W-BEN

Previously, when chatting with friends about earning interest on BTC, everyone's first reaction was not about how much profit it would bring, but rather, 'Is this centralized institution reliable?' From certain exchanges to various CeFi financial management platforms, there are already enough examples of failures. The balance on the account interface may look fine, but the assets behind it have long been used for high-risk operations, ultimately leading to a hasty conclusion with just an announcement. I later started to seriously explore the Lorenzo Protocol, simply because it moves many things that were originally hidden in the background onto the blockchain, at least letting me know what my coins are doing.
The basic path of Lorenzo is actually not hard to understand: users deposit BTC-related assets into the protocol and receive a liquidity certificate representing the staked position. This certificate can continue to participate in lending and market-making in DeFi; the underlying protocol uniformly allocates these BTC to pre-designed yield strategy combinations. The interface clearly indicates the general direction of the current main position, and even if it doesn't break down every transaction for you, at least you can distinguish between 'this is a relatively stable interest rate strategy' or 'this is an Alpha strategy with some directional risk,' and you won't be misled by the term 'comprehensive yield pool.'

If the main character of the last bull market was 'staking ETH in the staking pool for returns', then the increasingly clear trend of this round is to create yield around Bitcoin itself. In the past, holding BTC basically had two choices: either let it sit in a cold wallet waiting for the market, or give it to centralized institutions to earn a bit of interest, all while constantly worrying about counterparty risk. After seriously studying the Lorenzo Protocol this time, I have a very intuitive feeling: it is equivalent to giving BTC a 'new generation on-chain banking vault', rebalancing returns, liquidity, and security.

Having been on-chain for a long time, everyone has similar basic expectations for lending protocols: security must be solid, liquidity must be ample, and interest rates shouldn't be too erratic. I had always followed the old path of Aave until I encountered products like Falcon Finance that directly package 'leverage yield strategies,' which made me seriously compare which tools are suitable for different people. In simple terms, Aave is more like a toolbox where you rely on yourself to build strategies; Falcon, on the other hand, gives you a semi-finished 'portfolio position,' and you just need to decide how much risk you can take.

The first time I noticed Falcon Finance was because a friend sent a link saying it could turn idle stablecoins into leveraged mining combinations with one click, which is much easier than manually juggling positions. At that time, I was somewhat skeptical, as there have been many failure cases with such 'automated leverage vault' projects, so I chose a small amount to try it out as a guinea pig. The entire opening process is actually quite intuitive: select collateral assets, target leverage multiple, expected return range, and the system will directly provide a rough APR range and liquidation price prompt. This is much better than many competitors that just throw out a vague profit number; at least I know roughly what height I am walking the tightrope at.

Looking at the public chain ecosystem this year, the most intuitive feeling is that there are already so many L2 chains that it's hard to count. Every project is shouting about how high their TPS is and how low their Gas is, but many of my friends who are into quant and liquidation are actually more concerned with one question: how do the prices match up among so many chains?
This is the reason why I increasingly value APRO later on. It didn't try to compete with those slogans of 'our TPS can reach hundreds of thousands', but instead honestly positioned itself as a key piece in every chain, like a clockmaker responsible for 'multi-chain synchronization'. In today's fragmented liquidity market, the price difference on one chain can be instantly arbitraged by clever individuals on another chain; if the oracle reacts slowly, the anomalies that should have been arbitraged away will become the trigger for a liquidation accident.

In the past two years, every time there is a Federal Reserve meeting night, most of my friends involved in contracts stay up all night. It's not because they love work so much, but because they have seen it happen too many times: once the interest rates are announced, there's a big drop in the market, and before they can understand the situation, the clearing message has already been sent to their phones. Upon careful investigation afterward, sometimes it's not about how high their leverage is, but rather that in that instant, the oracle fed an outrageous price, causing the entire risk control system to be thrown off track.
It also started from those few times that I seriously focused on APRO, a project that is 'born to be an oracle'. It has a slightly different characteristic, treating itself completely as 'part of the risk system', rather than just a simple data intermediary. For example, during periods of extreme volatility, it employs many subtle strategies: increasing the weight of highly liquid exchanges, extending the price sampling window, and tagging obviously abnormal price jumps with an 'observe' label, rather than blindly pushing data onto the chain. You can understand it as preferring to be a little slow for a moment during the craziest seconds of the market, rather than stuffing a bunch of junk prices into the clearing contract.

