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.
That's what Europe is currently doing. The US has indicated that it does not intend to lead the current world order or the Western world anymore so Europe is adapting and forming new bonds and alliances as fast as possible.
Just closed a trade zone deal with South America that was 20 years in the making. Now in deep discussions with Canada that itself is desperate to reduce the bonds with the US.
In a few decades, US will be at the same spot as Great Britain has been before. They will realize they lost a lot of their standing in the world and are now just one of many countries.
The only good thing about Trump is that he is stupid. If he had been clever, Europe would have ended up in a much worse position.
However, he was super transparent from the beginning and even started a war, giving Europe the opportunity for an easy united (non-) response. The war even showed the reliance of the US on Europe bases for their power projection. Making the reducing of man power that followed even more dysfunctional.
Only thing he could have done to destroy all bonds with the western world faster is to actually annex Greenland. However, there's still plenty of time for that.
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).
The problem that I see is what you do if someone runs out of tokens. It doesn't very well work to say "well I guess you just get fired because you can't work at full speed for the rest of the month".
Personally, this feels like its just trying to push the work of managers in allocating resources onto developers so that they have more work to do and can be blamed if anything goes wrong.
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.
Sorry for not haveing been clear (and thanks for the replay) -- I was asking about the US, I am French and have a reasonable idea about the general European systems.
My main point was to undersatnd when (and if) the "in the US we get enough money to pay the insurance" breaks.
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.
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