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A friend asked me for some study/productivity tips, and I figured the most productive thing I can do is write a post about it. That way, it might help more people too. So here we go. Before we start, there are two things you need before any productivity advice will work: Be introspective. Be honest with yourself. My productivity rules Plan your day the night before Don’t leave decisions for your foggy, maybe lazy, morning self. Here’s the loop: wake up → review study plan → study → write down the plan for tomorrow → sleep → repeat. Check your energy during the day If you just ate and feel sleepy, don’t force deep study. Take a 30–60 minute break - nap, walk, exercise, or scroll your phone a bit, then come back refreshed. You can also take micro-breaks: finish a chapter, grab water or a piece of chocolate, and get back to it within five minutes. Don’t lie to yourself about effort Setting goals is great, but be honest about how hard you actually worked. You can check all the boxes, feel proud, and still know deep down that you took it easy. That’s fine sometimes, we all need rest days - but don’t confuse that with an intense study session. Review your day When you plan tomorrow’s tasks, reflect on today. Did you actually do what you set out to do? If not, why? Maybe your goal was too ambitious, or maybe you just spent too much time gaming. Either way, learn from it. There’s always room to improve, if the goal really matters to you. Limit distractions Put your phone on silent and out of reach. If you study on your computer, close anything that might tempt you, and even hide shortcuts to distracting apps. When I was in uni, I played a ton of League of Legends. The desktop icon was staring at me every time I opened my laptop — so I buried it under three folders. Sounds dumb, but it worked. A few more things Get good at breaking big goals into small tasks. If your goal is to pass an exam, start by mapping out all the smaller steps that’ll get you there. Spread them out over time, with a bit of buffer. Plans will change — that’s fine — but you should always know where you stand and adjust as you go. And one more time, because it’s that important: don’t lie to yourself. If you spent four hours on TikTok, felt bad, then studied a little to compensate — that’s not a “productive” day. Call it what it is. It’s okay to have those days, you’re human — just plan for them instead of pretending they didn’t happen. I believe that much of the productivity advice online includes some stupid ceremonies and whatnot, just do what you feel works for you. I think the ability to introspect, being honest with yourself and improving over time is all that matters. Take whatever breaks you need, in whatever order you want. You don’t need a fancy journal to write your tasks, open a txt file. You don’t need a perfect system, just something simple that works for you.
Most people solve their subscription fatigue by canceling Netflix. I solved it by vibe-coding my own workout app instead of paying another SaaS. Two weeks ago, I decided to get serious about my workouts again and start logging them. I looked for an existing solution that has the following: create workouts log sets, reps, and weights a calendar to track consistency simple enough. Free apps were crammed with ads. Paid apps had bloated features I didn’t want. Both annoyed me. Before vibe-coding, I’d either tolerate ads or pay. Now there’s a third option - build my own. Of course, you could always build your own, but pre-vibe-coding it would take much more time to be worth it. How did I choose the vibe coding platform? I logged into loveable, base44, bolt, and wrote the following (imperfect) prompt 1 2 3 4 5 6 7 I want to create a personal webapp for managing my own workout routines (kind of a workout logger) I want to be able to define "workouts" - collection of exercises including sets and reps I want to be able to track which workout i did on which day - calendar view I want to be able to log the weights I did for every exercise in every set. I want my workout templates to be a simple collection of exercises that are plaintext - don't create some kind of an exercise library.. i just want to type the exercise name myself make it stateful, including a db connection to store all the relevant data. Whichever tool gave me the best first shot, I ran with. This time it was Loveable. Iterating With that one-shot starting point from loveable, I published it, and went to my first workout at the gym, all excited and ready to use what I built. First workout: I needed notes for exercises. Another: supersets. Each time I wrote it down, went home, and 15 minutes later I published a new version with loveable. After two weeks of using this app at the gym and doing tweaks, the app feels solid. On my last iteration, I added a badges/achievement page, and a github-like consistency widget that looks cool I hope will help me stay on track and be consistent. Sharing Friends wanted to try it too - the problem? I didn’t make it secured by sign-in, I thought I only need it for myself, and even if someone’s going to find this weird loveable URL, they could only see my workouts - who cares… But of course for my friends I’ll write one more prompt - and so I added Supabase auth within minutes. Final Thoughts This post isn’t about showing off my little workout logger anyone could make in a few hours prompting. It’s about how easy it is today to scratch your own itch. You build the exact features you need, when you need them. No ads, no bloat, no adapting to someone else’s UX. Just something comfortable and fun to use. It’s never been easier to bring your ideas to life, small or big.
