AutoStartup, A Llama 2 Agent That Plans and Builds Your Startup Ideas

Hacker News

Hi HN! Last night, I live streamed myself coding this Llama 2 Agent on a Single GPU (Colab). After 6 hours it actually has some good results. How it works is it takes in your intuition (e.g. "I think x would be cool") and develops a business idea (with a name and branding colors) and a business plan. After the business plan is developed, it criticizes this plan recursively until the "Investor" prompt is satisfied with the plan. After all this it will generate the final MVP idea and pass it to a the React Engineer Agent I live coded 2 days ago ( https://github.com/jawerty/10x-react-engineer ). Finally once the code is generated you have an option to pivot. As for the AI's memory, I have a small vector db for history of good intuition+idea examples to use as context (builds as you generate) and a simple backlog of business idea criticisms. Overall, this experiment showed me that you definitely don't need OpenAI keys to have an effective LLM Agent for you. This is only using the Llama 13b parameter chat model and doing a wonderful job. I spend maybe 5 dollars of Colab compute building this for 6 hours which is way more than it would be to mess around with this. One caveat is the final react code base often needs some modifications to the package.json/webpack setup. However, It's generating the components/folder surprisingly well starting from the entrypoint file. I addressed most of these concerns in this video but still going to fine tune a model that's way better in my next video or just move to a fine-tuned starcoder for this portion. Let me know what you think and play around with the Colab if you can! The live stream: https://youtube.com/live/ZCyxDRSw0wE The 10X-React-Engineer: https://github.com/jawerty/10x-react-engineer If you want to see me live code more let me know any ideas you'd like to see built. I've been streaming projects like these most days this past month and don't feel like stopping haha.

Visit product View on Hacker NewsAug 17, 2023

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

92%92% 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 · Strong signals: para · Missing: supports, reddit linkedin, podcasting

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

52%52% predicted probability of success on TrustMRR, 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

48%48% 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

44%44% 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

15%15% 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

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