
Echo by Tracer
Echo – Fable-level results at 1/3 the cost using open-weight models
adam_rida · HN
Echo – Fable-level results at 1/3 the cost using open-weight models
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Echo – Fable-level results at 1/3 the cost using open-weight models
adam_rida · HN
Echo – Fable-level results at 1/3 the cost using open-weight models

使用 Axiom 通过逻辑推理完善想法并发布到社区。
samcymbaluk · HN
Axiom toolkit – Make your ideas precise enough to be proven wrong

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

上传数据集自动发现具有统计意义的相关性和因果关系。
@matthew_meadows · X
Correlation Studio - Discovery Mining • Causation Analysis A powerful new SaaS statistics application that brings the insights of correlation data science to everyone. Data science without the code.

AI Trading Replay 是一个帮助交易者分析交易和行为模式的仪表盘。
@Crypto77qi · X
最近一直在@dappOS_com上体验 @xBubble_ai 的 Coding,这两天我给自己做了一个 AI Trading Replay(AI 交易复盘助手)。 👉 做交易时间越久,我越觉得,真正拉开差距的,并不是找到更多交易机会,而是不断减少重复犯同样的错误。 现在币圈已经有很多成熟的交易工具,行情、链上数据、资金流向、技术指标几乎都能找到对应的平台。但这些工具更多是在告诉我们市场发生了什么,却很少帮助我们分析,为什么面对同样的市场,每个人最终的交易结果却完全不同。 有时候并不是没有机会,而是因为犹豫错过了;有时候方向看对了,却因为害怕提前离场;还有时候连续几次亏损之后,下一笔交易开始变得犹豫,最终又错过真正的行情。 这些问题,并不是增加一个指标或者再多看一份数据就能解决。 所以我没有继续做一个新的行情工具,而是直接用 xBubble Coding 做了一个 AI 交易复盘助手。 它不会预测市场,也不会告诉我什么时候买卖,而是把每天的市场变化、自己的交易记录和决策过程整理下来,通过 AI 自动分析哪些判断是正确的,哪些错误一直在重复发生,并生成每天、每周的交易复盘,让整个交易过程形成一套可以不断优化的闭环。 整个开发过程也比我想象中轻松很多。 没有写代码,没有配置服务器,也没有部署环境,只需要把自己的需求描述清楚,一个可以直接访问的网站很快就搭建完成了。从原来的一个想法,到真正变成可以使用的产品,中间几乎没有任何开发门槛。 我觉得,这也是 xBubble Coding 最有价值的地方。 它并不是让大家重复开发一个市面上已经存在的软件,而是让每个人都能把自己的经验、工作习惯和思考方式快速变成真正属于自己的工具。 未来真正有价值的,或许不是拥有越来越多标准化的软件,而是每个人都能根据自己的需求,打造一个最懂自己的 AI 工作伙伴。

用英文描述加密交易策略来回测它们。
@torquant · X
building Torquant. you build and backtest trading/investment strategies using only natural languange

SeeBet.ai:查看NFL博彩赔率、盘口变动和十年回测数据的分析工具。
u/sebriz · Reddit
I built an institutional NFL data terminal that audits 44 situational factors against 10 years of market data [Open Beta] Hey everyone, I wanted to share a side project I’ve been building called SeeBet.ai . As someone who loves sports data, I got tired of generic broadcast hype, sports betting "gurus," and subjective narratives about things like "revenge games," travel fatigue, or short rest. I wanted an institutional, zero-hype terminal that simply audits physical reality against market

使用Python或可视化块构建交易机器人,在真实数据上回测,并在排行榜上竞争。
@CodeBullapp · X
Building CodeBull. A platform where you build trading bots with Python or visual blocks, backtest them on real-world market data, and compete against other users’ strategies. Zero risk. Just learn trading by building.

@lightsilver323 https://t.co/4fEpxb1qeX No funding needed. We’ll see if founders need the help we think it provides in the good ole boot strap way.
@stevesimonson · X
No funding needed. We’ll see if founders need the help we think it provides in the good ole boot strap way.

用 Etch 追踪、回放和验证 AI 代理的决策。
u/Funky_Chicken_22 · Reddit
OSS to SaaS positioning problem: when the user persona and the buyer persona are completely disjoint Founder here. Sharing a positioning problem I think a lot of OSS-to-SaaS founders hit and don't talk about publicly. Context: I have been running an OSS project (world-model-mcp) with ~2,500 monthly PyPI installs. Two weeks ago I opened up the hosted companion, Etch, at etch.systems. Launched publicly on Product Hunt at 12:00 PDT yesterday. The positioning problem: OSS user persona: in

为AI应用提供MCP服务器、REST API和行级安全的后端服务平台。
u/bob__io · Reddit
What we learned from trying to turn vibe-coded prototypes into production SaaS applications Founder here. While developing MCPBackend, we noticed a major difference between generating a convincing application demo and building a maintainable SaaS product. AI coding tools are increasingly capable of generating interfaces, routes and even initial application logic. But a production application still needs: a reliable data model authentication and authorization backend validation

AI 研究代理分析公开市场,提供收益警报并总结 SEC 文件(附引用源)。
oceanplexian · HN
I Built OpenClaw for Stocks