Since I just gained a large(r) following after the quantum computing thread, I should re-introduce myself. I'm the founder of @RareSkills_io. It's a free education platform for web3/blockchain programmers and smart contract auditors (cybersecurity for web3). RareSkills is most known for the free content, which I will provide links to in the following section. Although we mostly target intermediate to (very) advanced engineers in the space, we do have some "new to web3 programming" resources I will share in the next Tweet. Our content tends to fall into the following topics: 1. Zero Knowledge Proofs cryptography 2. Explainers of DeFi (Decentralized Finance) protocols for programmers 3. Pretty much anything related to the Rust and Solidity programming languages. One thing that trips people up is that when we say "free" we actually mean "free." No email required to sign up, no gated content to upsell. So how do we make money? 1. We have paid training, but these use the same materials that we give out for free. The idea is that if you are busy, you probably want to learn as fast as possible, and thus paying someone who already knows the subject well and has a lot of experience teaching it will speed up the process. We intentionally limit the class sizes to 10, which is why they tend to be somewhat high in price. We prefer a few highly committed engineers than hundreds of people casually checking things out. 2. Since we get a good amount of traffic from experienced engineers to our website, we also have a recruitment firm called @RareTalent_xyz. @guy_de runs that company so reach out to him if you are hiring or passively looking for work. 3. For some cohorts, @base sponsors them, so they are free to you if you are accepted. Stay tuned for updates (from @RareSkills_io) about those because they come and go quickly. 4. Some of the education content was funded by other companies. This is always disclosed. ———— I tend to post commentary on the Web3 industry, usually from a technical perspective, although sometimes I post satire. As you can tell from my thread on Quantum computing, I'm a "cynical optimist." I really dislike hyping up tech to the point where people don't have a good picture of what is actually happening. I loudly and correctly predicted two years ago that LLMs would not significantly disrupt most white collar jobs in the time frame that the hype artists were claiming. Nonetheless, I do think LLMs are a useful tool and sometimes I dabble with them to improve my workflow. Overall, I believe that technological progress is a good thing and should be pursued. ———— My background is in machine learning -- I used to lead the video machine learning engineering team at Yahoo, but switched to blockchain full time in 2021. Yes, that seems like a bad career move, but technically, I find blockchain to be much more interesting. AI/ML is basically about how many experiments you can run in a limited time. It's very hard to do research if you aren't part of a big lab. Blockchain on the other hand can be reasoned about from first principles (the same way a regular computer or a quantum computer can be). Yes, I do think our industry (web3) can be very scammy and unethical, but if you know the right crowd to hang out with, you get to work on interesting, meaningful, and constantly-changing problems and will never be bored. If you've ever done business in a country with corrupt banks, then you know blockchain solves a real problem. For people in developed countries however, I will admit the biggest (but by no means the only) use-case is gambling. ———— Due to how wildly successful the last tweet was, of course I'm thinking about what else to publish about quantum computing, but frankly I think it's mostly an interesting novelty as a research problem and sometimes a fun brain teaser. My industry is blockchain engineering, so I hope you find that interesting!

By the way, this is only Part 1. So you'd better sign up now because early users get perks... PLONK, NTT, Coding Theory + ZK-STARKs, and even Bilinear Pairings are all in the works. If you liked the ZK Book, you'll like this even more. Quite literally 10x better.

