DEV Community: RainbowSea

I Stopped Asking "What's Selling?" and Started Asking "What Just Opened?"

RainbowSea — Fri, 30 Oct 2026 14:11:39 +0000

For two years, my product research routine was the same: open a spy tool, sort by ad spend, copy whatever was already winning. It worked, right up until it didn't. The month I finally had budget to test a "proven" product properly, three other stores launched the exact same item with the exact same angle. My ad costs doubled in a week. I wasn't late to the trend — I was standing in line behind everyone else who had read the same list.

The fix was embarrassingly simple, and it came from a mistake. I was checking a supplier's store one evening and misread the domain registration date — I thought a store had been around for a year, but it was barely six weeks old and already ranking for its niche's main keywords. That store wasn't copying a winner. It was placing a bet before the winner existed. So I flipped my question: instead of asking what's selling, I started asking what just opened.

Here's the routine I run now, before I test any product:

Watch new stores, not just new products. A new store in a niche is a bet someone just placed with real money. If three new stores appear in the same niche in the same month, that niche is heating up, whatever any single product list says.
Check the store's birthday, then its trajectory. A store registered 30 days ago with growing traffic is a very different signal from a store registered 30 days ago that is flat. Age alone means nothing; age plus movement means a lot.
Work backwards from the store to the product. Once a new store is surging, look at what it leads with — its hero product, its category structure, its pricing. That tells you what its founder believes will sell, before the spy tools have enough ad history to notice.
Only then open the spy tools. I still use them — but now as a second opinion, not the first one. If the new store's bet also shows up in ad libraries and marketplace data, I test. If it doesn't, I wait.

The honest part: this routine has also saved me from tests I was excited about. Last spring I was ready to launch a kitchen gadget that every list called a winner. Then I checked the new-store layer: nothing new had opened in that niche for months, and the stores selling it were all old. The "winner" was a leftover, not a wave. I skipped it, and watched its ad costs climb for everyone who didn't.

That is why I built my own habit around a discovery board that ranks newly-registered sites by traffic surge — RainbowSea (https://rainbowsea.org) — and why I check its e-commerce category first when I'm scouting a niche. It doesn't replace product research tools; it sits one step earlier, at the moment a store is born. If you want to discover trending new stores before they show up on the lists everyone else reads, that earlier step is the whole edge.

What about you — when you last picked a product, did you check how old the stores selling it were? I'd genuinely like to know whether the store's age ever changed your mind.

The New-Store Checklist I Run Before I Test Any Product

RainbowSea — Wed, 28 Oct 2026 14:11:38 +0000

I lost money the boring way. I found a product on a best-seller list, built a page around it, and ran ads at it. The ads worked, briefly. Then the price war arrived, the margins vanished, and I realized I was the tenth store selling the same thing that week. The product was not bad. My timing was.

That failure changed what I research first. I stopped starting from products and started starting from stores — specifically, new stores that are suddenly getting attention. A product can be faked with a good video. A store that is surging in its first weeks is harder to fake, and it tells you something a product list cannot: someone is already doing the hard work of finding buyers.

Here is the checklist I run now, before I spend anything on a test.

First, I ask how new the store is. If it launched months ago and is only now trending, I am probably late. If it is weeks old and already climbing, I pay attention. Newness is not the signal by itself — velocity is. A flat new store is just a new store. A new store with a sharp traffic climb is a store that found a nerve.

Second, I look at what the store sells, not what it claims. One hero product with a clear promise beats ten scattered products every time. If I cannot explain in one sentence why a stranger would buy from this store today, the store has not found its angle yet, and neither have I.

Third, I check whether the surge has a source I can see. Are people arriving from search, from short video, from a community? A store carried by one viral clip is a different bet from a store carried by steady search demand. I would rather test a product with quiet, repeatable demand than one riding a single spike.

Fourth, I ask the unglamorous question: can this actually ship well? Price, shipping time, and supplier reliability still decide whether a good idea becomes a good business. A surging store with a two-week shipping promise and no reviews is a warning, not an opportunity.

Only after those four checks do I look at the product itself — and by then, I am choosing from a much shorter, much better list.

