Kada javnost najviše reaguje?
Desentix je analizirao 650.270 komentara i 16.909 autora u periodu 1.1–1.6.2026.
Odgovor je konkretan.
Najaktivniji sat je 13:00.
U tom satu zabilježeno je 43.588 komentara.
Ali to je samo jedan signal.
37 od 100 komentara dolazi u periodu od 10:00 do 16:00.
A više od pola ukupne rasprave formira se između 12:00 i 19:00.
Javna rasprava ne teče ravnomjerno.
Ima svoje špiceve. I oni su predvidivi.
Za kampanje i PR timove, to nije samo zanimljivost.
Nije dovoljno pratiti šta se komentariše.
Važno je znati i kada rasprava dobija zamah.
Desentix pokazuje gdje se rasprava formira, kada ubrzava i gdje treba djelovati.
Ako vodite kampanju ili PR, zakažite prezentaciju https://t.co/2PoXYUV10H.
Analizirano: 650.270 komentara i 16.909 autora, 1.1–1.6.2026. Sati: Europe/Sarajevo. Podaci služe kao analitički signal, ne kao konačan zaključak.
U analiziranom uzorku Desentix je našao:
- 204.127 komentara
- 2.879 autora
A onda se pojavljuje ključni signal:
30 najaktivnijih autora napisalo je 54.990 komentara.
To je skoro 27% analizirane rasprave.
Drugim riječima:
Oko 1% autora nosi skoro trećinu komentara.
To ne znači da su ti profili botovi.
Znači da online slika može biti snažno oblikovana malom grupom najglasnijih učesnika.
Za kampanju je to važna razlika.
Ako gledate samo broj komentara, možete pomisliti da je reakcija šira nego što jeste.
Ako gledate ko nosi raspravu, vidite da li je pritisak organski, koordinisan ili koncentrisan u malom broju profila.
Sekundarni signali koje Desentix dodatno provjerava:
- 5.226 komentara nosi kombinovani botlike signal
- 3.214 komentara ima link/spam obrazac
- 1.537 dolazi iz hosting, cloud ili data-center mreža
- 1.110 dolazi iz VPN ili proxy mreža
Komentari su signal.
Desentix pokazuje ko nosi raspravu, gdje nastaje pritisak i gdje kampanja treba djelovati.
Zakažite prezentaciju.
Napomena: podaci služe kao analitički signal, ne kao konačan zaključak.
Botlike signal nije dokaz da je neko bot, nego indikator ponašanja koje zaslužuje dodatnu provjeru.
@milan_milanovic Spent last weekend deleting 14k lines of AI-generated code.
It looked useful. It wasn’t impactful.
Simplicity > illusion of progress...
The founder of Postman says you have to kill your existing org chart, especially if you're still operating with a pre ai hierarchy arrangement.
The modern org chart, according to @a85:
- wide span of control (even within exec team)
- work directly with ICs, not through layers
- either you're building, or you're selling
Projects are led by staff/principal engineers with high agency. They see across the board as well as deep in the stack.
Product managers are building APIs and prototyping in Claude instead of writing PRDs.
Designers are shipping PRs through Cursor directly instead of relying solely on Figma.
Everyone is building. And the management's job is to develop better judgment.
I've seen quite a few tech cycles by now... and honestly, every time a big shift happens, it feels like we fall into the exact same trap...
Lately more and more companies are adopting this "AI-first" label almost overnight. And to be fair, I get why they do it. Market pressure is huge, survival margins are getting tighter, and the business simply has to find some leverage. That's just reality, and it would be naive to ignore it.
But the thing that really gets me thinking... is how this is pushed down to the teams.
It's often a pure top-down directive, without any real consultation with the people who actually keep the system running. The problem isn't that we want to use AI to be faster and more efficient... the problem is when that decision comes purely from a spreadsheet, completely ignoring the company culture.
You can't just declare a new era in a meeting and expect things to magically sort themselves out.
If we're pushing an "AI-first" approach, it shouldn't just be an economic measure... it has to be a conversation with the people. How does this change our delivery? What does it mean for code ownership? How do the engineers actually feel in this new way of working?
At the end of the day, a company isn't built on management declarations... it's built by the people inside.
The point isn't what tech we adopt to survive... the point is whether we make that transition together with the team, or despite them.
