Thomas Broadfoot 🍁👻

Thrilled to share that I’ve successfully defended my PhD dissertation, “Transistor-Level Programmable Fabrics for Cost-Effective IC Redaction” It’s been an incredible journey, and I’m grateful to everyone who supported me along the way. Here’s to the next chapter! #PhD #Graduation #HardwareSecurity

@alexocheema Now do tokens/s/mm^2 TALOSv2 will smoke your M4 on that metric.

My M4 Max MacBook gets 3,756,165 tok/sec in pure C, compared to ~50,000 tok/sec with the FPGA. Try it yourself: github.com/AlexCheema/tal…

@Chris53626784 @alexocheema Paradigm shift is an overreach. People have implemented full ASICs, emulators of SoCs, these systems are generally trade secrets.

Solid performance on the M4 Max.
However, I think this comparison misses the actual point of the FPGA post. They weren’t trying to beat a laptop; they showed it’s possible to implement a full transformer in pure hardware (no software layers). That paradigm shift, built on Karpathy’s MicroGPT, is the real innovation here.

If the designed system has no mechanism to validate it’s outputs, it shouldn’t be vibe coded. However, when validation and regression is in scope for every task, failures drop dramatically. V&V must be automatic and Agents should take part in the process. They should have skills written for that purpose.

A new study just blew up the entire "vibe coding" movement. Researchers from UC San Diego and Cornell tracked 112 experienced software developers using AI agents in their actual jobs. The finding is the opposite of every viral demo on your timeline. Professional developers don't vibe code. They control. Here's what they actually found. The researchers ran two studies. 13 developers were observed live as they coded with agents in real production work. 99 more answered a deep qualitative survey. Every participant had at least 3 years of professional experience. Some had 25. The viral pitch of agentic coding goes like this. Hand the agent a vague prompt. Don't read the diff. Forget the code even exists. Trust the vibes. Andrej Karpathy coined the term. Tens of thousands of developers on X claim to run "dozens of agents at once" building entire production systems hands-off. The data says almost nobody serious actually works that way. Here is what experienced developers do instead. → They plan before they prompt. They write out the architecture, the constraints, and the edge cases first, then hand the agent a tightly scoped task. → They review every diff. Not because they're paranoid. Because they've seen what happens when you don't. → They constrain the agent's blast radius. Small, well-defined tasks only. The moment a problem touches multiple systems or has unclear requirements, they take over. → They treat the agent like a fast junior dev that needs supervision, not a senior engineer that can be trusted alone. The researchers also found something darker buried in the data. A separate randomized trial they cite showed that experienced open source maintainers were 19% slower when allowed to use AI. A different agentic system deployed in a real issue tracker had only 8% of its invocations result in a merged pull request. 92% failure rate in production. 19% productivity drop for senior devs. The viral demos lied to you. The paper's biggest insight is in one sentence: experienced developers feel positive about AI agents only when they remain in control. The moment they let go, quality collapses, and they know it. This matches what every serious shop has quietly figured out. The developers shipping the most with AI right now aren't the ones vibing. They're the ones with the strictest review processes, the tightest task scoping, and the clearest mental model of what the agent can and cannot do. Vibe coding makes for great Twitter videos. It does not make great software. The next time someone tells you they let Claude build their entire SaaS in a weekend, ask them how much of that code they've actually read. The honest answer separates real engineers from the demo crowd.

@pdoocy The guy behind him is like “I’m gonna give you an exclusive look at ….” Not sure they know what exclusive means.

Would have worn nicer shoes if I knew this was going to happen today: just became the 1st person (before President Trump, I am told by a WH aide) to walk on the new black granite outside the Oval Office since construction finally wrapped. Did my best not to scuff it up

@DarkSideAdvcate @SpencerHakimian LooksMax3000

@SpencerHakimian Anybody know who this guy is?

You can see it change in real time.

@MetabolicFactor Oats

High-protein meals that won’t leave you bloated, here’s what to eat.

@felixprehn In Dallas all of our lakes are extremely full this year. We can’t use the water fast enough.

