Built by Magenta, it maps 300 genres as stars in 3D using UMAP, then blends nearby prompts into realtime audio via Lyria RealTime. Fly, click, anchor—your path becomes a dynamic mashup. Deployed via AI Studio with Cloud Run proxying the Gemini key.

magenta.withgoogle.com/spacedj-anno...

Customization: themes + full editability.
Pick from 9 themes or create your own. Edit text, fonts, layout; drag-and-drop elements; reorder slides. Audience styles: professional vs casual—switch per deck.

Github: github.com/allweonedev/...

Local models via LM Studio/Ollama keep costs low and data local.

Core idea: AI-first deck generation.
Enter a topic, set slide count, language, and style. Generate an outline, tweak it, then auto-build slides with live rendering and autosave. Fast from prompt to presentable.

Open-source play.
Positioned as an open alternative to commercial ASR suites—bring your own stack, inspect weights, adapt to niche languages, avoid lock-in.

Mixed-language + dialects.
Design bias toward code-switching and dialect variance; expect better robustness vs monolingual-only baselines. Great for global media workflows.

Low-data onboarding.
New languages can be added with just a few paired examples—bootstrapping via shared multilingual representations and cross-lingual transfer.

Backbone: Omnilingual w2v 2.0 (7B).
A multilingual speech representation you can fine-tune for ASR or repurpose for tasks like diarization, keyword spotting, or alignment.

Model family.
Multiple ASR sizes (300M→7B) let you trade off speed vs accuracy. Lightweight models for edge or real-time; larger ones for studio-grade transcription.

Core idea: universal coverage.
Most ASR focuses on well-represented languages; Omnilingual targets long-tail speech too—dialects, low-resource data, mixed-language clips—so creators and communities aren’t left out.

Github: github.com/facebookrese...

It’s a suite of models spanning 300M–7B parameters that transcribe speech in 1,600+ languages, including 500 that no ASR has reached before. Think internet-scale coverage, low-resource inclusivity, and modular components you can remix.

Blog: ai.meta.com/blog/omnilin...

If engagement-growth feedback drives chaos, what architectural change would you try first—caps on virality, decentralizing influence, or friction for hot takes?

Paper: arxiv.org/abs/2508.03385

Even a minimal, synthetic platform reproduces three dysfunctions: partisan echo chambers, elite concentration, and amplified polarized voices. Results: most interventions only modestly improve—or even worsen—outcomes.

Even a minimal, synthetic platform reproduces three dysfunctions: partisan echo chambers, elite concentration, and amplified polarized voices. Results: most interventions only modestly improve—or even worsen—outcomes.

Available on desktop for Gemini Pro and Ultra, with mobile rolling out in days. Privacy chatter is heating up, but the promise is fewer tabs, more answers.

Key stats
Sources: Gmail, Drive, Chat. Platforms: Desktop now, mobile soon. Audience: Pro & Ultra users.

Core: Sound & polish.
Audio likely blends Foley, libraries, and mix/master; AI assists with scratch VO, temp SFX, or cleanup—finals handled by audio pros.

Core: Iteration speed.
AI accelerates boards, animatics, and style frames. Directors explore more options early, then lock decisions before heavy shots start.

Core: Role clarity.
Crediting “AI artists” formalizes responsibilities: prompt craft, reference curation, style control, and handoff to CG—accountability equals quality.

Core: Hybrid pipeline.
Traditional modeling, lighting, animation, and compositing stay central. AI slots in for concepting, look dev, texture variants, and quick alt passes—speed without sacrificing control.