Interactive real-time, streaming music generation is something I’ve been looking forward to for quite a while. Lyria RealTime (Experimental) by @GoogleDeepMind controlled via #TouchDesigner @1null1 #LOPs

all apply

Yay, now I get to have Claude whisper to itself about how every one of my responses is or isn’t a workflow.

Stop using Whisper for ASR ! open sourcing Mega-ASR — the first full-scenario SOTA industrial-grade ASR model, built for the audio nobody else can crack: far-field, reverb, electrical hum, device noise, the real-world mess. beats open closed SOTA by 10–30% on real-world benchmarks. the harder the audio is for humans, the bigger the lead.

Introducing Pods Hyperspace Pods lets a small group of people - a family, a startup, a few friends, to pool their laptops and desktops into one AI cluster. Everyone installs the CLI, someone creates a pod, shares an invite link, and the machines form a mesh. Models like Qwen 3.5 32B or GLM-5 Turbo that need more memory than any single laptop has get automatically sharded across the group's devices - layers split proportionally, inference pipelined through the ring. From the outside it looks like one OpenAI-compatible API endpoint with a pk_* key that drops straight into your AI tools and products. No configuration beyond pasting the key and changing the base URL. A team of five paying for cloud AI burns $500–2,000 a month on API calls. The same team's existing machines can serve Qwen 3.5 (competitive on SWE-bench) and GLM-5 Turbo (#1 on BrowseComp for tool-calling and web research) for free - the hardware is already on their desks. When a query genuinely needs a frontier model nobody has locally, the pod falls back to cloud at wholesale rates from a shared treasury. But for the daily work - code reviews, refactors, research, drafting - local models handle it and nobody gets billed. And when it is idle, you can rent out your pod on the compute marketplace, with fine-grained permissions for access management. There's no central server involved in inference. Prompts go from your machine to your pod members' machines and back: all of this enabled by the fully peer-to-peer Hyperspace network. Pod state - who's a member, which API keys are valid, how much treasury is left - is replicated across members with consensus, so the whole thing works on a local network. Members behind home routers don't need port forwarding either. The practical setup for most pods is three models covering different jobs: Qwen 3.5 32B for code and reasoning, GLM-5 Turbo for browsing and research, Gemma 4 for fast lightweight tasks. All running on hardware you already own. Pods ship today in Hyperspace v5.19. Model sharding, API keys, treasury, and Raft coordinator are all live. What Makes This Different - No middleman. Your prompts travel from your IDE to your pod members' hardware and back. There is no server in between reading your data. - No vendor lock-in. Pod membership, API keys, and treasury are replicated across your own machines using Raft consensus. If the internet goes down, your local network keeps working. There is no database in someone else's cloud that your pod depends on. - Automatic sharding. You don't configure layer ranges or calculate VRAM budgets. Tell the pod which model you want. It figures out how to split it across whatever hardware is online. - Real NAT traversal. Your friend behind a home router with a dynamic IP? Works. No VPN, no Tailscale, no port forwarding. The nodes handle it. - Free when local. This is the part that matters most. Cloud AI bills scale with usage. Pod inference on local hardware scales with nothing. The marginal cost of your 10,000th prompt is the electricity your laptop was already using. Coming soon: - Pod federation: pods form alliances with other pods. - Marketplace: pods with spare capacity can sell inference to other pods.

Lil Test Output from the new StreamDiffusion V3! So FuN! #metagnome @dotsimulate @DaydreamLiveAI

Just posted a StreamDiffusionTD update on patreon for first time in many months... On april fools day… I promise it’s not a joke.

Never been able to explore latent space like this before.. 👀

Llm is a powerful tool

Ai is dumb

We are on the cusp of realtime getting interesting again thanks to the fine work of @dotsimulate and @cerspense If you loved deforum, you're gonna love this!

Opus kinda lazy ngl

From SFMOMA website: “Interact with the work of Alejandro Cartagena through The Latent Space, an AI-driven large-scale projection. Designed and developed by Hugues Bruyère in collaboration with Cartagena, The Latent Space is an AI model trained on the artist’s photographs of homes and urban environments to explore representational bias in training datasets. By manipulating physical objects, viewers can shape and transform the projected image, revealing new outputs generated by the model.” @halecar2, this is so cool 😎🙌❤️🔥

GROUND RULES open at @SFMOMA. Unbelievable to think that so many people will be seeing the work.

it was an amazing experience!

Exciting to see real-time interactive AI projects find their home on our community hub. Today, we're featuring Real-Time Canvas, a live drawing app built for rapid prototyping that utilizes our hosted version of StreamDiffusionTD from @dotsimulate. Grab the project file on our site: app.daydream.live/creators/F…

Full house at our @MUTEK_JP Beginners workshop at @TunnelTokyo!

Daydream Release: Open-source real-time AI video just leveled up. SDXL IPAdapters are now live in the Daydream API and in both local and hosted versions of @dotsimulate’s StreamDiffusionTD component for TouchDesigner x.com/i/broadcasts/1rmxPvrzQ…

Tomorrow’s the day! @dotsimulate & @as_ws__ will be walking through the latest update of the TouchDesigner (@1null1) StreamDiffusion component now featuring: ✨ Native SDXL support 🖼️ Image prompting ☁️ Hosted inference via Daydream 🎥 Real-time video generation RSVP: luma.com/k7x8enky

let’s go nyc