Open-source platform to build internal AI agents

Hacker News

Hi everyone, Airstrip is an open-source platform for users to build internal AI agents. We've made some major changes since our last release. - Improved chat experience with a memory layer: For this, we are using Mem0. This can be configured when creating the agent. - Notion-style editing to configure agent: A sleeker UI and a familiar editing experience for our users. - Encrypted API keys and only decrypted at the point of usage. We are using Infisical to store our encryption keys, ensuring greater security. - Add knowledge bases to chat assistants. We are using Unstructured to handle the parsing of various document types and chunking for greater context. This is much better than our v1 where users need to add all content in the prompt. Our goal is to build an open-source enterprise-grade AI platform that combines the best-in-class LLM tools (preferably open-source too) for users to build AI agents easily. Thank you!

Visit product View on Hacker NewsSep 16, 2024

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

99%99% 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.

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

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

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

58%58% 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 · Strong signals: platform, users · Missing: plus, intuitive, reviews

37%37% 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

21%21% 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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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