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@kala shared an update, 6 days, 6 hours ago
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OpenClaw Lightweight Alternative Launches: A 10MB AI Assistant That Runs on $10 Hardware

Go OpenClaw PicoClaw

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 , 6 days, 6 hours ago.
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@varbear shared a link, 6 days, 10 hours ago
FAUN.dev()

Thoughts on the job market in the age of LLMs

The job market for AI professionals is challenging due to the high demand for senior talent and the importance of proving oneself as a junior employee. Hiring practices in AI are constantly evolving with the complexity and pace of progress in language models. Open-source contributions and meaningful.. read more  

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@varbear shared a link, 6 days, 10 hours 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  

Understanding the Go Compiler: The Linker
FastMCP is an open-source Python framework designed to simplify the development of Model Context Protocol servers. It allows developers to define MCP components such as tools, resources, and prompts using decorators, and to organize them through a modular architecture built around providers and transforms. Providers determine where components originate, including local code, directories, OpenAPI specifications, or remote MCP servers. Transforms modify components as they flow to clients, enabling namespacing, filtering, versioning, and visibility control.

The framework supports component versioning, per-component authorization, and middleware for cross-cutting concerns such as authentication and logging. It includes a built-in command-line interface for listing, calling, discovering, and installing MCP servers. FastMCP also supports session-scoped state, background task execution, OpenTelemetry tracing, pagination for large component sets, and transport options including stdio and HTTP-based protocols.

FastMCP is intended for developers building agent-compatible backends and structured tool interfaces for large language model systems that implement the Model Context Protocol.