Spycost - Fell for a discount again?
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Remember the last time you bought something at a discount, only to see it later for the same price without the discount? Or when the product costs more today than it did yesterday. Did you feel cheated? Are you ready to look behind the screen of manipulation and lies? Spycost helps you see when you’re paying more while thinking you’re paying less. It gives you the full price picture, so you can decide whether it’s actually worth it.
Replies
Would love a browser extension version so it can auto-track prices while I shop instead of me having to look things up manually every time, would make it way easier to catch those sneaky price jumps in the moment.
Spycost
@sevilckr0 thank you for the comment! A browser extension is actually the next highest-priority goal for Spycost.
I think it can make the whole experience much more natural: instead of switching between tools or remembering to check prices later, you’ll be able to keep shopping as usual and see the important price context directly on the product page. That’s the direction I’m most excited to build next.
Bhava
Spycost
@riya_jawandhiya yes, exactly. I think you described the problem really well. A lot of the time we’re not just seeing a discount, we’re being pushed into a decision through urgency, crossed-out prices, and “limited time” labels.
It doesn’t always mean the deal is bad, but without price history it’s very hard to understand what’s real and what’s just manipulation. That’s the part I wanted Spycost to make more transparent.
Jinna.ai
Congrats on the launch! Is it capable of tracking a product CLASS, not just exact single product? A good example would be buying a coffee machine. You know a set of features/parameters you need, but you are open to look at multiple brands.
Spycost
@nikitaeverywhere thank you! At the moment Spycost doesn’t track a whole product class yet, only specific products. But this is exactly the direction I want to move in: helping you define what you need, like a coffee machine with certain parameters, and then watching multiple relevant options across brands.
Right now the main priority is to cover more platforms and make product tracking reliable across more stores. After that, expanding into broader product categories and smarter matching is a very natural next step.
this is a real problem, fake discounts are everywhere. genuinely curious about the mechanics though - to know a price history you'd need to have been tracking a product before the "discount" showed up. does it only work for stores/products already in your database, or is there a way to check a price you're seeing for the first time?
Spycost
@omri_ben_shoham1 thanks for the thoughtful question. Right now, if a product wasn’t already being tracked, the most reliable price history starts from the moment it’s added.
But we’re actively working on making the first-time check much more useful too. Our priority is that every user can get as much price context as possible right at the moment they’re deciding, not only after waiting for weeks of tracking data.
So the goal is to improve how Spycost collects, compares, and shows price signals so you can better understand whether today’s price looks normal, inflated, or actually good, even when you’ve just discovered the product.
Price history tracking should be built into every e-commerce site but it never will be because it's bad for business.
Spycost
@ringo_td5 yes, I completely agree. Price history should be a basic part of e-commerce, but realistically most stores will probably never add it themselves because it goes against the way many discounts are designed to work.
What surprises me is how few people realize how much our buying decisions are shaped by price changes, crossed-out prices, urgency labels, and “limited time” deals. Often it’s not about helping us make a better decision, it’s about pushing us to buy faster.
That’s exactly why I’m building Spycost: to give people the missing context and make these pricing tricks easier to see.
Strong pain point, beautiful solution, and a great launch :)
Good job, amigo.
Next Feature Idea: call bullshit on "only X seats left, etc" :P
Spycost
@knolladrian Thanks, Adrian! I love that feature idea. Fake scarcity like “only X seats left” sits in the same dark-pattern family as fake discounts, and it would be powerful to show people when that pressure is just noise. Where do you see this tactic most often: travel, SaaS, marketplaces, or somewhere else?
@glebarios From my experience, the Travel industry is the unchallenged champion of the fake scarcity tactic :)
Spycost
@knolladrian That’s a good point, and honestly a very good idea. I’ll look into this more closely and consider adding travel-specific support to Spycost, since fake scarcity seems especially common there.
The duck-spy branding got me before I even finished the tagline, nice touch. The thing I kept hitting when I built price scrapers is that a lot of retailers personalize or geo-vary the price now, and some quietly A/B test it mid-session, so 'the price yesterday' isn't one number, it's a spread. Do you snapshot from a fixed region and session so the history stays apples-to-apples, or could two people see different Spycost baselines for the same product? That's the part that decides whether the 'is this a real deal' call actually holds up over time.
Spycost
@dipankar_sarkar thank you! And glad the duck-spy made you smile :)
You’re absolutely right: “the price yesterday” is not always one clean global number anymore. It can vary by region, currency, availability, delivery rules, session, cookies, account state, or even A/B tests.
The way I’m approaching this with Spycost is to treat price history as context-specific, not as one universal baseline for everyone. So the important part is keeping the comparison apples-to-apples for the same product and tracking context. If two people are in different regions or see different store conditions, their baselines may be different, and I’d rather make that explicit than hide it behind a false single number.
Longer term, I want Spycost to show more of that context directly: region/currency, confidence, and eventually price ranges when there is enough signal. The “is this a real deal?” call only holds up if the history is honest about where the data came from.
Congrats on the launch, Hlib.
Ran a quick manual QA pass over the site out of habit. One thing worth
checking right now: your Pricing section stays on "Loading available
plans..." and the plan data isn't in the served HTML, so the Pricing
link in your header currently leads to a section with no prices.
Found a few other bits too. Happy to send the write-up over if useful -
free, no strings.
Spycost
@day_qa thanks a lot for taking the time to check this and point it out. I really appreciate it. I’d be happy to take a look at what you found. Could you send the write-up to me on LinkedIn? https://www.linkedin.com/in/hlib...
Love the idea, I've been burned by fake discounts before, but I also wonder how often the best price is worth waiting weeks or months for.
Spycost
@reda_roqai_chaoui thank you, Reda! That's exactly the tradeoff I think about too. The goal isn't to make people wait forever for the absolute lowest price, because sometimes buying now is worth it. Spycost is more about giving you the context: was this price recently normal, is it trending down, and does the "discount" actually look real. Then you can decide whether waiting makes sense for that specific product.
Context specific is the right call. What saved us when we stored scraped prices was writing the request context into the same row as the number, so region, currency and whether we were logged in all travelled with it. Before that we lost an afternoon chasing a price drop that turned out to be a different geo answering the same URL.
Spycost
@dipankar_sarkar exactly, that’s a very useful lesson. The price itself is only half of the data; the request context has to travel with it, otherwise the history can become misleading very quickly.
That’s the direction I’m taking with Spycost too: each snapshot should be tied to the context that produced it, like region, currency, store conditions, availability, and whether it came from an anonymous or user-side session. Then the baseline is not just “this URL was X yesterday”, but “this product under this context was X yesterday”.
Your geo example is exactly the kind of false signal I want to avoid. If the data is mixed or uncertain, Spycost should make that visible instead of pretending the answer is cleaner than it really is.