If there’s one pattern I’ve seen across multiple companies, from scrappy startups to big corps, that causes endless headaches, it’s this: a single cache cluster shared across services. I recently shortly wrote about my lessons from building and maintaining distributed systems at scale, and the first point that came to mind is exactly this - it starts with an excuse of simplicity, “we already have a cache cluster up and running, let’s just make this other service use it, no need for more infra”, and ends with a confused on-call engineer trying to debug which services were affected by the last keys eviction. So I want to double down on this idea and explain in more detail why it becomes a nightmare once your system scales. One eviction policy You got different services each throwing keys at the same redis cluster. A sudden spike/bug just caused a dramatic increase in cache writes - your cluster wasn’t ready for this, it hits maxmemory and now different keys are being removed based on your eviction policy. What’s the problem? there’s no isolation - service A caused the max memory, and now service B, C, D also pay the price - their keys are being removed as well from the cluster, and could affect the latency, and correctness of other flows of your system. Monitoring is harder Our metric fires up — we see a drop in hit rate on the cluster. Which service is causing it? Who’s affected? Instead of thinking about one service, you’re now mentally juggling everything across the entire system. More noise, less clarity. Although monitoring is harder, you could set up application monitors that you send once you write/read from the cache, based on the prefix of the key. potentially if you are organized and each service that uses the cluster has a unique prefix and you can easily identify between the hit rates of different prefixes - that’s great, but you have to work to get there. Debugging is harder This ties back to my first point about the eviction policy. You had 10m keys. something happend. now you got 5m. The effect on the services is really hard to trace. One service might have lost 100k keys, and you barely see a difference in its monitors, but it doesn’t mean your users are not feeling something is off, maybe today the are waiting a bit more for the page to load, but it’s not too long to hit your monitors thresholds. In that case, if you didn’t have a monitor on the cache cluster for keys eviction, you might be totally blind..”oh I see a slight latency increase here, but no monitors popped - guess all is well” So, never use a shared cache cluster? No, that’s not the lesson here. In some cases it is totally fine to use a single cache cluster. For example: You don’t really have a lot of traffic read/written to the cache so most of it is free anyway You store shared static data (for example, feature flags) Also note that some of the points I was making here against using a single cache cluster, can be somewhat mitigated by having good monitoring set in place. For example, having a defined prefix for the cache key per use-case, per service, and publishing metrics in the application level so we have observability to which type of keys (by prefix) are experiecning a low hit ratio. But on the other hand, tracking keys eviction is harder to monitor, since it’s not initiated by your system. Anyway, I hope you get the point. If you are getting started, a single cache cluster is totally fine. Otherwise, spin up another cache cluster, and sleep better at night. 🚨 Become a better software engineer. practice building real systems, get code reviews, and mentorship from senior engineers. Get started with 404skill
when i was a student, everything was simpler. grind leetcode. build projects. get an offer. i knew the salaries. i knew what “winning” looked like. it was a somewhat straight line from broke student to backend engineer at a top company. and i did it, i 10x my life in the span of 4 years. five years in. and honestly? it’s… fine. it’s more than fine. but it also feels like i’m stuck on a plateau. the growth feels logarithmic. the peak that isn’t the peak things are good, but i can’t stop feeling like i want more, even though i am comfortable. i look back at the student version of me, and i see hunger. direction. i look at me now and i see someone who’s tried a bunch of things: built products that barely anyone used started a newsletter, got some nice traffic, but it didn’t stick thinking about podcasts, courses, maybe a dev