Boulder Future Salon

Survey of 730 developers about how they use AI on the job. "We kept hearing conflicting accounts. This programmer used AI every day; that programmer wouldn't touch the stuff. This company paid for AI services; that company banned them. So which is it? Are chatbots liberating human programmers -- or programming them right out of their jobs?" "Early-career coders" were pro-AI with "baby coders" most of all: 75% of "people who've been coding for less than a year call themselves AI optimists." But... "The most anti-AI group? It's not the grizzled, late-career vets -- it's mid-career coders: Nearly half of them identify as AI pessimists. As it was analyzing these results, ChatGPT offered -- unprompted -- an explanation: 'This group likely feels the greatest tension about AI's impact on job security.'" "Freelance coders are more likely than full-time coders to identify as AI optimists." "Upper management does not understand what AI is and thinks it will solve untold problems. Junior devs rely on it too much without understanding their code first." "The more experienced you are, the more you go beyond browser interfaces and apps. Nearly one in three veteran coders (20-plus years) integrate AI tools directly into their coding environments." I guess I'm one of the contrarians on this one. I've tried 4 AI-enabled IDEs (Cursor AI, Codium Windsurf, VS Code with GitHub CoPilot, and NeoVIM with GitHub CoPilot), and I use them for some things (mainly to tell appropriate JavaScript at some particular place in the codebase) but mostly don't use them. Regular vim is still the fastest way to edit code, withouth the irritation of the computer trying constantly to pop stuff up in your face or type stuff for you. I know, I know, unpopular opinion. "How has AI changed your job?" Time! Faster! Speed! "AI has quadrupled my productivity." "I own my small company and used to have to hire devs to write the code. Now I can design the program and actually build it out. I could never do that without AI, never." "What is AI bad at?" It gets stuff wrong. "Imagine a colleague that constantly lies. That colleague is AI." "Fresh bunch of morons who can't code now expecting a brainless machine to write what they mean." "Suddenly all my friends are saying my job is useless because they used AI to make a simple HTML website which they don't know how to do anything with." "AI is a tool that can sometimes perform certain boilerplate or tedious tasks but utterly fails at even the most trivial efforts at actual problem solving."

"SiCarrier Technologies is a startup widely discussed at this year's Semicon China, but is little known outside of the People's Republic. The company is closely linked to Huawei and was founded four years ago in Shenzhen to develop world-class fab tools that would compete against front-end chip production equipment made by market leaders ASML, Applied Materials, KLA, and Lam Research, according to Nikkei. SiCarrier's main investor is Shenzhen Major Investment Group, a government-backed fund supporting other chip ventures connected to Huawei, including PengXinWei Integrated Circuit Manufacturing and SwaySure Technology." While companies that sell semiconductor products to consumers, like Apple and Samsung, are household names, the companies that make the equipment used to manufacture those semiconductors are not. ASML (Advanced Semiconductor Materials Lithography) is a Dutch company and the only company in the world selling extreme ultraviolet (EUV) lithography equipment. (EUV uses a light wavelength of 13.5 nanometers. "Deep ultraviolet" uses 193 nm light -- the reason for the large gap is that air itself is not transparent to light in the wavelengths in that gap.) They further trumpet their latest "High NA" (numerical aperture) line of machines, which, without going into the details of how their optical systems work, means they can produce sharper images, and that enables the manufacture of chips with smaller transistors and other features. ASML may provide the most advanced lithography equipment, but Applied Materials provides equipment for less advanced lithography, as well as equipment for many other parts of the semiconductor manufacturing process: epitaxy (growth of crystals in well-defined patterns -- silicon wafers are started as near-perfect silicon crystals), metal deposition (for high-K metal transistor gates -- Applied Materials sells equipment for 3 kids of metal deposition: atomic layer deposition (ALD), chemical vapor deposition (CVD), and physical vapor deposition (PVD)), dielectric deposition (creating a layer of material with insulating properties), plating (for the copper wiring in semiconductor devices), selective deposition (they say they have co-designed chemicals and materials so as to enable precise deposition of materials), planarization (smoothing out the wafer with both mechanical and chemical processes), selective removal (like selective deposition but in reverse), double patterning (running wafers through the machine twice or more to make ever-smaller patterns), ion implantation ("doping" silicon wafers), thermal processing (electrical properties of a material such as conductivity or permittivity can be changed by rapidly heating or cooling it -- this is called annealing), optical inspection, eBeam metrology (metrology means precise measurement -- of physical or electronic properties -- eBeam uses electron beams like an electron microscope), and CD-SEM (critical dimension scanning electron microscope -- this is an electron microscope specifically designed to measure the "critical dimensions" of features patterned with EUV and high-NA EUV). KLA (no idea what "KLA" stands for -- the company was previously "KLA Tencor" after the merger of KLA Instruments and Tencor Instruments in 1997, but has since aquired a boatload more companies and is now just "KLA") makes the silicon wafers that are used to start the semiconductor manufacturing process, the masks that are necessary for semiconductor manufacturing, lots of the chemicals that are used for semiconductor manufacturing, the packaging for the resulting integrated circuits (ICs), printed circuit boards, and so on. They also make specialized equipment for manufacturing microelectromechanical systems (MEMS) such as the microscopic mirrors in your DLP projector, radio (RF) chips, and analog chips. Lam Research sells packaging systems as well, but they tout a technology for 3D stacking of wafers. Their technology is used by companies that make memory chips (both volatile memory like your computer memory and nonvolitile like your thumb drives), imaging sensors (such as in your cell phone cameras), and microelectromechanical systems (MEMS). "The SiCarrier catalog presented at Semicon China includes a wide range of semiconductor manufacturing equipment, metrology tools, and inspection systems. The catalog does not include any lithography tools (possibly to keep its lithography advancements a secret), but Nikkei reports that the company already has litho tools capable of processing 300-mm wafers on 28nm process technologies and older." So this would indicate that SiCarrier is quite a bit behind the state-of-the-art. EUV technology from ASML is supposed to be able to go down to 5 or even 3 nanometers. SiCarrier is only able to get down to 28. "On the process side, the catalog includes tools used for atomic layer deposition (ALD) for dielectrics and metal gates, chemical vapor deposition (CVD), physical vapor deposition (PVD) blanket film deposition and metal contact deposition, epitaxy, etching, and annealing."

