Seren Desktop – AI IDE with Publisher Store and X402 Micropayments

Hacker News

What makes Seren different: We're trying to build an AI IDE for non-devs, but that great devs will appreciate. Open marketplace – 90+ publishers for databases (SerenDB, MongoDB), web scraping (Firecrawl), AI search (Perplexity), email (Google), calendars, CRM, even eSignatures Micropayments that work – x402 USDC on Base network. No subscription tiers, no credits expiring. Pay per API call. Databases: Query SerenDB (serverless Postgres), MongoDB, Neon with natural language. Web tools: Scrape websites with Firecrawl, AI-powered search with Perplexity Integrations: Email, calendar, CRM actions. Coming soon: GitHub, Linear, Notion Compute: Run code sandboxes, agent templates, custom workflows Each publisher sets their own pricing. You pay per API call with USDC on Base (x402 standard). No subscriptions, no expiring credits. What's inside Semantic Indexing Monaco editor with Cmd+K inline AI editing Agent Client Protocol for running Claude Code and compatible agents Model Context Protocol for local MCP servers Multi-model support with free tier (Gemini 2.0 Flash) The Tech stack Frontend: SolidJS + TypeScript + Vite Backend: Rust + Tauri 2.0 Vector store: sqlite-vec (all local, no cloud) MIT licensed, ~10MB binary Architecture The client is fully open source. It connects to Seren Gateway for: AI model routing & authentication SerenEmbed API (embedding generation) Publisher marketplace Think VS Code (open source) + Extension Marketplace (proprietary). Try it GitHub: https://github.com/serenorg/seren-desktop Website: https://serendb.com Current: v0.1.0-alpha.9 (macOS, Windows, Linux)

Visit product View on Hacker NewsJan 28, 2026

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

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: gemini, compatible · Missing: supports, reddit linkedin, podcasting

84%84% 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: open source, ide, io · Missing: https docs, excited, just released

48%48% 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: google, way · Missing: mobile apps, ios, personal

24%24% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

23%23% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

22%22% 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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