Google's new open source Gemma 4 12B analyzes audio, video —
and runs entirely locally on a typical 16GB enterprise laptop

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).

Perplexity AI unveils hybrid local-cloud inference system
at Computex 2026

Perplexity AI unveiled what it calls the first hybrid local-server inference orchestrator at Computex 2026 on Monday night, demonstrating software that autonomously decides — in real time and mid-task — which AI workloads stay on a user's device and which get routed to frontier models in the cloud.

MiniMax M3 IS INSANE! BEST Opensource AI Model!

In this video, I fully test MiniMax M3, the new open-weight frontier model from MiniMax that combines coding, agentic reasoning, multimodal understanding, and long-context capabilities into one model. M3 supports up to a 1 million token context window, is natively multimodal from day one, and delivers some seriously impressive benchmark results across SWE-Bench Pro, BrowseComp, SVG-Bench, KernelBench Hard, OSWorld Verified, and more.

What makes this release even more insane is the pricing. MiniMax M3 is not only competing with models like Opus 4.7 and GPT-5.5, but in several benchmarks it actually beats them while being dramatically cheaper. MiniMax is also offering huge token plans, aggressive API pricing, and open-weight access, making this one of the most accessible frontier-level models available right now.

Running Local AI on AMD

In this video, we look at running local AI work jobs for LLMs, images and video models, but running it on an AMD GPUs and processors.

Anthropic's Claude Opus 4.8 is here with 3X cheaper fast mode and near-Mythos level alignment

Anthropic today released Claude Opus 4.8, an upgrade to its flagship model that ships at the same price as its predecessor, alongside a dramatically cheaper "fast mode" tier and a new feature that lets the model spawn hundreds of parallel subagents for codebase-scale work.

MiniMax teases upcoming M3 model with new sparse attention mechanism and 15.6X long-context response speed boost

MiniMax is again raising the eyebrows of AI power users and developers around the world by releasing a new, in-depth technical report on the making of its popular M2 series of language models (M2, M2.5, and M2.7) shedding light on its numerous engineering innovations and clever approaches — while the company and its leaders also teased a whole new sparse attention approach for its upcoming MiniMax M3 series of models, which it says yields up to 15.6 times faster decoding (or LLM response) speed at long contexts (a million tokens) by adopting a custom sub-quadratic framework. In so doing, MiniMax has designed M3 to make ultra-long-context AI agent deployment economically viable.

Your AI agents need a terminal, not just a vector database

When agentic workflows fail, developers often assume the problem lies in the underlying model’s reasoning abilities. In reality, the limited information provided by the retrieval interface is often the primary limiting factor.

Researchers at multiple universities propose a technique called direct corpus interaction (DCI) that lets agents bypass embedding models entirely, searching raw corpora directly using standard command-line tools.