Tuesday
December 31
Reading diary
Notes from 2024. 39 entries.
Tuesday
December 31
Saturday
December 28
Sunday
December 22
Saturday
December 21
Wednesday
December 18
Sunday
December 15
Wednesday
December 11
Sunday
December 8
Thursday
December 5
Tuesday
December 3
Wednesday
November 27
Tuesday
November 26
Monday
November 25
Thursday
November 14
Friday
November 8
Monday
October 28
Wednesday
October 23
Wednesday
October 9
Monday
October 7
Friday
October 4
Tuesday
October 1
Monday
September 30
Sunday
September 29
Saturday
September 28
Thursday
September 26
Tuesday
September 24
Monday
September 23
Friday
September 20
Gotten my reading from +100 articles to 58! 🎉
Wednesday
September 18
Saturday
September 14
Friday
September 13
Thursday
September 12
Tuesday
September 3
Monday
August 26
Wow, need to test this out on my side projects.
Skimmed the article but although high level I find the main points very true. Understanding the system, being on top of the state of the art. That and the tips for head of data
Great episode! Nice to see that css is getting better and better
Friday
August 23
Wow, I’d love this one man saas but knowing how it can burn you out…
Nice episode on the long sought features for node that have been introduced on bun and demo (single file, typescript support and top await async are the big ones for me)
Great to see a new version coming along! Is pdb worth using with vs code? 🤔
As a google to owner would be good to know that my working hardware wouldn’t brick just because I don’t want to upgrade
Tuesday
August 6
Although I had my eyes already on rye I certainly didn’t know it was so full feature. Gotta try and change some of my projects to it ,
Python support slowly getting good
The report raises a good point about an increase in workload. With a big productivity boost bosses might start loading even bigger workloads than what we gained from the productivity boost
Ofc this isn’t good as people will feel overloaded
Super interesting IMO. Might give it a try on my team when we start deploying solutions to our clients
Would be interesting to test this out. Have an example of a dataset that can be queried using duckdb. Given a question understand if a query is correct how to fix it and improve it’s performance. One in Sal and another in pyspark (or ibis/pandas)
Wednesday
July 31
English sdk looks awesome but requires an OpenAI key. Could it be replaced with ollama?
Friday
July 26
Interesting topics to research a bit more on CAP alternatives
In summary, to have a good AI product we need to have data with quality which requires good data governance. With this data we need to define useful products that we can measure it’s value using data driven metrics. We must also ensure the product has good practices avoiding security or bias issues
Resumed by:

This is something I would love to implement. Allowing to define the metrics on which to evaluate a new feature, the expected hypothesis and revert the feature (i.e feature flags) automatically with a report on the experiment
Could I replace Athena with this? I think the main blocker for me is I want to work with S3. And need to check how it runs for a really large dataset…
Good tip. For any project define the metric of code coverage goals and start increasing on the project
Thursday
July 25
This article wasn’t properly parsed by omnivore but the big takeaways:
After reading the statistics books I can see much more clear the value of proving a null hypophesis, this is the feeling I am getting out of the academia. We are seeing more research without any any added value. Goodhart’s law.
Sounds just like an airflow contender. with the plus of being able to run notebooks 🤔
Good theme for a blog post on the changes of spark 4, this is really useful for human errors (been there multiple times)
So this will be generated on the fly as views by the semantic layer?, This looked neat until the moment I understood that the semantic layer requires dbt cloud
Meta seems to be a couple of years ahead of the industry. The article doesn’t provide a lot of insights but gives a feeling of their model evaluation being mostly automated + having a good AI debug tool
First step in a long way before we can get run python without GIL. Interested on seeing if libraries like pandas will be able to leverage multithreading eventually with this
Mixed feelings here. Great to see Open Lineage implemented at AWS. However it feels again that AWS just created the integration and won’t be driving the development of open lineage
What could be improved to help this kind of migrations be done in a matter of days?, Livy might be deprecated in favor of spark connect. With their migration to Spark 3 and eventually 3.5 (not clear on this article) they could be interested in moving new jobs to connect , Basically solved issues by using the old behaviours. These will need to be migrated eventually. Would need to better understand these features , This looks like an important detail. With no explicit order spark can have random order of rows?, Cool to see these migrations and teams using open source solutions. EMR although expensive with a good engineering team can prove to be quite cost effective
The need to define a data platform is something I see everywhere. It really looks like we are missing a piece here. Netflix maestro for example seems like a good contender for to solve the issue of (yet another data custom platform)
This articles brings me the question. Can we improve dbt by using WAP? How does the rollback mechanism work when a process fails?
Super interesting to see how we can enable data quality visibility
Good to use open data lakes showing the big cost and speed improvements
Why is this so hard? 😭