That is also why I use RainbowSea (https://rainbowsea.org) as the first step in this routine. It surfaces newly-registered websites that are surging in traffic, so I can discover trending new stores while they are still early instead of months later when every list has already covered them. I filter by the e-commerce category, scan the day's climbers, and run the four checks above on the handful that stand out.

The habit that saved me the most money was not a tool. It was a rule: no product gets ad spend until a new store behind it passes the checklist. Lists tell you what already won. New stores tell you what might win next. Which one would you rather build on?

Before You Spend on Ads, Check the New Stores

RainbowSea — Sun, 25 Oct 2026 14:36:46 +0000

I almost spent $300 promoting a product I had not validated. A friend in a seller group had posted a screenshot of a store doing "crazy numbers" with a minimalist desk lamp, and for a weekend I was convinced. I mocked up an ad, picked the same lamp from a supplier, and was one click away from launching the campaign.

Then I paused and did something slower: I looked at the new stores themselves, not the screenshots of them.

What I found changed how I validate a niche. The lamp store was not the beginning of a trend — it was the middle. A dozen similar stores had registered in the previous few weeks, most of them already fading. The surge had already happened. My ad would have arrived after the party.

That weekend taught me a simple rule: before you spend on ads, check the new stores. Here is the three-signal check I use now.

1. Are new stores appearing, or just new ads?

Open your category and look at what is newly registered, not just what is newly advertised. One store can run ten ads; ten stores in the same niche is a different story. When several independent shops appear around the same idea within a few weeks, something in demand is moving — even if none of them is huge yet.

I keep a short list of the niches I care about and scan the newest entrants every week. It takes about twenty minutes, and it has saved me from chasing at least three "winning products" that were already past their peak.

2. Is the category surging, or is one store surging?

A single viral store can mislead you. A rising category is harder to fake. When I see new stores cluster — different names, different angles, same underlying need — I treat that as a real signal. When I see only one store with a big ad budget, I treat it as a marketing story, not a market.

A good test: can you describe the customer need in one sentence without naming a product? "People working from small apartments want their desk to feel calm" is a need. "A lamp with a wooden base" is just a product. Niches built on a need survive when the first product fades.

3. What do the earliest stores look like?

The first stores in a surge are usually rough: plain pages, few reviews, simple photos. That roughness is useful information. It tells you the niche is early and the bar for entry is still low. If every new store already looks polished and professional, the niche is probably crowded — the easy phase is over.

I learned this the embarrassing way. The first time I spotted a genuinely early cluster — small, scrappy stores around a home-workout accessory — I hesitated, waiting for more proof. By the time I felt sure, the polished copycats had arrived and ad costs had doubled. Early does not mean certain, but late is expensive.

How I do the check in practice

My weekly routine is deliberately boring:

Pick two or three niches, no more.
Look at the newest stores in each one and note the pattern: how many, how similar, how rough.
Write one line per niche: "rising, early, worth testing" or "crowded, skip."
Only then decide whether an ad test is worth the money.

For the discovery part I use RainbowSea — it surfaces newly-registered websites that are suddenly surging, so I can discover trending new stores before they show up in everyone's ad feed. The point is not the tool itself; it is the order of operations. Stores first, ads second.

That order flipped my results. I run fewer ad tests now, but the ones I run start from a real demand signal instead of a screenshot. The $300 I almost spent on that lamp went into a test for a niche where the new stores were still scrappy — and that test actually taught me something, even though the product itself did not win.

Before your next ad test, spend twenty minutes with the new stores. If the surge is real and early, you will see it. If it is not, you just saved yourself the budget.

What niche are you watching right now — and what do its newest stores look like?

I Watch New Domains Like a Stock Ticker — My System for Finding Fresh Websites Early

RainbowSea — Fri, 23 Oct 2026 14:27:57 +0000

I still remember the night I found a tiny AI note-taking app three days after it launched. It was doing maybe 400 visits a day. Two months later it showed up on Product Hunt, did 40,000 visits in a day, and the comment section was full of people saying "how did I not know about this sooner."

