Memex is a Claude Code alternative built on Rust+Tauri for vibe coding

Hacker News

Hi HN, TL;DR Memex is a cross-platform desktop app for vibe coding. Think ChatGPT + Claude Code rolled into one. Why we built it: We love chat tools like Perplexity and ChatGPT. We also love coding agents, like in Cursor and Windsurf. We don’t like that web-based app builders are opinionated about tech stack and we can’t run them locally. So, we built Memex to be a chat tool + coding agent that supports any tech stack. What it can do today: Claude Code-like coding. Agentic web search / research. Pre-built templates (e.g. fullstack, iOS, python + modal, etc). Inline data analysis + viz. Checkpointing (shadow git repo). Privacy mode. How it works: Written in TS+Rust+Python, using Tauri for the cross-platform build (macOS, Windows, Linux). It has a bundled python environment for data analysis. Agent uses a mix of Sonnet 3.7 + Haiku. Status & roadmap: Free download with free tier and paid plan: https://memex.tech . Up next: [1] Additional model support (e.g. Gemini 2.5). [2] MCP support. [3] Computer use. Ask: Kick the tires. Give us feedback on product + roadmap. If you love it – spread the word! Thanks! David

Visit product View on Hacker NewsApr 29, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agents, macos · Missing: apple, slack, google

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.

Indie HackersFits the IH revenue-focused audience · Strong signals: supports, ios, gemini · Missing: reddit linkedin, podcasting, created

78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

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

26%26% 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: ios · Missing: mobile apps, personal, entrepreneurs

23%23% 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

17%17% 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, paid · Missing: web3, crypto, cryptocurrency

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

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