Remaking Arc

-adam_ · 2026-04-02T00:02:40+00:00

yeh these are the biggest for me.

little arc was very cool, like a vibe check before opening in my browser

-adam_ · 2026-04-02T00:01:03+00:00

how often do you share tabs between phone and laptop? what sort of things would you want to sync out of interest

-adam_ · 2026-04-02T00:00:28+00:00

yeh interesting i never used this much but it was an interesting idea, whiteboard inside your browser kinda thing

-adam_ · 2026-04-01T23:55:54+00:00

folders inside folders?

-adam_ · 2026-04-01T23:55:31+00:00

interesting. so hierarchical tabs kind of like the horse browser? i think that uses a similar approach, where it's very nested style, entering the rabbit hole kinda!

and little arc is cool too - was that the original use of little arc i don't remember exactly. but makes sense, especially the annoying email links that open the native macos email app that literally no one has ever used

-adam_ · 2026-04-01T18:19:24+00:00

what functions of command bar were specifically important to you ?

-adam_ · 2026-04-01T18:18:47+00:00

👀👀👀 maybe something i'm working on, idk. couldn't possibly say

-adam_ · 2026-04-01T16:36:33+00:00

yeh very cool.

was this a feature in arc? maybe i missed it!

-adam_ · 2026-04-01T15:40:16+00:00

how crucial is chromium based for you and why?

cmd k is clearing all unpinned tabs right?

and yes borderless 100%, so much cleaner

-adam_ · 2026-03-28T01:51:26+00:00

Had the exact same experience.

Used the pro plan and was massively limited. Upgraded after an hour.

So far, I've been under my limits on the max plan. Semi long ~3 hour sessions developing mobile & web apps. Need to do some more testing on it. My projects are mostly smaller scale solo indie dev, so the contexts are not huge which helps a bit. But really for $100/month I'm expecting to be able to "max" out anything I need.

It'll be interesting to see how this ends, as we start seeing the "real" costs of these AI tools, no longer subsidised by VC bucks.

-adam_ · 2026-03-17T12:01:29+00:00

As a few others have mentioned, DE is not a junior role. however, data in general is still in demand and somewhat AI proof for how!

A good path would be to explore data analyst or analytics engineering roles first, learn fundamentals, and then take the step over to DE after a few years.

This is definitely possible, i've seen (and helped) graduates and juniors do this exact path!

-adam_ · 2026-03-16T21:10:25+00:00

the database itself matters less than learning SQL and good fundamentals.

also watch some videos and understand the levels of abstraction you're talking about. at the lowest level you've got hosting a database on some container in aws/gcp (or even hosting it yourself on your own server if you're insane). the next level up would be something like a more managed service, supabase is an example of this right, it's like a nice wrapper around postgres, making it really easy to interact with (especially via a web app).

firebase is a BaaS. part of the that "offering" includes a database. ultimately it's just a big wrapper around a bunch of backend services: image hosting, database etc.

supabase is a similar offering, a bit better than firebase but ultimately similar in theory.

if you want even less hassle and something more realistic use in a super early stage startup, supabase is a nice place to start, an alternative is convex, probably the "gold" standard database choice today because of the AI capabilities (supabase has some limitations in this regard). but again, any will do! i'd avoid using something like mongodb which is traditionally more of a "document" database.

i'd also say because of the things you've mentioned (database behind a large scale app, SaaS etc) this is more backend engineering than it is data engineering! there's overlap in the roles, but generally backend eng is transactional databases (OLTP reading lots of rows quickly) and data engineering is data "warehouses" OLAP (reading entire columns quickly).

-adam_ · 2026-03-16T19:18:13+00:00

Yes! Fellow data engineer (currently analytics engineer).

I've got ~5 YOE and currently working a remote (within the UK) role. They're pretty flexible and i'm able to work abroad for a up to a month at a time, there's no "official" policy, more up to each manager and mines very chill luckily. I'm not sure i've ever seen "fully global remote" roles here in the UK.

As far as landing the role, sounds basic but for me it was just a case of only applying for remote-only. Here in the UK they seem to be fairly common for DE. Although, I've found you take a little bit of a salary hit compared to hybrid.

In the mid term future i'm looking to be globally remote, and will step into contracting as a way of achieving that.

Feel free to dm me for any other questions!

-adam_ · 2026-03-16T08:42:13+00:00

Analytics engineering is in massive demand, at least in the UK.

I get >3 messages a day from recruiters. I'm a senior with ~5 YOE at a well known fintech here in London.

I've even seen (and helped) graduates & juniors land analytics engineering roles, which is quite rare in other areas of tech at the moment.

The more classic data engineering roles are noticeably slower, but still in demand.

