of work I poured into our course blog. Every student contributed two posts to the blog, and I gave multiple rounds of detailed feedback on all twelve posts, learning a lot from the students in the process. ​ and especially not Jekyll, since having a working Ruby environment is the hardest problem in computer science

The Ukrainian mathematician Maryna Viazovska has solved the centuries-old sphere-packing problem in dimensions eight and 24.

and pay more attention to consolidation, exposition and preservation of the knowledge we already have. more room for quiet study and reflection.

Catron felt during the staring contest “not just that I was really seeing someone, but that I was seeing someone really seeing me” — like a recursive reflection of a mirror in a mirror perhaps.

a blogging product without a publish button and create a space for collecting the dots. ​ It’s more common to think of “connecting the dots” but the truth is that you can’t connect the dots you can’t see.

People, in general, are terrible at valuing their time, perhaps because for most people monetary compensation for one's time is so detached from the event of spending one's time. Most time we spend isn't like deliberate practice, with immediate feedback. ​ We focus so much on product-market fit, but once companies have achieved some semblance of it, most should spend much more time on the problem of product-market unfit. ​ Twitter the product/app has hit its invisible asymptote. Twitter the protocol still has untapped potential. ​ The most obvious path to this is Groups, which can subdivide large graphs into ones more unified in purpose or ideology. Google+ was onto something with Circles, but since they hadn't actually achieved any scale they were solving a problem they didn't have yet. ​ In addition, perhaps there is a general limit to how far a single feed of random content arranged algorithmically can go before we suffer pure consumption exhaustion. Perhaps seeing curated snapshots from everyone will finally push us all to the breaking point of jealousy and FOMO and, across a large enough number of users, an asymptote will emerge. ​ Seduction is a gift, and most people in technology vastly overestimate how much of customer happiness is solvable by data-driven algorithms while underestimating the ROI of seduction. ​ just because a given person's product intuition might hit on the right moment at the right point in history to create a smash hit, it's rare that a single person's frame will move in lock step with that of the world. How many creatives are relevant for a lifetime? ​ Pattern recognition is the default operation mode of much of Silicon Valley and other fields, but it is almost always, by its very nature, backwards-looking. One can hardly blame most people for resorting to it because it's a way of minimizing blame ​ In my experience, the most successful people I know are much more conscious of their own personal asymptotes at a much earlier age than others. They ruthlessly and expediently flush them out. One successful person I know determined in grade school that she'd never be a world-class tennis player or pianist. Another mentioned to me how, in their freshman year of college, they realized they'd never be the best mathematician in their own dorm, let alone in the world. Another knew a year into a job that he wouldn't be the best programmer at his company and so he switched over into management; he rose to become CEO. By discovering their own limitations early, they are also quicker to discover vectors on which they're personally unbounded.

In the middle of my sketching hours, I don’t want to be worrying about whether I’ll be ready for my classroom prototype next month. Within a given day, action-oriented “butt-in-chair”-style advice does help; meta-thought is just distracting. But go too long without error correction, and you’ll misspend hours in the chair. ​ The rest of the day’s work becomes roughly deontological. I give myself permission to be satisfied with the day if I spent three focused hours sketching like I’d planned. ​ From time to time, I flip back into execution mode. It feels like an old friend. We say hello, dance for a while, and part ways smiling, just as it always was. Open-ended mode is more enigmatic, reserved—yet occasionally it sparks some moment so singular it lights up the whole year. Those moments don’t happen without the days spent together between those moments. I’m slowly learning to make the most of our quiet strolls.

But as it currently stands, the incentives for mathematicians reward one thing and one thing only: publishing influential papers.

Andrew Wiles, one of the world's most renowned mathematicians, wonderfully describes research like exploring a big mansion. You enter the first room of the mansion and it’s completely dark. You stumble around bumping into the furniture but gradually you learn where each piece of furniture is. Finally, after six months or so, you find the light switch, you turn it on, and suddenly it’s all illuminated. You can see exactly where you were. Then you move into the next room and spend another six months in the dark. So each of these breakthroughs, while sometimes they’re momentary, sometimes over a period of a day or two, they are the culmination of, and couldn't exist without, the many months of stumbling around in the dark that precede them. ​ But more often than not you'll find that by the time you revisit a problem you've literally grown so much (mathematically) that it's trivial.

