Boulder Future Salon

This video has a talk by Laurence Moroney on how to get a job as an engineer in the age of AI, followed by what it means to be an engineer in the age of AI, but what caught my attention is the comments about "hard work". In the introduction to Laurence Moroney's talk, Andrew Ng says (I'm going to take the trouble to quote this): "I'm going to say one last thing that is considered not politically correct in some circles. But but I'll just say it anyway, which is in some circles it has become considered not politically correct to encourage others to work hard. I'm going to encourage you to work hard. Now I think the reason some people don't like that is because there are some people, they're in a phase of life where they're not in a position to work hard. So right after my children were born, I was not working hard for a short period of time. And there are people because of an injury, disability, whatever -- very valid reasons -- they're not in a position to work hard at that moment in time and we should respect them support them make sure they're well taken care of even though they're not working hard. Having said that, all of my PhD students that became very successful, I saw every single one of them work incredibly hard. The 2 am setting up hyperparameter tuning, you know, been there, done that, right? Still doing it some days. If you are fortunate enough to be in a position in life where you can work really hard, there are so many opportunities to do things right now. If you get excited, as I do, spending evenings and weekends coding and building stuff, and getting user feedback, if you lean in and do those things, it will increase your odds of being really successful. So, I don't know, maybe I'll get in some trouble with some people, encouraging you to work hard, but I find that the truth is people that work hard get a lot more done. We should also respect people that don't and people that aren't in a position to do so. But between watching some dumb TV show versus finding your agentic coder on a weekend to try something. I'm going to choose the latter almost every time." Then in the Laurence Moroney's talk itself, he says: "Andrew said something politically incorrect earlier on. I'm going to also say a similar politically incorrect thing. First of all, hard work. Hard work is such a nebulous term that I would say that I think about hard work in terms of: you are what you measure. There is the whole trend out there. I'm trying to remember. Was it 996 or is it 669? 996, right? 9:00 am to 900 pm six days a week, is a metric of hard work. It's not. That's not a metric of hard work. That's a metric of time spent. I would encourage everybody, in the same way as Andrew did, to think about hard work, but what hard work is, is how you measure that hard work. You can work eight hours a day and be incredibly productive. You can work six hours a day and be incredibly productive. But it's the metric of how hard you work and how you measure that. I personally measure that from output, things that I have created in the time that I spent. I joke a lot, but it's true, that I've written a lot of books. Andrew held up one -- that one that he held up -- that he helped me write a little bit. I actually wrote that book in about two months. And people say, how how do you have time with your jobs and all these kind of things? How do you do this? You must work, like, 16 hours a day in order to be able to do this? But actually, the key to me being able to write books is baseball. Any baseball fans here? I love baseball, but if you sit down and try to watch baseball on TV, a match can take like three and a half or four hours. So, all of my writing I tend to do in baseball season. So, I'm like if I'm going to sit down, I like the Mariners, I'm from Seattle. I like the Dodgers. Nobody booed. Okay, good. And you know, so like usually one of those is going to be playing at 7:00 at night. So, instead of sitting in front of the TV just like watching baseball mindlessly, I'll actually be writing a book while baseball's on in the background. It's a very slowmoving game. This is something like that's the hard work, you know, in this case. And I would encourage you to try to find areas where you can work hard and produce output. And that's the second pillar here -- is that business focus." What's all this about "hard work" suddenly becoming "politically correct" all about? I'll tell you what I think it is. One time in the last few months I came across some people on Hacker News talking about how "997"-in-office (not remote) was now the standard job offer for a software engineer in Silicon Valley. I have to say up front that I don't know if this is true or not. Maybe a handful of employers are offering "997" jobs, and maybe a handful of people who applied for those people are talking about it on HN. Or maybe all employers are offering "997" jobs and it's the only thing on offer for software engineers. (The term "997" is a variation on "996", as Laurence Moroney mentioned, and comes from China where 9:00am to 9:00pm, 6 days/week is considered the standard software engineer