goostavos

I've been super happy with my boox. Full android, so you can install the Kindle app if needed. But it can natively read just about every format.

I sync books through Dropbox. Seamless as can be. (And no DRM or "leasing" nonsense)

Cleanup is indeed an art when you go with this approach. Namespacing helps a lot, as well as little things like randomizing individual queue names such that test run B never knows that infra from run A (that may still be hanging around from a failed cleanup) exists.

Meaty: Quarantine by Greg Egan. Easily my favorite book so far this year. It starts as a detective story, but then spirals into something much bigger. Forced beliefs, quantum super positions, and the existential dread of repeatedly facing your own non-existence. It's second only to Permutation City in my Egan tier list.

That book led me to Quantum Computing for Everyone. It's good so far, but more work than expected to follow along being that I'm dumb.

Since the question is what are you reading (not what have you read): Ant-Kind by Charlie Kaufman. It's too soon to say if it's good or not, but it has made me laugh out loud several times. I'm about 80 pages in, and it's only just now started to open up into Kaufman's recursive, meta story telling that makes his film stuff so cool.

Whoa! What the -- that's me! This made my night. Glad you're enjoying it!

These miss the point of integration testing (in my Humble opinion). Integration tests are valuable because they get around the problem of mocking. They let you reach out into the foreign thing you use and see if it behaves the way you expect it to. If you replace the real thing with a fake thing, you are, well, by definition, not learning anything about the real thing. It's the subtle behaviors and quirks of real services that often cause the most problems. (It's why testing the DDB Local often hides the painful eventual consistency problems you see with DDB proper.)

My $0.02: just use the AWS SDK to spin up real infrastructure on demand. You can provision SQS in < 10ms. Buckets in <100ms. DDB can take upwards of 30 seconds to a minute if you're adding GSIs, but it's a very small tax in exchange for the confidence it brings. (People often balk at this idea when they first hear it, but give it a shot! I have stamped out many a bug thanks to being able to bounce ideas off real infra.)

Home schooled kid here: consider not doing it ^_^

(although, to be fair, it was a different time. AI would have surely made it much better)

Heavens. Is it possible you're reading into it a bit? He's miserable to work with? From this one blog post?

A more generous read, or, at least the reading I took: "once we (think we) know what we're doing, we violently execute."

So, "boo" on you. This guy sounds awesome.

In contrast, here's how I under engineered my book lol: https://news.ycombinator.com/item?id=49336392

If our two styles found a middle ground we'd be unstoppable!

...I use email notifications. I follow very few people (fewer than 10), so I'm always happy when an email pops up letting me know they've written something.

I don't pay attention to my RSS reader, and barely use social media. For whatever reason, email works for me.

>session transcripts were the new oil

Something about this idea really resonates with certain personality types. I equate it to the Zettelkasten hype phase from several years ago. People (...like me..) got really wrapped up in the belief that the process was more important that the content. "Linking" was an "activity." Something good will happen as long as you (a) take notes on stuff and (b) link them to other notes on stuff.

You see the same thing with the session transcripts people. They're building ever more sophisticated setups of indexing and storing and cross referencing every conversation they've ever had on the (I would argue) mistaken belief that the transcripts are the valuable part, rather than the uncomfortable part where you go do something. A lot of it, I say from falling in the trap, is fancy procrastination.

(Although, I have found myself jealous on many occasions where their fancy system retrieves something they vaguely recall from a conversation they had 3 months ago. So, who knows.)

If you offload breaking up long sentences to the LLM how will you get better at not writing long sentences?

(as an aside, I also struggle with super long sentences and found a pretty good trick for curing it: rewrite by hand with pen and paper. My cramping hand finds entire passages that the work can survive without)

>no hope for writers like me continuing.

If it's any consolation, I'm releasing a technical book on Java this year. In 2026. When AI exists and, at according to the front page most days, programming is "solved." All of it was written by hand because, well, why else would I bother? What's the point of a craft when you're not the one doing it? Half the joy is getting better at it, and that requires doing.

It's demoralizing to know post-2022 books are devalued in people's minds. It's demoralizing that I also think that way. I spend enough time at work reading other people's copy/pasted LLM output. Books should be a relief from that world, but the risk is unavoidable unless you go back in time.

But hopefully good things rise above the noise. If they don't, it's still OK. Honing a craft is its own reward, even if not celebrated. (this is the lie I tell myself as I lay in bed awake realizing I've wasted that last several years of my life)

Same for domestic partners. They push you into their desktop version for some reason, but the desktop version is a buggy pile of trash. I wasn't able to use it at all even on a windows machine. To their credit, they did refund the software without too much of a hassle (I expected a giant fight).

Agreed. To quote Leslie Lamport, "the hardest part of TLA+ is learning to think abstractly."

There's always a moment, usually annoyingly late in the process, where I realize I've been massively overthinking everything or solving the wrong problem. Time is an essential an ingredient. Clear thinking is extremely hard.

LLMs are definitely useful along the way, but the thinking is the spiritually fulfilling part.

