Josh Brake @JoshuaBrake

Really enjoy Readeck, but like to read on my Kindle Scribe instead of the app and my phone/tablet. But the full featured website is not a good experience on the Kindle web browser. So naturally, built myself a custom web interface for the Kindle today, from my phone, with Claude Code in approximately 3 prompts. Just crazy.

Just can’t quite shake the intuition that there are real limits to motivation via machine. My working thesis is that lasting, authentic motivation requires human relationship, in one form or another. Feels like this is a really central thing to get straight as we think about integrating AI into education. As one example, I’m thinking about how activity rings on my Apple Watch, Strava streaks, or other sorts of machine-generated nudges or reminders are way less effective for motivating you than a running or walking buddy who expects you to show up at a specific time every week. To the degree that the nudges work, it’s because they are actually just reflecting your own expectations back to you. This is why the technology can help you to build a habit if you bring the motivation to the table, but isn’t much good otherwise. Same paradigm applies in education, where a motivated learner can use AI to learn but it will be net negative for a student who doesn’t already come motivated to learn. The technology can help to facilitate or amplify motivation, but it can never create it. That said, I’m very curious to hear how this lands with you. What are the best arguments/narratives for and against this thesis?

70k words, 600+ illustrations, and 1000s of hours (so far). Making Software is now available in early access. makingsoftware.com/early-access

We shouldn't destroy ourselves in arguments about AI. campuses need to focus on conversations that allow faculty to form shared values and shape what role, if any, AI should have in education.

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@jeffreyhuber Cooking up a few ideas this summer too for my classes this fall. Not super sophisticated, but I’m bullish on using AI for building interactive visualizations for teaching. Not exactly AI directly, but an example of how AI can amplify good teaching. teaching.aiprototypes.org.

@jeffreyhuber One of my buddies is building socrat.chat which I think is pretty slick…

Never been a better moment to have some imagination for what might be possible. Crazy fun to build right now.

If there is one thing you read today, read this. Brilliant analysis from Yuval Levin in @tnajournal. thenewatlantis.com/publications/i…

A post about Pope Leo XIV's encyclical on AI. Why the Pope is right, but perhaps not right enough. Artificial intelligence is reshaping the world in front of our eyes: how we communicate, how we access information, how we work, how income and status are distributed among us, and soon how we fight and kill each other. Yet the public conversation about AI remains stuck on the minutiae of competition between labs, or on a false dichotomy between AI as a “stochastic parrot” with no real capabilities and AI as an alien superintelligence poised to take command of humanity. The more important questions are about what we want from AI, and whether our current mindset, institutions, and control mechanisms are equal to the task of steering it toward our welfare. It is refreshing, then, that a bold and powerful voice has weighed into this debate: Pope Leo XIV. As an economist who has long argued that technology is a matter of choice rather than fate, I find Leo’s intervention welcome and, on most points, on target. But on the most consequential question of what AI should actually be designed to do, Leo stops short. Secular readers may bristle at the encyclical’s opening invocation of the Tower of Babel. They would be mistaken to stop reading there. Leo goes much further than most pundits, journalists and policymakers in the United States by recognizing that what happens to AI, and hence to humanity, is a under our control. There are multiple possible paths for AI, and which one we take will have sweeping consequences. He is also ahead of many commentators when he writes forcefully and unequivocally that “technology is never neutral, because it takes on the characteristics of those who devise, finance, regulate, and use it.” These were the central themes of the book I wrote with Simon Johnson, Power and Progress: Our Thousand-Year Struggle over Technology and Prosperity. It is heartening to hear them taken up by a voice with Leo's reach. The Pope is also right to question the current trajectory of AI in warfare and law enforcement. What was taboo only a few years ago – AI-driven mass surveillance, algorithms selecting targets for killing – has become routine. Many in Silicon Valley are now calling openly for a new military-algorithmic complex centered on AI as an instrument of American hard power. Leo captures something deep and too often ignored: “Any technology that facilitates attacks without seeing the face of human beings lowers the moral threshold of conflict.” His call for the “disarmament of AI” follows directly from these observations. As he explains, disarming AI means “freeing it from the mentality of ‘armed’ competition, which today is not limited simply to the military context, but is also an economic and cognitive phenomenon.” His moral clarity in stating that “there is no algorithm that can make war morally acceptable” should be a warning to technologists rushing to design new weapons of mass destruction. Underneath these specific concerns lies a more fundamental claim: that what is technically feasible is not the same as what is good for humanity, and that the difference depends on who controls the technology and what ideology and interests guide them. Leo edges toward what I take to be the most important point about AI's future when he observes that “while AI promises to boost productivity by taking over mundane tasks, it frequently forces workers to adapt to the speed and demands of machines, rather than designing machines to work with those who work.” But here he does not go far enough. He stops short of questioning the prevailing design philosophy of AI itself: a philosophy centered on mimicking human capabilities and automating human tasks, with the ultimate goal of artificial general intelligence (AGI) that can do everything a person can. This philosophy rests on a mistake. It assumes that artificial intelligence and humanintelligence are fundamentally similar, and therefore machines should naturally take over whatever humans currently do. Yet these intelligences are fundamentally different. Humans are “one-shot” learners. We form hypotheses from a few examples, mentally simulate possibilities, and refine our understanding through a social process of trial and error. This is how children learn language - imitating a few words, generalizing, and adjusting based on how others respond. We are not, however, very good at absorbing massive volumes of information or sifting through unstructured data for relevant patterns. AI models are almost the opposite. They thrive on enormous training sets and excel at pattern recognition at scale. But they have, as yet, no genuine creativity, no real-world embodiment, and no capacity for trial-and-error learning grounded in interaction with the physical and social world. When two things are different – you shouldn’t, and typically you couldn’t – use one to mimic the other. If you did, you would end up with suboptimal, disappointing results. It would have been a colossal mistake, and the Chicago Bulls’s legendary coach Phil Jackson would have gone down in the annals of basketball as one of the worst coaches in history, if he decided in the 1990s that because Michael Jordan was the better player, Jordan should mimic everything that Scottie Pippen and Dennis Rodman were doing in the team. The team went from championship to championship because these players worked together and complemented each other. The same applies to AI and human skills. The more productive path is complementarity – using AI to do what humans cannot, so that humans can do what they do best. An electrician aided by AI diagnostics, a nurse supported by AI in interpreting symptoms, a teacher using AI to personalize instruction for each student; these are the contours of a different AI future, one that raises rather than displaces human capability. Optimists and industry insiders will respond that automation-first AI can still benefit everyone, provided redistributive policy keeps pace. But this argument has a poor track record. Forty years of digital automation have already concentrated gains at the top, hollowed out middle-skill work, and produced disappointing aggregate productivity growth. There is little reason to expect that an even more powerful round of automation, deployed by even more concentrated firms, will end differently. We can and must demand a different design. The global stakes from the future of AI are even larger than those we can see around us in the United States. For the developing world, where billions still depend on the prospect of decent jobs as a path out of poverty, an automation-centric AI agenda is not merely suboptimal. It is simply transferring to foreclose the most important route to broad-based prosperity. The biggest failing of today's AI industry is its refusal to recognize any of this. It is guided instead by an ideology of control (the industry’s own over humanity) and by a conviction that machines are uniformly better than humans. As Leo rightly notes, this failure is enabled by the fact that a handful of companies now command the future of AI. What we need is a combination of moral clarity and a serious, society-wide debate about what AI can do and what we want it to do. That debate must move beyond exhortation toward concrete choices: antitrust action against the dominant platforms, public investment in human-complementary AI, regulation of surveillance and autonomous weapons, and meaningful rights for workers and citizens over the data on which these systems are built. The Pope's intervention makes such a debate a little more likely today than it was before. It is now up to the rest of us to carry it further than he was willing to go.

