14 articles tagged with this topic
Google Lets Chrome Run AI Models Directly — The Browser is Becoming the New OS
Google opens Prompt API: web apps call built-in Gemini Nano in Chrome—no servers or API keys. It shifts inference on-device, making AI a native browse
Google Multi-Agent Speeds Code Migration 6x: From Functions to Engineering
Google multi-AI agents accelerate TensorFlow to JAX migration 6x. AI proves it can handle systemic engineering tasks taking months of manual labor.
Chrome Silently Installs 4GB AI Model: Google Races Ahead in Local AI via Browser
Chrome silently installs a ~4GB local AI model without consent. Browsers are becoming AI runtimes—distribution rights now matter more than the models.
Google Doubles Gemma 4 Speed — Speculative Decoding Goes Mainstream
Google's Gemma 4 MTP models use speculative decoding for up to 2x speed with zero quality loss, boosting local LLM practicality and lowering compute b
Google Gemma 4 Fixes Chat Template — Local LLM Usability Inches Forward
Google fixed Gemma 4's chat template bug; community quantized versions updated. Not major news, but proves local AI usability inches up via detail ref
7 Years of Transformer Dominance: LLM Architecture Awaits the Next Reshuffle
Transformer underpins LLMs via self-attention, fixing old algorithms' parallel and long-context flaws. Grasping it reveals LLM capability limits and b
Gemma 4 Per-Layer Embeds: Knowledge-Reasoning Split, Hope or Hype
Gemma 4's per-layer embeddings spark debate: Can knowledge and reasoning scale separately? If so, 2B models could hold 20B knowledge, redefining local
Transformer: 7 Years, 120K Citations—Key to the LLM Race
Google's 2017 Transformer is the LLM bedrock, replacing RNNs with parallel attention. Grasping it reveals who takes shortcuts in the LLM race.
Gemma 4 Hits HuggingFace — Open Source Outpaces Official Toolchain
gemma-4-31B-it-DFlash on HuggingFace lacks llama.cpp support. We see models outpacing toolchains—having models you can't run is the new paradox.
Decade of Seq2Seq: The True Technical Starting Point of LLMs
Google's 2014 Seq2Seq architecture is the shared technical foundation of LLMs like GPT and BERT. Understanding its encoder-decoder division and info b
Google Lets AI Recompose Your Photos After the Shot
Google Research demos AI that re frames photos post -capture — shifting the " fr aming decision" from photographer to algorithm.
Google Engineers Want One Ruleset for Production - Ready AI Code — Harder Than It Sounds
Google engineers are tac kling why AI- generated code rarely ships to production, and the fix is more complex than expected .
Gemma 4 Has Hidden MTP Heads Disabled by Google at Launch
A developer found multi-token prediction weights inside Gemma 4's LiteRT files; Google confirmed MTP exists but was intentionally disabled.
Gemma 4 llama.cpp Issues Resolved With Recent Fixes
Google Gemma 4 models now run correctly in llama.cpp after critical fixes for output quality and crashes