sails

Fair enough, my experience is European where you usually have multiple options and most people decide by price.

I will say I was amazed by how few options I had to get from JFK to Denver and the price was insane relative to a transatlantic flight, so perhaps the market is structured differently.

I agree with your experience, it does feel a bit insane, but just to point out this is the result of too much competition selling a commodity product rather than a monopoly.

Having lived in Palma I can confirm that this is indeed a decent way to avoid the two nightmare tourist areas (and not just a population density, as the central Palma area would be larger).

I like this idea for proxy maps. Do the same for London with American candy stores

Not to mention the mess that is pasting between Notion, Slack and Gsuite. Especially between the first two.

Lending to a group of companies, one company owning multiple. We see this where owner wants to sell and the acquisition process then includes an additional company for various reasons.

A very real pain, especially in my experience with commercial lending.

Do you cater to more complex credit products like loans to support acquisitions? These often require servicing of multiple companies in a group. This multi-entity requirement is where my frustrations are most acute, the lenders I work with catering to this more niche market.

Edit - UK based direct lending, but I’m sure the same is relevant to some extent in the US

I was expecting company data but e-commerce data also makes sense.

Do you have a sense for how people store, transform, organise, model the data once they have bought it? Is it typically data warehouse and dbt or straight into a data science pricing algorithm, or something else entirely?

Author writes article about content creators for hyped tech spaces, author _is also_ a content creator for hyped tech spaces

If the irony of the author writing that piece and then slapping a patreon link at the end is lost on you then I probably can’t do much in trying to explain it further

The irony of the article ending with a patreon link.

(Their other posts are about Bitcoin, web3, meta verse etc.)

Interestingly he did share an anecdote. Startups fail quietly and obscurely?

https://twitter.com/lennysan/status/1687138482152783873

Great resource, especially that they take a stance on versions [] and include some history

[] https://www.animatedknots.com/midshipmans-hitch-knot

Along these lines, but the other direction and something I've seen developing is niche expert local forecasts. I follow a few, surfing, sailing etc.

Surfline [1] do an excellent (paid) regional analysis, and the best aspect is the long range outlook where they will incorporate any levels of uncertainty or conflict between the various weather models into their analysis. To the lay person there may be "the forecast", but interpreting the different models and incorporating local anomalies is very valuable.

Also excellent is the UK Met office 10 day trend[2], to get an idea of the uncertainty and macro patterns at play.

[1] https://www.surfline.com/surf-forecasts/north-cornwall/58581... [2] https://www.youtube.com/watch?v=rLccinRLccA&list=PLGVVqeJodR...

> But engineering hours saved is pretty much a wash compared to DIY once you hit that first edge case that the tool does not handle elegantly

This is probably the exact crux of the argument, and I wonder if it reduces the overall demand relative to the growth in the industry. Do you need 10 engineers (like a decade ago) when 1 (great engineer) can deal with the bulk of the work with "better tools" and then DIY on the edge cases. This has been my experience with the modern ETL tools.

The evidence points to demand for data engineers declining on a relative basis to other data roles, following the data science trajectory. Agree or disagree?

Maybe the analysis as to _why_ is wrong, but that is what I'm trying to unpack.

HN Guidelines: "When disagreeing, please reply to the argument instead of calling names"

It would be useful to give more clarity around your thoughts on the "Data Engineer" role. Is it also in decline? Is the market as a whole in a relative decline?

(I wrote a blog [1] making a call that Data Engineering was likely also to see something of a relative demand decline, or be better defined into Software Engineer and Analytics Engineer, so I am quite interested in your analysis here)

https://groupby1.substack.com/p/data-engineering

> Most businesses' data engineering needs have been solved or will shortly be solved by managed services that 10 years ago would require endless and extensive self-built ETL pipelines, databases and tools. For the exceeding majority of businesses, this means they can and should focus on building capacity for business logic, analysis and predictions instead of data engineering.

I like that you improve on the underlying database error messages, as they are really unhelpful, and I think this is a great place to add value.

I've been keeping track of a few dbt alternatives. Dbt have opened up the market to this use case, while only partially solving the business model and maturity side. Here are the more interesting ones:

sdf.com (ex Meta team)

sqlmesh.com (relatively new)

paradime.io (more an IDE)

cloud.google.com/dataform (GCP only)

In case anyone is wondering, if this isn't dealt with by the system designer, then what ends up happening far too often is the data team is tasked with inferring the sequence of events based on a series of clues across this and other business systems to come up with some kind of pre-eventual-consistency guess.

> Beyond cost, I was also frustrated with having to make sure my database was always in sync with MailChimp and the audience schema they enforced.

Could you explain why this was such a problem?

It would seem like this (API to submit data) is the sensible way to balance the cost against the security concerns raised in the other comment.

Does mean engineering and sync issues which your solution does address in an interesting way.

I like it.

The link https://degreeswhat.com/?100 too quickly tracks the mouse and you lose the pointer to the specific degrees

Unfortunately your compass bearings seems a bit off.

1. Compass degrees go 0 to 360

2. North is traditionally at the top

Yours only has half the compass (East) and seems to be reversed or mirrored

Edit: direct linking works https://degreeswhat.com/?270 and I get the correct bearing (270 = West), I think it is still mirrored for a compass because you increase from 0 anticlockwise.

Coverage for UK providers like Truelayer? Currently using Codat as an aggregator pass through for Truelayer as they manage auth flow and sync bank data to their platform, which saves us the infra hassle on both. Wonder how viable this is to for you to provide?

Thanks, I suppose GraphQL is a valid example, I was thinking niche in terms of application eg Zanzibar is auth only, and you would struggle to use it for much else

> A Flexible, Uniform Data Model for Authorization

Are there good examples of similar applications of data modules for similarly niche use cases? I get that there are obviously endless data models, but this seems to extend beyond that into a more integrated concept and I don’t quite know why that seems to be the case.

I was going to post hex as an ideal option, I definitely do not miss dealing with jupyter and all the Python env related headaches, hex solves all of these and then the rest of the more UX related issues with notebooks. Definitely recommend as the best compromise for notebook based Python data analytics tasks

I didn't suggest it is straightforward, I said they need a system, precisely because it isn't straightforward. Hotels in other reply a good suggestion.

The beds are all terrible. Airbnb needs a bed system.

Everything else is just building a system around your needs.

When I had to develop a custom tap for a client, the support I received from the team, the SDK and the general experience was absolutely top class. Love the new site too!

> I turn off notifications/alerts for most things and I simply check when I have some time.

This is the answer - the phone rings when it is urgent.

Data modelling is important to highlight, and if OP not familiar with the concept and need then likely won’t see the obvious value of Cube.

Late to the party but I'm somewhat sure that there are no true "power users" of notion (despite what the article says), because of the lack of a feature to see your upcoming reminders. This is such a basic Todo feature.

https://www.reddit.com/r/Notion/comments/12g97j1/how_to_see_...

I tried the ASUS ZenScreen MB16ACE and the quality/resolution/rendering was bad compared to a Macbook screen, I returned it straight away.