GPT-4-powered web searches for developers

Hacker News

Hi HN, Today we’re launching GPT-4 answers on Phind.com, a developer-focused search engine that uses generative AI to browse the web and answer technical questions, complete with code examples and detailed explanations. Unlike vanilla GPT-4, Phind feeds in relevant websites and technical documentation, reducing the model’s hallucination and keeping it up-to-date. To use it, simply enable the “Expert” toggle before doing a search. GPT-4 is making a night-and-day difference in terms of answer quality. For a question like “How can I RLHF a LLaMa model”, Phind in Expert mode delivers a step-by-step guide complete with citations ( https://phind.com/search?cache=0fecf96b-0ac9-4b65-893d-8ea57... ) while Phind in default mode meanders a bit and answers the question very generally ( https://phind.com/search?cache=dd1fe16f-b101-4cc8-8089-ac56d... ). GPT-4 is significantly more concise and “systematic” in its answers than our default model. It generates step-by-step instructions over 90% of the time, while our default model does not. We’re particularly focused on ML developers, as Phind can answer questions about many recent ML libraries, papers, and technologies that ChatGPT simply cannot. Even with ChatGPT’s alpha browsing mode, Phind answers technical questions faster and in more detail. For example, Phind running on “Expert” GPT-4 mode can concisely and correctly tell you how to run an Alpaca model using llama.cpp: ( https://phind.com/search?cache=0132c27e-c876-4f87-a0e1-cc48f... ). In contrast, ChatGPT-4 hallucinates and writes a make function for a fictional llama.cpp. We still have a long way to go and would love to hear your feedback.

Visit product View on Hacker NewsApr 12, 2023

Share card

Actual performance

1,401points

414comments

Made the leaderboard

Launch Intel predictions

Analyze your own launch →

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

93%93% 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.

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

84%84% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: answers, way · Missing: mobile apps, ios, personal

57%57% 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

38%38% 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

12%12% 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

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

Correct prediction on native model

Similar products

Web Search Powered by GPT and Bing71%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Web Search Powered by GPT and Bing

Hacker News37

Tipping culture for developers on the Web59%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Tipping culture for developers on the Web

Hacker News38

Driwwwle: Dribbble, but for Web Developers68%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Driwwwle: Dribbble, but for Web Developers

Hacker News4

ELI5 Powered by GPT-355%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

ELI5 Powered by GPT-3

Hacker News134

I made a GPT-powered webscraper51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

I made a GPT-powered webscraper

Hacker News2

Worldbuilding Experiments with GPT-336%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Worldbuilding Experiments with GPT-3

Hacker News2

Autosummarized HN (With GPT-3)51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Autosummarized HN (With GPT-3)

Hacker News6

I composed a sonata with GPT-3 DaVinci-003 and you can too51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

I composed a sonata with GPT-3 DaVinci-003 and you can too

Hacker News3

GPT Classifies HN Titles57%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

GPT Classifies HN Titles

Hacker News6

Cerebras-GPT-2.7B finetuned on Stanford Alpaca dataset65%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Cerebras-GPT-2.7B finetuned on Stanford Alpaca dataset

Hacker News4

Browse all products Analyze your launch