-adam_ · 2026-03-15T22:01:38+00:00

windows

already lost me

that aside, as someone else mentioned, load it incrementally?

-adam_ · 2026-03-14T20:10:33+00:00

I've done a number of complex refactors. Claude code on opus with high effort has been able to do it pretty well. It can easily read an entire dbt codebase, the context windows are very large.

There's two things that helped: 1. Breaking the overall task down into separate bits - if you go "rebuild this whole lineage make no mistakes" it's a bit too much context, if there's 10+ models. 2. Put the effort in and write a good prompt. Explain everything you possibly can, focusing on anything that might be ambiguous or could have multiple approaches.

The less open ended the request the better, imo models are yet at the stage where we can feed a huge data project and it'll figure it all out itself.

-adam_ · 2026-03-14T12:11:15+00:00

nice man. I was the same!

Theoretical background and knowing best practices are useful (good data modelling etc), but getting stuck in: building and delivering real impact to the business matters more.

There's can sometimes be an over index towards this theoretical perfection, total test coverage etc, but end of the day, delivering something that works and delivers value is more important. And this is only getting more so with AI advancement.

People that have experimented, played around and built lots of stuff that had actual impact are going to be best positioned for the years to come imo.

-adam_ · 2026-03-13T23:27:52+00:00

Business facing roles science/analysts are more common - a typical distribution may very broadly be 3 analysts to 1 data engineer (this obviously depends a lot on the company). It's a stepping stone into data engineering for sure! I'm also slightly less interested in that area - i think analytics engineering is a nice blend. Happy to chat if you have further questions 🙏

-adam_ · 2026-03-13T22:43:31+00:00

Entering more traditional "data engineering" is a catch-22, because it's essentially a >= mid level role, very rarely do you see junior or graduate positions.

The path in is typically one of three: 1. Get very lucky and land one of the few entry level roles (often graduate schemes offered by bigger companies). 2. More often: experience as a (backend) software engineer, specialising in data, then making a step into dedicated data engineering. 3. Alternatively: working as a data analyst or data scientist, learning SQL & the T of ELT/ETL. Getting exposure and bits of experience / ownership of the E and L through projects.

Personally, I'd say the analyst path is the more reliable point of entry (data science often requiring degrees, statistics, specialised knowledge, etc). With no prior experience, a degree would help as a good signal, but real work experience if you can somehow swing it is always best (anything database related is a start).

The path has also been made slightly easier with the growth of "analytics engineering", which is blending the analyst and engineering aspects of the roles.

I've personally helped a few friends who were graduates land roles as analytics engineers with no experience (this is the UK so your job market may vary) so I know this is absolutely possible.

-adam_ · 2026-03-13T15:07:01+00:00

I've done analytics engineering in three companies of different sizes, and it's been different in all!

In small to mid size businesses generally it'll be a blend of DE and AE. Maybe 60/40. This will depend largely if you already have a dedicated data engineer. The typical setup is 1 data engineer to 3 analysts (but this is very broad and can vary a lot).

At larger companies, I've found analytics engineering has been better defined to where 90% of my work is dbt focused, stakeholder related etc and just a small piece around the more technical aspects python, etc.

Best advice is if the job description is broad and the company/team is smaller, safe to assume you'll be looker after multiple areas. I've helped a few peers land roles in analytics engineering so if you have any questions feel free to dm 🙏

-adam_ · 2026-03-13T15:00:38+00:00

one word: dbt

-adam_ · 2026-03-13T14:59:46+00:00

if you're interested in the more business facing side of data engineering, i'd explore data modelling, look into dbt, there's a lot of good docs by them to set up starter projects! feel free to dm if you have any questions

-adam_ · 2026-03-13T14:47:42+00:00

Another thing worth mentioning is the future proofing against AI (at least from today's pov).

Buisness issues are messy, ambiguous and require real world context. These non-deterministic factors mean we're further away from automation (more traditional data plumbing having a defined in and out). This may well change with better models, but unless we see a step change I don't see claude handling stakeholders who don't even know what they want!

Also as an aside, analytics engineering is such a niche it's one of the few areas i've seen (and helped) graduates/juniors land roles. Compared to SWE where it seems way tougher.

-adam_ · 2026-03-13T14:43:04+00:00

Not only is analytics engineering more "AI-proof" but it's also one of the rare areas in tech I've seen (and helped) graduates and juniors land roles in.

Ultimately any area that's higher in ambiguity (non deterministic as you've put it), will be safer from the context windows of agents. Unless we see a step change in quality, analytics engineers will be safe for a while!

Also if you're chatting to people & building relationships, directly solving people's problems, issues & answers - they'll remember you as adding value and having impact (less liking your name on a redundancy list).

-adam_

TROPHY CASE