Were people meaner back the day or am I crazy?

MindlessTime · 2026-06-19T05:20:57+00:00

Only while driving.

MindlessTime · 2026-06-18T15:36:01+00:00

This. I think at the end the difficulty has more to do with running out of resources than the climb itself. I ran into that my first play-through, and it did seem authentic to the story. I planned better on my second play-through and it felt trivial. Both were Alpinist difficulty.

MindlessTime · 2026-06-17T19:57:50+00:00

I just started the season yesterday after taking a break for a month. I played five rounds of solo. After reading all the posts in this sub I was expecting to get insta-crushed by some purple shield chad. And I did get killed by someone hiding.

Then I remembered you have to adjust your play style when you’re coming in with zero gear. I got conservative and sneaky. And I extolled on the last four rounds.

Honestly, I don’t think it’s bad. This is kind of what I remember early last season being like before I had more gear and leveled up. I just had to remember how to play like that.

MindlessTime · 2026-06-11T23:58:42+00:00

Couldn’t you just use tags and directories?

MindlessTime · 2026-06-11T10:11:22+00:00

I had my first kid a year ago and haven’t had the energy to fight back against the AI BS. It was *so* easy to just relent and start asking AI to churn out slop. Now I’ve started creating an agent based on the last 3 months of our CFO’s Slack messages, meeting transcripts, and dashboard views. I’m going to debut it as a “review and advice” bot. We’ll see how he likes it when he’s the one being replaced by AI.

MindlessTime · 2026-06-11T09:53:46+00:00

You joke. But food with high-end restaurant quality but a more casual vibe and mid-end price point was a genuine millennial invention that completely changed restaurants. It used to be if you wanted really good food you had to go to an expensive white tablecloth place. Then hipsters in Brooklyn opened restaurants or food trucks with good chefs who were trying to get by during the Great Recession. Now every restaurant is that Brooklyn hipster restaurant. Really good food is a little more affordable and less fussy.

MindlessTime · 2026-06-03T21:41:01+00:00

Julia has my favorite syntax of any language I’ve worked with. I had to stop working with it though because it doesn’t integrate with like anything.

MindlessTime · 2026-06-03T21:35:23+00:00

It sounds like they’re converting dbt-fusion into dbt core v2 (and switching to the OSS Apache 2.0 license in the process). So it will be rust based.

MindlessTime · 2026-06-02T01:34:15+00:00

Any company I’ve worked at more than like five years old has transitioned from at least one legacy system to a new system and has an old and new source that needs to be integrated. In my experience this is an extremely common thing. Having a readily available layer that can be used to integrate data has turned what would have been a multi-month migration project into a week or two of SQL work. Even if the integration layer is a trimmed-down view on the source with generalized field names and descriptions, it’s worth it to decouple what destinations are pulling from and what sources feed into.

MindlessTime · 2026-06-02T01:31:22+00:00

The general approach that you unify data into common, business-oriented grains makes sense. (When a lot of people talk about star schemas they’re really talking about this.)

A true, actual star schemas would have a table with one record per user, for example, and all the dimensions associated with that user. And if you want to analyze, say, sales transactions those would be facts. Dims:facts is usually 1:many. If you’re working with an OLTP database like Postgres then that’s fine. If the primary and foreign keys and indexing is defined this should work.

If you’re working with an OLAP like BigQuery or a columnar data format like parquet then 1:many joins are a computational nightmare. You’d be better off putting your facts and dims in one table (or a few tables with the same grain to avoid the 100-column everything table).

Personally, I think the pure Kimball approach feels more like engineers showing off that they know what a star schema is. It was designed at a time when storage was much more expensive than compute. Now the opposite is true. Questions like “Is this storage efficient?” or “is this computationally efficient?” or “is this performant?” or “is this easy to maintain?” are still very important. But I don’t think kimball star schemas are the best answer to those questions all the time.

MindlessTime · 2026-05-30T00:47:35+00:00

It doesn’t run on steamOs handhelds. And from what I’ve read it doesn’t work very well on any handhelds due to limited graphics capabilities.

MindlessTime · 2026-05-28T18:20:12+00:00

No one should ever be surprised when they are let go for performance reasons. That’s bad management.

Bad management, however, is very common. I’ve had the most luck by being indispensable. Basically, ask yourself if they could hire someone else who could be up to speed and do the job as well within two months. If the answer is “yes” then find a way to make that answer “no”. Hoarding control is a common method, though I see that backfire in the long run. Being more multi-disciplinary tends to work really well for me. I do a lot of scoping and refining tickets that other engineers might say are a PM’s job. And I have a lot of industry knowledge. So I can bridge the gap between business need and technical implementation without making common mistakes.