agency? dreaming of 10x-ing my life again, but not sure where to invest my time. when i was younger, the path was obvious. now it’s all vague, i could do anything. do i go all-in on indie hacking? live off my rsu’s for a few years and just build? try again with another product? double down on the blog? start a podcast? well, the next level doesn’t seem to come with an instructions book. escaping means risking the fall as a cs student you learn that escaping a local maxima usually means exploring a few downs to find a higher maxima. well, applying it to life is scary. what if i lose everything i worked hard to build? i am no longer a student living off of scholarships, i have more obligations. and also, the scariest part of all is what if this is the best it gets? i prefer to be positive and believe there’s another jump out there. something worth building. something that might actually shift my trajectory again. i just haven’t found it yet. but i’m looking. 🚨 Become a better software engineer. practice building real systems, get code reviews, and mentorship from senior engineers. Get started with 404skill
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I listen to a lot of podcasts, and I like how they fit around other tasks. I press play, lock my phone, and put it down. I’m free to wash the dishes, fold the laundry, or shop for groceries. Unfortunately, more and more information is only published as a video. Technical talks, conference sessions, video essays – they don’t work in an audio-only podcast app. I could convert these videos to MP3 files, but that breaks down the moment a video isn’t pure spoken word. If a speaker says, “Look at this slide” or holds up a diagram, an audio-only file leaves me stranded. I don’t want to give up the podcast player I like, nor stare at a screen for an hour – but I do want the information in these videos. To solve this, I’m abusing my podcast player’s chapter support. This gives me the best of both worlds: I can listen to a video as audio-first, and glance at my lock screen if I need a moment of visual context. The idea: Chapters every few seconds MP3 files can have ID3 metadata, and ID3 metadata can include chapters. A chapter covers a particular time range, and it can have an associated title, description, and cover art. My podcast app of choice is Overcast, which can’t play videos, but it does have robust chapter support. I can jump between chapters, navigate a table of contents, and see per-chapter cover art. To get videos into Overcast, I’m creating MP3 files with a new chapter every few seconds, and the per-chapter cover art is a corresponding frame from the video. As I play the file, I get a slow, stop-motion-like rendition of the original video. If my phone is locked, I can glance at my lock screen and see the current frame in the Now Playing screen. Overcast is developed by Marco Arment, and I got this idea from Forecast, his app for adding chapters to podcasts. In particular, I was struck by its ability to create chapters that don’t display in the chapter list – ideal if I don’t want a table of contents with hundreds of entries. As I was developing my script, I compared my output to the output from Forecast to ensure I was creating the chapters correctly. The code: FFmpeg and Mutagen There are three steps in this process: Convert a video file to an MP3 Extract images from the video at a fixed interval Insert the images as hidden chapters in the MP3 file Let’s go through each in turn. 1. Convert a video file to an MP3 Converting a video file to an MP3 is a single FFmpeg command: ffmpeg -i video.mp4 audio.mp3 This is consistently the slowest step of the process, and I do wonder if I could use different settings or an alternative encoder to make it go faster – but it’s not slow enough to be worth further investigation. 