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@kala shared an update, 2 weeks ago

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OpenAI Hires OpenClaw Creator Peter Steinberger; Project Moves to Independent Foundation

#OpenCla... #autonom... #AI #OpenAI #Peter S...

Peter Steinberger, creator of OpenClaw, is joining OpenAI to work on bringing AI agents to a broader audience, while OpenClaw will move to an independent open-source foundation and continue development outside OpenAI’s direct control.

OpenAI Hires OpenClaw Creator Peter Steinberger; Project Moves to Independent Foundation

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News FAUN.dev() Team Trending

@kala shared an update, 2 weeks, 2 days ago

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OpenClaw Lightweight Alternative Launches: A 10MB AI Assistant That Runs on $10 Hardware

#OpenCla... #AI agen... #PicoCla... #$10 har... #Go

Sipeed has released PicoClaw an OpenClaw micro alternative that uses 99% less memory than . , an open-source AI assistant written in Go that runs in under 10MB of RAM and boots in about one second. Designed for low-cost Linux boards starting around $10, it supports multiple LLM providers, chat platform integrations, and automation workflows. The project is MIT-licensed and available on GitHub.

OpenClaw Alternative Launches: A 10MB AI Assistant That Runs on $10 Hardware

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@kala added a new tool PicoClaw , 2 weeks, 2 days ago.

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@varbear shared a link, 2 weeks, 2 days ago

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Understanding the Go Compiler: The Linker

Go’s linker stitches together object files from each package, wires up symbols across imports, lays out memory, and patches relocations. It strips dead code, merges duplicate data by content hash, and spits out binaries that boot clean - with W^X memory segments and hooks into the runtime... read more

Gemini 3 is Google’s third-generation large language model family, designed to power advanced reasoning, multimodal understanding, and long-running agent workflows across consumer and enterprise products. It represents a major step forward in factual reliability, long-context comprehension, and tool-driven autonomy.

At its core, Gemini 3 emphasizes low hallucination rates, deep synthesis across large information spaces, and multi-step reasoning. Models in the Gemini 3 family are trained with scaled reinforcement learning for search and planning, enabling them to autonomously formulate queries, evaluate results, identify gaps, and iterate toward higher-quality outputs.

Gemini 3 powers advanced agents such as Gemini Deep Research, where it excels at producing well-structured, citation-rich reports by combining web data, uploaded documents, and proprietary sources. The model supports very large context windows, multimodal inputs (text, images, documents), and structured outputs like JSON, making it suitable for research, finance, science, and enterprise knowledge work.

Gemini 3 is available through Google’s AI platforms and APIs, including the Interactions API, and is being integrated across products such as Google Search, NotebookLM, Google Finance, and the Gemini app. It is positioned as Google’s most factual and research-capable model generation to date.