Ggpt – Take advantage of GPT on the command line

Hacker News

Hi HN, with all of the hype recently surrounding GPT, i’ve been wondering what value it could bring to the table for developers. While chatGPT is wildly popular, and GPT incredibly powerful, I found it hard to integrate it into my CLI based workflows without lots of copy pasting into the terminal. With the interface being a nonstarter for my own adoption, I wrote ggpt. ggpt is a command line utility written in go, that allows for quick and easy GPT access. ggpt is drop dead simple; ‘ggpt prompt “this is my prompt” ’ to query gpt, and `ggpt last` to return the previous output again. One really successful use case i’ve found is with regex. ` ggpt prompt "can you explain what regex $REGEX is doing?" ` will return a breakdown that really helps explain the regex in question. Even if the output isn’t perfect, ive found it gets you about 90% of the way there, in 10% of the time, and the outputs will get better and better over time. I’d love to hear other ways people are using GPT.

Visit product View on Hacker NewsApr 9, 2023

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

91%91% 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

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

56%56% 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: way · Missing: mobile apps, ios, personal

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

40%40% 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

11%11% 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.

Incorrect prediction on native model

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