Gains Attention from Meta and OpenAI
2 developments
- Feb 13 · Lev Selector
OpenClaw Gains Attention from Meta and OpenAI - Feb 27 · Lev Selector
OpenClaw Gains Popularity with 236,000 GitHub Stars (This article)↳ OpenClaw reaches 236,000 GitHub stars.
More in Open Source
© Hugging Face BlogOpen Sourceagents
Tether AI launches BotPsy, a sub-billion agentic model
Tether AI Research has released BotPsy, an 0.8B-parameter model built on Qwen3.5 that specializes in tool-use and agentic workflows for on-device deployment. It achieves state-of-the-art performance in its size class, outperforming larger models like Qwen3.5-2B-Think on benchmarks such as BFCL v4 and ACEBench while maintaining strict privacy by keeping data local. The model is designed to handle routine tasks like calendar management and web search without relying on expensive cloud APIs, offering a viable path for private, self-hosted personal assistants. With open weights under Apache 2.0 and GGUF support, it provides immediate utility for developers building lightweight, autonomous agents.
Hugging Face Blog
© Hugging Face BlogOpen Sourceother
NVIDIA releases 9M synthetic Japanese finance personas
NVIDIA has released a massive synthetic dataset of 9 million Japanese financial personas, addressing the privacy and data scarcity bottlenecks in localizing AI for Japan. By grounding these profiles in official government statistics rather than real customer records, it allows developers to train and red-team agents without violating strict privacy regulations. The inclusion of specific attributes like household assets and financial literacy provides a level of demographic granularity that generic Western-centric datasets simply cannot match. This effectively lowers the barrier for building culturally accurate, compliant AI systems in one of the world's largest economies.
Hugging Face Blog
© Matt WolfeOpen Sourceimage
Pruna Releases PH3 Image Open-Weight Model
Pruna has released PH3 Image, an open-weight image generation model available on Hugging Face with specific hardware requirements.
Matt Wolfe