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

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

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

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.

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)?

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

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.

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).

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.

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).

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

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.

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.

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.

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

- state your intent more clearly.

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.

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.

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.