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I made a VS Code extension where you can use Mixtral 8x7B for free

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I made a VS Code extension where you can use Mixtral 8x7B for free

Hey guys, I made a VS Code extension, within which you can use the Mixtral 8x7B AI model for free. Mixtral 8x7B is a new AI model from the Mistral family, which outperforms GPT-3 in many benchmarks, a fact that I have personally confirmed in daily coding tasks, where Mistral 8x7B was the clear winner. You can use all the AI features, such as AI Lens with custom instructions and similar. I'm pretty sure some of features will be useful to you. I'd appreciate both, positive and negative feedback!

Visit productView on Hacker NewsDec 15, 2023
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, tasks · Missing: mac, agents, macos
87%87% 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.
TrustMRRLess likely to generate early MRR · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs
41%41% 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
36%36% predicted probability of success on AppSumo, 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
32%32% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
29%29% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% 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
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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