Main takeaway from Q
Anthropic's blog post on Agentic Analytics. Data quality and modelling are still the most important part the stack. Even more than ever.
https://t.co/76BUfpDHWk
@GergelyOrosz I would have thought AI and coding LLMs would bring business and tech teams closer (maybe making biz empathize more with tech). Completely wrong, the misalignment has never been bigger (and the ideal team has never been smaller!)
@juanmacias Supongo que como cualquier herramienta, hay que aprender a usarla.
Ademรกs entran en juego factores como el push-back de algunos managers que se ven amenazados o la falta de oferta formativa...
Pero el uptick reciente en la linea verde es revelador.
if you think AI chatter has reached an annoying level right now you're in for something else. it's going to be the only thing on anybody's mind starting shortly
40% discount for yearly payment sounds crazy!
After a few years in SaaS, hard not to wonder whatโs driving it: cash up front, monthly churn, both...
(Pricing pages can be weirdly revealing)
@hunvreus Looks like they are trying to serve all Marketing / Ops / Business folks that now feel like vibe coders and in full control when using Claude Code? Could be a good bet!
If your dashboard has 12 charts and 8 filters, it probably stopped being a dashboard...
It became a workaround for the fact that business users canโt explore data on their own.
AI agents change this: dashboards for monitoring, agents for investigation.
I've been helping a friend with his consumer goods trading business.
What surprises me is that the business can run for years without anyone really collecting, cleaning, organizing, or using the data.
Not that it's bad. But it just feels like he's leaving a lot on the table.
It makes me wonder how many traditional businesses are like this. Real revenue, real operations, real internal and industry knowledge... But the data layer is basically an afterthought.
The hardest data role for AI to replace right now is the one in the middle: the data modeler.
API-wiring data engineering is becoming increasingly automatable.
Getting insights from a clean dataset is getting easier.
The real leverage is still where the business logic lives.