Sarvam built two models for two very different jobs. The 30B is a deployment model. It was designed to run fast, stay cheap, and handle real-time interactions without breaking a sweat. If you need an AI that can take a phone call in Hindi, understand a tool request mid-conversation, and respond before the user notices a delay, that's what the 30B was built for.

Meta has a new AI model and for the first time in years it is not called Llama. Muse Spark launched yesterday under Meta Superintelligence Labs, a new internal division Meta quietly formed by bringing together researchers from Google DeepMind and other frontier labs. It is natively multimodal, supports multi-agent reasoning, and is available right now at meta.ai. It is also not being released as open weights. That last part is worth sitting with for a second. Meta built one of the most trusted brands in open source AI through Llama. Developers built on it, researchers published with it. Muse Spark continues none of that. No weights, no HuggingFace release, private API preview only. What you get instead is a genuinely capable multimodal model with some benchmark numbers that are hard to ignore and a new reasoning mode called Contemplating that puts it in conversation with Gemini Deep Think and GPT Pro. Whether that trade is worth it depends entirely on what you were using Meta AI for in the first place.

Windows can be shaped, refined, and extended far beyond its default look and behavior. Over time, small details start to matter. How the taskbar feels. How quickly you can preview files. Whether the interface feels calm or busy when you sit down to work. These things do not show up on spec sheets, but they strongly influence how enjoyable a system feels day to day. With a few carefully chosen tools, Windows can become cleaner, more visually consistent, and noticeably more pleasant to use without changing how you work or forcing unnecessary complexity.

Jensen Huang walked onto the GTC stage and said something that did not sound like a chip announcement. He called Vera Rubin "the greatest infrastructure buildout in history." That is a bold claim even for NVIDIA. But when you look at what Vera Rubin actually is the ambition makes more sense. This is not a faster GPU. It is seven chips designed to work together as one supercomputer, built specifically for a world where AI does not just answer questions but plans, executes, and runs continuously without stopping. Every GPU you have used until now was designed for training massive models or answering queries fast. Neither of those is the same as running an agent that plans, executes tools, checks its own work and keeps going for hours. Current infrastructure was simply never designed for that workload. Vera Rubin is NVIDIA's answer to that problem.

Most text-to-speech tools fall into two camps. The ones that sound good need serious hardware. The ones that run on anything sound robotic. MOSS-TTS-Nano is trying to be neither. It's a 100 million parameter model that runs on a regular CPU and it actually sounds good. Good enough that the team behind it built an entire family of speech models around the same core technology, one of which has gone head to head with Gemini 2.5 Pro and ElevenLabs and come out ahead on speaker similarity. It just dropped on April 10th and it's the newest addition to the MOSS-TTS family, a collection of five open source speech models from MOSI.AI and the OpenMOSS team. The family doesn't just cover lightweight local deployment. One of its models MOSS-TTSD outperforms Gemini 2.5 Pro and ElevenLabs on speaker similarity in benchmarks. Another generates voices purely from text descriptions with no reference audio needed. And one is built specifically for real-time voice agents with a 180ms first-byte latency. Nano is the entry point. The family is the story.

AI tools that respect no cloud uploads, no delays, no compromise & completely offline & Open Source. They run directly on your device, keeping your data safe, your workflow lightning-fast, and your experience completely under your control. Most of the tools listed here are cross-platform, compatible with Windows, macOS, and Linux, and come with direct installer files for easy setup or easy installation steps.

If you have been looking for open source tools to work with video using AI you have probably noticed something. Most of what gets covered is generation like creating new videos from scratch. The editing side, actually modifying existing footage with AI, has been much quieter. That is starting to change. There are now open source models that can swap outfits, replace backgrounds, remove objects, change characters and apply styles to existing video using plain text instructions. Some are built specifically for editing. Others are generation models that fit naturally into a creative video workflow. This list covers both honestly. Three models built specifically for video editing and two generation models worth knowing about if you are working with video content. All open source, all available today.