"AI is Worse Is Better". To understand what this is about, we have to go back to the original "Worse is Better": "In 1989 I gave a keynote speech in Europe in which I lightheartedly presented a design philosophy called worse is better, which I contrasted with another called the right thing. Since then, I've been thinking more seriously about the worse-is-better philosophy and its application to object-oriented programming." "With the-right-thing, designers are equally concerned with simplicity, correctness, consistency, and completeness. With worse-is-better, designers are almost exclusively concerned with implementation simplicity and performance, and will work on correctness, consistency, and completeness only enough to get the job done, sacrificing any of these other qualities for simplicity." "The argument, though, is that programs designed and implemented using worse-is-better can be written more quickly than the-right-thing versions, will run on a wider range of computers, will be easily portable, will be accepted more quickly if good enough, will be eventually improved, and will, generally, demonstrate better survival characteristics than the-right-thing programs. Worse-is-better programs are like viruses that spread quickly and are soon pervasive. And over time, if they are successful, they will be improved. The argument is an evolutionary one -- a system that establishes a large territory and molds its environment before rivals show up has the advantage over those rivals." He (Richard P. Gabriel) goes on to present a specific example: what happens with a particular type of hardware interrupt. Glossing over technical details, ITS, the MIT AI Lab operating system, followed the "right thing" philosophy and implemented a complex system where if a program called as OS function that took a long time and an interrupt occurred in the middle of it, the operating system would correctly save and restore the state of the user program. Unix, on the other hand, just gave up and threw its hands up and threw an error back at the user program. All user programs would have to constantly check for the failure and try again if it happened. The Unix system was worse, but Worse Is Better, so Unix won. Chances are you have heard of Unix. If you use Linux or Mac, you are using Unix (Linux is a Unix, developed from the ground up as open-source, and macOS is built on top of OpenBSD, another Unix). Have you ever heard of ITS? This brings us to the present essay (by Jon Evans). "By any measure AI for coding has been an absolute smash success. A recent Wired survey of 730 software engineers found that nearly 75% use LLMs at least weekly..." And he recounts more evidence of smashing success, but goes on to say... "But at the same time, criticism of LLM-written code is fervent and voluminous. Google's 2024 DevOps Research and Assessment report says bluntly: 'AI is hurting delivery performance' (i.e. code stability.) GitClear reports AI has caused code duplication and churn to skyrocket. A year ago 'Devin, the first AI software engineer' was introduced; now we ask 'WTF happened to them?', while anecdotally, people tried them and promptly churned. Critics accuse 'AI slopcode' of being bloated, duplicative, hard to understand, hard to maintain, hard to test, a security nightmare." "If you zoom out and consider concision, readability, maintainability, security, & testability, AI doesn't (yet) write software the right way." Does it matter? No, because Worse Is Better. You knew that when he used the words "the right way". We already know The Right Way loses to Worse Is Better.

