生产力1 week ago
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
分享链接上传收据，LLM 自动分类和整理。
jahnoikka · HN
Sorted Receipts - clients dump receipts in one link, LLM sorts them
自动监控 Codex 额度重置，通过 Telegram 推送及时提醒。
zemul · V2EX
每次 Codex 额度刷新全靠社区消息，反应慢一步直接错过 所以我搭了个自动化服务： - 抓取 OpenAI 员工的公开推文 - 新推文丢给 LLM 做语义判断：是不是在说额度重置 / 限额恢复 - Telegram 推送 地址： https://divin.cc/
用任何AI构建和托管应用
@akhileshrangani · X
i built codex micro and used it inside of claude to control codex AND claude code it uses a herdr bridge that is running on my mac talks it through a ngrok proxy uses to render inside of claude
通过现有 API 订阅同时运行多个 AI 模型。
@ContinuumCode · X
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
SoloPM 为独立创始人打造的 AI 工作空间，整合项目管理、CRM、着陆页和自动化。
ejxcll01 · Product Hunt
SoloPM Ship on Lovable. Manage everything else on SoloPM.
用你的LLM API密钥分析Hacker News公开个人资料。
Topfi · HN
Like everyone on HN, I love nothing more than to (re)read my own comments. Getting my intuition that I am among the smartest, most humble, highest quality commenters on here confirmed by an LLM so capable that the US government had to temporarily export restrict it [0] seemed only natural. Having had my perfection confirmed, I decided to share this joy with you as I had a few percent usage left before a reset. I took a few prompts, then did a review of the output which resulted in Selbstbild, a BYOK (Anthropic / OpenRouter) web app that gives you a summary and assessment of your public comments by one of our machine Gods, including Fable 5 (provided your can afford that luxury at API pricing). In all seriousness, I have, for a long time, used my own comments on social media (including HN) as part of a personal needle-in-haystack test, simply because I do know my somewhat peculiar style and what I tend to write, but also because I can sometimes write in a slightly confusing manner, ma
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
