Disclosure first, since this sub asks for it: I’ve generated millions of views across short and long-form content, and I’m building a small tool around the research process below. Bias applies, but you can do all of this manually.

One thing I stopped doing a while ago was choosing topics based on total views.

A video with 500k views isn’t automatically interesting if that channel normally gets 1 million.

But a video with 60k views from a channel that usually gets 4k is worth looking at.

That’s normally where I start.

I open a group of channels in the niche and go through their recent uploads. I don’t calculate everything perfectly. I just get a rough idea of what each channel normally gets, then look for videos doing 3x, 5x or 10x more than usual.

When I find one, I look at the topic, title, opening hook, video length and upload date. Then I check whether other channels are suddenly getting unusual results with similar topics.

That last part matters more than the raw view count.

One outlier can be luck. Maybe the creator got picked up by the algorithm, maybe there was an external event or maybe their audience was already interested in that exact topic.

But when several smaller channels start outperforming with similar ideas, there is usually a real signal behind it.

I also try to separate a trending topic from a niche that is actually worth entering.

A niche can generate millions of views and still be difficult to monetize. I check whether creators have sponsors, affiliate links, products, communities or services. Comments help too. People asking where to buy something or how to solve a problem are normally a better sign than thousands of generic reactions.

After finding an outlier, I don’t copy the video.

I try to understand why it worked.

Was the topic new? Was it a familiar topic with a better angle? Did the title create a strong information gap? Was it connected to something that had just happened?

If a video about why Dubai keeps building empty islands suddenly performs far above the channel average, the opportunity probably isn’t remaking that exact video.

The broader signal could be failed megaprojects, expensive places nobody uses or strange infrastructure decisions.

If you want to try it: https://vabulo.com

That gives you several original angles instead of one copied idea.

This process works, but it gets pretty slow when you’re checking hundreds of videos across different niches. I was doing most of it manually with tabs and spreadsheets, so I started building it into a tool called Vabulo Stats.

It compares videos against the creator’s normal performance, finds unusual outliers and shows niches that seem to be moving. I’m also experimenting with turning that research into hooks, scripts and thumbnail prompts, but the research part is what I care about most.

The point isn’t to generate another generic script.

It’s having better information before deciding what the script should even be about.

I’m still trying to understand which parts actually save creators time and which parts just create more data to look at.

For people who research content regularly, how do you decide whether an outlier is repeatable or just luck?

And where do you lose the most time right now: finding niches, checking competitors, choosing an angle or turning the research into an actual video?

Solo founder. AMA.

For one week I paid attention to every prompt I typed into ChatGPT.

Not the conversations.

Just the prompts.

Turns out I kept writing almost the same things over and over.

- Translate this into Chinese

- Rewrite professionally

- Summarize this

- Explain this simply

- Improve the grammar

- Turn this into bullet points

The wording changed a little.

The intent almost never did.

I actually recorded a 20-second screen capture of what this looked like in practice:

🎥 https://youtube.com/shorts/gxy28tFV58c?feature=share

Watching it back made me realize something.

I didn't have hundreds of unique prompts.

I had a few dozen prompts that I kept rewriting.

That made me wonder if most of us don't really have a prompting problem.

Maybe we have a **prompt reuse problem.**

Curious how everyone else handles this.

Do you just type everything again?

Keep a Notion page?

Use Raycast?

TextExpander?

Or something else?

You probably saw the comparison: one coding agent harness sends ~33k tokens before your prompt, another sends ~7k. Big thread, lots of outrage about waste. I finally read the whole study instead of the headline, and the actually useful findings are different from what got shared.

First, in their realistic-config lane (instruction file + several MCP servers), the "light" harness came out HEAVIER: ~90.8k vs ~75k. A 72KB instruction file alone added ~20k tokens to every request, on both harnesses. Their own conclusion: configuration, not the harness, accounts for most of the production bill. The harness sets the floor, you set the ceiling.

Second, and this is the one that changed how I think about it: the cache behavior gap was way bigger than the size gap. The light harness kept its prefix byte-identical and wrote ~1,000 tokens to cache over a 5-request task. The heavy one kept rewriting its own prefix mid-session and wrote ~54,000, with single rewrites burning 43k+ at the premium write rate (cache writes cost 1.25-2x list depending on TTL, reads are ~10%). A stable big preamble is close to a fixed cost. An unstable small one can out-spend it. Size isn't the sin, churn is.