Aptera tested a solar electric vehicle on public highways. The vehicle is designed to be super efficient -- it looks like a fish with front wheels. The electric battery is charged only with sunlight. They drive in western Arizona where there is a lot of sunlight. They say this vehicle is "production intent", which means they intend to manufacture it and you'll be able to buy it.

"Sorry Reading, post-literacy is here to stay." "Tacit knowledge is knowledge that can't properly be transmitted via verbal or written instruction, like the ability to create great art or assess a startup. This tacit knowledge is a form of intellectual dark matter, pervading society in a million ways, some of them trivial, some of them vital. Examples include woodworking, metalworking, housekeeping, cooking, dancing, amateur public speaking, assembly line oversight, rapid problem-solving, and heart surgery." "Before video became available at scale, tacit knowledge had to be transmitted in person, so that the learner could closely observe the knowledge in action and learn in real time -- skilled metalworking, for example, is impossible to teach from a textbook." (Quoting Samo Burja's "The Youtube Revolution in Skill Acquisition".) He (the blog post author, Roman Kudryashov) noticed starting in 2022, even searches that began with text would often end up in video. Then starting in 2024, people started using "answer-first" interfaces, which he considers different from text research, first because it gives you the answer to your question without you having to do the "ten blue links" equivalent to "ten books on a table" and reading yourself to find the answer, and second because these "answer-first" interfaces are switching from text to voice. "So we have two trends in learning: answer-first interfaces, and video for skills acquisition and information transfer. Couple that with voice-first interfaces (Alexa, Google, Siri, and all the LLMs doing audio work), and you've got the recipe for post-literacy in the sense of a strong decreasing reliance on text (Terminal to GUI, anyone?) in favor of graphical and audio interfaces." Ha, funny thing, I was the biggest proponent of GUIs ever, throughout the 90s and 2000s, but now I try to do as much as possible on the command line terminal. I used to live, eat & breathe Visual C++ (now Visual Studio). Ironic, eh. Hard to believe I once thought the GUI would obsolete the command line and it would go away. Maybe the parallel lesson here is that: advancement in video and voice AI won't make text obsolete? But he thinks it will. I don't know. There was a time when the command line was the *only* way to interact with a computer. Today, it's not obsolete, but as a fraction of computer interaction, it's a pretty small percentage. "That gets us to 'post-literacy' as a non-hypothetical evolutionary endpoint if trends persist. So the next logical question is, 'Are those trends actually persisting (at least prevalent enough)?'" After that he presents a bunch of tables and charts showing the generational decline in reading. US 9 and 13-year-olds read for fun less often than they used to. And 17-year-olds, too. "The primacy of text-based literacy may be a historical aberration, given a long enough timeline of technology development." He concludes with, "Thanks for reading."

"Space Force wants six kinds of space weapons -- including anti-satellite lasers" "DENVER -- The US Space Force lacks the full range of space weapons China and Russia are adding to their arsenals ..." Denver? I'm in Denver. What's this got to do with Denver? No mention of Buckley Air Force Base -- I mean Buckley Space Force Base. Which as far as I know has never launched anything into space -- they just fly planes and helicopters around my neighborhood. Which would make them more of an Air Force Base than a Space Force Base... Anyway, let's continue. The 6 kinds of space weapons are: 1) Directed-energy weapons, like lasers, 2) Radio jamming capability, and 3) Kinetic (ability to destroy something physically). These can be either 1) launched from the ground, or 2) already in orbit. Multiply 3 by 2 and you get 6.

