Fal优化MiniMax H3实现实时视频生成，被Twitch封禁后自建直播服务

For the entirety of the history of Generative Media, you basically had to design around the inconvenient fact that generating images and video takes time — even if you used consistency models to get a 30 second generation down to 1 second, you still only have a 1 FPS video at best… well below anything acceptable for consumer-grade human attention.

Fal took Minimax’s H3 release from last month and first posttrained it for both cost and quality improvement, then optimized it for their in-house inference engine for 35x speed of the official endpoint… resulting in crossing the infinite video singularity:

This was first noticed by Ethan Mollick:

Then productized by fal employees into an infinite twitch stream:

and then the floodgates opened:

with Twitch/Youtube kicking Fal off the platform immediately, so Fal made their own “twitch plays pokemon” live video service:

If you watch the stream for even a few seconds, you can tell this is pure slop - nobody will actually watch this fever dream mishmash of content with no plot and low quality RL tuned imagery.

And yet… this is the worst that this is ever gong to be. If you have not learned the lesson that the best engineers and entrepreneurs build for the future that is coming, and the existence proof of faster-than-realtime good-enough video is defeinitely possible, then you aren’t reading the room very well in the metagame of how to stay ahead in AI.

AI News for 8/29/2026-8/31/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!

AI Twitter Recap

Model Releases, Agent Benchmarks, and Open-Weight Competition

• Meta’s Muse Code exits beta with an SDK and subscriptions: Meta pushed Muse Code into general availability, positioning it as a bigger-task coding agent with a developer-preview SDK for embedding custom agents, connecting tools, streaming progress, and resuming sessions. Launch details came from @finkd, with follow-ups on the SDK and monthly plans; @alexandr_wang amplified the release. Separately, Ollama said it already supports the Muse Code harness.
• DeepSeek V4 Flash Vision weights are now open: Several posts pointed to the release of DeepSeek-V4-Flash-Vision-Exp weights, with @teortaxesTex noting the model adds vision parity with Moonshot and GLM, and @zizhpan linking the weights directly. The follow-up from @teortaxesTex suggested DeepSeek may be committing to releasing all checkpoints.
• GLM-5.3 Flash looks especially strong on agentic cost/performance: On Agent Arena, @arena reported GLM-5.3-Flash at #19 overall, #4 among open models, with +4.6% net improvement over 9K+ real-world sessions and a $0.12 median cost/task. Signal breakdown included +15.3% Confirmed Success and no tool hallucination issues in the thread. Vals also highlighted the broader GLM-5.3 family, including 95.4% on SWE-bench, 78.1% on Vibe Code Bench, 1M context, and 128k max output tokens in benchmark notes.
• Qwen3.8-Flash-Next enters the same arena, but below GLM-5.3 Flash: @arena placed Qwen3.8-Flash-Next at #24 overall, #7 among open models, with +2.4% net improvement across 8.7K+ sessions. It stood out more on Confirmed Success (+12.3%) than on steerability or praise-vs-complaint, according to the signal breakdown.
• Tencent Hunyuan’s Hy4 Preview appears to be moving into China’s top agent tier: A long-form roundup from @ZhihuFrontier described Hy4 Preview as an open-source 770B MoE model with 49B active params and >1M context, emphasizing gains in coding, agent stability, and practical office/research use. The notable engineering claim is not just capability but organizational acceleration: seven weeks after Hy3, Tencent allegedly closed much of the gap through post-training, agent-policy tuning, and better stability.

Agent Infrastructure, Harnesses, and Context Engineering

• Hermes Agent shipped a large feature release aimed at persistent, multi-agent workflows: @Teknium announced Hermes Agent v0.21.0 with Bots Mode, agent-to-agent comms, persistent multi-gateway connections, subagent steering, and broader connector access. A follow-up noted the release also cut default context usage by ~50%, a concrete sign that context-efficiency is becoming a first-class systems concern.
• DeepSeek Harness is evolving fast, but with breaking plugin-contract changes: The best summary came via @ZhihuFrontier: v0.1.2-alpha removes the legacy APIProxy, rewrites the web client, tightens session-event semantics, and expands subagent/model configuration. The key engineering takeaway is that plugin-heavy agent platforms are still defining their public boundaries; DOM injection, internal symbols, and custom session event types are proving especially brittle under rapid iteration.
• Context management is emerging as a distinct research frontier: Two papers got attention. First, WikiSkill / SKILL.state from Google and collaborators, summarized by @dair_ai and @omarsar0, replaces ever-growing conversation histories with explicit mutable state and persistent skill knowledge; the reported result is better long-horizon accuracy with lower cumulative token use. Second, Tencent’s ContextPilot, highlighted by @omarsar0, trains agents to edit their own working context and assigns reward at the level of specific context edits, a more targeted RL credit-assignment scheme for long-horizon tasks.
• “Harness engineering” is becoming a core AI engineering skill: This theme showed up repeatedly: @omarsar0 explicitly called out harness engineering alongside evals; @dejavucoder framed non-vibe coding as increasingly about watching traces and feeding RL environments; and @AlexatVester asked who will build an open-source Codex-style in-app browser for agents.
• Code-navigation and observability tooling continues to get more agent-native: @TheTuringPost highlighted Sonar Vortex, which gives agents a semantic graph of code relationships and reportedly cuts task cost by 5–36% versus text-search-heavy workflows. On the observability side, @wandb added live W&B panels directly into CoreWeave ARIA chats, and @hwchase17 emphasized trace-level cost reconciliation over coarse spend totals.

Inference, Compute, and AI Infrastructure

• Apple hardware may be an unexpected bottleneck for computer-use RL: The most-discussed infra anecdote came from @VaibhavSisinty, who claimed OpenAI bought tens of thousands of Mac minis and Mac Studios for training computer-use agents via RL, while Anthropic rents similar hardware through AWS. The reported consequences: high-RAM Apple configs disappearing from sale, long backorders, and scalping. If accurate, it’s a notable datapoint that desktop-class Apple silicon has become operationally relevant for agent training loops, not just local inference.
• Together AI and HUMAIN announced a 250MW Saudi data center for open models: @nikogallogly surfaced the NYT scoop, and @togethercompute framed it as one of the largest open-source-focused infra deals, with 250MW capacity and $5B+ annualized revenue attached to the partnership. The story matters less for the headline number than for the strategic pattern: compute access via geopolitical partnership, rather than every model company vertically financing its own capex.
• Inference specialization and serving architecture continue to fragment: @SemiAnalysis_ outlined three disaggregated inference configurations pairing Rubin and LPU components across prefill, decode, verification, and FFN paths. Meanwhile, @StasBekman highlighted Snowflake’s Semi-Persistence approach for multi-model serving, keeping weights in pinned CPU memory and rehydrating them to GPU on demand, with internal benchmarks showing 5.6x–19.9x faster sleep/wake cycles versus the compared vLLM baseline.