
MotoDataHub
Aggregate and compare used-car listings across European marketplaces to find vehicles at lower prices.
@andrejbuday · X
I hope I am not late into the party. This is mine
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Aggregate and compare used-car listings across European marketplaces to find vehicles at lower prices.
@andrejbuday · X
I hope I am not late into the party. This is mine

Run AI-powered sprint retrospectives that connect to your tools and identify improvement patterns.
@akadhanu · X
an ai sprint retrospectives for it teams (

AI research agents that analyze public markets, deliver earnings alerts, and summarize SEC filings with cited sources.
oceanplexian · HN
I Built OpenClaw for Stocks

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

Track investment opportunities and deal flow in sports technology ventures.
@Stemack · X
I vibe coded a sports capital tracker…would love your thoughts.

Trade on real-time public sentiment markets for athletes, politicians, and cultural figures.
u/Maleficent-Ad-5181 · Reddit
got roasted, made some changes, ready to get roasted again A few weeks ago, we posted about our marketing, and we got the following feedback on our website: I visited your site and have no idea what it is that you do This site is garbage 🙂 Very confused on what the website is for. Ai slop I have no fucking idea what your company does or how any of what I’m looking at works. Discouraging? A little. BUT it was actually very helpful to hear some brutal feedback we hadn't heard ye

Analyze cryptocurrency markets with Chan Theory patterns, K-line charts, and anomaly detection.
@onehopeA9 · X
用 DAPPOS @dappOS_com 做了一个加密货币缠论分析网站,也是我一直想做没时间做的! 来看看缠论三买的威力: 不是不会写代码。 我自己是程序员,真要硬写当然能写。 但问题是:没时间。🤡 做交易的人都懂,真正消耗人的不是“看一根 K 线”,而是把一堆信息拼起来: K 线结构。 缠论笔、中枢。 一买二买三买。 一卖二卖三卖。 资金费率。 持仓量变化。 成交量异动。 市场舆情。 这些模块涉及太多方面,写得出来,但太费时间。 所以我一直想做一个自己的交易雷达: 接币安历史数据。 用 TradingView 展示 K 线。 自动画缠论结构。 标注买卖点。 再把资金费率、持仓量、成交量和币安广场热度放进去。 想法很清楚。 但一直躺在 TODO 里。 最近用 xbubble 的 Coding 功能试了一下,直接把需求丢进去: “做一个加密货币缠论分析网站,接入币安一年历史数据,用 TradingView 展示 K 线,自动画笔、中枢、一买二买三买、一卖二卖三卖,增加异动分析和币安广场舆情热度。” 它真给我生成了一个能跑的原型。 这次最戳我的不是“AI 会写代码”。 而是 xBubble 把最消耗时间的工程杂活压短了。 以前我自己做: ❌ 搭项目结构 ❌ 爬数据源和图表库 ❌ 写 K 线处理 ❌ 写缠论结构识别 ❌ 做前端交互 ❌ 部署、改样式、修细节 一圈下来,核心想法还没验证,人先累了。 现在变成: ✅ 先描述业务目标 ✅ 让 xBubble 生成可运行原型 ✅ 我再校验规则、调整逻辑、优化体验 ✅ 把时间花在交易理解和产品迭代上 这对程序员其实很有价值。 因为程序员最缺的不是能力,而是时间和注意力。 我这个原型现在大概有几块: 第一,TradingView K 线主图。 用币安数据展示行情,再叠加缠论结构。 第二,笔和中枢。 把原本需要手动盯的结构,先自动画出来。 第三,买卖点雷达。 一买、二买、三买,一卖、二卖、三卖,先帮我筛可能的位置。 第四,异动面板。 资金费率、持仓量、成交量变化,和 K 线结构一起看。 第五,舆情热度。 把币安广场讨论热度也拉进来,看看市场情绪是不是和盘面互相印证。 你可以理解成: 我不是让 AI 替我交易。 我是让 AI 帮我做一个交易前的信息雷达。 我觉得 xBubble 和普通 AI

Log soil metrics to track field health and receive AI-powered agronomic guidance.
@batmanyussif · X
Check out what I just built with Lovable!

