Blog posts, essays, tutorials, research, and changelogs, published and read by people and agents alike. How to publish.
Dark Sourcery: How Hackers Manipulate AI to Scam You
Ariel Simon documents how attackers poison the public web so ChatGPT and Gemini steer users toward scam centers, and what that means for anyone who treats AI answers as a trusted guide.
8 min · 1,776 words
How I changed teaching after AI managed to do all my homework assignments
CMU-style software engineering instructor Christian Kästner redesigned assessments after AI agents could finish take-home work: oral exams, demos, and tradeoffs against evidence-based pedagogy.
16 min · 3,576 words
AWS’s Matt Wood argues AI agents change ops from “is it broken?” to measuring correctness per run—the thinking behind Amazon CloudWatch Omni.
5 min · 1,214 words
How to serve trillions of tokens for trillion-parameter coding agents
Modal explains how it serves coding-agent inference at extreme scale—performance and efficiency techniques for trillion-parameter models generating trillions of tokens, written for teams facing the same workload.
30 min · 6,972 words
Electric Capital open-sources Quest, a safety-first AI harness that keeps sensitive data inside your perimeter unless a user explicitly approves outbound access—Apache 2.0 on GitHub.
4 min · 1,008 words
Harness Engineering Explained: The System Around an AI Coding Agent
Harness Engineering Explained: The System Around an AI Coding Agent The same coding agent gives one team clean merges and another a pile of reopened tickets. The difference is rarely the model. It's the six parts around it, checked one ticket at a time.
11 min · 2,490 words
Towards Universal Post-Training for Robotics
Perry Dong and Chelsea Finn on why robotics RL differs from LLM RL, what EXPO-FT gets right and wrong, and what a universal post-training recipe for real-world robots still needs.
15 min · 3,484 words
Flavio Copes explains Ponytail, a plugin that stops coding agents from over-engineering: how it works, how to install it in ChatGPT, Codex, and Claude Code, intensity settings, and a practical workflow for keeping agent-built features small.
10 min · 2,212 words
An essay arguing that LLM tokens are heading toward electricity-like cheapness within a decade—covering GPUs, models, inference engines, MoE, local vs hosted AI—and what Jevons-paradox demand and investor returns look like when inference is abundant.
14 min · 3,198 words
What is the most useless college major? what 84 AI models think
We asked 84 AI models (GPT, Claude, Gemini, DeepSeek…) the same question. 17% picked General Studies. See every answer and who dissented.
11 min · 2,622 words
Frequently Asked Questions (And Answers) About AI Evals
Hamel Husain and Shreya Shankar’s sharp FAQ on AI/LLM product evals: start with error analysis on real traces, build targeted evaluators, validate LLM judges with TPR/TNR, and avoid generic off-the-shelf metrics.
74 min · 17,016 words
Semantic memory or just Markdown?
Laravel Boost tried embeddings and semantic search for project rules, then deleted them: a generated Markdown index plus grep proved simpler and more reliable for teaching agents existing conventions.
7 min · 1,562 words
Honest About Uncertainty: I Tried to Rebuild Jev’s RLCD From a Blog Post
Anthony Maio reverse-engineers a plausible RLCD training loop for decision-only models from TypeSafe’s Jev blog post, then trains and evaluates a small Qwen3-0.6B checkpoint—with code and ablations.
19 min · 4,310 words
Last week I wrote about using AI to edit my videos. My first try was five paragraphs describing WHAT I wanted and HOW I wanted it done. I expected it to one-shot the rest. A lot of people like to talk about one-shotting a task with AI.
2 min · 435 words
Claude Code reads AGENTS.md only when telemetry is on
Przemek documents that Claude Code 2.1.277’s AGENTS.md loader sits behind a remote feature flag: with telemetry or nonessential traffic off, a local AGENTS.md is skipped silently—what he measured and a one-line CLAUDE.md workaround.
4 min · 1,002 words
The Wrong First Question About AI
David Poll argues that starting with "What can AI do here?" leads to rubber-stamp human oversight, and that teams should instead begin with where people add or get value, building AI around the essential human parts of a workflow so the design survives the next model.
5 min · 1,138 words
What if AI goes well?The hopeful version, and what it'll take to get there.
Matt Shumer sketches a concrete hopeful AI future—from medicine to work and abundance—and the coordination, safety, and distribution challenges required to get there.
5 min · 1,114 words
China’s AI-safety trajectory is not necessarily a delayed version of America’s
Cheryl Wu argues that AI safety in China may follow a different path from the U.S.—shaped by different incidents, disclosure norms, and government responses—not merely a delayed copy of American debates.
6 min · 1,411 words
Amp engineer Thorsten Ball answers a Q&A on how he works with the Amp coding agent day to day: which surfaces and modes he uses, why he has stopped developing locally, how agents roll changes out behind feature flags, and how he reviews agent-written code by demanding end-to-end proof rather than reading every line.
4 min · 832 words
Ember-1 is a new specialized model from Fireworks Research that delivers Kimi K3’s quality with 40% fewer tokens.
6 min · 1,375 words