If so, my new website at colortheorymusic.com will feel instantly familiar.

Color Theory XP features highlights from the Color Theory catalog disguised as a Windows XP desktop, complete with boot screen, login, taskbar, Start menu, draggable windows, and fake apps that play real music.

A month ago, I wouldn’t have thought that this was even possible, much less that I could do it myself.

I suppose that’s overstating things. I didn’t actually do it myself. I asked Claude to do it, and it was up and running in an afternoon.

Here is the actual prompt I gave it:

I, Brian Hazard, have a recording project called Color Theory and I want to build a new website that I’ll host at colortheorymusic.com, leaving colortheory.com to host my email. For the purposes of this task, pretend that colortheory.com doesn’t exist and just pull images and info online.

I want you to build a website that looks like a Windows XP operating system, with a desktop and file navigation and apps to click on. Make it fun with lots of easter eggs. Try to tastefully encourage people to join my Patreon page and include WinAmp that can play my songs. Also include Cubase VST that can load and play my songs.

Bada boom, bada bing, done.

I figured it would need PHP and a database and a special hosting package, but to my surprise, it didn’t require hosting at all. It’s just HTML, CSS, and JavaScript that runs in the visitor’s browser.

Cloudflare, where I host my domains, has a Workers & Pages feature where you simply upload the files Claude spits out (a “deployment”), and they’re immediately live on the web with no monthly fee.

So I put it up and shared it, and it started spreading on social media! To capitalize on the momentum, I updated it to include my new single, “Couldn’t It Be Me?“

I rendered stems from Ableton so that they could be muted and soloed on a per-track basis in Cubase VST. Getting that to work took longer than everything else up to that point.

Ten separate audio files won’t stay in sync in a browser, so Claude made it download all of them up front, decode them into memory, and start them on one shared clock. On mobile it collapses the stems to mono at a lower sample rate to fit in memory.

As for traffic, Cloudflare reports 5,440 unique visitors in 30 days mostly from the US, UK, Netherlands, Brazil and Canada.

Is it an effective way to promote music? Who knows. Was it fun? Yes.

If you’re paying $20 a month for a Claude or ChatGPT subscription, you can do it too, though you might have to space out your sessions. You genuinely don’t need to know anything about coding or web design. Just tell it what you want in plain language.

My little demonstration video only scratches the surface. Feel free to play around to your heart’s content!

I had Claude prepare a full feature list, including every easter egg, in case you’d rather read than explore:

If you’ve got any ideas for what I should do next, let me know in the comments!

ICYMI, The Atlantic created an AI Watchdog search engine where you can check if your music was used to train AI models, for which you were almost certainly not informed or compensated.

In my case, 181 songs are in two datasets: LAION-DISCO (above) and SLEEPING-DISCO:

I should be really angry then, right?

Maybe I should be, but I’m like a 4 out of 10 tops.

For starters, I already knew my music was trained on. There’s a music AI service called Sonauto (now Treblo) that allowed you to prompt for specific artists until recently, most likely due to legal challenges.

When it first launched, I prompted for songs “in the precise sound and style of Color Theory” and the results were shockingly good. For free, mind you. Anyone could theoretically whip up a Color Theory album in a half hour.

I shared a track with my patrons and here are some sample comments:

Yeah I can’t tell BrAIan apart from you based just on a voice or lyrics, or the music style… Scary!

This is unreal, if you’d have posted this as your next demo I wouldn’t have thought twice To be fair the vocals are good, but the song hasn’t quite got that Color Theory Magic.

Yes we‘re doomed….a bit. I still can hear a difference, luckily…But to be honest: if you wouldn‘t have told me/us before, I might have not noticed it….

My friend Minute Taker was in there too so I whipped up some songs with him singing ridiculous things he’d never say, for our ears only of course. If nothing else, AI is always good for a laugh!

Even then, I wasn’t angry. I was amazed! And a little bit honored to have made the cut, I suppose.

I’m not saying we don’t need protections for our sonic identities. We absolutely do, and maybe we’ll even get some retroactive compensation via a class action lawsuit. I’m not holding my breath.

Another possible reason I’m not angry (who can really say, emotions being what they are) is that a couple of years ago, I opted in to AI training through a sync/radio platform called AudioSparx. I’ve been active there for many years, and I figured if my music was going to be trained on anyway, I might as well get paid for it.

To be clear, this isn’t how my music ended up in those datasets. The AudioSparx training is fully licensed, as an ethical alternative to the theft detailed above. So far I’ve made $168.85 in AI training royalties.

