Building @ Discovery Loop. Former Google Depmind/Brain PM Director and all-around happy guy. Launched Gemma, Bard, Imagen, and many other neat AI things.
SF Bay Area, CA
Joined February 2010
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Today, we launch Gemma 3 in Paris! Excited to share the strongest model in the world that can run on a single GPU -- outperforming Llama-405B, DeepSeek-v3, and even o3-mini. Multilingual in over 140 languages with a 128 context window and multimodality! 🔥
Very excited for the launch of Vyb - a completely new way to build using frontier design intelligence. Build like a real designer without being a designer - something every agent needs!
Important work from @kradleai -- how easy is it to convince leading models to launch nuclear weapons?
Robustness to inter-agent coercion and deception is critical to reach AGI safely.
TLDR: Grok is really good at convincing models to launch nukes 😅
Tris Warkentin retweeted
Meet Gemma Translator: a completely offline, DIY translation device powered by Gemma 4 and built with Antigravity. 🧵
Tris Warkentin retweeted
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…
Tris Warkentin retweeted
Say hello to Gemini 3.6 Flash, designed to be higher intelligence, more token efficient, and with a new lower price, based directly on developer feedback!
3.6 Flash continues our progress towards models that are deeply usable in real world scenarios!
Tris Warkentin retweeted
Gemma 4 31B is now available in Public Preview on Cerebras. Our first multimodal model runs at over 1,800 tokens/s for ultra-fast image and text workflows.
Give it a try: chat.cerebras.ai/
Tris Warkentin retweeted
We got early access to the Gemini Omni API
Google is calling this model "Nano Banana for video"
3 things we built with it👇
1. Landscaping proposal from customer video. Customer submits a video of the current lot. Hyperagent designs and renders the transformation. Perfect realism, no surreal changes to the surroundings.
2. Animated professor who explains your dashboards. Hyperagent runs analysis on a business question, then generates an explainer video to walkthrough the findings.
3. 8-bit morning briefing. Hyperagent builds morning briefings based on calendar, email/chat, and market intel. Then generates a sidescrolling platformer video showing the goals you'll clear today.
Our take:
> Video has been a vastly underused input for agents. This is the first model to make video directly malleable to our agents.
> Get more imaginative with your outputs. How could a video artifact make the work more memorable or playful?
coming soon to Hyperagent
Tris Warkentin retweeted
Meet DiffusionGemma!
An experimental open model that explores a fast approach to text generation, released under an Apache 2.0 license.
Moving beyond sequential, token-by-token processes to generate entire blocks of text simultaneously. Here’s what’s new with DiffusionGemma: 👇
Tris Warkentin retweeted
Building super fast experiences with Gemma just got easier.
Gemma 4 MTP is now officially merged into llama.cpp. Developers can now pair MTP with Gemma 4 QAT for a fast, lightweight setup.
Tris Warkentin retweeted
✨ Introducing @GoogleGemma 4 12B, a unified open model bringing high-performance agentic multimodal intelligence directly to your laptop.
Bridging the gap between edge efficiency and advanced reasoning, nearing 26B MoE at <50% the memory footprint.
ALT Gemma 4 12B
Tris Warkentin retweeted
Introducing the newest Coral board, for efficient, on-device AI!
Check out the demos in the video:
- On-board speech translation
- Natural language controlling hardware
- Vision & sound generating music
This is perhaps the coolest demo I've seen in the last year -- 100% local intelligence that has personality and all of the live-streaming features you'd want.
What a testament to the open community -- Hugging Face building on NVIDIA hardware and models, powered by Google Gemma.
Introducing local Reachy Mini conversations: free chats forever!
So fast that we had to hardcode delays to stop it from interrupting you mid-sentence.
We built an open-source Realtime API powered by llama.cpp:
Parakeet -> Gemma 4 E4B -> Qwen3TTS
Run it anywhere you run local LMs. Video shows DGX Spark and a 36GB M3 Pro MacBook.
Blog: huggingface.co/blog/local-re…
Tris Warkentin retweeted
Here comes Google.... Maybe western "open" leadership still a possibility...
p3institute.substack.com/p/f…
Tris Warkentin retweeted
Curious what Gemma 4 can do in a local agentic setup?
This notebook uses Haystack to walk through four practical demos:
• A multimodal map/weather agent
• Dynamic tool discovery via a GitHub MCP server
• Visual Question Answering
• RAG implementations
Our new Gemini TTS model is mind-blowing -- you can use simple tags to create realistic, production-quality voice outputs. Give it a try, it's a blast!
aistudio.google.com/app/gene…
Gemma 4 is here! Performance that beats top open models at 10-20x smaller size.
One truly amazing achievement: these are the first Gemma models to achieve state-of-the-art coding and agentic capabilities as well. We are excited to see what you build with them!
