Cloudflare's Code Mode MCP server exposes its entire API of more than 2,500 endpoints in roughly 1,000 tokens by offering just two operations — search for an API surface and execute code against the d…
Claude Code now supports AGENTS.md in addition to CLAUDE.md, letting developers change agent behavior by writing markdown documentation that harnesses auto-load when a session starts. The LLM reads sk…
TypeSafe's Jev has sparked community activity around a set of Jev-like models, though matching Jev's benchmark results remains a significant technical challenge, according to the article. Jev still le…
A system called Cache-to-Cache (C2C), published on GitHub by thu-nics, lets large language models communicate directly through their KV-Caches instead of generating text, achieving 8.5-10.5% higher ac…
A debugging guide for large language model training runs recommends that engineers first test whether a model can overfit a tiny dataset of 100 examples, then check training and validation loss, data …
OpenJev has been renamed SemIf, an independent research project that provides the same API as TypeSafe's Jev but may use a completely different underlying architecture, since TypeSafe never disclosed …
PrismML released Bonsai 2 27B, a ternary-weight compression of Qwen3.8 27B that cuts model size from about 56 GB to 5.9 GB while retaining roughly 98% of the original model's benchmark score. In a han…
TypeSafe announced Jev, a model that takes unstructured state as input and returns typed, probabilistic decisions — Choice, Score, and Noul — rather than free text, claiming it is 20-200x faster and 4…
Ponytail, a coding skill that makes AI agents write less code, cut code volume by 80-94%, cost by 42-75%, and time by 3-6x in benchmarks published in its GitHub repository, according to the project's …
AI models behave probabilistically rather than deterministically, meaning the same input can produce different outputs on separate runs, according to an explainer on how AI models work. The explainer …
A technical explainer on AI agents details how autonomous loops chain tool calls through repeated cycles of deciding, acting, and reviewing results until a task completes or a stopping condition halts…
Large language models hallucinate because they are trained to predict plausible next words rather than verify facts, producing false outputs such as nonexistent library methods, wrong import paths, an…
Large language models process text as tokens rather than words, and providers charge separately for input and output tokens, usually in price-per-million units, with output tokens almost always costin…
The fourth installment of the "AI Foundations" series explains that a model's context window functions as short-term memory rebuilt on each request, holding the system prompt, user prompt, additional …
Tool calling lets a language model request actions it cannot perform itself, with the application executing the tool and returning results to the model's context, according to the AI Foundations 5 exp…
A guide in the AI Foundations series, "AI Foundations 6 - Prompting and Evaluation," argues that prompt structure, few-shot examples, and automated evaluation loops determine model output quality as m…
A 499-neuron circuit extracted from MaleCNS v1.0, the Janelia-published CC-BY electron-microscopy reconstruction of a male fruit fly's central nervous system, was run as a reservoir on MNIST in PyTorc…
Anthropic published a finding that several Chinese AI firms secretly routed LLM requests to Claude, including API keys, credentials, rocket code, and military information, according to the article. Th…
Managing an agent's context window is a key engineering decision because every token adds computation cost, and an oversized window causes context rot, making the model forget information. The article…
Researchers at ORUK, in collaboration with FlyEM at Janelia, Cambridge, the MRC Laboratory of Molecular Biology, and Google Research, have developed a speech emotion classifier using a 499-neuron circ…