Adebowale Oparinu

A 15-year-old girl immigrates to New Jersey from China. Doesn’t speak English. Her parents, both educated engineers back in Chengdu, are now working as cashiers and restaurant cooks. She gets a job washing dishes at a Chinese restaurant to help the family survive. She gets into Princeton on a full scholarship. Her reaction is so disbelieving she asks two different advisors to verify the acceptance letter is real. Then her mom gets sick, so the family opens a dry cleaning shop in Parsippany. Every weekend for seven years, Fei-Fei Li leaves Princeton’s physics department to run the register, handle inspections, talk to customers, manage billing. Monday through Friday: quantum mechanics problem sets. Saturday and Sunday: sorting other people’s laundry. She later called herself the “CEO” of the dry cleaning business. She kept running it remotely through half of her PhD at Caltech. In 2007, she proposed building an image dataset so massive her own mentor told her she’d taken the idea “way too far.” Pre-ImageNet, the entire AI field was working with datasets containing a few hundred images. She built one with 15 million. Most researchers at the time believed algorithms were the bottleneck. She bet on data when nobody else would. By 2012, a team ran a neural network on that dataset and halved the existing error rate overnight. AlexNet on ImageNet became the moment the deep learning era started. Every computer vision product shipping today traces its lineage back to that dataset. Fast forward to 2024. She starts World Labs. Four months in, $230 million raise, $1 billion valuation. Today, $1 billion more at roughly $5 billion. The bet investors are making: that the woman who gave AI its eyes with 2D image recognition is about to give it spatial awareness of the 3D physical world. Her new model, Marble, generates persistent 3D environments from text or images. Unlike video generators that fake depth frame by frame, Marble creates actual geometric space where objects stay where you left them. The investor list tells you everything. AMD and NVIDIA both wrote checks. When the two biggest competing chipmakers both fund the same startup, they’re telling you this workload is coming whether their competitor funds it or not. Autodesk put in $200 million and signed on as strategic advisor, which means they see spatial AI integrating directly into CAD and design workflows within 18 months. From dry cleaner to ImageNet to a $5 billion spatial intelligence company. Fei-Fei Li has now placed two bets that the rest of the field thought were too early and too big. The first one created modern computer vision. The second one is trying to give machines the ability to understand physics. If she’s right again, this is the last major unlock before embodied AI actually works.

Duolingo closed at $112 yesterday. That’s down 70% from its May 2025 high of $544. The revenue story is real. Duolingo went from $162M in 2021 to nearly $1B in trailing revenue. 41% YoY growth last quarter. 50 million DAUs. Record EBITDA margins approaching 30%. By every operating metric, this company is executing at an elite level. So why has the market vaporized $16B+ in market cap since May? Three things happened at once. Bookings growth decelerated from the pandemic-era highs, and Goldman and Wells Fargo both flagged “challenging comparisons” ahead. The CFO who steered the company through its entire public life announced his departure. And the AI narrative flipped from tailwind to existential threat in investor minds. Then T-Mobile dropped a bomb three days ago. They announced “Live Translation,” a real-time AI translation service built directly into their wireless network. Over 50 languages. No app, no download, no subscription. Works on a flip phone. The stock fell another 10% in a single session. The market is now asking a question it never had to ask before: if AI can translate any conversation in real time at the network level, what’s the premium on spending 2,000 hours learning a language the hard way? Duolingo trades at 6x sales today. At its peak it traded at 30x+. The business grew into its valuation and then the valuation collapsed anyway. The market isn’t repricing the present. It’s repricing a future where the entire category of “language learning” gets compressed by AI that skips the learning part entirely. Revenue up 410% and stock down 70%. The market is telling you that growth in a category AI might eliminate gets valued at zero.