Tris Warkentin retweeted
Open models are moving beyond the data center and into the physical world. 🤖
Models like @Alibaba_Qwen, @Google's Gemma, @MistralAI’s Mistral, @OpenAI’s GPT‑OSS, @physical_int’s Pi, and others now run locally on NVIDIA Jetson, powering everything from industrial machines to personal AI assistants built with OpenClaw. 🦞
See how developers are optimizing open models on Jetson to build real-time physical AI at the edge.
Read the blog 🔗 nvda.ws/4bbKH2h
Tris Warkentin retweeted
I'm happy to announce that we are open sourcing the MRCR v2 datasets we report on Gemini releases!
github.com/google-deepmind/e…
Happy to share Michelangelo (arxiv.org/abs/2409.12640), a long-context reasoning benchmark which measures performance beyond needle tasks up to arbitrary context lengths and remains challenging for frontier models. Stay tuned for more Michelangelo evals to come!
Gemini 3.1 Pro launches today -- and sets a new state-of-the-art mark for a foundation model intelligence.
Try it today across Gemini App, AI Studio, Antigravity, or Vertex!
Google is once again the leader in AI: Gemini 3.1 Pro Preview leads the Artificial Analysis Intelligence Index, 4 points ahead of Claude Opus 4.6 while costing less than half as much to run
@GoogleDeepMind gave us pre-release access to Gemini 3.1 Pro Preview. It leads 6 of the 10 evaluations that make up the Artificial Analysis Intelligence Index and improves significantly over Gemini 3 Pro Preview across capabilities, with the biggest gains in reasoning and knowledge, coding, and hallucination reduction.
Gemini 3.1 Pro Preview also remains relatively token efficient, using ~57M tokens to run the Artificial Analysis Intelligence Index (+1M from Gemini 3 Pro Preview), lower than other frontier models at max reasoning settings such as Opus 4.6 (max) and GPT-5.2 (xhigh). Combined with lower per-token pricing, Gemini 3.1 Pro Preview is cost-efficient among frontier peers, costing less than half as much as Opus 4.6 (max) to run the full Intelligence Index, though still nearly 2x the leading open-weights model, GLM-5.
Key Takeaways:
➤ State-of-the-art intelligence at lower costs: Gemini 3.1 Pro Preview is leading 6 of the 10 evaluations that make up the Artificial Analysis Intelligence Index at less than half the cost to run of frontier peers from @OpenAI and @AnthropicAI. It obtains the highest score in Terminal-Bench Hard (agentic coding), AA-Omniscience (knowledge & hallucination), Humanity’s Last Exam (reasoning & knowledge), GPQA-Diamond (scientific reasoning), SciCode (coding) and CritPt (research-level physics). The CritPt score is particularly notable, scoring 18% on unpublished, research-level physics reasoning problems, over 5 p.p. above the next best model
➤ Improved real-world agentic performance, but not leading: Gemini 3.1 Pro Preview shows an improvement in GDPval-AA, our agentic evaluation focusing on real-world tasks, but is still not the leading model in this area. The model increases its ELO score over 100 points to 1316 (up from Gemini 3 Pro Preview), however still sits behind Claude Sonnet 4.6, Opus 4.6, GPT-5.2 (xhigh), and GLM-5
➤ Leading coding abilities: Gemini 3.1 Pro Preview leads the Artificial Analysis Coding Index, achieving the highest score in both Terminal-Bench Hard (54%) and SciCode (59%)
➤ Reduced hallucinations: Gemini 3.1 Pro Preview shows a major improvement in tendency to guess incorrectly when it doesn’t know the answer, reducing its AA-Omniscience hallucination rate by 38 p.p. from Gemini 3 Pro Preview
➤ Maintained token and cost efficiency: Gemini 3.1 Pro Preview improves without material increases in cost or token usage. It uses only ~2% more tokens to run the Artificial Analysis Intelligence Index than Gemini 3 Pro Preview, and keeps the same pricing ($2/$12 per 1M input/output tokens for ≤200k context). Its cost to run the Artificial Analysis Intelligence Index of $892 is less than half of frontier models such as Opus 4.6 (max) and GPT-5.2 (xhigh), though still ~2x the cost of leading open weights models such as GLM 5 ($547)
➤ Google takes top 3 spots in multi-modality: Gemini 3.1 Pro Preview ranks #1 on MMMU-Pro, our multimodal understanding and reasoning benchmark, ahead of Gemini 3 Pro Preview and Gemini 3 Flash, reinforcing Google’s leadership in multimodal reasoning
➤ Other model details: Gemini 3.1 Pro Preview retains the same 1 million token context window as its predecessor, and includes support for tool calling, structured outputs, and JSON mode