To sum things up, I suppose I’m pro-AI, anti-theft. You know I’ll be asking Claude to proofread this post before I hit publish!

How do you feel about AI training on our music? Are you angry? Tell me why I should be too in the comments!

I learned that I’ve spent over $12K on Spotify Marquee and Showcase campaigns since 2022.

And what did all that money get me besides a hole in the wallet?

Exactly 107,327 streams from 23,100 listeners at an average cost of $0.55 per listener.

And how do I know this?

It’s all thanks to the free Spotify Campaign Analyzer I built for you. Export your results from Spotify for Artists, drag the file onto the page, and you get everything S4A shows you, plus the analysis it doesn’t:

• Every campaign in one sortable table. Spend, listeners, streams, and intent rate side by side across your entire campaign history.
• The cost metrics Spotify doesn’t calculate: cost per listener, cost per stream, and total streams, including streams of your non-promoted releases.
• Side-by-side comparisons. Pick any set of campaigns and see them charted against each other, metric by metric.
• Benchmarks with context. Any campaign can be measured against your own history or against typical independent-artist results. Tiny, stopped-early campaigns can be disregarded (you set the dollar threshold) so as to not skew your results.
• Apples-to-apples mode. Campaigns that target potential listeners (cold audiences) always look worse on paper. One click recomputes the numbers from warm audiences only.
• A straight answer on in-progress campaigns. Enter the numbers from a campaign that’s still running and get a verdict with clear reasoning: keep going, watch it, or stop.
• Completely private. It’s a single web page. Your data never leaves your browser. Nothing is uploaded, and there’s no signup. There’s not even a donate button!

What my 123 campaigns taught me

Pulling my whole history into one place surfaced a few things I would’ve never spotted on Spotify for Artists:

• Latin America is a bargain. In Brazil I pay about $0.25 per converted listener and in Mexico $0.38, versus roughly $0.60 in the US and UK. Brazil and Mexico also post my highest intent rates (~22%), well above the US (~17%).
• Albums beat singles, by a lot. Album campaigns convert listeners at $0.44 and generate ~5 streams per listener, while single campaigns cost $0.60 for ~1.6 streams each. More tracks means more to explore, but the cost-per-listener differences wasn’t obvious. EPs land in between.
• Almost 40% of my streams came from my back catalog. Across these campaigns, listeners drove ~64k streams of the promoted release, plus another ~41k streams of my other releases. Kind of important to factor in, wouldn’t you say? But S4A keeps it tucked away.
• Marquee and Showcase are different tools. My Marquee campaigns earn higher intent (~22% vs ~15%), but Showcase drives nearly double the streams per listener (3.0 vs 1.7).

Getting your data in

The tool reads the same results file Spotify already lets you download. It never connects to your account or asks for a login.

1. In Spotify for Artists, go to Campaigns → Display.
2. Click the “…” menu next to + Create campaign and choose Download all results. That’s a single CSV covering every campaign you’ve ever run.
3. Drag that file onto the analyzer. Done.

A few things worth knowing: it’s one file for your whole history. There’s no need to download individual campaigns, but it’ll accept them if you do. Your data stays in your browser; nothing is uploaded. It reads both Marquee and Showcase data all the way back to my first campaign in 2022.

It even works on my iPhone via the Files app! That was a pleasant surprise.

Give it a try at passivepromotion.com/spotify-campaign-analyzer

And share your results and suggestions for improvement in the comments!

But are those things actually connected, or am I paying for what the algorithm would do anyway?

I vibe coded a tool to find out!

The Meta Ads to Spotify Analyzer is a single-page, browser-based calculator with no login required. Your data never leaves the page.

Try it here, or use the version baked into this post.

I’ve completely overhauled it, and this post, since I first published the tool. Instead of entering data manually, you simply drop in CSV files from Ads Manager and Spotify for Artists.

The tool re-runs the whole analysis on 300 random shufflings of your conversions and only claims an effect if random data can’t match it.

See for yourself! If you need help locating the proper files, click on “Where do I get these files?” in the import box.

Or if you just want to see what it can do, click the Load Example button below the table.

The minute you click Load Example (or Run Analysis on your own files), you’ll get a plain-English verdict. For the example (a real 44-day campaign plus a couple of baseline days), it comes back with:

 Streams: strong evidence your ads are driving this (chance matched this result in fewer than 1% of 300 trials)
 Intent (saves + playlist adds): suggestive, but not conclusive yet (chance matched this result in about 10% of 300 trials)

Then you get a pretty graph and lots of numbers before the part that actually matters: what’s attributable to the ads.

