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Hundreds of customer conversations in hours

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    I think agent-first chat interfaces will be a primary software modality and busy dashboard/UI will go away. I’m not sure who exactly wins it, but I want my knowledge to grow/go with me. A lot of the “knowledge” ie research, analysis, reasoning will be done by agents as the primary user. Our current notes tools & tasks management systems were built for humans… I don’t care what the 17th thing on my bug backlog is. I want to conduct agents that can execute for me and do great work. What I built OzBrain to do: + Create a central place for agent reasoned knowledge to live + Be agnostic…

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    N71141

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  13. 13IA

    I am working on an AI that uses multiple LLM based agents to do medical research on any topic you choose! The program terminates after a set number of iterations and all of the findings are saved. Still a work in progress but it is showing some promising results imho! Would love to receive any critical and constructive feedback, collaborate, Review your PRs, or discuss your ideas!!

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  18. 18CA

    Current AI chat assistants face a fundamental challenge: context management in long conversations. While current LLM apps use multiple separate conversations to bypass context limits, a truly human-like AI assistant should maintain a single, coherent conversation thread, making efficient context management critical. Although modern LLMs have longer contexts, they still suffer from the long-context problem (e.g. context rot problem) - reasoning ability decreases as context grows longer. Memory-based systems have been invented to alleviate the context rot problem, however, memory-based…

    Nov 2025

  19. 19

    turns out marketing isn't that hard

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  20. 20IB

    Excited to share a project I’ve been building for months! Would love to receive honest feedback :) My motivation: AI is clearly going to be the interface for data. But earlier attempts (text-to-SQL, etc.) fell short — they treated it like magic. The space has matured: teams now realize that AI + data needs structure, context, and rules. So I built a product to help teams deliver “chat with data” solutions fast with full control and observability (agent tracing, quality scores, etc) — am I wrong? The product allows you to connect any LLM to any data source with centralized context…

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  21. 21BY

    we had hundreds of discussions with engineering leaders over the past few months, and everyone's trying to understand where they are in the AI journey. we collected all this data into a benchmark and built a free grader to let you know where you stand. you answer on a 1–5 scale (e.g., autonomy runs from "suggestions only" to "agents own multi-hour workflows across code, infra, and external systems") - takes about 5 minutes. https://agent-benchmarks.com/software-factory/ waiting for your results!

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  22. 22IY
  23. 23LA

    Hey HN, I’m building Lenzy AI - probably the first product analytics platform for AI agents. From my research: Companies building AI agents have thousands or even millions of conversations. In these, users express what they need, use, love, or hate. Often long before they reach out to support (or churn). Some teams try to read chats manually, some build in-house pipelines to analyze them, others completely miss out on this data. The idea: Lenzy continuously analyzes conversations users have with your AI agents to: 1. Discover missing features (e.g. "Fetch info from a URL" mentioned 42 times…

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  24. 24AA

    Made this project in a couple of weekends after playing around with NotebookLM and seeing the hype for more projects like this. Let me know what you think!

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