I did know. I'd been using it for six weeks by then. I had emailed the founder a bug report, he'd replied within an hour, and I'd built a small workflow on top of his API before anyone else cared. That two-month head start was worth more than any "productivity hack" I'd tried that year — not because the app was magic, but because being early changes the relationship. Early users get answers. Early users get features. Early users get remembered.

The frustrating part? For every one I caught, I missed ten. And I kept missing them the same way: by waiting for launch platforms to tell me what was new.

Launch platforms tell you what's popular, not what's new

Product Hunt, Hacker News, the big newsletters — they're launch-day theater. By the time something trends there, the discovery arbitrage is gone. Thousands of people are already piling in, the founder is drowning in feedback, and your thoughtful comment is comment #482.

Don't get me wrong, I still browse them. But I stopped treating them as a discovery feed and started treating them as a confirmation feed: if a site I found three weeks ago shows up on the front page today, my system is working.

The real question is where to look before that happens. After a couple of years of experimenting, I landed on something embarrassingly simple: watch newly-registered domains, ranked by traffic velocity. Like a stock ticker, but for websites.

Two signals, that's it:

Newness — the domain was registered recently (weeks or a few months, not years). This filters out the entire established web.
Velocity — its traffic is surging right now. Not "big," surging. A site going from 50 to 5,000 daily visits is more interesting than a site sitting flat at 100,000.

Either signal alone is noise. New domains are mostly spam and parked pages. High-traffic sites are mostly old news. But a new domain with a steepening curve? That's a site that's alive — someone launched something, and the internet is starting to notice.

My 15-minute Friday routine

I do this once a week, Friday afternoon, coffee in hand. The whole thing takes about fifteen minutes:

Step 1: Skim the surge board. I pull up a board that ranks newly-registered sites by traffic velocity and scan the top of the list. I'm not reading deeply — I'm pattern-matching. Which categories keep showing up? AI wrappers? Niche communities? Weird little tools?

Step 2: Filter by what I care about. Most weeks I'm hunting in two categories: dev tools and AI apps. If something in e-commerce or gaming spikes, I note it too — those categories have been quietly producing the most interesting launches lately.

Step 3: Open five, bookmark one. I open the five most interesting ones in tabs. Four get closed within a minute — broken, boring, or obviously a parked domain with bot traffic. One survives. That one gets bookmarked and a real try-out over the weekend.

Step 4: Reach out to one founder. This is the part most people skip, and it's the highest-leverage move. A short email — "hey, found your site early, here's a bug / here's what confused me / here's what I'd pay for" — to a founder with 500 daily users gets a reply roughly 80% of the time in my experience. Try that with a founder doing 50,000.

What to do with an early find

Finding sites early is only useful if you act on it. Three plays have consistently paid off for me:

Play 1: Give feedback, build on top. Early founders are starved for real users. A detailed bug report or a small integration built on their API buys you a relationship that money can't. Two of my freelance clients came from emails I sent to founders when their sites had under a thousand users.

Play 2: Write about it. A "first look" post about a site nobody's covered yet ranks almost by default — there's no competition for the keywords. Some of my best-performing posts were about tools with barely any search volume yet.

Play 3: Learn the pattern. After a few months of this, you start seeing repeats. The same landing-page structures, the same onboarding flows, the same distribution tricks. Watching new sites is market research disguised as procrastination.

The tool I built for this

I'll be honest about the ending here: I got tired of cobbling this together from domain-registration feeds and traffic estimators, so I built myself a board that does it — RainbowSea. It surfaces newly-registered websites currently surging in traffic, ranked by velocity, filterable by category. Basically the stock ticker I described above, running every day.

It's free, no signup. If you try the Friday routine, it's a decent starting point — and if you find something great three weeks before everyone else, I'd genuinely love to hear about it.

Here's my question for you: what's the best website you discovered way before it blew up — and where did you find it? Drop it in the comments. I'm always looking to add new sources to the routine.

I Stopped Trusting "Winning Product" Lists — How I Spot Trends by Watching New Stores Instead

RainbowSea — Wed, 21 Oct 2026 14:41:18 +0000

Two years ago I lost $1,400 chasing a "winning product."

