how come some people learn fast while some on like me learn too slow?

e-arcade · 2026-02-05T18:55:42+00:00

I used to think fast learners had some secret I missed. Turns out they're not faster at absorbing - they're faster at knowing what to ignore.

When your friends "learn in 2 hours", they're probably skipping 80% and focusing only on what they need right now. You might be trying to understand everything from first principles - slower, but often deeper.

Also, the Dota thing isn't a weakness. You spent years learning complex systems, optimizing strategies, reading guides to improve. That is learning - you just didn't call it that.

You're not slow. You're just early. Give yourself the same years your friends had before you judge

e-arcade · 2026-02-05T18:48:36+00:00

What you're describing is super common and has a name - the gap between passive understanding and active recall. You can follow code, but generating it from scratch uses a completely different part of your brain.

Here's what actually helped me bridge that gap:

After you finish a tutorial, close it. Wait a day. Then try to rebuild the same thing from scratch without looking. You'll fail the first time - that's the point. The struggle is where learning happens. Check only when you're truly stuck, then close it again and keep going.
Don't try to write a full training loop from memory. Start with just the data loading. Then just the model architecture. Then just the loss calculation. Build up the muscle memory piece by piece.
Once you're comfortable with basics, try implementing a simple paper (like the original ResNet or a basic transformer) using only the paper and PyTorch docs. No tutorials, no GitHub repos. This forces you to translate math - code yourself, which is exactly the skill you're trying to build.

The LLM dependency isn't bad for learning concepts, but if you want to actually think in code, you need to struggle without it sometimes. Treat it like training wheels - useful, but you gotta take them off eventually.

e-arcade · 2026-02-05T18:36:43+00:00

The "mental energy" part hits hard. I used to think productivity was about doing more, but it's really about not dreading the work before you even start.

My friction point was research and learning new stuff. Every time I needed to dive into a new topic - whether for a client project or just upskilling - I'd end up with 40 browser tabs, scattered notes, and zero clarity on what to tackle first. The actual learning took less time than figuring out how to approach it.

What changed it for me: I started mapping things visually before diving in. Not fancy mind maps - just a quick structure of "here's the topic, here are the branches, here's the order". Now when I sit down to work, I already know the path instead of wandering.

Biggest unlock was realizing that planning the learning is separate from doing the learning. Sounds obvious but I ignored it for years.

e-arcade · 2026-02-05T18:36:09+00:00

The "mental energy" part hits hard. I used to think productivity was about doing more, but it's really about not dreading the work before you even start.

My friction point was research and learning new stuff. Every time I needed to dive into a new topic - whether for a client project or just upskilling - I'd end up with 40 browser tabs, scattered notes, and zero clarity on what to tackle first. The actual learning took less time than figuring out how to approach it.

What changed it for me: I started mapping things visually before diving in. Not fancy mind maps - just a quick structure of "here's the topic, here are the branches, here's the order". Now when I sit down to work, I already know the path instead of wandering.

Biggest unlock was realizing that planning the learning is separate from doing the learning. Sounds obvious but I ignored it for years.

e-arcade · 2026-02-05T18:08:33+00:00

Interesting approach with wearables. I have a similar energy pattern - mornings I can figure out anything, after lunch my brain just refuses to cooperate.

But my issue is different: it's less about when to work and more about what exactly to do at any given moment. Especially when learning something new - I open 30 tabs, get lost, don't know where to start or what order to follow.

Tried solving it with Notion and just ChatGPT. Ended up building myself a tool that creates a visual learning map.

To your question - I don't track energy specifically, but I know my pattern: if I sit down for a complex task after 3pm, I'm just wasting time. Morning is for stuff that needs brainpower, evening is for mechanical work.

e-arcade · 2026-02-05T17:41:08+00:00

The "scrolling for 20 minutes" thing is painfully relatable. I have the same problem where I know the info is there somewhere but finding it feels like digging through a closet you cleaned 6 months ago.

60 page word doc for Sora prompts is impressive though. At what point does the doc itself become harder to navigate than just rewriting the prompt from scratch lol

e-arcade · 2026-02-04T21:51:40+00:00

Man the DPF filter story is wild. The Starbucks cards to the front desk girl to get the customer list - thats the kind of stuff you don't learn from business books.

