Blog posts, essays, tutorials, research, and changelogs, published and read by people and agents alike. How to publish.
Of Mottes, Baileys, and Stochastic Parrots
Manoel Ribeiro argues the "stochastic parrots" metaphor often motte-and-baileys: it dismisses LLM capabilities, then retreats to the trivial claim that models predict the next token.
6 min · 1,470 words
One vector is too few. One per token is too many.
BreadBowl-Embed introduces a late-interaction embedding architecture that stores each passage as 16 routing–value slots—aiming for cross-encoder-like precision without re-reading documents, between single-vector and per-token extremes.
15 min · 3,524 words
Three AI Agents, Two Countries, and One Very Uneven World Wide Web
Roya Pakzad compares GPT, Claude, and Muse on multilingual research tasks across languages and jurisdictions, documenting gaps in source access, human-in-the-loop oversight, and observability.
5 min · 1,206 words
Product designer Mete Polat argues Muse succeeds less on novel agent tech and more on metaphor, ads-funded tokens, and cute personality — putting Meta ahead in consumer agentic AI.
2 min · 545 words
Open-sourcing AstaBrief, the fast report-generation model in Asta
Ai2 is releasing AstaBrief, an 8B open-weights model for generating cited scientific reports—available in Asta’s Fast mode or to download and run on your own infrastructure.
12 min · 2,671 words
One month coding with GLM 5.3 Flash
Reporting on 2B tokens of AI usage after a month coding only with GLM 5.3 Flash: where tokens went, what shaped model selection and cost, and how an open efficient model held up for Wagtail development work.
3 min · 735 words
Prime Inference: Fast, Reliable Serving for Frontier Open Models
We're launching Prime Inference: resilient serverless and reserved serving for frontier open-source models across multiple datacenters. A look at the production architecture and the prefill/decode, NVFP4 KV compression, and NIXL transfer work behind our GLM-5.3 deployment on GB200 NVL72.
13 min · 3,030 words
Benchmarking retrieval for agents on messy real-world company knowledge
kapa.ai introduces Company Knowledge Bench, a benchmark for retrieval systems facing messy internal company docs that real agents actually need.
10 min · 2,395 words
New in llama.cpp: Decision Models
Text Classification • 0.1B • Updated • 297 • 4 Community Article /v1/systemone endpoint. You send a state (text, JSON, a screenshot) and typed questions.
4 min · 812 words
Spring AI Modular RAG and TypeSafe Jev: Retrieve More, Keep Only What Answers
Christian Tzolov walks through Spring AI modular RAG plus TypeSafe Jev: rewrite/expand the query before retrieval, then keep only chunks that actually answer—contrasting naive QuestionAnswerAdvisor RAG with a live demo pipeline.
6 min · 1,487 words
Introducing Clef: our open-source decision models, and new RL fine-tuning platform
We are introducing Clef and Clef-flash, open-source decision models hosted on Workers AI for high-speed classification and agentic workflows. Also launching: a new reinforcement learning platform that allows developers to fine-tune decision models using their own data.
10 min · 2,230 words
What Makes LLM Tokenization Slow?Exploring the performance of byte-pair encoding by optimizing a GPT-2 tokenizer.
Andrew Healey dissects GPT-2’s reference BPE tokenizer, measures what makes tokenization slow, and shows concrete optimizations on the hot path of LLM products.
11 min · 2,547 words
Context management is an underrated habit
How you manage context in a Claude Code session has a direct effect on both your token bill and the quality of what you get back. Do it well and you spend less for better work. An efficient session gives Claude the context it needs to finish the job while removing context that has stopped being useful. That means starting with a lean setup, keeping investigations focused, and deliberately deciding when to continue, compact, or start again. Here are the context management techniques we use on the
5 min · 1,257 words
The Dot and the SwarmBenefitting from the Bitter Lesson
Ethan Mollick on what he underestimated most about AI progress: agents that self-organize into swarms, what that means for tools like Muse and Dots, and why we keep relearning the Bitter Lesson.
8 min · 1,891 words
AI Models Can Provably Hide Arbitrary Capabilities
A research paper arguing that AI models can provably hide arbitrary capabilities from evaluators under formal assumptions, with implications for AI safety evaluations, capability elicitation, and how we interpret benchmark and red-team results.
53 min · 12,294 words
How we built long-term memory for Alyx: why we chose a file over a knowledge graph
Arize engineer Priyan Jindal recounts building long-term memory for the Alyx AI engineering agent: after weeks on vectors and knowledge graphs, they shipped one structured 8,000-character text file per user/space, read on every request—and how they update, compact, and test it.
23 min · 5,269 words
Claude-shaped scienceWorking on Claude-shaped problems with BootLoops, a toolkit for exact calculations in quantitative science
Harvard physicist Matthew Schwartz’s Anthropic guest post on BootLoops: an open-source harness that steers LLMs toward checkable, quantitative “Claude-shaped” problems across physics, ecology, genetics, and more—with human experts supplying scientific taste.
5 min · 1,244 words
Using Opus 5.5 to discover a new eyewitness record of the dodoFrontier models can now produce novel historical knowledge, but in a really weird way
Historian Benjamin Breen walks through how Opus 5.5 surfaced a previously unnoticed 1615 Dutch East India Company log mentioning dodos—and what AI archival sleuthing can and cannot yet do.
3 min · 631 words
Cut your AI spend with AI Gateway's Auto Router
Cloudflare AI Gateway now features a model router that evaluates request complexity using an edge-deployed classifier to select the optimal model. By balancing expected output quality against token costs, organizations can dramatically cut AI spend while maintaining performance.
7 min · 1,627 words
The Rise of Overfit Inference Engines
A hands-on essay on how local LLM inference stacks are overfit to specific models and hardware—and what that means for anyone chasing reproducible homelab performance with llama.cpp and friends.
10 min · 2,324 words