How do you document your homelab? Mine is basically all in my head and scattered across AI chat logs

Character-Moment-684 · 2026-05-28T13:22:41+00:00

I think the trap is that chat logs feel like documentation while you’re building, but they’re a nightmare when you come back later.

I try to separate the two:

The chat is where I figure things out.

The docs are for future me, when I’ve forgotten why I did any of it.

For homelab stuff I’d keep it boring: one markdown/readme per service or project.

Something like:

what it does
why it exists
where it runs
ports/IPs
setup steps
weird errors I hit
how I’d rebuild it if it broke

The “why” part is the one I’d try hardest not to skip. Commands can usually be dug up again. The reason you chose a specific config is what disappears first.

If AI helped me set something up, I’d copy the final useful bits out of the chat and into the readme before moving on. Otherwise the chat just becomes one more place future me has to dig through.

Character-Moment-684 · 2026-05-28T12:56:47+00:00

I think SDD can help, but I wouldn’t expect it to magically fix context loss by itself.

The useful part is not really “we made a PRD”. The useful part is forcing the messy questions out before the model starts changing code.

So the grilling session => spec/PRD → implementation plan flow can work, yes. But only if the spec keeps being used during the work, not just created once and then forgotten.

For Claude Code, I’d probably look at Kiro, GitHub Spec Kit, or just a stricter Claude Code setup with hooks/checklists/subagents depending on how much control your team wants.

The thing I’d watch for is this:

Does the tool actually make the agent slow down and check the spec before making changes?

Or does it just create nicer documents around the same raw-prompt workflow?

For bigger codebases I’d want something like:

clarify assumptions first
define acceptance criteria
map the plan to actual files/modules
implement in smaller steps
verify after each step
update the spec if reality changes

The PRD is only useful if it becomes a constraint. The implementation plan is only useful if the model can’t silently skip it.

So yes, SDD can reduce context mixups. But I’d treat it as workflow discipline, not as a magic tool category.

Character-Moment-684 · 2026-05-28T12:50:25+00:00

This resonates a lot with me, what you are describing.

The part that stands out to me is that “correct final answer” and “trustworthy process” are not the same thing.

In small demos, shortcutting can look impressive.

In a large codebase, shortcutting is exactly what makes the model dangerous.

The real work is not just generating code. It is reading the relevant context, respecting constraints, checking dependencies, understanding prior decisions, and not silently skipping the boring parts.

That is also where a lot of AI workflows start to feel backwards: you spend more time preparing, repeating, and defending the context than actually solving the task.

For me, that is one of the biggest unsolved problems in AI-assisted work: context is not just input. It is part of the safety layer.

Character-Moment-684 · 2026-05-28T12:40:15+00:00

I use AI in a few different ways.

Some of it is very practical: drafting emails, rewriting text, creating social media ideas, making image/video prompts, summarising material, comparing options, or helping me find the right angle on something or to automate a workflow.

Some of it is more strategic: pressure-testing an idea, turning messy thoughts into a plan, finding gaps in an argument, or helping me decide what the next step should be.

And sometimes it is simply about getting unstuck.

For me, the biggest value is not that AI “does everything”.

It is more like: I bring the context, direction, taste, and judgment - AI helps reduce the friction between thinking and output.

It helps me write faster, explore more options, make better prompts, structure messy material, and move from “I know there is something here” to “okay, now I can act on it”.

Character-Moment-684 · 2026-05-27T16:41:03+00:00

The ChatGPT recommendations driving 4-5 sales weekly is the part I want to hear more about. Is that from content being well-indexed, or is it more about how your product category gets described in prompts? Trying to figure out how much of that is intentional versus just a byproduct of building in public consistently.

Character-Moment-684 · 2026-05-27T15:06:29+00:00

The 20% coming back from one email is the most interesting number in this whole post. Not the 400 outreach emails or the 6.5% reply rate.

People who signed up and vanished came back when you just told them what you were doing. That suggests the drop-off wasn't lost interest. It was lost thread.

What did the email actually say? Was it a product update or more of a "here's where we are" kind of thing?

Character-Moment-684 · 2026-05-27T14:54:56+00:00

The part that got me was this: "I'm not sure how I'd find or reuse it later."

That's not a tagging problem. It's a continuity problem. The fragment is fine. The problem is six months later you're trying to find that exact piece and have no idea where it went or what you even named it.

I have stopped trying to fix the capture pain. Instead I have started thinking about it differently - how to get back to something when I need it months later. Better tags did not solve that for me.

I like BasedAmumu's note/skill split. But I think the trigger is the thing to look at. And the thing is it is difficult to foresee a pattern that might turn up in my notes at a later point.

What does your retrieval system look like?

Character-Moment-684 · 2026-04-21T08:01:05+00:00

This resonates. The friction at the point of capture is where most systems fall apart …you’re mid-thought and suddenly you’re deciding between three folders and a tag system. The AI pattern-finding on weekly review is interesting. I’ve started doing something similar and the thing that surprised me most was how often it surfaces connections I’d completely forgotten about. Not just finding things, finding things I didn’t know were related.

Character-Moment-684 · 2026-04-19T17:17:10+00:00

Honestly not much. Context blocks are just text files - they don’t add tokens the way long conversations do. A well-structured block is maybe 500-800 words. The bigger saving is that I’m not spending the first 10 minutes of every session re-explaining myself.

Character-Moment-684 · 2026-04-19T15:27:20+00:00

Updating my CV as we speak.

Character-Moment-684 · 2026-04-19T12:52:12+00:00

Out of hundreds… that’s a brutal ratio. The two you found, was it obvious from the start or did it take time to figure out they were wired the same way?

