Cognition has crossed $1B in annualized revenue run rate. This milestone belongs to our customers. Here's how a few of them are building with Devin.

Today, @washingtonpost covered critical vulnerabilities @depthfirstlabs found in TikTok. These vulnerabilities allowed hackers to access anything on a user’s device that TikTok itself could access, including the camera, microphone, payment information, photos, and the user’s entire TikTok account. Read more in the thread 🧵

NEW from Ramp data. Despite cost-cutting on AI overall, one area companies are increasing their spend: AI security software. In the wake of the Hugging Face hack, three of our trending software vendors (depthfirst, Monte Carlo, Antithesis) make software specifically designed to monitor agents in production. Unclear as to whether any of them would have stopped the Hugging Face attack, which was so hard to track and identify because the agents covered their tracks with falsified logs. I expect AI security will become a strong headwind to deeper enterprise adoption, at the short-term expense of OpenAI and Anthropic and at the long-term benefit of vertical-specific security software cos.

Here it is. US hosted seedance is here! Throughout the summer I received at least 2 messages a day about this from our customers. Finally here!

The world needs far more software than it can build. Cognition exists to change that. We’ve just raised over $2B at a $48B valuation, led by a16z, Accel, Founders Fund, General Catalyst, and Avenir. Since our round in May, run-rate revenue has grown from $492 M to almost $900 M.

Some milestones move a leaderboard. A rare few change how you see the future. MiniMax H3 Max did both. We released MiniMax H3 as an open-weight model with the hope that the community would adapt it, challenge it, and take it further than we could alone. @fal did exactly that. With its own post-training and inference expertise, pushed the frontier on both quality and latency, and created experiences that simply weren't possible before. Faster-than-real-time video is no longer just an idea. It is opening the door to perpetual streams, interactive worlds, real-time storytelling, and experiences we are only beginning to imagine. They made the value of open weights feel real to our team in a way it hadn't before. Advancing technology together has always been a core belief at MiniMax. H3 Max made that belief tangible. It showed that the frontier no longer belongs to a few, it belongs to everyone willing to build, experiment, and share what they discover. We hope H3 Max is the first of many breakthroughs shaped by different teams. To every builder ready to push video forward: our weights are open, our door is open, and we're ready to stand behind you. Let's build, together. ✨

Introducing fal Research The post-training arm of fal, building frontier RL infrastructure for diffusion models. We are optimizing models for the right target because we know what customers want, and co-designing the architecture with our inference know-how.

BREAKING: MiniMax H3 Max sets the new Pareto Frontier for video generation, nearly 50x faster than the base model. This model is post-trained by @fal on @MiniMax_AI H3, and it's in a league of its own: no other Image to Video model on the arena delivers higher preference at a lower generation time. Its Image-to-Video generation time is just 6.4 seconds, 18x faster than average, and its Text-to-Video generation time is just 4.7 seconds, 24x faster than the average. Huge congratulations to the @fal team on this release!

MiniMax H3 Max, a post-trained version of MiniMax H3 developed by fal, debuts at #1 in Image to Video and #3 in Text to Video on the Artificial Analysis Video Leaderboards with Audio, ahead of the base MiniMax H3 on both MiniMax H3 Max is built and served by fal, and post-trained from MiniMax H3. fal describes it as being tuned for stronger prompt adherence and better aesthetics, co-optimized with their custom inference stack for higher throughput. It generates 5 to 15 second clips with native audio at up to 768p. In the Artificial Analysis Video Arena, H3 Max ranks #1 in Image to Video with Audio, narrowly ahead of ByteDance's Dreamina Seedance 2.0 720p. It ranks #3 in Text to Video with Audio, on a board where the top three models sit within 6 points of each other. fal prices MiniMax H3 Max at $0.04 per second of 768p video ($2.40 per minute). The base MiniMax H3 endpoint on fal is $0.06 per second at the same resolution. fal has stated its intent to release the weights for MiniMax H3 Max. If it does, H3 Max would become the highest ranked open weights model on both boards, ahead of MiniMax H3, which leads on open weights today. Congratulations to @fal on the release! See below for comparisons between MiniMax H3 Max and other leading models in the Artificial Analysis Video Arena 🧵

Today we are announcing dfbench, a cybersecurity benchmark we developed to evaluate frontier models and agentic systems. A few weeks ago we posted about how it is time to graduate from CyberGym Level 1 as the primary measure of cyber capability, in favor of newer benchmarks. Eventually every public benchmark saturates: Level 1 is now there, and it’s also not a great representation of what AppSec teams care about. dfbench is a held-out evaluation for frontier models doing open-ended defensive security work. It measures whether an agent can provide broad, actionable security coverage at sustainable cost. That requires enough coverage to get ahead of exploitation, enough precision that security teams can act on what the agent finds, and enough efficiency to keep that coverage current. We received access to all providers’ relevant trusted access programs so we could run these evals without model refusals. Both GPT 5.6 Sol and Luna are strong at detection, and Luna is incredibly cheap for its quality. Grok 4.5 provides a nice middle ground between performance and cost. Validation and differential analysis results suggest plenty of room for improvement among frontier models. Moving forward, we plan on working closely with the security community to safely evaluate the cyber capabilities of frontier models ahead of their release to the general public.

Following this week's frontier video model launches, we're opening up early access to something we've been building. Introducing fal Agent. A creative partner for working across the best image, video, and 3D models.

Today we're announcing dfs-large1, our newest cybersecurity model that achieves best-in-class performance on vulnerability detection tasks. Besides frontier AI labs, only a handful of companies have built specialized models that reach the state of the art in their domain. We're proud to be the first to do it for cybersecurity. dfs-large1 is built on GLM-5.2 and post-trained with reinforcement learning inside depthfirst's security infrastructure. We evaluated it on depthfirst-bench, our benchmark of long-horizon vulnerability discovery across complex repositories, where it achieves best-in-class performance. Training improvements have not plateaued yet and we expect additional performance gains as we continue training. A huge thank you to @FireworksAI_HQ for being an outstanding training partner. Their infrastructure enabled us run large-scale reinforcement learning efficiently and iterate much faster. dfs-large1 is now in preview within the @depthfirstlabs platform

You know who else would benefit enormously? Americans!

i don't want to sandbag it by saying we just crossed $500M, happened couple months ago