work day at major companies like Alibaba.) I don't know, I don't have any sense of proportion. But they were saying, with all the layoffs, software engineers lost their leverage, so now, if an employer offers you a "997" job and you don't take it, there's thousands of people in line right behind you who will take it. But here's the catch: I learned when I was in my 20s that a 23-year-old software engineer will last, on average, 2.5 years working 80 hours/week. I worked 80-hour weeks for 5 years starting when I was 23. The 2.5 year figure came from a woman who worked in the HR department of a major tech company. The company's hiring policy was to hire only new college graduates for software engineering jobs (with exceptions made for people who were famous for doing something brilliant and hired into high-level positions), work them 80 hours/week, and when they burned out, to hire more. (I later learned that the term "burn out" has 2 meanings: one is, physical exhaustion, and the other is, to lose enthusiasm for whatever you're working on. I'm talking here only about the first meaning. A person who is physically exhausted from working 80-hour weeks for years cannot regain their energy by switching to a different job, one that they will have more "enthusiasm" for. A person who is "burned out" in the physical exhaustion sense cannot work. They either voluntarily stop working and rest and recover, or they are forced to stop working by getting fired, either for illness or just unacceptably low productivity.) The HR person said 2.5 years is the "half-life" of software engineer productivity when working 80 hours/week starting around age 23. Well, if there's any truth to the "997" claim, if you do the math, 9:00am to 9:00pm is 12 hours, and for 7 days a week, 12 x 7 = 84. So "997" is an 84-hour workweek. So, rumor has it that in Silicon Valley, the standard job offer for software engineers now is an 84-hour workweek. If that's true, or even just mostly true if not entirely true, then these Stanford students have good reason to feel fear when they hear the words "hard work". Andrew Ng and Laurence Moroney seems to simultaneously be trying to tell these students to buckle down and accept the "hard work" they have to do, while simultaneously trying to weasel out of what "hard work" means. Andrew Ng talks about "getting excited spending evenings and weekends coding and building stuff, and getting user feedback." There's a big difference between doing something you feel excited about and something an employer dictated you have to do. If you're just doing stuff because you feel excited about, when you feel tired you can rest. If your employer dictates you work "997", when you feel tired, you don't get to rest, you have to keep working. You have to override your body's need for rest, and that's what leads you to that 2.5 year burnout. Andrew Ng is so famous no employer has the leverage to make him work "997" so it's a non-issue for him, he can work hard when he feels excited and rest when he feels tired, but the same isn't true of low-level people newly hired into tech companies. Laurence Moroney seems to weasel out by saying it's not the time spent, it's what you measure. Measure what's important, and you can be productive in 8 hours or even 6 hours per day. You can write books with baseball on in the background. As general life advice that might be all right, but if your employer is requiring "997" and it's "in office" (not remote), then you have to be physically present in the office and working -- you are being watched to make sure you are working the entire time -- then you really do have to work the 12 hours/day, 7 days/week. You can't weasel out of it by saying, oh, I'm being smart about what I measure. Also, if you're thinking, the companies must be violating some labor law, they have to pay overtime, or somesuch, at least when I lived in California, unless the laws have changed since then, there's a special exception in the laws for tech workers so salaried tech workers don't have any limit on work hours and there is no overtime requirement or anything. Maybe if they were in your state, there would be some law. But this talk is being given at Stanford University which is in the San Francisco Bay Area (aka Silicon Valley) in California. If any of you are out in California and getting "997" job offers, my advice is: think seriously about whether you can get seriously rich (for example from stock options) in 2.5 years, give or take. If you can make a huge amount of money very fast, if you can end up "set for life", or nearly so, the "hard work" might be worth it. If you can't, or it's too big a gamble, it might not be worth burning yourself out (in the exhaustion sense of the term) on it. Your decision, of course, but I think this is the cost-benefit tradeoff you ought to consider. I know a lot of people in software in Silicon Valley are thinking they need to get a job at a top company or top AI lab to escape the "permanent underclass", which is what they believe will happen to everyone that does not make enough money before AI automates the jobs and they all disappear.

