aerhardt

I haven’t typed a line of code in like six months but I still review all production code and stay very connected to the codebase. I don’t feel my skills have withered at all.

I am pretty sure non-technical people are not going to be able to compete in any meaningful way with technical people.

If the habit's not there I would always recommend starting with readings that are immediately pleasurable, like you mention on the post. But for me increasingly challenging myself has been an important part of the process, and I never stopped believing in the benefits because I had previously experienced them.

I literally couldn't focus for more than five minutes. I would try to start books and leave them unfinished. The help consisted in talking in some depth about why I was consuming more digital media than I really wanted. Then I layered on top some behavioral interventions like using app and website blockers. But the latter would've never worked without the former.

Reading makes me feel addicted to life.

I was a decent reader as a kid, then for the longest time I couldn’t read. I got psychological help to recover my attention, and gently started reading 10 mins of something easy after waking up. Now I read at a pace of about 1.5 books per week and I can’t get enough of it. There are so my worlds that I want to explore further. Russian literature. The history of ideas. Sci-fi. 19th century France. Computer science. So many books, so little time!

Reading can generate many virtuous cycles. It requires attention, but begets attention. It can calm the mind and feel meditative. It can inspire thought and sometimes useful action. It can help you be more interesting in conversation. It can become addictive, but rarely as much as screens and won’t make you miss social opportunities.

It’s not for everyone but my theory is that if you’ve ever been immersed in two or three books at any point in your life, there’s a good chance that the potential is in you to scale it to a fulfilling habit.

I really hope you sort things out and find meaning one way or another. I’m glad that at you’re in good professional hands - that is a great first step.

LLMS are terrible at writing in terms of style, and in terms of content or creativity they couldn’t come up with a short story any better than what you’d find at an amateur writer workshop. To declare we have reached AGI in textual media seems premature at best.

I work in tech for Executive Search, which is often (way) lower volume than generalist recruitment, but scheduling is still an issue. Keeping an eye out on this - best of luck.

Bro, it reeks of AI.

I get instantly turned off by a mere whiff of AI when reading something, and consequently I refuse to foist such garbage on my fellow human beings. But by god if I read another two-line (!!!) comment with an emdash on LinkedIn, I'm going to drop a bollock.

Please.

I am seeing similar dynamics at work, but also in my graduate studies.

I am currently doing the OMSCS at Georgia Tech and taking Machine Learning (7641) which has always had a reputation for being difficult. I don't mind a challenge, but I feel that the AI policy creates a sense of permanent and unpayable cognitive debt and learning deficits.

The class has traditionally taken a "data-first approach" to ML, where instead of focusing on the details of the different algorithms, students must apply them to datasets and analyze their performance and trade-offs. There are four colossal end-to-end ML projects which culminate in an 8-page IEEE-style paper each. (I actually prefer this general direction rather than an algo-heavy one - I find it more valuable to my work in business applications.)

For their AI policy, they've decided that all code can be generated by AI - the only rule is that the paper contents must be original analysis. To avoid taking any risks, I do not even use spell-checking AIs on the paper.

However, it seems to me that to compensate for the AI help, they've cranked up the amount of ground that needs to be covered in the projects. In the first project we were given two datasets, six algos to test, and a bunch of params and metrics to experiment with, producing a real combinatorial explosion of stuff to work on. This is on top of up to around 150+ pages of scientific reading on some weeks.

I am leaning very heavily on LLMs to generate massive chunks of the code, but I feel like I can't keep up at all.

I don't even feel my skills coming in where poor. I am a confident programmer, recently brushed up on math, and this is actually my second CS degree and my fourth course at Georgia Tech. I am rather familiar with the feeling of difficult courses or work problems pushing me to my intellectual limits where I stare into the abyss, but this feels radically different.

I am pushed to work at a higher (less detailed) level of abstraction, as many have foretold LLMs would do. I feel like I am learning about the data science meta-process but cannot keep up with details that are not even that fine. There is some complex math in there that could probably make my head spin but I cannot even get to that - I am cognitively stuck at higher abstractions like keeping up with so many families of algos, datasets, APIs, and thousands of AI generated codes.

In some sense this may be a shape of things to come at work too, but here's where that analogy breaks down: the performance of our work doesn't matter and we're not even graded on it. As long as we convincingly explain why things happen, we should be good, but even as I start to get the class and focus on that, I feel like I can barely keep up. If only they had made a bit of room with the AI productivity increase to focus a bit longer on that!

I thought I was losing it but this morning I found a Reddit thread with dozens of current students venting and found some solace in seeing that I'm not alone.

I also feel for the teaching staff, who I think are absolutely well-meaning, competent and attentive, but who just like the rest of us are trying to wing it in this brave new world.

AI is transformative for the good and the bad, and it's going to take us all many years to sort it out. We're not even started understanding social media and AI could be orders of magnitude more complex and also further complicating the former.

A question I've been asking myself and which I honestly want to put out there - and I apologize in advance, because you will see me repeat it in other threads, out of genuine curiosity:

Does your life have so much friction that you need a digital agent to act on your behalf?

Some of the use cases I saw on the OpenClaw website, like "checking me into a flight", are non-issues for me.

I work in business automation, but paradoxically I don't think too much about annoyances in my private life. Everything feels rather frictionless.

In business, I see opportunities to solve friction and that's how I make money, but even then, often there are barriers that are very hard to surmount:

(a) problems are complex to solve and require complex solutions such as deterministic or ML systems that LLMs are not even close to being able to create ad-hoc

(b) entrenched processes and incumbent organizations create moats that are hard to cross (ex: LinkedIn makes automation very hard)

(c) some degree of friction, in some cases, may actually be useful!

