The full gallery
Tech stack
26 projects
AI research agents that analyze public markets, deliver earnings alerts, and summarize SEC filings with cited sources.
oceanplexian · HN
I Built OpenClaw for Stocks
Train reasoning skills with adaptive AI-generated puzzles and real-time questions.
u/connerpro · Reddit
IntelligenceMax - Adaptive reasoning practice with live AI questions (claim-safe near vs far) submitted by /u/connerpro to r/SideProject [link] [comments]
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。
Test and benchmark AI trading agents against historical market data.
remote_ctrl · HN
BotTrade – a replayable benchmark for autonomous trading agents
AI-powered training coach for runners with biometric tracking and personalized coaching.
@LeoSparr · X
Compare AI coding models on real tasks with live previews, cost tracking, and ELO rankings.
@intheworldofai · X
On the World of AI Bench (vibe-coding composite): Claude Fable 5 → 85.2 GPT-5.6-sol → 82.4 kimi-k3 → 81.5 Moonshot’s K3 just walked in and claimed bronze on one of the toughest coding-focused leaderboards out there.
See how AI systems interpret, rank, and represent your brand.
@RealSamuelAudu · X
Automate LinkedIn prospecting and sales outreach to book meetings
rohanrecommends · Product Hunt
Fuzzy AI We warm your prospects before reaching out
Scan AI prompts and endpoints for vulnerabilities, then monitor LLMs in production for security and compliance.
@CognisafeUK · X
Analyze US stocks by watching AI investment personas debate each company.
hobbescotch · HN
Noema – AI personas debate US listed stocks
Compare AI model benchmarks across coding, reasoning, agents, and multiple evaluation domains.
davidtsong · HN
Benchmarklist: track AI benchmarks (2.4k+), models, and capabilities
Query NFL statistics using natural language or YACQL, instantly create shareable tables and charts.
u/Fun-Calendar8486 · Reddit
I am building yacdb.fyi, letting users ask natural-language questions about NFL data and turning them into queryable results. Yacdb.fyi goal is to allow users to construct questions about NFL data, think "Best 1st down conversion rate in 2025", and exposing a custom query layer on top (think SQL) allowing users to define their own queries to build data sets. They can chart in the app, using built in tooling, but can export the data as well if they want to use their own tooling. I am look