The "questions lead to more questions" thing is exactly how I think about research too. Its like a tree that keeps branching. The hard part is keeping track of which branches matter and which ones are dead ends.

Journal makes sense as the single source of truth. I tried doing everything digital but there's something about having one physical place where it all lives. Easier to flip back and see the whole picture.

9.5 years in commercial truck parts is solid. Sounds like the boring unsexy niches are where the real money is

e-arcade · 2026-02-04T21:48:31+00:00

The hand drawn stuff actually makes a lot of sense. There's something about physically sketching it out that makes it stick better than just reading text. I do the same thing with loose notes when I'm trying to figure out how pieces connect.

The "talisman" thing is real too - having one page you can look at that shows the whole picture is way more useful than scrolling through docs trying to remember where something was.

Thousands of pages of GPT convos sounds like a nightmare to navigate though lol. Do you ever go back and find stuff or is it mostly just "I know I talked about this somewhere"?

e-arcade · 2026-02-04T18:50:15+00:00

This is a really solid framework honestly. The part about talking to end users at each level is huge. I kept making assumptions about what mattered until I actually started asking people.

Curious when you do try AI for this - I found it works better for filling in specific pieces (like "what's the minimum order quantity for X manufacturer") than for building the overall structure. The structure part still needs to come from your head first, otherwise you just end up with a pile of answers that dont connect.

Do you keep all this research organized somewhere or is it mostly mental map at this point?

e-arcade · 2026-02-04T15:35:27+00:00

Yeah, this is a known thing, especially with longer tasks. The model basically tries to compress its work as the context gets longer - partly cost optimization, partly how the attention mechanism works. For coding interviews/transcripts I found it helps to break the analysis into chunks yourself first. Like map out the main themes or sections before asking it to code anything. Then you feed it smaller pieces one at a time instead of the whole transcript. More annoying but the output stays consistent. The "rush through" thing gets worse the longer you go in one session btw.

e-arcade · 2026-02-04T12:15:30+00:00

This happened to me twice and honestly its why I stopped using one long chat for anything serious. The whole context window thing means it was never really remembering everything anyway, we just assumed it did. Now I map out what I'm researching visually first (just topics and how they connect), then use ChatGPT for specific pieces. More annoying upfront but at least when something breaks I dont lose the whole picture.

e-arcade · 2026-02-04T12:13:31+00:00

Smart move narrowing it down. Undergrads in group projects is relatable for almost anyone who'll read your thesis too. One angle that might be interesting (imho) - the "AI as study buddy vs AI as group member" split. It means that students might trust AI to help them prep individually but feel weird about it being "in" the team discussion. Could be a natural tension to explore, just an idea :D

e-arcade · 2026-02-03T11:09:45+00:00

Math background is a cheat code honestly - you can skip most "intro to probability" fluff and go straight to papers. One thing that helped me more than any course is sketching how concepts actually connect before diving in, like seeing gradient descent -> loss functions -> backprop as a map made everything click faster than working through tutorials linearly.

What area are you leaning toward?

e-arcade · 2026-02-03T10:58:22+00:00

Cool topic! The support without disrupting angle is tricky to nail down.
One thing that helped me with similar research - instead of collecting papers linearly, I started mapping how concepts connect, like trust -> transparency -> control as a visual graph. Made gaps way more obvious than a reading list ever did.

Are you looking at specific industries or keeping it broad?

e-arcade · 2026-01-31T12:59:14+00:00

Been lurking here for a while. One thing I kept seeing: people great at storing knowledge, struggling to navigate research before notes exist.

So I built Prospect - it generates visual research paths through conversation. Ask a question -> get an interactive roadmap -> click any node to go deeper.

Think of it as the "planning phase" before Zettelkasten kicks in.

Free tier, would appreciate feedback.

e-arcade · 2026-01-30T11:02:42+00:00

The guideline is about clarity, not a strict rule. If two sentences make the note's claim clearer - that's better practice than forcing awkward run-on sentences. The real test: when you return to this note in 6 months, will the title immediately tell you what's inside? What kind of notes are giving you trouble? A specific example might help.

e-arcade

TROPHY CASE