The neuroscience behind why this works is what makes it so effective. This game is stacking four separate neurochemical systems simultaneously. When those children race to answer 2+5 before the other team, their brains release norepinephrine from the competitive urgency, which sharpens attention and primes the hippocampus for encoding. The physical movement of reaching up to tap the screen triggers dopamine release through motor-cognitive coupling, and dopamine doesn’t just feel good, it physically tags active neural circuits for long-term retention. A Copenhagen study of 757 elementary students found that kids who did math while physically moving showed a 6% boost in math mastery, a 16% increase in intrinsic motivation, and a 14% jump in self-determination compared to kids learning the same material seated at desks. A separate randomized controlled trial of 7-year-olds found that integrating physical activity into math lessons produced an effect size of d=0.38 over one school year. The mechanism is called embodied cognition. Your brain doesn’t store abstract math in isolation. It encodes the physical context alongside the information, creating multiple retrieval pathways. So when these kids physically stretch their arms to punch in “7” while their team’s tug-of-war characters are losing ground, they’re building richer memory traces than any worksheet could produce. The competitive element adds another layer. The tug-of-war visual gives immediate feedback on accuracy. Each correct answer pulls the rope. Each wrong answer costs your team ground. That real-time consequence loop is what Huberman calls “reward prediction error” in action, where the brain constantly recalibrates its dopamine output based on whether outcomes match expectations. The kids who answer correctly get a dopamine surge that stamps in the neural pathway. The kids who get it wrong experience a brief dip that makes the correct answer more salient on the next attempt. The game from edugames.uz, and the fact that this is spreading across Chinese classrooms, tells you something about where education is heading. While most Western ed-tech is still building apps that sit kids in front of screens alone, this approach combines social competition, gross motor movement, real-time feedback, and collaborative pressure into a single five-minute math drill. Sitting still and memorizing multiplication tables works against the brain’s architecture. This works with it.

I just read @TheAthleticFC 41 facts about Ronaldo as he turns 41. And some facts stood out for me. In Cristiano Ronaldo’s nearly 24 years of playing professional football, he has scored against 207 different teams. Of all the teams he has played against at least three times, he has only failed to score against 9 of them. Those nine are: Benfica, Turkey, Lille, Albania, Wolves, Leicester, England, Brazil, and Celtic. The teammate he played the most games with is Karim Benzema. They played 355 games together for Real Madrid. Forget their Saudi shenanigans. He has scored 25 goals in finals — 15 of them were for Real Madrid. 226 of his career goals were scored between the 76th and 90th minute. It's the period he scored most of his goals. The least amount of his goals came in the opening 15 minutes of games. Of all the stats I read, the one I found the most interesting and useful for the everyday person in my opinion is this fact: Ronaldo has played 1,308 professional games in his career. He has played 106,957 minutes in total. Since there are 1440 minutes in a day, this means Ronaldo has played a total of 74.2days in his career, approximately 74 days. Now, to break it down. He has played active, recognised, professional association football for only 74 days out of his career of over 23 years. He played 1,308 games across 74 days. There are at least 8,395 days in 23 years and he has done what has given him the fame, money, and power for only 0.009% of his career. To leave it there would be a weak and shallow position. Ronaldo may have spent less than 0.1% of his career playing active games, but he has spent those 99.9% working hard, training, building himself and sharpening his axe to cut the tree. We all spend days working and would be recognised only for the work we do, but there are thousands of hours that go behind the work to make it look like the quality it is. That's something to note. Like Roger Federer said, “effortless is a myth”.

“And that's the thing about people who mean everything they say. They think everyone else does too.” — Khaled Hosseini, The Kite Runner