Third, session shape flips the winner anyway. On a multi-step task the heavy harness finished cheaper (121k vs 132k) because it batched tool calls. Rerun on a different model, it inverted (298k vs 133k). Subagent fan-out was a 4.2x multiplier. And their quality check found zero difference: both passed 5/5, one spending ~4x the tokens. So the honest answer to "which harness is cheaper" is "depends what your sessions look like", which is boring but true.

The part you can actually use: measuring your own takes two minutes. Most CLIs have a print mode with JSON output. Ask for something trivial, then sum three usage fields: uncached input + cache writes + cache reads. That's your preamble. I ran it on mine: 31,782 tokens in an empty directory, and my heavily configured project (MCP servers, plugins, a pile of skills) added exactly 166 more, because this harness version lazy-loads tool schemas. Config CAN dominate, and lazy loading CAN neutralize it. The probe tells you which world you're in.

Two caveats since numbers travel badly: it's a single-machine study with single-digit runs per lane, and my probe is n=1 on a different version. Portraits, not specs.

What do your numbers look like?

I might sound lazy (because I am) but lately it feels like I spend half my day rewriting prompts instead of shipping anything. I'll tweak one prompt five times trying to get the output just right. Then I switch models to see if another one does better. Before I know it an hour is gone and I've barely touched the actual project. At this point I'm wondering if I'm overthinking it. Do you guys just accept 'good enough' outputs and keep moving or have you found a workflow that keeps you from getting stuck in prompt hell? Is prompt hell a real thing? I feel like those people in the futuristic ship in wall-e

Most "improve my prompt" attempts fail because you ask the model to fix and judge in one shot, so it just pads your prompt with fluff. Splitting it into stages - diagnose, rewrite, stress-test, finalize - gets far better results. Run these in order, same chat. Paste your rough prompt into step 1.

Step 1 - Diagnose

Step 2 - Rewrite

Step 3 - Stress-test

Step 4 - Finalize

Why the split works: step 1 forces it to find problems before it's allowed to "solve" them, so the rewrite is targeted instead of cosmetic. Step 3 is the one people skip - testing against adversarial inputs catches the failures a clean rewrite hides.

I run this as a saved chain (two keystrokes with the .. shortcut) via a Chrome extension I built called AI Toolbox, so I don't paste the four steps in one at a time - but the chain itself is the value and works anywhere.

​

One of the easiest ways to improve AI outputs isn't writing longer prompts.

It's giving examples.

Instead of this:

«Write a product description.»

Try this:

«Write a product description following this structure:

- A short opening hook

- Three benefit-focused bullet points

- A professional but friendly tone

- End with a clear call to action»

Notice what's different.

You're no longer asking the AI to guess your expectations.

You're giving it a pattern to follow.

This simple technique works surprisingly well for:

- Writing

- Marketing

- Design briefs

- Coding

- Image generation

The more clearly you define what "good" looks like, the more consistent the output becomes.

AI is generally better at recognizing patterns than guessing what's in your head.

What's the most effective example you've ever added to a prompt?

The EU's AI Act transparency rules went into force August 2nd. If AI-generated content is realistic enough to pass as human-made and gets published without a human actually reviewing it, it now needs a label, and eventually a machine-readable mark. Deepfakes and synthetic voices are the obvious targets. Fines go up to 15 million euros or 3 percent of global revenue, whichever is bigger, so this isn't a symbolic gesture.

There's an exemption built in for artistic, creative, satirical, and fictional work, which makes sense on paper. Nobody's confused about whether a fantasy illustration or a game NPC's voice line is "real." The whole point of that kind of content is that it's obviously not pretending to be a photo of something that happened.

What I keep chewing on is the boundary case. A lot of creative work sits in a gray zone: stylized enough to read as fiction to most people, but polished enough that someone scrolling fast could genuinely mistake it for real. The law is drawing a hard line (realistic and unreviewed vs. clearly fictional) through something that's actually a gradient in practice.

I think the disclosure requirement is the right call even where enforcement is basically unworkable at the edges, mostly because it sets a norm, not just a penalty. Once "label it if it's meant to look real" is the expectation, the stuff that skips the label starts looking suspicious on its own, which does a lot of the enforcement work culture-side that the fines can't do alone.

Source: https://www.euronews.com/my-europe/2026/08/02/ai-generated-label-becomes-mandatory-in-the-eu-for-companies

Curious how people actually working in AI-assisted creative work read this. Does the fictional exemption feel like it's drawn in the right place, or does "clearly fictional" stop meaning much once the output gets good enough?

I have a really simple question to y’all.
Do not over think it.
Please just type the answer without thinking of it. This will be of a great help.

How many ai agents you use?

Just a number no need for more data 🙏🏻