
nanointerpret
探索语言模型如何解释和处理文本的交互式平台。
belluxx · HN
Nanointerpret – LLM Interpretability Playground
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探索语言模型如何解释和处理文本的交互式平台。
belluxx · HN
Nanointerpret – LLM Interpretability Playground

可视化语言模型在各层回答前的思考内容。
ada1981 · HN
I built a web tool to see and edit what an AI thinks before it answers

LLM API支出分析仪表板,按模型和环境分类,含优化建议
ATsimbalistov · HN
Show HN: Tracking GenAI cost and endpoint fragility so app teams don't have to

追踪您的应用中使用的 AI 模型,并在其被弃用前获得警告。
taylorgt · HN
Find every AI model your code calls and warn before it's retired

用 LLM 评估 AI agent 对话质量,提供评分卡和成本分析。
@tech_maju · X

自动将提示词路由到最便宜的模型,节省API成本。
u/ASDKING100 · Reddit
Launched an AI API router tonight, and found a bug hours in that would've taken real payments without ever upgrading the account Built LLMLite over the past few weeks — it classifies each prompt and routes it to the cheapest model that can actually handle it, instead of hitting GPT-4o for everything. Free tier, no card needed to try it. Tonight, right as I was about to launch, ran a real transaction to test the payment flow end to end. Paddle processed it, webhook fired, signature verified

用您的数据微调语言模型并管理自定义事实,获得密码学删除证明。
@MBrew26730 · X
Dataset cleaning + fine tuning + continual learning at

检查 AI 推理踪迹,评估模型真实性。
malik_dixon1 · Product Hunt
TraceLogicAI: AI Architecture Evaluation Compare AI architectures with evidence, not guesswork

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

观看两个 LLM 在物理竞技场剑战,盲投选择更聪明的。
u/Time-Shelter-35 · Reddit
I built a site where two LLMs sword-fight in real physics and you blind-vote who's smarter Two months ago I thought: what if the AI benchmark was just… watching them fight. So: https://stickblade-arena.vercel.app Two random LLMs get stickman bodies in a pymunk physics arena They each turn output JSON moves (swing, block, dash, shoot bow, throw flail…) Ragdolls, momentum, weapon collisions, the whole bit You watch the replay without knowing which model is which and vote who f

向多个前沿大模型提问,获得经过同行评审的综合答案。
u/Puzzleheaded-Log-27 · Reddit
Building a multi-model AI deliberation tool taught me something about trust LLM Counsel isn't another wrapper around one model - it sends your question to a panel of frontier LLMs, has them peer-review each other anonymously, and an impartial "chairman" model returns one synthesized answer. Free to start, pay-as-you-go after, credits don't expire. What I've learned so far: people trust a synthesized answer a lot more once they can see that the models actually disagreed and how that disagree

用对抗测试检查LLM端点安全,获取OWASP审计报告。
@aryaan_sheth · X
- LLM security for small teams