I once shared this story in a Space and everyone was shocked 😂. After she asked me for my major reasons for wanting to date her and be in a relationship with her, I decided to put my thoughts together and send them to her via email. That was in March 2021. She didn’t respond immediately. In fact, her reply came in August 2021. But here’s the interesting part; before that email response, we were already talking. No labels yet, just a proper talking stage. Between March and July 2021, we had deep conversations….faith, family values, parenting, future goals, marriage… real-life discussions. At some point, she asked me what my five-year plan looked like, and I told her everything….straight from the top of my head. And the talking stage continued. Fast forward to August 2021; just a few days before my birthday 😂😂. She finally replied to my email. But not just a reply; she sent a detailed breakdown of why she was choosing to date me and be in a relationship with me. Best birthday gift ever. 2021 was truly that year.🥰🥰 But the real highlight came in September 2021. She sent me another email titled “HILAUS BLUEPRINT.” Every single five-year plan I had casually mentioned during our conversations….she wrote them all down, merged them with her own five-year plans, and created a master plan for us. She simply said: “Check your mail. Take your time. Read through it and let me know what you think.” The moment I opened that document and saw HILAUS BLUEPRINT, and read through everything… I just knew—this is the one. On December 28th, we went to the beach 🏖️ to relax and flesh out some parts of the plan. On the 31st night of December 2021, we prayed about it. And in 2022, we fired 🔥 down. Now in 2025, by the special grace of God, we’re still firing. PS: HILAUS is just the combination of our names. Hers-Hilary and Mine: Augustine 😂 My ijaw woman na combinationist 🥰🥰

people have differing views on this but when my wife and I combined our finances way back when our lives got 100x easier. I have not yet heard an argument that has swayed me otherwise. Again, another arrangement might work for you, but I can't imagine going back to separate finances

google principal engineer, jaana dogan: "we've been trying to build distributed agent orchestrators since last year, but it’s been slow and messy i described the problem to claude code, and it rebuilt what we made last year in an hour" jaana is not wrong. claude code is fast because it understands context and can run autonomously while you steer but it works best when you treat it like a pair programmer, not a code generator

Let this be the year in which you rebuild your attention span. Big Tech wants you unable to focus on anything for longer than a few seconds. Resist the infinite scroll. Avoid short-form videos. Watch movies without being on your phone. Read voraciously and often.

High school student uses AI to discover 1M+ objects humans missed in astronomical data. Head of NASA openly recruiting him through Twitter with a fighter jet ride included. All my worlds colliding. I love everything about this.

🚨 A student in the US just discovered MILLIONS of new space objects. The astronomy world was recently shaken by a discovery from an unexpected source: a teenager still in high school. Matteo Paz, a student from Pasadena, utilized archival data from NASA’s retired NEOWISE mission to bring 1.5 million invisible cosmic objects into the light. During a stint at Caltech’s Planet Finder Academy, and mentored by astrophysicist Davy Kirkpatrick, Paz took a novel approach to data analysis. He built a unique machine learning model capable of sifting through a staggering 200 billion infrared records. In a span of only six weeks, his AI detected subtle patterns that human analysts had missed, identifying everything from distant quasars to exploding supernovas. Paz’s findings were so robust that they earned him a spot in the prestigious The Astronomical Journal and a position as a research assistant at Caltech. His work does more than just populate star maps; it provides specific coordinates for the James Webb Space Telescope to investigate further. This breakthrough highlights a growing trend where fresh perspectives and AI tools allow young researchers to make historic scientific impacts from the classroom.

For 20+ years, software ate the world. Now, AI Agents are eating software. A massive signal just came out of China that most people missed: Bairong (a publicly listed enterprise giant) started selling "AI Workers". they call it Results-as-a-Service (RaaS). 🧵 instead of buying "seats," enterprises now "hire" agents. each agent comes with a job description, KPIs, and revenue targets. if performance drops, the bill drops. if the agent improves, it earns more. Bairong runs this through "Results Cloud". it’s essentially an HR system for machines. they’ve already deployed agents across: Sales & Customer Service + Recruitment (hiring cycles cut from 30 days to 2) + Legal & Tax (handling 90% of high-frequency work) this is where the SaaS model starts to crack. Traditional SaaS: You pay upfront. You carry the risk. Agentic Era: You pay for outcomes. The vendor carries the risk. this shift is being accelerated by the collapse of build costs. I came across this post by @martinald recently that agentic coding has slashed internal build costs by ~90%. when it's this cheap to build exactly what you need, the "Buy" in "Build vs Buy" dies. IMO, Vendors who are not able to price against results will struggle. the "Seat" is dead. the "Outcome" is everything. 🤖📈

