My posts on two things I like to do. linkedin.com/posts/carldea_d…

The AI containment debate may already be over. @friedberg argument is that powerful AI is no longer controlled by a handful of American companies running enormous data centers. Open weight models are becoming capable and efficient enough to download, modify, and run on consumer hardware, making the technology nearly impossible to contain once released. The recent launch cycle shows how quickly this is happening. DeepSeek released a faster and cheaper model, Alibaba published downloadable weights for Qwen Image 2.1, and PrismML compressed a 27 billion parameter Qwen model into just 5.9 gigabytes. PrismML says its compressed model retained 98.2% of the original model’s benchmark performance while becoming small enough to run on a laptop or consumer GPU. At the same time, closed model companies continued releasing more capable and affordable systems. Anthropic’s Opus 5.5 reportedly matched its more expensive flagship while costing approximately 40% less to run than its predecessor. Stanford found that the cost of running a model with GPT 3.5 level performance fell more than 280 fold in roughly 18 months. It also found that the performance gap between leading open-weight and closed models narrowed from 8% to 1.7% on some benchmarks in one year. That is the real meaning of Friedberg’s argument. Governments can regulate large data centers, chip exports, and commercial AI providers but they cannot easily remove models that have already spread across the internet and onto personal computers. The debate must therefore shift from whether powerful AI should exist to how it should be used and who becomes responsible when it causes harm.

Driving around to see the flooding in our area. Nor’easter Sept 24-27, 2026

Major Flooding Hits Ocean City, MD - Streets Under Water - Ocean's Calli... piped.video/Zbl62AIqmms

Unlock the 11 Times Table Trick #shorts piped.video/shorts/Sw-pRuuV1…

holy smokes - should this have been our launch video? 🥹

a Stanford professor used JEV to build a data science system that analyzes 40.000.000.000 data points every 15 min his system can process massive datasets and automatically decide which results deserve deeper analysis. the professor spent more than a year developing the system with his research team. the first version relied heavily on LLMs to interpret every result. it worked, but analyzing millions of records created unnecessary latency and huge inference costs. then they redesigned the decision layer around Jev. instead of generating explanations for every data point, Jev evaluates the results and decides what the system should investigate next. I found a breakdown of the pipeline they used and the way Jev filters the data is surprisingly clever. I’ll show you how it works next so stay tuned if you want to see what Jev Engineering looks like inside a real data science system. would you let an AI decide which patterns in your data are worth investigating?

Quite brilliant. This will be the standard to follow. Once stable I hope there is an open source on prem version.

As of today, six @OpenJDK distributions have #JDK27 GA available through actions/setup-java@v6. If you are on the bleeding edge, update your setup-java on your @GitHub projects for java-version: 27.

A great example of what Ai should be doing: understanding biological sciences (and other sciences) and discoveries to help humanity. What your cells actually look like (it's chaos) - Eric Betzig piped.video/shorts/MgLQiIIk1… Pardon the cursing. Not every textbook(LLM) is accurate.

Caveman Issues Dire Warning That The Wheel Is Going To Eliminate Countless Jobs buff.ly/u9nCQoL

MIT Just Discovered What Jobs AI Actually Killed (by Dylan John) piped.video/watch?v=UGVz615A…

Day 12 building the Figma for hardware until somebody invests in it. This week I’m focusing entirely on jewelry. Technology is getting closer and closer to our bodies, and I think the objects around it will matter just as much as the technology itself. A necklace, ring or charm can carry something digital without asking for your attention all the time. So I want to explore the space between hardware, jewelry and everyday objects: connectors, chains, rings, charms, carriers and ways of turning technology into something you actually want to wear. The system I’m building will eventually go beyond devices. I want it to be a way to design objects in general.

Best AI Ad ever..

