Ben Affleck (Hollywood star & Artists Equity CEO) talks about how he fine-tunes open video models by unfreezing weights and trained only the last cinematic layer so a film crew can hit real production standards. for context, Ben Affleck founded InterPositive in 2022, a 16-person AI shop for film post and Netflix bought it in March 2026 for $587 mn in cash. He needed that model because public video models were trained on his peers' films, and he did not think that was a real business. So InterPositive raised money, shot its own dataset for 8 months on a controlled stage, and used it only as late-stage training. Each new film then trains a private model on its own dailies, so the production keeps the footage and the learning. That is the product Netflix paid $587 million for. ---- From "Bloomberg Live" YouTube channel, (link in comment)

Ben Affleck (Hollywood star & Artists Equity CEO) talks about how he fine-tunes open video models by unfreezing weights and trained only the last cinematic layer so a film crew can hit real production standards. for context, Ben Affleck founded InterPositive in 2022, a 16-person AI shop for film post and Netflix bought it in March 2026 for $587 mn in cash. He needed that model because public video models were trained on his peers' films, and he did not think that was a real business. So InterPositive raised money, shot its own dataset for 8 months on a controlled stage, and used it only as late-stage training. Each new film then trains a private model on its own dailies, so the production keeps the footage and the learning. That is the product Netflix paid $587 million for. ---- From "Bloomberg Live" YouTube channel, (link in comment)

Ben Affleck (Hollywood star & Artists Equity CEO) talks about how he fine-tunes open video models by unfreezing weights and trained only the last cinematic layer so a film crew can hit real production standards. for context, Ben Affleck founded InterPositive in 2022, a 16-person AI shop for film post and Netflix bought it in March 2026 for $587 mn in cash. He needed that model because public video models were trained on his peers' films, and he did not think that was a real business. So InterPositive raised money, shot its own dataset for 8 months on a controlled stage, and used it only as late-stage training. Each new film then trains a private model on its own dailies, so the production keeps the footage and the learning. That is the product Netflix paid $587 million for. ---- From "Bloomberg Live" YouTube channel, (link in comment)

Introducing Griffin, the first model to pass the video Turing test. 48% of people who talked to it live thought it was a real human. Previous systems have had a pass rate <3%. It is #1 on NVIDIA's benchmark for full-duplex AI video. It’s the first Human Interaction Model (HIM).

Generate a commercial or brand video for your small business with Seedance 2.5 in Perplexity Computer. This commercial was created entirely inside Computer, including the brand look, product mockups, and full video edit.

力，起源于差异。一大一小，两个同样材质的东西，能在人脑中映射出“压迫感”。这个压迫感，就是力。把房子建在房子群落中，房子就不够有力。把房子建在旷野上，方圆百里，没有其他房子，这个房子就有力。力，就是差异。 有了力，就有了结构。一大一小，就是一种结构。荒野上的孤独的房子，就是结构。结构就是稳态的差异。海浪也有差异，但不稳定。海浪，不是结构。结构若想稳定存在，就要形成系统。系统保存着结构，结构保存着差异，差异，保存着力。 少部分人，发抖音。大部分人，刷抖音。 一少一多，就有力。 少部分人，购买奢侈品。奢侈品每年花大价钱，买最大的广告牌，让大部分人看到、得知它的贵重。 一多一少，就有力。 越有力的系统，结构上的差异越大。女人，细枝挂硕果，最有力。男人，毛发旺盛，但粉鸡巴，最有力。 结构上的差异越大，包含这个结构的系统，就越难以自持。抖音，通过算法控制，来维系力。让少部分精彩视频，被分发给大部分人。一多一少，让力释放其价值。没有推荐算法，任由内容传播，会使得精彩和平庸混为一谈，那是对力的亵渎。 越难自持的系统，就需要越强的外部能量的控制。在强力的控制下，围绕力，释放价值，价值反哺外部能量，实现自持。如同涡扇发动机，进入自持，需要外部能量的点火激励。 用草纸，画出一个二八定律下的结构，一个有力的平台，便被描绘出来。少部分人，做什么。多部分人，做什么。一多一少，一薄一厚，一尖一盾，就形成力。有力，就有价值。结构，保存力，等同于保存价值。系统，组织结构，让价值可视、可流转、可维护。 大而全，是亵渎力。小而美，是小的力。大而精，最有力。力在悬殊中，力在差异里。

We're releasing HeyGen Video, built for businesses that need production-quality video without production-level costs. Pricing starts at $0.01/s through October (50% off) Built on @Minimax_AI H3, post-trained by HeyGen. Learn more: https://developers.heygen.com/heygen-video-1.0-catalog

Introducing Boreal-H3 — a video model built for ads and our next step toward recursive self-improvement in video generation. A good-looking video isn’t enough. The product has to stay the same. The actor has to stay the same. The label has to be right. And the action in the brief actually has to happen. So we post-trained MiniMax H3 specifically for advertising. But this isn’t a one-off SFT or LoRA fine-tune. We built a closed-loop system that learns what to improve next. Human-calibrated evaluation diagnoses failures and guides the next intervention: targeted data collection, reinforcement learning, or inference optimization. When the feedback is unreliable, we revise the evaluator or reward—not just the generator. Every experiment feeds into shared memory, informing the next training decision. The model improves, and so does the process that produces its successor. The results: → 85.3% reference fidelity — highest among the frontier video generation models we evaluated → Brief success: 28% → 50% → Identity match: 83% → 94% → Visible defects per clip: down 70% → Generation time and estimated cost: down 20% Boreal-H3 doesn’t just make better-looking video. It makes more usable ads. Credit to the @MiniMax_AI team for the foundation we’re building on. This launch is a checkpoint, not the finish line. We’re building more than a better video model. We’re building a system that learns how to make the next one better.

SPARE。由 可灵 Kling 4.0 满血版生成的短片。 感谢可灵 AI 的内测邀约。本片使用 可灵 Kling 4.0 满血版全能参考生成。 满血版比 Flash 还是强不少。 - 原生 30 秒直出 - 画面与声音更清晰 - 口型匹配更精准 - 21:9 电影画幅 可灵 Kling 4.0 将于 10 月上线。 中文字幕版：

来了 @stark_nico99 @YouMind_AI ，Manus 风格的YouMind 宣传片。