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.
Pi is the Claude Code Killer Nobody Saw Coming...
Alan Walsh examines the critical role coding harnesses play in AI performance, comparing the feature-rich, "batteries-included" approach of tools like Claude Code with the modular, minimalist design of Pi. Learn how these competing philosophies influence agent behavior and discover strategies for building and extending custom coding environments tailored to specific development needs.
Moonshot AI, the Beijing-based artificial intelligence startup backed by Alibaba, on Thursday released Kimi K3 — a 2.8-trillion-parameter model that the company says is now the largest open-source AI model in the world, and one that benchmarks show performs neck-and-neck with the most powerful proprietary systems from Anthropic and OpenAI.
The release, timed to land just ahead of the 2026 World Artificial Intelligence Conference in Shanghai, is a dramatic escalation in the global AI arms race and a watershed moment for the open-source AI movement. It also marks a remarkable comeback for a company whose market position had eroded significantly over the past 18 months following DeepSeek's meteoric rise.
Thinking Machines Lab, the AI startup founded by former OpenAI CTO Mira Murati, released its first in-house AI model Wednesday morning, called Inkling. And unlike the flagship models from OpenAI, Anthropic, or Google, it’s open-weight, meaning outside developers and companies can download it and modify it directly.
Inkling is a mixture-of-experts system with 975 billion total parameters, though it only draws on a fraction of that — about 41 billion — for any given task, a common design that keeps very large models faster and cheaper to run. It was trained on 45 trillion tokens of text, image, audio, and video, and reasons natively across all four.
Everyone is talking about writing loops instead of prompting agents like Claude Code, Codex and Cursor. But what actually are loops and how do you write them?
In this video I break down the agent loop in simple terms so that you can start writing loops that 10x your outcomes from agents like Claude Code, Codex and Cursor - even if you're non technical.
Because loops don't just have to be for software engineering, they can also work for marketing, design, research, strategy, data analysis and much more.
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.