Texas just admitted it needs $174 billion for water. Not for roads. Not for schools. Not for energy. Water. The stuff that comes out of your faucet. The Texas Water Development Board released the numbers last week. $174 billion over the next 50 years to prevent the state from running out of water. Double the last estimate from 2022. Texas is adding 17 million people by 2080. A 53% increase. Water supply is dropping 10% over the same period from depleting aquifers. Without action, shortages could cause $177 billion in economic losses by 2030 alone. More than the cost of fixing it. And it's not just population growth draining the system. Tesla's Giga Texas factory uses 556 million gallons of water per year. A single factory. Data centers are consuming 0.4% of the state's entire water supply and growing fast. In Austin, data centers and industrial demand are straining a water system built for residential use. This isn't a Texas problem. It's a global one. The World Bank just launched a program called Water Forward targeting water security for 1 billion people by 2030. 14 countries signed on. They're calling it one of the defining infrastructure crises of the century. Water is the only commodity on earth with no substitute. Oil has renewables. Gold has Bitcoin (if you believe that). Copper has aluminum for some applications. Water has nothing. You need it or you die. Every person, every farm, every factory, every data center. And it's running out faster than any government projected. Where this creates an investment thesis almost nobody is talking about: Xylem (XYL). The largest pure-play water technology company in the world. Builds the infrastructure that treats, tests, transports, and analyzes water. Revenue above $8 billion. Every dollar of that $174 billion Texas plan flows through companies like Xylem. American Water Works (AWK). Largest publicly traded water utility. Serves 14 million people across 24 states. Water utilities are natural monopolies. You can't build a second pipe to someone's house. The customer can't switch providers. Pricing power is absolute and demand is non-negotiable. Veolia (VEOEY). Global leader in water treatment and waste management. Operates on every continent. When countries need to build water infrastructure from scratch, Veolia gets the call. Essential Utilities (WTRG). Growing through acquisitions of small water systems. Rural water infrastructure across America is crumbling. Most small systems are municipally owned with no budget to upgrade. Essential buys them, upgrades them, and charges the regulated rate. Mueller Water Products (MWA). Builds the valves, hydrants, and pipes that make up the physical water distribution network. Every infrastructure dollar spent on water flows through components these companies manufacture. The Invesco Water Resources ETF (PHO) gives you diversified exposure to the entire water infrastructure chain. When governments start writing $174 billion checks for water, every company in this ETF benefits. Water infrastructure is the most boring and most inevitable investment thesis on earth. Nobody talks about it because it's not AI and it doesn't have a ticker on CNBC's bottom scroll. That's why it's still cheap. every week i cover where the money is actually going before it makes headlines. former banker. felixfriends.org/live (texas just said it needs $174 billion for water. double the last estimate. the state is adding 17 million people while aquifers are depleting. tesla's single factory uses 556 million gallons a year. data centers are draining supply in austin. the world bank just launched an emergency water security initiative for 1 billion people. water is the only commodity on earth with zero substitute. nobody on financial TV is covering this. $174 billion has to go somewhere.)

@contextconor Palantir’s Ontology is the key

Strategy has acquired 34,164 BTC for ~$2.54 billion at ~$74,395 per bitcoin and has achieved BTC Yield of 9.5% YTD 2026. As of 4/19/2026, we hodl 815,061 $BTC acquired for ~$61.56 billion at ~$75,527 per bitcoin. $MSTR $STRC strategy.com/press/strategy…

@DataChaz Also add Ontology like Palantir. And create multiple experts on top of it. CEO, CTO, CFO, Chief Council, Chief of Staff.

this guy literally breaks down the exact Claude + Obsidian playbook to build your own AI employee in a single afternoon 🤯

@ArtemXTech Everyone wants a second brain. Everyone talks about Palantir. Build Ontology for your Second Brain. Then build experts on top of it. Give them motivation, a mission. Goals achieve them selves. This is the way.

@InnovatingCoin I didn't realize they can go to North Park Mall. That's cool

My friend just shared his first unsupervised Robotaxi ride in Dallas, TX!

@pbeisel Also important to note that HBM is from SK Hynix

Early engineering sample, also called “first silicon”, of Tesla's AI5 chip. Likely officially taped out a few weeks earlier. The main AI5 compute die surrounded by HBM memory stacks (to the left and right). This is a Samsung chip: Bottom-right corner: “KR 2613” → “KR” = Korea (South Korea). → “2613” = production week 13 of 2026 (late March 2026).

