Ideon – open-source spatial canvas for project context

Hacker News

Hi HN, Ideon is a self-hosted workspace that maps project resources (repositories, notes, links, checklists) on a spatial canvas instead of hierarchical lists. The goal is to preserve the "mental model" of a project visually, reducing context-switching friction when returning to development after a break. Stack: - Next.js (App Router) + TypeScript - PostgreSQL + Prisma - Docker Compose for self-hosting - AGPLv3 License Key features: - Spatial organization of resources (drag & drop blocks) - Direct GitHub integration (live issue tracking on canvas) - Markdown notes with real-time sync - Fully self-hostable It's designed to run on a cheap VPS or a home server. I'm looking for feedback on the spatial approach compared to traditional linear project management tools. Docs: https://www.theideon.com/docs

Visit product View on Hacker NewsFeb 25, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, dock, context · Missing: mac, agents, macos

95%95% 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.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

52%52% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

15%15% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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