dynamic_rick
54 posts


Compared Qwen3.6 35B and 27B in the same conditions with Google TurboQuant
Device: MacBook Pro M5Max 64GB RAM
Outputs characteristics:
Qwen3.6 35B: 6672 tokens, 2m 10s, 65 tok/s
Qwen3.6 27B: 7344 tokens, 5m 22s, 24 tok/s
Conclusion: Both models were asked to draw waves using HTML, 35B responded quickly but the result feels weak and messy, while 27B took more time and delivered a much cleaner and more consistent result, because it is built for thinking and planning, so it works better on tasks that need structure, overall 27B is a better choice for tasks where planning matters, while 35B is more suitable for everyday use when you just need a fast response
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I'm on the waitlist for @perplexity_ai's new agentic browser, Comet: perplexity.ai/comet
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This Cursor Extension is awesome
Accurate tweaking of UI was always a struggle,
But @stagewise_io allows you to bring full context to Cursor, just point and command:
1. Directly choose specific elements in browser
2. Send to Cursor with full context
And it's open source
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Cursor Agent is just wild.
Now i use Gemini PRO 2.5 to scan the codebase and sonnet 3.5/3.7 to execute code.
In this workflow you need 3 things:
1. Detailed project documentation
2. Use multiple AI coding models
3. 50-step implementation plan
I spend 30 hours/week on cursor. I've found out the best cursor practices and the workflow.
I attached the best practices below and here's the best workflow.
Your project docs (PRD, Tech stack & APIs doc, app flow doc stec) works like a knowledge base for AI models.
If AI models find all necessary information within the knowledge base, they don't hallucinate, assume things and don't ruin the codebase.
So must add project docs in your root directory. Ideal place is add them under project rules (.cursor/rules)
Then you need to use multiple AI models. Now I am using Gemini PRO 2.5 to scan the entire codebase (cus it has 1M context) and find errors or update docs.
And I use Sonnet 3.5 to execute code. If it's a bit complex step then I also use Sonnet 3.7.
Sometimes I also use GPT o1 model to debug but rarely (mostly done by Gemini pro 2.5)
So 2.5 to scan, update, and 3.5/3.7 to execute.
Each model has its superpowers. We need to maximize those.
Lastly, you need to write an end-to-end plan to code your app. I call it "implementation plan."
This implementation plan works as a blueprint for Cursor Agent and it just follows the tasks and executes those.
I use @CodeGuidedev to generate coding docs + it provides 50-step implementation plan to code the entire app.
Now it also supports MCPs. Imagine Cursor using Supabase MCP to create database tables, and add policies autonomously. It just saves so much time.
So wrap up of the workflow is:
Attach your coding docs + use multiple AI models in your flow + have a solid 50-step implementation plan.
And you'll see how powerful Cursor Agent is.
I hope this'll refine your Cursor coding workflow. Let me know your findings.

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@H0ckeyWorldwide Proud of Team Hong Kong...shows great restraint and discipline to not further the situation. As for Turkmenistan, I'm lost for words...
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Thank you @dayvid_JS for writing this helpful article.
How to Create and Send Email Templates Using React Email and Resend in Next.js
freecodecamp.org/news/create-an…
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Deploy your next app in seconds. Get $200 in cloud credits from @DigitalOcean using my link: m.do.co/t/eabfadad20ca
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@iamnot_elon Yes 👍 only if has unique functionality with my model Y!
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