schrodinger

Quick tangent if you’re willing to humor me…

I've been noticing that many new projects that would have been written in Python or Node a year ago are starting to be written in Go, Rust, etc.

Theory: people realized there’s little benefit to Python for agents. As Zep wrote, an “agent is a long-running, concurrent, I/O-bound process that spends most of its time waiting on a model, a tool, or a human[1]” — not a particular strength of Python.

I'm wondering if you'd considered Go (or others—Go’s just my fav ) before landing on Node, and more broadly whether you've noticed a similar pattern?

1: https://blog.getzep.com/agentic-development-in-go/

I must be misunderstanding something, because I can't imagine that training would-be predators would significantly reduce the rates of abuse.

Perhaps I'm mischaracterizing it, and it's about training those around potential abusers on how to recognize signs of abuse and adopt tactics that prevent it? Or it's about training _children_ to recognize what's unacceptable and how to report it safely? Or something else entirely?

Regardless, your conviction in the efficacy of the training grabs my interest. Could you share more about it so I'm not working off of incorrect assumptions?

You also mentioned that their abuse rates are lower than schools. Do you happen to have a source for that — I’d be interested in educating myself on that as well!

I'm not a Catholic (nor religious at all), and do question whether schools truly are worse than churches in terms of how children are treated.

But that's not the point. You made up a quote I didn't say ("demonization"), and accused the former poster of doing something they weren't: defending the church.

Saying that schools are worse yet ignored is not defending the church. It may also be incorrect, but that's a separate assertion.

Where did you see apologies?

I read it basically as “schools are even worse than churches and scouts yet are overlooked,” nothing apologizing for the churches.

I hear you. Coincidentally, I was actually called out just last night for a message with em dashes:

“Are you AI-ing me? Em dash giving you away”

It’s a pretty load-bearing (lol) example, yet it was clear in context it wasn’t serious. I’m beginning to notice people getting pretty good at detecting AI content, which I find reassuring.

It makes sense: AI-written prose is basically the homogenization of all styles of writing into a single voice, so it’ll stand out against the unique personalities we are used to seeing. It is just taking a minute for the average person to gain literacy — but it seems to be happening rather quickly, again unsurprising since we’re such social monkeys with a lot of our 15 watt brains dedicated to socialization and identity recognition.

Use your em-dash proudly!

Truly enjoyed this article, but hate to admit one thing: I'm so burned by AI slop that once I noticed this seems to have been "assisted" by an LLM, I got irrationally angry because I felt tricked.

It hit me at the end, when I read this:

> The idea behind Ambiance is simple: the model's priors [...] Everything else here is just in service of that.

I've noticed "in service of" take off similar to "load-bearing" with LLMs, and the whole structure just pattern matched to Claude for me.

I went back and scanned it over again, and noticed several other tells:

* "Think of U/L [Unix / Linux] as a motivating analogy rather than a direct comparison." * "Priors" _and_ "a priori" used in the same article. * "A real kernel […]. The Ambiance Kernel […]. The Kernel […]." — LLMs love this pattern.

To be abundantly clear, *I'm not calling this AI SLOP*; it's obvious that a human put a lot of thought into this, gives a shit about the topic, and shared interesting ideas leading to a productive discussion. I really did like it.

There's just something empty or hollow about the LLM style; paraphrasing Joni Mitchell "there's something lost and some thing gained" [1] when using AI. Your writing, your code is more consistent, more structured, more planned out — generally better but in a way that loses the character behind human writing.

[1]: From "Both Sides Now", a great song about looking something from two perspectives: youthful innocence, and jaded cynicism. Listen to the original 1969 version first, and then the 2000 remake as you can tell she's singing from the respective perspectives. Deeply meaningful song!

I believe you and OP are in agreement — they were saying that the 2019 book had them, therefore the terms _do_ predate AI. Your point that AI was being trained on material than is load-bearing (lol) but in agreement with OP, not contradictory.

Only mentioning because your "actually" may imply you thought you were disagreeing, when in fact it's one big happy family!

Interesting! I'd love to hear what type of work you do where you can get away without any of those because frankly I think most of those are pretty garbage too.

Postgres is decent for a free ($$$) database, although it's lack of clustered indexes and in-place updates (its MVCC approach) sucks for many use cases. I find it a sensible default but not the best at any one use case.

