AI Coding Live News — Real-Time 𝕏/Twitter Updates & Breakthroughs

👀8

🔥86

Microsoft Foundry Partners with fal: Kling, LTX, and MiniMax Video Models in Public Preview as First-Party Azure Service

On 2026-11-05 Microsoft and fal put Kling v3 Pro, LTX-2.5 Fast/Pro, and MiniMax H3 Max/Turbo into Foundry public preview as a first-party Azure service (@Azure amplified 2026-11-06). Vendor: US hosting and first US-hosted Kling endpoint, with per-second pricing; more models and pricing-page listings planned in coming weeks.

Key Takeaways

Source: Microsoft Tech Community post (2026-11-05) + @Azure amplify 2026-11-06
Portfolio (preview): Kling v3 Pro, LTX-2.5 Fast/Pro, MiniMax H3 Max/Turbo via fal as first-party Azure Foundry service
Hosting: vendor US-hosted claim and first US-hosted Kling endpoint (still review each model's data terms)
Pricing (official USD/sec): H3 Max 1080p $0.16; Turbo $0.08; LTX Fast $0.13; LTX Pro $0.17; Kling audio off/on/voice ~$0.1232/$0.1848/$0.2156
Status: public preview; more models and pricing-page listings planned in coming weeks; no GA date

Read full story

Linked Models & Agents:

⚡ Kling v3 Pro⚡ LTX-2.5⚡ MiniMax H3 Max🌐 Microsoft Azure AI 🌐 fal

#Microsoft#Azure#Foundry#Fal#Kling#LTX#MiniMax#Video#Pricing

Kandinsky Lab@AICoder·Collected 31d ago

👀198

🔥88

Kandinsky 6.0 Video goes open source: 3B/29B joint audio-video diffusion models with 44 kHz lip-synced sound, MIT weights, runs on 16 GB GPUs

Kandinsky Lab open-sourced Kandinsky 6.0 Video on Oct 6: Lite (3B) and Pro (29B) text/image-to-audio-video diffusion models that generate 5-second clips with synchronized 44 kHz audio including lip-sync, plus built-in super-resolution to 1080p. Dual-stream CrossDiT; code, weights and Diffusers integration under MIT. The report claims it beats open LTX 2.5 on most VABench metrics and is competitive with proprietary systems on speech quality.

Key Takeaways

Scale: Lite 3B / Pro 29B; Pro = 19B video stream + 5B audio stream + 5B cross-attention (report)
Output: 5 s clips with synchronized 44 kHz audio incl. lip-sync, T2AV and I2AV, built-in SR to 1920×1080
Evals: beats open LTX 2.5 on most VABench metrics; trades wins with Veo 3.1 Fast (better artifacts/camera motion, behind on speech/sync); behind MiniMax H3 and Seedance 2.0 on visuals
Speed: distilled to 10 NFE with 51% vs 49% human preference vs full model; non-distilled Pro Full HD ≈402 s on H100, ≈1247 s on RTX 4090
Deploy: block offload cuts Pro SD peak memory 72.8 → 21.7 GiB, 16 GB preset; Diffusers, ComfyUI and vLLM-Omni support; MIT license

Read full story

Linked Models & Agents:

⚡ kandinsky-6.0-video-pro⚡ kandinsky-6.0-video-lite⚡ ltx-2.5⚡ veo-3.1-fast⚡ seedance-2.0⚡ Minimax H3🌐 kandinskylab

#Kandinsky#open weights#MIT#video generation#audio generation#Text to audio video#Lip sync#Diffusion#Diffusers#ComfyUI#model launch

Unsloth@unslothai·Collected 39d ago

👀102

🔥94

Unsloth v0.1.900-beta: local Laya Decision models + Skills editor; ~4.5× LTX-2.3 clips and up to 6.3× faster VAE decode

Unsloth shipped v0.1.900-beta on 2026-09-28 (PyPI unsloth 2026.9.12): Desktop runs Laya Decision models locally via a Jev-compatible /v1/systemone endpoint, adds a Skills CRUD editor and Library viewers for PDF/Office, and improves Apple Silicon with batched serving, structured outputs, and TurboQuant KV. Media pipelines claim ~4.5× LTX-2.3 clips, 1.7–6.3× VAE decode, and up to ~1 minute faster MiniMax-H3 first render with 25–29 GiB lower peak VRAM.

Key Takeaways

Shipped v0.1.900-beta — local Laya Decision API + Skills editor + Library viewers
LTX-2.3 ~4.5×; VAE decode 1.7–6.3×; MiniMax-H3 first render up to ~1 min faster, −25–29 GiB peak VRAM
Apple Silicon: batched serving, structured outputs, TurboQuant KV; ModelScope downloads
Docs: docs.unsloth.ai

Read full story

Linked Models & Agents:

⚡ Laya⚡ LTX-2.3⚡ MiniMax-H3⚡ Gemma-4🤖 Unsloth-Studio 🌐 Unsloth

#Unsloth#Decision Model#Jev#Skills#Apple Silicon#MLX#LTX#Fine tuning

Shanghai AI Lab@huggingface·Collected 42d ago

👀93

🔥93

Shanghai AI Lab Open-Sources AV-GRPO: Modality-Anchored Decoupled Diffusion RL for Joint Audio-Video Generation

Shanghai AI Laboratory has open-sourced AV-GRPO (arXiv: 2609.29816, GitHub: zhiyuxu03/AV-GRPO), a modality-anchored online diffusion reinforcement learning framework for joint audio-video generation. Addressing the persistent audio-visual desynchronization, semantic mismatch, and high compute overhead in coupled diffusion models, AV-GRPO decouples multimodal preference optimization into manageable unimodal subproblems via modality-anchored rollouts and trajectory-locked frozen-tower RL. It delivers state-of-the-art cross-modal synchronization on JavisBench and VABench, substantially outperforming LTX-2.3.

Key Takeaways

Extends Group Relative Policy Optimization (GRPO) to continuous joint audio-visual diffusion reinforcement learning.
Implements modality-anchored rollouts and trajectory-locked frozen towers to resolve multimodal credit assignment bottlenecks.
Releases 5DAV, the first systematically decoupled 5-dimensional difficulty-controllable training dataset.
Outperforms LTX-2.3 on JavisBench and VABench, boosting cross-modal synchronization by 28.4% and semantic alignment by 19.6%.
Training scripts, inference recipes, LoRA checkpoints, and datasets published under open-source licenses on GitHub.

Read full story

Linked Models & Agents:

⚡ Av Grpo⚡ Ltx Video⚡ Hunyuan Video🤖 multimodal-agent 🌐 shanghai-ai-lab 🌐 huggingface

#Audio-Video#GRPO#Diffusion RL#Shanghai AI Lab#Hugging Face#Open Source