The most popular joke in the past two years was 'Whoever controls the big model controls the world,' but later everyone discovered that models can pop up overnight, and what really makes the difference are those seemingly dirty, tired, and inconspicuous 'data tasks.' Whoever can continuously obtain clean data, cheap computing power, and stable demand for calls is qualified to stay in the game. The emergence of protocols like KITE is essentially about grabbing tickets for the second half of AI.
In the traditional internet, the logic of data flow is: users contribute for free, platforms centrally harvest, and then monetize through ads or subscriptions, with almost no direct returns given to individuals. Everyone has gotten used to this set of rules of 'I can't help being taken advantage of.' But on the blockchain, things start to look a bit different. KITE breaks down data participants into several layers: those who actually collect raw data, those who do cleaning and labeling, those who provide computing power, those who develop algorithm models, and those who build terminal applications. The contributions of each layer can be written into distribution formulas through smart contracts.

The most magical thing in the past two years is that while everyone is complaining on social media that 'they are about to be replaced by AI', they can't live without various smart tools every day. They may say they don't believe it, but their actions tell a different story. If we really want to say who is reaping the benefits, it's neither the researchers publishing model papers nor the influencers in the crypto space, but those who are genuinely reconstructing the 'data-computing power-application' closed loop, and KITE is a typical example.
Many people only regard KITE as 'just another AI narrative token', which actually underestimates its ambitions. What KITE aims to do, to put it in a somewhat colloquial way, is to repack the 'data fragments' scattered across various platforms and applications into tradeable, verifiable, and sustainably feedable 'data assets' for models. In traditional Web2, data belongs to the platform, and users are merely 'statistically counted'; but in KITE's network, the collection, cleaning, labeling, invocation, and feedback of each piece of data can theoretically generate a profit distribution path through on-chain records.

In the traditional financial world, initial public offerings are a business of "threshold": quotas, relationships, and information asymmetry are all indispensable. In the Web3 gaming track, YGG Play is trying to break down this opportunity, which originally belonged to only a few, into a "task-based initial offering" that any ordinary player can participate in. It sounds a bit idealistic, but the mechanism has its own intricacies.
We can imagine the YGG Play Launchpad as a "game asset issuance assembly line": every newly onboarded game project must first "queue for a health check" on this assembly line. The health check includes dimensions such as community foundation, product form, economic model, long-term operation capability, etc. Only projects that pass the screening have the opportunity to activate the Launchpad task pool. What players need to do is not to grab the white list report, but to decide based on the task list which games are worth spending time and effort to participate in.

In the past two years, you will notice a subtle change: more and more veteran players are beginning to complain, 'No matter how fun the game is, in the end, it's just working for others.' The ambition of Web3 is to rewrite this complaint into, 'I am willing to work for the game I invested in.' YGG Play Launchpad has entered the scene at this moment, turning the idea of 'playing games = mining early chips' into a complete platform.
Traditional games release new works, either relying on pre-sales or relying on buying volume, but most of this money goes into the pockets of channels. The core players who truly provide early feedback and help test pressure and services often only receive a simple 'thank you for participating.' YGG Play's logic is completely the opposite: through a task system, it structures behaviors such as 'testing, attracting new players, ranking, and creating topics' into tasks that can earn new game tokens. Every click and every match has the opportunity to become an asset accumulation rather than just a consumption of time.