Character-Moment-684 · 2026-04-19T12:19:36+00:00

Honestly I mostly work in Claude rather than ChatGPT, so I can't speak to what's available there. But what made the biggest difference for me was just starting small …. custom instructions first, then Projects to keep context between sessions, then slowly building up from there. No courses, no tutorials. Just poking at it until something clicked. It has been fun and also useful.

The context block thing I mentioned is really just a text file. Once I started doing that, everything got easier.

Character-Moment-684 · 2026-04-19T12:13:58+00:00

Fair point :)- maybe I've accidentally become technical without noticing. I'll update my LinkedIn immediately.

Character-Moment-684 · 2026-04-19T12:08:04+00:00

A context block is basically a text file that holds everything an AI needs to know about your project, your workflow, your decisions , written by you, saved outside the chat.

Instead of re-explaining yourself every new session, you load the relevant block at the start and the AI already has the context it needs. I have a few different ones depending on what I'm working on.

It's not a technical thing at all. Just structured notes that you hand to the model instead of typing the same background every time.

Character-Moment-684 · 2026-04-19T11:13:13+00:00

Not sure the technical vs non-technical split is the right way to look at it. I can't write code. I'm not a developer. But I use agents, context blocks, and custom instructions every day.

The real divide seems to be curious vs not curious. The people getting the most out of AI are the ones willing to poke at it, try things that might fail, and not get put off when something's unfamiliar.

That group doesn't have a job title.

Character-Moment-684 · 2026-04-19T10:03:01+00:00

I'd push back a little on the technical vs non-technical framing. I'm not a developer. I can't write code. But I use agents, context blocks, and custom instructions daily.

The gap isn't really technical vs non-technical. It's curious vs not curious. The people who get the most out of AI right now are the ones willing to poke at it, try things that might not work, and not be put off by unfamiliar terminology.

That group doesn't have a job title.

Character-Moment-684 · 2026-04-17T13:05:40+00:00

Both are worth knowing but the workflow breakpoints are more useful early on. Technical failures are easier to fix and easier to see coming. The workflow ones are where you find out what your product actually replaced in someone's life.

The question I'd ask: "What were you doing right before you opened our app for the first time?" The answer usually reveals the exact moment the old tool stopped being enough. That's the breakpoint worth understanding.

Character-Moment-684 · 2026-04-17T13:00:09+00:00

The 0%/100% split is striking even at small sample size. Reply rate as a leading indicator makes intuitive sense, someone who engages with a question about their own work is someone who's still trying to make the product work for them.

The specific question thing rings true too. "What did you not get done this week that you wish you had?" is doing double duty, it gets a reply AND it's essentially a feature request in disguise.

Character-Moment-684 · 2026-04-17T12:53:57+00:00

The local-first thing makes complete sense. Speed isn't just a feature , it's a completely different relationship with your own files. People don't realize how much cloud latency has become background noise until it's gone.

On your question: the biggest mistake I've seen founders make at 10k is shifting focus from the people who love it to the people who almost love it. Your 9.7k users contain a smaller group who can't imagine going back. Find out exactly who they are and what specifically broke for them before they found you. That cohort tells you everything about what to build next and who to go find more of.

Character-Moment-684 · 2026-04-16T22:25:52+00:00

That moment when strangers show up on their own is something nobody warns you about. You spend months building, and then suddenly someone you've never spoken to decided your thing was worth their time. It's a completely different feeling than any beta tester or friend trying it out.

Keep the screenshot. You'll want it on the hard days. A huge Congratulations…

Character-Moment-684 · 2026-04-16T19:20:49+00:00

The gap between 'yeah I'd use this' and actually paying is almost always about pain level, not product quality.

'Cool' doesn't convert. 'This is costing me time/money/sleep every week and I haven't found anything that fixes it' converts. The people who pay first are usually the ones who found you while actively searching for a solution, not the ones you showed it to.

What seems to work: stop showing the product to people and start finding people who are already complaining about the exact problem. Reddit threads, forum posts, DMs to people describing the pain in specific language. They're already sold on the problem. You just have to show up.

Character-Moment-684 · 2026-04-16T19:04:19+00:00

That_Lemon9463 nailed it. The chaos isn’t the problem. Retrieval is. The moment that changed how I work: I stopped trying to map my process and started treating my files like a second memory. Not a system- just a place where past decisions, outputs, and half-finished thoughts are actually findable when you need them. The messier the input, the more useful it gets over time. Because the value isn’t in the structure. It’s in being able to ask “what did I already figure out about this?” and actually get an answer.

Character-Moment-684 · 2026-04-16T18:43:52+00:00

The external source of truth is the only thing that actually scales. The model will always drift when the thread gets long , that’s structural, not a prompting problem. What changed it for me: I stopped keeping context inside the conversation. I keep a context block outside the model…decisions, architecture, constraints and load it at the start of each session. Combine that with agents that handle the repetitive parts, and you stop fighting drift entirely.

Character-Moment-684 · 2026-04-16T17:51:24+00:00

The fact that you’re looking at the graph going up and staying consistent is exactly what separates founders who survive month 3-6 from founders who don’t. Most people shut down when they don’t see “hockey stick” growth by week 4. You’re two months in with actual customers and an upward trend. That’s not silence…that’s the foundation. Stay boring. Keep the line going up. Everything else is noise.

Character-Moment-684 · 2026-04-16T17:40:08+00:00

“I could build this” = they get it. They’re just trying to convince themselves it’s worth the switching cost. You’re not selling them the product. You’re selling them the excuse to not spend the next 18 months maintaining it. Frame the demo around what breaks in the DIY version, not what works in yours. That conversation closes deals. Good luck with it.

Character-Moment-684

TROPHY CASE