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🔥 TOP SIGNAL
Boris Cherny (Head of Claude Code) is blunt: for the kinds of programming he does, “coding is largely solved”, and the frontier is shifting to adjacent, end-to-end agentic work (project management, paying tickets, general ops) rather than better IDE autocomplete . In that world, throughput isn’t hypothetical: he says Anthropic saw +200% productivity per engineer (PRs), and Claude now reviews 100% of pull requests (with human review still in the loop) .
🛠️ TOOLS & MODELS
Claude Code — stability + performance signals
- v2.1.47: long-running sessions use less memory.
- Team guidance: keep reporting issues and they’ll fix them .
- Practitioner complaint: Theo reports Claude Code has “regressed an absurd amount” with UI/feedback issues (timestamps not updating, missing “thinking,” multi-minute hangs with 0 output) and suggests it “needs to be rewritten from scratch” .
Cursor — agent sandboxing shipped across desktop OSes
- Cursor says it rolled out agent sandboxing on macOS, Linux, and Windows over the last three months .
- Mechanism: agents run freely inside a sandbox, only requesting approval when they need to step outside it .
- Implementation write-up: http://cursor.com/blog/agent-sandboxing.
OpenAI Codex — pricing/availability + compute pressure
- @thsottiaux: Codex is included with a ChatGPT subscription (even Plus has “very generous” usage) ; they attribute this to gpt-5.3-codex achieving “SoTA at lower cost” .
- Same source: candidates increasingly ask how much dedicated inference compute they’ll have, and usage/user is growing faster than user count → compute could be scarce.
Gemini 3.1 Pro — dev-workflow positioning (ramping up)
- Google Antigravity: Gemini 3.1 Pro is ramping to Google AI Ultra/Pro users, pitched around “advanced reasoning” and “long horizon planning” for dev workflows . Details: https://antigravity.google/blog/gemini-3-1-pro-in-google-antigravity.
GitHub Copilot → Zed editor (GA)
- GitHub: Copilot subscription support in Zed is generally available . Changelog: https://github.blog/changelog/2026-02-19-github-copilot-support-in-zed-generally-available/.
Model choice drift + self-hosting pressure (reported trend)
- Salvatore Sanfilippo says he’s seeing excellent programmers move off US models (Codex, Claude Code) toward Chinese open-weight models like Kimi 2.5 and GLM5, often via providers or by building in-house Nvidia GPU inference to avoid outages and keep sensitive data internal .
- He frames DeepSeek v4 as a potentially major moment if it lands as SOTA (as rumors suggest), putting pressure on OpenAI/Anthropic business sustainability .
💡 WORKFLOWS & TRICKS
“Plan mode → execute” as a default loop (Claude Code / Boris Cherny)
- Start the task in plan mode (he says he does this for ~80% of tasks) .
- Iterate on the plan (model goes back-and-forth) .
- Once the plan is good, let it execute; he’ll auto-accept edits after that .
- Implementation detail: plan mode is literally a prompt injection: “please don’t write any code yet” .
Parallel agents, but treat “state” as a first-class problem
- Cherny: he runs ~5 agents in parallel while working (terminal/desktop/iOS) and highlights you can run many sessions in parallel .
- Kent C. Dodds: similar “utter chaos” workflow—multiple projects, “a couple cloud agents” each, plus a locally guided agent .
- Failure mode (real): Simon Willison describes “parallel agent psychosis”—losing track of where a feature lives across branches/worktrees/instances .
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Recovery trick: after hacking in
/tmpand crashing, he recovered the code from~/.claude/projects/session logs, and Claude Code could extract and recreate the missing feature .
Turn your feedback firehose into PRs (fast iteration loop)
- Cherny’s pattern: point Quad/Cowork at an internal Slack feedback thread; it proposes changes and opens PRs quickly, which encourages more feedback because users feel heard .