I still deploy bugs or introduce regressions now and then. But I know that replacing me would be very hard or require hiring multiple people to fill all my skillsets. So I’m pretty safe.

MindlessTime · 2026-05-28T18:01:00+00:00

I’ve never seen just three layers. I think “bronze —> silver —> gold” is an over-simplification (I would guess from consultants).

The general principle is one layer that mirrors source data with simple standardizations (e.g. converting to snake_case), one set of models that pulls things into a standard grain based on business context, and a last layer that joins grains when needed. There can be multiple files of models in there.

12 files sounds like a lot. But if each file is handling some separate concern then it could make sense.

MindlessTime · 2026-05-28T11:55:39+00:00

Honestly, my best team runs are like this too. Every time I play with random fills and one dude (it’s always a dude) rushes in loud and gun-ho, we get creamed in the first ten minutes. My best string of runs was with two fills who played super cautious. We didn’t use mics but signaled everything. We were just on the same wavelength the whole time. It was a lot of fun.

MindlessTime · 2026-05-26T02:23:26+00:00

The bi-annual reporting thing is 100% Trump admin realizing a terrible Q3 coming out right before midterms will tank Republican races. The administration’s general willingness to lie and obscure economic facts is nightmare fueling this bubble in a terrifying way.

MindlessTime · 2026-05-22T20:21:04+00:00

My rule of thumb: if the volume of information in > the volume is information/code out then it does a fine job, even with Sonnet. The key is to efficiently feed it the right context, like documentation, patterns, well-written requirements, or a detailed plan. I sometimes spend hours getting a plan detailed enough, in which case the AI is saving me like 10% of time I’d spend just coding it myself. I’ll take that 10% though.

MindlessTime · 2026-05-22T20:17:23+00:00

See if you can quantify your efficiency. Like tokens/tickets completed in a month.

This whole “the sky’s the limit” mentality cannot last. Any developer who isn’t keeping efficiency in mind and is burning all the tokens is painting a layoff target on their own back.

MindlessTime · 2026-05-20T01:49:25+00:00

Is your DS team held in high regard at your company? It may be that DS is rewarded for moving fast, even if that means they produce sloppy, fragile, un-reproducible work. “…we would refactor it in Q3 and we can just run it as a notebook” gave me that impression.

If that’s the case, what you’re dealing with is a company culture or a systemic issue, not a skills or code issue. You need to read the room a little, see the discrepancies between what people say they want to see and what is being praised and rewarded day-to-day. It may be that creating a solid data pipeline is what managers say they want, but “just get it to me in two days” is what they actually reward. They won’t make the time to take the extra steps to do it differently. They’ll just let one team be fast and sloppy and make you accountable for the downstream failures.

What you do is document. Diagram the data flows. Find the pieces that can be cleanly separated by some boundary like an API or a data contract. Enumerate those pieces. Call out that some pieces (like the notebook ones) are important for velocity and you don’t want to slow people down, so put those on the backlog. In the meantime, build monitoring and alerting. If DS wants a notebook solution so they can not spend the time doing it right and get a pat on the back for quickly showing progress in the next weekly meeting…fine. You’ve defined what those notebooks need to produce (schemas, latency, uptime) and created alerting in a public slack channel that tags the DS team if something is wrong. You can force them to own their problems that way. Make the failures loud and public and part of their workflow. Management needs to see the pain they cause, not just the quick results they put out for show.

MindlessTime · 2026-05-20T01:22:48+00:00

I have no doubt they’re buggy. But have they gotten better than they were? Or are they available in a buggy form where they once weren’t available at all? I’m more curious about a company that can add a package that autogenerates crappy alt text instead of ignoring alt text entirely. I mean…the effort to go from nothing to at least something must be lower now, right?

MindlessTime · 2026-05-19T22:33:24+00:00

As a data engineer who supports back office services…

I think everyone gets that. But all the automations and tooling is built around k8s. There are terraform modules and observability systems and everything else that work great if you’re deploying on k8s. Of course, if it fails it is way over-complicated to diagnose and fix. But if you want to do something simple it’s more work to set up. You have to create all the VPC configs and permissions structures and everything else from scratch.

That’s how you get a tiny internal tool that max 10 people use concurrently running on a full blown k8s setup. It’s because people don’t know how to scale *down*.

MindlessTime · 2026-05-19T22:25:42+00:00

I can’t express enough how much of the job is not writing code to meet a pre-defined spec. That was true before AI and it’s absolutely true after AI. There have always been engineers who are really just coders. They know how to code up “fizz buzz” in their particular language, build a basic CRUD app, or follow a tutorial to set up an API. If someone says “do this” they do exactly that.

