jochem9

If you're at a stage where code doesn't compile and doesn't do what you want, then that's really easy to improve.

- let the llm (agent) compile the code and iterate until it does.

- state your intent more clearly.

This implies that the government respects the rules or at the very least pretends to do so. To me it's pretty clear that the US federal government has moved beyond that.

One does not rule out the other. In the end it's nerds messing with hardware.

Lots of computer culture is rooted in anarchism, anti-capitalism and a fight for fairness. E.g. early internet culture, the open source community.

Imo it's very nice to see explicit anti-capitalist movements within tech, because the other side of tech is so completely over the top capitalist.

To me these all sound pretty plausible?

You have browser extensions that allow AI to control it. There are apps that create a virtual microphone for text to speech. All pretty easy to get going if you allow an AI to control a computer.

Generating designs is also easy. Just use nano banana or something similar.

I'm more curious about the quality of the output. I've seen some ugly stuff. The article also includes one example (the outdoor seating design).

I'm also curious about the reaction of people to AI running the shop. Do some people decide not to deal with it? Do some people abuse it? In the article there seem to be only positive examples of human-AI interactions.

Says a lot about the state of society when parenting is outsourced to technology, so that the parents can be further enslaved (because almost no one chooses to work two jobs).

EU severely reducing its fossil fuel imports from Russia in 2022 cut down natural gas usage by 17% and overall energy consumption by 3%. So yeah, increased price due to scarcity help a lot in shifting around the energy mix.

It's a bit shit that hits poorer people relatively more than richer people. Governments can reduce this impact by subsidizing sustainable alternatives (like heat pumps). It's still leading to inequality (unless you give more subsidy to the poor), but at least overall people will hopefully benefit.

I'm on a very similar train. You cannot dump all the data into an LLM (for many reasons) and we also already have clearly defined rules that an LLM doesn't have to figure out.

So keep organizing data (LLM powered, of course), so that you can query data as usual (multi modal, so not just graphs, but also time series, relational, etc). Feed that to deterministic computations. Let an LLM reason about the outcomes.

Give the LLM the freedom to orchestrate the retrieval and computations. Make sure the way it orchestrates it is auditable.

The key thing I want to achieve is beyond this system: I want to uncover hidden things in the system (missing in the ontology, computations, etc) and propose to add these. This will effectively give you a generic approach to create ever evolving systems aliging with reality while being fully auditable.

I'm also doing openspec for a few months now and it's really good if you invest enough in the specs (in the beginning I skimmed over much, now I pay attention to all details and fix anything that's wrong or where I see a gap).

The one thing I like that OP brings is to tie specs and code together. The openspec flow does help a lot in keeping code synced with specs, but when a spec changes, AI needs to find the relevant code to change it. It's pretty easy to miss something in large codebase (especially when there is lots of legacy stuff).

Being able to search for numbered spec tags to find relevant bits of code makes it much more likely to find what needs to be changed (and probably with less token use too).

Not the original commenter, but I share the same sentiment.

The harm done is that there is less human interaction outside one's bubble. Before deliver-anything-at-home you were forced to be in environments where you'd interact with people you'd normally not see.

At some level you're also building a connection if you're interacting with the same service employee or bump into a neighbor.

Strong communities is a massive boost to public health. It reduces loneliness and prevents all kinds of deceases (heart stuff, dementia, etc). It also builds a safety net so people don't have to rely so much on official healthcare.

Now obviously just some food delivery is not destroying community by itself. There's probably worse offenders out there. However, society becomes more and more parallel and robots do contribute to it. Unfortunately it's not something that's discussed a lot.

Most products you consume are probably not software. Pretty much all products you consume are created by companies that use software.

If those companies don't keep up with software, they might not have a competitive edge that their competitors who are keeping up gain.

Yes.

Your message needs to find other people. The how is irrelevant, it just shapes and transmits it.

Oh, I thought it was opium.

This is the crazy part with LLMs. It knows much more than you as a single user will ever realize, as it only shows the part that matches with what you put in.

I was building a tool to do exploratory data analysis. The data is manufacturing stuff (data from 10s of factories, having low level sensor data, human enrichments, all the way up to pre-agregated OEE performance KPIs). I didn't even have to give it any documentation on how the factories work - it just knew from the data what it was dealing with and it is very accurate to the extent I can evaluate. People who actually know the domain are raving about it.

You could build (code, if you really want) tools to ease the review. Of course we already have many tools to do this, but with LLMs you can use their stochastic behavior to discover unexpected problems (something a deterministic solution never can). The author also talks about this when talking about the security review (something I rarely did in the past, but also do now and it has really improved the security posture of my systems).

You can also setup way more elaborate verification systems. Don't just do a static analyis of the code, but actually deploy it and let the LLM hammer at it with all kinds of creative paths. Then let it debug why it's broken. It's relentless at debugging - I've found issues in external tools I normally would've let go (maybe created an issue for), that I can now debug and even propose a fix for, without much effort from my side.

So yeah, I agree that the boring part has become the more important part right now (speccing well and letting it build what you want is pretty much solved), but let's then automate that. Because if anything, that's what I love about this job: I get to automate work, so that my users (often myself) can be lazy and focus on stuff that's more valuable/enjoyable/satisfying.

It's all kinds of patterns. What stood out in this one is that almost all subheadings start with "The".

I notice that I'm even changing my writing style the moment I feel like I'm writing how an AI might write. Doesn't feel good either.

I think my sweetspot is having one (30min+) features a day. And then after spend synchronous time iterating on it to fix edgecases or tweak stuff.

The rest of my time goes to prepping those big features (designing, speccing, talking, thinking, walking).

