Python deps can be dynamic, so it can be necessary to download the package and execute code just to find out.

Would be nice to see a resource that lists out the statically defined ones, though. Perhaps that’d pressure the dynamic ones to change – it’s a cause for some of the notorious pain of Python packaging.

IMO it will “succeed” in the early phase. Pre-seed startups will be able demo and get investors more easily, which I hear is already happening.

However, it’s not sustainable, and either somebody figures out a practical transition/rewrite strategy as they try to go to market, or the startup dies while trying to scale up.

We’ll see a lower success rate from these companies, in a bit of an I-told-you-so-moment, which reduces over-investment in the practice. Under a new equilibrium, vibe coding remains useful for super early demos, hackathons, and throwaway explorations, and people learn to do the transition/rewrite either earlier or not at all for core systems, depending on the resources founders have available at such an early stage.

First suggestion is impractical. Not going to be able to memorize 100 names to look up and research later

Second suggestion should already be happening, but doesn’t capture the desired use case.

The use case is this: in physical life, there is a gradient of “boundaries/leashes” to match maturity and development. For example, the gradient of movie ratings, or:

• Very young - stay within arms reach/sight
• Young - stay in the yard/park/neighborhood
• Child - stick with what’s familiar, I’ll be nearby
• Pre-teen - go and try it, I can be right there
• Teen - go and try it yourself, call me if needed

We could argue about whether a gradient is too steep or shallow, but the point is that one exists.

In contrast, digital in many ways is very often all-or-nothing

Not saying digital should be “gradient-ed” in all cases, that leads to tone-deaf rules and bad security practices. Just trying to show what the problem is

I think there is a difference. Because software is so flexible and quick to build, it’s orders of magnitude easier to build something known and understood.

A promising startup with its systems in a knot, but their initial team is still on retainer? Brains can be picked, abstraction boundaries placed, surgical rewrites deployed. Despite the mess, they still understand it, and development can expand.

It remains to be seen if AI-generated code is recoverable, if any existing strategies can be applied so humans can contribute, or if the company is forever beholden to AI providers to release a better AI to manage/improve what they’ve already got.

Highly recommend having some scripting/interpreted language in your stack – in fact you likely already do (consider how shell scripting makes up a significant part of Dockerfiles)

It’s an incredibly useful intermediate between freeform-but-non-executable text/docs/wikis and “industrial-grade”-but-inflexible tooling

In other words, a great fit for capturing this partial/incomplete/tribal knowledge space the post is talking about. I personally even go a bit further and actively advocate for converting “onboarding/operational docs” from wikis into scripts that print out the equivalent text that can be committed and incrementally automated.

In my experience, LLMs aren’t really that good at summarizing

It’s more like they can “rewrite more concisely” which is a bit different

https://f-droid.org/packages/de.t_dankworth.secscanqr/

Also have the option of selectively/strictly enforcing in CI, to get an experience & protections similar to “compile-time type checking”

it lacks clear and enforced type restrictions which help with clear code contracts

Not anymore! Gradual typing is supported by the core language and pyright is a fantastic incremental type checker that you can use both in your editor and in CI.

You cannot, and that’s why that type declaration models a NonEmpty that a type checker can enforce

Hm, that’s kind of interesting

But my first reaction is that optimizations only at the “Python processing level” are going to be pretty limited since it’s not going to have metadata/statistics, and it’d depend heavily on the source data layout, e.g. CSV vs parquet

What’s hard about vanilla Ruby?

What kind of query optimization can it for scanning data that’s already in memory?

What did you go over?

No so much that YAGNI falls short, but more like “When YAGNI means ‘You Are Gonna Need It’”

Bad abstraction is worse than no abstraction

If the code is going to poorly organized, I’d prefer it to just be one single gigantic standalone script than some wrong and misleading arrangement of objects or functions that adds more complexity than they solve

“never see addressed”? What do you think currently happens in (real, non-hypothetical) cities with good bike infrastructure?

https://www.youtube.com/watch?v=j2dHFC31VtQ&t=365

Oh look, emergency vehicles work even better on bike infrastructure than on car infrastructure

Bicylists and pedestrians can’t hard block a firetruck the way car traffic can

absolute beginner? Start at https://www.hedycode.com/

Which will ultimately lead into vanilla Python. This is the creator explaining why Hedy is uniquely designed for learning: https://www.youtube.com/watch?v=fmF7HpU_-9k

inheritance to avoid code duplication

What no, inheritance is not for code sharing

Sound bite aside, inheritance is a higher level concept. That it “shares code” is one of several effects.

The simple, plain, decoupled way to share code is the humble function call, i.e. static method in certain langs.

If you used good objects, you’ll only have to make the change in one place

IMO that’s generally a retroactive statement because in practice have to be lucky for that to be true. An abstraction along one dimension – even a good one, will limit flexibility in some other dimension.

Occasionally, everything comes into alignment and an opportunity appears to reliably-ish predict the correct abstraction the future will need.

Most every other time, you’re better off avoiding the possibility of the really costly bad abstraction by resisting the urge to abstract preemptively.