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I’ve always thought about writing more and sharing my thoughts, but a mix of imposter syndrome and my naturally laid-back, lazy attitude has held me back. Honestly, I don’t worry too much about what others think, but I do hesitate when it comes to posting things that might feel like nonsense—which, looking back, might actually be a good filter. For example, if I were to write about neural networks, I’d wonder: aren’t there already hundreds of posts on this topic? What would make mine stand out? And then comes the self-doubt—am I even qualified to write about it? Do I actually know enough? These kinds of thoughts have stopped me in my tracks more times than I can count.
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But last week, I came across an inspirational article by [Simon Willison](https://simonwillison.net/2025/Jan/2/ending-a-year-long-posting-streak/)about his experience completing a year-long streak of posting every single day. It got me thinking: why not start my own streak? Maybe not as intense as his daily commitment, but something I could stick to, like one post a week—every Tuesday.
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I’ve always thought about writing more and sharing my thoughts, but a mix of imposter syndrome and my naturally laid-back, lazy attitude has held me back. Honestly, I don’t worry too much about what others think, but I do hesitate when it comes to posting things that might feel like nonsense which, looking back, might actually be a good filter. For example, if I were to write about neural networks, I’d wonder: aren’t there already hundreds of posts on this topic? What would make mine stand out? And then comes the self-doubt, Am I even qualified to write about it? Do I actually know enough? These kinds of thoughts have stopped me in my tracks more times than I can count.
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The topics could be anything: technology, machine learning, books, travel, sports, or cinema. The only rule is to publish something every week. To give my streak a clear end goal, I’ve chosen December 30th, the last Tuesday of this year, as the final day. That’s 52 weeks of consistent writing—a manageable yet challenging target.
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But last week, I came across an inspirational article by [Simon Willison](https://simonwillison.net/2025/Jan/2/ending-a-year-long-posting-streak/) about his experience completing a year-long streak of posting every single day. It got me thinking: why not start my own streak? Maybe not as intense as his daily commitment, but something I could stick to, like one post a week, every Tuesday.
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The beauty of streaks lies in their ability to push you into action. I’ve been reading this self-help book called _Compound Interest_, which emphasizes how consistent efforts over time yield exponential benefits. Initially, the growth is imperceptible, but over time, the results become extraordinary. The key is not just consistency but also striving to improve incrementally. Not every day will be better than the last—bad days are inevitable—but over the long run, the goal is to see progress, even if it’s microscopic.
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The topics could be anything: technology, machine learning, books, travel, sports, or cinema. The only rule is to publish something every week. To give my streak a clear end goal, I’ve chosen December 30th, the last Tuesday of this year, as the final day. That’s 52 weeks of consistent writing, a manageable yet challenging target.
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The beauty of streaks lies in their ability to push you into action. I’ve been reading this self-help book called _Compound Interest_, which emphasizes how consistent efforts over time yield exponential benefits. Initially, the growth is imperceptible, but over time, the results become extraordinary. The key is not just consistency but also striving to improve incrementally. Not every day will be better than the last, bad days are inevitable, but over the long run, the goal is to see progress, even if it’s microscopic.
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Simon Willison’s inspiration for his streak was [Tom Scott](https://www.tomscott.com/), the well-known YouTuber and science enthusiast who posted a video every week for ten years. That’s a remarkable level of commitment. In Tom’s final video, he shared how this streak impacted his daily life, including the immense pressure to maintain it. While streaks are a powerful psychological tool, they can also be stressful and overwhelming. Breaking a streak can feel devastating, and restarting often feels impossible.
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The solution, as Simon suggests, is to incorporate flexibility. For example, Duolingo’s “streak freeze” allows users to forgive occasional slip-ups without breaking their streak. I’m planning to adopt a similar approach, allowing myself up to three “streak freezes” throughout the year. This doesn’t feel like cheating; it’s a way to make the streak sustainable and reduce the stress of perfectionism.
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Of course, I’ll have to be cautious not to misuse this flexibility. The ultimate goal is self-improvement, and the only person benefiting from this commitment is me.
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With that, I’m ready to start my streak. Oh, and guess what? It’s my birthday today! Seriously, could there be a better day to start something fresh? It’s like a little birthday gift to myself—a promise of consistency, growth, and maybe even a bit of fun along the way. Here’s to 52 weeks of writing, learning, and surprising myself. Let the streak begin!
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With that, I’m ready to start my streak. Oh, and guess what? It’s my birthday today! Seriously, could there be a better day to start something fresh? It’s like a little birthday gift to myself, a promise of consistency, growth, and maybe even a bit of fun along the way. Here’s to 52 weeks of writing, learning, and surprising myself. Let the streak begin!
Ever since I decided that I want to be a machine learning engineer, I have never stopped feeling overwhelmed by the topics I need to learn and the concepts I need to cover. It's absolutely staggering, both in depth and breadth.
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The resources are plentiful—so many choices, so many options, so many books, articles, and more. It's very difficult to choose what is best for you. Every time I sit down to learn, I am bombarded with these resources.
