Single-file homepage with typeahead find (spec-driven LLM codegen)

Hacker News

I needed a single-file dashboard that I could use as a browser home page to quickly access my most-used links when working in restricted environments and that replicated the keyboard-oriented type-ahead search functionality of my homelab homepage, since I am so used to doing type-ahead search to find links that I find it hard to work without it. As it happens, I was in a middle of a series of discussions about spec-driven development and LLM code generation, so I wrote a very simple spec for this and then iterated on both the spec and the code to demonstrate the basic principles--so this ended up as a bit of "show and tell", and I thought HN might find it both useful and fun to discuss.

Visit product View on Hacker NewsFeb 21, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: single, code · Missing: mac, agents, macos

83%83% 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

63%63% 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: io · Missing: https docs, excited, just released

46%46% 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 · Missing: mobile apps, ios, personal

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

32%32% 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

14%14% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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