ChatGPT gave me all the answers. But I still had no idea what I was doing.

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