In the past two years, everyone has been talking about EVM and Rollup, as if all problems can be solved by 'Ethereum compatibility'. However, when it comes to the derivatives track, especially in achieving extremely low latency on-chain trading, you'll find that the logic of EVM was not originally designed for high-frequency trading; forcing it will only create obstacles.
The approach of Injective is somewhat different: I simply integrated the 'exchange logic' into the core of the chain from the very beginning. What does that mean? In simple terms, other chains offer a 'general execution environment, and you write a contract to implement an order book'; whereas Injective provides a preset matching system at the consensus and execution layers, allowing all DEXs to directly call my core capabilities. The performance and determinism differences between the two are significant.

If you've been involved in crypto derivatives over the past two years, it's hard not to have heard of Injective. Many people's first reaction is: another so-called 'high-performance' public chain, who can't tell a story? But if you really dig into the data, you'll find that this chain is a bit different.
First, let's look at the most straightforward dimension: trading volume. Injective focuses on on-chain order books + derivatives DEX track. It is not the kind of 'farm chain' that just piles up numbers based on TVL, but rather has real users trading and holding contracts, with genuine users opening long and short positions daily. The top protocols on-chain can often achieve daily contract transaction volumes comparable to small centralized exchanges, which is actually quite impressive among the many 'public chains that claim to take down CEX.'

Many people, upon first seeing INJ, just regard it as 'another public chain coin': it has nodes, staking, and deflation, seemingly no fundamentally different from a bunch of L1s or L2s. However, if you really see it only as a 'platform coin,' then you are somewhat underestimating its design. In my own view, INJ is closer to a 'stock-like' asset centered around on-chain financial infrastructure, and it can even be roughly understood through cash flow discounting.
From the publicly available economic model, INJ integrates multiple dimensions such as transaction fees, fuel costs, protocol revenue, and staking rewards. Simply put, when there are transactions on-chain, there is real income; a part of the real income will be transmitted to holders and staking participants through deflation, buybacks, or rewards. This logic is completely opposite to the traditional internet approach of 'burning money to grow users first, then figuring out how to monetize.' It resembles the type of assets favored by old-school value investors: you don't need to hype up news every day; as long as the data steadily rises, the value will accumulate thicker over time.

If you've been complaining about liquidation, slippage, and front-running on centralized exchanges over the past two years, then you probably haven't truly researched Injective. The biggest 'rebellion' of Injective is that it has nearly moved the entire stack of traditional centralized derivatives exchanges onto the blockchain— but not in the slow AMM way, rather a combination of high-performance order book matching and a modular chain.
To put it simply: it wants to provide you with the familiar order interface, leverage, funding rates, and order book depth; but it also strives for transparency in liquidation, matching, and settlement, making it far more challenging than just 'launching another meme coin.'

The first time I encountered the concept of a guild was when a friend jokingly told me: 'In the past, we would rent a room in an internet cafe to grind dungeons all night, and now we can grind dungeons on-chain while also earning some gas.' At that time, GameFi was still the domain of P2E (Play to Earn), and everyone was rushing to get in, mainly competing for the early traffic dividends. It wasn't until later, when wave after wave of P2E models collapsed, that it really filtered out who was just making 'quick money' and who was building 'long-term infrastructure'.
YGG has an interesting aspect as an established guild, which is that it has not simply remained in the stage of 'gold farming guild + asset custody', but has evolved into a combination of 'task platform + player profiling + Launchpad'. You can understand it as: before, it was about teaming up to grind dungeons; now, it is about teaming up to complete tasks, with new game projects acting as 'release laboratories' behind those tasks.

