Coding agents

• Every Claude Code sub-agent we ran was Opus 5.5. Then Sonnet 5.5 scored 40/40 for $0.02.: 72 hidden-test runs show Sonnet 5.5 at high effort hits 40/40 at ~2k tokens/task ($0.02), versus 23k tokens ($0.23) at max for the same score, so route sub-agents by task and cap effort.
• This week Claude Sonnet 5.5, GPT-6.1 Sol and Gemini 4 Argon all launched near the top of the Coding Agent Index: Sonnet 5.5 (max) in Claude Code leads at 68, but the three trade performance against cost differently, and the index scores model plus harness together.
• Stripe cut payment integration timelines from up to 6 months to 2–6 weeks using coding agents: their LLM orchestrator was slow, costly and did tasks itself, burning its own context, so they moved predictable coordination into code, which matches the sub-agent routing lesson above.
• Muse Spark 1.3: Meta's update targets agentic and coding work, with longer-horizon threads, clarifying questions, and confirmation before consequential actions.
• Asked Fable 5.5 for the three-body problem. One-shot.: about 1000 lines of Bend2 with GPU-computed pixels at 60 FPS and four formally proven properties, a sign that proof-carrying output is reachable one-shot.
• The future is verification-engineering.: Rauch argues proofs, e2e tests, benchmarks and linters (deterministic and agentic) become the main engineering work as agents write the code.
• I gave Claude Code the CEO job for my free Chrome extension: week-1 anecdote of an agent running a product, with the human still approving every public post.

Claude Code mods

• v2.1.287: ships Claude Mods (plugins can modify deeper behavior) plus a built-in "You should know" mod, a side agent that flags what you or Claude missed, enabled via /plugin enable cc-plugin-you-should-know@builtin.
• You can now mod Claude Code: behavior, UI and features are customizable with a few lines of TypeScript or by asking Claude to write the mod, installed as plugins via /plugin.
• Mods are absolutely insane: Boris Cherny pitches prompt-driven customization of Claude's look and workflow, with mods shared as plugins.

Other agent harnesses and tooling

• earendil-works/pi: open toolkit with unified LLM API, agent runtime with tool calling, TUI and a self-extensible coding agent CLI; new-contributor issues and PRs are auto-closed by default.
• DeepSeek Harness: DeepSeek's open-source agent harness; HN commenters say its one-shot benchmark misses long-running use, so published metrics mislead.
• DSH Desktop: official desktop app for the DeepSeek harness.
• OpenCompanion: one desktop app to start, watch and answer multiple AI coding CLIs.
• Crontick: local scheduler for running prompts on a cron-style schedule.
• Effect v4: zero-dependency TypeScript ecosystem pitched as the base for reliable software and AI agents.

Copilot

• GitHub Copilot in VS Code, September 2026 releases: VS Code v1.136–1.140 add automations for repeatable tasks and agent merge, covering the path from implementation to PR merge.
• Dynamic workflows in Copilot CLI and the Copilot app: define orchestration in code across the CLI, app and SDK for more reliable multi-step runs.
• GitHub Copilot can now interact with desktop apps with computer use: public preview of computer use in Copilot CLI and the app on macOS and Windows.

Runtimes, sandboxes and security

• agent-substrate/substrate: secure-by-default agent runtime for millions of sandboxes, with sub-500ms resume, 500+ suspend/resume activations per second, and zero-trust kernel and network isolation.
• Private AI Proxy: checks the AI service with hardware attestation and TLS key pinning, and forwards fail-closed, before your codebase context leaves your machine.
• Polylane: agents that fix production issues unattended.
• Semitexa: PHP framework designed so an AI agent can inspect it.

Models and local inference

• Claude Opus 5.5 on NEAR AI Cloud: now hosted there with 1M context, 128K output and adaptive reasoning for long agentic coding.
• Qwen3.8-Flash-Next on a 64GB Mac: the Slipstream release runs the 95.5 GiB model at 41–52 tok/s, 1.76x faster than llama.cpp, with 130k context scaling and a Swift variant.