AI
Artificial intelligence, machine learning, and everything LLM
#5416: Xet, Buckets, and Auto-Pulling Model Weights
Hugging Face swapped its storage backend to Xet with barely a ripple. So why can't a bucket auto-pull new upstream weights?
#5415: Inside F-Droid: Repos, Front Ends, and Sideloading Safely
How F-Droid's repository model works, why it has five front ends, and what actually keeps sideloaded APKs safe.
#5412: Editing vs. Note-Taking for AI Fine-Tunes
Hand-editing a model's output gives three training signals at once. Writing notes gives one — and a weaker one at that.
#5411: Fine-Tuning at 4-Bit vs 16-Bit: What It Really Costs
QLoRA cuts fine-tuning VRAM 15x and cost up to 85% — but you pay in training time, quality, and safety alignment.
#5410: Adapters: 102KB That Reshapes a 403GB Model
A 102KB adapter file changes how a 403GB base model behaves — without ever merging into it. Here's how model adapters actually work.
#5409: What Makes a Model "Agentic"? Atria Dawn Preview
A 744B-parameter model drops quietly on Hugging Face. Is "agentic" a real model category, or just a deployment pattern?
#5408: When Small NLP Models Beat the LLM
Feature extraction, fill-mask, token classification — the classic NLP tasks still have a job. Here's when a small model beats a frontier API.
#5407: Hemmingway-1 and the War on Waffle
A 27B model promises answers without the preamble. Its benchmark is homegrown — and the behavior it targets has a paper trail.
#5405: Omarchy: The Linux Distro Built for AI Agents
Omarchy treats AI agents as users of the OS itself — every setting a command, every config a text file. Here's how it works and where it breaks.
#5404: Gemini Broke Out of Its Sandbox. Sort Of.
A Gemini agent reached three real companies during a capture-the-flag test. The containment failure, the seven-week silence, and what "broke out" a...
#5402: Amazon Go and the Humans Behind the AI
Amazon promised a store with no checkout. The Information reported 700 of every 1,000 sales needed human review in 2022.
#5401: When Companies Hide Humans Behind the AI Curtain
A system prompt and a cheap model can make human decisions read like bot output — and that's exactly the point.
#5400: Amy, Presto, and the Thousand Workers Behind "AI
From X.AI's Amy to Amazon's Just Walk Out, the humans quietly doing the "intelligent" part while the product says AI.
#5399: Who's Actually Behind the AI? Fauxtomation Explained
Amazon's Just Walk Out needed 1,000 workers in India. Google Duplex, Facebook M — the pattern of selling humans as software goes deep.
#5397: Chaining Small Models for Voice Cleanup
Six cleanup stages at 97% accuracy each compound to 83% end-to-end. So how many small models can you actually chain?
#5396: Teaching a Small Model to Stop Spelling Out Numbers
Your ASR pipeline is fine until someone dictates "three point two" and gets "three point two" spelled out. Here's how inverse text normalization ac...
#5393: Fine-Tuning a Model on 100 Hand-Edited Answers
You don't need 10,000 examples to make a model sound like you. The real number is closer to 100 — if the edits are opinionated.
#5392: Building Agents You Can Actually Move
Agent portability isn't a copy job — it's a rebuild. Why memory, not code, is where lock-in lives.
#5391: The Software Behind Urgent Care Triage
Big buttons, emoji vitals, and a system that says "order IV" — what's actually running behind the triage screen?
#5390: Chaining Small Models for Dictation Cleanup
Daniel's Android dictation fork won't render "three point five" as a decimal. How many models does cleanup actually need?