Microsoft todo app

I’ve recently done it again and switched from google task to Microsoft todo. For some reason I wasn’t able to get things done through the old app. But now, having switched to the Todo I feel like I’ve gotten more control over things. The Day list is really nice to and triggers a productive flow that I find pleasant and keeps me going through. As soon as I know of a new tasks I write it down. Quick and painless. If it’s something I can do in less than 10 minutes and I’m not doing anything else I just get it done. Lazyness issomething I’ve been struggling for the past months and this quick iteration gets me going. However, as I struggle to do more than 1 thing at the same time, I quickly write on the Todo and let it be a problem for later.

The recommendation for the Day is also great. At the end of the day I can quickly look at the list and add for me to worry on the next day. One must always leave space in the day for the unexpected tasks. Or for the ones that take more than expected. And that for me is the last thing I think I miss. Be able to add a label on how long the task should take. For now I try to separate the tasks into things I can do in 1 hour or less. More than that and I lose motivation to pick it up. But it’s a rule of thumb and even now, looking at some of the tasks it’s clear this is reason I haven’t picked them up yet.

But for now, I feel I’m back. Pick, do, done, repeat.

Semester middle review

Looking at the semester it seems I’m a bit behind. I’ve schedule the data engineer exam but i still have to study for it. I’ve gotten myself to give another talk and I’m quite happy about it. I feel I can do one more and that should be around my open source project. My goal will be to pick it fully next week, push through and only then get to study for the AWS exam.

That leaves me with the remaining big goal, reading the statistics book. I don’t know where I’ll find the time but I guess it will have to be done between the open source project and the AWS exam. And I already know that I’ll be finishing the semester one week late, with the exam on the 26 of July.

Readings

• Is star-schema a thing in 2024? A closer look at the OBTs | by Adrian Bednarz | May, 2024 | Medium by Adrian Bednarz: Really interesting take. The usage of big tables for streaming and getting stronger data qualities was something I hadn’t thought previously
• 1-bit LLMs Could Solve AI’s Energy Demands: TIL: 1-bit LLM’s seem like a very good way to run LLM’s locally without being a resource hog
• 20 Years of Blogging; On my own website - jeena.net by Jeena: Not a technical post but this is exactly what I’m striving for. By writing I’m hoping to sort my thoughts and hey! Someone might find something useful in it
• Introducing support for Apache Kafka on Raft mode (KRaft) with Amazon MSK clusters | AWS Big Data Blog
• Data Platform Explained Part II  - Spotify Engineering : Spotify Engineering by Spotify Engineering: Being able to deploy a data platform on this scale is something I would love to try out. Setting something easy to use and that caters to so many user needs is something I can’t even fathom. Would be interested to see if there’s a need for an open source framework?
• Codestral: Hello, World! | Mistral AI | Frontier AI in your hands by Mistral AI: I’m a user of GitHub copilot but having open models is a pro to me. Might be worth considering in a couple of months
• AWS Amplify 2024: A New Era by Michael Liendo: Great to see improvements on this! Might be enough for me to consider replacing my vergel projects
• On Orchestrators: You Are All Right, But You Are All Wrong Too | dlt Docs by Anuun Chinbat: Funny to see how hard orchestrating still is. I’ve had experience with airflow and more recently have been using aws step functions. The idea I have is that because we don’t have a common interface like it was introduced with kubernetes, we struggle a lot and each project reinvents the wheel with no perfect devX

Goal of the week

• Trim down on the omnivore reading list