AI AGENTS

LM Studio launches Bionic, a standalone agent harness for open models with local inference, voice input, and zero data retention cloud options.

A July 2026 paper from Tencent Hunyuan turns agent harnesses into behavior-level maps. The useful lesson for builders is simple: code search is not enough when one behavior spans prompts, tools, state, permissions, and runtime policy.

SkillHone is a July 2026 paper about evolving agent skills across sessions. The useful takeaway for developers is simple: do not save only the latest SKILL.md. Save the decisions that explain why it changed.

How to set up Entire's regional Git mirrors for AI coding agents. Covers installation, mirroring, integrations with Claude Code, Codex, Cursor, and Factory AI.

Long-Horizon-Terminal-Bench tests coding agents on 46 terminal tasks that can run for 90 minutes. The takeaway is not that agents are useless. It is that evals need to measure endurance, recovery, and partial progress.

Open-source tool gives Claude Code, Codex, and other agents their own isolated Linux VM on your machine - network firewall included, no cloud account required.

A deep dive into the agent orchestration behind the Bun Rust rewrite - the workflow architecture, adversarial review gates, what one human actually did, and the Zig vs Rust debate including Andrew Kelley's response.

A companion guide to the Composio CLI video: one command-line layer that lets Claude Code, OpenClaw, Codex, and other agent harnesses search, authenticate, and execute tools across 1,000+ apps.

ByteDance's Dockerless paper asks whether coding-agent patches can be verified without spinning up per-repo environments. The practical answer is not replace CI. It is use cheaper evidence before CI.

The architecture side of loop engineering: plan/act/verify cycles, convergence criteria, retry policies, budget-bounded loops, and the loop-until-dry pattern. Concrete TypeScript-shaped patterns for building agent loops that stop when they should.

A new Vera paper tests Codex, Claude Code, OpenClaw, and Hermes with executable safety cases. The useful lesson is not panic. It is evidence-grounded agent QA.

A new benchmark shows GLM 5.2 processing 59 transactions and producing VAT returns off by only 7 pence - at $2.73 versus typical accounting fees of $1,000+. Here is what the benchmark actually tested, where the model failed, and why the HN discussion focused on liability.

Meta launches Muse Spark 1.1 through the new Meta Model API - a 1M-token-context model for personal agentic tasks with OpenAI-compatible endpoints, $20 free credits, and pricing that undercuts the competition.

pgvector, Pinecone, Qdrant, Weaviate, Chroma, Milvus, and Turbopuffer compared on hosting model, filtering, scale, and cost for RAG.

Security researchers discovered a prompt injection vulnerability in GitHub's Agentic Workflows that allows attackers to extract private repository contents through public issues.

Lilian Weng argues self-improving AI won't start with models rewriting their weights - it starts with the harness. Here's what that means for developers building agents.

Microsoft Execution Containers (MXC) give your AI agents policy-driven sandboxing across Windows, Linux, and macOS. TypeScript SDK, JSON config, multiple isolation backends. Here is how to use it.

Claude Code and Codex both ship great agents and terrible transcripts. AgentCanvas is a visual adapter that puts the artifacts, decisions, and handoffs on one board so the next agent and the next human can see them.

DeepSeek, Kimi, and GLM are cheap enough to run as sidecar subagents for drafts and exploration. The catch is that cheap work you cannot inspect is just expensive noise. A shared canvas makes the output reviewable.

MCP makes tools callable by agents. That solves invocation. It does not solve visibility. The next agent and the next human still need to see what the tool calls produced, and a transcript is the wrong place for that.