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GPT Classifies HN Titles

Hacker News

GPT Classifies HN Titles

With all the "what can GPT do", I went to dabble with it to see if I could do something that plays to its strengths rather than just speculating about "would it be good at that?" https://gist.github.com/shagie/ff2f67b33bb517f6a297e2da66530cf4 The basis for the prompt is: Classify following titles into one or more of {US Politics}, {World Politics}, {Technology}, {Security}, {Current Events}, {Boasting}, {Curiosity}, {Unknown} The results from the front page as I type this: 34152137 Technology: Lite XL: A lightweight text editor written in C and Lua 34151880 Curiosity: Hydrochloric acid is more complicated than you think 34156235 World Politics, Current Events: Apple Japan hit with $98M in back taxes for missing duty-free abuses 34152333 Technology: The Architecture of the Lisa Personal Computer (1984) [pdf] 34151951 Curiosity: The Zen anti-interpretation of quantum mechanics (2021) 34155009 Unknown: Added 49 volumes of Arkansas, Mississippi, and Tennessee law 34145665 Technology: FEA Solver that doesn't need defeaturing 34151107 World Politics, Current Events: Amazon packages burn in India, final stop in broken recycling system 34155783 Technology: Your Cellphone Will Be a Satphone 34154778 World Politics, Current Events: Vandals destroy 30k-year-old indigenous cave drawings in Australia 34146397 Boasting: Ask HN: Elevator Pitch for a Polymath? 34151708 Technology: All About USB-C: Illegal Adapters 34149340 World Politics, Current Events: Southwest cancels 5,400 flights in less than 48 hours 34154896 Technology: Beaker Browser is now archived 34145811 Technology: Run Third-Party Scripts from a Web Worker 34152978 World Politics, Current Events: When Algorithms Rule, Values Can Wither 34152981 Security: Experian Vulnerability Shows Any Report with just SSN, DOB, ADR 34151382 World Politics, Security, Current Events: US Government demands direct police access to European biometric data [pdf] 34143272 Curiosity: Archaeologists devise a better clock for Biblical times 34149804 Technology: Using eBPF and predefined inspections to minimize “observability tax” 34155875 US Politics: Ventura Issues 34152100 Technology: Anki SRS Algorithm : Spaced repetition explained with code 34154406 Technology: How RISC-V Timer Interrupts Work 34146493 Curiosity: Old-school computing: when your lab PC is ancient (2021) 34152369 Technology: BorgBackup: Deduplicating archiver with compression and encryption 34147825 Technology: Show HN: Self-hosted CMS on serverless Cloudflare 34153648 Boasting: Brooklyn Bar Menu Generator (2015) 34117429 Unknown: “Blue Light” creating capacity for nothing (2007) 34149239 Curiosity: Visually symmetric words There are certainly some that one could argue would be different though I think its fairly correct based on titles alone. I'll also note that there are some different results when I was working in a smaller sample size - the entry "Added 49 volumes of Arkansas, Mississippi, and Tennessee law" was classified as "US politics, Current Events" rather than "Unknown". I do want to note that isn't that bad. Running this: "prompt_tokens":560,"completion_tokens":222,"total_tokens":782 So, this is just a hair under $0.02 to do. Thinking about it, some cleanup can be done by removing the year and media type which could trim a few tokens off the request.

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
90%90% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: apple, computer, visual · Missing: mac, agents, macos
66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: ios, personal · Missing: mobile apps, entrepreneurs, apps
58%58% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: lua, io · Missing: https docs, excited, just released
57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: host · Missing: plus, platform, intuitive
53%53% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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