Logitech MX Keys with Qwerty layout
Readit Newspaeselhz commented on Ask HN: What keyboard and keyboard layout are you using? · Posted by u/psychoslave
I've used Quarto[1] to build a personal blog and it has been really easy and straightforward. Especially if you want to run some code alongside the post (like Python, R, or Julia). As far as I know, you can also use it to write books and presentations.
[1]: https://quarto.org/
paeselhz commented on Ask HN: Does anyone have experience running LLMs on a Mac Mini M2? · Posted by u/etewiah
I bought a Mac Mini M2 last year to start playing around with some personal projects, and I did some tests using LM Studio running Mixtral models with pretty good throughput, I also tested Open AI's whisper models to do some transcriptions and those ran fine as well.
I do, however, recommend that you upgrade the RAM, 8GB is barely enough as is, so getting at least 16GB would be better. (I don't recommend upgrading the SSD though, since because of Thunderbolt 4 you can have a fast external SSD for half the price that Apple charges for storage).
paeselhz commented on Ask HN: What's the stack for your "home-cooked meal" apps? · Posted by u/joshspankit
It’s really cool that you’re doing this for yourself… but reading this also makes me kinda sad that solitaire gets flooded with ads.
Hahaha, exactly! A simple game that has been around for centuries, and now even Microsoft riddled it with ads and (no idea how or why) paid features. Looks like they took a page from EAs book...
paeselhz commented on Ask HN: What's the stack for your "home-cooked meal" apps? · Posted by u/joshspankit
Recently I've felt overwhelmed by the amount of ads in a simple solitaire game (from many distributors). So I've spent some time learning Unity and built my own Solitaire Game to play during my commute, with no required internet connection, and no loading screen, just like the old days of Solitaire in Windows 98. I'm still pending on adding some features to make the game more complete, but it has been a joyride to learn a new stack while building something completely personal.
Other than that I've built some tools just for the sake of exploring some hypothesis that I had, but all of those always felt like work. The game development was something that truly felt like "cooking at home".
Can you recommend good resources on MLOps?
I did the Deep Learning Specialization on MLOps which is available on Coursera (https://www.deeplearning.ai/courses/machine-learning-enginee...), however, I found it too "Tensorflow-oriented" and it did not match the current development stack of the team that I was working on.
Most of the knowledge I was able to glue together came from webinars/lives/tutorials/books I found that already solved the same problems I was looking for.
If you have a solid Python foundation and already have some experience in Machine Learning in general, I would recommend the Practical MLOps book( https://www.oreilly.com/library/view/practical-mlops/9781098...), or even Introducing MLOps (https://www.oreilly.com/library/view/introducing-mlops/97814...) as starting points.
EDIT: Also, nothing beats a good project end-to-end, where you take a toy problem, and try to build an entire stack around it, from the training of simple ML models, tracking the models' versions, creating APIs to serve these models, monitoring, and so on.
My career started as a Data Scientist, but as a necessity from the company I used to work for (and because it was a subject of my interest), I shifted to an MLOps hat. We didn't have many people in that role at the time, but it was really interesting to make this move.
Most of my time, I spent trying to better understand how to "approximate" the Data Science/ML workloads to the Development stack of the company, so I spent a lot of time learning about containerization, and multiple ways to deploy those artifacts. On top of that, I started learning about the CI/CD stack, and introducing the CT (Continuous Training), by tracking metrics of the live models that were being served, and triggering data-drift alerts.
Most of my work was done using Python, and the FastAPI library, and the containerization was done mostly using Docker, but I had to gain an understanding of how to deploy it in cloud environments, at the time it was really valuable to learn Terraform to understand how to use Infrastructure as Code.
paeselhz commented on Ask HN: Seeking Advice – Relocating to Canada as a Software Engineer · Posted by u/engineerick
Hey, recently my wife and I decided to make a similar change, we moved from Brazil to Canada, for her to pursue her studies, and for me to make a professional change, in Brazil I was a Machine Learning Engineer.
1. The demand for software-related jobs is quite high, but the breakthrough in some companies (especially big ones) is quite challenging, in part because of the recent waves of layoffs, but a lot of the hiring market here is based on connections. For me, it was especially hard to get a job offer before arriving in the country.
2. It depends on where you want to live, but most of the jobs are in large metropolitan areas like Toronto, Montreal, and Vancouver, and a lot of them have been requiring at least hybrid work, so you need to factor in the cost of living in this cities. Afaik, the average Canadian household spends half of their income on living expenses (shelter, heating, power). Also, telecommunication services here are quite expensive, so if you plan to have good internet to work from home, you might need to add a couple hundred dollars to your monthly expenses for that.
3. About immigration, the best way is for you to reach an Immigration Attorney or Consultant, each case is particular and there is no one-size-fits-all for this. I've met people from the most diverse backgrounds, with completely different immigration strategies, and it worked out for them. Find what works best for your scenario and customize your immigration strategy based on that.
4. I'm still planning to visit Vancouver, but having known Toronto and Montreal, I feel that Toronto is a really good city to start, there are plenty of opportunities, and many Canadian companies choose to have their offices.
I would agree with another commenter who said that building a solid portfolio might go a long way in getting job interviews.
Good luck on your path!
Hastie and Tibshirani's class on Statistical Learning is a must for someone that wants to learn about ML. All of the classes are available here (https://www.youtube.com/playlist?list=PLoROMvodv4rOzrYsAxzQy...) and the book recently got a Python version to complement the original R classes.
I believe it is a great tool, especially if you want to skip the beginner's lessons, however, it seems to me to have diminishing returns with time.