In this example, each conversion is generating about 8 streams, for a cost of $0.03 per stream.

Keep in mind that this example is catalog-wide. For any individual song, Spotify for Artists only lets you download daily streams. Saves, listeners, and playlist adds are for all of your songs.

So if you include a CSV for an individual song, the streams conclusions will be tighter and ultimately more honest.

I realize it sounds ridiculous to pay that much when Spotify pays us back a paltry $0.003 in royalties, but that doesn’t factor in future streams or algorithmic impact. Good luck measuring that!

When it comes to streams, there are loads of confounding variables:

• organic activity
• The Algorithm
• Marquee and Showcase campaigns
• playlist adds and removals
• proximity to release day
• weekdays vs weekends

It’s so messy, we’re lucky to see any signal at all in the noise, and some of these can fake a correlation as easily as hide one.

Intent paints a cleaner picture. A save or playlist add is a deliberate, same-day reaction to your ad.

In my experience, saves are the best way to judge the success of an ad campaign. If you don’t see an obvious relationship between conversions and saves, something is probably wrong, and this tool will help you spot it.

Alrighty then, that’s a lot of data talk and my brain hurts.

If you’ve got a few spare minutes, try it for yourself and share your experience in the comments!

Seeing as “set it and forget it” is Passive Promotion’s whole reason for existence, I took it for a test drive with my latest Color Theory single “The Naked Eye.”

I’ll walk you through the campaign creation process and share my results, but keep in mind that SubmitHub founder Jason Grishkoff is constantly updating the platform. Things will likely look different for you!

Alright, let’s get this show on the road.

Easy Mode vs. Advanced Mode

For starters, I went with Easy Mode for the ultimate kick-back experience. Advanced Mode has extra options like influencer marketing, album reviews, and graphic design.

Pick a package

There are three preset Spotify-focused bundles: Starter (120 credits), Boost (250 credits), and Scale (550 credits). There’s also a Custom mix builder if you want to dial in your own ratio of playlists, Meta ads, and blogs. Lurking at the bottom of the page is a Branding option (100 credits) specifically for blog submissions.

I went with Boost: Work toward your first 1,000 streams. 150 credits for 10 days of Meta ads to Spotify, plus 30-40 playlist submissions and blogs.

I also checked the “Blogs: submit to 10-15 for 30 credits” box, bringing the total to 280 credits. Why? Because I happened to have exactly 285 credits after converting all my curator earnings.

At this point, you may be wondering what 280 credits translates to in US dollars.

The sticker price for one credit is $1, but you can get up to 25% off of that by buying in bulk:

Additionally, you can get a 10% off coupon from me here, which should stack.

Jason told me that people pay around $0.80 per credit on average, so I’ll use that figure in my calculations going forward.

For me, 280 credits is $140, because I’m able to convert my curator earnings to credits at $0.50 per.

Pick a song

Next up, you select a previously uploaded song or add a new one. I always upload a high-quality mp3 and provide SoundCloud and YouTube as additional sources.

Of course, you’ll also want to add the Spotify link when you get it. In my case, I uploaded nine days before release and didn’t have it yet. DistroKid’s Spotify URI looker-upper has been broken for me since the start of 2026 and I can’t get them to fix it.

And so it begins…

That’s all there is to it! Select Easy Mode, pick a package, pick a song, done.

It took five minutes from start to finish, and half of that was taking screenshots.

I received an email confirmation explaining what happens next:

1. Learning phase – We’ll send your track to a selection of curators to see how it performs. This takes about 3 days.
2. Analyze & optimize – Based on the results, we’ll adjust our targeting for a second round of submissions.
3. Meta ads – We’ll set up and manage ads to drive engagement.

The Learning Phase

Three hours later, I received another email saying they’d submitted the track to 15 playlisters and 7 blogs to start the learning phase.

At this point I’d worked through a few more submissions and earned more credits, so I added 35 more to Playlists.

Round one results

The next day I received an email summary with way more than 15 playlisters and 7 blogs:

For the most part, these were great picks.

There are a few that I knew wouldn’t accept the track, either because they only accept instrumental synthwave or they’ve told me a dozen times that they don’t like my voice.

Balancing that out, a few names I didn’t know turned out to be receptive.

A couple of days later, I received an email with the subject line: Round 1 results: 35% approval rate

• Sent to 59 curators
• 18 approved (35%)
• 33 declined
• 8 didn’t respond in time

Solid results. We now have a good sense of which curators are responding well, and we’re using that to guide round two.