I imagine there are similar dynamics in the consumer space, but more than anything, I may not be seeing issues with such a critical eye (I like to relax after work, after all)

So, do you have problems in your private life that you'd want to take on the risks - and friction - of maintaining these agents?

I take him to be a good programmer on top of a pioneer venture capitalist and entrepreneur but Hackers and Painters contains some pretty bad predictions and takes on programming, and if he didn't have that good foresight then, it has probably become worse with the years.

The article is hard to read, paywall notwithstanding, and tells us very little about Herzog's book other than that the critic didn't like it.

I really appreciate Herzog as an artist. I think Grizzly Man is a unique piece of art, and Herzog's commentary is an integral part of it - original, and very worth listening to.

Tonight I was planning to watch either Fitzcarraldo or Aguirre after having listened to Herzog on the Freakonomics podcast earlier this week. But after hearing about the book there, I was really put off by some of the things he said and concluded that the book would be a hard pass for me. Nothing persuaded me that he had anything interesting to add - neither rationally, nor aesthetically - about a topic which has been extensively covered by very diverse thinkers throughout the millennia.

"Calvinism makes pretty lofty claims for a religion who has yet to demonstrate soul salvation anywhere near Lutheranism if they're leaning into the reformation angle"

- Someone in the 16th century, probably

I feel the same. I can't believe the amount of shit I am throwing at Codex for a measly 20€.

I read Hyperion last year. It's an ode to the English letters and a phenomenal exercise in world-building. RIP.

Even in our fabulously wealthy societies, people are mostly worried about paying their bills, taking care of their families, and putting food on the table, not in getting together in a quixotic enterprise and paying for thousands of kilometers of communal fiber. Also like in most communist utopias what would probably happen is that the control infrastructure would be captured by special interest groups and now you’ve traded one evil for another, but in addition you’re left holding the capex bag and you’re poorer for it.

Dario admitted in the same interview that he's not sure whether current AI techniques will be able to perform well in non-verifiable domains, like "writing a novel or planning an expedition to Mars".

I personally think that a lot jobs in the economy deal in non-verifiable or hard-to-verify outcomes, including a lot of tasks in SWE which Dario is so confident will be 100% automated in 2-3 years. So either a lot of tasks in the economy turn out to be verifiable, or the AI somehow generalizes to those by some unknown mechanism, or it turns out that it doesn't matter that we abandon abstract work outcomes to vibes, or we have a non-sequitur in our hands.

Dwarkesh pressed Dario well on a lot of issues and left him stumbling. A lot of the leaps necessary for his immediate and now proverbial milestone of a "country of geniuses in a datacenter" were wishy-washy to say the least.

Low? Put it this way. You work in a company or go to class with 45 people. Do you really think that 14 crashes in the last ~9 months is low?

In terms of miles driven, which is the fair comparison, from the article:

> Electrek analysis found that the vehicles have traveled roughly 800,000 paid miles in that time period, amounting to a crash every 57,000 miles. According to the NHTSA, US drivers crash once every 500,000 miles on average.

> We also have someone who vibecoded without reading any of the code. This is self admitted by this person.

This is isn’t right. He says very clearly in the recent Lex Fridman podcast that he looks at critical code (ex: database code). He said he doesn’t look at things like Tailwind code.

I've used their Pro models very successfully in demanding API workloads (classification, extraction, synthesis). On benchmarks it crushed the GPT-5 family. Gemini is my default right now for all API work.

It took me however a week to ditch Gemini 3 as a user. The hallucinations were off the charts compared to GPT-5. I've never even bothered with their CLI offering.

I'm coding about 6-9h per day with Codex CLI on the $20 Plus sub, occasionally switching to extra-high reasoning and feeding it massive contexts, all tools enabled, sometimes 2-3 terminal sessions running in parallel and I've never hit limits... I operate on small-ish codebases but even so I try to work in the most local scope possible with AGENTS.md at the sub-directory levels.

Are you really hitting limits, or are you turned off by the fact you think you will?

Hetzner doesn't even have an RDS service. I've heard rumors for years but they haven't done it. Also, while I agree that leaning too much on the cloud leads to lock-in - this is an abstract concept that needs to be guarded against when managing technology, always, anyway - and vendor-driven hellish architectures, "vanilla cloud" offers other conveniences other than compute, bucket, storage managed and load balancers, like IAM, good CLIs, secrets management, etc. Only Scaleway or OVH seem to be timidly developing what I would consider "vanilla cloud".

Well for one thing, call me a sell-out or accuse me of lacking craftsmanship, but I like my databases managed. Then also storage buckets, IAM, general cloud security and other niceties.

And I don't think it's for a lack of skills, I know my way around a Linux box - it's just that I save so much time. I'll occasionally build small projects in a VPS (sometimes cramming the db in there too!) but I don't feel I can do it for other more serious work projects.

Hetzner has basic load-balancing and security around the VPS and that's it, OVH has a bit more but it all looks quite green.

I don't predict ever going back to writing code by hand except in specific cases, but neither do I "vibe code" - I still maintain a very close control on the code being committed and the overall software design.

It's crazy to me nevertheless that some people can afford the luxury to completely renounce AI-assisted coding.

I've been recently researching if I could replace American cloud providers with something like OVH or Hetzner (the latter I occasionally use for VPS) and there is no fucking chance. It's great that 37signals and DHH can do it, and I have no trouble believing they have saved money, but for situations in which I operate, both startup and enterprise environments but where devs are scarce and teams small, it's simply not realistic.

Pretty sure the parent comment was being sarcastic but I cannot quite tell if you are, too.

I don’t type code into the editor anymore and I still think as deeply about the problem as I ever did, if not more.

Margaret MacMillan is a modern historian with a liberal world view and no ideological incentive to defend Bismarck, and in her phenomenal work The War that Ended Peace (2013) she largely supports this view.