AI, courses, knowledge work, and conceptual breakdowns.
12 postsA breakdown of the defense-in-depth used by Claude Code, Codex, and others: rule-based keyword matching, classifiers, input/output scanning, execution sandboxes, cross-session behavioral monitoring, and the role of system prompts and skills.
An npj Digital Medicine study analyzed 84,395 users and 282,893 sessions on the self-hypnosis app Reveri. Self-rated stress dropped with a Cohen's d of −0.71 to −0.78 (a 'large' effect). But it's a retrospective observational study with no control group and a single-item Likert measure — a great case study in why 'big N + big effect' still can't prove causation.
When an agent loads tools on demand, it's essentially applying RAG's 'retrieve-then-inject' pattern to tool schemas — the only difference is the retriever is the LLM's own reasoning instead of vector similarity.
Fireship walks through 10 landmark CS papers from Turing to GPT-3, showing how each idea set the stage for the next — and how the path to modern AI was anything but linear
Reading guilt comes from treating 'finishing the book' as the goal. Hao Xu-Lie's point in Haovoice EP680: the real purpose of reading is finding your own connection to the material—not page count.
There's no universal answer on grad school timing, but one question cuts through the noise: are you going to solve a specific problem, or because you haven't figured out what's next?
The LLM Wiki isn't a query tool—it's an architecture that lets knowledge compound over time. The LLM actively builds and maintains a markdown knowledge base, instead of re-fetching from raw documents on every single query.
OpenAI's Michael Bolin breaks down the Codex CLI agent loop in depth: from prompt assembly, token inference, and tool execution, to context window management and auto compaction.
The concept of first principles isn't hard; actually applying it is. Using three familiar scenarios — the PMP exam, going to grad school, and learning personal finance — Joe from Dadu (大人學) demonstrates three thinking exercises you can turn into daily intuition.
ChatGPT's capabilities come from the first stage of massive self-supervised pre-training; but 'answering questions, understanding instructions, and not making things up' come from the latter two stages — instruction fine-tuning and RLHF. Capability and alignment are two separate things that are forged independently.
Time granularity is the basic unit you use to schedule your time. Shrinking it from hours to 15 minutes isn't just a quantitative change — it's a fundamental upgrade in how you perceive the cost of time.
Treat a conversation as raw material for an article: with a structured prompt and a template, Claude Code turns a debugging thread into a publishable technical post.