My average approval rate is around 45%, with my highest being 68%, so seeing 35% with a bunch of new names thrown in is respectable.

Round two results

There weren’t many credits left to spend on playlists and blogs, but the second round netted me four more approvals:

• Sent to 74 curators total
• 22 approved (35%)
• 40 declined
• 12 didn’t respond in time

You still have 8 credits remaining. We’ll continue sending to curators in the background to make the most of them. You won’t hear from us on every send, but you can always check your package page for updates.

Keep in mind that this all took place before release day! The main event, Meta ads, was still to come.

Before it kicked off, I topped up the Meta Ads budget by 130 credits, bringing it to 280 total (28 per day over 10 days).

Release day: Meta ads begin

It typically takes me 4-6 hours to set up a Meta campaign, most of which is devoted to creating videos in Canva. Offloading that chore to SubmitHub was glorious.

First, they generate a landing page. Mine is here.

By default it only includes Spotify, which is fine if maximizing Spotify streams is your only goal. I added the other links myself because around 20% of my conversions typically go to Apple Music.

Then, they select targeting:

Country targeting was a little different from my usual suspects, but nothing jumped out at me as problematic.

Finally, they make the actual creatives, 5 to start, which are generally stock footage with text overlays. With your song playing underneath, of course!

SubmitHub tells you what will happen next in an email:

• First week: Meta enters a “learning phase,” where it optimizes your creatives. Conversions are typically slower and more expensive during this period (1-2 per credit).
• Second week: Meta identifies what works best, or we step in to make adjustments. We’ll email you an update on your ad’s performance.
• Third week: Your ad should be running smoothly, with costs per conversion decreasing. If not, we’ll help you decide the best course of action.

When your remaining budget starts to run out, we’ll email you about automatically renewing the ad. To opt out, visit the “status” section of your ad and look under the “budget” section.

By default, they run the ads from their own Meta ad account, but I chose to have it run from mine instead. Meta sends an email invite with a link to approve or deny the access request.

Meta ads results

After a few days I was able to see the winning ad. It’s no longer available on the dashboard, but from what I saw it was simple, appropriate, and seemingly effective. At its peak, it was generating 1.6 conversions (clicks from the landing page to a streaming service) per credit. At an average cost of $0.80 per credit, that’s $0.50 per conversion.

That’s not horrible in and of itself, but only 8% of users who clicked on the ad were clicking through to a streaming service from the landing page. I’m used to seeing at least 40%.

That tells me that either people didn’t understand what they were clicking on, or Meta was optimizing for the wrong sort of user. The vast majority of conversions were from Mexico, which is normally reliable.

They continued testing new visuals, and I added four of my own.

I decided to test a quick campaign on my end via Hypeddit with those four visuals, and managed $0.18 per conversion the following morning.

Figuring I’d just handle the ads on my own, I pulled the plug on the Meta ads portion of the SubmitHub campaign and was immediately refunded 107 credits.

And then…

My cost per conversion quickly climbed back up to roughly what SubmitHub had been doing, depending on how you price a credit.

I ran my campaign for another week and decided to save my money for the next song.

Here are my final SubmitHub Meta ads results:

In retrospect, I should’ve just let the SubmitHub Meta campaign ride, if only for the sake of this post! It’s not clear that I did any better on my own.

SubmitHub Packages Conclusions

So, was it worth it?

My playlisting and blog submission results were par for the course, the key difference being that it didn’t cost me an hour of my time. SubmitHub hit up all the usual suspects and then some, in an intelligent fashion.

The Meta ads campaign saved me another 4-6 hours, or would have if I hadn’t decided to get my grubby little hands dirty and launch my own.

My Meta ads budget was 280 credits, of which 107 were refunded back to my SubmitHub earnings when I pulled the plug, leaving 173 credits actually spent on ads. They generated 192 conversions, which works out to $0.45 per conversion at my cost of $0.50 per credit, and $0.72 per conversion at the typical $0.80 per credit.

And that’s not factoring in that someone had to actually make the 10 creatives!

So yeah, it was worth it!

Here are my results in Spotify for Artists. My SubmitHub ads campaign stopped on April 9, and as you can see, streams dropped off sharply afterward:

Maybe next time I’ll try a bigger campaign, while also launching a Meta campaign on release day to see which performs better.

Have you tried SubmitHub Packages for yourself? Share your results, observations, and questions in the comments!