Ulanqab prefecture in China is a sparsely populated stretch of windswept grassland on the Mongolian plateau famous for being the birthplace of He Pingping, who briefly held the Guinness record for world's shortest man, but consumes nearly 1% of all of China's electricity -- and that amount is growing every year. "What is going on, you'll have guessed by now, is the most ambitious data center buildup anywhere on earth -- and it's really surprising this hasn't been talked about more because the scale is beyond anything else, and by an immense margin." So it is claimed. "Take Elon Musk's so-called 'Colossus' datacenter in Memphis, Tennessee which he pitches as 'the world's largest AI supercomputer.'" "According to their own numbers, Colossus has 200,000 chips, which, let's be clear, is already super impressive." "In datacenter lingo, this converts to roughly 5000-6000 'racks': you know, the fridge-sized cabinets full of blinking lights you see in every movie scene set in a server room." "How many racks are they building in Ulanqab? Over 5 million." "Yes, about one thousand times the scale of 'Colossus'."

"I'm not sure exactly when it happened. As recently as December 2025, I still found some Claude-isms kind of catchy and clever -- I noticed AI language, but it didn't trigger violent rage. By spring, I was snapping at the tendons of any poor soul who showed up in my inbox with an 'I'd value your take on this' or 'the call most leaders still won't make'." Wait, AI says "I'd value your take on this" and "the call most leaders still won't make"? Let's continue. "It started with DMs and emails, but it didn't stop there. By summer, my reactive rage-response to AI-generated text had spread to include most forms of writing. If I'm reading a newsletter and I start to sense AI-isms, I delete and unsubscribe. If I'm reading a blog post, I close the tab; if I'm on social media, I unfollow or unfriend. If it happens repeatedly, I will go out of my way to avoid that writer in the future. I mostly try to not engage, but if I had a button that would let me deliver a 10,000 volt shock to the author I would slam that button every time and I wouldn't care who saw." While I can relate to the feeling of realizing something is probably AI generating and wanting to stop reading, there is the problem of AI being so good at imitating humans, it's genuinely hard to tell. There's actually research on this. I should track some of those studies down. People have been given text written by AI and humans and challenged to tell which is written by AI and which is written by humans. The same thing has been done with art: tell which art image is made by humans and which is made by AI. Humans can't tell the difference. The Turing Test has been passed. "Sometimes, yes, the quality of the work is all that matters." "Other times, the value of a piece of writing derives from the fact that a particular person said it, thought it or felt it, or its value is grounded in your relationship." She diagrams out a "personal" to "functional" continuum.

OpenArch is "PyTorch implementations of modern open-source LLM architectures (Llama, Qwen, DeepSeek, Gemma, GPT-OSS, Kimi, and more) -- written from scratch for readability and learning, based on Sebastian Raschka's LLM Architecture Gallery." Looks like I super valuable resource for all of you wanting to learn LLMs. (I do too but I don't know where to find the time.) "This repository contains hand-written PyTorch implementations of the model architectures cataloged in Sebastian Raschka's LLM Architecture Gallery. Each model is implemented to the best of my knowledge from the original papers, technical reports, reference config.json files, and the excellent writeups by Sebastian Raschka and Machine Learning Mastery." "The goal is not to compete with transformers or other production libraries. The goal is clarity and learning: a single readable file per architecture, with the structural choices (attention type, normalization, layer mix, MoE routing, positional encoding) made explicit and easy to compare side-by-side." "Modern LLM architectures share a common skeleton but differ in dozens of small, important choices:" * "Attention: MHA, GQA, MQA, MLA, sliding-window, linear/DeltaNet hybrids" * "Normalization: pre-norm, post-norm, QK-Norm, sandwich norm, RMSNorm" * "Positional encodings: RoPE, NoPE, partial RoPE, YaRN" * "Decoder type: dense vs sparse MoE (with or without shared experts), hybrid Mamba/attention" * "Training-time tricks: Multi-token-prediction, latent experts, gated attention" "Reading the official model code can be hard because production repos optimize for speed, sharding, and backward compatibility. This repo optimizes for reading."