Congrats to the @Tesla_AI chip design team on taping out AI5! AI6, Dojo3 & other exciting chips in work.

@andweknow Schwab moved in 2021... not breaking at all

🚨 CALIFORNIA JUST LOST $10 TRILLION IN MANAGED WEALTH OVERNIGHT! 🚨 Charles Schwab — after 50 years in California — is officially packing up and moving to Texas. $10 trillion in client assets. A financial powerhouse that helped millions of Americans invest. Gone. Why? Even Schwab couldn’t stomach California’s crushing taxes, insane regulations, and skyrocketing costs anymore. Newsom is spinning and campaigning around the country while his state hemorrhages wealth and businesses. How many more giants have to flee before California admits the truth? You can’t tax and regulate success into oblivion and expect companies to stay. California is bleeding out. Texas is winning big. America First states are rising — socialist experiments are collapsing. 🇺🇸

@RoundtableSpace all agents are meant to run on repos!

EX ANTHROPIC ENGINEER SAYS CLAUDE IS MEANT TO RUN ON REPOS NOT PROMPTS AND THAT’S WHERE THE EDGE IS

@HowToAI_ its not just about how the ai will think about math, science and engineerings... the silicon computation that comprise ai is now immensely cheaper in hardware.

Researchers just proved that every single elementary function, sin, exp, log, sqrt, comes from one single binary operator. It is like finding the “God Particle" for calculus. In computer science, every complex program breaks down to a single logical operator: the NAND gate. It is the fundamental building block of all digital reality. But for continuous math, physics, engineering, machine learning, we thought we needed a massive toolbox. Addition. Subtraction. Trigonometry. Logarithms. Every scientific calculator and neural network has to juggle all of them. Until today. But this paper proved that every single mathematical function can be generated by a single, bizarre binary operator. eml(x,y) = exp(x) - ln(y). Combine that with the number 1, and you can build everything. Pi. The square root. Sine and Cosine. Arithmetic. It is all just the exact same operator, repeating over and over again in a binary tree. Nobody anticipated this existed. It was found by systematic exhaustive search. But the implications for AI are massive. Instead of an AI struggling to combine different mathematical rules to discover a new scientific law, it can just use a single, uniform architecture. One trainable circuit. One repeatable node. We thought the language of the universe was complex. It turns out, it's just one equation repeating in the dark.

@hridoyreh This is the way. Ideation, strategy, planning, and documentation all built around the code base.