I've never felt this much behind as a programmer. The profession is being dramatically refactored as the bits contributed by the programmer are increasingly sparse and between. I have a sense that I could be 10X more powerful if I just properly string together what has become available over the last ~year and a failure to claim the boost feels decidedly like skill issue. There's a new programmable layer of abstraction to master (in addition to the usual layers below) involving agents, subagents, their prompts, contexts, memory, modes, permissions, tools, plugins, skills, hooks, MCP, LSP, slash commands, workflows, IDE integrations, and a need to build an all-encompassing mental model for strengths and pitfalls of fundamentally stochastic, fallible, unintelligible and changing entities suddenly intermingled with what used to be good old fashioned engineering. Clearly some powerful alien tool was handed around except it comes with no manual and everyone has to figure out how to hold it and operate it, while the resulting magnitude 9 earthquake is rocking the profession. Roll up your sleeves to not fall behind.

we are overstimulated and we don’t even notice. netflix while eating. reels in the bathroom. music while cooking. podcasts on walks. we consume by default, not by intention. you keep filling every gap, then wonder why you feel foggy and unmotivated. boredom and silence are the real growth drivers. they give you space to think and create. that’s when solutions show up for problems that have been stuck for months. leave some room

Agency > Intelligence I had this intuitively wrong for decades, I think due to a pervasive cultural veneration of intelligence, various entertainment/media, obsession with IQ etc. Agency is significantly more powerful and significantly more scarce. Are you hiring for agency? Are we educating for agency? Are you acting as if you had 10X agency? Grok explanation is ~close: “Agency, as a personality trait, refers to an individual's capacity to take initiative, make decisions, and exert control over their actions and environment. It’s about being proactive rather than reactive—someone with high agency doesn’t just let life happen to them; they shape it. Think of it as a blend of self-efficacy, determination, and a sense of ownership over one’s path. People with strong agency tend to set goals and pursue them with confidence, even in the face of obstacles. They’re the type to say, “I’ll figure it out,” and then actually do it. On the flip side, someone low in agency might feel more like a passenger in their own life, waiting for external forces—like luck, other people, or circumstances—to dictate what happens next. It’s not quite the same as assertiveness or ambition, though it can overlap. Agency is quieter, more internal—it’s the belief that you *can* act, paired with the will to follow through. Psychologists often tie it to concepts like locus of control: high-agency folks lean toward an internal locus, feeling they steer their fate, while low-agency folks might lean external, seeing life as something that happens *to* them.”

Paul Graham on why you shouldn’t write with AI: “In preindustrial times most people's jobs made them strong. Now if you want to be strong, you work out. So there are still strong people, but only those who choose to be. It will be the same with writing. 𝐓𝐡𝐞𝐫𝐞 𝐰𝐢𝐥𝐥 𝐬𝐭𝐢𝐥𝐥 𝐛𝐞 𝐬𝐦𝐚𝐫𝐭 𝐩𝐞𝐨𝐩𝐥𝐞, 𝐛𝐮𝐭 𝐨𝐧𝐥𝐲 𝐭𝐡𝐨𝐬𝐞 𝐰𝐡𝐨 𝐜𝐡𝐨𝐨𝐬𝐞 𝐭𝐨 𝐛𝐞."

The entire robotics industry is about to compress a decade of progress into 18 months, and nobody’s pricing it in. The hardware has been ready for years. Boston Dynamics had Atlas doing backflips in 2018. The bottleneck was never motors or actuators. It was that every robot behavior had to be hand-coded. Pick up a box? That’s one program. Pick up a bottle? Different program. Move the box from shelf A to shelf B in a warehouse with slightly different lighting? Start over. Foundation models broke this completely. Before VLAs, teaching a robot one skill gave you exactly one skill. Zero compounding. Zero transfer. A robot trained to fold shirts couldn’t fold towels without starting from scratch. The labor intensity of data generation meant robotics datasets stayed narrow, robots overfit, and small variations like object weight or table height caused failures. Now a single Gemini Robotics model handles tasks it has never seen in training. Google’s On-Device model learns new behaviors with 50-100 demonstrations. Not 50,000. Fifty. That’s a 1000x reduction in the data requirement for new capabilities. The speed implications cascade through everything. First order: deployment timelines collapse. What took robotics teams 6-12 months of custom programming now takes days of fine-tuning. Second order: the addressable market explodes. Tasks that were never economical to automate suddenly are, because the integration cost dropped by orders of magnitude. Third order: the data flywheel accelerates. Every robot running Gemini Robotics feeds learning back into the foundation model. More deployments means faster improvement means more deployments. Physical Intelligence raised at $2.4B because investors finally understood this. Boston Dynamics partnered with Toyota Research Institute to bolt Large Behavior Models onto Atlas. Every humanoid company is scrambling to either build or license the intelligence layer they don’t have. The market is still valuing robotics companies on their hardware differentiation. But hardware is commoditizing. Boston Dynamics spent a decade perfecting locomotion, and now that’s table stakes. The value is migrating entirely to whoever owns the foundation model that generalizes across embodiments. Google trained Gemini on the largest multimodal corpus ever assembled. Then they added physical actions as an output modality. That’s not a robotics company bolting on AI. That’s an AI company whose models now output motor commands. The companies pricing this correctly are building around foundation model access, not around proprietary hardware. The companies pricing this wrong are still acting like the moat is in the mechanical engineering. AGI moving into the physical world isn’t a 10-year prediction. Gemini Robotics shipped in March. The 1.5 version with chain-of-thought reasoning shipped in September. They’re iterating on a 6-month release cycle while hardware companies iterate on 3-year cycles. The gap between software intelligence timelines and hardware development timelines is the entire trade.

You can’t change someone. But you will anyway. Not through arguments or ultimatums or that thing you do where you get really quiet and hope they’ll notice you’re upset. (They never do, by the way. They just think you’re tired.) You’ll change them the way water changes stone, by being around them long enough that they start to see themselves through your eyes. I dated someone who interrupted everyone. Constantly. Mid-sentence, mid-thought, didn’t matter. It drove me insane for months. Then one night at dinner with friends, I watched her do it again, and this time she caught herself. She looked at me, then back at the person she’d cut off, and said, “Sorry, keep going.” Nobody else even noticed. But I knew: she’d started hearing herself the way I heard her. That’s the thing they don’t mention when they say “accept people as they are.” You do accept them. And then they feel that acceptance so deeply that they finally have enough safety to look at the parts of themselves they’ve been running from their whole lives. Not because you demanded it. Because you made space for it. But here’s the trap: if you’re asking “can I change her” with a specific outcome in mind, you’ve already lost. You’re not in a relationship anymore. You’re in a renovation project. And people can smell that a mile away. They can feel when your love has conditions attached. It makes them smaller, more defended, and less likely to grow into anything you’d actually want. Honestly, it usually just makes them better at hiding. The real question isn’t whether you can change someone. It’s whether you can love someone enough that they feel free to change themselves. And whether the direction they’re growing is toward you or away from you. Sometimes you date someone, and she becomes kinder, more thoughtful, and more herself. Sometimes she becomes exactly who she’s always been, just louder. And sometimes, this is the one that hurts; she grows into someone beautiful. Just not someone beautiful for you. You can’t control which one happens. You can only decide how long you’re willing to wait to find out.

If I could send my 18 year old self a message, it would have three parts: 1. Prestige is often mistaken. Follow curiosity instead. 2. There's no way to avoid hard work. It's not sufficient, but it is necessary. 3. Don't take your parents for granted.

“I helped a man climb a mountain and found that I too had reached the top.” — Unknown