Grouped around GitHub project review, agent tooling, model releases, developer workflows, and AI news interpretation.
developer workflowsRun OpenSSF Scorecard locally, preserve the exact revision and findings, and decide which repository risks require investigation before adoption.
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AI ops and monitoringAudit GitHub Actions token permissions job by job, pin third-party actions, and test the workflow after removing write access it does not need.
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agent tools and workflowsAudit an MCP server’s tools, credentials, file reach, and network side effects before connecting it to an AI client or production account.
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GitHub AI project watchlistTest a ComfyUI workflow on a clean profile, record models and dependencies, and identify custom-node lock-in before a graph becomes hard to reproduce.
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Open-source AI comparisonsCompare Ollama and llama.cpp using one pinned model, repeatable prompts, latency, memory, concurrency, API needs, and recovery behavior.
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model release analysisEvaluate a coding agent on one real bug with a frozen repository, hidden test, clean-diff rubric, cost record, and reproducible review artifacts.
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developer workflowsA 10-minute method to judge whether an open-source project is safe to depend on: release cadence, issue triage, maintainer count, security policy, and license.
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Local models and LLM infrastructureA practical decision tree for small teams choosing between self-hosting an open model and calling a hosted API: privacy, cost curve, latency, and ops burden.
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agent tools and workflowsPrompt injection is not a wording problem. Why instruction-based defences fail, and the architectural containment that actually limits the damage.
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model release analysisPublic benchmarks measure a different task than yours. How to build a small evaluation set that makes model choice, prompt changes and upgrades decidable.
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AI ops and monitoringWhat to record around every model call so failures are debuggable: inputs, retrieved context, tool calls, the model version string, cost and latency.
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model release analysisRAG and fine-tuning solve different problems: knowledge versus behaviour. Why fine-tuning on facts often makes hallucination worse, and how to decide.
Read the guideLocal models and LLM infrastructureGitHub AI project watchlistOpen-source AI comparisons