Search YouTube channel transcripts to research content and build fair-use clips.
@johncalvo · X
Built an entire YouTube research platform with @AnthropicAI Claude Code. a YouTube research tool that treats entire channels as a searchable transcript database rather than individual videos. Any YouTube channel. Every word. Fully searchable. Track how topics trend, peak, and evolve across hundreds of videos. Researching what someone said on YouTube means watching hours of footage and hoping you find it. ChannelScout extracts full transcripts from any channel, makes every word instantly searchable, and tracks how topics trend over time, without watching a single video manually. Paste a YouTube channel or playlist URL and it transcribes and indexes every transcript. Follow multiple channels and they all land in one private library. New videos on followed channels are auto-transcribed and added to your library the day they publish. Beyond YouTube it accepts any yt-dlp-supported URL including Vimeo, TED, and SoundCloud, and you can upload local files directly

US stock trading signal platform analyzing market data to identify trading opportunities and signals.
@MEJ50749 · X
用 @dappOS_com coding 做了一个美股信号网站: 它和普通美股行情网站最大的区别是:普通网站更多是“给你看行情”,而这个网站更像一个“辅助交易工作台”。 大多数美股网站会把价格、涨跌幅、新闻、财报、K 线分散展示出来,信息很多,但用户还要自己判断: 哪只股票值得看? 现在是什么信号? 这个信号强不强? 有没有触发提醒? 后续要不要复盘? 我这个站的核心思路不是堆信息,而是把交易前的观察流程做成工具: 1. 市场总览不是单纯看涨跌 首页会把美股标的放进观察池里,比如 AAPL、NVDA、MSFT、GOOGL、AMZN、META、TSLA 等,不只是显示价格,还会结合涨跌幅、成交量、市值、PE/PB、行业、52 周区间等信息,让我先快速判断哪些票值得继续看。 2. 最重要的是信号扫描器 这个功能是普通行情站很少直接做成工作流的地方。 网站里有“智能信号扫描器”,可以扫描技术形态,并把信号归类成突破、反转、动量、超卖、超买、金叉、死叉、背离等类型。 比如: NVDA 出现强势突破、量能放大、均线多头排列; AAPL 出现 MACD 金叉、RSI 超卖反弹、布林带下轨支撑; MSFT 出现 MACD 背离,需要注意短期回调; TSLA 出现均线死叉,提示中期下行压力。 这就不是简单告诉我“今天涨了多少”,而是帮我回答一个更交易化的问题: 这只股票现在为什么值得关注,关注点在哪里? 3. 个股详情页更像交易前检查清单 普通网站的个股页往往是行情 + 新闻 + 财务数据。 我这个站的个股详情页更偏“交易前判断”:价格、评级、52 周区间、K 线/成交量、基本面、技术信号、资讯都放在一起。 它的价值是把“发现信号”之后的下一步接住。 扫描器告诉我哪只票有异动,详情页帮我进一步看这个信号有没有质量。 4. 提醒中心是我最喜欢的功能 这个站不是看完就结束,而是有 Alerts Center。 里面可以看到全部提醒、已触发提醒、等待中提醒,还能新建价格/信号提醒。 比如: NVDA 突破 900 美元触发提醒; AAPL 出现 MACD 金叉提醒; TSLA 等待成交量异常触发; META 跌破支撑提醒止损风险; AMZN 突破近期高点提醒动能增强。 这和普通美股网站最大的差异就在这里: 它不只是信息展示,而是把“观察 →

Answer surveys to earn credits, then create your own for verified campus students.
@g8zxtrp9p6 · X
🚀 Just launched CampusVerify No more begging for survey responses. Reach real verified students on campus, get genuine answers fast, and earn credits by participating. Sign up & get free credits → @Lovable @supabase #Lovable #StudentsMatter

Analyze Shopify and DTC sales data to find hidden profit leaks by SKU.
@okiela_io · X
Sales up but profit unclear? I help ecommerce sellers find hidden profit leaks by SKU. Send me 1 order export → 3 leaks: