This Is One of Cardano’s Biggest Upgrades in Years

Added: 2026-05-15

[00:00:00]DeFi on Kadena has always had one silent killer, and that's bad data, and nobody had a real way to fix it until now.

[00:00:08]Because Pyth Pro is now live on the network, and it could fundamentally change the types of apps Kadena will be capable of supporting. This is truly one of the biggest upgrades we had on Kadena in a long time. So, let's get into it.

[00:00:22]Hi, I'm Linda, and if you'd like to see more content like this, make sure to give me a follow or support me by staking to my Malapool on Kadena. And remember that this is not financial advice. Always do your own research. One of the biggest missing pieces in Kadena's DeFi stack may have finally arrived. And to understand why this shift is so meaningful, we have to talk about what oracles are and how they traditionally work on Kadena. [music] So, blockchains are essentially closed systems. They're very good at recording and verifying transactions, but they have no way of knowing what's happening on the outside world on their own. They can't look up the price of Bitcoin, check a stock price, or know what the weather is like. They only [music] know what's already inside the chain. Now, oracles are the bridge that fixes [music] this. They fetch real-world data and bring it on chain in a way that smart contracts can actually read and use. So, [music] when a lending protocol needs to know if your collateral has dropped below a certain value, it's the oracle doing that job. And without a reliable oracle, DeFi basically can't function properly.

[00:01:23]>> [music] >> You'd have no way to trigger liquidations, settle derivatives, or price any asset fairly. [music] The quality of an oracle directly determines the quality of everything [music] built on top of it. A bad oracle means bad data, and bad data means that entire protocol is flying blind. [music] Now, let's take a look at how all of this works on Kadena. Because of the e u t x i models design, bringing data on chain can be a bit of a bottleneck. Most [music] existing solutions use a push model, where the oracle decides when And price is different enough to pay the transaction fee and updates the record on chain. Now, the downside here is that this creates lag and in [music] fast-moving markets even a few seconds of delay can completely change the outcome of a trade. Pyth Pro changes this entire dynamic of a push model by introducing a pull-based Oracle model.

[00:02:15]So, instead of waiting for a third party to push data to the chain, developers can now pull the exact price they need when [music] they need it right into the transaction. We're finally looking at sub 100 milliseconds latency, which puts Cardano's data speeds on par with the high-frequency [music] trading environments we see on chains like Solana or in traditional finance. So, for context, it takes you about 150 milliseconds to blink. So, Cardano is now getting price data faster than your eyes can process what's happening. And while speed is definitely a big plus, the real value here is where the data comes from. [music] Most traditional Oracle systems act as aggregators, meaning that they scrape prices from public APIs, average them out, and hope for the best, which, to be fair, is also how I make my financial decisions, but probably not the standard you want when billions of dollars are on the line. Pyth Pro, on the other hand, uses first-party data sourced directly from over 125 institutional publishers like major global exchanges, market makers, and trading firms. This creates a level of data integrity that makes institutional investors happy because they don't want a black-box logic or scraped data that might have errors.

[00:03:27]They want transparent, cryptographically verifiable proof of what a price is at a specific millisecond. Basically, the guys in suits want receipts, and Pyth gives them a receipt for every single millisecond. Another important technical detail here is the confidence interval.

[00:03:45]So, instead of giving you a single price, Pyth gives a range of where the market currently is. So, in a volatile market, different exchanges might show Bitcoin at slightly different prices, and Pyth tells the protocol how much variance there is between these big publishers. The protocol can then detect uncertainty and automatically protect its users by adjusting its risk parameters in real time. This becomes super useful during periods of extreme volatility when, instead of blindly trusting a single number, the protocols can see that the market is unstable and react accordingly. Pyth also unlocks a whole of composability across the ecosystem. By having access to reliable, low-latency price feeds, developers can now build applications on top of each other. Lending protocols can interact efficiently with derivatives platforms.

[00:04:36]Liquidation systems can react faster, and automated trading strategies can operate with much tighter execution windows.

[00:04:43]And the best part is this has already been tested. Pyth network launched on Solana in 2021 and has since become one of the most widely used oracle networks in crypto. By 2024, it had already secured over $500 billion in total value enabled across DeFi and has facilitated data for trillions of dollars in cumulative trading volume across the protocols that use it. So, it's not just Solana. Pyth is now live on over 90 blockchains including Ethereum, BNB, Sui, Aptos, and of course, Cardano. And now that Cardano has access to this level of infrastructure, it can safely support things like perpetual futures, complex lending markets, and even equity-linked products that require a level of precision that simply wasn't possible a year ago. I think we're moving away from the era of experimental apps and towards a suite of tools that banks and hedge funds can actually trust. Cardano's security and Pyth's institutional-grade data is a pretty strong combo for institutional money.

[00:05:46]Now, let's also look at Pyth in practice. [music] Indigo is the first protocol on Cardano to integrate it.

[00:05:52]Indigo's core mission has always been about I assets, which are synthetic versions of Bitcoin, Ethereum, or the US dollar. And while these are very lucrative instruments because they give users price exposure without needing to leave Cardano, they face a constant uphill battle when it comes to maintaining their peg. If the price of Bitcoin moves 5% in a minute and your oracle only updates every minutes, the system becomes a playground for arbitrage players who can drain the protocol's liquidity. [music] So, by integrating Pyth Pro, Indigo is fixing that problem straight from the roots. Because Pyth uses a pool-based model, Indigo's contracts now fetch the most recent price directly from the Pyth network at the second a transaction is initiated. As a result, price manipulation becomes much harder.

[00:06:39]Spreads remain tighter, and for the pros, this means they can now treat Indigo like a high-frequency trading platform. And on top of that, Indigo puts the confidence interval to work in a very practical way. If the market is too chaotic and the confidence interval is too wide, the protocol can automatically adjust its risk parameters or even pause liquidations to prevent bad debt from forming during a flash crash. Since liquidations are now based on institutional grade data, there's much lower chance of a false liquidation happening due to a faulty oracle feed.

[00:07:13]So, what Indigo is showing here is that when you fix the data layer, everything that's built on top gets so much stronger. And if this is only the first Cardano project using Pyth and we're already seeing this level of improvement, it makes me wonder how far this can actually go once protocols start adopting it. In my opinion, this is no longer a question of if Cardano can handle big-ticket DeFi, but a question of when the first billion-dollar asset class is tokenized on chain. So, let me know in the comments if you think this is the missing piece that finally brings the big money over to the network. And don't forget to like this video, and I'll see you in my next one. Bye.