Grothendieck views the mathematician and the problem as complimenting each other, the mathematician using the problem’s natural structure in its solution, rather than striking it with a foreign, invasive method. My view is that any problem that has resisted repeated direct attack from problem solvers, should naturally be of interest to theory builders. If you can’t solve a problem directly, then grow a crystal of theory around the problem and then hope that the solution you are looking for can be located somewhere inside the crystal. This is hard because the same person needs to know about both category theory and the problem domain, which is quite a heavy demand on a human brain.

But I've also become allergic to that language—the rhetoric around "solving problems" and "building things" that springs from the privileged mindset of the lone techno-savior. I suspect this mindset is one reason the old-school punks didn't quite make it. Sometimes, there are no clear-cut problems and nobody needs to build anything new. Sometimes we just need to talk about it. If we build anything, it should be not software, but consensus. I, too, want to help shape these emerging computational layers to be less coercive and extractive, more expressive and equitable, before it's too late. So I think the most effective thing I can do now is to join various publics, listen to how people understand and engage with technologies, and let that understanding shape my work as I work to shape that understanding. I don't know how to do it yet, but I'm trying.

While no level of type safety is sufficient to ensure all programs' correctness, the fact that the Semantic Code team spends the majority of its time working on features rather than debugging production crashes is truly remarkable—and this can largely be attributed to our choice of language. ​ but in Haskell: its brevity, power, and focus on correctness lets researchers focus on the nature of the problem rather than the onerous task of fitting advanced research into conventional languages. Writing in Haskell allows us to build on top of the work of others rather than getting stuck in a cycle of reading, porting, and bug-fixing. ​ a reputation for being difficult to learn. Some of that is well deserved, but half of it has more to do with how many of us first learned imperative programming and the switch to a functional paradigm takes some patience.

With his students, he was able to write a program capable of typesetting the entire 700-page revised volume of his book by 1978. The program, called TeX, revolutionized how scientific papers are formatted and printed. It’s also one of the oldest OSS projects still in use. The disconnect between technical or scientific and nontechnical authors is also fundamental to understanding TeX’s mainstream obscurity: In nontechnical publishing, typesetting is usually not essential for conveying the author’s intent. Typesetting is considered ornamental; authors of popular material are content to send a Word document to their publisher and let professionals do the rest. Technical authors, on the other hand, need to convey their meaning precisely through glyphs, sizes, and placement. TeX lets them do that, as well as exchange their documents in a widely understood format.

Category theory and intelligence seem to be deeply linked - both are guided by the goal of organizing and layering abstractions. Automation of exactly that - organizing and layering abstractions - seems to be the quest of machine learning.

But of course, email and social media and games are obvious distractions. In my experience, the more subtle threat -- particularly for non-fiction writers -- comes via the eminently reasonable belief that you’re not ready to start writing, because you haven’t finished your research yet. ​ But as much as I enjoy it, I have learned the hard way that you are never done with your research. Waiting around for the research phase to be complete is a recipe for infinite postponement. ​ when you’re researching in media res, the new ideas or details or stories that you stumble across are much more useful to you, because you can immediately see the slots where they belong.

Why we should all spend more time in search boxes and less time in news feeds.

https://twitter.com/jasdev/timelines/1109142617063866368

The idea that groundbreaking work is driven by individuals probably makes sense to a lot of people, yet in practice, there’s no readily available funding for individuals Sometimes, an individual just needs a check, and a vote of confidence from someone they respect, to keep going. I found that formalizing the program actually helped to decouple it from my identity. It’s either that, or just go off the radar entirely and fund opportunities privately. I hadn’t planned on giving these grants a name at all, but a friend suggested I should, because it would make it something that the recipients could be proud of and take ownership in. After seeing a few more grant cycles, I think he was right. I was surprised to learn how many applicants said that the validation mattered more than the money. If I can help validate someone’s idea, amplify their work, or connect them to others, that’s often more meaningful than capital. I think these are potentially good ideas. I think someone ought to pursue them. But when I consider what gives me energy, I realize I have no desire to scale anything up here. I like having enough skin in the game to help me think about interesting questions in philanthropy, but this isn’t my full-time focus. The process of making new ideas legible to others is emotionally taxing.