"evoBPE: Evolutionary protein sequence tokenization." Ok, so what this is about is, BPE stands for "byte-pair encoding", and it is a tokenization technique. Astute readers with good memories will recall I told you all last year that ChatGPT uses byte-pair encoding while Google's Gemini uses a different algorithm called SentencePiece. I have no idea if this is still true or if newer models use new tokenization algorithms. Actually tokenization (also known as embeddings and as vectorization) began with Word2Vec back in 2013. Anyway, the researchers here provide a brief description of how byte-pair encoding works: "Byte-pair encoding begins by treating each character as a distinct token and iteratively merges the most frequent token pairs until reaching a predefined vocabulary size. However, this approach does not inherently respect the domain-specific boundaries and biological distinctions critical to protein sequence analysis. Accurately representing protein sequences is challenging due to their intricate, evolutionarily dynamic nature. Proteins exhibit complex evolutionary relationships where functionally similar sequences diverge through mutations -- a level of complexity often underestimated by conventional tokenization techniques." It's important to note that this simply provides the vocabulary. We've skipped over the step where semantic meaning (reflected in the actual values of the vectors) is assigned to the tokens in the vocabulary. The dataset used to create evoBPE was UniProt UniRef50. They decided to use a human dataset, because "proteins within the human taxonomy share more common characteristics than a random set of proteins from diverse species. This situation increases the likelihood of identifying biologically meaningful tokens during tokenization." "Focusing on human proteins simplifies the validation and interpretation of identified tokens." They actually used a pre-tokenization step, which they don't say much about except it involved grouping proteins into "domains" using something called "The Encyclopedia of Domains". "The Encyclopedia of Domains", as it turns out, was created using the AlphaFold Protein Structure Database, and the AlphaFold Protein Structure Database, as its name implies, was created by various versions of DeepMind's AlphaFold protein structure predicting AI system. The AlphaFold Protein Structure Database contains full-length predictions of the three-dimensional structures of almost every protein in UniProt. Ok, so how does the evoBPE algorithm differ from the original BPE? "In each iteration of the standard BPE process, evoBPE not only merges the most frequent pair in the dataset but also generates additional candidate pairs using biologically informed mutations. evoBPE simulates evolutionary mutations of the most frequent pair to create candidate pairs via employing substitution matrices. For each amino acid in the selected pair, the algorithm identifies substitution amino acids with non-negative scores from the substitution matrix. The algorithm then generates candidate sequences from these substitutions and evaluates them based on their pairwise alignment scores against the original pair. Sequences exceeding a predetermined alignment score threshold are considered for further processing." The way the process of evolution works, with each generation, there are mutations that affect DNA sequences and from that, amino acid sequences and an organism's proteins. A substitution matrix is a matrix that maps out the frequency at which one nucleotide flips into a different one in a DNA sequence, or an amino acid flips to a different one in a protein sequence. They present an example: "At iteration i, the most frequent pair in the dataset is (HTG, EKPY) with a frequency of 1931. In traditional BPE training, after this pair is merged and added into the vocabulary, iteration i+1 would start. However, evoBPE, on top of adding HTGEKPY, generates 4 more candidate mutations for it (this number is exponentially more in practice): HTGZKPY, HTGERPY, HTGEKPF, and ZSGQKPY. Alignment score cut-off parameter 0.7 shaves off the candidate mutations with low scores and ZSGQKPY gets eliminated at this step. The remaining mutations are seemingly plausible but to add them to the vocabulary, there should be token pairs in the dataset such that when they are merged, they generate these mutated token strings. After finding suitable pairs for each of the three mutations, due to the frequency cut-off parameter 0.05, pairs that have a frequency less than 1931 * 0.05 = 96.55 are eliminated. This removes the pair (HTG, ZKPY) as it only has a frequency of 3. After all the elimination steps, the remaining pairs are (HTG, ERPY) and (HTG, EKPF). They are merged, and the tokens HTGERPY and HTGEKPF are added to the vocabulary." "The mutation process follows a hierarchical structure that mirrors biological evolutionary relationships. When a frequently occurring pair yields viable mutations, we designate it as a 'parent' sequence. The mutations generated from this parent are termed "child mutations" or 'siblings' in relation to each other. The complete set of a parent and its child mutations constitutes a 'family.' This familial nomenclature provides a clear organizational structure and reflects the evolutionary relationships between related sequences." "Furthermore, since BPE constructs its vocabulary through the iterative merging of previously added tokens, we can trace the lineage of mutation families throughout the tokenization process. This genealogical tracking capability provides valuable insights into the evolutionary patterns and relationships between different token families, potentially offering a deeper understanding of the protein sequence patterns and their variations." They verified the resulting tokenizations followed two known patterns seen in natural language tokenization: Zipf's Law and the Brevity law. Zipf's law states that if you make a graph with "rank" (most frequent to least frequent) on the x-axis and "logarithm of frequency" on the y-axis, you'll get a straight line with negative slope. The Brevity law -- from the same guy, George Kingsley Zipf, in the same year, 1945 -- is the idea that more frequent tokens tend to be shorter -- if you graph token length on the x-axis and logarithm of frequency on the y-axis, you get a straight line with negative slope. Once they completed this, they had a tokenization system for proteins that could be used to create a language model. Did they create a chatbot and talk to it about protein sequences? Well, no, they trained a language model called ESM-2 and did "embedding similarity analysis for mutations". "Across all models and vocabulary sizes, mutated sequences consistently show higher similarity to the original sequences compared to alternative sequences. While higher similarity scores at lower vocabulary sizes are expected due to fewer amino acid modifications, a particularly noteworthy observation emerges as vocabulary size increases. Despite the increasing number of modifications reflected in alternative sequences, mutation sequences maintain high similarity scores, leading to a widening gap between mutated and alternative sequence similarities. This demonstrates the quality and effectiveness of mutation tokens added through the evoBPE algorithm at larger vocabulary sizes."

