Nice build! I always wanted one of those AR robot arms but I'd rather buy it off-the-shelf than assemble from scratch. I don't think anyone sells them pre-built though. This type of 'high level hobbyist robotics' is in a bit of business dead-zone unfortunately, as it's some combination of too small a market and not enough use cases to justify having a company around. Or maybe there's potential there but no one's thought of it yet.
Btw I bet your arm's movement smoothness can be improved with some different deceleration rates. It was really fun watching it sort those beans!
My research goal with one of these 'good enough' 1mm repeatability robots would be vision-guided adaptive control to complete tasks after seeing examples of them done via human manual control. Would be a really interesting ML/AI problem. Just need a reasonable hardware platform to get started. Right now I'm leaning more towards simple/smaller servo motors like in the OP of this post (plus there's the cost/time trade-off).
> 90% of professional python developers believe that python dependencies is a solved problem (with pip and virtualenv).
I doubt that even 90% of those professional python developers who believe that python dependencies is a solved problem believe that it is solved with pip and virtualenv; the conda faction has to be bigger than 10%. Plus there's the people that think pip/venv aren't enough, but that tools on top of them plug the gaps (poetry).
But I think that the share of professional developers who see it as a solved problem at all is less than 90%. Obviously, we've all got some way of working with/around the issues, that doesn't mean that we don't feel that they exist.
If I can give my anecdote, the last 3 companies I worked at that were heavily using Python and the hundreds of developers in them were all relying on pip and virtualenv. And it worked just fine no matter what the HN crowd would have you believe.
conda had minor usage in the last one for building a handful of special projects mixing C++ and Python code (highly specific code in the finance industry), after build the artifacts could go into the python repository (internal pypi) and be usable with pip. Everything was down to pip at the end of the day. As a matter of fact, the guys who used and pushed for conda were also the ones pushing the hardest for pip because pip is the answer to everything.
Well - you can add one more to your anecdote - my current job is 13 active developers, 200+ megabytes of 1500+ .py files developed over 9 years by 30+ developers. It's all virtualenv/wrappers + pip.
Our data scientists like Conda - but our developers don't touch it.
I'm talking about Susie Q and Joe Sixer. The amount of fiddling with package systems, build, and container systems is an anti-pattern. One guy on a small team sets up and controls the stuffs. Individual contributors shouldn't be messing with the env or putting new packages in :)
I often wonder what problem is being solved by Poetry, that pip with virtualenv(+wrappers) doesn't solve perfectly well. requirements.txt ensures you have the right version of everything in production, and virtualenv lets you explore various versions of libraries without risking any destabilization of your development environment.
I struggle to see what spending time looking at Poetry will yield in terms of any actual benefit, though I would love to be informed/educated otherwise.
virtualenv always feels like a hack to me. Too many times I’ve forgotten to activate the virtualenv halfway through a project and now I’m troubleshooting all the problems I just caused with some packages installed in the virtualenv and some globally and oh half of them aren’t in my packages.txt so now I can’t remember which package I needed for this...
I don’t expect my dev tools to be idiot proof, but they should at least try to be “I’ve been hacking for 18 hours straight and I just need to commit this last line and I can finally go to bed” proof.
One thing that helps is:
# Don't let people touch anything in the root environment
export PIP_REQUIRE_VIRTUALENV=true
That prevents you from ever doing a pip install in your root environment.
I've got about 20 different pip environments, and virtualenvwrappers (workon xxxx) makes it pretty seamless for me to hop back and forth between sessions. I'm also pretty dedicated to doing all my work in tmux windows - so my state in a half dozen virtualenvs is changed by changing my window (which I've done a workon)
I guess what I'm really interested in is, "The last three years of my life I've used virtualenv/wrappers + pip, and haven't run into any problems. What can Poetry do for me, and why should I change my work habits? Genuinely interested in using new and better tools.
Same here. 20 years on python and I've never done anything more than clone a repo, venv and pip install -r requirements. And then cd && python -m to run the source code, maybe set PYTHONPATH in some cases.
I don't think I've even written a setup.py .
Obviously there's a whole world of development and deployment where these things are relevant, but there's also a massive world where nobody even understands what they are missing.
My current gig has a 9 year python repo, 3526 .py files over 228 megabytes, 13 (current) developers. It's all managed with virtuanenv/pip install. So - even larger projects seem to get by okay - would love to read something on Poetry that just says, "Here is why it's a lot better than virtualenv/pip"
Agreed. I don't get the fuzz about pip/setuptools/virtualenv. I have shipped and deployed Python code countless times, never encountered an issue a `setup.py` couldn't solve.
