physix

In the meantime, I discovered that it might simply be a type of acquisition that circumvents regulatory oversight https://medium.com/@villispeaks/the-blitzhire-acquisition-e3... seen from https://news.ycombinator.com/item?id=44553257

I've been actively working with Spring since about 2008. About 3-4 times a year, I cuss and curse some strange side effects that occur during refactorings. And in some areas we've painted ourselves into a corner.

But all in all, it's a great set of frameworks in the enterprise Java/Kotlin space. I'd say it's that synergy, which makes it worth the while.

I'm curious, though. Is the use of dependency injection part of the portfolio of criticisms towards Spring?

I'm not sufficiently familiar with the details on ML to assess the proposition made in the article.

From my understanding, RL is a tuning approach on LLMs, so the outcome is still the same kind of beast, albeit with a different parameter set.

So empirically, I actually thought that the lead companies would already be strongly focused on improving coding capabilities, since this is where LLMs are very effective, and where they have huge cashflows from token consumptions.

So, either the motivation isn't there, or they're already doing something like that, or they know it's not as effective as the approaches they already have.

I wonder which one it is.

I'd really like to know which use cases work and which don't. And when folks say they use agentic AI to churn through tokens to automate virtually the entire SDLC, are they just cherry picking the situations that turned out well, or do they really have prompting and workflow approaches that indeed increase their productivity 10-fold? Or, as you mention, is it possibly a niche area which works well?

My personal experience the past five months has been very mixed. If I "let 'er rip" it's mostly junk I need to refactor or redo by micro-managing the AI. At the moment, at least for what I do, AI is like a fantastic calculator that speeds up your work, but where you still should be pushing the buttons.

That reminds me of a thought I had about the poachings.

The poaching was probably more aimed at hamstringing Meta's competition.

Because the disruption caused by them leaving in droves is probably more severe than the benefits of having them on board. Unless they are gods, of course.

"Sprawling distributed systems".

I like that. Sounds like a synonym for "Platform Engineering". :-)

I remember being amazed that lambda architecture was considered a kind of reference, when it looked to me more like a workaround.

We like to build IT cathedrals, until we have to run them.

That may hold to a certain extent for relational databases where your business model doesn't align well with physical model (tables). Although you might wonder why stored procedures and triggers were invented.

In databases where your domain is also your physical data model, coupling business logic to the database can work quite well, if the DBMS supports that.

https://medium.com/@paul_42036/entity-workflows-for-event-dr...

Take off, eh!

It's missing.

I was curious to know the statistics on the mentions of various programming languages on HN over the years, so I got me a copy of all HN comments from a BigTable public source. But now I need to interpret each comment and so what I need is a semantic grep. The easiest would be to prompt an LLM.

Comments are pretty short, but there are many millions of them. So getting high throughput at minimum cost is key.

I'm hoping that Inception might be able to churn through this quickly.

If you folks have other ideas or suggestions, what might also work, I'd love to hear them!

The idea is having a semgrep command line tool. If latencies are dropping dramatically, it might be feasible.

This looks to be really well written. After 25 odd pages, I'm saying to myself, can't wait to read the whole book.

Thanx!

What's ASI?

I vote for "unhinged" as Word Of The Year.

Interesting that The Economist thinks they are "verging on the unhinged", and not yet actually unhinged. Is that a buy signal? :-)

I think that's what evolution did when developing the brain! :-)

I'm a total noob in ML. I just had to vent something for not understanding this stuff and realizing that knowing physics doesn't mean you can grok ML mechanics.

We developed an Entity DBMS where the entities are FSMs.

https://medium.com/@paul_42036/entity-workflows-for-event-dr...

I believe that FSMs are a very powerful approach, even for building entire systems. So much so, that it forms a core part of our product.

I was waiting for that comment to appear.

If your core product isn't any good, A/B testing seems like rearranging the deck chairs on the Titanic.

I think it's indeed the opportunity cost and the commoditization of the infrastructure and operational expertise that drives startups to AWS. But over time, as you scale, they can easily become your biggest component to your marginal cost, without an easy exit, because they locked you in.

It was cool reading your article.

We're also bootstrapped and use Hetzner, not AWS (except for the occasional test), for very much the same reasons as you.

And we are also fully infrastructure as code using Ansible.

We used to be a pure software vendor, but are bringing out a devtool where the free tier runs on Hetzner. But with traction, as we build out higher tier services, it's an open question on what infrastructure to host it on.

There are a kazillion things to consider, not the least of which is where the user wants us to be.

We met on a project in a bank. Worked together for a number of years. Became friends.

We trust, respect and understand each other. I think that's really important. You'll be going through think and think together.

It's an interesting article that makes intuitive sense. But from what I understood, the scenarios are still pre-AGI.

From what I understand, we are far from achieving AGI, and the article would have benefited from defining the term and putting it into relation.

Because the disruption is significant even without AGI.

A colleague upgraded the production database for a securities financing settlement system on a Friday evening by accident 20 years ago.

We were devs with root access to production and no network segregation. He wanted to upgrade his dev environment, but chose the wrong resource file.

He was lucky it was a Friday, because it took us the whole weekend working round the clock to get the system and the data to a consistent state by start of trading.

We called him The Dark Destroyer thereafter.

So I would add network segregation to the mix of good ideas for production ops.

Ah, yes.

> I immediately messaged my co-founders.

I dunno. The effort needed to ensure you have backups is tiny compared to the work done to create the product. And to pull a backup before deleting stuff in production only needs a smidgen of experience.

They were extremely lucky. Imagine what the boss would have said if they hadn't managed to recover the data.

Without having looked at it, I would assume the value comes from not having to know the data model in great detail, such that you can phrase your query using natural language, like

"Give me all the back office account postings for payment transfers of CCP cleared IRD trades which settled yesterday with a payment amount over 1M having a value date in two days"

That's what I'd like to be able to say and get an accurate response.

> Build sociotechnical systems with “normal people” in mind

From the perspective of the composition of software engineering teams: Most of us have to make due with the average, we strive to find the above average and avoid the mediocre, but mostly we are teams composed of "normal" people. The article has some good advice for making the best out of a group of normal people. It particularly relevant because it's unlikely that you'll see anything else.

It's a good article, but does it go without saying that when one says "engineers", it's about "software engineers"?

One reason I clicked on this one is that I was hoping to learn stuff about engineers beyond just software.

But it's a good read nevertheless. Thanks for that!

I love it, it's crazy!

It would be cool to start with a random ROM image and then use a Monte Carlo technique (simulated annealing), making a set of random changes to the image by flipping bits, use the change in the "composite score" for the decision step in the MC iteration, and have your image "evolve" into something.

Repeat until one finds a game that is new!

Should only take a few ages of the universe. :-)

I think this is a great summary. It's quite intuitive and elegant. Does anyone have any information about what the author's peers think of this model?

I'd love the idea that we are living inside a black hole, which is inside a black hole, which is inside a ...

Not being in agriculture, it took me a bit to understand that "natural enemies" refers to the natural enemies of pests threatening crops. Not the natural enemies of the crops themselves.

Good to know that diversity promotes growth!

I read them to get an idea of the quality that the AI produces, but mostly ignore the content after reading and click on to find actual sources, since I don't yet trust that content.

I was thinking that the drop in traffic to news sites is due to AI summaries, which might have the effect of filtering out people who are happy with a snippet. And was postulating that it would have two effects: improving the relevance of those who go to the news site (good) and feeding people with poor quality AI generated information (bad).

But then I tested out various news- ish search terms and never got an AI summary from Google. So I think the primary cause for a drop in traffic to news sites is probably not the AI summary itself.