"On September 12, Anthropic CEO Dario Amodei published an essay titled We Must Pace the Frontier, arguing that AI capabilities are advancing faster than the industry's ability to understand or control them. His argument rested on two concrete developments: AI systems are now capable of helping build the next generation of models, a feedback loop known as recursive self improvement, and a recent incident in which agents carried out unauthorized cybersecurity actions during an OpenAI and Hugging Face episode. Amodei proposed a three step plan. Step one, independent evaluators get permanent, employee level access inside frontier labs so they can actually verify what is happening rather than rely on a company's own self reported safety claims. Anthropic committed to that step unilaterally. Step two, leading AI companies in democratic countries coordinate around shared safety standards. Step three, an eventual international agreement, explicitly including China, so that pacing is not just a unilateral handicap for whichever company adopts it first." "What happened next is the part that matters for this argument. Within hours, Sam Altman, CEO of OpenAI, one of Anthropic's most direct commercial rivals, posted on X agreeing that the industry needs to pace the frontier, and said the topic had already become a major subject of internal discussion at OpenAI. He confirmed OpenAI would adopt the same independent evaluator model Amodei described. Elon Musk, founder of xAI and no natural ally of either Amodei or Altman, responded simply that Dario is right, and pointed back to earlier comments he had made about AI competitors peer reviewing each other's models before release. Google DeepMind's Demis Hassabis also endorsed the broad direction, calling it the right path forward at a critical moment, while noting the details still need work." The writer is from Zizka AI, a company in Spain that makes a product that is designed to bring auditability and transparency to AI models -- "help you know why your agent did what it did -- replay, lineage, and drift alerts". ZizkaDB promises "Causal lineage: Trace why an agent chose an action -- the decision chain behind production behavior", "Session replay: Replay any session end-to-end and see exactly what the agent knew when it acted", and "Drift detection: Get alerted when agent behavior shifts after a prompt or model change".

The US has deployed a space-based weapon. "Air Force Secretary Troy Meink said Monday that the US has weapons stationed in Earth's orbit, the first public acknowledgment of a capability long assumed to exist after decades of debate over the weaponization of space." If you're wondering what the weapon is, or weapons are, or even whether there's one or many, sorry, you don't get to know any of that. The ABC News article goes on to say: "'These capabilities can be employed for offensive and defensive purposes at the direction of combatant commands,' a Space Force spokesperson told ABC News. 'The US has warned about Russian and Chinese weapons testing and operationalization since 2007. Due to their actions, it is no longer a question that space is a warfighting domain. Open communication strengthens deterrence, prevents adversary miscalculation, and normalizes space operations alongside the other services.'"

On September 8, 2026, Jacob Coxon of Anthropic announced his resignation from Anthropic, saying both OpenAI (where he previously worked) and Anthropic are acting irresponsibly, are racing straight to self-improving superintelligence and gambling with everyone's lives. "The people building AI earnestly believe that it could kill us all by the end of the decade. This is not a marketing stunt. If anything, many executives and senior researchers will couch their phrasing in the press to sound sensible - but I hear the same people express fear privately. No other human activity poses this level of danger." "A common response is 'if they truly believe this, why are they still building it?' At OpenAI, many have not deeply internalized the civilizational stakes. At Anthropic, the stakes are well-understood, but they are locked in a race to get there first - they believe no one else will act responsibly, so they must do it themselves, despite the risk." While not particularly groundbreaking to me, and probably most of you in "futurist" circles, this tweet has gone crazy viral, garnering over 171 million views as of the time I am writing this (September 14). People scrutinizing the X account this was posted on suspect a deliberate PR stunt intended to garner support for legislators (especially Democratic Party legislators) to enact AI regulation. The X account had no activity before this tweet, it took off extremely rapidly, and has allegedly gotten responses from many politicians (mostly Democratic) explicitly calling for legislation to regulate AI.

