Why it feels like AI is never happy with us

API pricing vs subscription: what my month of Codex would cost on the API

I priced 30 days of my real Codex usage at API rates. A $100 plan covered $745 of API-equivalent work. Here's the math, the script to run on your own logs, and when the API still wins.

Red Flags to Watch for When Choosing Between AI Pentesting Vendors

Choosing between AI pentesting vendors requires looking past marketing labels like 'autonomous' or 'agentic' because the underlying systems vary widely. A Stanford-led study ran different AI agent frameworks against a live 8,000-host network and found that between 55% and 82% of reported findings were confirmed as real vulnerabilities. Swapping only the underlying model moved one framework's valid finding rate from 57% to 80%, yet that same framework still scored lowest overall since its findings stayed shallow. On a CTF-style benchmark two frameworks scored near-identically (48.6% vs 45.9%), but on the live network one scored nearly three times higher on severity and complexity. Buyers should demand proof of exploitation, real-network results, clear safety controls, data handling answers and clarity on what 'continuous' actually means.

What Frontier Labs Buy: A Data Deep Dive with Handshake AI — Garrett Lord & Team

Handshake went from launch to $1B in one year pushing the envelope of specialized data for frontier labs. Our first deep dive into a data lab with specific examples across Finance, Bio, and Alignment

RAG vs. Vectorless RAG, clearly explained!

...explained visually (with tradeoffs in production).

Issue #147 - Reinforcement Learning Explained with a Practical Python Example

How agents learn from rewards, why exploration matters, and how to build your first Q-learning agent.

DeepSeek Super-Agent Architecture Explained

How DeepSeek Harness's Super-agent works: a Router, isolated Workers, event triggers and a Watchdog let one AI agent run many tasks. Diagrams inside.

How I use AI (Oct 2026)

I document how I use AI, one year after my first writeup. Everything's completely different.

Build Your Own Reasoning Model: A Full Video Course

Khóa học video kéo dài 11 giờ tập trung vào việc xây dựng model reasoning với Python và PyTorch. Nội dung bao gồm các kỹ thuật advanced như evaluation, inference-time scaling, reinforcement learning và distillation. Qua khóa học, bạn sẽ học cách tối ưu hiệu suất của các reasoning model hiện đại. Các bài thực hành code chi tiết giúp hiểu sâu về quy trình training và fine-tuning. Giá trị lớn nhất nằm ở việc kết hợp lý thuyết với thực hành để xây dựng được hệ thống AI có khả năng reasoning mạnh mẽ.

Lập trình viên muốn nâng cấp kiến thức về mô hình học máy từ cơ bản đến ứng dụng thực tế—đặc biệt là trong scaling mô hình, reinforcement learning, và distillation—để áp dụng ngay trong dự án AI của mình với Python và PyTorch.