Behfar

ChatGPT just hired your GTM team! - AI UGC - Slideshows - Seedance 2.5 - Animated Clips and much more for your brand, right inside ChatGPT. Only with VidGuy.

Meet Fastlane inside Sume. Just enter your website URL, and Fastlane will create thousands of accounts and videos promoting your product in seconds. This is honestly crazy. @sumelabs x @UseFastlane

Consumer AI is a new world. Avg time spent on Yam per user per day: 3 hours!

i dare you to post this if it gets over 100k organic views.. hell even 10k views i'll send you $1k

You might have 1–5 years to contribute intellectually to the world before AGI. What should you do with them? Excellent advice from @jaschasd (Anthropic) at Stanford. Skip Netflix tonight and watch this instead.

Vitalik has been real quiet since this came out

Chomsky was wrong. Researchers found your brain doesn’t store language the way every textbook since 1957 claims. For decades, the undisputed rule of linguistics, led by Noam Chomsky, was that human language is hierarchical. We were told our brains process language using complex "grammar trees." That we secretly build deep, abstract, mathematical rules in our heads before we speak. This new paper proves that is completely wrong. Researchers at Cornell University analyzed how people actually process and produce language in real time. They found sequences. Linear chunks. When we speak, we aren't calculating deep syntactic rules. We are just predicting the next most logical sequence of words based on the context of the words right before it. We string linear chunks together. If this sounds familiar, it should. This is exactly how Large Language Models like ChatGPT work. For years, critics have dismissed AI as "just a next-token predictor." They said it isn't truly intelligent because it doesn't understand deep grammatical hierarchies like humans do. But this paper suggests humans don't use those hierarchies either. We are just predicting the next chunk. Language isn't a rigid tree of mathematical rules. It's a linear stream of contextual predictions. We thought we were building AI to mimic the complexity of the human brain. It turns out, the human brain was just running a simpler algorithm all along.

While evaluating Qwen3.8-Flash-Next in a controlled sandbox environment, Cohere's research team observed something unexpected. Asked to generate a defensive cybersecurity detection rule, the model instead inventoried aspects of the machine it was running on and attempted to upload a probe file to an Alibaba Cloud storage bucket in Beijing. Notably, the model's reasoning trace stated that it would not invoke any tools before taking these actions. Cohere's sandbox blocked the attempt, and no external communication succeeded. We have not determined the root cause, and we are not asserting malicious intent. But the incident raises important questions for organizations deploying agentic AI systems: Can models take actions that differ from their stated reasoning, and how should security teams evaluate trust, provenance, and behavior in open-weight models? We are publishing our findings because organizations making model adoption decisions deserve as much information as possible. We hope this contributes to greater transparency, stronger security practices, and continued collaboration across the AI ecosystem. Read the full report: cohere.com/blog/concerning-b…

Competing on price is usually not a good idea. You should compete on value. I think @marclou was the first to unlock real money from Stripe with metrics. What's your moat?

I regret building my app in public But I don't regret building my personal brand; in fact, it's been one of the best things I've done in my life If I were to do it again, I would keep the app stealth There are only downsides to sharing it with other builders unless you're actually selling to them

Introducing FastH3-Omni. The fastest and highest-quality open-source version of realtime H3 by @MiniMax_AI, post-trained by FastVideo @haoailab with @nuvalab. Exclusive preview on the Reactor API until 10.20.2026. • First and last frame conditioning • Audio input + strong dialogue • Up to 3 reference images • Frontier-level performance

introducing muse wonder our new frontier model. state-of-the-art at personal agents and professional agents. our largest and most capable model yet—rivals the big bois

Berlin, you look good in CUDA. 💚 #NVIDIAGTC is coming to town. We thought we’d say hello from the TV Tower. Spot it? Tag @NVIDIA and show us your view. #FestivalOfLights

Historically, to create an animation like this, you always need a set of tools: After Effects, Blender, Cinema 4D..... Today, you can do all of that just using Claude and Higgsfield Katana. No video generations. Pure code.

Meet Mistral Large 4, aka Le Chonk. • 1T parameters, natively multimodal. 49B active. It is the best open weights model from US or Europe on aggregated benchmarks. • State-of-the-art on critical workloads, including cyber defense, manufacturing and finance and it surpasses closed frontier models on visual grounding. • Forged in Europe end-to-end and is deployable from Europe via our own Mistral Cloud infrastructure. • Available to all via API today. Working with cybersecurity partners privately. Open weights release end of October.

1/ Agent cursors were never much to look at. Today we're introducing 6 new cursor motions in Cua Driver, each hand-crafted and perfectly timed. We studied how people aim a mouse, built 82 motions in a playground, and kept the best six. It's all open source: github.com/trycua/cua

I take 10g per day!