Prompt Tuning Firefox Shake to Summarize for Recipes

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.

I put a local AI model on a bootable USB, so I can troubleshoot a dead PC with a chatbot that doesn't need the internet

My rescue USB now also helps me make sense of disk errors.

RAG vs. Vectorless RAG, clearly explained!

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

I replaced Grammarly Pro with a local model on my GPU, and my drafts never leave my PC

My local LLM replaces yet another subscription for effectively $0

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.

I replaced Gemini and ChatGPT on my phone with my gaming PC’s 26B model, and the app that does it is free

Farewell, bloated AI apps!

I changed one llama.cpp flag, and my 8GB laptop GPU now runs a 35B model faster than I can read

Một lập trình viên đã tìm ra cách chạy model Llama 35B trên laptop GPU chỉ 8GB VRAM với tốc độ đáng kinh ngạc. Nguyên nhân kỹ thuật nằm ở việc chỉnh sửa cờ n_gpu_layers trong llama.cpp, cho phép tận dụng hiệu quả bộ nhớ VRAM thay vì để nó trở thành điểm nghẽn. Hệ quả là model 35B parameters chạy nhanh đến mức người dùng không kịp theo dõi, vượt xa các kỳ vọng ban đầu về giới hạn phần cứng. Điều đáng học hỏi là đôi khi vấn đề không nằm ở cấu hình phần cứng mà ở cách tối ưu hóa phần mềm để khai thác tiềm năng thực sự của hardware.

Bài viết tiết lộ cách tối ưu hóa một cờ trong llama.cpp để chạy mô hình 35B trên GPU 8GB nhanh hơn tốc độ đọc của bạn.