Python, frankly, sucks nowawadays. Maybe it had its time, but there are so many better lingos now. It's got type hints that are ignored, really bad patterns ("dependency injection" that's really just the singleton pattern, FastAPI encourages you to open a db connection and a transaction at the front of every request and commit at the end while you're making other requests, writing to disk, etc), and it's slow in both user experience and runtime (no real parallelism).

But generally I have to make some trades to get a great job. I love Go, personally, and the incredible simplicity it encourages.

Seriously, if you'd be willing to share, I'd love to hear what you do!

If you're actively looking for a job, you should have some familiarity with the common dev stack when you're looking. Today you should be comfortable working on a Mac, know Bash / Zsh, a little bit of Vim for SSHing, git, docker, react, postgres, etc.

If you don't, spend a few weeks before you start your search. You're almost definitely going to need them. Unless you're in a niche where the common stack is different.

This isn't me gatekeeping or something, it's just common sense. When 80% of the jobs are Python + Javascript / Typescript, running in Docker, using Postgres, using React on the frontend, FastAPI on the backend, and git plus github for deploying and reviewing, you're going to stumble without cursory knowledge. You don't need to be an expert in it all…

First come first served is capitalism in disguise. Someone will automate a tool that watches for these and jumps on them, and the person with the most resources, can get a close network connection, etc will "buy" it.

Same with waitlists.

It's impossible to avoid capitalism!

To those who have used it: is it handy for situations where you have multiple repos that want to share a little code, but it's not worth the trouble of extracting a library, referencing it, publishing versioned releases, updating dependent repos, etc?

And instead just "sync" a code folder from one main repo (perhaps containing common domain models) to other repos?

Basically the Go philosophy that a little bit of copying is better than a lot of dependency?

I don't get it. How do you handle 10k people wanting, say, garden.com, without a free market?

As someone who spent over half a decade using MS SQL Server daily, I have to admit it's a very nice database, and better than Postgres for some use cases. This could be interesting!

Postgres made the foundational design decision that every update is an insert leaving behind a stale row that'll get cleaned up eventually with its MVCC model. It's great for non-blocking read-heavy workflows (presumably the most common), but it suffers for the inverse.

For example, I wrote a simple job queue in Postgres, which had a lot of contention over the oldest rows non-terminal rows. It had many runners trying to either mark a row as completed or claim a row by atomically marking it as started and returning the row's contents (using the "skip locked" functionality). This ran much faster using similar semantics on MS SQL Server or MySQL because they have in-place updates, not an append-only tuple log.

Anyone notice how they linked to a "Hacker News Discussion" on their homepage? It's empty unfortunately, but hey, they tried!

https://news.ycombinator.com/item?id=42807210

Interesting point:

""" A popular story that made the rounds a few months ago was “an LLM wrote a web browser in one week, completely autonomously! It’s buggy but it works.” I won’t deny that it’s an impressive demo, but in a practical sense … what was accomplished? We trained on every open-source web browser ever written to produce a version of “git clone firefox” that’s orders of magnitude more expensive, slower, and buggier. """

That would imply that all of the knowledge held with these models is an uninteresting waste of everyone's time. I don't believe that's true.

I could tell it was AI, but it was interesting nonetheless. FWIW, I don't think AI enablement is inherently bad; this can be a game changer for an individual with an interesting thought yet difficulty expressing themselves in written word. (Obviously, it's also created a real problem with the ability to create near infinite well-written content, especially in the case of propaganda.)

I do agree with you that the quotes cited out are literary constructs used by humans, and there's a risk we get trigger-happy in calling out AI-slop. Still, those are just the most obvious tells — there were absolutely other, less notable mannerisms that confirmed it for me. If you interact enough with an LLM, you can become quite good at detecting their output through subtle subconscious cues that are hard to put to words.

I do wonder where some of the tropes came from. Claude tends to say "____ is doing a lot of work in this sentence", yet I don't recognize that as a common construction for humans overall or even a specific community (e.g. journalists). Perhaps I'm just unfamiliar with some vernaculars found in training data. Yet sometimes, it legitimately feels like they've actually developed a lingo of their own — an emergent property.

I find it all truly fascinating (along with other feelings…), and I never expected computers to be able to "understand" language anywhere near the degree we see today. Will it soon plateau, requiring another breakthrough? Or is there plenty of juice left to squeeze?