@eugeneychan Yup, totally agree this is not a new property. How are you thinking about the tractability of the new problems?

Two things can be true at the same time: 1. AI is very powerful and is helping us solve problems. 2. In solving those problems, AI is creating new problems and some of these new problems are gnarlier than the ones AI helped us solve. The only way that AI helps us flourish is if we can figure out how to direct it in ways that address our existing problems without creating even more. Unfortunately we are likely in a situation with a hydra effect where every problem we solve spawns two or more new ones. There is a real possibility that AI may be a very powerful tool for solving problems but will leave us worse off than when we started. This is the most pressing challenge of our current moment.

@AndrewDNoble I would also say that I do feel we ought to have a healthy respect for the number of problems that are being “solved” right now that are simultaneously creating lots of others that we won’t see or understand until some time passes.

@AndrewDNoble Fair enough. Although it is Twitter after all, overstatement is in the water :)

@jamescham @danielrock +1. I've often thought that it would be very cool to unleash some network theory into an event app. Would want to figure out how to find connections that don't exist but should, ways to facilitate new connections, allow folks to request intros, etc.

So disappointing, @Anthropic. 95% of my usage of my usage of Claude Code is in @conductor_build. Seems absurd that this is now going to be effectively billed as API use.

ClaudeDevs @ClaudeDevs

4 weeks ago

This means that third-party tools built on the Agent SDK like Conductor and OpenClaw work with your Claude plan, but will draw from your credit the same way your own scripts do.

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Worth a read here from @tobi. Interesting to think about what this would look like in a classroom: you can use AI however you want, but must use it in public so everyone else can see the interactions.

tobi lutke @tobi

4 weeks ago

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I understand the general sentiment that one way to combat the temptation for our students to outsource their work to AI is to move the work in class. But shouldn’t the real goal be to help them build the character and grit needed to resist the temptation and do the work even outside of the perfectly curated environment? We can and should attend to the environment to help our students grow, but it cannot stop there. We need to help our students to grow to love the work for its own sake, to experience the deep joy of struggling with ideas and work that is worthwhile, to build not only the raw skills that are part of an intellectually rich life, but to help them to learn how to cultivate their own energy, environment, and engagement. I’m not interested in letting the specter of AI’s temptation restrict my imagination about what my students can be and do. If they can only do the work in class, I will count that as a failure and loss on both of our parts.

Holy memory leak, Batman. Some things not even 128GB of RAM can save you from.

The Sam Altman and @miramurati texts from the day he got fired from @OpenAI in 2023 just became evidence in the @elonmusk v. @sama trial. It felt like a meaningful moment in AI history, so I turned it into a musical. The lyrics are the texts.