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I wrote a technical blogpost on autonomous agents using AI

Hacker News

I wrote a technical blogpost on autonomous agents using AI

Wrote a technical walkthrough that dives into the fascinating world of autonomous agents in Langchain. Do give it a read, especially if you're curious about how these agents actually work. The explanations used in the article were prepared using Genie, a "Codebase explainer" that we built using langchain. Our hopes is that developers would be able to use it to accelerate their understanding of new codebases and empower themselves with tools to become an 10x open-source contributor! BTW, if you're curious to learn more about Genie and how it can supercharge your code repositories with QnA capabilities, do visit our website at www.birdlabs.ai for more details :)

Visit product View on Hacker NewsJun 18, 2023

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

90%90% 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

64%64% predicted probability of success on Indie Hackers, 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

44%44% 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 · Missing: mobile apps, ios, personal

38%38% 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

29%29% 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

17%17% 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.

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