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HumanLayer – Human-in-the-Loop for AI Agents

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HumanLayer – Human-in-the-Loop for AI Agents

I found myself building a bunch of LLM-backed features that needed to use tool calling, and some of those tools involved doing things that were somewhat high stakes - communicating on my behalf or modifying shared / production data. one example - I wanted to replace a marketing website with a chatbot + vector DB loaded with the previous content, docs, and blog posts. Between hallucinations, missing knowledge base info, and the LLM generally writing like an psuedo-intellectual high schooler, I realized I couldn't trust it to communicate unsupervised with my website visitors. I needed a way to improve the percent of high-quality responses, and do it this at scale. I wired up a prototype that would 1. consult me in slack before sending any response down to a website visitor 2. incorporate my feedback into the knowledge base, and 3. reformulate answers until I approved the message 4. send it to the visitor That prototype evolved into what is now HumanLayer https://github.com/humanlayer/humanlayer#why-humanlayer

Visit productView on Hacker NewsAug 21, 2024
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, slack · Missing: mac, macos, cursor
91%91% 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 NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
57%57% 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
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: answers, way · Missing: mobile apps, ios, personal
37%37% 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
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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