Launched this week
Spycost
Fell for a discount again?
439 followers
Fell for a discount again?
439 followers
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.
Spycost
Spycost
@sasha_buratynskyi thank you! The goal was to keep Spycost very simple visually. I didn’t want users to feel buried under hundreds of charts, numbers, and distracting elements. The idea is to show the price story clearly, so it’s easy to understand when a deal is actually worth it.
The duck came from the name itself: Spy + Cost. In other words, a little spy watching prices for you.
And yes, the goal is worldwide coverage. So far, we’ve tested around 100 different stores across different regions, and Spycost worked successfully with 95 of them. Popular stores and global brands are supported well, so you can track prices from almost any online shop.
Spycost
@adana thank you! I felt the same problem myself. Discounts often make it harder to tell whether I’m making a good decision or just reacting to the label. Spycost started as a small attempt to make that a bit easier to see.
@glebarios I'll definitely give it a try!
Spycost
@tehreem_fatima5 thank you so much for coming over from LinkedIn and taking the time to check it out! You captured the problem really well. Fake discounts often don’t just change the price, they change how we feel about the purchase and push us into acting faster than we normally would.
That was one of the main reasons I started building Spycost: to make the price context more visible, so buying decisions can be based less on pressure and more on what actually makes sense. Really appreciate your support, Tehreem!
@glebarios What signals does Spycost use to decide whether a discount is real or just a pricing trick?
I really like the idea, especially because the problem is so relatable. Price history can change the whole buying decision when you are planning a purchase instead of reacting to urgency.
Spycost
@vahid_davoudi Thanks, Vahid. That is exactly the behavior I wanted Spycost to make visible.
The main signal is price history, not the discount label itself. Spycost looks at how the current price compares with previous prices, recent lows, the usual price range, and whether the price was increased shortly before a “discount” appeared. If the sale price is basically the normal price, or only looks good because the reference price was inflated, that is where it starts to look suspicious.
I do not want it to make the decision for the user. The goal is to show enough context so you can tell whether it is actually a good moment to buy or just urgency dressed up as a deal.
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.
the duck-as-a-spy branding is doing a lot of work here, it's memorable. question on the price history itself - when I add a product I just found today, do you backfill history from somewhere, or does the chart only start from the moment I add it, so the "is this actually a good deal" signal takes a while to become useful?
Spycost
@galdayan thanks! Right now we don’t backfill old data: the price history starts accumulating from the moment you add a product.
I know that can feel inconvenient at first, because the “is this a real deal?” signal needs a bit of time. But I also think that’s part of the value: it makes buying less impulsive and more transparent.
From my own observations, large retailers, especially in electronics, change prices almost daily. We just usually don’t notice it. For example, I’m tracking an Elegoo 3D printer, and over the last month they changed the price 7 times, with a difference of around €40 between the highs and lows. The changes often follow the same pattern, so I’m planning to add AI that can predict likely price drops in advance.
@glebarios that Elegoo example sells it well, 7 price changes in a month is way more volatility than I would've guessed. makes sense to lean into the transparency angle rather than trying to fake instant backfill data
Spycost
@galdayanexactly, that’s the tradeoff I’m trying to be honest about. I’d rather show a smaller amount of reliable data than invent confidence from a messy or unverified backfill.
The first snapshot is useful as a starting point, but the real value grows as Spycost keeps watching the same product over time and shows how often the price actually moves. That transparency is what I want the product to be built around.
@glebarios that's the right philosophy honestly, a fake backfilled history would just erode trust the first time someone catches an inconsistency. building the value around watching forward is the more honest and probably more durable approach anyway
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.
Congrats on the launch, Hlib — the spy-duck is perfect for this. One thing I’m genuinely curious about: when I add a product, do I see its price history from before I started tracking (so I can judge today’s “deal” right away), or does the history only build up from the moment I add it?
Spycost
@andrei_rebrov1 thanks a lot! Right now Spycost doesn’t backfill old price data: the history starts accumulating from the moment you add a product.
I know that means the “is this actually a good deal?” signal needs a bit of time to become useful. It can feel less convenient at first, but I also think it makes the purchase less impulsive and more transparent. Instead of trusting a discount label, you start seeing how the price really behaves over time.
From my own observations, big retailers, especially electronics stores, change prices almost every day. We just usually don’t notice it. For example, I’m tracking an Elegoo 3D printer, and over the last month its price changed 7 times, with around €40 between the highest and lowest prices. These changes often follow repeatable patterns, so I’m planning to add AI that can help predict likely price drops in advance.
@glebarios Honestly, the way you frame it, the no-backfill choice grew on me — watching how the price actually behaves beats trusting a discount label, even if it needs a little time to warm up. The Elegoo example makes it click: 7 changes and ~€40 spread in a month is exactly the movement nobody notices. And AI that flags likely drops sounds like the perfect next layer on top of that. Thanks for the thoughtful reply, Hlib — congrats again!
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.
This is a very relatable problem. The product seems strongest if it turns “I bought too early” into a simple post-purchase routine rather than another tracker to manage. Do you monitor price drops automatically from receipts or orders, or does the user manually add each purchase?
Spycost
@simon_dao Right now the flow is user-initiated: you add the product you are thinking about buying, and Spycost tracks it from there automatically with price history and drop notifications.
I agree with you that receipts/orders would make the post-purchase use case much smoother. That is exactly the direction I want to explore next: less manual tracking, more “show me when I overpaid or when a better moment appears.” For launch, I kept it focused on planned purchases first so the core price history and fake-discount detection is reliable.