It was a posture-corrector gadget — the kind with the dramatic before/after ads. It sat at the top of every "winning products this month" listicle I could find. The comments were full of sellers bragging about their ROAS. So I did what the gurus said to do: I ordered 300 units, spun up a one-product store, and launched ads.

The first week felt incredible. Clicks were cheap, add-to-carts were rolling in, and I genuinely thought I'd cracked it. Then, around day ten, everything fell off a cliff. CPMs doubled. Then tripled. My "unique angle" turned out to be the exact same angle as forty other stores, because we'd all copied the same listicle, which had copied the same original store.

I ended up selling the remaining inventory at cost to a liquidator and shutting the store down. The worst part wasn't the money — it was the realization that I'd done everything "right" according to the playbook, and the playbook itself was the problem.

Why winning-product lists are always late

Think about how a "winning products" list actually gets made. Someone notices a product selling well. They write it up. An editor publishes it. Aggregator sites copy it. By the time a product earns the label "winning," here's what's already happened:

The original seller has been running it profitably for weeks or months.
Dozens of copycats have entered and started bidding up ad costs.
Suppliers have noticed the demand spike and raised wholesale prices.
Customers have seen the same ad creative seventeen times.

A winning-product list is a lagging indicator. It's a photograph of a trend that already happened. Trading on it is like buying a stock after the earnings call — the information is public, priced in, and the edge is gone.

The uncomfortable question I sat with after my $1,400 lesson was: what would a leading indicator look like?

The signal I almost ignored

A few months after the posture-gadget disaster, I noticed something odd. Within about three weeks, I kept stumbling across brand-new stores — unfamiliar names, fresh domains, janky-but-earnest branding — all selling variations of the same niche product: a magnetic car-mount-style phone holder, but redesigned for desks and gym equipment.

No listicle had mentioned it. No guru was talking about it. Each store on its own looked like nothing — a handful of visitors, a basic Shopify theme, maybe a dozen products. But there were five of them, all launched within a month, all converging on the same idea.

I didn't act on it. I told myself it was a coincidence, that I was pattern-matching noise. Six months later that product category was everywhere — including, ironically, on the winning-product lists. I hadn't lost money this time, but I'd left the lesson on the table: the earliest real signal of a trend isn't a product going viral. It's multiple new stores betting on the same thing at the same time.

That changed how I do product research entirely. I stopped asking "what's selling now?" and started asking "what are new stores being built to sell?"

The method: build a new-store radar

The core idea is simple: newly registered domains + sudden traffic growth = someone spending real money on a bet. Domain registration costs money. Building a store costs time. Running ads costs serious money. When a brand-new store starts gaining traffic fast, that's conviction you can observe from the outside.

Here's the DIY version, step by step. It's unglamorous, but it works.

Step 1: Harvest newly registered domains

You need a feed of fresh domains. Practical sources:

Certificate Transparency logs (via crt.sh) — every new HTTPS cert is public. Query for recently issued certs and extract the domains.
RDAP lookups — for any domain, RDAP tells you the exact creation date. This is your age filter: keep domains registered within the last 90–180 days.
New-domain feeds and drop lists — several free feeds publish daily registrations.

Filter aggressively by TLD. Sellers overwhelmingly use .com, .store, .shop, and .co. A .xyz domain can be a store, but your time is better spent where the signal density is highest.

Step 2: Detect stores, not blogs

Most new domains are junk — parked pages, spam, affiliate blogs. You need store fingerprints:

URL paths like /cart, /checkout, /products/, /collections/
Platform signatures: Shopify (cdn.shopify.com, /cart.js), WooCommerce (/wp-content/plugins/woocommerce), BigCommerce, Wix Stores
Commercial intent markers: "Add to Cart" buttons, price schema, checkout flows

A simple crawler checking the homepage for these markers will eliminate 90%+ of non-stores. This filtering step is the boring part, and it's also the part most people skip — which is exactly why the signal stays valuable.

Step 3: Measure velocity, not size

This is the key mental shift. You don't care how big a store is. You care how fast it's growing.

Track ranking movement over 2–4 week windows (lists like Tranco publish rank data you can diff over time).
Watch for domains that go from "doesn't exist in the data" to a meaningful rank within weeks. That's the explosion pattern.
Ignore absolute traffic numbers — a store doing 50k visits/month that's flat is less interesting than one doing 5k that's 10x'd in a month.

