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Overture – Interactive plan graphs for AI coding agents (open source)

Hacker News

Overture – Interactive plan graphs for AI coding agents (open source)

As a daily Claude Code and Cursor user, I kept hitting the same wall: the agent shows me a plan as a numbered list in the terminal, then starts coding. I can't reorder the steps. I can't monitor what's happening at step 8 without scrolling through a wall of output. I can't attach my Stripe API key to just the payment step without it leaking into every other step. And I can't tell step 3 to use the Figma MCP while step 7 uses Supabase. Overture is an open-source MCP server that intercepts the agent's planning phase and renders it as an interactive node graph in the browser before execution starts. Each step becomes a node. Dependencies become edges. You click into a node and attach exactly what it needs: files, docs, API keys, MCP servers, natural language instructions, all scoped to that step only. You drag to reorder, delete what you don't need, add what's missing. At decision points, you pick an approach or run branches in parallel and compare the output. During execution, each node updates individually so you see exactly where things stand. Under the hood, agents generate plans in structured XML (<node>, <edge>, <field> elements). Overture parses it into the graph. Agents can also declare typed input fields per step, so if a step needs a Stripe key or a database URL, you fill it in during planning instead of getting interrupted mid-execution. Works with any MCP-compatible agent. npx overture to try it. Everything local. Biggest open problem: getting agents to consistently produce well-structured plans. Claude Code cooperates well. Cursor is hit or miss. If anyone has experience with prompt engineering for structured output from coding agents, I'd love to compare notes. GitHub: https://github.com/SixHq/Overture.git

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, cursor · Missing: mac, macos, model
99%99% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: para, compatible · Missing: supports, reddit linkedin, podcasting
76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal
32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
27%27% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: open source, io · Missing: https docs, excited, just released
25%25% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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