I've seen quite a few tech cycles by now... and honestly, every time a big shift happens, it feels like we fall into the exact same trap...
Lately more and more companies are adopting this "AI-first" label almost overnight. And to be fair, I get why they do it. Market pressure is huge, survival margins are getting tighter, and the business simply has to find some leverage. That's just reality, and it would be naive to ignore it.
But the thing that really gets me thinking... is how this is pushed down to the teams.
It's often a pure top-down directive, without any real consultation with the people who actually keep the system running. The problem isn't that we want to use AI to be faster and more efficient... the problem is when that decision comes purely from a spreadsheet, completely ignoring the company culture.
You can't just declare a new era in a meeting and expect things to magically sort themselves out.
If we're pushing an "AI-first" approach, it shouldn't just be an economic measure... it has to be a conversation with the people. How does this change our delivery? What does it mean for code ownership? How do the engineers actually feel in this new way of working?
At the end of the day, a company isn't built on management declarations... it's built by the people inside.
The point isn't what tech we adopt to survive... the point is whether we make that transition together with the team, or despite them.
I was looking at some numbers recently... 40% more websites, a 50% jump in iOS, GitHub commits going wild. People with zero experience are shipping software left and right.
And it is impressive... the barrier to entry practically doesn't exist anymore.
It reminded me of a story I heard recently... about a doorman. A hotel installs an automatic sensor because management figures the doorman just pushes doors and it's an easy way to cut costs. Technically, everything works... but they lose the guest recognition, that welcoming feeling, and the premium status. They optimized the operational process, but long-term, they killed the value.
Honestly, this is exactly what's happening to us in development right now.
AI tools are those automatic doors. And the point isn't how fast and cheap we can generate code anymore... the point is who will actually want to pay for it and use it.
When anyone can spin up an app in a day, the code itself becomes a pure commodity. Its value drops drastically. What remains as the real advantage is that invisible work... empathy, deep UX, and truly understanding the problem. In a flooded market where software production is practically free... the only ones who will win are those who realize that you just can't prompt a good product.
As AI speeds up our delivery... I'm starting to think the main question isn't actually productivity.
It’s not just about how fast we can push code... it’s about how much of that change a team can genuinely absorb, while keeping shared understanding and control.
Because when things break in production, the speed of your tools won't save you... what saves you is how fast you can form a solid hypothesis of where the problem is.
And you can only do that if you still have a clear mental map of the system.
I wrote a bit more about this... along with some practical steps we’ve started taking.
https://t.co/NfjWeWDuSl
The biggest impact in the AI era... definitely won't come from those who write code the fastest.
Looking at it purely from an economic standpoint, code production itself is actually becoming a commodity. Something incredibly cheap and available at scale.
It's not about typing speed. The market simply doesn't reward that the way it used to.
It's about systems thinking.
Keeping a complex architecture in your head... and predicting what one fast, generated, and "cheap" change is going to break somewhere on the completely opposite end of the system.
That right there is the real capital. The skill that brings actual ROI.
Because honestly... cheap code that brings down your production ends up costing the absolute most.
@milan_milanovic AI isn't erasing our value, it's elevating it. Our magic isn't typing fast... it's our human vision, shaping systems and guiding the chaos. Cheers.
@komentatoricaba Sve više društvene mreže potiču narcizam i konstantnu potrebu za potvrdom, što vodi do gubitka slobode, samorefleksije tj. sve više površnih diskusija, koje često zamagljuju važna politička pitanja...
@komentatoricaba Zar ne postoji Zakon o sukobu interesa u institucijama Bosne i Hercegovine. Mislio sam da zakon definiše situacije u kojima postoji sukob interesa, odnosno kada javni dužnosnici koriste svoju poziciju za privatne interese?
@komentatoricaba Nemam ništa protiv nijednog kandidata, ali čini mi se da nije baš primjereno da se na listama, pored obrazovanih ljudi, nalaze i oni koji imaju samo osnovnu školu. Zar ne bismo trebali birati najstručnije i najkompetentnije osobe da vode državu?
Love the idea Raul Junco of simplifying data app development with Taipy! Especially for folks new to web dev. Does Taipy integrate well with existing data science libraries like Pandas or NumPy, this will help me a lot? Seems like a powerful combo for data folks looking to build quick data dashboards?