@kitbasedev · X

We just launched, check us out at

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

taosx · HN

I created a simulation for coding harnesses based on my own pi sessions. When taking into account all factors, DS-v4-Pro is cheaper than gpt-5.6-luna due to caching. Look at the bill segments difference for cache read cost and uncached cost between deepseek and the other models. At this point is cheaper to use ds-v4-pro than the luna models from openai. ignore the numbers except the classic and keep in mind that classic is based on pi with the only change limiting tool output to 10kb https://har

@JackiePeters · X

@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

@Havey1001 · X

公共数据被研究得多，并不等于没有创新。更关键的是问题设定、识别策略、机制解释，以及同类结论和引用证据链是否已被覆盖。投稿前先系统比对公开论文、专利和既有技术路线，能少走不少弯路。我们做了一个投稿前风险预审工具，专门把这些重合与薄弱点提前摊开：

u/dimabreezy · Reddit

Hey, I built a tech that predicts human attention (it's Machine Learning + Data project). I've being using it for the past 2 months and it gives amazing results to AI agents I'm a software engineer and I also love good visuals. And I hate when AI build UI but it doesn't understand what should be GRABBING the attention, so I've build a tech that solves that https://attentionproof.com/ Here you can sign in with the ChatGPT account and get free 2 tries (I got limited compute) so please g

@dt_sqr · X

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