I made an IDE to build AI apps

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Hi HN, I've been working on building AI agents and ended up building a tool to easily chain together prompts, vector db, function call, ReAct agentic flows etc. I am doing a soft launch today, mainly because I want to get feedback. How acris.ai works: 1. Each step is an LLM prompt that you can chain together and execute. You can choose the type of a step: Text, JSON, Function Call, ReAct Agent, Vector DB etc. 2. Deploy it as a web application or JSON API 3. All free for now. Will implement usage based charging later Feedback is welcome. If you want to try it out, sign up here: https://app.acris.ai How to use https://x.com/siraj_hs/status/1868986296372543912

Visit product View on Hacker NewsDec 17, 2024

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

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.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

18%18% predicted probability of success on AppSumo, 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.

Correct prediction on native model

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