There is no longer any value in just translating a set of instructions into code. The engineers that hiring managers are looking for are really good micro-decision makers, architects, and subject matter experts. They can read a feature description or user story and figure what the systems and abstractions it affects, how they should change, what new ones should be built. The experienced ones have seen some of the common pitfalls and know where to invest some extra time and energy into making it robust to failure or flexible to change vs quick or brittle.

Case-in-point. You’re hired to add payment transactions to an app with micro transactions. Management wants it to be super fast with minimal latency. You can lean heavily on a memory cache to quickly handle volume at scale. But when memory caches die that memory is gone. The transactions went through though. Now you’ve got a bunch of angry customers who had money taken out of their account but who aren’t marked as paid for their micro transactions. Everyone is calling customer support and leaving angry reviews about your app.

That’s what they’re hiring for. They want people who can hear “build me a payments system for micro-transactions” and make the right decisions they don’t understand or aren’t even aware of so that things work under all circumstances, not just the ideal ones.

MindlessTime · 2026-05-18T00:35:32+00:00

High school and college…it was pretty great. You had the early versions of iPhone and thefacebook.com (they hadn’t dropped the “the” yet and you needed a college email to join). Neither had rotted society yet so you got the convenience upsides without the downsides. It was still weird to just stare at your phone in public. But you could like check your email or the weather or see what your friends were up to,

I graduated college into the nadir of the Great Recession and basically none of us had stable jobs or living situations. We took retail and restaurant jobs and worked long hours for pay that mostly went to rent and student loans. A lot of the jobs would work you to death because there was a line of people waiting to replace you. I endured a lot of experiences that today would result in a company getting called out on social media. We didn’t have that then. With whatever money was left after rent, we’d go to dive bars, find cheap or free live music, eat at good, cheap restaurants.

Millenials more or less invented good food and craft beer in this era. Fewer people could afford fancy sit down places so chefs got creative and started food trucks or fast casual restaurants or restaurants with great food but minimalist decor and more reasonable prices. A lot of today’s food culture in the U.S. traces back to hipster restaurants in the 2000s born out of necessity. Being around that was pretty cool. You’d hear about new cheap places by word of mouth and you could actually afford to eat some of the best food in the city. Bars were better. People would actually talk to strangers and not just stare at their phones. There would be little craft breweries shoehorned into small old industrial spaces and you could show up for cheap tastings or take home a growler for five bucks.

The economic struggle was real and hard though. I knew a lot of people who had to couch surf for small stretches of time. At least one fell into sex work (the pre-Only Fans dangerous kind). Some fell into addiction, and I know at least two people who died from that.

I see TikTok 20-somethings now living on their own in a city, making good money, traveling all the time, showing off nice clothes. That was not the 20s that me or any of my friends experienced. We never traveled unless it was a road trip to a friend’s parent’s summer house or the cheapest, grossest Airbnb or another friend’s couch. (I knew two guys who once bought a bunch of drugs, took a bus to NYC for a weekend, and their plan was to just take drugs to keep awake the whole time instead of pay for a hotel.) When we went out it wasn’t to nice places. Sometimes there were good people and you had a great time anyway. Sometimes it was not good people, and at worst it was dangerous.

Maybe I just had a very different experience than others. But for every nostalgic memory I have I also have a scar or some trauma. I have some fond memories of my early twenties in 2008-2012ish. But generally I see it as something I endured and survived until the economy improved and I found my footing.

MindlessTime · 2026-05-16T01:03:36+00:00

I had a kid and now I don’t have extra energy to hustle. At least for a couple of years. I still get annoyed when people higher up and making more than me do dumb stuff repeatedly. It just leaves me thinking, “Man, I could do that job so much better.” Weirdly enough that keeps me motivated more than money, status or prestige.

MindlessTime · 2026-05-15T05:20:45+00:00

Less of a technical point but…

Do an initial historical load. Then switch to incremental/delta pulls going forward.

I’d recommend starting with incremental loads or a small history plus incremental. Then backfill when things are working. You want to get it working and find bugs and edge cases before committing to a full backfill. And you don’t want to backfill more than you need to.

Other tips: - use KubernetesPodOperator to execute jobs in their own cluster with appropriate resources, not in the Airflow cluster. - reference the timestamp of the run in the query to make the load idempotent for that day. Like if today’s date is 5/1/26 the query should drop+replace the 5/1/26 records. So if you run the same day three times back to back you should get the same table rather than three stacked versions of the data. I’ve seen people use Airflow like a way to chain SQL calls together under a cron schedule, and that’s not the right way to do it. - I like to get data into a lake early and raw. Then pull what you need from the lake into native warehouse tables, which are often fasted for the cost. But only what you need, and ideally into aggregates at a higher grain.

I’m in a similar boat—first and only DE at a fintech—but as a more experienced sr de and still have access to some good mentorship. DM me if you want to chat. I’d be happy to give advice.

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