DDB has two use cases:

1. You need an "infinitely scalable" key/value store and have deep pockets[0]

2. you work at AWS and your deployment pipeline has so many stages and regions and fabrics that you can no longer even conceptualize what it means for there to be a "current version" of your software (the hell in which I live).

But for some awful reason it's sold as a general purpose "NoSQL Database." Pair that with the Pavlovian response developers have to the word "scale" and you've got an army of people using the worst possible tech for their usecase. Everyone eventually pairs DDB with Elastic whenever "Oh, wait, so we need to be able to query our data?" hits.

[0] And you ONLY need PK reads. Querying turns "infinite scale" into "infinite throttles."

>so distant from daily routine that it seems completely pointless

imo, this is a problem with how it's taught! Order theory is super useful in programming. The main challenge, beyond breaking past that barrier of perceived "pointlessness," is getting away from the totally ordered / "Comparator" view of the world. Preorders are powerful.

It gives us a different way to think about what correct means when we test. For example, state machine transitions can sometimes be viewed as a preorder. And if you can squeeze it into that shape, complicated tests can reduce down to asserting that <= holds. It usually takes a lot of thinking, because it IS far from the daily routine, but by the same rationale, forcing it into your daily routing makes it familiar. It let's you look at tests and go "oh, I bet that condition expression can be modeled as a preorder on [blah]"

Agree. AI is (currently) fantastic at "de-bullshitifying" the internet. "Give me a table that compares Products A & B by z, y, and z." Companies have gone out of their way to make comparison shopping near impossible. Specs are hidden, if they're shown at all. Just figuring out if a certain TV had an ARC-HDMI out required downloading the manual.

I dread the day when ads inevitably make their way into the main AI models. One of the things its currently good at will be destroyed.

You do all of that when leaving a comment on HN? Why...?

I'm confused by this need(?) desire(?) to polish things that are irrelevant.

No amount of FBI stats about how often "assault" rifles are used will change people's minds. They don't like them and so want to take them away.

I don't know how to square the same people saying we're living under a tyrannical government also pushing legislation that makes sure said tyrannical government is the only one with guns.

Sigh.. same.

The real AI fatigue is the constant background irritation I have when interacting with LLMs.

"You're not imagining it" "You're not crazy" "You're absolutely right!" "Your right to push back on this" "Here's the no fluff, correct, non-reddit answer"

>software engineers today are 100x more productive

Somebody needs to explain to my lying eyes where these 100xers are hiding. They seem to live in comments on the internet, but I'm not seeing the teams around me increase their output by two orders of magnitude.

A lot of pride is wrapped up in the craft of writing software. If that goes away (I don't think it will) it would leave a lot of people wondering how they spent all their time.

(or something like that. Obviously I'm too well adjusted to have these existential worries)

I had my first interview last week where I finally saw this in the wild. It was a student applying for an internship. It was the strangest interview. They had excellent textbook knowledge. They could tell you the space and time complexities of any data structure, but they couldn't explain anything about code they'd written or how it worked. After many painful and confusing minutes of trying to get them to explain, like, literally anything about how this thing on their resume worked, they finally shrugged and said that "GenAI did most of it."

It was a bizarre disconnect having someone be both highly educated and yet crippled by not doing.

I found the first season OK enough, but the second season to be unwatchable.

If the only reason you write is as a means to and end, sure. Inevitable. If you pursue it as a craft then the struggle and imperfections are part of the process. LLM usage would sand away those wonderful flaws.

Oh, hey -- you're that guy. I learned a lot of what I know about TLA from your writings ^_^

Consider my behavior changed. I thought the "high school math" was an encouraging way to sell it (i.e. "if you can get past the syntax and new way of thinking, the 'math' is ultimately straight forward"), but I can see your point, and how the perception would be poor when they hit that initial wall.

I find the same. Even those who are interested in it in theory hit a pretty unforgiving wall when they try to put it in practice. Learning TLA+ is way harder than leaning another programming language. I failed repeatedly while trying to "program" via PlusCal. To use TLA you have to (re)learn some high-school math and you have to learn to use that math to think abstractly. It takes time and a lot (a lot!) of effort.

Now is a great time to dive in, though. LLMs take a lot of the syntactical pain out of the learning experience. Hallucinations are annoying, but you can formally prove they're wrong with the model checker ^_^

I think it's going to be a learn these tools or fall behind thing in the age of AI.

Fictional, but it captures something about work and life in that unique way that art is supposed to.

One of my favorite scenes:

Peggy: "You never say thank you!" Don: "That's what the money is for!"

It captures a lot of the mismatch in perspective between employer/employee boss/subordinate. You're there to do something for someone who is paying you to do it. That's as far as it goes (despite the constant human pull to perceive it as more).

FWIW, even with the "simple explanation," I'll echo OP's statement that the README doesn't really explain what it is or what it's solving. "Generates new versions of the structures" might mean something really clear to you, but even the phrase "data modeling" is enough to trigger lots of conflicting baggage in my head. Also: it took awhile it realize it's for Scala. I initially assumed this was a Smithy-like competitor.

It looks neat (once I found your docs)! Show what it is and what it solves in your README! The structural inheritance is slick.