Ideon – An open source, infinite canvas for your project's segmentation

Hacker News

The problem: I want to use my tools. You want to use yours. The contractor we hired uses yet another set of tools. This causes unnecessary friction for all participants. Ideon is a self-hosted visual workspace designed to bridge this gap. It doesn't replace your existing stack (GitHub, Figma, Notion, etc.) but provides a shared context where all these pieces live together on an infinite canvas. We built this because projects often die from fragmentation—code is in one place, decisions in chat logs, and visuals in design tools. Ideon aims to keep the project "mentally navigable" for everyone involved. Key features: - Visual Blocks: Organize Repositories, Notes, Links, Files, and People spatially. - State History: Track how decisions evolved with workspace snapshots. - Multiplayer: Real-time collaboration. - Self-hosted: Docker-based and AGPLv3 licensed. Tech stack: Next.js, PostgreSQL, Docker. Would love to hear your feedback on the approach!

Visit product View on Hacker NewsJan 26, 2026

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2points

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Launch Intel predictions

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

92%92% 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: exist, open source, existing · Missing: https docs, excited, just released

63%63% 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

59%59% 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

49%49% 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

27%27% 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

11%11% 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.

Incorrect prediction on native model

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