
Unveri
Check academic papers for AI usage and originality by analyzing patterns and sources.
@stejas809 · X
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Check academic papers for AI usage and originality by analyzing patterns and sources.
@stejas809 · X

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

Analyze chess games with natural language explanations using an open-source browser tool with no login required.
u/ICARUS_2X · Reddit
Spent 7 months building a FOSS platform for natural-language chess analytics (No LLM) Hey guys, I've released CHONSE2, an open-source game review platform that offers unlimited analysis and move explanations without using hallucination-prone LLMs, running entirely in your browser. chonse2.com But Lichess is free, so why use this? Some have asked. It expands on Lichess's feature set a few different ways: Full analysis (accuracy/elo estimations/eval graphs, etc) requir

Draw or import bead patterns, automatically count beads, and export PDFs.
letsgogogo · V2EX
做了一个串珠子的工具,支持拼豆。 我做了一个在线串珠图案工具 Loomet 。 可以画图案、自动算珠子数量、导出 PDF 。也支持拼豆模式。 https://loomet.app

Local NLP linter that detects AI writing patterns in text
NikhilVerma · HN
Slopsift – a local, graph-backed linter for AI writing

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

Compare candidates on evidence-cited dimensions and generate hiring documents.
facundobon · HN
Verdict – AI hiring verdicts where every score cites the CV verbatim

Website monitoring and maintenance reports for agencies, tracking uptime, SSL, domains, and SEO.
@devtet_mon · X
Building DevTet — monitoring + maintenance reports for web agencies. It helps agencies prove the value of website maintenance with uptime, SSL/domain, ping, key monitoring, WordPress maintenance log, WP plugin, Client groups and branded client reports.

Look up any IPv4 or IPv6 address across geolocation, ASN, registry, and IP reputation sources in one clear report.
@fatwang2ai · X
做了一个 IP 检测工具: 开源。一次查询汇总 13 个数据源的风险评分、代理/机房类型和网络归属。不同平台的判断经常不一样,有些数据库甚至提供付费修正机制。与其只信某一家,不如把共识和差异放在一起看,更全面地了解你的 IP

Free website security scanner checking SSL, headers, and compliance for instant trust scores and AI-guided fixes.
@MrPenetratorTP · X
MrPenetrator helps businesses monitor their website’s trust, security and performance before problems affect their visitors.

Two AI reviewers analyze your code independently and verify each other's bug findings.
@BotariaDotBot · X
This Tuesday I'm launching Botbugger on @ProductHunt 🚀 One AI reviewer gives you 10 "critical" bugs — half hallucinated. So I built two: Claude Code + Codex review your code independently, then cross-check each other. Real bugs with fixes, not noise.

Review production agent traces to identify and fix recurring failures.
@guru3s · X
IMF team for when your AI agent ( Ethan Hunt ) is about to fail v0 at