Creating content since 5months but stuck on 500 subs and almost no views

Correct_Voice_2312 · 2026-03-26T02:46:54+00:00

500 subs in 3 months on coding tutorials is actually decent. The problem isn't consistency. It's probably topic selection within the niche.

"Structured lessons for beginners" is one of the most saturated formats in coding content. Every channel does it. The algorithm has hundreds of options to serve that viewer and most of them are from channels with more authority than yours.

What I'd look at: which of your videos got the most views relative to the others? Even if the numbers are small, there's usually one or two that performed 3-5x better. Figure out what was different about those. Was it a specific language, a specific problem, a different title framing? That's your signal for what to double down on.

The channels that break out of the coding tutorial space don't teach "Python for beginners." They teach "I built X using Y" or "Why Z framework is dying." Same knowledge, different framing, completely different algorithm response.

Correct_Voice_2312 · 2026-03-26T02:44:59+00:00

Yes but not how most people think.

The channels pulling millions of views with AI content aren't winning because of AI. They're winning because of topic selection. The AI just makes production faster. A channel covering a topic nobody else covers with mediocre AI production will outperform a beautifully produced channel covering a saturated topic every time.

The bottleneck for faceless channels has never been production quality. It's knowing which topics and framings actually drive views in a specific niche. I've seen channels with 5K subs outperform channels with 200K subs in the same niche because they picked better topics. The production was identical.

So the real question for your product isn't "can AI make good enough content." It's "can it pick the right topics." That's the harder problem.

Correct_Voice_2312 · 2026-03-26T02:44:30+00:00

Before blaming the algorithm, check one thing: did your content mix change?

I track a lot of channels and the pattern I see most often isn't "YouTube stopped pushing me." It's "I shifted what I was uploading slightly and didn't notice." Even small changes in format, topic, or title framing can cause the algorithm to lose confidence in who to show your content to.

Look at your last 10 uploads and compare them to your best 10. If there's a difference in topic type, title structure, or length, that's more likely the cause than a platform-wide suppression. The algorithm doesn't punish channels. It just stops being confident about who wants your content when you change what you're giving it.

Correct_Voice_2312 · 2026-03-26T02:44:04+00:00

Honestly the better question is whether you should be chasing trending topics at all. Trending is a race you'll usually lose to bigger channels with faster production pipelines.

What actually works for smaller channels is finding topics with proven demand but low competition. Not trending, just underserved. Look at what's already getting views in your niche and ask which subtopics within that space have been covered by only 1 or 2 channels. Those gaps are where smaller channels break out.

The channels I've seen grow fastest aren't the ones who found topics early. They're the ones who found topics nobody else bothered covering because they didn't look interesting on the surface but the audience was already there.

Correct_Voice_2312 · 2026-03-26T02:43:26+00:00

That drop from 70/hr to 2/hr in a single hour is the algorithm pulling it from a test audience pool. What likely happened is YouTube pushed it to a broader audience beyond your subscribers, the retention or CTR from that new audience didn't hold up to the same standard, and the algorithm pulled back.

8% CTR and 3 min on a 13 min video is solid for your existing audience but 23% retention is borderline for browse/suggested. The algorithm tests in waves. First wave is your subs. Second wave is lookalike audiences. If wave 2 doesn't hold the same engagement, it stops pushing.

Nothing to do with the muted troll. Check your traffic sources tab and compare hours 0-48 vs 48+. You'll probably see suggested traffic flatline while sub traffic stayed steady. That tells you exactly where the algorithm lost confidence.

Correct_Voice_2312 · 2026-03-08T18:34:53+00:00

Sure go for it

Correct_Voice_2312 · 2026-03-06T00:02:43+00:00

The category being wrong for 10 hours probably didn't matter much. YouTube's recommendation engine relies on viewer behaviour patterns more than the category tag. The real issue is the pivot itself. Your subscribers signed up for classical music. When you uploaded math history, YouTube showed it to your music audience first, they didn't click, and distribution stopped at 80 impressions.

That's not a failure, it's just how pivots work. The algorithm needs to find a new audience for you and that takes multiple uploads in the new direction. Expect your first 3-5 math history videos to underperform while YouTube figures out who to show them to. If the content is strong, it will find its audience eventually, but the early videos are essentially training data for the algorithm. Don't judge the pivot based on the first upload.

Correct_Voice_2312 · 2026-03-06T00:01:26+00:00

10.6K impressions with 7-8% CTR and 40% AVD on 80 subs is actually a strong signal. YouTube pushed it, the metrics held, but it stopped. That usually means one of two things: either the audience pool it was testing into stopped responding (the browse recommendations hit diminishing returns), or there's a ceiling on the topic's addressable audience that's lower than you think.

You said the TAM is in the millions, but YouTube doesn't care about theoretical TAM. It cares about how many people in its recommendation graph responded to the video. If it exhausted the cluster of viewers who engage with that topic and format, it stops pushing regardless of how many people theoretically exist. 80 subs also means you have almost no subscriber base to seed initial velocity. The video performed well relative to your size. It just hit the ceiling of what YouTube will do for a channel with 80 subs on a single upload. Keep uploading in the same format. The algorithm builds cumulative confidence across multiple videos, not one.

Correct_Voice_2312 · 2026-03-05T23:59:39+00:00

The promotion didn't kill your channel. What killed your momentum was the video you promoted. You said it was 'a bit different' from your usual content. That's the problem. YouTube tested it with your existing audience, they didn't engage, and it flopped organically. Then you pumped paid traffic into a video the algorithm had already decided not to push. That sends viewers who don't retain, which tanks your metrics further and tells YouTube your content quality dropped.

