Topics
A running table of contents, my archive of learning and occasional write ups.
- Browser Automation exploring
Driving a real web browser programmatically for testing, scraping, and increasingly for AI agents that navigate the web.
- C4 Architecture exploring
A lightweight model for visualizing software architecture at four zoom levels — Context, Containers, Components, and Code — so diagrams stay consistent and easy to follow.
- Classical Verification exploring
Verifying correctness empirically through testing, simulation, and coverage, the practical counterpart to formal (proof-based) verification.
- CUDA Programming exploring
Programming NVIDIA GPUs directly: the execution model, the memory hierarchy, and how to write and launch kernels.
- Diffusion Models exploring
Generative models that create data by learning to reverse a gradual noising process, behind most modern image generation and now text.
- Edge Models exploring
Running models on-device (phones, laptops, embedded) under tight memory, latency, and power budgets.
- Finetuning exploring
Adapting a pretrained model to a specific task or domain, from full finetuning to lightweight LoRA.
- Formal Verification exploring
Proving software and hardware correct against a specification, instead of only testing for bugs.
- Kernel Engineering exploring
Writing and optimizing the small, hot compute kernels (often on GPUs) that make ML and numerical workloads fast.
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Reverse-engineering what neural networks actually compute, and the tooling (like Goodfire) that makes internals inspectable and steerable.
- Open Source Models exploring
Openly available model weights (Llama, Mistral, Qwen, Gemma, DeepSeek) and the licenses and ecosystems around them.
- Rust Programming exploring
A systems language that guarantees memory safety without a garbage collector, through ownership and borrowing.
- Shor's Algorithm exploring
A quantum algorithm that factors large integers in polynomial time, threatening the RSA encryption that secures much of the internet.
- Small Language Models exploring
Compact language models (often a few billion parameters or fewer) that trade scale for speed, low cost, and on-device use, while staying surprisingly capable.