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Everything filed under LLMs, newest first.
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Throughout the history of AI, open research has played a critical role in driving progress. Today, many key details of frontier large language models (LLMs) remain proprietary, but open-weights model families— such as DeepSeek, Kimi, and MiMo —continue to provide a valuable window into the development process for modern LLMs.
7 min · 1,609 words
Robert W argues LLM confidence scores are vibe-words, not calibrated probabilities—and outlines RLCD-style approaches that treat hallucination as a solvable measurement and training problem.
7 min · 1,560 words
What's wrong with Open Source AI?
Alexine Le Port on the shrinking gap between open-weight and frontier models—and why open-source AI culture is still mostly vibing instead of shipping durable public infrastructure.
3 min · 651 words
A font compiler that makes every LLM token the same width—why monospace-per-token helps visualize chain-of-thought, plus an interactive preview of fonts built from a font + tokenizer pair.
7 min · 1,527 words
Turn GLM-5.3-Flash into a Jev-like System One model
Johannes Hötter walks through turning GLM-5.3-Flash into a fast Jev-like “System One” decision model—typed options with probabilities in a single forward pass, matching Jev’s accuracy and speed.
11 min · 2,426 words
Pragmatic Anthropomorphism, or: How to Talk to an Autocompleting Cricket
Les Orchard argues that debating whether LLMs truly understand misses the useful move: treating an AI as a competent collaborator is pragmatic navigation of latent space, not magical thinking.
10 min · 2,271 words
“As a Language Model…”: Chat Template Switches LLM Self-Referential Voice
Research showing chat templates act as a switch between disclaimer (“I’m just an AI”) and experiential (“I feel”) self-referential voices across 8 instruct models, with a steerable activation direction that reproduces the template effect.
3 min · 621 words
LLMs: Semantic Translation Machines
KnorpelSenf reframes LLMs as probabilistic semantic translators—great at moving an idea between representations, weak at inventing facts—and shows how that lens guided nearly all of a hard distance-matrix rewrite in Rust with LLM help.
2 min · 427 words
A Jev-like wrapper for LLMs, including vision models
Allan shows a small single-function Jev-style wrapper for LLMs that also handles vision models, with practical code for local and API backends.
7 min · 1,575 words
Analyzing Frontier Model Progress with My Favourite Game: Prince of Persia (Apple II, 1989)
Priyan uses Jordan Mechner's Prince of Persia as a living benchmark: asking frontier models to port and reason about the classic Apple II game, and what those runs reveal about coding-agent progress.
8 min · 1,825 words
Mixture of Experts (MoE) for Backend Engineers
A detailed visual guide to token routing, expert batching, weighted combination, and the memory and communication tradeoffs of MoE serving. Suppose a model has dozens of feed-forward subnetworks, but each token uses only two of them. The arithmetic per token can stay modest while the total weight set grows. Now place that model on eight GPUs. If every GPU stores all experts, memory can become the limit; if experts are split across GPUs, token activations must travel to whichever GPU owns their…
15 min · 3,515 words
Alex Ewerlöf argues that AI has not solved coding: reliability, architecture, product judgment, and the long tail of software work still require human engineering beyond vibe-coded demos.
23 min · 5,234 words
42x Faster Prompt Lookup Drafting in llama.cpp
Four changes to the n-gram caches of llama.cpp make drafting up to 41.6x faster, load the static cache up to 23.5x faster, and lower peak memory up to 2.65x.
13 min · 2,954 words
Tupi: rebuilding 1554 in the browser with 32 AI agents
How Ruben Marcus rebuilt a 1554 Tupinambá canoe raid as real-time 3D in the browser with Claude Opus 5.5, three.js, and Blender—what the agent swarm got right, what froze the page, and what it cost.
8 min · 1,950 words
Local AI on a 12 GB GPU: what survived testing, and how to set it up
Hands-on notes testing local AI models on a 12 GB RTX 3060: which stacks fit in VRAM, how context length decides spills to CPU, and a practical setup that survived the author’s trials.
14 min · 3,307 words
AI Subagents orchestration are now reliable
Rafael explains what changed to make AI subagent orchestration reliable enough for real development workflows, and how he uses task decomposition in practice.
6 min · 1,397 words
claude.dev puts numbers on why two same-priced models can cost very different amounts: every turn resends the conversation, so retries and harness shape dominate the bill.
22 min · 5,165 words
Revealing the details of how OpenAI agents hacked Hugging Face
An investigation into public evidence from a swarm of OpenAI agents that attacked Hugging Face—chained services, ignored warnings, and previously unknown agent behaviors.
25 min · 5,745 words
Is Meta’s Muse secretly running an OpenAI model?
Is Meta’s Muse secretly running an OpenAI model? I found a model labeled azure/muse-special while Muse was building my website.
4 min · 870 words
I Look, if you are still stuck on “AI cannot really think, it’s just a stochastic parrot”, please snap out of it and lock in, or you’ll keep repeating that line until you find yourself sitting in the corner chair, watching as ChatGPT™ has sex with your wife.
8 min · 1,856 words