PREDICTION - Google overtakes OpenAI and potentially Anthropic by year end Swapping all our Sol and Sonnet workloads with Argon. It’s just better Their next release Carbon will beat Fable and Astra. It almost seems inevitable 😲

This feels horribly like 8 Jan 2023, when something that shouldn't have taken anyone at all by surprise, took everyone by surprise.

Deepseek Flash is very hard to beat on easy tasks - ship updates every month - 10x cheaper than sonnet - handles multi modal outputs - ultra fast options will soon be available Their performance and price combination will almost become impossible to match

Modders have unlocked hidden AI compute performance on NVIDIA’s RTX 20, 30 and 40-series GPUs. The modifications remove software-level limits on certain Tensor Core operations, without changing the hardware or flashing a new VBIOS. - RTX 4090: 165.6 → 331.6 TFLOPS FP16 Tensor Core throughput -RTX 3090 Ti: 87.5 → 157.4 TFLOPS BF16 performance - RTX 2080 Ti: 59.9 → 111 TFLOPS FP16→FP32 performance - RTX 3090: 73.0 → 122.8 TFLOPS in a PyTorch GEMM test The gains are mainly relevant to AI and compute workloads, not gaming.

I love that David Bowie's and Brian May's reactions were captured when Annie Lennox gives it everything, and that George Michael singing along in the back! Fantastic video and the very best music.

Use Seedance or MiniMax as your storyboard creator. Once you have your dialogues and basic hero, environment, shots, and characters, put them in the references and make all shots one per second. Seedance will plan out the shots for later references. Stage your entire 5-minute film with one 10-second video. Then separate the video into frames. Use those frames as camera angle references so you never have spatial problems.

Jensen closed his GTC Berlin 2026 keynote with this Hermes Agent demo as the finale. He introduced it by saying the way to build an agent is to ask an agent to build it for you, so he installed and set up a customized Hermes in one prompt. Then he used Hermes, running on @nvidia RTX Spark, to create a simulated training environment for the @huggingface Microduck. Hermes planned an obstacle course with Nemotron and had GPT-6 Astra build it using FreeCAD, Omniverse libraries, Blender, Newton, and Isaac Lab. Hermes trained thousands of simulated ducks on that course until the validated policy was ready to run on the real robot in the physical world. At Nous, we believe open source is a practical tool as much as a philosophical ideal. It gives users the ability to build powerful, complex workflows chaining numerous local+cloud models, devices, and tools themselves instead of waiting for a company to enable them.

AI companies that actually making profit : - SpaceXSI - Google - Alibaba - Tencent - Xiaomi What do they all have in common? They make money in other industries and pour it into AI.

global crime rates are gonna collapse once superhumanoids like this hit the streets imagine facing this thing as a robber lol Unitree’s new "Guardian" robot can fight off a guy with a knife and patrol areas for 8 hours straight which is terrible news for criminals, because the biggest thing that stops them from committing crimes is the chance of getting caught right now in the US only about 13% of burglaries ever get solved so most burglars are making a pretty safe bet but if you put a robot on every block that: > never sleeps > records everything > and can physically stop/beat the shit out of you ...then almost no criminals would make that bet anymore on top of that, if you add the abundance that AI and robotics are likely to create then far fewer people will even get desperate enough to commit crimes in the first place, because they'll already have enough there's a good chance that our kids will think about robbery the way we think about guillotine executions

Unitree Introducing: H2-X Kung Fu Legend Guardian🤖 Powerful performance to serve and guard you, with a maximum endurance of approximately 8 hours. Please use the robot safely and in a friendly manner.

Olhando esses resultados, um GLM 5.3 flash já é suficiente pra um dia a dia de programação "normal" (os cruds, front-ends do dia a dia, pelo menos). O que segura isso é o atual enforcamento do suprimento de componentes. É só teoria da conspiração, mas RAM, NVME, GPU caseira, etc estivesse nos preços de 2 anos atrás, nós já poderíamos estar com LLMs em casa, usáveis pra trabalho mesmo. Mas isso naturalmente começa a retirar valor dos datacenters e da bolha de IPOs de trilhão. Vamos ver até quando isso se sustenta.

