
Cursor
Claude Code
Windsurf Editor
GitHub Copilot
replit
Lovable
warp by spolu
bolt.new
SIMY.one
Codex by OpenAI
Claude Code
GitHub
Codexโโ
GitHub Copilot
SIMY is an AI development platform built to reduce the gap between team conversations and implementation. It turns discussions from Slack, meetings, Gmail, and other workplace tools into structured engineering context, then uses that context to generate code, tests, and GitHub pull requests.
We built SIMY from a problem we kept seeing in our own work: a team discusses a feature, everyone seems aligned, but once implementation starts, the intention shifts. Assumptions no longer match, edge cases get missed, and the final PR gets feedback like, โusers wouldnโt actually use this.โ
The issue is often not lack of effort. It is that the assumptions inside the conversation never fully make it into the code. PMs may optimize for user value, while engineers optimize for correctness and safety. Both are reasonable, but the same words can still mean different things in practice.
Traditional fixes like writing better specs rarely work as cleanly as they sound. Specs are often incomplete, outdated, or never written at all, while Slack and meetings remain the real source of truth.
Most coding tools assume a human has already translated the discussion into a clean prompt or specification. SIMY is designed to remove that translation step. It uses the conversation itself as the input, reconstructs the relevant context, makes the done state explicit, and generates implementation from that shared understanding.
In internal benchmarks, SIMY achieved 86.7% first-shot success and increased pull requests per engineer by 17.7x on tested tasks. These are early internal results, and real-world performance will vary.
SIMY is currently connected to the Claude Code API, with support for additional backends planned over time. It is still early, and we are especially interested in feedback on where this breaks down in real teams.
Cursor
SIMY.oneCursor is recommended for small to medium-sized businesses looking for an efficient customer relationship management (CRM) solution. It's ideal for teams that need an integrated system to manage customer interactions, support operations, and sales tracking.
SIMY.one's answer:
SIMY uses real team conversations as the source of truth for software development. Instead of relying on manually written prompts or specs, it captures context directly from Slack, meetings, and emails, reconstructs intent, and generates code, tests, and pull requests from that shared understanding.
SIMY.one's answer:
Most AI coding tools require developers to translate discussions into prompts. SIMY removes that step. By working from original conversations, it reduces misalignment, avoids lost assumptions, and produces outputs that better reflect what the team actually decided.
SIMY.one's answer:
Product and engineering teams building software collaborativelyโespecially teams that rely heavily on Slack and meetings and want to reduce friction between decision-making and implementation.
SIMY.one's answer:
SIMY was built from repeated experiences where teams aligned in discussions but diverged during implementation. Feedback like โthis isnโt what users needโ often came from assumptions that never made it into the code. SIMY was created to close that gap by making conversations directly executable.
SIMY.one's answer:
AI agents, large language models (via Claude Code API), cloud infrastructure, and integrations with collaboration tools (Slack, Teams, Gmail, Zoom) and GitHub.
SIMY.one's answer:
SIMY is currently in an early pilot phase and has not publicly disclosed customer names.
As a user, I had the opportunity to contribute to the development of SIMY.
Perhaps due to the systemโs complexity, there are still some bugs, but it has become much more stable over time. What impressed me most is the concept: capturing conversations from Slack, Gmail, Zoom, Teams, and more, then using AI to automatically create and update actions. Additionally, incorporating historical action data as contextual input for coding is a brilliant idea.
For these reasons, Iโm giving it 5 stars.
Based on our record, Cursor seems to be more popular. It has been mentiond 9 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.
Cursor has spent the past week in headlines after confirming a partnership with SpaceX that could eventually lead to a $60 billion acquisition. The deal, for now, centres on training more capable coding models using SpaceXโs compute infrastructure. - Source: dev.to / 13 days ago
The step up from there is an editor with a built-in agent like Cursor, Google Antigravity, Windsurf, or VS Code with a coding extension. These are code editors with an AI agent living inside them, and the difference is the responsible party for getting things from place to place. Instead of the software creator shuttling code between windows, the AI agent edits the project files directly and runs the GitHub and... - Source: dev.to / 24 days ago
Where llms.txt genuinely gets read is a different layer: coding and agent tooling โ Cursor, Claude Code, GitHub Copilot, Windsurf โ pulling a documentation site's pages with less token waste, plus emerging agent protocols like OpenAI's Agents SDK. That's real, and it's growing fast. - Source: dev.to / 25 days ago
If you donโt believe me, go to Google AI Studio, get you an API key, create a project, then open Cursor, add the key, add whatever model they have available to use, run a task and you will see how models like Gemini 3.5 or 2.5 Flash which gives you 5 Requests Per Minute and 20 Requests Per Day will scream at you with hitting a limit rate. - Source: dev.to / about 1 month ago
Here is an example how to connect Prometheus DB to Cursor AI code editor. - Source: dev.to / 11 months ago
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