When we say "the results have been amazing" frankly that's a bit of an understatement. In the most extreme case, one tester with only a high school education wrote their first ZK-SNARK in less than two weeks of intense study. I've seen extremely smart people need a month to do that (with a lot of guidance), so the efficiency gain is nuts. But the consistent outcome I see in my weekly calls with users is that students can explain what they learned for 10-20 minutes straight, correctly, uninterrupted, and without preparation. And they aren't just repeating what the app is asking them to memorize because they can answer "out of distribution questions" about the subject. This is a MASSIVE improvement over the typical response students give in a classroom when ask to "what did you learn in the lecture/homework?" Students giving talks for 10min+ straight unprompted is a HUGE improvement over the typical book/lecture/homework format. --- Building the RareSkills AI Tutor for Zero Knowledge Proofs was far harder than I thought (coding was easy, thanks Claude/Grok/GPT) 1) Asking good questions is the meat of goodteaching. A good self-directed learner (autodidact) asks themselves good questions, and this informs the direction of their practice/research. Most of the effort of building the app was coming up with questions that make the student engage with the core of the subject rather than tangentially "think about" it. Also, questions need to be carefully calibrated to the student's (predicted) level. A question needs to be clear, optimally difficult, and help the student build a mental model of the subject in the background. Sometimes this means mechanical drills, sometimes this means simply repeating the definition back, sometimes this means asking the student to connect concepts... the space of possible questions to ask is massive. AI could not help with this at all. I had to draw on my experience of face-to-face teaching to know what open-ended questions work. Math questions with a definitive correct answer is easy, but questions that "get the student to think correctly" were challenging. AI doesn't come up with the questions, it's just too unreliable. Some questions the student will encounter are AI generated on the fly, but this can only happen in very specific circumstances with tight guardrails. Asking the right questions in the right sequence is 80% of making the app work well. The other 20% is not screwing up the rest of the implementation, which is the next point... 2) Review algorithms are very hard to get right. The common failure case from spaced repetition is "review overload" and the first version of this app fell into that trap. You cannot expect students to remember everything you teach them. It's your job to determine what is the ultra-important information and drill that with correctly designed questions (see above). The "easy part" is modeling how well they remember specific topics and how good their overall memory is. However, asking the student to memorize something comes at a very high cost. This is why spoken languages are so hard to learn -- you literally have to remember thousands of words and when to use them. So the course needs to be extremely selective about what it expects students to explicitly memorize. The "memorization budget" must be spent wisely. 3) Getting questions wrong is very demoralizing for students, but the solution cannot be to make every question easy. One seemingly trivial, but very important feature in the app is that it gives the student more than one attempt at a problem (depending on the nature of the mistake). Most edtech platforms mark the student wrong, show the correct answer, and move on. But what if the student made a silly mistake? What if the student had incomplete justification? Allowing students to "learn from their mistakes" rather than "punish them with a grade" seems simple, but it's actually (excuse the AI-ism) gamechanging. I think educators are too socially RL-fried into thinking "homework should be graded" that it shows up in how edtech platforms get buit. Making mistakes is part of learning and it provides an incredibly valuable signal into the "learning state" of the student. The app also applies the philosophy in reverse where appropriate -- the student can get the answer right, but if the student's thought process is wrong, the app forces them to correct it before considering the answer correct. In fact, right answer, wrong justification can be marked wrong if the student can't justify the answer with escalating hints. Testers have repeatedly said that getting evaluated on their thought process rather than just the correctness of their answers has been a very positive experience (and it's given me a lot of valuable data). This does not mean correctness is unimportant. But the app strongly weights getting things correct AFTER you learned it rather than while you learned it for the first time. The grader is quite punishing if you don't remember key points from the lesson during later reviews, and that's important. What we care is that you REMEMBERED CORRECTLY not that you cruised through it the first time. Having an environment where "there is no grade, but I want you to get the answer right" makes learning far less stressful and productive. The app fully leans into this style of teaching during the "free recall" sessions (you have to try it to experience it, I can't type it all up here). The broken aspect of homework is that grades are used as BOTH and incentive and feedback mechanism, which means they don't allow for mistakes, exploration, and non-scary feedback. This app nicely solves it by separating incentives from feedback. 4) Active learning does not replace videos and articles. A common feedback I got on an earlier version of this product is that it would benefit from videos. I resisted this feature because I thought the exercises should be so good videos are unecessary (also, writing scripts for videos is time consuming and AI takes a long time to make animations). I was falling into the same trap people who are religious about not commenting their code are. Yes, code should not need comments, but good code has good comments. A good course has good exercises, but passive learning has value if used correctly. A common failure case with videos is that people watch them and then fool themselves into thinking they understand the topic. So in this course, instructional videos unlock AFTER the exercises are completed. Usually, after someone has done a bunch of exercises, their brain is a little cooked. So watching the lectures is a good way to take a "motivational and productive break." Lectures "consolidate" information rather than "teaching" it. Oh, and the lectures feature a lot of sick animations, so they're definitely fun to watch. 