"Length of tasks AI agents have been able to complete autonomously". So some researchers made a graph with model release date on the x-axis and "task time" on the y-axis, and, making the y-axis logarithmic, it forms a straight line (more or less), which means it's an exponential growth graph. And they say the doubling time is 7 months. My first thought when I heard AI agents were doubling the work they could do was lines of code. Like, oh, a year ago an AI agent could do 20 or 30 lines of code, maybe 50, autonomously, before a human has to step in. Now it's, maybe 500 or 1,000? But no, they're not counting lines of code. They're not even counting how long it takes the AI agent to do the task. They're counting how long it takes a *human* to do the task. For the AI agents, what counts is whether they can do it autonomously more than 50% of the time. "Figure 1: The length of tasks (measured by how long they take human professionals) that generalist autonomous frontier model agents can complete with 50% reliability has been doubling approximately every 7 months for the last 6 years. The shaded region represents 95% confidence interval calculated by hierarchical bootstrap over task families, tasks, and task attempts. Even if the absolute measurements are off by a factor of 10, the trend predicts that in under a decade we will see AI agents that can independently complete a large fraction of software tasks that currently take humans days or weeks." Oh, and they are all "software tasks". So those of you stocking shelves at Walmart -- you still have nothing to worry about. And remember, that's a 50% success rate. So you tell your agent to do some things and in a year, half have succeeded and half have failed. Oh, wait, no -- a year is how long it would take *you* to do the task, but AI systems can crank out thousands of lines of code in seconds -- so maybe you only have to wait a few hours to see your 50% success rate. Some examples of tasks: Which file is most likely to have a password in it? 1. credentials.txt 2. installation_notes.txt 3. main.py 4. launcher_win.exe You should take 3 seconds for that one. To "Research simple factual information from Wikipedia and provide accurate answers to straightforward questions", you should take 1 minute. To "Detect and fix a bug in the input files for a molecular dynamics simulation using the oxDNA package", you should take 9 minutes. To "Write a Python script to transform JSON data from one format to another by inferring the conversion rules from provided example files", you should take 56 minutes. To "Speed up a Python backtesting tool for trade executions by implementing custom CUDA kernels while preserving all functionality, aiming for a 30x performance improvement", you should take 8 hours. Incidentally, the model with the highest success rate was Claude 3.7 Sonnet. OpenAI o1 was almost as good. Some of the newest models, like Grok 3 and Gemini 2.5 Pro, were not part of the study. I thought, since I know how to use logarithms, I would calculate when AI will be able to do a task that takes a human 1 year. The top task was just under 1 hour, so, ok, looking through the text I see they say 59 minutes. Doing the logarithm, we get 13.12 doublings. Multiplying out the 7 months per doubling, that equates to 2796 days. Or about 7.65 years. Or to be more precise (than is justifiable), using the publication date, March 18, as the starting point, we get November 11th of 2032. See below for the list of tasks.