If the main theme of the last bull market was DeFi and NFT, then this time more and more funds are seriously examining GameFi: not the kind of short-lived GameFi that pops up every few days, but infrastructure-level projects that can connect 'players, assets, tasks, and distribution' together. Many people are focused on the suddenly surging meme games on certain chains, but what truly holds lasting value is often the 'guild infrastructure' that stands behind all games, responsible for attracting new users, distributing, educating, and accommodating incremental users.
This is why, in the long-established guild competition track, we can still see funds continuously revolving around YGG. YGG is not a single game token but a composite of 'traffic + assets + tasks + distribution', especially the entire closed loop built around the Launchpad of 'players → tasks → game tokens → secondary market', which is quietly becoming a highly cost-effective distribution channel in the eyes of many new game teams.

If you think back carefully to the times you lost the most, it's likely not because you misread an indicator, but because your emotions were grinding you down: when chasing highs, you feel certain you can escape this time, and when cutting losses, you always think that waiting a bit longer will result in a rebound. No matter how many lines technical analysis draws, as long as you can't manage these two things, in the end, there usually remains just one word: regret. So in these two years, everyone has started to pin their hopes on 'AI to calm me down.'
The problem is that most so-called 'AI trading' is actually just a rebranding: taking a few traditional indicators, feeding them into a model, and outputting a bunch of incomprehensible signals, ultimately either forcing you to trust unconditionally or simply becoming another form of emotional amplifier. KITE's approach is different; it first acknowledges that 'humans are prone to making mistakes,' then attempts to break down and quantify these typical error patterns, allowing the agent to become a 'negative emotion filter': not making all decisions for you, but specifically responsible for stepping on the brakes when you're about to make a big mistake.

If I had to summarize the cryptocurrency market over the past two years in one word, I would choose 'overload'. Information overload, order book overload, news overload, even emotions are overloaded. When you open a trading interface, on the left are dozens of currency pairs, on the right are countless indicators, above is a news feed, and below is community chat—resulting in the end, the logic for actually placing orders often comes down to just one phrase: go with the flow. It's no wonder that at the start of this round, more and more experienced players are saying that it might be better to leave part of the decision-making to systems that 'can see more than they can'.
What KITE aims to do is essentially to 'turn AI into an auxiliary trader', rather than creating another cold, impersonal black box. Its approach is quite straightforward: first, gather structured data such as prices, transaction volumes, depths, and funding rates scattered across various chains and exchanges, and then absorb semi-structured information from social platforms and news opinions, using a unified 'intelligent decision-making hub' to underpin strategies. You can simply understand it as: combining a relatively professional quantitative team with a news editor who understands emotions, all packed into one system.

The most fascinating aspect of the crypto world is that 'everyone thinks they won't be the one left holding the bag this time.' A single message, an image, or a statement like 'big funds are entering the market' is enough to make countless people forget about taking profits or cutting losses. The reality is that after every emotional peak, there are always those who end up holding the last position, and those who withdraw early are often not smarter, but simply hold a bit more data that others can't see.
The APRO data network essentially attempts to systematize this 'additional information gap.' By tracking leverage ratios, liquidation density, position concentration, capital flows, and the basis between futures and spot prices, it can provide a more objective 'market sentiment thermometer' for protocols and even end applications. In some integrations, this is visualized directly as a risk level; in more advanced applications, it is incorporated into strategies to automatically trigger position adjustments.

When extreme market conditions arise, it's always the same scene on Twitter: screenshots, liquidation records, and insults directed at exchanges. But if you take a moment to calmly analyze the chain of events, you'll find that the real 'mastermind' is often not the contract that ultimately presses the liquidation button, but rather the oracle that discreetly nudges things along a few seconds earlier. Price precision, update granularity, and anomaly filtering capabilities—any slippage in these areas could lead to a complete loss of control over the entire liquidation chain.
The first thing APRO does is acknowledge that 'the market is dirty'—price data is always noisy, rather than the perfect curves found in textbooks. So, when designing the data inflow, it implemented a three-tier filtering system: source diversification, anomaly detection, and liquidity weight adjustment. In simple terms: it’s normal for quotes from different markets to be inconsistent at the same point in time, but those prices with almost no trading volume and are easily pierced should only be marked as references, not directly used to trigger liquidations.