5) Whatever an uber-smart AI says is irrelevant compared to empirical data. AI will have convincing explanations for why a question is "redundant" but then you see some students actually struggle with the "redundant" question. It's important to treat AI as a grunt-level teaching assistant rather than as a professor. It's just too likely to fly off the rails into some random pool of training data that does the opposite of what you want. --- The app does things slightly unconventionally: lectures come after "homework" and "homework" is not strictly graded. Then of course there is the RareSkills touch of being very selective about what technical terminology gets introduced and the tendency to prefer intuition over rigor. For those wondering, the app doesn't teach ZK-SNARKs the same way the ZK Book does. The app is a lot more thorough, incremential, and digestible. When you restrict yourself to communicating in 5 minute increments, your mental model of the subject must be extremely granular. The ZK Book has 5,000+ word chapters. The book is clear, but granular and incremental it is not. So the "framework" the app uses is quite different. Some examples: - QAPs are taught *before* Rank 1 Constraint Systems in the app. This is highly unusual, but we've found it scaffolds from polynomial prerequisites better. - The app teaches a version of Pinocchio before reaching Groth16. The app treats Groth16 as an evolution of less efficient algorithms that the book doesn't cover. - The treatment of abstract algebra is significantly better than elsewhere. This is the first time I've seen people get homomorphisms on their first encounter. The teaching sequence is really effective. In fact, I kid you not, an early student on the sunsetted v1 platform was able to explain homomorphisms after only five days of using the app, and that person did not have a math background. If you know anything about teaching homomorphisms, then you know that outcome is (again excuse the AI-ism) *wild*. The tester didn't even know what a "group" was when he started, then five days later he could do basic reasoning about homomorphisms and explain it. --- It's hard to boil down to one thing that makes the app so great... it's more that it does so many things almost flawlessly. 1) At the early stages, drill the basics to get key definitions and concepts down. The app actually expects you to do some math by hand and memorize definitions. These are not fun things to do, but the app (successfully) frames these exercises as "building intuition" rather than "grinding." 2) Later, ask questions that draw out the student's train of thought, then give feedback on the train of thought. Giving feedback on the train of thought, not only the correctness of the answer, is a FAR more efficient teaching mechanism, and the results show. 3) The app isn't stuck to a certain way of teaching. Sometimes it uses direct instruction, sometimes it uses Socratic instruction, sometimes it leans on worked examples, other times it uses inductive teaching. Different topics and different student "learning states" benefit from different approaches, and I think we generally got the combinations correct. 4) The app uses spaced repetition (as any good edtech platform should) but spaced memorization is not the core memory tool -- everything the student learns is part of a whole. "Isolated facts" were aggressively pruned. The spaced repetition algorithm itself adapts to students very quickly and accurately. Students consistently say "I'm getting just the right amount of review" and statistically, students get review questions correct ~90% of the time. 5) The "free recall" technique the app uses is so visibly effective that students tell me "I'm going to start using this in other areas of my life" 6) The coding exercises are designed in a way that is AI-aware. We know nobody writes code by hand anymore, but there is value in writing code to test your own understanding. The coding exercises are carefully designed around this reality. 7) What made RareSkills great was the quality of the explanations on the blog. This DNA still carries over to the platform. The app's explanations are crisp and easy to understand. Interactive tools are used where appropriate. Obviously a good explanation is only the beginning of learning, but it's important and it's done right. Most of the mediocre explanations have already been pruned out by the testers. 8) The app has a very graceful recovery mechanism if you've been off the platform for a while and you aren't sure what you remember. Starting to learn again after taking a two week break normally feels daunting, but on this app, taking a break can actually be productive, and re-entry is smooth! I'll admit this feature isn't perfected, but it's FAR better than anything else I've seen. 9) The app was heavily shaped by over a dozens of people who give really amazing feedback (both explicitly and implicitly). And feedback is still continuing to shape it. To give credit where credit is due: - The YouTube channel "Benjamin Keep, PhD, JD" had some really fresh teaching ideas I definitely borrowed. - Mathacademy opened my eyes to how far you could push "learning through active learning." I thought the exercise-driven approach in our bootcamps was well tuned, but I had no idea how much further the concept could have been pushed. - The substack carlhendrick has some brilliant pieces about gaps between "education research" and "education in the real world." His work actually made clear to me why some aspects of an earlier version of this app were not working out even though the app was doing things correctly "in theory." He really drilled that "just because the student is producing correct answers doesn't always mean they understand the subject." Fill out the form, and get your access code. As long as you remember (or can quickly relearn) how to divide polynomials and multiply matrices, you'll be able to take the AI tutor for a spin.

