Artist radios almost inevitably become a mix of songs I’ve listened to the most that have even the faintest crossover with the artist genre. It’s very, very frustrating and my biggest peeve about Spotify. I now ask an LLM for an artist radio playlist and copy it over to spotify, which is kind of a pain.

That's really weird. I have heard of people having the same experience as you though. I'm not sure why my radio plays songs that I have never listened to

I find the "self loathing american on the internet" act to be about as performative as putting a pink ribbon on your social media avatar after a shooting. It's not introspective in any meaningful way.

I've seen that sentiment shared a couple of times in the past day and little else before. Just something I've noticed...

I find the self loathing is usually well qualified and not at all a performative act. Performative for who exactly?

The rest of the world, presumably

I know there’s a ton of resources out there for this sort of thing but is there any particular sites/books that stood out to you while learning?

its more like the people with the highest disposable money want other users that interrupt their experience to back off so its not bothering them.

tech industry suffering from a kind of zoocosis / stockholm syndrome perhaps.

Do LLM's not benefit from the abstractions higher level languages like Javascript/node offer?

Perhaps I'm speaking out of depth because I haven't done a lot of Golang, but I've always thought of it as a systems language first, which means by necessity you have you to handle lower level problems yourself. I'm sure there's plenty of libraries that paper over this - but the philosophy of the languages themselves is different. Javascript was designed to solve CRUD like interfaces/problems quite well.

Maybe this is just an outdated argument though that isn't really relevant with modern golang/rust though.

You can implement apps in any popular language you want with modern LLMs (they still need to be trained on that language). Tell it what to write and it'll do it. But the downside is, if the language is a memory hog or takes up tons of disk space, you're not gaining anything. If you don't have to manually write the code yourself, you might as well pick a language that has the fewest problems when executed.

Go has a really solid standard library which removes a lot of what you'd typically implement separately. You don't solve lower level problems because the language already solved it. Nodejs has the opposite issue, where there's virtually nothing standard, so people made libraries to implement 5 lines of code. Rust also has a minimal standard library, and is more complex than Go. If you want a dead-simple, batteries-included compiled programming language, Go is pretty much it. Since you write less code with Go, there's less context use, so actually, the LLM has an easier time with Go apps.

It started as a 'systems language' but it has many projects that extend its usefulness. There are two separate frameworks that let you write one Go app and compile it as an Android app, iOS app, Mac app, Windows app, Linux app, both GUI and console. It has multiple web frameworks, and one is even an Electron replacement. The thing it doesn't have is a REPL.

All of those things are smells imo, you should be very weary of any code output from a task that causes that much thrashing to occur. In most cases it’s better to rewind or reset and adapt your prompt to avoid the looping (which usually means a more narrowly defined scope)

A person has a supervision budget. They can supervise one agent in a hands-on way or many mostly-hands-off agents. Even though theres some thrashing assistants still get farther as a team than a single micromanaged agent. At least that’s my experience.

Just curious, what kind of work are you doing where agentic workflows are consistently able to make notable progress semi-autonomously in parallel? Hearing people are doing this, supposedly productively/successfully, kind of blows my mind given my near-daily in-depth LLM usage on complex codebases spanning the full stack from backend to frontend. It's rare for me to have a conversation where the LLM (usually Opus 4.6 these days) lasts 30 minutes without losing the plot. And when it does last that long, I usually become the bottleneck in terms of having to think about design/product/engineering decisions; having more agents wouldn't be helpful even if they all functioned perfectly.

I've passed that bottleneck with a review task that produces engineering recommendations along six axis (encapsulation, decoupling, simplification, dedoupling, security, reduce documentation drift) and a ideation tasks that gives per component a new feature idea, an idea to improve an existing feature, an idea to expand a feature to be more useful. These two generate constant bulk work that I move into new chat where it's grouped by changeset and sent to sub agent for protecting the context window.

What I'm doing mostly these days is maintaining a goal.md (project direction) and spec.md (coding and process standards, global across projects). And new macro tasks development, I've one under work that is meant to automatically build png mockup and self review.

What are you using to orchestrate/apply changes? Claude CLI?

I prefer in IDE tools because I can review changes and pull in context faster.

At home I use roo code, at work kiro. Tbh as long as it has task delegation I'm happy with it.

I work on 1M LOC 15 yr old repo. Like you it's across the full stack. Bugs in certain pieces of complex business logic would have catastrophic consequences for my employer. Basically I peel poorly-specific work items off my queue into its own worktree and session at high reasoning/effort and provide a well-specified prompt.

These things eat into my supervision budget:

* LLM loses the plot and I have to nudge (like you) * Thinking hard to better specify prompts (like you) * Reviewing all changes (I do not vibe code except for spikes or other low-risk areas) * Manual thing I have to do (for things I have not yet automated with a agent-authored scripts) * Meetings * etc

So, yes, my supervision budget is a bottleneck. I can only run 5-8 agents at a time because I have only so much time in the day.

Compare that vs a single agent at high reasoning/effort: I am sitting waiting for it to think. Waiting for it to find the code area I'm talking about takes time. Compiling, running tests, fixing compile errors. A million other things.

Any time I find myself sitting and waiting, this is a signal to me to switch to a different session.

weary (tired) -> wary (cautious)

Wary, not weary. Wary: cautious. Weary: tired.

This is really common, I think because there’s also “leery” - cautious, distrustful, suspicious.

Its a lot less odd when you remember that he's running an AI company himself.

I'm seeing your comment's downvoted, I'd like to hear from those that did as to why. Doesn't his current venture with his AGI startup Keen Technologies deserve being called out as a potential conflict of interest, here?

Because whether there is a conflict of interest or not, the argument can and should be examined on its own merits.

Ah.. So the old “It is difficult to get a man to understand something, when his salary depends upon his not understanding it!”.

Yes, but likely in the exact inverse than what is implied here. Carmack has generational wealth, he is likely fine financially regardless of how AI pans out. The many individuals who feel they should be financially compensated for code they open sourced are likely far more invested financially in that particular outcome.

Are you telling me that my home assistant enabled humidity sensors in my garden that trigger the arduino hose valve could just be replaced by a watering can??

It’s not X it’s Y is one of the most obvious LLM writing patterns. Especially the heavily punctuated sentence structure.

I'm on FB primarily because my local buy-nothing group is on it, so I am logging in multiple times a day. I'm so used to this slop it's pretty funny at this point, but as is the case with all social media, you tune your algorithm as you engage. At this point it pushes things like cooking videos and hockey clips more than the AI slop for me.

Sometimes I'll go down a rabbit hole of clicking AI generated videos just because my curiosity is piqued, and then I'll be stuck getting that slop fed to me for the next week. I have to make a mental note to actively disengage with it as quickly as possible to tip the algo in the other direction.