Azmat
Watch or Listen on YouTube IQuest Coder V1: Benchmaxed Or Breakthrough A Reality Introduction A new coding model drops, a leaderboard lights up, and within five minutes the internet has reached consensus. Not on whether it’s good, that part is boring. The consensus is on whether it’s “benchmaxed.” The funny thing is that both camps … Read more
Watch or Listen on YouTube Deep Delta Learning Explained: Introduction ResNet’s big trick is almost insultingly simple. Add an identity shortcut, stop gradients from dying, train deeper models. The shortcut is a safety rail, and in deep residual learning for image recognition it turned “very deep” from a heroic gamble into a default option. That … Read more
Watch or Listen on YouTube Weekly AI News January 3 2026: The Pulse And The Pattern Introduction Some weeks feel like a neat list of headlines. This one felt like watching a distributed system under load. Everything speeds up, the bottlenecks move, and the dashboards start arguing with each other. We got faster decoding for … Read more
Watch or Listen on YouTube DeepSeek mHC Explained: Stable Hyper-Connections For Wider Transformers Introduction If you have ever watched a large run go sideways at 3 a.m., you know the weird part is not the failure. The weird part is how sudden it is. Loss looks fine, gradients look normal, then a small architectural “improvement” … Read more
Watch or Listen on YouTube Qwen Image 2512 Review: Open Weights, Closed Model Realism Introduction The last year of image models has felt like a tug-of-war between two worlds. Closed systems ship the “wow,” open weights ship the control. Qwen Image 2512 is one of the first releases that makes that trade feel less mandatory. … Read more
Watch or Listen on YouTube TTT E2E: 128K Context Without the Full KV Cache Tax Introduction Long prompts feel like a superpower right up until you pay for them. You paste in 80K tokens of logs, code, or chat history, and the model spends the next few seconds doing what looks like “thinking,” but is … Read more
Watch on YouTube Tencent WeDLM 8B: Topological Reordering & KV Cache Diffusion Introduction Speed claims are cheap. Latency is not. Anyone can make a language model “faster” by picking an easy prompt, a short output, and a baseline that was never tuned. The harder problem is shaving seconds off the stuff people actually wait on. … Read more