I’d like to announce two projects I’m working on: First, I am launching a non-profit with the aim of gathering Christians in tech to help build “software for the kingdom”. There isn’t much on the site yet, but to stay in the loop, drop your email --> awakenusa.com. Second, I have taken on the role as president for a new TPUSA Communities San Francisco Bay Area chapter. I’ll be focused on building stronger local communities through social and civic engagement, volunteer opportunities, and events, all united behind the shared values of faith, family, and freedom. More on this --> tpusasfbay.com. What led me here? Today marks one year since Charlie Kirk was assassinated. His death had a profound impact on me. I know many disagree with his views, but having listened to him for years on campuses, on air, and in churches, there’s no denying his unwavering commitment to dialogue, truth, and freedom. He gave everyone a mic, especially those who disagreed with him, listened intently, and treated them with respect and decency. And in his last few years his faith was front and center, boldly proclaiming the gospel. I can see the fruits of this in our own church, especially with the younger generation. I’ve been in tech for almost 30 years, much of that running my own businesses. But over the last several years, something has shifted. AI brings hope, but also separation. Truth and freedom have been inverted, giving rise to deception, anger, and loneliness. As technologists, we are builders and optimists at heart, but it’s hard to ignore the negative consequences. What world will my three kids grow up in? Can technology be used for good or are we just building for profit? Will technology unite us or divide us? Will it be used to increase agency or control us? This is where faith and tech must find a deeper union. And this is what led me to create Awaken and team up with TPUSA. As the sun rises on 9/10, and tomorrow on 9/11, and soon 10/7, so many people remain divided, confused, and lonely. As technologists and entrepreneurs, let's use our gifts and talents to inspire dialogue over division, to seek truth over deception, and to stand for freedom above control! Ultimately let’s “be not overcome of evil, but overcome evil with good”, and to “Let your light so shine before men, that they may see your good works, and glorify your Father which is in heaven.” Here I am Lord. Send me.

For years, Maryland was considered the poster child for extreme partisan gerrymandering in the nation. Our congressional districts were so unfair, twisted, and distorted that they became a punchline on every list of the worst-drawn maps in America. We appointed an independent, non-partisan redistricting commission that held hearings all across the state and heard the testimony of thousands of Marylanders. The legislature's extreme partisan maps were thrown out by the courts, and the legislature was ordered to redraw fairer maps. These are the ones we currently use today. Our fair maps took eight years to win in Maryland, and the effort received bipartisan praise across the country. Now, to ensure that their partisan, illegal maps can never be thrown out again, the monopoly in Annapolis is attempting to rewrite our state Constitution to clear the way for the same kind of maps that the courts already said violated the law. Let me be clear, the way I've been clear about this for decades: partisan gerrymandering is a cancer on our democracy, and it's wrong no matter who does it. Marylanders, regardless of party, should say NO to extreme partisan gerrymandering. NO to politics as usual. NO to being the worst in America. We are better than this. wbaltv.com/article/redistric…

META'S CHIEF SCIENTIFIC ADVISOR, YANN LECUN, SAID, "CHATGPT, CLAUDE, AND ALL CURRENT LANGUAGE MODELS ARE A DEAD END, AND INVESTORS ARE WASTING TRILLIONS OF DOLLARS." This scandalous statement was made by a Turing Award winner and a man considered one of the "godfathers" of modern AI, which has the entire IT industry in stitches. While IT giants are buying up hundreds of thousands of NVIDIA graphics cards and promising "superintelligence by next year," LeCun is blunt: attempting to create strong artificial intelligence (AGI) based on LLM is a fundamental misconception for the entire industry. Here are four compelling arguments from an AI legend about why the chatbot era has hit a wall: • Lack of a model of the real world: A 4-month-old baby or a cat understands physics, gravity, and space better than the most powerful neural network. Training AI solely on text is like trying to learn to drive by forcing a person to read the instructions on the couch. • The Illusion of Reason Instead of Thinking: Current LLMs are simply mathematical next-word predictors (pattern matching). They lack logic, true memory, and planning ability. The models produce plausible text, but have no understanding of its semantic essence. • The Scaling Wall: Increasing data centers and budgets by 10x yields only a symbolic increase in quality. The industry is trying to squeeze the maximum out of a technology that has reached its architectural limits. • The Shift to "World Models": True AGI will emerge not from text chats, but from fundamentally new architectures-"world models"-capable of perceiving physical reality and cause-and-effect relationships, just as the human brain does.

Jeff Hinton dismissed gradient descent in the 1970s because he thought models would get stuck. He won the Nobel Prize in 2024 partly for being wrong about that. Every time ChatGPT gets better after a training run, it's because 1.2 billion parameters fell through a wormhole in a loss landscape - not because they crawled downhill one step at a time. This video visualizes exactly what that looks like inside Llama 3.2. 20 minutes. Bookmark & watch today. The misconception that almost stopped AI is more interesting than the solution.