Ta

Talk to AI Models in Terminal

Hacker News

Talk to AI Models in Terminal

Hi everyone, nice to meet you and I am a newcomer of HN. I have made a binary tool Aih that could communicate with Bard, ChatGPT, Claude, and Llama(HuggingChat) from the terminal. https://github.com/Databingo/aih Since CAPTCHA challenges and bots detecting have become increasingly difficult, I've changed my strategy from hacking the APIs to simulating a real browser's action. The tool first takes the logged-in cookies of Google, ChatGPT, Claude, and HuggingChat accounts from the real Chrome browser, then it opens an invisible instance of Chromium for communication, then displays the answers in terminal. I think it's useful especially when I am researching some topics and need to compare answers of those AI models at the same time. Feel free to test and welcome provide feedback!

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Actual performance

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, model, google · Missing: mac, agents, macos
82%82% 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.
TrustMRRFits verified-revenue profile · Strong signals: google, answers · Missing: mobile apps, ios, personal
52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
49%49% 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: llama, ide, io · Missing: https docs, excited, just released
37%37% 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
36%36% 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
22%22% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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