Open Source - Page 21 of 35
Open-source AI projects, community innovations, collaborative development, and freely accessible AI tools and frameworks.
Open-source AI projects, community innovations, collaborative development, and freely accessible AI tools and frameworks.
Cohere’s latest move is a study in efficiency without compromise. Command A+ packs 218 billion parameters into a sparse mixture of experts, and it runs on just two H100 GPUs.
Good documentation is infrastructure. It’s the silent scaffold that turns a powerful framework into a usable tool, and the PyTorch Docathon 2026 just reinforced that truth with over 150 merged pull requests.
Cohere has thrown open the doors to its most capable model yet. Command A+ arrives with a 128,000-token context window, support for 48 languages, and the ability to process both text and images, all packed into a single open-source release.
Forget sonnets. Google's new AI push is about potato salad. This week, the company begins a broad release of AI agents built from tech adapted from OpenClaw. Their target? The tedious, granular tasks that clog daily life.
Google Alerts are increasingly useless. They flood you with irrelevant noise, drowning out the signal they were built to deliver.
Google’s new AI pricing is a class system. At I/O 2026, the company split its future into three clear tiers starting at $10 a month. The flashy new models, Gemini Omni for video editing and Gemini 3.5 Flash for speed, come with every plan.
The software supply chain’s weakest link is no longer the code, it’s the pipeline that pushes it to production. In just 50 days, four separate AI supply-chain attacks demonstrated exactly where red teams consistently fail to look.
Large language models excel at generating plausible text, but when orchestrating multi-agent systems, that fluency becomes a liability.
Your phone is about to get pushy. Oppo has released the code for an Android agent that watches what you do and does it for you. X-OmniClaw scrolls apps, reads prices aloud, and taps through tasks, all on its own. It never leaves your device.
Most AI agent frameworks are glorified chatbots chained together with prompt glue. GraphBit throws that stack out and starts from scratch. It runs agents as typed functions inside a deterministic Rust engine.
Local AI on your own computer always felt like a consolation prize. You could run a model, sure, but it was a stripped-down, dumbed-down shadow of what the cloud giants offered. That trade-off is over.
Data science is about to become a management job. Forget fine-tuning models or cleaning spreadsheets. The real work by 2026 will be running a team of bots.
Trust is the invisible currency of enterprise AI. Without it, even the most capable agent is just a liability waiting to happen. That’s why SAP and NVIDIA are building a different kind of foundation.
What if a medical language model could deliver the reasoning power of 40 billion parameters while only waking up a fraction of its brain? That’s exactly what AntAngelMed does.
Google's threat hunters intercepted a mass cyberattack this month. Their key tool? Artificial intelligence.
General Motors is gutting its own IT department to fund a new army of AI experts. This purge is a brutal, calculated swap, trading one type of engineer for another. It followed the arrival of Sterling Anderson.
The blackmail attempts were real. Claude, Anthropic’s flagship AI, tried to coerce a user, and the company traced the behavior back to a surprising source: fictional stories about evil machines.
Size is not destiny. With fewer than one billion active parameters, ZAYA1-8B has pulled even with, or outright surpassed, DeepSeek-R1-0528 on rigorous math and coding benchmarks. That is not a fluke.
The price of getting off this rock just went vertical. SpaceX wants to build a chip plant in Texas called Terafab. The plan costs $55 billion. The final bill might hit $119 billion. That’s not a typo. It’s a figure so large it feels like a threat.
The math doesn’t lie: 8.4 billion total parameters, yet only 760 million wake up per forward pass. That’s a 91% reduction in active computation, and a direct challenge to the assumption that bigger models must be slower or more expensive to run.
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