2. Extract images from the video at a fixed interval Extracting images from a video needs a more complicated FFmpeg command: ffmpeg -i video.mp4 \ -vf 'fps=1/5,scale=iw*sar:ih,scale=min(iw\,945):min(ih\,945):force_original_aspect_ratio=decrease' \ thumbnail_%04d.jpg This extracts an image every 5 seconds, downscales any image larger than 945 pixels square (while preserving the original aspect ratio), and saves the results as sequentially numbered JPEG images (thumbnail_0001.png, thumbnail_0002.png, and so on). The key is the -vf flag, which defines two FFmpeg filters: The fps filter selects one frame every 5 seconds (fps=1/5). The first scale filter scales the width based on the sample aspect ratio (scale=iw*sar:ih). Without this filter, frames can be stretched and distorted. The second scale filter scales the input video, preserving the original aspect ratio (force_original_aspect_ratio=decrease), and ensuring the output images fit within 945×945px or the size of the input video, whichever is smaller. My limit is 945 pixels because that’s the largest size that cover art is shown on my iPhone. This filter still isn’t completely correct – it sometimes creates images from portrait videos that are smaller than I’m expecting – but it’s good enough. These are only thumbnails for glancing at, and if I want to change it later, I can always do the image resizing outside FFmpeg. 3. Insert the images as hidden chapters in the MP3 file Inserting the chapters into the MP3 file is more complicated. Although FFmpeg has basic support for ID3 metadata, as far as I know, it can’t insert chapters with per-chapter artwork. Instead, I’m going to reach for Python and the Mutagen library. Here’s the code to add a chapter to an MP3 file: from mutagen.id3 import APIC, CHAP, ID3, PictureType audio = ID3("audio.mp3") with open("thumbnail_0001.jpg", "rb") as f: img_data = f.read() image_frame = APIC(mime="image/jpeg", type=PictureType.OTHER, data=img_data) chapter_frame = CHAP( element_id="chp1", start_time=0, end_time=5 * 1000, sub_frames=[image_frame] ) audio.add(chapter_frame) audio.save() This creates a single chapter that lasts the first 5 seconds (0 to 5000 milliseconds), and the per-chapter cover art is thumbnail_0001.jpg. If we ran this in a loop, we could add images for every 5 second slice of the original video. This code is inserting two frames into the ID3 metadata: The CHAP (chapter) frame contains the timing information, and it can have subframes for metadata like title, chapter art, or associated URL. The APIC (attached picture) subframe contains information about a picture, which can either be a blob of image data or a URL to an image on the web. Normally, you’d also insert a CTOC frame which defines a table of contents, but I don’t want a TOC with hundreds of 5-second chapters, so I’m deliberately not doing this here. This is allowed by the ID3 spec – you’re not required to insert a CTOC frame if you’re using chapters, and you can have chapters that aren’t listed in your table of contents. To work out which frames I needed, I used Forecast to create some chapters by hand, and I inspected their frames. In particular, loading an MP3 and calling Mutagen’s pprint() method shows a human-readable list of frames, and then I could drill into the individual fields: from mutagen.id3 import ID3 audio = ID3("audio.mp3") print(audio.pprint()) I wrapped all this code in a project called glancecast, which allows you to convert a video file with a single command, with optional flags to set the frame length and chapter art size: $ python3 glancecast.py interesting_talk.mp4 interesting_talk.mp3 The process takes a minute or so to complete, most of which is spent transcoding the video file to MP3. The resulting MP3s are usually 40 to 50 MB in size, which is very reasonable. The outcome: How it looks in practice Here’s what one of these “glanceable” podcasts looks like in Overcast and on my lock screen: Maggie Appleton presented this talk over two years ago and it’s been on my “talks to watch” list ever since. Once I put it in Overcast? I listened to it in less than a day. It’s not a lot of extra information, but enough that I can quickly glance down and get the gist of what a speaker is saying. Both views update with a new frame every few seconds, or I can put my phone in my pocket and ignore the screen. I’ve used this approach for half a dozen videos so far, and I’m happy with the results. I expect to keep using it, because I have a long queue of videos I’ve been meaning to watch. If you’d like to try this, check out glancecast for the full code and instructions. [If the formatting of this post looks odd in your feed reader, visit the original article]
Andrew Baker, the current Group CIO at Capitec Bank wrote an interesting piece on AI and open source, and how these tools that generate code according to one’s specification may replace the general reliance on open source implementations done by contributors around the world. I’d really recommend reading it. I have great admiration and respectContinue reading "AI Isn’t Replacing Open Source"
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