Dexicon – Capture AI coding sessions so your team never loses context

Hacker News

We built Dexicon because there's invaluable context in AI coding sessions that disappears the moment you close the tab. Architectural decisions, debugging rabbit holes, the "why we did it this way" - gone. Dexicon captures sessions from Claude Code, Cursor, Codex, and others, then makes it all searchable via MCP. You can also upload sessions manually along with relevant docs. It extracts atomic pieces of context into a knowledge graph - for V1, that means completed tasks and debugging/root-cause analyses, the non-trivial stuff that helps when someone hits the same issue a few weeks later. It's designed to be useful for solo devs who want searchable insights into their own sessions, but scales to teams as a way to solve the tribal knowledge problem. We're pre-seed with a handful of paying customers. The developers we've been working with have surprised us with use cases we didn't anticipate: encoding team best practices, speeding up onboarding for new teammates, and generating optimized agent instructions from their own session history. Now we're opening up access to more users and would love feedback from HN community.

Visit product View on Hacker NewsJan 27, 2026

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

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.

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

52%52% 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

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

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

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

nativeThis product was originally launched on this platform.

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

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

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

25%25% 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.

Correct prediction on native model

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