Tesseract – 3D architecture editor with MCP for AI-assisted design

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Hey HN. I'm David, solo dev, 20+ years shipping production systems. I built Tesseract because AI can analyze your codebase, but the results stay buried in text. Architecture is fundamentally visual — you need to see it, navigate it, drill into it. So I built a 3D canvas where AI can show you what it finds. Tesseract is a desktop app today (cloud version coming) with a built-in MCP server. You connect it to Claude Code with one command: claude mcp add tesseract -s user -t http http://localhost:7440/mcp I use it for onboarding (understand a codebase without reading code), mapping (point AI at code, get a 3D diagram), exploring (navigate layers and drill into subsystems), debugging (trace data flows with animated color-coded paths), and generating (design in 3D, generate code back). There's also a Claude Code plugin (tesseract-skills) with slash commands: /arch-codemap maps an entire codebase, /arch-flow traces data paths, /arch-detail drills into subsystems. Works with Claude Code, Cursor, Copilot, Windsurf — any MCP client. Free to use. Sign up to unlock all features for 3 months. It's early but stable. I've been dogfooding it on real projects for weeks and it's ready for other people to try. Demo video (1min47): https://youtu.be/YqqtRv17a3M Docs: https://tesseract.infrastellar.dev/docs Plugin: https://github.com/infrastellar-dev/tesseract-skills Discord: https://discord.gg/vWfW7xExUr Happy to discuss the MCP integration, the design choices, or anything else. Would love feedback.

Visit product View on Hacker NewsFeb 25, 2026

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

98%98% 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 NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: video, month · Missing: mobile apps, ios, personal

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

42%42% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

28%28% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

23%23% predicted probability of success on AppSumo, 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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