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This has made me pause and give up many times. When I start reading about a topic, I often come across related concepts or ideas that catch my attention, causing me to shift focus. This constant switching makes it difficult to retain progress, leaving me feeling as though I'm not learning effectively. This happens a lot.
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Hence, I thought I would block out all the unnecessary noise and just focus on what I want to learn—what I need to learn. I decided to choose a book, a course, or a video, commit to it, and focus only on that before moving on to the next.
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What you're looking at is my carefully curated collection of resources that I have decided to conquer in the next few months.
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### Traditional ML
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The resources are many. so many choices, so many options, so many books, articles, and more. It's very difficult to choose what is best for you. Every time I sit down to learn, I am bombarded with these resources.
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-[Introduction to Statistical Learning (ISLR)](https://www.statlearning.com/): have read the first 2 chapters, needs to complete the rest
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-[ML Engineering - Andy Burkov](https://www.mlebook.com/wiki/doku.php): completed this book about 2 years back, need to revisit.
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This has made me pause and give up many times. When I start reading about a topic, I often come across related concepts or ideas that catch my attention, causing me to shift focus. This constant switching makes it difficult to retain progress, leaving me feeling as though I'm not learning effectively. This happens a lot.
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### Deep Learning
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So I decided to write down only what felt necessary and put it all in one place. The goal was to keep the list short, doable, and focused on resources that are widely recommended and actually worth finishing. As I complete each book or course, I plan to add my own notes alongside it, so this slowly becomes more than just a list.
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-[Dive into Deep Learning](https://d2l.ai/)
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-[Deep Learning with Python - Francois Chollet](https://www.manning.com/books/deep-learning-with-python)
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-[Deep Learning at Scale](https://www.oreilly.com/library/view/deep-learning-at/9781098145279/)
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What you’re looking at below is my personal, evolving list of resources. This isn’t meant to be exhaustive or trendy. It’s simply a collection of things that, in my experience, explain concepts clearly and reward patience. I’ll keep updating it over time as I find better ways to learn.
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### Concepts
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### LLMs (Large Language Models)
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1.[Introduction to Statistical Learning (ISLR)](https://www.statlearning.com/)
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2.[ML Engineering - Andy Burkov](https://www.mlebook.com/wiki/doku.php):
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3.[Dive into Deep Learning](https://d2l.ai/)
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4.[Deep Learning with Python - Francois Chollet](https://www.manning.com/books/deep-learning-with-python)
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5.[Hands-on Large Language Models](https://www.oreilly.com/library/view/hands-on-large-language/9781098150952/)
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6.[Build a LLM from Scratch](https://www.manning.com/books/build-a-large-language-model-from-scratch).
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-[Hands-on Large Language Models](https://www.oreilly.com/library/view/hands-on-large-language/9781098150952/): In Progress
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-[Build a LLM from Scratch](https://www.manning.com/books/build-a-large-language-model-from-scratch)
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### Engineering
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### ML System Design
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1.[DDIA - Designing Data Intensive Applications](#)
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2.[CPU Land](https://cpu.land/)
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3.[Designing ML Systems - Chip Huyen](https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/):
5.[Deep Learning at Scale](https://www.oreilly.com/library/view/deep-learning-at/9781098145279/)
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6.[Machine Learning Engineering Open Book by Stas Bekman](https://github.com/stas00/ml-engineering)
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7.[How to Scale your Model](https://jax-ml.github.io/scaling-book/)
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8.[The Smol Training Playbook](https://huggingface.co/spaces/HuggingFaceTB/smol-training-playbook#introduction)
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-[Designing ML Systems - Chip Huyen](https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/): this is done and highly recommended
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-[Reliable Machine Learning](https://www.oreilly.com/library/view/reliable-machine-learning/9781098106218/): In Progress
Copy file name to clipboardExpand all lines: data/blog/7_setting_up_my_mac_mini.mdx
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Starting the year with a thoughtful upgrade, I decided it was time to level up my tech game—and what better way to start than by upgrading my trust old [laptop](https://support.apple.com/en-la/111924)? I initially thought a MacBook Air, a natural upgrade, would be a good choice, but after reading reviews about the Mac Mini, my decision was clear. I ordered a new Mac Mini at the tail end of last year and received it in the second week of January. Since then, I have been exploring various Mac setup articles and videos to optimize my system. In this article, I will list all the apps and modifications I made on my new Mac Mini and include references at the end.
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## Mac mini specification
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Starting the year with a thoughtful upgrade.
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I decided it was time to level up my tech game and what better way to start than by upgrading my trusty old [laptop](https://support.apple.com/en-la/111924)? I initially thought a MacBook Air, a natural upgrade, would be a good choice, but after reading reviews about the Mac Mini, my decision was clear. I ordered a new Mac Mini at the tail end of last year and received it in the second week of January. Since then, I have been exploring various Mac setup articles and videos to optimize my system. In this article, I will list all the apps and modifications I made on my new Mac Mini and include references at the end.## Mac mini specification
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