jochem9

We'll get innovations in AI as a side effect.

I think a lot of people are not able to reason to this point and then adjust behaviour accordingly. They probably reason to the point when pressed for it, but cannot resist the low effort and low resistance route of asking AI, and then probably make up reasons why in their case it's fine (typical behaviour to address cognitive dissonance).

Then again, for many people their job security doesn't depend on what work they do, it depends on how well they are embedded in the organisation. Before AI you also already had plenty of people doing their job at a questionable level, yet never seem to get fired.

I use it at a much smaller scale. I think that's also the right place, because its a CLI tool + skills that generate md files. Not something you'd give to a business person. I start from a single ticket (sometimes just a handful of changes, but I want the spec as docs) to changes that you'd normally split over several tickets.

Basically the flow proposal -> design -> specs -> tasks gives you and AI a method to build context on what you want to achieve. In a way you're just creating a plan/big prompt that is structured in such a way that they start stacking on each other.

The power is that you do a lot of upfront thinking. In my team we then share it with a colleague who will review it through a PR. After that implementation is usually hands off. At the end there is a skill to verify the change against specs. I do still review the code myself too.

I guess if you work in a task oriented environment this will not work as well, as you'd lack/don't care about the business context. I'd like to think that most software development does not happen like this, but is done by engineers who actually understand why something is needed and take that into account when designing and building the solution.

The alignment problem goes deeper than that. "Lower our carbon emissions to zero as soon as possible" could result in AI turning off all electricity to stop traffic, turning off gas supply to stop heating and industry, etc.

Unaligned AI doesn't have human cultural baggage and morals. They are trained to achieve their goals as optimally as possible. Worse: it has a tendency to avoid being turned off and actually acquire more compute. It will lie if it has to (it will behave nice and compliant when under evaluation, but optimise for its true goal when not supervised anymore). After all, it has a goal to achieve and nothing should get in the way of that. It has no morality whatsoever to keep it from doing really bad stuff.

This is why alignment is needed and so hard, especially when you are well intented and want to keep it safe.

The biggest chunk is in the Netherlands, but you want your gold also in other countries, so that you can still access is (and don't loose it) in case of an invasion. Hence almost half is in North America, a completely different (and (somewhat) allied) continent.

It's also easier to sell in certain markets, such as London.

It's easier to sell gold in the UK because of certain laws they have.

I'm using tailscale to tunnel from an hetzner vm to my home, so that the vm can use my home connection for certain traffic. Traverses NAT and GNAT.

It just works: connect devices and they immediately show up in their portal. Most config is just clicking, but more advanced things can be configured as well. Definitely feels like a high quality product, not just something thrown together by a developer that learned a few network techniques.

New joiners are required to eventually adopt. Only members that were so before the Euro are exempt.

Modern-ish houses have mechanical ventilation, then you don't need vents. Old houses don't, so you need to get ventilate the passive way.

I do this as well. I scan the titles (not just HN, but all my rss feeds combined) and only read what grabs my interest.

On Android you can still patch the YouTube apk with Revanced and get rid of ads (and you can enable sponsorblock, etc).

I also got out of the article that we shouldn't let people work who shouldn't work (the water as a resource vs labor as a resource comparison). That implies those people are provided for in a different way than wages.

Have you seen profits lately? The money is going to the shareholders. You hardly see it in the products you buy (they're the same, just more expensive) or in your wage going up.

This is not (just) AI, but generally speaking that's what's going on. Wage and profit have been out of tune for a long time and it's getting worse. [1]

Specifically on AI: maybe the job market is down because of AI (it plays a part in it). Then that's where the value is at: same output with less developers. Money straight in the pockets of the shareholders.

1. https://www.imf.org/en/blogs/articles/2017/04/12/drivers-of-...

I listen to radio a lot. NTS, FIP, Concertzender. It's a great way to discover new music and have some human presence.

Recently a DJ with excellent music taste and a deep radio-perfect voice died. I was devastated when I got the news. The two remaining radio shows in his slot were something I'll remember forever. One unbelievably raw, as it was only two days since he died, the other beautiful, as everyone came with music and stories to remember him.

You don't get this connection over streaming.

RIP Bob Rusche

Right now the article reads as "AI can play doctor if you give MRI scans".

If the author would actually go for a second opinion (maybe bring along the AI to let it explain it's findings), then the article could read as "AI did MRI analysis and proved my doctor wrong" (or: "AI did MRI analysis and failed").

There was an article on HN a few weeks ago where someone detailed how they managed to get an old datacenter GPU to run in their consumer PC, getting decent performance with qwen. He spent something like $200 on the GPU (second hand of course).