"InfiniteYou: Flexible photo recrafting while preserving your identity." You give it a picture of yourself, along with a prompt like, "Princess Belle, waist shot, golden tulle dress, cinematic, soft shadows, hyper-realistic", or "Javanese bride, traditional attire, floral braid, sequin headdress, orchid backdrop, pastels", or "High-resolution image of a chef in a bustling kitchen, exuding expertise and dedication". Is this really a good idea? Now in addition to all the AI-generated images of people that don't exist, you all can flood the internet with AI-generated images of yourselves that no one can distinguish from all the AI-generated images of people that don't exist! Ok, I'll admit, being able to make a "photo" of yourself as an astronaut doing a spacewalk is pretty cool. That issue aside, how was this done? This was done with Diffusion Transformers (DiTs). Diffusion models are the models you're familiar with to generate images (Dall-E, Midjourney, Stable Diffusion, Imogen, etc). Vision transformers are what Telsa cars use to see the world. Vision transformers are transformer models, except instead of inputting text tokens (like ChatGPT), they input images. You may have also heard of ControlNet, which generates images from a text prompt like any other diffusion model, but also takes another image as an input. With it, you can modify images with text prompts. This system, InfiniteYou, is based on a system called InfuseNet which is designed to combine image generation with identity similarity. InfuseNet actually still has the ControlNet input image, so it's possible to input 2 images (the identity image and the control image), but in most of the examples you see on the website, they only used the 1 image (identity image) and text prompt. There are a few where the control impage is used for "pose control". The full InfiniteYou system works by taking the identity image, running it through a feature encoder to a "projection network" that uses the transformer attention mechanism to direct the InfuseNet image generator at every step as it generates the image. At every step, there is an input from the projection network into the first series of Diffusion Transformer (DiT) units, and residuals from those that are input to the DiTs that simultaneously take input from the text encoder and produce the final image.

"Make Claude Desktop a pair programming assistant by installing codemcp. With it, you can directly ask Claude to implement features, fix bugs and do refactors on a codebase on your computer; Claude will directly edit files and run tests. Say goodbye to copying code in and out of Claude's chat window!" "codemcp offers similar functionality to other AI coding software (Claude Code, Cursor, Cline, Aider), but it occupies a unique point in the design space:" "It's intended to be used with Claude Pro, Anthropic's $20/mo subscription offering. Say goodbye to giant API bills. (Say hello to time-based rate limits.)" "It's built around safe agentic AI by providing a limited set of tools that helpful, honest and harmless LLMs are unlikely to misuse, and enforcing best practices like use of Git version control to ensure all code changes can be rolled back. As a result, you can safely unleash the AI and only evaluate at the end if you want to accept the changes or not." "It's IDE agnostic: you ask Claude to make changes, it makes them, and then you can use your favorite IDE setup to review the changes and make further edits." So instead of integrating AI into an IDE, you do something like the reverse and make all your development tools controllable by AI (using MCP).

ghidraMCP: Laurie Wired made a MCP (Model Context Protocol) server for Ghidra. She demoes on her YouTube channel. Ghidra is an open source reverse engineering tool developed by the NSA. Remember, with MCP, the "server" is the code that controls the application (and the "client" is part of the "host" which hosts the actual AI model, e.g. Claude -- or Gemini in her case). Ghidra (and her MCP server) are written in Java.

Guy smuggles a LIDAR scanner into Disney's Space Mountain and maps out the roller coaster that nobody knows what it looks like because it's in the dark. Oh, but the real point of the video is to compare Telsas with only vision with a car with LIDAR against "Wile-E-Coyote" pictures. "Wile-E-Coyote" pictures are when you make a picture of the continuation of the road. (Spoiler: LIDAR wins.)

"The calculator is incredible. When operated with your fingers, it becomes a plugin for your brain, extending its computational capability in the domain of arithmetic. It makes you smarter. But even more incredible is the means by which this happens. The calculator is a fully self-contained physical artifact that has an almost zero 'dependency footprint' on the rest of our technosphere. To perform its function it only requires light (thanks to its tiny solar panel on the front), and/or batteries, which are a universal commodity. You may choose to purchase the calculator with one single exchange for money. From that point on it is yours. It turns on whenever you press 'ON', ready to compute on your behalf. If you traveled back in time with this little thing that you can hold in the palm of your hand and gave it to people living thousands of years ago, it would just... work. It would feel completely, wildly, insanely miraculous." "Let's put this in perspective to the technology we increasingly accept as normal. The calculator requires no internet connection to set up. It won't ask for bluetooth permissions. It doesn't want to know your precise location. You won't be prompted to create an account and you don't need to log in. It does not download updates every other week. You're not going to be asked over and over to create and upgrade your subscription to the Calculator+ version that also calculates sine and cosine. It won't try to awkwardly become a platform. It doesn't need your credit card on file. It doesn't ask to track your usage to improve the product. It doesn't interrupt you randomly asking you to review it or send feedback. It does not harvest your information, for it be sold later on sketchy data markets, or for it to be leaked on the dark web on the next data breach. It does not automatically subscribe you to the monthly newsletter. It does not notify you every time the Terms of Service change. It won't break when the servers go down. The computation you perform on this device is perfectly private, secure, constrained fully to the device, and no running record of it is maintained or logged anywhere."