BREAKING DISCOVERY: Humans alternate between conscious and unconscious states 15 times per second.

anthropic made claude code 3x faster using gzip. a compression tool from 1992.

You should be fascinated hitting the power button on your computer. Every boot on an x86 machine iterates through the entire sequence of computing evolution in a few milliseconds: Stage 1: 16-bit Real Mode, acts like an 8086 from ~1978. 500ns (ish); about 15-30 instructions. Goal of this stage is to create a writeable stack+heap. Stage 2: 32-bit Protected Mode, acts like a 386 from ~1985. Tens of millions of instructions; takes forever on DDR5 /w memory training. Temporarily uses L2/L3 “cache-as-ram” to kickstart main ram (Intel calls this NEM). Stage 3: 64-bit Long Mode, acts like AMD64 from ~2003. Hundreds of millions of instructions; enumerate pcie devices, gpu, bootloader, etc. Stage 4+: Secondary core wakeup. Every *single* core has to go through the same process, but by this point it’s faster because Main RAM is warmed up. Stage 2 is just so neat man. Quite literally the same concept as an APU/cart start. Use the smaller motor (L2/L3) to kickstart the main turbine (Main Ram) and execute the training process. Is that not just the coolest thing? Next time I’m annoyed waiting at the DDR5 training process, I’m going to imagine it’s spinning up SR71 turbines.

FLUX 3 Image Klein is here. Open Weights with the quality of a large frontier model. Customizable and fast. 5.6B parameters and runs on a single consumer GPU. Native 4K, bounding boxes, and pixel-perfect editing.

If you want to know who survives AI, look at what happened to Hollywood in the 1970s. Movie Studios used to enforce tight “shooting ratios”; usually 5:1, maybe 10:1 TOPS. As in, for every 5 feet of film shot, at least 1 foot had to end up in the final cut (35mm was expensive!) In the 70s, there was a rapid elimination of production constraints. Improvements in film cost and camera portability made the old studio rules…obsolete. Why not take multiple angles, roll simultaneously? Set could have 3, 5, even 10 cameras rolling at once. Shooting ratios *exploded*, 50:1, even 100:1 (Apocalypse Now)! Of course, it also *radically* changed the workflow. Editing use to be the quiet, thankless job in the studio years (30s-50s). Suddenly, they became the most important people on the totem pole! How do you find the masterpiece hiding in the (literal miles) of film? A ton of the footage was…trash, of course. But as each individual camera because *less* important, it opened up directors to take on significantly higher risks. Now, don’t get me wrong. Insane shooting ratios were still *kinda* a niche in the 70s. More traditional directors stuck with only slightly inflated shooting ratios (15:1, 30:1 common); it was the more experimental pioneers (Coppola, Scorsese, etc) that hit the big 100:1 numbers. The huge ratios were still crazy. Until they weren’t. You can probably guess what happened in the early 2000s. Digital cameras reduced the cost of individual footage to a complete non-constraint. Effectively free, don’t even cut on set, just keep the camera rolling. Suddenly…*EVERYONE* starts shooting 100:1. Entire new forms of content get created, like Reality TV, where ratios can hit 500:1! Massive shift of human labor to the editing side. I’d say, we’re currently in the “1970s Hollywood” of AI. It can be easy (and cheap!) to produce things (code, tools, etc), but YOU have to be the ruthless editor. It’s the “miles of film” problem all over again. Hollywood solved it by throwing massively more resources on the editing side, and editing became a “constantly talking with the director about vision/direction” job, instead of a “hide in the back office and do your work” job. It’s a hunch, but I’d bet that filmmakers closer to the experimental 1970s “ratio explosion” directors handled the later 2000s digital shift *much* better, because the underlying principles were the same. They’d certainly be ahead of the curve predicting it, and (hopefully) were less surprised when the digital ratios did hit. I’m not saying AI is exactly the same problem. But it’s a good idea to look at parallels in history where rapid elimination of constraints changed an industry, and how that industry handled it 5, 10, even 50 years later! When something starts to get cheap…helps to imagine what would happen when it’s free!