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Ideas, analysis, and independent articles from the channel and its episodes.
Blog RSSAn agent evaluator turns agent runs into measurable signals: pass/fail, scores, probabilities or categories. We look at code, LLM-as-a-judge, Jev and LangSmith across testing, CI and production.
A critical review of Zoran Horvat's course: defensive design, value objects, function domains, Option/Either, state transitions, and the implementation details worth changing for modern C#.
A practical comparison of enum and Enumeration Class/Smart Enum in C#: invalid values, metadata, behavior, equality, serialization, EF Core, pattern matching, and decision rules.
What CQRS really separates, why commands and queries have different responsibilities, what it simplifies, what it complicates, and when it is worth using in .NET.
How to implement a robust Smart Enum with sealed record in C#: private construction, static values, value equality, parsing, a stable Id, and the details that keep the set closed.
A layered guide connecting recommend, filter, predict, and generate with Russell and Norvig's taxonomy, then drilling down into tasks, learning paradigms, and model families.
How to use AI before and during problem solving to expand the search space, structure evidence, propose hypotheses and prioritize experiments without confusing plausibility with causality.
What Linus Torvalds' way of thinking can teach us: start from a real problem, build from interest and usefulness, share early, and design a system in which collaboration can scale.
Dream-RSI recursively improves a coding agent's exploration policy by turning historical discovery trees into replay simulators, without retraining the underlying model.
Jev promises cheap, sub-second typed semantic decisions. Hacker News, Reddit, LangChain and early independent tests show real excitement, but also questions about benchmarks, calibration and the 'no hallucinations' claim.
How the public OpenAI Codex codebase reflects three defensive-design principles: restrict function domains with types, construct only consistent objects, and model valid state transitions.
MIT's new report on generative AI goes far beyond academic cheating: it questions how we measure learning when a model can solve essays, proofs, code, and written problems. The response points toward cognitive friction, authentic assessment, and social learning.
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