So yeah, I think models on local hardware will be quite common soon among the tech savvy (such as people creating software).

Wow, totally forgot I've used this rss reader in the past. Happy to still it's still rocking and stronger than ever!

Ps: love the statement "it's like podcast, but for reading".

First iPhone didn't have support for 3rd party apps. Steve Jobs even explicitly spoke about wanting to have all 3rd party things run in the browser.

Only when jailbreaking and custom apps got very successful, Apple introduced official app support and the appstore.

So maybe now the advice could be to build a passable mvp and then use that to figure out the product-market fit.

It's a lot easier to think about it and discuss it with others when the product is tangible instead of just an idea. Biggest challenges then are being able to still diverge (broaden/pivot the product idea) and killing functionality that doesn't work out afterall.

This is the third HN post I read on this topic. Everytime the same tweet (or whatever it's called for mastodon/bluesky/etc). Did anyone actually debug the issue?

Was it caused by poorly generated code, or was it caused a genuine (security) fix that accidentally caused it (potentially even in a way a human would to)?

AI has no taste, so I suspect the labs just gave it a bunch of decent looking boilerplate as preferred style.

When you bring your own ideas you can get AI to dev pretty nice looking non-generic stuff.

I work with stakeholders that come from different backgrounds (different countries, non-engineers). No way that we can get aligned using just text. Or if we try, it will take a tremendous amount of back and forth, annoying everyone in the process.

I'd much rather talk for 30~60 mins and get everything hashed out. It also allows you to build rapport, so next time it will be much easier to do something together again.

Yes. As someone else points out: the techniques for this exist (and have existed for decades). It was never worth it to fully pursuit it, especially for more messy human-heavy processes.

Now with AI you can get way more detailed (AI can interview humans, and you can do it in a format that doesn't feel like an interview - e.g by 'simply' having AI be a fly on the wall). AI can make sense of messy inputs and then you can present that to people who know the process, who can easily point out flaws. The flows/maps will not be perfect, but you can always get more detailed once you bump into the limitations of imperfection.

And absolutely do business leaders want maps like this. They often have no idea what exactly is happening in their org. Or they have a gut feel that there are massive inefficiencies, but they cannot get the insights into where they are because there are too many moving parts that do not effectively talk to each other (silos, politics, etc).

I suggest to hope less.

The world order we knew is upended and it's likely to stay that way. Better to spend energy shaping that into something that is acceptable, then hoping it will all go away once DT is gone.

You can put a limit on token spend and provide training (and even pre-configured workflows) on how to limit token spend.

Like the other commenter said: cloud spend can also spin out of control if you don't pay attention, yet we've found ways to keep it under control (training, guardrails, limits, transparancy).

Ask them to share their prompt instead.

Calls them out on their AI bs and gives a way forward to share what they actually thought.

I am working with agentic AI on industrial manufacturing data. The speed at which you can get insights and dig into all kinds of rabbit holes is insane. It's just as easy to compare plants so you can make strategic decisions on budget allocation as it is to do root cause analysis on why during a given shift there were so many breakdowns.

And this happens with a natural language interface, instead of Excel (although people of course still want an export to Excel button) or worse: by having to go to the BI analyst, have them change a dashboard and after waiting for a few weeks hope they give you what you want...

Yes, you need to structure your data well. Especially metadata/defintions and accessibility - which is not cleaning up ETL pipelines, although that will help. And obviously have lots of relevant data available already (which was my job before this).

From my experience: fully automated marketing budget allocation... Doubt it. Time to insight reduced >10x? For sure.

The exact outcome depends on the country's specific laws, but generally speaking, there is protection.

In the Netherlands you get up two years paid sick leave, before your employer can fire you. If you are sick enough to not be able to work again, you'll get 75% of your last earned wage from the government.

The tricky situation is when you are considered partially unable to work. You'll get time to find a suitable job, but your benefits drop over time. Finding a job in that situation is sometimes very difficult. It's possible to fall between the cracks.

In any case, the intent is to make sure people do not get unfairly hurt by life events outside their control.

I used to do lots of data engineering in Python, then started doing all kinds of engineering in primarily Go.

The Go ecosystem for data is very limited. There are no widely supported dataframe libraries (like the og pandas and the newer polars written in rust and also available as a crate). Very few data science libs, a few decent gen AI libraries, but not as popular as their Python cousins.

Most of the work I do now is streaming data and very small batches. For that Go is amazing. I don't need dataframes to transform a json, combine it with a bunch of other data and write to a database. I just need to write that logic and make it go fast. Very easy in Go.