April 2026

Audio AI has had a breakout year. Automatic speech recognition has gotten dramatically better with models like OpenAI’s Whisper variants, NVIDIA’s Parakeet, and Mistral’s Voxtral. Audio understanding stepped forward with models like NVIDIA’s Audio Flamingo 3. Dialogue-grade text-to-speech arrived via Nari Labs’ Dia-1.6B. And Meta shipped the Perception Encoder Audiovisual (PE-AV), a multimodal encoder capable of learning a shared embedding space across audio, video, and text. The frontier has never moved faster. The catch? The practical knowledge required […]

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Researchers at Meta’s FAIR lab have released NeuralSet, a Python framework designed to eliminate one of the most persistent bottlenecks in Neuro-AI research: the painful, fragmented process of getting brain data into a deep learning pipeline. https://kingjr.github.io/files/neuralset.pdf The Problem: Neuroscience Data Is Stuck in the Pre-Deep-Learning Era Neuroscience already has excellent, battle-tested software. Tools like MNE-Python, EEGLAB, FieldTrip, Brainstorm, Nilearn, and fMRIPrep are the gold standard for signal processing across electrophysiology and neuroimaging. The trouble is that these […]

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In this tutorial, we build a complete, production-style pipeline for detecting and redacting personally identifiable information using the OpenAI Privacy Filter. We begin by setting up the environment and loading a token classification model that identifies multiple categories of sensitive data, including names, emails, phone numbers, addresses, and secrets. We then design helper functions to normalize labels, extract structured spans, and transform raw model outputs into usable formats. From there, we implement a configurable redaction system that allows […]

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The race to make large language models faster and cheaper to run has largely been fought at two levels: the model architecture and the hardware. But there is a third, often underappreciated frontier — the GPU kernel. A kernel is the low-level computational routine that actually executes a mathematical operation on the GPU. Writing a good one requires understanding not just the math, but the exact memory layout, instruction scheduling, and hardware quirks of the chip you are […]

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As large language models scale to longer context windows and serve more concurrent users, the key-value (KV) cache has emerged as a primary memory bottleneck in production inference systems. For a 30-billion-parameter model with a batch size of 128 and an input length of 1,024 tokens, the resulting KV cache can occupy up to 180 GB of memory. For reference, a 7-billion-parameter model’s parameters consume 14 GB of GPU memory, while the KV cache for the same model […]

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