📂 SaaS ┃ ┣ 📂 Idea ┃ ┣ 📂 Problem Discovery ┃ ┣ 📂 Market Research ┃ ┣ 📂 Niche Selection ┃ ┗ 📂 Competitor Analysis ┃ ┣ 📂 Validation ┃ ┣ 📂 Customer Interviews ┃ ┣ 📂 Landing Page Test ┃ ┣ 📂 Waitlist ┃ ┣ 📂 Pre Sales ┃ ┗ 📂 Demand Testing ┃ ┣ 📂 Planning ┃ ┣ 📂 MVP Scope ┃ ┣ 📂 Feature Prioritization ┃ ┣ 📂 Tech Stack ┃ ┣ 📂 Roadmap ┃ ┗ 📂 Time to Market ┃ ┣ 📂 Design ┃ ┣ 📂 Wireframes ┃ ┣ 📂 UI UX ┃ ┣ 📂 Prototype ┃ ┣ 📂 Design System ┃ ┗ 📂 UX Copy ┃ ┣ 📂 Development ┃ ┣ 📂 Frontend ┃ ┣ 📂 Backend ┃ ┣ 📂 APIs ┃ ┣ 📂 Database ┃ ┣ 📂 Authentication ┃ ┗ 📂 Integrations ┃ ┣ 📂 Infrastructure ┃ ┣ 📂 Hosting ┃ ┣ 📂 CI CD ┃ ┣ 📂 Monitoring ┃ ┣ 📂 Logging ┃ ┗ 📂 Security ┃ ┣ 📂 Testing ┃ ┣ 📂 Unit Tests ┃ ┣ 📂 Integration Tests ┃ ┣ 📂 QA ┃ ┣ 📂 Performance ┃ ┗ 📂 Beta Testing ┃ ┣ 📂 Launch ┃ ┣ 📂 Landing Page ┃ ┣ 📂 Product Hunt ┃ ┣ 📂 Beta Users ┃ ┣ 📂 Early Adopters ┃ ┗ 📂 Public Release ┃ ┣ 📂 Acquisition ┃ ┣ 📂 SEO Wins ┃ ┣ 📂 Content Marketing ┃ ┣ 📂 Social Media ┃ ┣ 📂 Cold Outreach ┃ ┣ 📂 Communities ┃ ┣ 📂 Influencer Outreach ┃ ┗ 📂 Affiliate Marketing ┃ ┗ 📂 Paid Ads ┃ ┣ 📂 Distribution ┃ ┣ 📂 Directories ┃ ┣ 📂 SaaS Marketplaces ┃ ┣ 📂 Partnerships ┃ ┣ 📂 Integrations ┃ ┗ 📂 Communities ┃ ┣ 📂 Activation ┃ ┣ 📂 Onboarding Flow ┃ ┣ 📂 First Value ┃ ┣ 📂 Time to Value ┃ ┗ 📂 Product Tours ┃ ┣ 📂 Conversion ┃ ┣ 📂 Pricing ┃ ┣ 📂 Free Trial ┃ ┣ 📂 Freemium Model ┃ ┣ 📂 Sales Funnel ┃ ┣ 📂 CRO ┃ ┗ 📂 Demo Flow ┃ ┣ 📂 Revenue ┃ ┣ 📂 Subscriptions ┃ ┣ 📂 Usage Based ┃ ┣ 📂 Upsells ┃ ┣ 📂 Add-ons ┃ ┗ 📂 Enterprise Deals ┃ ┗ 📂 Lifetime Deals ┃ ┗ 📂 Annual Plans ┃ ┣ 📂 Retention ┃ ┣ 📂 Onboarding ┃ ┣ 📂 Email Automation ┃ ┣ 📂 Customer Success ┃ ┣ 📂 Feature Adoption ┃ ┣ 📂 Churn Reduction ┃ ┗ 📂 Feedback Loops ┃ ┣ 📂 Analytics ┃ ┣ 📂 Tracking ┃ ┣ 📂 Funnel Analysis ┃ ┣ 📂 Cohorts ┃ ┣ 📂 Attribution ┃ ┗ 📂 A/B Testing ┃ ┣ 📂 Growth ┃ ┣ 📂 Product Led Growth ┃ ┣ 📂 Referral Programs ┃ ┣ 📂 Viral Loops ┃ ┣ 📂 Community Building ┃ ┣ 📂 Growth Experiments ┃ ┗ 📂 Network Effects ┃ ┣ 📂 Monetization Expansion ┃ ┣ 📂 Enterprise ┃ ┣ 📂 Add-on Revenue ┃ ┣ 📂 Upsell Flows ┃ ┗ 📂 Cross Sell ┃ ┣ 📂 Customer Success ┃ ┣ 📂 Support ┃ ┣ 📂 Help Docs ┃ ┣ 📂 Live Chat ┃ ┣ 📂 Success Metrics ┃ ┗ 📂 Management ┃ ┣ 📂 Legal ┃ ┣ 📂 Privacy Policy ┃ ┣ 📂 Terms ┃ ┣ 📂 GDPR ┃ ┗ 📂 Data Security ┃ ┗ 📂 Scaling ┣ 📂 Automation ┣ 📂 Hiring ┣ 📂 Systems ┣ 📂 Global Expansion ┗ 📂 Exit Strategy

@gabriberton Yes it’s true. But now you’ve got an AI who doesn’t care about incentives and will work 24/7 finding exploits. Either way there is a risk, whether the risk existed before but wasn’t exploited because people were too lazy or unskilled. That constraint doesn’t exist anymore.

Super interesting take from one of the greatest hackers He says Mythos is not as good as they claim, because zero-day vulnerabilities are not that hard to find for skilled hackers I'm far from the hacking world but sounds reasonable Any thought?

@SqSehrish $BTC Buy

Say it out LOUD... not in your HEAD 😭