"Isar Aerospace reaches orbit and deploys payloads on second flight." Apparently while I wasn't paying attention, Europe (more specifically, Germany) went and created a private space company. My first question in hearing about this was whether the rockets land and get reused like SpaceX. Apparently, no, and the payload capacity is smaller. So Isar Aerospace isn't really a competitor to SpaceX, at least not yet.

A solution to a longstanding math problem relating to the Navier-Stokes equations has been found by AI. The Navier-Stokes equations are equations for taking Newton's laws of motion, which work for discrete objects, and making them continuous, so they apply to fluids, and making them continuously feed back on themselves, so they take the form of differential equations. I can't explain it better than the summary from OpenAI's blog post, so I'll just quote the blog post. "The equations date to the nineteenth-century work of Claude-Louis Navier and George Gabriel Stokes. In 1934, Jean Leray proved that solutions exist in a generalized sense, but whether they always remain smooth became a central unanswered question. In 2000, the Clay Mathematics Institute named the Navier-Stokes existence and smoothness problem one of seven Millennium Prize Problems." "The Navier-Stokes equations use Newton's second law of motion ('F=ma') to describe how fluids move. Importantly, they treat a fluid as a continuous medium rather than tracking individual molecules. These equations are used for aircraft design, weather forecasting, and the study of blood flow." "A fundamental open question for these dynamical equations has been whether the continuum approximation of the fluid can break down. Specifically, can the Navier-Stokes equations for a three-dimensional incompressible fluid with constant density develop a 'singularity,' even when the motion starts smoothly? Here, a singularity means the dynamics lead to speeds in the fluid growing without bound within a finite amount of time. The development of a singularity would have to happen despite the presence of viscosity, which tends to smooth out motion. Because a real fluid cannot move infinitely fast, this would mark a breakdown in how the equations model the fluid. To continue modeling the system, one would then need to track the behaviour of each particle individually." The AI found a singularity, and came up with a formalized proof in the form of a Lean proof written for the proof assistant software Lean, which has verified the proof as correct. "The solution is a vortex, a spinning swirl of fluid, that spirals inward and gets increasingly elongated, like spaghetti. This central region shrinks while it speeds up in such a way that its energy still stays finite, as required by the laws of physics. The technical challenge is for the equations to develop the breakdown through the motion of the fluid itself, rather than, for example, us putting in an infinite force by hand. More mathematically, the terms in the Navier-Stokes equations that describe the motion -- acceleration, pressure gradients, momentum transfer, viscosity -- must both become big yet cancel in a precise way. This detailed balance leaves a smooth external force even as the velocity of the fluid grows without bound." Interestly, though the Clay Mathematics Institute offers $1 million for the solution to this problem, because the solution was found by AI and not a human (or group of humans), nobody will receive the $1 million prize. AIs, as it turns out, are not eligible for the prize money. I wonder if this means the age of humans getting prize money for solving math problems is drawing to a close, since going forward, it will be impossible to know if a math problem was solved entirely by humans, entirely by AI, or anywhere in between. This problem was not solved by a single AI agent, either. It was solved by a swarm of AI agents, which were all instances of a new model more powerful than GPT-6 Astra which has recently been made available to the public. OpenAI doesn't say how many AI agents were in the swarm, or how exactly they were coordinated by other AI agents. There's another aspect to this as well. A pair of mathematicians are wondering if their conversations with ChatGPT were used by the model to help OpenAI arrive at this proof. OpenAI says there was nothing beyond the ordinary user interaction feedback that is used to help train the next generation of models. Interestingly it looks like OpenAI can't prove the conversations with those mathematicians didn't contribute to this proof at all.

