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Given NYT article, I think Jensen is gone at $NVDA, potentially by tomorrow morning with my guess the CFO as interim-CEO. But I think the stock bottoms on a RELATIVE basis compared to the semi index post earnings next week at the latest. The new interim CEO will probably want to set a low bar and the stock could trade at a single digit PE vs 11x currently on CY26. There are 40K employees at Nvidia and Jensen was a generational CEO but the pipeline of products I believe will sustain the company for ~2 years. There is a deep bench of direct reports. The analogy I would give is Steve Jobs took his medical leave in Jan-2011 and sadly died in October. $AAPL stock however rose 26%/26% in 2011/12 vs S&P 0/+12% Nasdaq -2/+14% on blistering revenue growth of 66%/45% in FY11/12. It took till year 3 for Apple revenue growth to slow dramatically to <10% in FY13 and then the stock massively underperformed that year. For the AI trade and Nvidia’s stock, the bigger question for me remains the same. Was June 22nd the top in the AI trade overall? I do not think so given Agentic AI just started this year. OpenClaw came out in January for the tech savvy and Muse from Meta for the rest of us in September with others introduced since then. Agentic requires 10-100x more tokens. But time will tell if that was THE TOP. From a technical standpoint, it does not matter if that was THE TOP. There has been forced selling in AI stocks and this Nvidia news is likely to clean out the rest. Why does it not matter? Even if it is March of 2000 and Nasdaq declines 78% from peak to trough over 2 ½ years, there were still 7 rallies that averaged 25% along the way back then. I think another one started on 11/5 but time will tell. Stay nimble and best of luck.

Reasoning from scratch, round number 6! An introduction (and implementation) of Reinforcement Learning with Verifiable Rewards (RLVR) and Group Relative Policy Optimization (GRPO). 00:00 Introduction 01:54 What makes a reasoning model different? 04:25 Reasoning traces and model capability 08:29 Accuracy and format rewards 11:34 Aha moments and DeepSeek-R1 training 14:41 Reasoning effort and answer length 18:38 RLHF and RLVR 23:04 GRPO vs. PPO 26:40 GRPO explained with a cooking analogy 31:43 The KL term and simplified GRPO 35:04 Loading the pretrained model 36:07 Loading the MATH training data 39:26 Sampling model responses 46:30 Computing verifiable rewards 49:55 Computing advantages 51:54 Token and sequence log probabilities 55:29 Implementing sequence log probabilities 57:37 Fixing the inference-mode error 1:02:24 Computing the GRPO loss 1:04:37 Putting the GRPO step together 1:09:19 The GRPO training loop 1:12:57 Training settings, logging, and checkpoints 1:17:24 Running training and inspecting outputs 1:19:28 Loading and evaluating checkpoints 1:22:33 MATH-500 results and training stability 1:24:05 Memory requirements and next steps

Testing Grok Imagine 2.0 against other image models: Prompt: "A mathematician drinking from an object of genus 1" ChatGPT gets trapped by the donut analogy: it turns a donut (genus 1) into a mug, accidentally creating an object of genus 2. Grok gets it right: a regular coffee mug already has genus 1.

