
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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24 projects

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

Refine ideas through logical reasoning and publish them to a community of verified knowledge.
samcymbaluk · HN
Axiom toolkit – Make your ideas precise enough to be proven wrong

Analyze Hacker News profiles with your own LLM API key, fully client-side.
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

Upload datasets to automatically find statistically significant correlations and causal relationships.
@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.

A dashboard for traders to replay trades, analyze behavior patterns, and optimize their trading system.
@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。

Backtest crypto trading strategies by describing them in plain English.
@torquant · X
building Torquant. you build and backtest trading/investment strategies using only natural languange

Analyze NFL games with devigged odds, line movements, and ten years of backtested market intelligence.
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

Build trading bots with Python or visual blocks, backtest on real data, and compete on leaderboards without risking money.
@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.

Trace, replay, and verify AI agent decisions with signed audit trails for debugging.
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

Backend-as-a-service with MCP server, REST APIs, and row-level security for AI apps.
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 research agents that analyze public markets, deliver earnings alerts, and summarize SEC filings with cited sources.
oceanplexian · HN
I Built OpenClaw for Stocks