Userdoc Reviews

GitHub Reviews

1. Reinhard

   · Boss at CLOUD Meister · over 5 years ago
   perfect 4 open Source

Userdoc mentions (6)

• Ask HN: Dear Product Managers – How do you use LLM's in your day to day work?

  Tools like Userdoc (https://userdoc.fyi) help in a few ways, you can easily create requirements (stories, personas, journeys, test cases), but also reverse engineer existing source code into detailed docs, then ask natural language questions etc. AI helps us plan our product in Userdoc, and our devs connect via MCP to bring those requirements directly in to Cursor (full disclaimer, I work at Userdoc - but we eat... - Source: Hacker News / about 1 year ago

• Ash HN: What tool do you use for changing acceptance criteria tracking?

  Userdoc has versioning of stories and acceptance criteria, aimed to at helping this exact issue https://userdoc.fyi (transparency: I’m the founder). - Source: Hacker News / over 1 year ago

• Generative AI and Product Requirements

  I'm interested to hear how other people are using tools to help them, we've been playing with userdoc.fyi and having good success, but there are just so many AI related tools I feel like I can't keep up! Source: about 3 years ago

• Requirements

  Great read, I spent years thinking requirements should live somewhere like Jira, and I feel for many teams this is the case. But as the author mentions, Jira contains tasks related to bugs, text changes, colour changes etc.. These are not product requirements per se. Like the author, I came to the conclusion requirements are best kept in a requirements management system, that integrates with your project... - Source: Hacker News / about 3 years ago

• Ask HN: Those making $1000/month or more on side projects – Show and tell

  I built Userdoc (https://userdoc.fyi) a requirements management system for software projects. After running a development consultancy for 8 years, I wanted a dedicated system for gathering and confirming requirements, and only syncing them with project management tools like Jira when they are ready (but keeping them in Userdoc as the living documentation and source of truth. Things are going well, we have some... - Source: Hacker News / over 3 years ago

GitHub mentions (2476)

• Como criei um agente de IA para selecionar testes automaticamente em PRs (Parte 1)

  """Clones a single branch of a GitHub repository into a temporary directory for local scanning.""" Import logging Import shutil Import subprocess Import tempfile Logger = logging.getLogger(__name__) Class RepoFetchError(RuntimeError): """Raised when the target repository/branch cannot be cloned.""" Class RepoFetcher: """Shallow-clones a single branch of a repository so its tests can be scanned... - Source: dev.to / 5 days ago

• I Treat My Video Descriptions Like a Build Artifact Now. Here's the Pipeline.

  # video-meta.yml Topic: > Walking through how we cut cold-start time on a Lambda-backed GraphQL API from 2.4s to under 400ms, including the two things that didn't work. Target_keyword: lambda cold start optimization Audience: backend devs who already ship serverless, not beginners Model_channels: - the three channels currently ranking for this keyword Links: repo: https://github.com/... slides:... - Source: dev.to / 6 days ago

• I audited a 5.5 GB AI training dataset by downloading 0.8% of it

  Import struct, json, urllib.request REL = "https://github.com/{owner}/{repo}/releases/download/{tag}/" PART = ["...part1.zip.001", "...part2.zip.002", "...part3.zip.003"] SIZE = [1992294400, 1992294400, 1893639808] # from the releases API Def grab(part, start, end, out): # HTTP range fetch req = urllib.request.Request(REL + PART[part], Headers={"Range":... - Source: dev.to / 10 days ago

• Superlogical – Mitchell Hashimoto

  Is published at https://github.com/.keys so an SSH server to which you connect could do a reverse lookup. This is the reason why my ~/.ssh/config has those 2 lines at the end:

          Host *.

  - Source: Hacker News / 18 days ago

• Your Agent's Confidence Score Is Not a Probability

  All of this assumes you can actually inspect what the agent did — the real inputs after resolution, the real tool outputs, the real intermediate steps. That is the other half of the workflow. AgentLens captures the trace: every model and tool step, resolved inputs, raw outputs. agent-eval scores and gates the output; AgentLens gives you the unforgeable, agent-didn't-author trace data for Tier 1+2 to score against... - Source: dev.to / 18 days ago