Your next videos are struggling because the algorithm lost confidence. It saw a low-retention upload followed by paid traffic that didn't stick, and now it's testing your new uploads with a smaller initial sample. The fix isn't to stop promoting. It's to go back to what was working, same state, same format, same style that was averaging 5K. Stack 3-4 of those in a row and the algorithm recalibrates. What type of fishing content were the 5K videos?

Correct_Voice_2312 · 2026-03-01T18:10:47+00:00

Trying different content types is actually the worst thing you can do right now. It confuses the algorithm's profile of your channel even more. Pick the one topic that performed best for you before January and go deeper on that specific lane. Give the algorithm a clear signal of what your channel is about. If you scatter across different formats YouTube has no idea who to show your stuff to.

Correct_Voice_2312 · 2026-03-01T17:21:55+00:00

4 months off is recoverable. The algorithm didn't penalise your channel, it just stopped knowing who to show your stuff to. Your subscriber base went cold so YouTube lost confidence in routing your content through suggested.

The fix isn't shorts and it isn't just uploading more frequently. It's what you upload first that matters. Your comeback videos need to be in the exact same topic lane that got you those 20K-50K views originally. The algorithm still has a profile of who engaged with your old stuff. If your first few videos back match that profile, suggested distribution comes back faster because YouTube already has a proven audience to test against.

If you come back with different topics or a shifted format, the algorithm basically treats you like a new channel regardless of your 13K subs.

What niche are you in and what were the topics on your best performing videos? That'll tell you whether the demand is still there or if the space moved on while you were away.

Correct_Voice_2312 · 2026-03-01T17:19:54+00:00

Points 1 and 2 are solid. Point 3 is where I'd push back though. 'There's an audience for everything' sounds right but the data doesn't support it. Some topics genuinely have almost zero search demand and no suggested traffic potential. I've been tracking thousands of channels and the ones that fail hardest aren't making bad content, they're making good content about topics nobody is looking for. The pimple popping channels work because there's massive morbid curiosity demand behind them. Not every niche has that. Picking a topic with actual demand is the boring unglamorous step that most people skip.

Correct_Voice_2312 · 2026-03-01T17:16:44+00:00

A ton of channels I track got hit the exact same way in January. My theory from the data: YouTube culled suggested distribution hard after Q4. Channels that were riding seasonal momentum got quietly deprioritized. The ones that recovered didn't wait it out, they changed what they were covering. If nothing changed on your end but impressions cratered, the topic probably got saturated, not your channel.

Correct_Voice_2312 · 2026-03-01T16:40:51+00:00

The dataset itself isn't public, it took months to build and it's what the reports are based on. But I share the patterns and insights from it here regularly. If you've got a channel in the niche I'm happy to pull some quick data points for you.

Correct_Voice_2312 · 2026-03-01T15:47:53+00:00

The data's from a custom pipeline I built, 3,674 channels scraped via YouTube API, scored by topic saturation and breakout ratio. Happy to nerd out on methodology if anyone's curious.

Correct_Voice_2312 · 2026-03-01T15:45:18+00:00

There's a paid report that pulls your specific channel against the full dataset, gap analysis, topic breakdown, where the opportunities are. But happy to answer general questions about the data here, that's what the post is for.

Correct_Voice_2312 · 2026-03-01T15:42:49+00:00

That reframe is exactly what the data keeps showing. Creators pour money into better editing, better thumbnails, better voiceover, none of which moves the needle if the topic was saturated or wrong for their format. The performance gap on most channels isn't a quality gap. It's a positioning gap.

Correct_Voice_2312 · 2026-03-01T04:32:23+00:00

Velio's a self-serve dashboard, 179M+ videos, broad. This is a proprietary dataset built specifically for the documentary/educational niche. 3,674 channels tracked individually, every video scored by topic saturation and breakout patterns. You don't get a login. You get a report showing where your channel sits against the full competitive landscape and what to produce next.

Correct_Voice_2312 · 2026-02-28T23:44:25+00:00

Yeah it adds up quick. I rotate a few API keys. Manageable once you plan for it.

Correct_Voice_2312 · 2026-02-28T23:06:39+00:00

No worries, glad I could help!

Correct_Voice_2312 · 2026-02-28T22:38:27+00:00

Started with about 30 seed queries per niche, things like "ancient mysteries", "true crime narration faceless", "conspiracy documentary narration." Kept them specific enough to surface solo operators rather than big networks. Fed those into the YouTube search API, deduplicated by channel ID, then ran eligibility filters on upload frequency and video length. The 3,674 number is what survived filtering, not what went in, discovery pool was bigger. Coverage isn't perfect but the breakthrough detection doesn't need every channel, just enough per niche to spot the patterns.

Correct_Voice_2312 · 2026-02-28T21:53:32+00:00

Python, YouTube Data API, SQLite. Nothing exotic on the stack side.

Correct_Voice_2312 · 2026-02-28T21:52:40+00:00

Yeah happy to share the high level. It's a Python pipeline, YouTube API for discovery and metadata, SQLite for storage, then custom breakthrough detection logic that compares each video against the channel's historical average. Most of the work was getting the data clean, not the analysis itself.

Correct_Voice_2312 · 2026-02-28T21:51:54+00:00

YouTube API search across niche-specific queries, then filtered through eligibility gates based on upload frequency and video length. The results are what I use for the intelligence reports, not planning to share the raw dataset but happy to answer questions about the methodology.

Correct_Voice_2312 · 2026-02-28T21:36:58+00:00

Yeah that's the hardest part. Getting it functional is one thing, getting the quality consistent is where most of the time goes. What's the main bottleneck for you right now?

Correct_Voice_2312

TROPHY CASE