I really don’t know how to answer this besides work 100x more

Just found out about recency bias. Maybe the most important bias ever

ngl "let there be light" sounds like a prompt

Absolute shameless behavior. Imagine being a designer, getting excited because a successful startup wants to hire you, spending hours working on an assignment for free, only to be told they’re not actually ready to hire. Then seeing them publicly begging for more designers. All while the founder celebrates his nine-figure company. If true, heads need to roll here.

After 7 months of writing, I finished my essay! I think agency might be the most important idea of the 21st century highagency.com

After months of writing, 'How to Unclench' is finally live! It's an interactive essay packed with stories, science & guided practices to help you unclench. → howtounclench.com

They say cockroaches will outlast us all. We'd settle for outlasting your database problems. New look, same resilience: distinct parts, adapting together, make the whole stronger.

My agent tells me that inference engineering job openings have doubled in the past 6 months. I think that's an underestimate. Demand for inference has inflected, again. So too has demand for engineers who can operate AI models in production.

New tutorial youtube.com/watch?v=ii6pAwAZ…

In this (extremely long) video, I'll build a distributed training framework from first principles using PyTorch. We'll derive everything from first principles, explaining the mathematics behind distributed training as well as every auxiliary component we build along the way, including Multi-head Latent Attention (MLA), Rotary Position Embeddings (RoPE) and YaRN. I'll explain process groups, ranks, collective communication operations, distributed autograd, device meshes and distributed tensors, and then use these foundations to implement the major forms of parallelism used to train modern language models. We'll derive and code pipeline parallelism, data parallelism, FSDP, HSDP, tensor parallelism and context parallelism, including pipeline schedules, block matrix multiplication, model sharding and ring attention. We'll then combine these techniques and examine how tensor layouts and gradients must be tracked as data moves between devices. Most importantly, we won't study each parallelism technique in isolation. We'll combine pipeline, data, tensor, context and expert parallelism into a single working framework and follow the tensors, communication operations and gradients as they move across GPUs. By the end, you'll understand not only how these techniques work individually, but how modern distributed training systems compose them to train large dense and mixture-of-experts models. We'll also build the important components of a modern transformer, including rotary embeddings, long-context extensions, multi-head latent attention and a mixture-of-experts architecture. This leads into expert parallelism, expert tensor parallelism, token dispatching, all-to-all communication and combining different parallel dimensions through dense and sparse device meshes. We'll also build the supporting training infrastructure, including the training loop, optimizer, learning-rate schedule, checkpointing and metrics. The goal is to give you the foundations required to understand how distributed training frameworks work internally and how to design and implement new parallelization strategies yourself. No prior knowledge of distributed training is required. Link to the video: youtube.com/watch?v=XoGvCBRn…

Celebrating small wins is hardwired into "Achieve," our goal-tracking sample app. We wanted the simple act of checking off a task to feel like a celebration. Instead of the standard #Material ripple, we used Styles to drop in a custom, high-contrast completion animation—specifically, a brand-colored ring that expands and fades on tap, paired with a subtle pressed-state gradient. It lets us focus entirely on polishing those custom, rewarding micro-interactions while Material handles the foundation. #GoogleDesign #MaterialDesign #JetpackCompose #AndroidDev #UXDesign #InteractionDesign

Guess what? local ai for everyone (: we really did it local.ai