20 lines of Python. 90% of cuBLAS. Zero thread management. The catch: NVIDIA owns the compiler.Continue reading on Towards AI »
No boilerplate here. No DI container, nothing-no middleware whatsoever. Just resultsContinue reading on Towards AI »
Release: llm-all-models-async 0.1
LLM plugins can define new models in both sync and async varieties. The async variants are most common for API-backed models – sync variants tend to be things that run the model directly within the plugin.
My …
A hands-on tutorial generating synthetic linked tables (customers, orders, invoices) with Faker and Pandas, and the hidden complexity that breaks at scale.Created using GPT imageFlat seems so simple and safe. “Yeah, that’s how I ended up with a broken …
Learn Prompt Engineering, Function Calling, RAG, and Multimodal Memory with Cumulative Code Examples Using GPT-5 and OpenAI Responses APIContinue reading on Towards AI »
How abstraction, registries, and a clean CLI turn chaotic text into structured, queryable knowledge — without locking you into a single…Continue reading on Towards AI »
Accuracy could be lying to youContinue reading on Towards AI »
A Deep Dive into Andrej Karpathy’s microGPT ImplementationIn the world of artificial intelligence, Andrej Karpathy is a well-known figure. He has worked at Stanford, led AI efforts at Tesla, and contributed to OpenAI. Beyond that, he has a rare talent …
Using just Python and a real CSVContinue reading on Towards AI »
NVIDIA researchers introduced ProRL AGENT, a scalable infrastructure designed for reinforcement learning (RL) training of multi-turn LLM agents. By adopting a ‘Rollout-as-a-Service’ philosophy, the system decouples agentic rollout orchestration from the training loop. This architectural shift addresses the inherent resource conflicts between I/O-intensive environment interactions and GPU-intensive policy updates that currently bottleneck agent development. The […]
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