Finance & Business3 weeks ago
mtok.market - spot pricing for AI tokens
Buy and sell AI inference token capacity on a non-custodial spot market.
royashbrook · HN
Show HN: Mtok.market – a non-custodial spot market for AI inference tokens
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Buy and sell AI inference token capacity on a non-custodial spot market.
royashbrook · HN
Show HN: Mtok.market – a non-custodial spot market for AI inference tokens
Deploy AI inference models on serverless GPUs with sub-200ms cold starts and pay-per-second billing.
@svpino · X
You can check out Runpod here: Thanks to the Runpod team for partnering with me on this post.
Verify AI agent decisions locally with transparent reasoning and MCP integration.
brandon_grutkowski · Product Hunt
Aurora Glass-box Quantitative AI for Humans and Agents
Inject engineered cognitive abilities into AI agents at inference time.
@frank_brsrk · X
reasoning tools for ai agents
Compare and evaluate AI models across coding, reasoning, agents, and other benchmarks.
davidtsong · HN
Benchmarklist: track AI benchmarks (2.4k+), models, and capabilities
Query spreadsheets and datasets with plain-English questions to generate instant answers and reports.
u/maybeImakemoney · Reddit
I built the thing. Now I am not sure the base use case is one people will pay for. Founder here. This started as a side learning project to see whether an LLM could answer questions about Excel data, back when they could not do it well. I built the first version on n8n, with workflows that ingested files, generated metadata with an LLM, and answered questions against the converted data plus that metadata. Then I started using it for my own analysis and report generation, saw that the time sav
Add persistent memory and knowledge to AI agents with drop-in files, URLs, and native MCP integration.
kitforai · GitHub
kitforai Kit for AI developer hub — official SDK, Claude Code plugin, MCP setup, and llms.txt.
AI agent that generates comparable product concepts from ideas, sketches, images, and constraints.
@hedge8og · X
4月23日，我开始用 Codex 搭自己的产品。到现在，满3个月了。 截至7月19日，Codex 页面显示247.1亿个 Token，连续使用54天。标题里的“250亿”是取整，准确数字以截图为准。 这三个月，我把 做上线了，产品介绍视频改了很多版，也借助 Codex 做了不少以前一个人很难完成的工作。 具体一点：我用它整理过 BP、导出过 PDF、做过 PPT 工作稿，也把调研、文档、图片、浏览器操作、网站、视频和邮件这些常见 Skills 跑过一圈。有些后来真的进入了工作，有些试完就放下了。 现在回头看，最大的收获不是“我做过这么多种东西”，而是我越来越能分清：哪些能力能进入真实工作，哪些只是演示起来很厉害。 这些数字确实挺夸张。说实话，我自己看到也会觉得：是不是做了很多事？ 但如果现在让我总结，我不会把它们当成绩。它们只能证明我很投入，蹬得快冒烟了，不能证明产品已经有价值，更不能证明商业已经成立。 我大学辍学，最高学历是高中，之前一直在制造业工作，也不会写代码。写这些不是为了卖惨，更不是想证明学历没用，只是先把我的真实起点说清楚。 2月 OpenClaw 火起来以后，我开始认真试各种 AI 工具，想先解决工作里那些重复、又不得不做的事情。 这期间还有一次，客户马上要我提案，但公司的设计师手上也有急活。我用 ChatGPT 做了产品效果图，最后拿到了订单。 老板后来跟我说：“早知道你可以帮我做这些，那些外包设计早就可以不用买了。” 到了4月，我又用扣子搭了发货模板和记账系统。原来需要手工对比核验的条形码、外箱码，我也开始想办法交给工具处理。 这张订单图只能证明订单记录是真实存在的，不能单靠它证明订单一定是 AI 带来的，更不是 Ohwait 的客户案例。AI 参与了产品效果图制作，之后拿到订单，是我对那次真实工作过程的记录。 那一刻让我兴奋的，其实不是发现了什么创业机会，而是我第一次觉得，模型真的可以成为人的“数字外骨骼”。 回头看这三个月，我现在有5个比较确定的结论。 ### 1. AI最好先从一个真问题开始 发货、记账、核验、临时要效果图，这些都不是什么宏大命题，但它们有一个共同点：问题原本就存在，而且有人正在为它花时间、花钱或者承担压力。 我现在判断一个 AI 工具有没有价值，还是看两件事：它有没有少让我重复
Ask one question to multiple AI models, compare their answers, and watch them debate to consensus.
u/trekhleb · Reddit
I kept pasting the same question into ChatGPT, Claude, and Gemini in three tabs; so I built a Yes-Brainer — a council of AI models, that answer your question in parallel, debate to consensus, or get judged to a verdict. submitted by /u/trekhleb to r/SideProject [link] [comments]
Deploy AI agents trained on your docs to automate customer support and sales conversations.
@iietmoon · X
Need sales x3 here is conversational AI Agent
Explore AI-generated book interpretations and curated knowledge across business, psychology, investment, and health.
晨笙阅读 — 书籍解读平台，AI 辅助+人工校审，提炼经典书籍的核心问题、论证框架与实践洞见，支持分类浏览、全文搜索和主题阅读
AI companion that learns from your interactions and autonomously completes tasks or works with you.
@cyrus_zad · X