Marfa is an amazing little town. I was there 3 months ago; while it is out of the way, even as a visitor, everyone is nice and genuinely there to provide an amazing artistic experience. If you ever want to experience the actual weird, southwestern, cowboy country, go to Marfa. And have a drink outside this public radio station. It's quite a nice getaway.
Please be careful. I'd love to adopt Dagger, but the UI in comparison to GHA, is just not a value add. I'd hate for y'all to go the AI route that Arc did... and lose all your users. There is A LOT to CICD, which can be profitable. I think there's still a lot more features needed before it's compelling and I would worry Agentic AI will lead you to a hyper-configurable, muddled message.
default values! Since type hints are *hints*, it is difficult to set default values for complicated types. For instance, if you have lists, dicts, sets in the type signature, without a library like pydantic, it is difficult and non-standard. This becomes even more problematic when you start doing more complicated data structures. The configuration in this library starts to show the problems. https://koxudaxi.github.io/datamodel-code-generator/custom_t...
The issue very much is a lack of a standard for the entire language; rather than it not being possible.
We've migrated our pyenv poetry application - that's pretty complex with data pipeline flows and apis. The only issue we had was loading .env files - we had done some custom env var scripting as a workaround to an AWS issue and that was hard to migrate over. However, once that was done (and was due to bad implementation initially outside of poetry), moving from poetry to uv was rock solid. No issues and it just worked. I was surprised.
I would really encourage you to think more on this. Good enough for you... is not really the goal. We want standard tools that help with packages and virtual environments that can scale to an organization of 2 python devs... all the way up to hundreds or thousands of devs. Otherwise, it fragments the ecosystem and encourages bugs and difficult documentation that prevents the language from continuing to evolve effectively.
I think this is a great take. I'm on board on the HCL hate - it really reminds me of shoe-horning a templating solution into a complex, code level concern - but just removing YAML is not enough. My one caveat to this is a lot of the "platform engineering" products out there right now seem to want to abstract away all concerns behind a UI, which is just replacing YAML with UI problems. What we really need are open-source, cross-language sdks that allow self serving by developers using the tools they already know, but with additional configuration being able to color that settings by more specialized folks in their areas. For instance, an sdk that lets developers say ram is light, medium, heavy, etc. in whatever language they operate in; followed by a review of the sdk and some additional configuration layered on by the ops folks who monitor the entire company's spend and define what light/medium/heavy is. Too many of the platform engineering "solutions" seem to be about vendor lock-in.
It feels like you've determined there's nothing wrong with pyenv, pip, and virtualenv so any issues brought up, you will reject.
If that's not the case, here's the issue - someone used pyenv and did not exactly specify the python type - I believe we were on 3.9 and prod was 3.9.11 and the current python version was 3.9.12. There was a downstream package that had an OS dependency - I believe it was pandas - that conflicted and needed to be updated locally to work with 3.9.12. This broke and raised an error in production that was not reproducible locally - and when you deploy on AWS, reproducing can be a pain in the butt. I'm sure if the data scientist had used perfect pyenv, virtualenv, and pip commands; we would have caught this. However, they're very complicated - especially for people who focus on math - so requiring full knowledge of these tools is unrealistic for most data scientists.
BUT importantly... it REALLY does not work well for lots of teams. For me, this setup has caused production outages multiple times across multiple teams. Maybe the root python ecosystem should learn and adopt from other ecosystems that have figured out complex deployment in a much easier way.
I REALLY want to use mojo...but they still don't have basic backwards compatibility features like lambda done. I worry mojo won't get off the ground because they're too focused on hardware performance and aren't building enough features to justify the switch... to get that hardware performance.
I wish there were fixes to devcontainers before doing adding copilot. I really want declarative, repeatable builds that are easily used in both Codespaces AND Actions. I know all of the functionality is theoretically there in devcontainers.json, it is so manual to configure and confusing, that anytime I've done it, I use it for 2 weeks and then just go back to developing on local because I don't have time to keep that up. ESPECIALLY if you're deploying to AWS cloud and also, want to use alternative package managers like poetry, uv, yarn, jsr, etc.
I love Ruff and I am so excited to see python ecosystem developers tackling some really big and core table stakes problems with python. Especially now that it is being used beyond scripting and has become foundational to lots of apps.
I highly doubt they work as well as an engineer believes. If there are no absolutely no user requested changes - even something as simple as support a new browser type or device type - then maybe. Otherwise, I've seen this viewpoint kill multiple companies because it lead to uncontrollable technical debt.
Jaclaz, yes, you are right, there are quite a few companies in this field. Every company has its own approach. For example, Curb does not make device detection, but shows you statistics on the circuit level (you need to connect it to every circuit breaker), Neurio detects only devices which consume more than 400W, etc.
In our case, we are focusing on reliable device detection of major home appliances (we detect home appliances which consume more than 100W). We detect devices by our algorithms and users can teach the system via training mode for more precise recognition. Beside this, we support 3-phase electrical networks (for both grid and grid+solar) and we support 220V/50Hz networks (so Ecoisme works in both Europe and US).
Yep, Ecoisme isn't a robot which turns off lights in your home ;) Ecoisme provides you with statistics of energy usage for every major home appliance and specify you clear steps how you can reduce your energy consumption. Also Ecoisme can be integrated with IoT devices in your home and then it will be possible to control them remotely.