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Sashiko: The AI That Reviews Linux Kernel Code Better Than Most Humans (And Everyone Knows It)

Jun 9, 2026

Sashiko: The AI That Reviews Linux Kernel Code Better Than Most Humans (And Everyone Knows It)

When AI Catches the Bugs That 100% of Human Reviewers Missed, the Question Isn't Whether to Use It. It's Whether You Can Afford Not To.

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MOSS-TTS: Why the Audio Tokenizer Is the Entire Stack

Jun 8, 2026

MOSS-TTS: Why the Audio Tokenizer Is the Entire Stack

Every component in the MOSS-TTS family, the flagship TTS, the spoken dialogue model, the voice generator, the sound effects model, the realtime streamer, sits on top of one shared foundation: MOSS-Audio-Tokenizer, a 1.6-billion-parameter pure Transformer audio tokenizer trained on 3 million hours of audio.

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KumoRFM-2: The Foundation Model That Made NVIDIA Pay $400M to Own the Enterprise Prediction Layer

Jun 7, 2026

KumoRFM-2: The Foundation Model That Made NVIDIA Pay $400M to Own the Enterprise Prediction Layer

NVIDIA acquired Kumo AI for over $400 million on June 4 2026. The acquisition was not about chips or inference hardware. It was about a specific technical bet: that the most valuable layer in enterprise AI is not the model that generates text, but the model that predicts outcomes directly from business databases, without feature engineering, without a data science team, and without months of ML pipeline work.

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rmux: Playwright for Terminals, Written in Rust

Jun 7, 2026

rmux: Playwright for Terminals, Written in Rust

Every AI agent that needs to drive a CLI or TUI application has the same problem: there is no reliable, typed API for terminal interaction.

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vLLM Semantic Router: The Infrastructure Layer That Decides Which Model Should Handle Your Request Before the Model Sees It

Jun 6, 2026

vLLM Semantic Router: The Infrastructure Layer That Decides Which Model Should Handle Your Request Before the Model Sees It

The hard problem in multi-model LLM deployments is not having good models. It is routing every request to the right model, at inference time, under simultaneous constraints on cost, privacy, latency, and safety, without building a custom decision system for each deployment scenario. vLLM Semantic Router (arXiv:2603.04444, vllm-project/semantic-router, 4.3k stars) solves this with composable signal orchestration: extract heterogeneous signals from the request, compose them through Boolean rules into deployment-specific decisions, execute through plugin chains. The same architecture expresses a cost-optimized deployment and a privacy-regulated enterprise deployment as different signal-decision configurations, without code changes.

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Gemma 4 QAT: How Google Trained the Quantization Into the Model Instead of Bolting It On After

Jun 5, 2026

Gemma 4 QAT: How Google Trained the Quantization Into the Model Instead of Bolting It On After

Quantization-Aware Training (QAT) is not a compression technique applied after the model is done. It is a training technique that makes the model learn to be quantizable. Gemma 4's QAT models, released June 5, 2026, demonstrate why this distinction matters: the 12B QAT at Q4_0 scores 67.07% on MMLU versus the BF16 baseline's 67.15%, a gap of 0.08%. Standard post-training quantization of the same model drops 2-4 points. The difference is architectural, not cosmetic.

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