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Trained on billions of lines of public code, GitHub Copilot puts the knowledge you need at your fingertips, saving you time and helping you stay focused.
GitHub Copilot
Sinatra.devNo features have been listed yet.
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.
It definitely increases my productivity.
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Snagly is a free, MIT-licensed set of 30 skills for AI coding agents — GitHub Copilot, Claude Code, Cursor, Codex and 70+ others — that turn "an AI that can drive a browser" into "an AI that tests like a QA professional." A skill, if you haven't met them yet, is a reusable instruction set that teaches the agent a specific working method — when to use it, what rigor it requires, what evidence to capture, and what... - Source: dev.to / about 1 month ago
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You need an active GitHub Copilot subscription. Plans are available at individual, business, and enterprise tiers at github.com/features/copilot. Once active, all tools use your GitHub account credentials. - Source: dev.to / 3 months ago
For over a decade PhpStorm (starting in my WordPress era) and later WebStorm have been my main IDEs for web development. So when GitHub Copilot launched, it was a natural choice to try it out in WebStorm. It was one of the first AI coding tools I used, and it had a big impact on how I thought about AI-assisted coding. - Source: dev.to / 3 months ago
Before we get into it, there are some things about AI usage worth addressing. I've had my fair share of scepticism in the past, but recent model releases have made it increasingly difficult to argue that AI isn't a viable tool for the majority of workstreams, including building user interfaces. Most large language models are trained on public data scraped from the internet, which means your internal design system... - Source: dev.to / 3 months ago
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