https://sauravpanigrahi.com/tags/systems/Recent content in Systems on Saurav PanigrahiHugoen-usFri, 01 May 2026 00:00:00 +0000https://sauravpanigrahi.com/reading/ml-systems/Fri, 01 May 2026 00:00:00 +0000https://sauravpanigrahi.com/reading/ml-systems/<p>Long-form references on training, infrastructure, and implementation practice.</p> <h2 id="training-systems">Training Systems</h2> <ul> <li> <p><a href="https://djdumpling.github.io/2026/01/31/frontier_training.html">Frontier Model Training Methodologies</a><br> Survey of open frontier training recipes and implementation choices.</p> </li> <li> <p><a href="https://jax-ml.github.io/scaling-book/">Scaling LLMs with JAX</a><br> Book-length treatment of distributed training practice.</p> </li> <li> <p><a href="https://arxiv.org/abs/2603.03276">Beyond Language Modeling: An Exploration of Multimodal Pretraining</a><br> From-scratch multimodal pretraining study with useful details on representation choices and scaling behavior.</p> </li> </ul> <h2 id="embeddings-and-retrieval">Embeddings And Retrieval</h2> <ul> <li><a href="https://blog.jxmo.io/p/how-to-train-the-best-embedding-model">How to Train the Best Embedding Model in the World</a><br> Detailed engineering writeup on embedding model training, label noise, verification, and dataset scale.</li> </ul> <h2 id="gpu-programming">GPU Programming</h2> <ul> <li> <p><a href="https://www.gpumode.com/home">GPU MODE</a> Community and resource hub for GPU programming.</p>https://sauravpanigrahi.com/reading/research-engineering/Fri, 01 May 2026 00:00:00 +0000https://sauravpanigrahi.com/reading/research-engineering/<p>Selected references on research taste, engineering judgment, and doing useful technical work.</p> <h2 id="research-taste">Research Taste</h2> <ul> <li> <p><a href="https://distill.pub/">Distill</a> Essays with a high bar for clear technical exposition.</p> </li> <li> <p><a href="https://www.cs.virginia.edu/~robins/YouAndYourResearch.html">You and Your Research</a><br> Hamming&rsquo;s classic essay on choosing important problems and organizing a life around serious work.</p> </li> <li> <p><a href="http://joschu.net/blog/opinionated-guide-ml-research.html">An Opinionated Guide to ML Research</a><br> Practical advice on developing taste and becoming effective in machine learning research.</p> </li> <li> <p><a href="https://michaelnielsen.org/blog/principles-of-effective-research/">Principles of Effective Research</a><br> A useful frame for research as a skill that can be deliberately improved.</p>