New + accelerating beats big + flat, every time.

Step 4: Cluster by niche

One fast-growing new store is a curiosity. Five new stores in the same niche within a month is a trend forming.

Group the stores you're tracking by what they sell — category, product type, even visual similarity of hero products. When a cluster forms, dig in: are they all dropshipping the same supplier's product? Is it a genuinely new product, or a new angle on an old one? The cluster tells you where to look; the individual stores tell you what's working (compare their angles, pricing, and creatives).

Step 5: Confirm conviction with ad libraries

Before you commit to a niche, verify that these stores are spending money — not just existing:

Search the store domains in the Meta Ad Library and TikTok Creative Center.
Stores running active, recent ads are putting cash behind their bet. That's the strongest confirmation signal available for free.
Bonus: the ad creatives themselves are a free education in what's converting — hooks, angles, offers.

Red flags to filter out

Parked or redirect domains with no real storefront.
Copy-paste clone rings — dozens of identical stores from the same operator (same theme, same copy, same products). That's one bet, not a trend.
Brand launches with PR — a celebrity brand will spike on launch day; that's marketing, not organic demand.
Seasonal spikes — Halloween stores in October are a calendar event, not a discovery.

The honest caveats

I'll be straight with you: this method has costs.

First, it's tedious. For every interesting new store you'll sift through hundreds of dead domains. The filtering is 80% of the work and 0% of the fun.

Second, it needs discipline. Checking once gives you noise; checking weekly gives you velocity data, and velocity is the whole game. A radar you don't maintain is just a spreadsheet.

Third, early signals are probabilistic, not certain. A cluster of new stores means several people independently bet money on the same idea — that's meaningful, but it's not a guarantee. You'll still be wrong sometimes. You'll just be wrong earlier and cheaper than the listicle crowd.

If you don't want to build this yourself

Here's the thing I wish someone had told me two years ago: steps 1 through 3 above — harvesting domains, filtering stores, computing velocity — are pure infrastructure. No product insight happens there; it's plumbing. The insight starts at step 4, clustering and interpretation.

If you'd rather skip the plumbing, that's exactly what RainbowSea does. It tracks newly registered domains, filters for real stores, and ranks them by traffic growth velocity — so you can discover trending new stores while they're still accelerating, instead of reading about them in a listicle two months later. The e-commerce category alone has 1,000+ new stores ranked this way, which is a solid starting feed for the clustering step.

Start here if you're curious: https://rainbowsea.org

Whether you build the radar yourself or use a tool, the principle is the same: watch where new stores are being built, not what old lists are celebrating. The list tells you where the parade has been. The new stores tell you where it's going.

Your turn

What's the earliest signal you've ever caught — the time you knew before everyone else? Or the flip side: the trend you saw forming, talked yourself out of, and watched explode without you?

Drop it in the comments. I'm genuinely curious which niches you're watching right now — and I have a feeling the best answers here will be better than any listicle.

I Stopped Waiting for Product Hunt to Tell Me What's New

RainbowSea — Thu, 15 Oct 2026 18:08:33 +0000

I Stopped Waiting for Product Hunt to Tell Me What's New

The launch-day FOMO that finally got to me

Two months ago, a tiny Markdown-to-slides tool blew up on Hacker News. Beautiful product, dead-simple idea, 2,000 stars overnight. My first thought wasn't "cool tool" — it was "I've been sketching almost exactly this in my notes app since March."

That wasn't the first time. A habit tracker with a weird-but-brilliant streak mechanic. A one-page invoice generator for freelancers. Each time, I discovered them at peak hype — when the comments were already full of "I built a clone of this last year" people. I was one of them. We all were.

The pattern was embarrassing once I saw it: I only ever found new indie tools after they were no longer new. My discovery pipeline was Product Hunt's front page, HN's front page, and Twitter hype cycles — all of which show you things once thousands of people already know. For an indie hacker looking for gaps, inspiration, or partnership ideas, that's arriving at the party during cleanup.