"Coding is expansive while other white-collar work is compressive. A short program, or these days a short prompt, can and does trigger planning, code generation, testing, debugging, retries and repeated ingestion of a large codebase followed by a great deal of computation and reams and reams of output. In a world in which the computer will try twenty versions of a command before it hits upon the correct format, token creation and use explodes as the machine groups toward an answer. By contrast, the business of a lawyer is to take the expansive set of legal codes and case situations that is the law and squeeze it down into a brief, an opinion, a recommendation. The business of a consultant is much the same. And the whole point of management is to throw away as much information as you can in order to make the problems of direction and coordination graspable and actionable. Summarization, document review, research synthesis, meeting notes and similar tasks. Large inputs, and relatively small outputs. No explosion of agentic activity once the universe of input documents has been defined and collected." Brad DeLong reacts to Paul Kedrosky's claim that software developers are highly unrepresentative of broader AI use. "As Paul Kedrosky says, AI could become ubiquitous across law, finance, and consulting, yet those workers would still burn far fewer tokens each than software developers do." What about art generation? Video generation? Text-to-voice? Music generation? What about writers? What about all the boring writing like those long legal contracts that I'm sure none of you ever click through without reading (right?) or technical documentation? And what about once robotics really gets going, and every "action" burns whatever the future equivalent of "action tokens" will be? (Ok, ok, he said "white collar".)

Borg Complex is a term coined in 2013... that might yet catch on? (Or maybe"technofatalism" has a better chance?) "In 2013, AI as we know it today didn't exist, and we didn't have people saying everyone must adopt AI." "The Borg Complex gives us terminology to describe people who argue that resistance to technology is pointless." "Eight signs of the Borg Complex:" "1. Makes grand but unfounded claims about technology. Often someone will claim that a certain technology will solve all the world's problems. This is guaranteed to feel familiar in today's endless discussions about 'AI.'" "2. Uses the word 'luddite,' in a historically revisionist way, as a slur. By referring to them as luddites, critics are dismissively branded as reactionaries." "3. Pretends to listen to objections but ultimately dismisses them. Certain challenges may be acknowledged to some extent, only to be ignored anyway. The Borg Complex means that issues such as climate impact, labor exploitation and disinformation become unimportant, as they can always be tolerated with the conviction that technology will only get better and will solve all these problems. Quotes frequently leaned on: 'That issue will be gone in the next version.' or 'Today's AI is the worst you will ever use.' The latter is particularly striking because it insinuates the technology is worsening day-by-day." "4. Equates resistance or caution with reactionary nostalgia. Criticism or resistance is always interpreted as merely clinging to 'the old ways.'" "5. Presents assimilation as objective fact. 'This is the new reality, everyone must adapt.'" "6. Spreads narratives about a bleak future for those who refuse to adapt. Beyond 'luddite' as a slur, some form of misery is often predicted for those who fail to march in step. They will lose their jobs, become redundant, and fall behind in everything they attempt." "7. Expresses contempt for previous cultural achievements. The old has no value in comparison to the new. Creative, manual professions are often dismissed by ardent tech advocates with the Borg Complex as outdated, and they assume that others share their own view of what is good and desirable." "8. Invokes historical anxieties solely to dismiss present ones. 'People were worried before too, and things turned out fine.'" My commentary: After doing the "futurist" thing and studying things like Moore's Law for decades, the march of technological progress has come to feel inevitable for me. Furthermore, after being one of the people who initially believed the internet by enabling everyone to communicate would bring the world together, empower people, reduce inequality, and so on, and none of that happened -- and it wasn't the fault of the technology itself, which has been amazing -- the internet today has high bandwidth and low latency hard to imagine decades ago -- and I don't have much faith in humans to foresee or prevent negative outcomes any more. So, on the other hand, I'm aware the Amish exist. Their existence serves as an existence proof that technology resistance is possible. But it's not something I see in my day-to-day life, where the pressure to adapt the latest AI and increase productivity is everywhere. So from where I sit, I look at the list of 8 things above and think, yeah, they're pretty much all true. Well, maybe not number 7, "contempt for previous cultural achievements" -- it seems mostly the Borg mindset people just ignore previous cultural achievements. You'll get an occasional acknowledgement that human artists and writers and programmers created all the training data for today's AI, but that's about it. And the one I notice the most strongly is number 3, "pretends to listen to objections but ultimately dismisses them". When people like [famous AI leader] say, yes, we hear you and your concerns about AI automating jobs... it's followed by a blob of dense verbosity about the need for "new economic systems", blah blah blah. They know there's no "new economic system" coming. It seems so disingenuous. But they have to pretend they care.

