Deep Work Plan
Models matter. Context matters more. Give your agent a plan.
Deep Work Plan launched on June 17, 2026 with 114 votes, #317 of 22,647 launches that month and more than 95% of that year's launches.
What it does
Deep Work Plan turns any repo into a harness with the context of your best engineer — so any AI agent codes like your smartest model and can't drift from the plan. Not a chat window it forgets, a spec written into the repo: atomic tasks, acceptance criteria, validation gates, resumable state. Long runs survive context resets; any agent picks up where the last left off. Point an agent at it, walk away, come back to work you can verify. Any agent, any repo, no lock-in. Open Source, MIT.
Context matters more than models. Deep Work Plan turns any repository into a structured environment where any coding agent finishes long-horizon work.
Deep Work Plan turns any repository into a structured environment — context, guardrails, and a durable plan — where any coding agent executes with precision and finishes long-horizon work. Copy the init.md prompt and paste it into your coding agent — Claude Code, Cursor, Codex, or any other — to make any repository AI-first. Deep Work Plan is spec-driven development where the repository itself becomes the harness. AI coding agents are remarkably effective in short bursts. On long-horizon work — a migration, a new subsystem, a refactor across dozens of files — they drift: context fills up, decisions are forgotten, and multi-hour tasks are abandoned halfway through. Deep Work Plan answers…from deepworkplan.com
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ReplicasJun 2026 · replicas.dev · ▲239Hey HN! I'm Connor, creator of Replicas. I'm a heavy Codex/Claude Code user, and I wanted to hand off tasks to agents like I would to an engineer - kick something off before work, let it scaffold while I'm at lunch, even come back to a ready PR. The problem: existing alternatives use workspace VMs that are black boxes. I have no access to any of them - they can't run tests, read logs, or use tools like the Playwright MCP (which I use to verify agents actually fixed what they claim to fix). Replicas gives agents real cloud workspaces where they can: - Run your test suite and read the…


Context GatewayMar 2026 · github.com · ▲220We built an open-source proxy that sits between coding agents (Claude Code, OpenClaw, etc.) and the LLM, compressing tool outputs before they enter the context window. Demo: https://www.youtube.com/watch?v=-vFZ6MPrwjw#t=9s. Motivation: Agents are terrible at managing context. A single file read or grep can dump thousands of tokens into the window, most of it noise. This isn't just expensive — it actively degrades quality. Long-context benchmarks consistently show steep accuracy drops as context grows (OpenAI's GPT-5.4 eval goes from 97.2% at 32k to 36.6% at 1M…
AgenMar 2026 · agenhq.com · ▲138AI changed the way we code, but we're still using the old processes, and we've become the bottleneck, the AI is waiting for us - to reply, to open our laptops, to review the code, and so on. We're building the future of AI software development. The agents are autonomous, they run in sandboxes, automatically fix the pipelines, and deliver you the final, working code. You can use live preview to see the changes they made. Working across multiple repositories, all within the same session. This is the future - you don't need an IDE, and you don't have to run anything locally.

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