A developer shared a GitHub project providing a local audit trail for Claude Code tool calls, paired with a rule-based risk analyzer. Users can supply a llama-cpp-compatible GGUF model to get a local second opinion on the risk of tool calls, keeping the whole workflow on-device.
A developer shared Conduct on Hacker News, an open-source tool providing guardrails for LLM and MCP tool calls. It gives practitioners a way to validate and control tool invocations in agent pipelines. The project is hosted on GitHub.
A developer shared Conduct on Hacker News, an open-source tool providing guardrails for LLM and MCP tool calls. It gives practitioners a way to validate and control tool invocations in agent pipelines. The project is hosted on GitHub.
Sentrint is a security scanner designed for projects built with LLMs. It helps developers identify vulnerabilities specific to AI-generated code, addressing the unique security challenges introduced by LLM-assisted development.
Sentrint is a security scanner designed for projects built with LLMs. It helps developers identify vulnerabilities specific to AI-generated code, addressing the unique security challenges introduced by LLM-assisted development.
Tracelint is a new open-source linter that statically analyzes AI agent traces to catch errors without relying on an LLM judge. It provides deterministic checks for agent workflows, aiming to improve reliability and debuggability.
Tracelint is a new open-source linter that statically analyzes AI agent traces to catch errors without relying on an LLM judge. It provides deterministic checks for agent workflows, aiming to improve reliability and debuggability.
Telemetry.dev provides observability tools for coding agents and LLM applications, helping developers monitor, debug, and optimize AI-powered workflows. It solves the problem of understanding agent behavior and performance in production.
Telemetry.dev provides observability tools for coding agents and LLM applications, helping developers monitor, debug, and optimize AI-powered workflows. It solves the problem of understanding agent behavior and performance in production.
A homelab builder used Claude Code as a supervised background agent to design, benchmark, and configure a three-Mac-mini cluster serving one 35B MoE model across two machines. The write-up documents working delegation patterns, concrete benchmarks, and the failures Claude hit. It matters to practitioners as a case study in agent-led infrastructure setup with human rule-setting.
A homelab builder used Claude Code as a supervised background agent to design, benchmark, and configure a three-Mac-mini cluster serving one 35B MoE model across two machines. The write-up documents working delegation patterns, concrete benchmarks, and the failures Claude hit. It matters to practitioners as a case study in agent-led infrastructure setup with human rule-setting.