- Bug-fix loop: “as long as the description is good,” he can fix a bug in minutes by delegating to Claude .
Token policy as a productivity lever (especially early)
- Cherny recommends giving engineers as many tokens as possible early (even “unlimited tokens” as a perk) so they try ideas that would otherwise feel too expensive; optimize/cost-cut after an idea works .
Avoid over-orchestration: tools + goal > rigid workflows (model-first design principle)
- Cherny: don’t “box the model in” with strict step-by-step workflows; give it tools + a goal and let it figure it out—he argues heavy scaffolding mattered a year ago but often isn’t necessary now .
“Ephemeral app” mindset + AI-native interfaces (Karpathy)
- Karpathy built a one-off cardio experiment dashboard with Claude; it had to reverse engineer a treadmill cloud API, process/debug data, and build a web UI; he still had to chase bugs (units, calendar alignment) .
- His takeaway: the app-store model feels outdated for long-tail needs; instead, the industry needs AI-native sensors/actuators with agent-friendly APIs/CLIs so agents don’t have to click HTML UIs or reverse engineer services .
Agent “memory” ops in practice (LangSmith Agent Builder)
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LangChain’s concrete guidance:
- Tell your agent to remember what works
- Use skills to inject specialized context when needed
- Edit agent instructions directly when it’s faster
- Entry point: https://blog.langchain.com/how-to-use-memory-in-agent-builder/?utm_medium=social&utm_source=twitter&utm_campaign=q1-2026_ab-philosophy_aw.
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LangChain’s concrete guidance:
👤 PEOPLE TO WATCH
- Boris Cherny — production-grade Claude Code habits (plan mode, parallel sessions) + strong claims about where “after coding” goes .
- Andrej Karpathy — high-signal framing: ephemeral bespoke apps + “AI-native CLI/API” requirements for tools and hardware vendors .
- Simon Willison — the best micro-case study of parallel-agent failure/recovery using session logs as the source of truth .
- Steve Ruiz (tldraw) — pragmatic company-building: code gets easier, but alignment/positioning/communication get harder—and he’s automating the overhead away .
- Theo — sharp practitioner critique on Claude Code regressions plus continued pressure on “harness vs infra” policy differences across vendors .
- François Chollet — frames agentic coding as ML optimization (spec/tests as constraints) and asks what the “Keras of agentic coding” will be ; @swyx suggests DSPy as the presumptive community default .
🎬 WATCH & LISTEN
1) Boris Cherny — “Plan mode” as the default starter move (~1:09:52–1:10:41)
Hook: a simple, copyable workflow: force planning first (no code), iterate the plan, then execute + auto-accept when the plan is solid .
2) Boris Cherny — “Coding is largely solved… what’s next?” (~0:18:19–0:19:06)
Hook: his thesis on why the frontier is shifting from IDE coding to adjacent operational tasks and general automation .
3) Steve Ruiz — daily automated release notes from landed PRs (~0:20:35–0:21:02)
Hook: treat agents like scheduled staff: every day, Claude scans the last 24h PRs and drafts “release notes we’d publish if we shipped main today” .
📊 PROJECTS & REPOS
- NanoClaw — “Clawdbot” in ~500–700 LOC TypeScript using Apple container isolation for sandboxing/security; posted as Show HN . Repo: https://github.com/gavrielc/nanoclaw • HN: https://news.ycombinator.com/item?id=46850205.
- Nullclaw — “fastest, smallest OpenClaw clone”: 678 KB static binary, no runtime/VM/framework overhead . Repo: https://github.com/nullclaw/nullclaw.
- tldraw agent starter kit — Cursor-like agent panel next to a canvas; cloneable starter for agent+canvas UX: https://tldraw.dev/starter-kits/agent.
Editorial take: As agents make code cheap, the new edge is orchestration discipline: plan-first loops, sandboxing, session-log recoverability, and AI-native interfaces that don’t force your agent to “be the computer.”