Practical skills & actionable AI news for engineers
No hype. Every article answers three questions: What changed?, How does it affect real systems?, and What should engineers try next?
AI Skills
Evergreen, production-oriented learning
Build real capability over time: structured paths, operational playbooks, and patterns that hold up in production.
- Learning Paths — 2–6 week tracks (RAG, evals, agents, LLMOps, security)
- Playbooks — step-by-step guides, checklists, reusable templates
- Patterns & anti-patterns — trade-offs, failure modes, when not to use a technique
AI News
News translated into engineering impact
Short briefs for awareness, deeper analysis for decisions, and release digests for implementation.
- Briefs — fast summary + impact + next actions
- Analysis — production trade-offs: cost, latency, reliability, security
- Releases — model/tool updates explained for developers
Why this is different
Production-first
Cost, latency, reliability, security, observability, and operational trade-offs come first.
Evidence-aware
Sources are linked. Assumptions are stated explicitly when evidence is incomplete.
Built for busy engineers
Most posts are readable in minutes and end with clear next steps.
Trade-offs over hype
You’ll see when to use something—and when it’s the wrong tool.
Latest AI News
Latest AI Skills
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Prompting Is Not Magic: What Really Changes the Output
Prompting does not make models smarter or more truthful. This article explains what prompts actually change under the hood, why small edits cause big differences, and how engineers should think about prompting in production systems.
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How LLMs Actually Work: Tokens, Context, and Probability
A production-minded explanation of what LLMs actually do under the hood—and why tokens, context windows, and probability matter for cost, latency, and reliability.