nvidia just backed an indian startup that helps companies run AI with fewer of nvidia’s own GPUs it’s called simplismart, bengaluru-based, $14 million series B with accel. valued at around $100 million their software sits between AI models and the computing infrastructure, and automatically tunes how models are deployed so companies get the right balance of speed, cost, and performance without manually managing it invideo cut their GPU serving costs by 56% using it. revenue grew 14x in six months. more than half comes from the US the obvious question is why nvidia would back software that reduces demand for nvidia chips the founders’ answer: making AI inference cheaper and easier gets more companies to adopt AI in the first place, which ultimately sells more GPUs nvidia is also working with simplismart on technical integrations with its own Dynamo inference software, introducing them to customers and partners so nvidia isn’t just investing, it’s embedding itself into a startup that competes in the same space as nvidia’s own inference products either nvidia genuinely believes efficiency drives more adoption, or they’d rather own the efficiency play than watch someone else take it

Peter Steinberger, the creator of OpenClaw, estimates he spent around $10M in tokens last month. At that rate, it's $120M a year on one person. That's enough to pay 500 senior engineers for a year. His employer, OpenAI, covers the bill. He also admitted not all of that spend was useful. So when someone tells you "AI is cheaper than hiring", ask what the tokens actually bought.

AI art is not ART !! BOOOOOOOO i like it, reminds me of my nvidia

I think the whole space x ecosystem is amazing. Something I like doing is liking tweets I see on here that have to do with AI and when in my Cybertruck. I have Grok read out my liked tweets and to explain them to me while in self drive.

Bullshot just announced $7.7M raised across funding rounds, with backing from @animocabrands and @PolygonVentures, also raising $1M with @MindoAI The intersection of token creation, trading and AI agents makes @Bullshot911 a project worth watching👀

HOLY F*CK, ELON MUSK CREATED GROK BOT THAT REPLACED 28+ PAID TOOLS it runs on its own cloud computer, signs into your apps and runs routines on a schedule. each bot can hold up to 50 routines. what it replaces: → ai agents: chatgpt plus/pro, manus, perplexity pro, genspark, devin, lindy → automation: zapier, make, n8n cloud, bardeen, phantombuster, apify/browse ai → social: hootsuite, buffer, sprout social, typefully, hypefury → sales: apollo io, clay, lemlist/instantly, salesloft → operations: superhuman, motion/reclaim, otter/fireflies, intercom fin/zendesk ai, feedly/brand24/mention → content: jasper/copy ai, gamma start small: pick the one subscription you open every day and hand that job to a bot as a routine. save this and read the article below to set up grok bot like the spacexai engineers do ↓

HOLY SH*T. OPENAI JUST MADE AI AGENTS CHEAPER AND BETTER AT USING YOUR COMPUTER. Meet GPT 6.1 Terra. And this update is about much more than generating better answers. OpenAI is pushing AI toward something far more useful. Actually getting work done. Terra is designed to understand what is happening on your screen, interact with computer applications and complete complex sequences of tasks. Think about what that means. An AI can help prepare proposals, interpret technical drawings, fill out forms, edit videos and handle repetitive office work. Instead of explaining how to do something, it can work through the steps required to get it done. And OpenAI says the new model is significantly better than GPT 5.6 Terra at these kinds of tasks. But here is the part that really caught my attention. The price. GPT 6.1 Terra's standard API pricing is 50% lower than GPT 5.6 Terra. That matters because AI agents can make many calls to a model while completing a single workflow. Every step costs money. Reading information, analyzing a screen, deciding what to do next and checking the result can all add up. Lower costs make it more practical to run these systems at scale. OpenAI also reports major improvements in coding performance and visual reasoning, alongside fewer factual errors in its internal evaluations. This combination is important. A model that can understand a task, operate software and write code becomes much more useful when it can do all of that faster and at a lower cost. Imagine AI agents processing invoices, updating spreadsheets, organizing customer information or handling repetitive tasks across multiple applications. Not just telling you what to do. Actually helping you get it done. Of course, computer use is not the same as perfect autonomy. Complex workflows still need testing, permissions and human oversight. But the direction is becoming clear. The next big AI race is not only about which model can think better. It is about which model can reliably turn that intelligence into action. And now, OpenAI is making that capability cheaper to use.

🚨HOLY SH*T. JENSEN HUANG JUST HELD A PETAFLOP OF AI POWER IN ONE HAND. And the comparison he made is absolutely insane. In 2016, Jensen Huang delivered an NVIDIA DGX 1 system to a small company called OpenAI. It was a massive machine built specifically for AI computing. It delivered one petaflop of computing performance and came with 128GB of memory. And Jensen reminded everyone of one detail. It took two people to carry that machine upstairs. Now, ten years later, he is holding a compact AI computer in one hand and showing the same headline numbers. One petaflop. 128GB of memory. Let that sink in for a second. The new RTX Spark lineup was developed through four years of work with Microsoft and the wider PC industry. According to Jensen, the project involved a new chip called N1X and an entirely new family of computers, including desktops, laptops and workstations. The goal is to bring serious AI computing power into a much smaller form factor. And this is where things get interesting. For years, access to powerful AI hardware meant relying on expensive cloud infrastructure or specialized machines that were far beyond the reach of most individual developers. Compact systems like this point toward a different approach. Imagine developing AI agents, experimenting with models and running demanding AI workloads on your own desk. Less dependence on remote infrastructure. More control over your development environment. And potentially fewer recurring cloud costs for workloads that can run locally. Of course, there is an important distinction. Matching the same peak petaflop figure does not automatically mean two machines deliver identical real world performance. The precision used, memory architecture and workload all matter. But the physical transformation is still remarkable. In 2016, this class of AI computing came in a machine that required two people to carry. Now, Jensen Huang can hold a system with comparable headline specifications in one hand. That is what makes this more than just another computer launch. AI hardware is not only becoming more powerful. It is becoming dramatically more accessible. And the next generation of AI developers may not need a server room to get started.