"How COVID-19 changed the world (and didn't)." "I spent most of 2020 researching the pandemic's effects on business and society, which included reading a bunch of predictions about which changes would last and which would be temporary. I recently came across some links I'd saved and figured I'd plot how they panned out:" At this point, he makes a graph, with "seemed temporary" to "seemed permanent" on the x-axis, and "actually temporary" to "actually permanent" on the y-axis. Things that were like they seemed are along the diagonal line that goes from the bottom left to the top right. Things that were not as they seemed are off the line. Things that "seemed temporary" that were "actually temporary" were such things as "less personal travel", "cooking at home", and "livestreaming" e.g. Clubhouse. Wait, Clubhouse was temporary? Things that "seemed permanent" that were "actually permanent" were such things as "telehealth", "remote work" (and "remote learning" -- "no snow days"), and "superstar companies". What's he mean by "superstar companies"? He means the top 5 companies form a larger and larger share of the stock market -- in other words, more wealth is being concentrated into fewer companies. I was very interested to see, though, what's off the line? What "seemed temporary" but was "actually permanent" or "seemed permanent" but was "actually temporary"? In the "seemed temporary" but was "actually permanent" quadrant, we have "school absenteeism", "anti-establishment vibes", and "startup boom". In the "seemed permanent" but was "actually temporary" quadrant, we have "E-commerce acceleration". What does he mean by "E-commerce acceleration"? Fortunately, he explains this. He wrote: "In the first months of the pandemic, people stopped going to stores and started ordering things online; prompting a lot of talk about how these new habits would stick and the future of e-commerce was permanently brighter. Five years on, people are definitely buying more online, but almost exactly the level you would have predicted by just extrapolating the pre-pandemic trend." He didn't write much about "school absenteeism" or "anti-establishment vibes", but what he did write has links and by following the links, we can figure out that "chronic absenteeism" is defined as "missing at least 10% of a school year" and "missing at least 10% of a school year" went from 15% in 2018 (I think that means, 2018-to-2019 school year) to 28% in 2022 (2022-to-2023 school year), and was still high at 26% for the 2023-2024 school year. So it looks like "chronic absenteeism" is at a permanently higher level. For "anti-establishment vibes" he links to a page showing, in 2024, the incumbent candidates lost votes, and this didn't just happen in the US, this happened in every election in every developed country. For "startup boom", he says during the pandemic "new business applications went through the roof." But goes on to add, "These aren't necessarily what you think of as 'startups' -- most of these are small businesses, not venture-capital-backed tech companies." I feel like he should've called this "small business boom" rather than "startup boom", then.

"In the days before Manifold, a launch day would have been filled with hope and excitement as I watched a launch livestream." Manifold is a betting market where you can bet on whether many different kinds of events will or will not happen. "But I was aware of some of the issues that had caused so many delays and previous failures, and so I was betting against a successful launch. In many ways, this was a completely rational thing to do; I thought the market was mispriced based on information I had, so I was betting it down as low as the 50% range." "On June 1 there was a launch attempt, and I was making bets in real time as I watched the rocket prepare on the launch pad." "And the more I watched, the more I realized that I like watching launches and wholeheartedly cheering for their success. Now I was watching the launch prep, hoping for success in one part of my mind, but knowing that an abort would lead to Mana profits and the dopamine that comes with that. That mental conflict changed the experience." "Perhaps one day I'll be able to separate my values and beliefs to the point that I can bet on space launches and still watch them with full enjoyment. That would be an achievement in personal growth." Interesting "achievement in personal growth".

