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).
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)?
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).
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).
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.
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.
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).
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").