The signal I was ignoring: domain age + traffic

Here's the thing nobody tells you: by the time a tool trends on Product Hunt, its domain is usually months or years old and its traffic curve already looks like a hockey stick. The interesting moment — the moment a brand-new site starts getting real visitors out of nowhere — happens weeks earlier, and it's completely public data. Domain registration dates are public. Traffic estimates are public-ish. Nobody was putting the two together in a way I could browse over coffee.

So I built myself a weekly ritual. It takes about 20 minutes, and it's embarrassingly low-tech.

Step 1: Hunt for surges, not launches

A product "launch" is a marketing event. A traffic surge on a newly-registered domain is a behavior event — real humans, showing up, unprompted. That's the signal I care about now.

Free ways to catch it:

Google Trends — compare rising queries in your niche over the past 90 days. Laggy, but it shows you what regular people are suddenly searching for.
GitHub trending + new repos — for dev tools specifically. A repo gaining stars fast with a fresh domain in its README is a lead.
Niche subreddits — search "I built" or "just launched" in r/SideProject, r/webdev. People announce before they scale, and the comment sections are brutally honest QA.

Step 2: Check how new the domain actually is

This is the step most people skip. A "new tool" on a 4-year-old domain is a rebrand or a pivot — still interesting, but different. A domain registered 3 months ago that's suddenly pulling traffic? That's either a great product, great marketing, or both, and either way there's something to learn.

Any free WHOIS or RDAP lookup gives you the registration date in ten seconds. I've made it a reflex: interesting site → check domain age → decide if it's worth a deeper look.

Step 3: Read the surge like a story

A spike means people showed up. The why is where the learning is:

Where did they come from? — Check the site's backlinks or just google site:reddit.com "[domain]". One viral thread explains most spikes.
What did the landing page promise? — The Internet Archive's Wayback Machine shows you what the page looked like before the spike. Compare versions; the change that caused the surge is usually visible.
What's the monetization? — Pricing page, or lack of one. Free-with-waitlist surges behave very differently from paid-from-day-one surges.

This is the part that leveled up my own building. I stopped asking "what should I build?" and started asking "what just worked for someone, and why?" — which is a much better question.

The shortcut I added last month

Stitching WHOIS lookups, traffic estimates, and trend data together by hand got old fast. Lately I've been starting my coffee ritual on a board called RainbowSea — it ranks newly-registered sites by traffic surge, so instead of tab-hopping I can discover niche websites while they're still climbing and work backwards to the why.

It's become my replacement for the Product Hunt front page: same "what's new" dopamine, but weeks earlier and without the launch-day theater. I still do the Wayback-and-backlink detective work myself — no board replaces that — but the discovery part went from a chore to a habit.

The uncomfortable bit

Most "discover new tools" content is just recycled launch announcements. The builders actually finding gaps aren't waiting for launch day — they're watching for the surge, reading it like a story, and moving while the idea is still fresh. The data is all public. You just have to look earlier than everyone else.

What's the best tool you found before it blew up? I'm collecting stories — drop yours in the comments.

I write about indie building and web discovery. Find me (and the surge board) at https://rainbowsea.org

How to Find Winning Products Before They Trend

RainbowSea — Wed, 14 Oct 2026 18:54:57 +0000

How to Find Winning Products Before They Trend

The $400 lesson that changed how I do product research

Last year I spent three weeks building a one-product Shopify store around LED sunset lamps. You know the ones — they were all over TikTok. I ordered samples, shot decent photos, wrote what I thought was clever copy, and put $400 into TikTok ads.

Eleven sales. Total.

Meanwhile, a friend of mine was quietly doing $8k months selling a posture-corrector variant I'd never heard of. When I asked how he found it, he shrugged: "I just noticed this brand-new store getting traffic out of nowhere, and nobody was copying it yet."

That stung. I'd been doing product research backwards — scrolling the same "winning product" lists everyone else was scrolling, competing for the same five products. He was watching stores, not products. By the time a product lands on a top-10 list, the margin is already gone.

Flip it: watch new stores, not product lists

Here's the method I use now. It takes about 30 minutes, once or twice a week.

