AI Studio

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AI Studio

Hey all! I've just launched a new service called AI Studio - https://aistud.io - a web based node/graph editor and runtime engine for building and executing complex intelligent systems. It makes it easy to build all sorts of complex AI systems with feedback loops, memory, conditional logic, interaction with the outside world, and lots more. AI Studio has an open source execution core built in Elixir, and you can export the systems you create with AI Studio and run them on your own hardware. There's some more detail in the announcement blog post here: https://aistud.io/blog/introducing-ai-studio There are templates to get started with common patterns, like the Parallel Conversation pattern for extracting structured information from a chat interaction, or the Aritist/Editor/Publisher pattern which can be used to automatically improve the quality of generated text and images. I'm taking inspiration from modular synthesizers, so you'll see some funny modules like the Sample and Hold and the Clock, but there's also some general programming nodes like a Key-Value Store, a Ring Buffer, JSON parsing, GET and POST, a Comparator and a Switch Case. AI Studio is a totally independent project, but if anybody is interested in collaborating, let me know! I've got some wild plans for the next phase of development. There's more information in the blog post, and I'll be writing more about how it works under the hood and exploring different patterns, but if you've got any questions I'll be happy to answer them. Cheers!, Rich

Visit product View on Hacker NewsApr 13, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, open · Missing: mac, agents, macos

94%94% 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: started, para · Missing: supports, reddit linkedin, podcasting

84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

70%70% 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: para · Missing: mobile apps, ios, personal

49%49% 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

46%46% 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

18%18% 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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