Google DeepMind has released Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber. Gemini 3.6 Flash is Google’s “workhorse model” that promises improved capabilities in coding, knowledge work, and multimodal performance while reducing token usage by up to 17%, making it cheaper than its predecessor 3.5 Flash.
Alphabet, Google’s parent company, is designing a new server chip to help its in-house Gemini models operate more efficiently.
The new chip, internally dubbed “Frozen v2,” is slated to be released sometime in 2028, The Information reported, citing anonymous sources. According to the report, the chip could be between six and 10 times more efficient than Google’s existing AI chips, measured by the number of tokens generated per unit of power.
A few weeks ago Google Cloud announced the Open Knowledge Format, or OKF — a vendor-neutral open standard that, in their words, "formalizes the LLM-wiki pattern" into a portable folder of markdown for handing an AI agent the curated context it needs.
I pointed Codex at my own full-stack app and asked it to walk the codebase and generate a full knowledge layer: one concept per markdown file, each one pointing at where the live code actually lives rather than copying it. Around 45 concept files, cross-linked into a browsable graph.
Google's DiffusionGemma, released this week, is an open source experimental model that applies diffusion to text generation at production scale. Built on the Gemma 4 backbone and released under the Apache 2.0 license, it is the first diffusion language model natively supported in the open source vLLM inference platform. It generates a 256-token block in parallel rather than sequentially, with every token position attending to every other. Google says DiffusionGemma generates text up to 4x faster than standard models on GPUs.
While many AI open source model providers are pursuing larger and more powerful models, Google is still giving attention to the smaller, more local side of the market. Today, the tech giant released Gemma 4 12B, an 11.95-billion-parameter open-weights model with permissive Apache 2.0 license optimized to execute locally on a standard enterprise laptop using just 16GB of VRAM or unified memory.
That means those enterprise users looking to keep working with AI while on a flight without WiFi, or trying to keep it offline for security reasons, can now do so far more easily and at far less cost (free to download and operate).
Google basically split Antigravity into multiple apps and the internet is LOWKEY crashing out trying to understand what happened.
This honestly feels like Google’s direct answer to tools like Claude Code, Codex, OpenAI Agents, and the entire rise of autonomous AI workflows.
At its Google I/O conference on Tuesday, Google unveiled an AI-powered overhaul of Search centered around a reimagined “intelligent search box” — what the company describes as the biggest change to this entry point to the web since the search box debuted more than 25 years ago.
Google announced a number of new Gemini Intelligence-branded AI features at its Android Show: I/O Edition event on Tuesday. These include the ability for AI to complete tasks across apps, browse the web, fill out forms, dictate speech, and even allow you to vibe-code your own Android widgets.
Google launched its Gemma 4 open models this spring, promising a new level of power and performance for local AI. Google’s take on edge AI could be getting even faster already with the release of Multi-Token Prediction (MTP) drafters for Gemma. Google says these experimental models leverage a form of speculative decoding to take a guess at future tokens, which can speed up generation compared to the way models generate tokens on their own.
Cirrascale Cloud Services today announced it has expanded its partnership with Google Cloud to deliver the Gemini model on premises through Google Distributed Cloud, making it the first neocloud provider to offer Google's most advanced AI model as a fully private, disconnected appliance.
Gemma 4 arrives as four distinct models organized into two deployment tiers. The "workstation" tier includes a 31B-parameter dense model and a 26B A4B Mixture-of-Experts model — both supporting text and image input with 256K-token context windows. The "edge" tier consists of the E2B and E4B, compact models designed for phones, embedded devices, and laptops, supporting text, image, and audio with 128K-token context windows.
Google Research recently revealed TurboQuant, a compression algorithm that reduces the memory footprint of large language models (LLMs) while also boosting speed and maintaining accuracy.
TurboQuant is aimed at reducing the size of the key-value cache, which Google likens to a “digital cheat sheet” that stores important information so it doesn’t have to be recomputed.
In this video, we go through Google's update to the Stitch app and look at the new features that it's added, like the Design.md files and the ability to replicate the theme of various sites.
Antigravity AgentKit 2.0 just dropped and it’s a massive upgrade for AI developers and vibe coders. With the new Agent Skills system, support for AGENTS.md, and a powerful collection of agents, templates, and workflows, Antigravity is pushing autonomous coding to the next level.
Google Workspace — the umbrella term for Google's suite of enterprise cloud apps including Drive, Gmail, Calendar, Sheets, Docs, Chat, Admin — is moving into that pattern with a new CLI that lets them access these applications and the data within them directly, without relying on third-party connectors.
The project, googleworkspace/cli, describes itself as “one CLI for all of Google Workspace — built for humans and AI agents,” with structured JSON output and agent-oriented workflows included.
Google's newest AI model is here: Gemini 3.1 Flash-Lite, and the biggest improvements this time around come in cost and speed, especially for enterprises and developers seeking to leverage powerful reasoning and multimodal capabilities from the U.S. search and cloud giant.
Positioning it as the most cost-efficient and responsive model in the Gemini 3 series, Google is offering a solution built specifically for intelligence at scale.
In this video, we look at the recent updates to Google's Opal agent system. How it's now set up to take advantage of the Gemini 3 models and how you can use it to build simple apps and agents
Google just dropped the new Antigravity AI Studio update, and it’s transforming the way we develop AI code! In this video, I break down all the latest features, show real examples of building AI applications faster than ever, and explain how this release could change the AI coding landscape forever.
Late last year, Google briefly took the crown for most powerful AI model in the world with the launch of Gemini 3 Pro — only to be surpassed within weeks by OpenAI and Anthropic releasing new models, s is common in the fiercely competitive AI race.
Now Google is back to retake the throne with an updated version of that flagship model: Gemini 3.1 Pro, positioned as a smarter baseline for tasks where a simple response is insufficient—targeting science, research, and engineering workflows that demand deep planning and synthesis.
Browser agents are broken. Not because the models are bad, but because the entire internet was built for human eyes, not machines. WebMCP changes the approach entirely: instead of making agents better at reading websites, it makes websites better at talking to agents.