Going to see how big a feature can be before the quality suffers too much and it becomes unmaintainable. This highly depends on how good I spec it out and how good I orchestrate the agentic workflow.

ASML has a near monopoly on the most advanced chip machines. They maintain that by 'just' being the most advanced and having lots of patents.

They haven't branched off into making chips themselves. They keep their focus on selling the factories.

I think they haven't, because ASML itself doesn't have production lines. Every machine is one off. It even gets delivered with a team of engineers to keep it running.

The same probably holds true for software factories: the best ones are assembled by the smartest people (wielding AI in ways most of us don't). They are not in the business to produce software at scale, they are in the business to ensure others can do that using increasingly advanced software factories.

This relies on the premise that such a factory cannot produce a more advanced factory without significant human intervention (e.g. high ingenuity and/or lots of elbow grease). If this doesn't hold true, then we are in for some interesting times x100.

Why do you bring up communism? It's not true that a society either has to organize as capitalist or communist.

So far in my career I have always had more requests coming in than implementations going out. If I can go 3 or 10 times faster, than I will still have plenty of work. Especially for the slew of ideas that are never even considered to put towards a dev, because it's already considered to be too low value to have it even be considered to be build. Or the ideas that are so far fetched they were never considered feasible. I am not worried work will dry up.

What I believe is going to be interesting is what happens when non-engineers adopt building with agentic AI. Maybe 70 or 80% of their needs will be met without anyone else directly involved. My suspicion is that it will just create more work: making those generated apps work in a trustworthy manner, giving the agents more access to build context and make decisions, turning those one off generated apps into something maintainable, etc.

In the US people take on personal debt for something like a car. In the Netherlands (where I live) this is very uncommon: people save up, then buy it.

The US is considered to be a flawed democracy for about 10 years now[1]. Europe, especially the powerful west, has the most healthy democracies.

It's absolutely not a given that the European democracies will survive, people here need to step up in strengthening it against illiberal forces as well, but it's in a much better starting position.

Example: in the Netherlands there was a government with an illiberal far right party (Wilder's PVV). They didn't achieve much, but there was a year of stagnation and the far right talking points have become even more normalized. Other democratic institutions, like judges had to be more on the defense. However, nothing fundamental is broken.

1. https://cpsblog.isr.umich.edu/?p=3417

In the Netherlands we call that a razzia. Very popular in the early 40s.

Vinyl is populair, inconvenient and doesn't have crisp audio quality. Cassettes are also inconvenient and have poor audio quality, plus they are cheap and portable. So I definitely also see them stick around. I also see plenty cassettes being issued on e.g. bandcamp for years already.

The poor audio quality can be seen as desired feature btw. It brings a certain lofi or warmth with it.

The Dutch government just explicitly denied any foreign involvement, while at the same time acknowledging that a few years ago the export restrictions for ASML were implemented because of US involvement.

Tbh, I don't know what the truth is. I'm a Dutch citizen and what is happening now is unheard of. I don't know what the motivation is and it seems to have happened out of the blue. Maybe the motivation literally is protecting the European semiconductor industry.

I seriously doubt it is aimed against China. Europe is not looking for a trade war, especially now that the US is an unpredictable ally (if they still are) and basically the whole geopolitical situation is shifting. China is also not a topic for Dutch politicians, so it's not winning anyone votes.

One thing that I don't see mentioned but that does bug me: data engineers often use a lot of Python and SQL, even the ones that have heavily adopted software engineering best practices. Yet both languages are not great for this.

Python is dynamically typed, which you can patch a bit with type hints, but it's still easy to go to production with incompatible types, leading to panics in prod. It's uncompiled nature also makes it very slow.

SQL is pretty much impossible to unit test, yet often you will end up with logic that you want to test. E.g. to optimize a query.

For SQL I don't have a solution. It's a 50 year old language that lacks a lot of features you would expect. It's also the defacto standard for database access.

For Python I would say that we should start adopting statically typed compiled languages. Rust has polars as dataframe package, but the language itself isn't that easy to pick up. Go is very easy to learn, but has no serious dataframe package, so you end up doing a lot of that work yourself in goroutines. Maybe there are better options out there.

Last time I worked with Databricks you could just create branches in their interface. PRs etc happened in your git provider, which for us was azure devops back then. We also managed some CI/CD.

You're still dealing with notebooks. Back then there was a tool to connect your IDE to a Databricks cluster. That got killed, not sure if they have something new.

In the Netherlands there was a brief spike of inflation, but that was due to rounding up, definitely not converting 1 NLG to 1 EUR.

The inflation did correct itself the years after (aka lower than usual). The perception with many people still is that the euro made everything more expensive, but that's only based on feelings. The inflation numbers tell a different story.

All EU countries are required to join the euro. This was agreed in the 1992 Maastricht treaty when the EU was founded (and the EMU, which was the starting point of the euro). Only Denmark and the UK negotiated an opt-out at the time.

Only problem is that there are no deadlines and it's up to the country to make a plan for adopting the euro.

Solution: reduce CO2 emissions to carbon neutrality.

This has been agreed multiple times, most notably the Paris agreement of 2015, which set deadlines for countries to achieve carbon neutrality.

The plan failed in e.g. the US because politicians didn't follow through. Trump literally withdrew from the Paris agreement.

All but 3 countries in the world participate in the Paris agreement. This includes all big CO2 emitters, like China, US (now withdrawn), India and EU countries.

This reminds me of a story I read in an Escher biography: Escher would receive letters from mathematicians, saying that his work exactly visualized this or that theory. Escher himself did not understand what they were talking about, as he was not into mathematics. He did of course enjoy that others got so much out of his work.