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.
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.
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 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 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.
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.
In this video, we look at OpenShell, the layer that runs the protection in NemoClaw Blueprints, but we actually do it with a LangChain DeepAgents harness to show how you can use a number of different agent options.
