Edifest.fyi – browse the Edinburgh Fringe with embeddings and MCP chat

Hacker News

I didn't know how absurdly large the Edinburgh Fringe was until I performed there. In roughly three weeks, there are about 3800 shows and more than 50,000 performances of those shows. That inspired me to start a SaaS for festival schedules, and this is my new site for navigating the Fringe as well as the other festivals in Edinburgh (as if it wasn't big enough already). The site is using embeddings for semantic search, so "comedy duo about fruit" has some surprisingly relevant results. The assistant has MCP tools for the same search as well as geo coordinates, and it's been making very good suggestions, schedules, and conversations. A big thanks to Festivals Edinburgh for making the data available. Happy to answer questions about it! Tech stack: FreeBSD, PostgreSQL, GraphQL, Node, Remix, React AI by Anthropic and Voyage

Visit product View on Hacker NewsJul 28, 2025

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

78%78% 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 · Missing: supports, reddit linkedin, podcasting

71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: 000, io · Missing: https docs, excited, just released

53%53% 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 · Missing: plus, platform, intuitive

47%47% 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

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

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

13%13% 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

5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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