One thing I really dislike about Medium (and a large part of why I stopped using it) is that it rewards authors for using sensationalist titles like "Node JS/Python/Rust is dead. Long live [replacement]" This kind of garbage content was obviously written to attract clicks, not say anything useful. X needs to algorithmically penalize this kind of stuff also. Grok should be smart enough to notice if the content's primary purpose is to play on the reader's emotions about major (alleged) changes.

The unlock for AI as a tutor is that it can give rapid feedback on your thought process, not just the correctness of your answers. Humans can do that too of course, but AI is more scalable.

The most powerful learning intervention I've discovered is putting students into an ice bath and not letting them out until they get the answer right. Beats spaced repetition and AI analysis of past answers by approximately +1.5 SD. Sign up here: RareWeek.RareSkills.io Learning tracks are Zero Knowledge Proofs, ZKVMs and Advanced Rust Programming. (you can also just cowork in a nice villa with high-caliber people if none of the learning tracks interest you)

Jobs will not disappear. Not in 10 years, and not in 1000. Not because AI won't become capable enough to replace most human labor. Maybe it will. But because humans are competitive. We want hierarchies. We want status. We want to climb ladders and feel more successful than our peers. Then we assign meaning to the competition and call it meritocracy. People who predict the end of work assume labor exists because things need to be produced. Automate production and cognitive work, and labor becomes unnecessary. But production stopped being the only purpose of work a long time ago. Work is also the civilized way humans compete with each other. Instead of throwing rocks at each other screaming oonga boonga, we send emails, sit through pointless meetings and invent increasingly fancy job titles to establish who is important. AI can automate the work. Good luck automating the human need for status.

I think a lot of software engineers felt this way in December of 2025 when Claude code could actually run unsupervised for an hour. Lo and behold, software engineers are still here 9 months later, and the job market is still improving. Now, I understand math is not quite the same because the incentive structure is different. Almost nobody pays money for theorems to get proved. However, I don't think it's a foregone conclusion how AI being superhuman at math plays out. Most of the sensationalist commentary we see on X about "AI replacing XYZ" is engagement farming, not serious/informed thinking. Dear algorithm: please show me some smarter takes on AI being superhuman at math than the basic "omg mathematicians are cooked."

Learning is primarily a social activity. This is why, even though classrooms are not effective, they have a far higher completion rate than self-paced moocs. This isn't unique to learning: - gym = build cardio/muscles with buddies - office = work on a project with coworkers - clubs = activity + social Anything difficult requires a social dimension if you want a high rate of success.

Explaining a subject in your own words is the most efficient way to learn a subject, and it continues to work even as you get really advanced. It forces a durable encoding of the subject in your own mind (you memorize it better) and it exposes your knowledge gaps. Explaining in your own words also improves your ability to coordinate with other humans and AI.

Take 4 minutes out of your day and watch this piece of art.

This is a prime example of why X is my favorite news source. "What if AI 'just' builds a virus..." The problem is that 'just' glosses over a lot of physical constraints. You can't 'just' build an app or 'just' start a company or 'just' invade another country. I'm not saying AI is something we should be totally careless with, but it's good to hear an informed perspective on what goes into 'just' synthesizing a virus. The physical world doesn't operate at 2.4 GHz and 1TB bandwidth. Good read.

Some random AI thoughts: I don't get all these posts about Astra quotas. I rip it on max fast every other day I haven't been hit with a usage block. $200 plan if that makes any difference. Fable 5.1 is smarter than Astra and Grok 4.6 is surprisingly not that far behind. I'm judging this by vibes when solving open-ended, real-world problems. I don't care what the benchmarks say. I do think Astra has gotten slightly dumber recently, probably some nerfing due to the server loads. Still a great model. No model has impressed me with the ability to "reformulate" a way of thinking into one it hasn't seen before. I've been messing with build an AR app this weekend. All of the models cannot one-shot something that works. It's taken a surprising amount of back-and-forth (and some domain knowledge of computer vision) just to make tangible progress. I mean, it still is far better than coding it by hand, but it's nowhere near "I don't know how to code haha AI go brrr." I can do in a couple days what used to take my team (previous company) a week to do.

If you are an engineering manager and you care about the continuous technical growth of your team... you won't get a better ROI on $3,000 than this. To learn fast, you need both quality instruction (which RareSkills excels at) and long periods of intense focus (a dedicated environment makes a huge difference). RareWeek provides both.

Back in the day, when only the Moon Math Manual was around, it would take engineers -- even those with advanced math backgrounds -- nearly a year to understand ZK end-to-end. When we published the RareSkills ZK Book, the typical learning time dropped to ~3-4 months. Now, with our new AI Tutor, it takes weeks -- or a single week if you are very motivated and prepared. A tutorial series is necessarily one-size-fits all, but interacting with an specialist AI cuts the time even further -- especially because the practice part of learning is closely guided. Explanations of a subject are not usually the bottleneck, but rather how efficiently you can "encode" new information permanently. This means carefully choosing what subtopics to understand, and how to go about memorizing it. Interactive platforms can do this better than any book. So I believe this is the future of learning: in-person events for maximum focus and peak efficiency AI tutoring. Oh -- and since part of the Navier-Stokes solution was likely... how shall we say it... "borrowed" -- ZK is going to play a greater role going forward. So get started now.