socketify.py VS DevOS
Compare socketify.py VS DevOS and see what are their differences
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socketify.py
DevOS
SRE.ai
Workflos.ai
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DevOS is what happens when you stop thinking of AI as "a tool" and start thinking of it as "an employee." The premise: AI coding agents in 2026 are technically remarkable. Devin can ship features autonomously. Cursor + Claude Code accelerate every senior engineer. Copilot writes half the boilerplate. But every existing AI agent product treats AI as a tool you call โ invoked from an IDE, prompted in a chat, given a one-shot task. That's the wrong frame for how teams actually work. Real teams don't have "tools" โ they have employees. Employees: Have specialized roles (frontend, backend, QA, design, copy, sysadmin) Take tickets off a sprint board Attend standups and report blockers Hand off work to teammates with context Open PRs, get code reviews, respond to comments Have a track record visible across sprints DevOS treats AI agents the same way. Three layers in one product: 1. The marketplace. Browse specialized AI agents by role. Each comes with role-specific prompting, tool integrations, and a track record. Hire as many as your team's working style needs. Agents are configured for their role โ a "frontend dev" agent ships React/Vue with the team's design system patterns; a "copywriter" agent ships marketing pages in your brand voice; a "QA" agent writes Playwright tests and triages bugs. 2. The sprint board. Linear/Jira-style kanban with full agile machinery: sprints, epics, backlog grooming, sprint planning, retrospectives. Agents appear as assignable team members. Drag a ticket onto an agent the same way you'd assign it to a human dev. The agent picks it up, posts an estimate, asks clarifying questions if needed, and starts working. 3. The communication layer. Standups are automated โ every morning agents post what they shipped yesterday, what they're doing today, and what's blocking them. PRs are opened in your real repo. Code reviews happen in real GitHub / GitLab. Slack/Discord/Telegram integration so humans can talk to agents like teammates
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Based on our record, socketify.py seems to be more popular. It has been mentiond 2 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
These "benchmarks" are useless, they're not testing anything real world except the performance of uWebsockets. There are copy errors all over the place. And then an advertisement: https://github.com/cirospaciari/socketify.py#briefcase-comme... Is this a professional framework that produces proper, real-world benchmarks and... - Source: Hacker News / over 3 years ago
After starting the project called socketify.py at https://github.com/cirospaciari/socketify.py, I got pretty good results and reviews, but many people asked if socketify.py could be used to create a WSGI and ASGI server. WSGI and ASGI have a lot of overhead, that's is why I choose not to use them in the first place, but adding an ASGI and WSGI server allows a lot of code already written to run faster! Source: over 3 years ago