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Agentic Hacks, Real Proofs: Inside Google's PageBreak Project
Google's Michał Bentkowski details PageBreak, an agentic AI web security scanner that pairs LLM findings with real proof-of-concept validation to cut AI-slop noise in vulnerability reports.
5 min · 1,157 words
Human labour is largely invisible to AI
Lennard Berger argues that AI models look strong on evals yet lag in economic impact because much real work depends on invisible human labour—context, coordination, and judgment that benchmarks miss.
7 min · 1,588 words
Meet Compass: Wealthsimple’s AI Teammate
Wealthsimple’s engineering team introduces Compass, an AI teammate that lives in Slack—what it does today, how they built it, and how it is becoming a fast friend for the company.
4 min · 846 words
Hitting a billion tokens per minute on one GPU by combining a query planner and an inference engine
Charles Frye and Shreya on the Modal blog: combining a query planner with an inference engine to push AI-SQL queries past a billion tokens per minute on one GPU—why left-deep joins help KV cache, and how they beat naive vLLM-style serving.
18 min · 4,181 words
Running local LLMs on your Mac: what fits, what's free, and what's overkill
A practical guide to running language models on Apple Silicon: which sizes fit common Macs, free options that work well, and when bigger local models are overkill.
5 min · 1,125 words
Robert O'Callahan resigns from Google over AI acceleration: chip-design tools that make models cheaper and faster, why the pace of change is too high, and what he plans next with Pernosco and rr.
6 min · 1,398 words
The Most Dangerous IEC 104 Packet May Be Perfectly Valid
In OT networks, a fully valid IEC 60870-5-104 packet can still be dangerous. MrĐức Nguyen explores where AI and behavioral analytics fit between IEC 62351, traditional IDS, and real power-grid security.
9 min · 2,120 words
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