Productivity1 month ago
Fold — one place for tasks, files and team communication
Organize your team's tasks, files, and chat in a single shared workspace.
@foldtaskapp · X
The full gallery
Tech stack
6 projects
Organize your team's tasks, files, and chat in a single shared workspace.
@foldtaskapp · X
Spreadsheet tool where each column is an AI agent that automates research, extraction, and data processing.
u/Capital-Top3289 · Reddit
I built a spreadsheet where every column is an AI agent (research, scrape, or API) — looking for feedback I got tired of pasting the same 50 company names into ChatGPT one by one, then copying websites / CEOs / LinkedIn URLs back into Excel. So I built MySheets: it looks like a spreadsheet, but each column can be: an AI prompt that runs row by row a web scrape an HTTP API call Columns can read previous cells, so you chain: name → website → product summary → CEO → LinkedIn. You
Workspace with focus timer, tasks, projects, notes, and routines for freelancers.
@mesbahlab · X
Track applications, research companies, remember deadlines, and follow up from one calm place. Built by Zyntro.
@shahsyeddanish4 · X
orbbt is a job tech saas, which helps you keep your job hunt sane and organized in this economy where it takes on avg of 100s of job applications
Organize your tools, workflows, and resources into one accessible workspace.
@mj_jimmi · X
Manage academic conferences: submit and review papers, handle scheduling and registration.
@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