More accurately, how many jobs are probabilistically mechanical. That is, how many jobs are really the execution of a serious Bayesian decisions with a strong prior. LLMs are really great at displacing such jobs.

I don't think the replacement is binary. Instead, it’s a spectrum. The real concern for many software engineers is whether AI reduces demand enough to leave the field oversupplied. And that should be a question of economy: are we going to have enough new business problems to solve? If we do, AI will help us but will not replace us. If not, well, we are going to do a lot of bike-shedding work anyway, which means many of us will lose our jobs, with or without AI.

Ape coding sounds harsher and more insulting, implying mindless or sloppy work rather than humor.

So, what exactly are we worrying about? The social security is not sustainable? The medical cost will go through the roof? There's no enough military power? There won't be enough consumption to support the growth (in that case, why do we have to keep growing? Why can't we just stay where we are? Again, not rhetorical questions but honestly curious about the answers)?

Personally, I find Boaler's advocacy extreme. Her famous quote: "Every student is capable of understanding every theorem in mathematics – and beyond – the mathematics curriculum. They just need the opportunity to struggle with rich tasks and see mathematics as a conceptual, creative subject.” This sounds inspiring, but in practice she advocated the policy of truly dumbing down math curriculums and text books. To say the least, shouldn't she at least demonstrate that she could understand any theorem? But instead, she advocated that SFUSD eliminate algebra from 8th Grade . Another example was that the curriculum that she advocated, College Preparatory Mathematics, was so boring and trivial. She also said something along the line "Traditional mathematics teaching is repetitive and uninspiring. We give students 30 similar problems to do over and over again, and it bores them and turns them off math for life.” What's funny is that the alternatives that Boaler prescribed were quite uninspiring and low level: https://www.youcubed.org/tasks/. All I can derive from her policies and complaints is that she couldn't do math. Why people would listen to someone who sucked at math about math education is beyond me.

The school districts like SFUSD are actually sabotaging the growth of our kids in the name of equity. They're committed to ideas from people like Jo Boaler, and they tried very hard to dumb down the curriculum. The real tragedy is that kids from wealthy families will just get other means of education to make up the difference. It's the kids who desperately need the quality education who are going to be left behind.

If it were up to me, I'd send those people to jail (yes yes, I know. I'm just angry and lashing out)

On the other hand, we certainly learned more after graduation (or something is wrong, right?). When I was in the AI course, the CS department was all about symbolic reasoning I didn't even know that Hinton was in the same department. I think what matters is the core training stayed with me and helped me learn new stuff year after year.

And my wife's experience: https://www.quora.com/What-is-it-like-to-learn-computer-scie...

In short, the training that we got from our universities was invaluable, and I always feel fortunate and grateful to my CS department.

On a side note, I believe it is an individual's responsibility to find the coolness in their project. What's the fun of building a dashboard that I have done a thousand times? What's the fun of carrying out a routine that does not challenge me? But solving a problem in a most rigorous and generalized way? That is something in which an engineer can find some fun. Or maybe it's just me.

What was surprising, though, was how reluctant the engineers are to learn such basic techniques. It's not like the math was hard. They all went through the first-year college math and I'm sure they did reasonably well.

Keep this in mind: it’s really hard for companies to hire good engineers. The onsite-to-offer ratio might be 20:1 or worse. So when a recruiter says they’ll just move on to the next candidate, they’re probably bluffing.

But what if they do have 20 people lined up? Then you don’t have leverage with that company—and that’s fine. Take the offer if it’s good enough, or walk and try elsewhere.

P.S., a fun anecdote: when Netflix was extending an offer to a renowned engineer, he brought his PR to negotiate. Apparently, it worked well for him.

P.P.S, always interview for a higher title. I get it — it’s tough with hot companies like OpenAI. But for most places, it’s worth a shot. At the very least, don’t aim lower than your current level. It’s funny how the human mind works—interviewers anchor their expectations to your title. And ironically, a senior engineer interview is often just as hard as a staff-level one. If you’re feeling cynical, just remember: title inflation is real and everywhere, and plenty of high-level ICs are great at navigating politics, drawing boxes, and sounding confident, but not necessarily skilled at offering real values like solving hard engineering problems. So if you can’t beat the game, why not play it?

``` []((Q & <>R) -> ((!P & !R) U (R | ((P & !R) U (R | ((!P & !R) U (R | ((P & !R) U (R | (!P U R)))))))))) ```

I'm doubtful. Remember when Google said their strategy was AI First? Baidu too? I'm old enough to remember that the criticism then was along the line "AI is technology. What problems do you want to solve?". The line of thinking seems still relevant to me today.

These two tactics per se are alright, right? If anything, I'd appreciate that Duolingo tries to keep me engaged. Besides, the more one spends time on learning language, the faster they learn.

The issue with Duolingo is not about gamification, but that translation is ineffective and boring, no matter how much gamification there is. Personally I find that the most effective way to learn a new language is starting with Comprehensible Input and then moving on with tons of output. Take Spanish for example, Easy Spanish, Dreaming in Spanish, Español Sí!, Extra, and Destinos offers lots of fun input for beginners. Paco Ardit's graded readers are great too.

Another problem with Duolingo is that it does not help listening comprehension at all. It turns out that we can only pick up sounds in context with tons of repetitions and combinations in consecutive sentences - a feature that is exactly what Duolingo misses. Yes, it has introduced listening and stories, but the amount of them is too little to be useful. Another lesson is that reading does not help improving listening much. When we read, we see individual words and phrases easily, while it's really hard to pick up individual words when listening. I didn't understand the difference and spent a lot more time reading than listening. As a result, my reading was at the level C1 yet I could only understand slow Spanish at the level of A2.