Buildable – Project management through MCP

Hacker News

I am an old developer who started using an AI Assisted code editor about 6 months ago. After building a few MVPs I started to notice a pattern in my process to go from idea to a complete MVP. It would go something like: 1. Ideate together with ChatGPT, thoroughly come up with a solid product that would seem both fun to build and give me some value from building it. 2. Dig into a good tech stack, I usually have a good stack that works for my web apps, but some times I want to try something new and that's when ChatGPT or Claude would give me some good advice on something that would work well. 3. Create a task list on everything I need to do before the MVP would be ready. 4. Start coding and tick off the boxes in the task list. 5. Launch the product. So I figured, why not streamline the process, make it easy to run through these steps and save some time in the process. With a couple young kids at home you don't have that much time so, time saved on getting started is gold. So lets go through what Buildable solves right now: - Creating a PRD or a context document your AI Assistant can consume through MCP. - Creating tasks that your AI Assistant can consume through MCP. - Managing tasks through MCP. - Setting up a repository with a starter .workflow doc and README. - Setting up issues in your github repository. - Centralize your project so you can jump between AI chats and get the assistant up to speed quickly. Yeah, I guess that's it for now, I have a few cool ideas I want to add in the near future. I'm interested to hear what you think, let me hear your brutally honest critique!

Visit product View on Hacker NewsJun 20, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, mcp, apps · Missing: mac, agents, macos

94%94% 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: started · Missing: supports, reddit linkedin, podcasting

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: apps, month · Missing: mobile apps, ios, personal

50%50% 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

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide · Missing: https docs, excited, just released

30%30% 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 · Missing: arr, mrr, revenue

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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