They might have opinions about it, but look at the pay for frontend engineers at the same company. It's not uncommon to see the same seniority be 20% lower than a backend role.
I see a lot of people say you have to use methodology X, or that methodology Y is worthless.
I'm the end, I think we have maybe different uses for notes. Journaling, scratchpads, to-do lists, research, etc.
Take a methodology with a grain of salt. If it doesn't fit, there's a good chance it's solving someone else's problem, but you can always inform your own approach with it.
I think you're operating in a scale that is small enough that there's little risk.
You'll be able to iterate if you run into anything that doesn't work. You should however be clear on what problem you and your team are solving, and not just "get some rag".
Out of curiosity, the post you linked mentions that it won't work for renames. What's the approach for these and other types of procedural migrations, such as data transformations (ie: splitting a column, changing a type, etc.)
With a declarative model, would you run the migration and follow immediately with a one off script?
Seems great for really small apps where you want your resource definitions colocated with the code using them. I'd imagine the benefits start to break down as your infrastructure gets more complicated.
The bigger answer is that if you're proficient and happy with CDK or anything else to wire resource up, you're probably not going to see much (if any) benefit.
I think the make in the title is a bit misleading, the author is actually just advocating for having a consistent file you use for adhoc scripting and testing in your application.
The thrust of the article could be summarized as: if you type more than one command into the shell, make a script.
I think it really depends on how teams use their estimates. If you're locking in an estimate and have to stick with it for a week or a month, you're right, that's terrible.
If you don't strictly work on a Sprint schedule, then I think it's reasonable to have high variance estimates, then as soon as you learn more, you update the estimate.
I've seen lots of different teams do lots of different things. If they work for you and you're shipping with reliable results then that's excellent.
I did too, and I've had a challenging time convincing people outside of those ecosystems that this is possible, reasonable, we've been doing it for over a decade.
I worked on a product that was built around planning an estimation with ranged estimates (2-4h, 1-3d, etc)
2-12d conveys a very different story than 6-8d. Are the ranges precise? Nope, but they're useful in conveying uncertainty, which is something that gets dropped in any system that collapses estimates to a single point.
That said, people tend to just collapse ranges, so I guess we all lose in the end.
It natively supports vector embeddings, which seems like it could be nice. The sqlite extensions I've tried for vector embeddings have been a challenge to get working (may just be me though).
I'll bet you could take a relatively tiny model and get it to translate the transcribed "git force push" or "git push dash dash force" into "git push --force".
Likewise "cd home slash projects" into "cd ~/projects".