janpmz · HN

Ilya's 30 papers, explained in audio

@fedeponte1 · X

my first saas. i think i built something people actually want and completely failed at the business side. would genuinely love help. openpaper ( writes full, cited research papers from a prompt. research, outline, writing, real citations, export. the honest numbers: - ~2,300 signups - ~1,500 generated a full paper. people actually use it. - ~5 have ever paid. ~$45/mo. activation is great, revenue is basically zero. i've been staring at this for weeks. the paywall is on export (pay ~$12 to download the pdf). but the full cited paper is right there on screen, free, you can read it and copy-paste it. so i'm charging for the gift wrap after handing over the gift. almost nobody even reaches checkout. 76% generate exactly one paper and leave. one-and-done, so subscriptions make no sense. the audience is mostly students and researchers on personal gmail, all over the world, almost none on university or company emails. traffic is x and github. so i genuinely can't tell

u/korro_ai · Reddit

I built Korroresearch: an AI that writes academic papers, then checks every single claim against 8 verification engines Most AI writing tools just generate text and call it done. Korroresearch does the opposite. Generation is step one. Verification is the real product. How it works: Describe your idea. It writes the full academic paper : research paper, grant proposal, white paper, pitch deck, conference talk, magazine article. English or French. Then the real part starts. 8 engine

PaperCheck — 论文提交前自查工具，提供 Turnitin 查重 + AI 写作检测报告，帮助学生在正式提交前发现问题

@saim645 · X

ashaney · HN

Curio, a place for HTML files

@Anastasiiakdmk1 · X

Just shipped major updates to ContractAI 🚀 ✅ Professional contracts in 8 languages ✅ AI Credits packs (buy extra generations) ✅ Full plan comparison table ✅ Onboarding tour ✅ Chrome extension Try free: Demo: #buildinpublic #saas #legaltech #ai

@sahiinthehood · X

@harryjwang · X

I've used many of the major conference systems. Plenty still have docs as an unsearchable static site — or worse, as PDFs that stopped tracking the product three versions ago. Somehow, still true in 2026. We took an AI-first path with instead. Our docs are generated from the codebase itself. A custom Claude Code skill writes each page and retakes the screenshots whenever a feature changes — so the docs don't drift from the product. Every page still gets reviewed by a human before it ships. Now you can talk to them, too. We just shipped "Ask AI" over our docs: This feature also turned out to be a nice teaching case for agentic RAG vs. classic RAG: • Classic RAG: chunk → embed → vector DB → retrieve once, by fixed rules • Agentic RAG: hand the model a search tool and let it drive — write a query, read the results, rewrite, retry At more than 100 pages, still no vector database needed. And every answer cites the exact page it came from. #Age

@manudecegliee · X

Proposals for agentic era.

@MrDave_id · X

I built an ai flashcard generator

@ChenglongW98225 · X

做了一个AI新闻日报，感兴趣的可以点下方链接看一下 有什么需要改进的也可以直接评论我，我都会认真回复