
Mockaroo
FakerBox
Data Creator
Dummy File Generator
DDL to Data
RandomPhoneNumber.online
Nodeflip
GenerateData.com: free, GNU-licensed, random custom data generator for testing software

Devin by Cognition
GitHub Copilot
Cursor
Codex by OpenAI
Jules
Claude Code
Ara.so
A cloud coding agent for your backlog. Assign a Linear issue or label a GitHub issue; it writes the code in an isolated sandbox, opens a pull request, runs your tests, and reviews its own diff. Free tier runs on your own Claude or Codex subscription.

Which is more popular?
Based on our record, Generate Data seems to be more popular. It has been mentioned 14 times since March 2021.
Website, pricing, platforms and company facts side by side.
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| Website | generatedata.com | sinatra.dev |
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What each product offers, as listed by its team.


Possible disadvantages
No features have been listed yet.
Walkthroughs and reviews on video.
Generate Data Science/Data Analysis Report of your DataSet in 5 Minutes
Sinatra demo: GitHub issue to pull request, end to end (22 seconds)
More videos
How often each product is chosen within a category, 0–100% relative to the other.


As answered by people managing Generate Data and Sinatra.dev.
Sinatra.dev's answer:
It runs on the model subscription you already pay for. You assign a Linear issue or label a GitHub issue, the agent does the work in its own isolated sandbox and comes back with a pull request, and the model bill goes to your own Claude or Codex subscription or API key. We don't resell tokens or mark them up. The free tier is 5 tasks a day on your own credentials, every day, or 1 task a day where Sinatra covers the tokens. Pricing was the reason we built it in the first place, the agents we tried billed in credits you couldn't predict.
Sinatra.dev's answer:
Pricing and entry points. The paid plan is $20 per member per month for the hosted sandboxes and orchestration, with no markup on tokens because inference runs on your own Claude or Codex subscription or API key. And it works from Linear issues as well as GitHub, so a team whose tickets live in Linear can assign work to the agent the way they'd assign a teammate. It also reviews its own diff and posts the findings on the PR, and pushes revisions when a reviewer leaves comments. To be honest about the limits, it only works from GitHub and Linear issues today, and the agent never merges its own PRs, a person does that.
Sinatra.dev's answer:
Small engineering teams and solo founders with a backlog of well specified tickets they never get to: reproducible bugs, small features with acceptance criteria, the work that is clear enough to hand off but keeps getting pushed behind bigger things. Teams already working out of Linear or GitHub Issues who don't want another tool to learn, and who would rather run agent work on the Claude or Codex subscription they already have than open a new metered account somewhere else.
Sinatra.dev's answer:
I built a pet sitting marketplace with my wife. She always had features she wanted shipped and every one of them went through me, so I spent about three months building an agent she could assign tickets to instead. She writes the ticket, assigns it, and a PR comes back. She's now a big contributor to that codebase without me being in the way. I started handing it my own backlog too and eventually it turned into a product. It has been a lot harder to build than I expected, lots of edge cases, which is why it only works from Linear and GitHub issues right now and why the agent doesn't merge its own PRs, you still do that last part yourself.
Sinatra.dev's answer:
TypeScript throughout. A Fastify API for webhooks and OAuth, a Temporal worker that orchestrates each agent run, Next.js for the dashboard and the site, and Prisma on Postgres. Every task runs in its own Daytona sandbox that gets cloned, worked and torn down. Model access is bring your own: an Anthropic or OpenRouter API key, or a Claude or ChatGPT subscription connected to the workspace.
Share your experience with using Generate Data and Sinatra.dev. For example, how are they different and which one is better?
Recommendations tracked on public social media and blogs since March 2021.


When you're learning SQL or testing queries, having access to realistic mock data is essential. Tools like Mockaroo and GenerateData can quickly create large datasets that you can upload into your database. You can define custom fields... - Source: dev.to / over 1 year ago
Since you will almost certainly need data to work on, I recommend generatedata.com. Source: over 3 years ago
Like this one I just found randomly. https://generatedata.com/. Source: over 3 years ago
Tracking Sinatra.dev since Sep 2026.
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