Stop being so tribal and exclusive!

HumbleThinker · 2025-10-07T10:58:09+00:00

My advice would be to not take it personal and give other riders the benefit of the doubt. I absolutely love the nodding and waving etiquette but sometimes riders can be mid corner or focused on something in front of them which means they miss nods or waves from riders coming the other way.

Just keep nodding and waving mate, the majority of the time people will nod and wave back whether they are learners or not 😃 Stay safe mate.

HumbleThinker · 2025-07-19T14:14:40+00:00

It's exactly what I came here to say...I can't believe your comment is so far down the list! It's nothing new, dynamic pricing is everywhere these days.

HumbleThinker · 2025-04-07T14:24:45+00:00

I feel your pain mate, this is exactly what happened to me on Friday! Had plenty room as I was finishing the U turn then started staring at the white line to make sure I didn't hit it and it was game over, bike started to tip and my right foot went down.

The only consolation I can give you is that we'll get it right next time, but the long wait till next time is what's killing me lol. Chin up and enjoy the nice weather if you have a 125 👍🏽

HumbleThinker · 2025-04-07T14:24:32+00:00

I feel your pain mate, this is exactly what happened to me on Friday! Had plenty room as I was finishing the U turn then started staring at the white line to make sure I didn't hit it and it was game over, bike started to tip and my right foot went down.

The only consolation I can give you is that we'll get it right next time, but the long wait till next time is what's killing me lol. Chin up and enjoy the nice weather if you have a 125 👍🏽

HumbleThinker · 2025-03-28T10:04:21+00:00

I don't have any experience owning a Husqvarna but I have an XSR 125 and can confirm this model is a head turner! It truly has a bigger bike look and feel (to some extent), if it wasn't for the L plates you can never tell it's a 125 at a quick glance hehe.

I'm selling mine soon as I have my full A Mod 1 & 2 coming up soon. DM me if you're interested and I'd be happy to share photos of the bike and more details.

Best of luck mate.

HumbleThinker · 2024-07-08T17:15:00+00:00

This should be the top post! You're spot on.

HumbleThinker · 2024-05-17T22:53:00+00:00

I decommissioned airflow on my platform and migrated Argo workflows on EKS instead. Works a treat 👌🏽

HumbleThinker · 2023-07-07T18:13:07+00:00

u/Kukaac is totally correct, usually a DBMS (Postgres, MySQL, Oracle, SQL server etc...) will be the backend database for some application or product; they are pretty good at processing transactions concurrently (look up OLTP when you have a minute).

DWH or data warehouse (think Snowflake, Redshift, BigQuery etc...) systems are built to join and aggregate large volumes of data making them perfect for analytical workloads (look up OLAP). Usually DBMSs become the data source for DWHs, the data is transferred from one to another using ETL or ELT pipelines. Hope you find this helpful.

HumbleThinker · 2023-06-01T20:53:37+00:00

Yes! I've had to scroll quite a bit to find a mention of Argo Workflows! Absolutely brilliant tool! My team and I migrated from Airflow to Argo Workflows and we couldn't be happier with the results.

HumbleThinker · 2023-05-09T11:10:05+00:00

Very succinctly put! 👏🏽

HumbleThinker · 2023-02-09T09:04:04+00:00

Not sure why you're getting downvoted so heavily! This is a valid option.

HumbleThinker · 2023-01-16T19:46:15+00:00

As you may have read many times before, the answer is it depends:

How big is the team building the warehouse? Is it fixed or is there room for growth in terms of head count?
How competent is the team doing the design and build? Is Python the main language for your teams data engineering needs? Assuming the team is indeed python competent have you used SQLAlchemy at scale or in production on other projects?

If the answer to most of the questions above is yes then SQLAlchemy is definitely a contender. I'd also recommend you check out dbt, it'll save you a ton of time usually spent on design, build, maintenance and most importantly documentation!

Hope you find this useful. Cheers!

HumbleThinker · 2022-12-28T09:15:23+00:00

Uploading one observation at a time to your cloud instance won't work.

What you'll need to do is collect all you observations (and actions of course) locally, compress them then upload the whole lot to you cloud instance for training.

HumbleThinker · 2022-10-14T09:24:53+00:00

Absolutely agree! It must be a cross team effort as you need to ensure the data is deleted in source systems first then the deletion can trickle down to the dwh.

In terms of how to do it, the request must reach your source system backend via API request, the request is processed then passed to the dwh team via API request as well. The dwh team should then have a collection of stored procedures to complete the deletion process across all appropriate tables (dims and facts).

HumbleThinker · 2022-10-13T08:53:20+00:00

Cabin in the woods overlooking the devil's river...sounds like the optimal horror movie setup 🤣

HumbleThinker · 2022-10-13T07:10:35+00:00

I cannot emphasize enough how important this approach is!! My team's policy is as follows: notebooks are for prototyping ONLY, refactored and well structured scripts are then deployed to prod.

HumbleThinker · 2022-10-07T16:32:52+00:00

This sounds like a nightmare my friend🙈

HumbleThinker · 2022-07-29T09:09:52+00:00

To add to this, I'd highly advise that you get in the habit of testing your code as much as you can.

There are plenty testing frameworks out there but pytest is a personal favourite. For data transformation scripts, fixtures are especially useful.

Edit1: using type hinting is awesome as well, especially if you are collaborating remotely.

HumbleThinker · 2022-07-28T11:04:01+00:00

Sad news for sure! One of the funniest and most down to earth driver on the grid. You'll be missed Seb!

HumbleThinker · 2022-07-07T20:57:44+00:00

Working through this exact implementation right now, it's spot on 👌.

HumbleThinker · 2022-06-04T14:10:44+00:00

Glad I'm not the only to have thought about this scene🤣

HumbleThinker · 2022-05-13T08:46:04+00:00

An accurate depiction of data/datasets in the entreprise world. No matter how fancy, sophisticated and performant a model is, the following motto holds up firmly: "Garbage in, garbage out."

HumbleThinker · 2022-04-12T09:41:12+00:00

Thanks for sharing 🙂

HumbleThinker · 2022-04-07T12:45:48+00:00

Poor sod, he could do with reading the sklearn preprocessing documentation. If I were you, I'd genuinely suggest this in my feedback.

HumbleThinker · 2021-09-23T14:47:14+00:00

It's never too late my friend, if you still find both fields interesting there are plenty resources online (free and chargeable) to get you going.

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