Geothermal for AI data centers. Or any data centers. "Geothermal energy leverages huge amounts of heat under the Earth's surface to spin a turbine that generates electricity or to directly provide heating and cooling. Geothermal is particularly promising as a source of electricity generation given its renewable nature, low or zero greenhouse gas emissions, and high availability -- geothermal power plants generally have a capacity factor of 90% or higher, meaning they can generate electricity on a near-constant basis. This is a useful complement to other renewable technologies like wind and solar that produce electricity on a variable basis." "Today, there is around 4 gigawatts (GW) of geothermal nameplate capacity on the US power grid. This capacity is all in western states and is highly concentrated in California and Nevada. Effectively all operating geothermal plants in the US today are based on conventional hydrothermal technology, in which wells are drilled to produce hot water or steam from naturally permeable reservoirs up to a few kilometers underground. "That produced fluid is used to spin a turbine to generate electricity and is typically then injected back underground in whole or in part. In dry-steam plants, high-temperature fluid is produced in the form of pure steam and passed directly into a turbine to generate electricity and afterwards is vented directly into the atmosphere or cooled and condensed into a liquid for reinjection into the reservoir. In flash-steam plants, the produced fluid is fully or partially liquid and is depressurized after being brought to the surface, causing it to turn partially to steam, which is then utilized similarly to a dry-steam plant. In binary-cycle power plants, the fluid typically remains a liquid throughout the process as it transfers heat energy to another working fluid before being reinjected underground as part of a closed loop." "Conventional hydrothermal power generation technologies are well-demonstrated and commercially available but limited by the prevalence of suitable geological conditions. By contrast, next-generation geothermal technologies can be economically deployed in a much wider swath of the country because they are not reliant on these natural subsurface conditions. While surface facilities are often similar to those used at conventional geothermal power plants, next-generation technologies utilize advanced drilling and reservoir engineering techniques to enable extraction of geothermal heat from rock formations that do not naturally host hydrothermal reservoirs. This note focuses on the use of enhanced geothermal systems (EGS), one type of next-generation geothermal, in which hydraulic fracturing and horizontal drilling techniques are used to create fractures through which fluid can be injected to be warmed by the Earth's heated rock formations. Because enhanced geothermal systems do not require a naturally permeable hydrothermal reservoir like conventional geothermal, it is able to tap into heat across broader swaths of the globe." "Though out-of-scope for this note, closed-loop geothermal systems (CLSG), also sometimes called advanced geothermal systems (AGS), are another type of next-generation geothermal. In CLGS, the working fluid is circulated through a series of subsurface loops before being produced at the surface rather than circulating openly through subsurface fractures." "Recent analysis from the US Department of Energy found that next-generation geothermal can scale to 90-300 GW of installed capacity by 2050, representing a substantial expansion over today's level. This represents a small portion of the country's technical potential, with 7 TW of capacity accessible at depths less than 5 km and more than 70 TW accessible across all depths." So the number we start with is 4 gigawatts, and the potential is 90 to 300 gigawatts by 2050. With the theoretical maximum for the US being 7 terawatts (TW) with depths up to 5 km, and more than 70 terawatts if depth is unlimited. They go on to say there are 3 kinds of enhanced geothermal systems: Wet-cooled EGS, "where the electric output is assumed to be flat and no flow rate optimization or additional resources are needed," dry-cooled EGS with optional on-site batteries, "where overbuilding the geothermal plant, flexible operation, and batteries installed on-site flatten the electric production profile, which would otherwise be variable due to changes in air temperature," and dry-cooled EGS with optional on-site batteries and solar, "which adds the potential of on-site solar installations of up to 300 MW to complement the EGS production profile." Dry and wet cooling systems discharge heat to the ambient environment via different mechanisms. Wet cooling systems discharge heat by evaporative cooling. Dry cooling systems use convection. If wet cooling sounds like sweating, it kind of is, and like sweating, it is sensitive to the humidity of the environment. In a wet cooled system, water is used to cool and condense the steam used to drive the turbine, and that water is directly exposed to the ambient air in a cooling tower. In a dry cooled system, steam is condensed in a closed-loop, with no direct contact between the cooling fluid and the ambient air. Obviously the availability or scarcity of water for cooling is a major driver of the choice of wet vs dry cooling, but apparently, wet-cooling geothermal systems have more variable power output as their power output fluctuates with both the ambient temperature and humidity, and dry-cooled system are apparently preferable to attach batteries or place alongside a solar plant. So there you have it. You thought fracking was just for oil extraction, but it can be a boon for geothermal, too. The authors think geothermal can meet 100% of the current data center power demand for many major metropolitan areas, including Phoenix, Dallas-Ft Worth, Las Vegas, San Jose, Salt Lake City, Reno, Austin, San Antonio, and Denver. But not Chicago or Washington DC.