我也是从外网看到的 猜猜是真泄密资料还是4月1日愚人节版 出处是x上人转发到Reddit 10 个帖子 - 10 位参与者 阅读完整话题
求一个有text-embedding-3-small或者Qwen3-Embedding-8B的公益站，感谢佬们 3 个帖子 - 2 位参与者 阅读完整话题
I started EDDI in 2006 as a rule-based dialog engine. Back then it was pattern matching and state machines. When LLMs showed up, the interesting question wasn't "how do I call GPT" but "how do I keep control over what the AI does in production?" My answer was: agent logic belongs in JSON configs, not code. You describe what an agent should do, which LLM to use, what tools it can call, how it should behave. The engine reads that config and runs it. No dynamic code execution, ever. The LLM cannot run arbitrary code by design. The engine is strict so the AI can be creative. v6 is the version where this actually became practical. You can have groups of agents debating a topic in five different orchestration styles (round table, peer review, devil's advocate...). Each agent can use a different model. A cascading system tries cheap models first and only escalates to expensive ones when confidence is low. It also implements MCP as both server and client, so you can control EDDI from Claude Desktop or Cursor. And Google's A2A protocol for agents discovering each other across platforms. The whole thing runs in Java 25 on Quarkus, ships as a single Docker image, and installs with one command. Open source since 2017, Apache 2.0. Would love to hear thoughts on the architecture and feature set. And if you have ideas for what's missing or what you'd want from a system like this, I'm all ears. Always looking for good input on the roadmap. Comments URL: https://news.ycombinator.com/item?id=47793245 Points: 2 # Comments: 0
So there are a ton of tools that let you visualize trajectories in semantic space but I just had the idea to compute the flow field out of these trajectories The story behind it was that I initially analyzed human emotions over time in terms of repeating behavior patterns, and from that I had the idea to generalize it to any data. It’s very experimental alpha at this stage Comments URL: https://news.ycombinator.com/item?id=47780357 Points: 1 # Comments: 0
For some reason, embedding a Jupyter notebook in a documentation site still means doing one of these: screenshot every cell, run nbconvert and clean up the HTML, copy-paste the code into fenced blocks (losing the output), or iframe in nbviewer. Every time you update the notebook, you redo the whole dance. notebook-mdx lets you just link the `.ipynb` file and it renders inline in your MDX. No export step, no cleanup, no re-pasting. Update the notebook, rebuild the docs, done. :::notebook{file="./analysis.ipynb"} ::: That's the API just link the file. You get authentic Jupyter styling (In/Out prompts, syntax highlighting, rich outputs including images and HTML), build-time rendering so there's zero client-side JS, multi-language support (Python, R, JS, SQL). Works with Next.js, Docusaurus, Fumadocs, and any MDX framework. Example output: - Example 1: https://notebook-mdx.vercel.app/docs/examples/notebook-demo - Example 2: https://notebook-mdx.vercel.app/docs/examples/directive-exam... Repo: https://github.com/abhay-ramesh/notebook-mdx Docs: https://notebook-mdx.vercel.app Comments URL: https://news.ycombinator.com/item?id=47780138 Points: 2 # Comments: 0
I built this because I kept losing good travel recommendations buried in Reddit threads and forum posts. You read a trip report, someone mentions a specific ramen shop in Osaka or a viewpoint that isn't on any tourist list, and then it's gone — closed tab, forgotten. RememberMap lets you create trips, drop pins on a map, and attach notes, photos, and voice transcriptions to each one. The interesting part is the Explore tab: I scraped travel forums with Scrapy, embedded the posts with Gemini, and stored the vectors in Firestore. When you open Explore near your saved pins or current location, it surfaces relevant recommendations pulled from real trip reports — not generic "top 10" listicles. Everything is private by default. You can share a trip via a public link if you want, but nothing is public unless you explicitly share it. Comments URL: https://news.ycombinator.com/item?id=47756207 Points: 1 # Comments: 0
On a mission to provide the cleanest, AI-ready social media data on the internet. Comments URL: https://news.ycombinator.com/item?id=47755116 Points: 5 # Comments: 3
I've been building an AI system to automate parts of the NRC Combined Operational License process: gap analysis against the Standard Review Plan, FSAR strength scoring, and RAI prediction using vector similarity to historical NRC requests. I intended this as a SaaS business, but was ultimately beat to the market. What I think is the most interesting artifact is the dataset: 37,734 chunks of NRC regulatory documents (NUREG-0800, 10 CFR Parts 20/50/51/52/72/73/100, and Regulatory Guides) embedded with OpenAI text-embedding-3-small. It covers the full regulatory corpus an applicant would need for a COL submission. I'm not aware of anything like this being publicly available before. The embeddings are ready to load directly into ChromaDB, Pinecone, or any other vector store. If you're doing nuclear AI, regulatory NLP, or just want a large real-world RAG dataset to experiment with, it should be useful. Here's the full codebase if you're interested: https://github.com/Davenporten/nrc-licensing-rag Comments URL: https://news.ycombinator.com/item?id=47753102 Points: 1 # Comments: 0
我放弃reddit了，新建一个ban一个； 现在得出的结论是环境不纯净了，要搬家。。。 3 个帖子 - 2 位参与者 阅读完整话题
I built xlg to browse Reddit, HN, GitHub, Wikipedia from terminal using Unix pipe syntax. Example: xlg 'reddit "r/Art" | take 5 | print' Also works as MCP server for AI agents, OpenClaw GitHub: https://github.com/auraz/xlg Comments URL: https://news.ycombinator.com/item?id=47748323 Points: 1 # Comments: 1
带屏的要加一千还是有点肉疼的，但是刷了下reddit好多人都说带屏的信号好很多，有佬友有真实体验吗，信号差距大吗，而且我看遥控器屏幕也才分辨率1k还不如用手机呢。 其实在neo2,mini4k,flip纠结了好久，看中了neo2的自动跟随，但是又想要mini4k的三轴云台和续航，只能咬咬牙加价上flip了 现在还要再咬咬牙上带屏吗 (买无人机最近的目标是去香港某海岛拍摄，以及国庆去北疆) 1 个帖子 - 1 位参与者 阅读完整话题
AI 早报 2026-03-11 视频版：哔哩哔哩 ｜ YouTube 概览 要闻 谷歌发布 Gemini Embedding 2 原生多模态嵌入模型 ↗ #1 Google 升级 Workspace Gemini 功能 ↗ #2 模型发布 Tencent AI Lab 开源 LeVo 2 音乐模型，支持 4 分半全曲生成 ↗ #3 Fish Audio 开源 S2 文本转语音模型 ↗ #4 Hume AI 开源 TADA，基于 Llama 3.2 实现语音语言统一模型 ↗ #5 开发生态 OpenAI 调整 Codex 服务，免费层级移除 gpt-5.4 模型权限 ↗ #6 Claude Code 引入 /btw 命令支持后台任务对话 ↗ #7 JetBrains 上线 Air，支持多 Agent 并行执行…
Teddi Mellencamp ain't ditching the dating world just because she's battling cancer -- in fact, she's revealed she's dating an "older" man! The 'Real Housewives of Beverly Hills' alum dropped the dating news on her "Two Ts in a Pod" podcast with…