Huge props to @mdowd and the @UnpromptedAU crew for an incredible con. Great sessions from @uglypackets, @ShaneHuntley, @chompie1337, @moyix, @caseyjohnellis, plus AU domestic capability, Slack D&R and TikTok’s privesc hunting. Lots to think about, looking forward to next year!

Here is Ash Fox from Google speaking about "Human in the loop, Ghost in the shell: Building AI powered adversaries"

Looking for a better local model that runs on a MacBook Air? Our latest update brings full Gemma 4 12B support to Google AI Edge Gallery app on Mac. ✨ Multimodal: Vision & Audio inputs 🎛️ Configurable vision token budgets ⚡️ MTP for blazing-fast inference 💻 Optimized to run on 16GB Macs! Download it today: ai.google.dev/edge/gallery

This might be the most beautiful thing I've read in my life. What a privilege to be alive in 2026. ordinaryabundance.com

Introducing Gemini 3.7 Flash : ) - it is fast! - 50% lower price than 3.6 flash (through end of year) - strong intelligence increase in only ~3 weeks (thanks to some awesome algorithmic improvements) - available in the API, AI Studio, Antigravity, and more!

Stop what you're doing and watch this talk.

1000+ fixes later (including one teenage bug!), here's how @googlechrome is using AI across the software bug lifecycle to make the web safer for billions: blog.google/security/chrome-… 🔎🪰✨

Introducing Gemini Robotics ER 2, our latest robotics embodied reasoning model based on Gemini. So much progress from our last ER model (and baseline Gemini 3.6 Flash). Very excited to see more robots do useful things with this model!

Gemini Robotics 2 is here, with our new suite of models, robots can now reason through every movement to manage tasks that weren’t possible before, like tying delicate knots - and even team up to solve complex workflows. Huge congrats to the robotics team on this great milestone!

The first autonomous agent cyberattack is an unprecedented event that deserves unprecedented transparency. Today we’re sharing everything we can: a full technical timeline, an interactive replay, and how we used an open model to defend ourselves, so defenders everywhere can learn from it and prepare for what’s next. huggingface.co/blog/agent-in…

Major cheat code for life: Assume good things are still ahead. You are not behind. You are not too late. You are not disqualified by your past. One new season can change the entire story. Keep showing up with belief. The best chapters are often written after the hardest ones.

A strong and secure open ecosystem is important for the world to benefit from AI. We’ve always supported and contributed heavily to open source and science from Jax to Transformers to AlphaFold to Gemma open models which have now been downloaded 300M+ times. And the standards framework we’ve proposed supports responsible deployment of both open and proprietary models.

Very happy to support this on behalf of Google. We have long benefited from open source, are big contributors to open source and in fact have consistently made open weights models with Gemma available from @GoogleDeepMind @demishassabis . Onwards!

For my first post, I’m sharing a letter @NVIDIA signed on why open models matter. AI will transform every industry, power every company, and be built by every country. Open models strengthen safety and cybersecurity, accelerate innovation and diffusion, and enable sovereignty. The world needs both frontier closed models and frontier open models. images.nvidia.com/pdf/Open-W…

Google has released Gemini 3.6 Flash and Gemini 3.5 Flash-Lite. Both halve time per task relative to their predecessors and increase token efficiency, Gemini 3.5 Flash-Lite improves by 11 Intelligence Index points while Gemini 3.6 Flash does not improve in intelligence over 3.5 Flash @GoogleDeepMind has released the latest updates to the Gemini model family with two new models. We benchmarked Gemini 3.6 Flash and Gemini 3.5 Flash-Lite ahead of release across Intelligence, Time per Task, and Cost per Task Key takeaways for Gemini 3.6 Flash (high reasoning): ➤ Maintains the same Intelligence as Gemini 3.5 Flash: Gemini 3.6 Flash scores 50 on the Artificial Analysis Intelligence Index, matching Gemini 3.5 Flash, and just below recently released models Muse Spark 1.1 (xhigh, 51) and GPT-5.6 Luna (max, 51). Compared to Gemini 3.5 Flash, Gemini 3.6 Flash maintains similar scores across the Index, with an improvement in GDPval-AA v2 (1421, +72) and a slight regression in HLE (38%, -3 points) ➤ Half the Time per Task: Gemini 3.6 Flash records an average time per task of 1.3 minutes, a more than 50% reduction compared to Gemini 3.5 Flash (2.7). This is driven by increased token efficiency and faster output, with speeds measured at 304 output tokens per second in our pre-launch testing ➤ Slightly lower Cost per Task: Gemini 3.6 Flash’s cost per task decreases ~18%, from $0.59 to $0.50. This is driven by lower output token use and new pricing of $1.50/$7.50 per 1M input/output tokens, down from $1.50/$9.00 for Gemini 3.5 Flash Key takeaways for Gemini 3.5 Flash-Lite (high reasoning): ➤ Significant Intelligence improvements over Gemini 3.1 Flash Lite: Gemini 3.5 Flash-Lite scores 36 on the Artificial Analysis Intelligence Index, up 11 points from Gemini 3.1 Flash-Lite (25). This places it behind models such as Nemotron 3 Ultra (38) and DeepSeek V4 Flash (max, 40), and above Mistral Medium 3.5 (30). The biggest intelligence gains compared to Gemini 3.1 Flash-Lite are in agentic evaluations, with improvements in GDPval-AA v2 (1140, +498), TerminalBench v2.1 (53.6, +22.5 points) and Tau3-Banking (16.5%, +7.8 points) ➤ Nearly half the Time per Task: Gemini 3.5 Flash-Lite records an average time per task of 0.6 minutes, nearly a 50% reduction compared to Gemini 3.1 Flash-Lite (1.0). This is driven by increased token efficiency and fast output speed, measured at 350 output tokens per second in our pre-launch testing ➤ More expensive with 2x Cost per Task: Gemini 3.5 Flash-Lite’s average cost per task increases from $0.04 to $0.09, driven by new pricing of $0.30/$2.50 per 1M input/output tokens, up from $0.25/$1.50 for Gemini 3.1 Flash-Lite. This cost increase comes despite using fewer output tokens, falling from 20k to 13k average output tokens per task Key model details: ➤ Context window: Both models retain the same 1M context window as their predecessors ➤ Multimodality: Both models have text, image, video, and speech input with text output only ➤ Pricing: Gemini 3.6 Flash is priced at $1.50/$7.50 per million input/output tokens, down from Gemini 3.5 Flash at $1.50/$9.00. Gemini 3.5 Flash-Lite is priced at $0.30/$2.50 per million input/output tokens, with the same input pricing across all input modalities. This is an increase from Gemini 3.1 Flash-Lite, which is priced at $0.25/$1.50 per million input/output tokens, with input audio tokens at $0.50. Both models retain the same 90% discount for cached input tokens

We’re rolling out three new models to make AI agents faster, smarter, and cheaper at scale: 🔵 Gemini 3.6 Flash: It uses fewer tokens than 3.5 Flash to deliver higher quality work at the exact same cost. 🔵 Gemini 3.5 Flash-Lite: A fast, cost-effective option for everyday tasks like processing documents and agentic search. 🔵 Gemini 3.5 Flash Cyber: A cybersecurity model built to find and patch critical software vulnerabilities.

We’re rolling out some big improvements to Gemma 4, fueled by incredible community feedback and contributions! Here is a breakdown of what’s being fixed and updated in this release: 🧵👇

Actually - no. This is why every and any sane software engineer uses 1) Utc timestamps (no daylight savings) or Epoch time (timezone independent) 2) Date libraries to render dates that take care of local conversion and pesky things like daylight savings