The "Web app" standard for the Agentic Web/OS has arrived. It is essentially the open standardization of the third-party app/plugin ecosystem found in ChatGPT. It consists of Skills and MCP, with MCP covering standards such as MCP Apps and MCP-UI, which allow third-party apps to embed GenUI-based interactive interfaces directly into the main Agentic Web/OS interface. WebMCP is about "embedding Tools within UI," while Agent Plugins / MCP Apps are about "optionally embedding UI within Tools." The core of this kind of "app" ecosystem are Tools, rather than any specific UI representation, such as cards, icon + Activity, or windows. AppFunctions / AppIntents / AppActions on Android, iOS, and Windows, like WebMCP, also belong to the "embedding Tools within UI" model. The current CLI ecosystems around Coding Agents such as Codex and Claude Code are effectively local "native app" ecosystems: apps have to be pre-packaged, downloaded, and installed, and users cannot realistically install too many of them. By contrast, Agent Plugins / MCP Apps can be regarded as a kind of "Web app" ecosystem, because their core functionality is retrieved from the Web on demand. Skills, like manifest-style files such as mcp.json, are lightweight distributions of code with declared metadata, somewhat like HTML: they can be discovered, loaded, activated, and used on demand, in a process analogous to HTML rendering but much more intelligent. This creates an opportunity to extend existing Web standards, which are centered around the distribution of UI resources. Instead of RESTful HTTP exposing only a limited set of actions over resources, the Web could expose a vast long tail of Tools that AI can discover and understand, and that can optionally embed UI. A general-purpose Agent capable of discovering, loading, and running Agent Plugins / MCP Apps on demand would be equivalent to "the browser + Google" of the desktop Internet era. If such an Agent becomes a super app, it could easily abstract away the operating system and become the single dominant super app for most user scenarios, much like the desktop Internet era, rather than the mobile Internet era with many parallel super apps. So such an Agent will naturally tend to integrate into the entire OS and even the hardware itself, much like IE vs. Netscape. That is also why Google, OpenAI, and even ByteDance are determined to build consumer electronics hardware products.

THIS GUY MAKES COMPLEX AI AGENT CONCEPTS RIDICULOUSLY EASY TO UNDERSTAND. No jargon wall. No assuming you already know what a reward model or an evaluation harness is. Just a straight line from "I have no idea how agents actually work" to "oh, that's genuinely simple." The best explainers do not simplify by cutting corners. They simplify by finding the one analogy that makes the whole thing click.

Many have asked how this photo was made. This is my explanation. The ISS rotates about its pitch axis as it orbits Earth at 17,000mph to keep its nadir view constant. Because of this long exposure photography turns stars into arced lines. Sidereal clock drives on Earth match the planet's rotation to see stars clearly. I knew one designed for space could do this too. So a camera mount was custom built at @RITtigers to rotate once every 90 minutes using a 60 to 90 toothed gear system, cancelling out the orbital motion of ISS and allowing the capture of distant stars as fixed points! In essence, it is a a rotational coil spring powered analog star tracker. It was machined out of aluminum and is entirely mechanical to bypass NASA's strict electrical requirements for cargo. We had to get creative! After Expedition 72 it was safely returned to Earth and is to be displayed at RIT. Which I am pleased to say because a similar barn door tracker I made from spare parts on Expedition 6 for nighttime city photography was destroyed in an atmospheric trash disposal! Photography is an important part of sharing the journey to new frontiers for all people of Earth!

No pixels No graphics card No lines of code This is Tennis for Two, created in 1958 at Brookhaven National Laboratory by physicist William Higinbotham To play, you held a custom aluminum box, twisted a knob to dial in your trajectory angle, and slammed a button to hit the ball Inside? An analog computer, vacuum tubes, and clicking electromechanical relays calculating gravity and bounce physics in real time The entire $200B modern gaming industry started as a glowing dot bouncing across an oscilloscope screen

Texas Instruments $TXN posted Q2 revenue of $5.5B, driven by strength in automotive and data center markets. Q3 OUTLOOK: Management is targeting up to $6.15B in revenue for Q3 on broad-based demand recovery. CASH & DIVIDENDS: $TXN delivered $6.5B in trailing free cash flow while maintaining an A+ dividend consistency rating. THE RATING: Elevated inventory balances keep the Seeking Alpha Quant score at a neutral HOLD for now. Are you holding $TXN for its top-tier dividend consistency as the analog cycle recovers?

50 years ago, @NASA's three-legged Viking spacecraft landed on Mars. It was our 1st ground-level glimpse of the Red Planet. 🤩 Today, analog missions on Earth are helping us prepare to send humans to Mars!