Codepointer

Multi-agent systems can configure agents with different roles, context, tools, permissions, and execution environments. That separation supports specialization, parallel work, failure containment, and verification against evidence independent of the agent

Understanding how an agent works at each layer helps explain its behavior, locate failures, and improve the right part of the system. This post breaks one agent run into those layers and shows where to debug and which layer to improve. The next post cover

Kimi K3 is an open-weight model with 2.78T total parameters, 104B activated per token, and a 1M-token training context. The weights are on Hugging Face. Moonshot reports 88.3% on Terminal-Bench 2.1 against GPT-5.6 Sol's 88.8%, 42.0% on SWE-Marathon agains

Every frontier model launch leads with a benchmark table. Benchmark scores are also heavily used in comparison threads and in decisions about which model to build on. Some accept those numbers as a direct measurement of capability. Others have seen enough

An LLM can write a math proof that looks right but is wrong. For most text a wrong sentence is a nuisance, but for a proof it is fatal, because a proof only counts if every step holds. This is why people reached for Lean. Its checker either accepts a proo

Three open-source projects promise to reduce the tokens your coding agent uses. rtk, headroom, and caveman all drew tens of thousands of GitHub stars and report 60 to 90 percent savings. This post is a deep dive into what each one actually does, tested on

TL;DRAgent skills became a packaging format. A large share of the trending repos were curated "skill" bundles for Claude Code, Codex, and other agents, spanning design, marketing, research writing, finance, and security. Google, OpenAI, and Vercel all shi

TL;DRAI agent tooling dominated. Most of the trending repos were coding agents, agent runtimes, sandboxes, memory layers, or the frontends that drive them.Skill packs spread across domains. Curated bundles of Claude Code and Codex skills showed up for des

TL;DRAI agent tooling dominated. Most of the trending repos were coding agents, agent runtimes, or add-ons for them.Skill packs were everywhere. Curated bundles of agent skills and plugins kept trending, and OpenAI, Anthropic, Cursor, and Google all shipp

Four popular agent-memory libraries each pick a different answer to the same design question. How should an agent remember things across sessions? Letta edits plain text blocks and files. Mem0 keeps a vector store with entity hints. Graphiti indexes facts