Maybe this is making a slightly different point (i.e. the usage of immutable physical features as "passwords", which I agree is largely useless), I think we're just entering a post-privacy era. Being around 40, I willingly fed the machine (Facebook) with hundreds of pictures during university, and had I not, my friends would have more than made up for it. It's absolutely possible to photograph every person you pass by every day, and Facebook most certainly has the keys to identify most all of the people. That dystopian combination means that unless you're welling to move to a largely uninhabited place, each of our whereabouts can be largely resolved with technology available _today_ at a completely reasonable cost.

Not only that, almost everyone on this forum walks around with a device that shares their identity and location with unscrupulous companies (cell phone carriers) whose data is available en masse to the government. (N.B. I have an iPhone and appreciate and even _trust_ all the privacy work put into it; however, the cell phone tower thwarts location tracking, and participation in social networks thwarts face tracking.)

I've long thought that rather than try to limit the information about us, we ought to _flood_ the Internet with information about us. Make the data available untrustworthy.

Or, accept it. So long as it remains in the hands of corporations and not solely the government, it guts both ways — a senator can no longer be publicly opposed to same-sex equality legislation while engaging in a homosexual relationship themselves.

AI seems to be pushing us down the former road.

Why wouldn't Robotic AI take over the sex worker industry as well?

Wonder if you could run your password manager in an isolated sandbox that couldn’t provide the secret behind the TOTP, only the current value.

That’s a good point.

Maybe a good compromise is to use 1pw for most TOTP but keep your gmail / iCloud and a few others in an iPhone only app?

Gmail is what scares me the most. It’s basically keys to the kingdom.

Idk if this is intentional or just part of an innocent site that’s unwittingly hosting these but I just got a “we’re verifying your browser” page, as if _I’m_ the suspicious one. Nice social engineering.

IYKYK

Is there any chance you made a typo in this comment? I'm not sure why your manager being long-term would result in less frequent one-on-ones...

I don't understand — I use AI to write email particularly _because_ I care about the recipient, and am confident the resulting email will more eloquently and accurately express my feelings. I'll also often edit it afterwards to ensure it's in my voice. Regardless, I don't think it's fair to presume that my boss doesn't case because an LLM generated the email.

^ This was written 100% by hand. Let's have Claude proofread it and make any suggestions:

I'd argue the opposite — I reach for AI because I care about the recipient. It helps me express my thoughts more precisely and eloquently than I might off the cuff, and I'll often edit the result to make sure it sounds like me.

Presuming that an LLM-assisted email signals indifference seems like a category error. The care is in what you're trying to communicate, not which tool you used to get there. -- https://claude.ai/share/3d3d1a78-381c-4fcf-9354-69b10f2d6f4a

Single inline backticks like `this` aren't recognized (although still useful in my opinion, they just don't change the rendering).

Triple backticks also aren't recognized. However, if you indent by I believe 4 spaces, it formats it in a fixed width font presuming it's code.

Let's try (4 spaces):

    func main() {
        fmt.Println("Hello, HN!")
    }

None for comparison:

func main() { fmt.Println("Hello, HN!") }

As someone who's been a hiring manager for around 7 years, I agree with you, but note that the people who screen resumés before they even _get to you_ very well may be looking for those references.

For my own resumé, I include the stack used at each job which I feel strikes a fair balance.

Same.

If it's something like "Refactored the apartment list service improving P99 Latency from 2s to 180ms", it definitely boosts the resumé in my mind. A good engineer would be measuring their impact and likely have numbers like that off the top of their head.

But if it's like "Increased revenue by $18.7M by reducing time-to-first-interaction latency from 2.3s to 117ms, increasing conversion by 47% and LTV by 28%," with the same fidelity on each bullet, I'm very skeptical.

--

I don't summarily reject AI-written resumés to be clear, as honestly, it's basically a necessity at this point to be competitive with others; it'd be putting yourself at a severe disadvantage on pure principles in a way that has no real positive net effect on society. Even if you disagree with AI resumé screeners, you're only hurting yourself — especially at a time that has the largest impact on your compensation (i.e. negotiating salary at job start is one of the most valuable ways to spend your time since it will pay you back every paycheck).

Though I _do_ tend to question resumés that look like they were written almost entirely by an LLM without the candidate providing significant context and refinement.

This sounds correct. When I implemented push notifications for an iPhone application, I remainder needing to obtain a store a separate token for each device a user has, and subscribing to a feed of revoked delivery tokens. Seemed like an interesting design intended to facilitate E2E encryption for push notifications.

How's it work? Embed tokens and use euclidean distance or something?