Voyagard — AI 学术论文编辑器，有 AI 编辑，一键查重降重，AI推荐文献索引等功能， 帮助你完成从选题调研，文献索引并管理， AI协助撰写等功能，帮助你轻松完成论文撰写

janpmz · HN

Ilya's 30 papers, explained in audio

@kr812345 · X

enterprise ai orchestration platforms to automate using tools. @

@marcellafjacob · X

@JohnTremendol · X

does this look vibe coded?

Sriram1729 · HN

Tampa, FL | Remote (US Eastern) | Contract, ~20 hrs/week (evenings + weekends, around a full-time job)I'm a full-stack + AI engineer, 5 years in. Looking for one side engagement I can actually commit to — nights and weekends, not a flaky "I'll get to it" thing. Short version of what I'm good for: give me a problem and I'll hand back something deployed. I do the whole thing — backend, frontend, the data model, the infra, the deploy. You don't need to spec it out for me or check my work line by li

@latent_node · X

@pdfproindia · X

@torgimaOS · X

Voice to any document in 30 seconds

@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

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