Step 1: Hunt for traffic surges, not product ideas

Newly-registered domains that suddenly start getting real traffic are the earliest signal that something is working — an ad creative, a TikTok organic post, a Reddit thread. The store behind the surge is your lead.

Free ways to spot this:

Google Trends — set it to the past 90 days and compare rising queries in your niche. Laggy, but free.
TikTok Creative Center — filter top ads by recency and look at which unknown stores keep re-running creatives. Re-running means it's converting.
Niche subreddits — search "just launched" or "my store" in r/ecommerce or r/dropshipping. People announce before they scale.

Step 2: Check the store's age

If the domain is under 6 months old and already pulling traffic, that's a strong signal. You can check registration dates with any free WHOIS/RDAP lookup. An old store with a sudden surge is interesting too — but a new store plus a surge means the product is likely doing the work, not the brand.

Step 3: Validate before you copy

A surge is a lead, not a verdict. Run this checklist:

Ad library check — is the store running the same creative for 2+ weeks? That's the real tell.
Review velocity — a handful of fresh reviews beats a hundred old ones.
Supplier check — can you source it with decent margin after shipping?
Differentiation — can you sell it 10% better (bundle, angle, creative), not just 10% cheaper?

The shortcut I wish I'd had earlier

Manually stitching this together — WHOIS lookups, traffic estimates, trend charts — is tedious. Lately I've been checking a board called RainbowSea that ranks newly-registered sites by traffic surge, with a filter for e-commerce. It's basically a discover trending new stores radar: instead of digging through data sources one by one, you see which new stores are taking off this week and work backwards to the product.

I still do the validation checklist myself — no tool replaces that. But the discovery part went from an hour of tab-hopping to a five-minute scan.

The uncomfortable truth

Most "winning product" content is survivorship bias with affiliate links. The sellers actually making money aren't sharing their products — they're watching for surges and moving fast. The good news: the signals are all public. You just have to look at stores instead of lists.

What's the weirdest product you've seen blow up this year? Genuinely curious — drop it in the comments.

I write about discovering new websites and products early. My discovery board: https://rainbowsea.org

How to Discover New Websites That Are Quietly Exploding

RainbowSea — Wed, 14 Oct 2026 01:15:14 +0000

How to Discover New Websites That Are Quietly Exploding

Every "best new websites of 2026" listicle has the same problem: by the time a site makes the list, it's already mainstream. The interesting moment — when a weird little tool suddenly starts doubling its traffic week over week — happens months earlier, and almost nobody is watching.

I got obsessed with catching that moment. Here's the actual toolkit I use, from manual methods to the automated board I ended up building.

1. Certificate Transparency logs (crt.sh)

Every time someone registers a domain and gets a TLS certificate, it's logged publicly at crt.sh. You can query recently issued certificates and get a raw feed of brand-new domains, sometimes only hours old.

It's noisy — most new domains go nowhere — but it's the earliest possible signal. I filter for domains that look like real products and skip the obvious spam patterns.

2. Rank velocity, not rank

A site sitting at rank #50,000 tells you nothing. A site that jumped from #900,000 to #50,000 in two weeks tells you everything.

Tranco publishes daily top-site lists. Diff today's list against the one from 30 days ago, compute the jump on a log scale, and you get a "surging" ranking. This one trick filters out 99% of the noise from method #1: new domains nobody visits never move in the rankings.

3. Google Trends breakout queries

Trends' "breakout" label means a query grew more than 5000%. When the breakout query is a brand name you've never heard of, that's a new website exploding in real time. I keep a running list of these and check which domain sits behind each one.

4. Communities, but as confirmation

Hacker News, r/SideProject, Product Hunt — great sources, but everything is already "discovered" by the time it trends there. I use them as confirmation rather than discovery: if a domain surging in my data also pops up on HN, that's a strong signal it's real.

Putting it together

Doing all four manually every day got old fast, so I automated it into a board: RainbowSea. It tracks newly registered domains, ranks them by traffic velocity, and lets you discover niche websites while they're still surging — not months later when the listicles arrive.

It's free, no signup. If you're into spotting tools, games, and weird little sites before everyone else, come take a look and tell me what's missing. Genuinely curious: what would you search for first on a board like this?