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Brainglue, an empirical playground for AI experimentation

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Brainglue, an empirical playground for AI experimentation

Hi HN. My name is Juan and I'm the creator of Brainglue. Brainglue is a fun and empirical playground for large language models that allows anyone to build powerful prompt chains that can solve complex generative AI problems. Brainglue focuses on providing an easy-to-use environment for prompt chaining. It's now well understood that chaining prompts is one of the most effective ways to leverage LLMs for GenAI problems. Prompt chains yield better reasoning and more accuracy, but experimenting and productizing these chains isn't yet trivial. With Brainglue, you get an environment where is easy to build these chains and configure them for specific GenAI tasks. Brainglue also comes out-of-the-box, comes with a straightforward API that allows you to use your AI chains from other applications and services. Still early days, but I have high hopes for this kind of AI scripting form factor. If you try it out and have any feedback, please let me know at brainglue@rasterwise.com

Visit productView on Hacker NewsSep 15, 2023
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Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, tasks · Missing: mac, agents, macos
81%81% 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
79%79% 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
61%61% 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
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, 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 · Missing: web3, chat, crypto
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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