Grouped around GitHub project review, agent tooling, model releases, developer workflows, and AI news interpretation.
developer workflowsInspect seven dated Scorecard fixtures, including a fabricated commit pin that still scored 10, and separate scanner signals from workflow execution.
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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 workflowsReview an open-source dependency in ten minutes: releases, issue triage, maintainer concentration, public security evidence, and license fit.
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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 workflowsUnderstand direct and indirect prompt injection, the limits of instruction hierarchy, and how tool permissions, approval checks, and egress controls reduce risk.
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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 monitoringTrace a 200 response that times out while reading its body, inspect limited logging fields, and preserve unknown usage without collecting every payload.
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model release analysisChoose retrieval, fine-tuning, or both by inspecting failures: source freshness, access, answer support, task behaviour, and held-out evaluation results.
Read the guideLocal models and LLM infrastructureGitHub AI project watchlistOpen-source AI comparisons