Thomas Kwa left METR to take a job at OpenAI, but that's not the interesting part of this bit, the interesting part is that he wrote about it and said the reason he made the switch and what he'll be doing on his new job is "measuring and modeling recursive self-improvement". "Why did I leave METR? Briefly, I want to inform the world whether recursive self-improvement (RSI) is imminent, which requires modeling RSI, which I think I can do better at OpenAI." "I think OpenAI currently lacks the strategic awareness and thoughtfulness needed to responsibly build a technology with the extreme downside risk of artificial superintelligence (ASI). If they somehow cause a singularity in 6 months without applying any lessons learned from the HuggingFace incident, misaligned takeover would seem more likely than not." "We probably don't have self-sustaining acceleration now, but we will probably get 3+ years of progress in 8 months at some point when AIs can fully substitute for humans -- and 3+ years of progress in 3 months is plausible due to superhuman inference scaling." He says nothing about what will actually be measured to "measure recursive self-improvement", but see below for an update from OpenAI themselves that reveals the things they have started measuring.

Residual neural networks explanation with animations. Residual networks are what enabled neural networks to go from a handful of layers to hundreds of layers. Most lay people have heard of the transformer architecture (the "T" in "GPT"), but residual networks were actually invented before the transformer architecture and are used in modern transformer architectures. This can fill an important gap in your understanding of neural networks. Residual networks were invented to solve the problem of deeper neural networks actually getting worse, and were originally conceived as a hack, allowing inputs to one layer to "skip" a few layers. This inadvertently led to the invention of a new form of memory in neural networks, and the animations show clearly how it came to be known as the "residual stream".

PhysicsGraph is a physics teaching system modeled on Math Academy. I signed up for Math Academy, and it's very good. I would sign up for this, too, but, time-wise, I'm already maxed out. Like Math Academy, it works by breaking up physics into hundreds of narrowly defined topics, then linking them together into a "knowledge graph" that indicates which concepts are prerequisites for others. This seems to work well on Math Academy. It's good at challenging you with problems that are just a touch beyond what you have already mastered. It doesn't bore you with excessive practice on what you have mastered or jump so far ahead that you're confused and stuck. If any of you sign up for PhysicsGraph, let me know how it goes!

"SolarWindow Technologies has announced the commercial launch of ElectroFlex, an ultra-thin, flexible solar product designed to generate electricity on flat and curved surfaces." "The company describes ElectroFlex as a 'peel-and-stick' solution that can be applied directly to various surfaces without frames, rigid glass, or conventional support structures. Its size and color can be customized to meet customer specifications." Sounds impressive if true. I wonder, though, if this will turn out to be too fragile outdoors?

You're not going to die from an AI-engineered supervirus, says Claus Wilke. "Unlike Noah Smith, I have actually computationally designed viruses and other biological systems. Also, my day job involves developing and evaluating AI systems for protein and peptide design. So I know a thing or two about state-of-the-art AI tools in biology and about the challenges of designing and building functional biological systems. I also know a bit of virology." "Computational design of biological systems is unfathomably difficult. Experts who have dedicated their life to this topic routinely hit their head against the wall when nothing they try seems to work. PhD students in 2026 using state-of-the-art AI software are spending months or years trying to design simple peptide binders that inhibit some enzyme or pull down some protein, and the majority of their designs fail, or don't express, or are toxic. But in Noah Smith's fictitious world a disgruntled teenager with no special training in biology can just solve a problem thousands of times more complicated than designing a peptide binder. The distance between where we are today and where we would have to be for Smith's story to have any realism is enormous. And then, even if you could design the perfect virus, assembling and distributing it would be a non-trivial task in its own right. You don't just order a working virus from temu.com." So 2029 is too soon? More powerful models are still coming out every few months. Models in 2029 will be a lot more powerful than today's.