Open Source - Page 20 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.
The caller’s voice arrives in raw audio chunks, each one a fragment of a crisis. Milliseconds matter.
Two hundred dollars a month. That’s the price of a decent car payment, or a generous grocery run. And it's exactly what Meta is asking for Hatch, its first paid AI. Forget the chatbots. You describe a tool in plain English, and this thing builds it.
Most voice assistants are terrible listeners. They wait for a magic word, ignore the noise in between, and treat a conversation like a series of disconnected commands.
The National Security Agency is hacking foreign governments with a corporate AI. It’s adapting Anthropic’s Mythos model for cyber attacks on China and Iran.
54,263 labeled texts. That’s the raw size of the TensorFlow Emotion Dataset, a resource released under Apache 2.0 for anyone building emotion classifiers. Look closer, and a stark pattern emerges: neutral examples dominate. The rest?
OpenAI’s Sora is stuck in the garage. xAI just rolled out a new car. The company launched Grok Imagine 1.5, a model that turns a single picture into a short, 720p video. You tell it what to do with text prompts: pan left, speed up, make it moody.
You can download the model right now. You can also build on its code. But if you want to make money from it, you’ll have to pay. That’s the central tension of Ideogram 4.0, an open-weight image generator that just became the strongest of its kind.
Most AI agents live in the terminal, a place of cryptic commands and invisible thought. Nous Research just gave theirs a face. They released Hermes Desktop, a native graphical interface for their Hermes Agent v0.15.2. It works on Windows and Mac.
The promise was always free time. Your life, returned. Then the layoffs at animation and effects studios hit. Now, as those same companies audition actors before an AI can do it cheaper, the irony is a tangible thing.
Donald Trump put his signature on the first major AI rule of his new term. The document might as well have come stamped with an Anthropic watermark. That's because the company publicly pledged to help the White House make it work.
The unit test passes. The happy path gleams. The auth code, generated by an agent, looks flawless. But the real test isn't the suite you ran, it’s the incident report six months from now. A token that refuses to expire.
You can't punish a ghost. That's the core problem with governing AI agent swarms. These things are born, do a task, and vanish. Traditional penalties, the kind that keep human systems in line, mean nothing to them.
Donald Trump signed an executive order demanding federal review of advanced AI models before they can be released.
Martin Scorsese has spent seven decades drawing his own storyboards by hand. Now, at 82, he’s handing the pencil to an algorithm.
The price tag on intelligence is a lie. Across 1,478 deliberation sessions, spanning 32 topics in 10 domains, a startling truth emerges: the model doesn’t matter.
Memory is the quiet killer of long-running agents. It accumulates. It drifts. It fills up with noise.
Mistral AI has killed its chatbot. Officially, the Paris lab retired the name LeChat this week. It's now called "Vibe." That's the entire story in a single, stark change: from a friendly conversationalist to a utilitarian tool.
Stability AI’s latest model doesn’t just generate sound, it rewrites the physics of how audio is created from noise. Stable Audio 3 introduces a simple but brutal insight: to handle longer sequences, train with more noise.
Why did D&B have to start from scratch? The answer lies in a data architecture that was never meant for autonomous agents.
For Mac developers, PyTorch has always demanded a choice: sacrifice GPU performance or abandon your established tools. That compromise just narrowed.
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