
Treequence AI
在空间节点画布上协作AI项目,组织语言模型上下文。
jebuehler55 · HN
I built a spatial node canvas to fix LLM context drift
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在空间节点画布上协作AI项目,组织语言模型上下文。
jebuehler55 · HN
I built a spatial node canvas to fix LLM context drift

查找与您硬件兼容的AI模型并查看性能和价格估计。
cdnsteve · HN
Tokenstead, find AI models for your hardware

为中国财经网站新闻添加 AI 解读按钮,支持多个 AI 模型。
EliteOtaku · V2EX
搞了个解读财经数据的油猴脚本,适配金十,财联社,汇通,华尔街见闻 比较简单,但挺好用的,在快讯旁边加了一个 AI 按钮,点击后由 AI 解读该新闻/数据的影响,需自己准备 API key,支持 DeepSeek 和 OpenAI、Anthropic 格式 https://greasyfork.org/zh-CN/scripts/590009-%E9%87%91%E5%8D%81%E6%95%B0%E6%8D%AE%E5%87%80%E5%8C%96-ai-%E8%A7%A3%E8%AF%BB-deepseek

TokenLab MCP:对比 LLM 价格,访问 31-80 个多模态工具。
tokenlabai · HN
TokenLab MCP, model discovery, pricing, and native AI endpoint tools

查看和对比主流AI模型的公众意见和基准评分。
u/TasteMysterious5285 · Reddit
I built AI Census, a live field bulletin for how people are actually talking about AI models I’ve been building AI Census, a public “field bulletin” for how people are talking about current AI models. I kept running into the same problem: benchmark tables tell me how a model performs on a test, but not whether people are actually finding it useful, frustrating, reliable, etc. So I built a rolling view from public technical conversations across Reddit, Hacker News, Bluesky, GitHub, and Huggi

向任何LLM发送前屏蔽敏感数据,然后在回复中恢复。
@velumprivacy · X

查看 GPU 兼容的 LLM 模型及成本预算方案。
jaeseok614 · HN
Open-source calculator for "will my GPU run this LLM?"

用WebAssembly在浏览器运行和管理LLM模型
userfrom1995 · HN
Goku – WASM (wllama)-powered LLM inference and model manager

压缩提示词以减少向LLM API发送的token数量和成本。
@asgujjuasitgets · X

通过统一的 API 接口访问多个 AI 语言模型。
u/DanTahirCode · Reddit
I built an open source coding agent with a personality - meet Klenny Code 🐾 Hey r/SideProject, my name is Dan Tahir, and I'm here to show off something I'm really proud of: Klenny Code, the open source coding agent with personality. A fully capable coding agent with memory and cross-project referencing, plus an assistant who can read your email, run scheduled tasks, pilot your browser, and be your corgi pal. Here's the pitch: bring your own OpenRouter API key, and Klenny wil

用多个模型实时审计AI回应以判断其可靠性。
u/inc_23 · Reddit
Hey, I created a tool that catches when your LLM is confidently wrong, in production, in real time — looking for beta testers. Your bot sounds sure of itself even when it's wrong, and you usually only find out when a customer complains. Auscope audits every LLM response in the background: 3 models from 3 different providers independently check it, a 4th "chairman" model resolves disagreements, and you get one verdict — verified, uncertain, or unreliable. Runs async, doesn't slow your respon

FlexInference: 通过多个提供商路由LLM API请求,降低成本和延迟。
Aperswal · HN
Made a Free LLM Router