ayush @opcode01

Best thing is human AI collaboration in auto research The big jumps are still suggestions I gave from my intuition But I was able to get those approaches because Opus gave me so many experiments to analyse

Tejas @tejasktwt

5 days ago

Autoresearch is so crazy! I left this new model architecture I'm working on to Opus 5.5 and forgot, and a week later it has hill-climbed crazyyy

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ayush @opcode01

a week ago

@ary_aks

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aks.ary @ary_aks

a week ago

23. "Not just another" builder/founder/developer from India🇮🇳. Looking to connect with more builders, indie hackers and founders across the globe. Let's join hands if this sounds like you.

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Adam @adamdotdev

a week ago

Me helping the agent ship a feature

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ayush @opcode01

2 weeks ago

@Guillamaury Dropped a follow

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ayush @opcode01

2 weeks ago

Python "asyncio": "await" vs "asyncio.create_task()" They’re not interchangeable. "await" waits for a coroutine to complete. "asyncio.create_task()" schedules a coroutine to run concurrently with other tasks. Here’s the difference: # Sequential: ~4 seconds await fetch("A") await fetch("B") # Concurrent: ~2 seconds task_a = asyncio.create_task(fetch("A")) task_b = asyncio.create_task(fetch("B")) await task_a await task_b Why does this matter? With sequential "await", B starts only after A finishes. With "create_task()", both tasks can make progress while waiting for I/O. Remember: - "await" → wait for a result. - "create_task()" → schedule concurrent work. - "await task" → wait for scheduled work to finish. When to use what? Use "await" when the next operation depends on the current result. Use "create_task()" when independent operations can overlap. One caveat: "asyncio" provides concurrency, not automatic CPU parallelism. Writing async code isn't enough. You need to structure it for concurrency.

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ayush @opcode01

2 weeks ago

@NadyaVoynich 🤝 one way connection done 😃

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ayush @opcode01

2 weeks ago

@v1shnyak0va Happy to connect 😊

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ayush @opcode01

2 weeks ago

@thepiyushnegico Dropped a follow 💫

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ayush @opcode01

2 weeks ago

@Ldataengineer Thanks for sharing, it's really helpful 🫡

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ayush @opcode01

2 weeks ago

A common FastAPI misconception: lifespan events run once for the entire application. Not exactly. They run once per application instance, typically once per worker process. For example, if you start FastAPI with 4 Uvicorn workers: uvicorn main:app --workers 4 And your lifespan looks like this: @asynccontextmanager async def lifespan(app: FastAPI): print("Starting application") app.state.cache = await create_cache() yield await app.state.cache.close() You should expect the startup code to run in each worker process. That means: - 4 workers can create 4 cache instances. - An ML model may load 4 times into memory. - Each worker may create its own database connection pool. - Shutdown cleanup runs separately for each worker. Why does this matter? If each worker's database pool allows 20 connections, 4 workers could allow up to 80 connections in total. The key lesson: use lifespan for resources owned by a worker process. Use dependencies for request-scoped resources, and use external infrastructure when state must be shared across workers. In FastAPI, understanding the process boundary is just as important as understanding the request lifecycle. #FastAPI #Python #BackendEngineering