Crypto460

@Injective a Layer-1 blockchain built for finance. Now it is delivering over 25,000 transactions per second while offering a trading experience with virtually zero gas fees. This is not just a benchmark. It is a new approach to building decentralized exchanges that are fast, reliable and cost efficient.

The Architecture: A Purpose Built Financial Layer-1
Injective is built on the Cosmos SDK with a Tendermint based proof-of stake consensus.
Optimized Consensus and Instant Finality
Injective achieves sub second block finality, typically around 0.6 seconds. This ensures transactions cannot be reversed and eliminates delays common on other chains. Low latency is crucial for high frequency trading and a stable real time experience.
The Native On Chain Order Book
Injective has a fully on chain order book and matching engine. Unlike AMMs or hybrid solutions, it works like a traditional limit order book:
• Custom Modules – Order submission, matching and settlement are handled directly on chain.
• MEV-Resistance – A Frequent Batch Auction batches orders at set intervals to prevent front running and ensure fair trades.
This design combined with Tendermint speed allows Injective to process massive trading volumes efficiently.

Zero Gas Fees for Traders
Transactions on Injective generate a fee, but this cost is decoupled from the end user experience.
• Trading Fees, Not Gas – Users pay a transparent trading fee on trades rather than unpredictable gas fees.
• Validator Compensation – Fees collected from trades are used to reward validators for securing the network.
• Trader Experience – Many dApps absorb small transaction costs, making trading feel gas free. Fees for staking or governance remain very low.
This allows traders to execute complex strategies without worrying about fees eating into profits.

The Economic Engine: INJ Token and Deflation
The INJ token is central to Injective’s economy. A portion of trading fees is used in weekly auctions where INJ is used to acquire collected fees. INJ used in winning bids is burned.
As trading volume grows, more fees are generated, more INJ is burned and the token value is reinforced. This aligns network growth with token holder incentives.

Interchain and Future Ready
Injective is fully interoperable with the Cosmos ecosystem via IBC. It also supports multiple virtual machines including EVM and SVM through Electro Chains. This makes high speed and low cost trading accessible across Web3.
Injective proves that specialized Layer-1 design can overcome the trade offs of older networks. Traders get high speed and low cost, and reliable infrastructure for sophisticated on chain financial activity.
#Injective $INJ

@Yield Guild Games (YGG) selecting games for its scholarship programs is not a casual choice. It is a careful strategic process that underpins its entire economic and social mission.
This process goes beyond potential ROI. It evaluates three key areas: the game’s economic structure, gameplay quality and retention potential and its fit within YGG’s broader decentralized autonomous organization (DAO) framework.

Economic Foundation: Ensuring Sustainable Yield
The first filter is the game’s tokenomics and asset utility. A successful scholarship program depends on long term sustainability and profitability.
Play to Earn Mechanism and Token Utility
$YGG looks for games where playing generates a reward token with real ongoing use. Key questions include:
Token Utility – Does the token have real use in the game such as crafting, upgrading or governance staking? Tokens without proper sinks can lead to unsustainable earnings.
NFT Utility and Entry Barrier – Scholarship programs rely on leasing high-value NFTs. YGG selects games where NFTs are essential for earning. This creates a barrier to entry that scholarships address.
Asset Scalability – The game must allow thousands of scholars without collapsing the market value of assets.

Capital Efficiency of Assets
The cost of acquiring and maintaining in game assets must match realistic earning potential. YGG evaluates projected returns after factoring in maintenance costs, asset wear and the value of the reward token. Only games with a plausible path to competitive earnings for scholars, managers and the guild are chosen.

Gamer Experience: Retention and Value
Web3 gaming is evolving beyond simple token farming. YGG requires games that are engaging to ensure scholars stay active and ecosystems thrive.

Gameplay Quality and Depth
Games must retain players even with minimal financial incentives:
• Engagement – Is the game fun and challenging? Strategic depth, skill development, and social interaction create stronger communities than repetitive clicker games.
• Progression – Clear progression systems that reward persistence encourage scholars to stay and succeed.

Community and Developer Commitment
YGG evaluates partners for long term dedication:
• Team and Roadmap – The background, funding, and roadmap of the development team are analyzed.
• Community Health – A vibrant and active community indicates a sustainable ecosystem and strong social engagement.

Strategic Alignment: DAO and Ecosystem Fit
The final step checks how the game aligns with YGG’s DAO and metaverse mission.
Interoperability and Ecosystem Value
YGG favors games that:
• Support interoperable assets across different virtual worlds
• Allow early governance participation through SubDAOs for game specific strategy
DAO Governance
Major asset acquisitions are approved by the YGG DAO. While the core team vets games, final decisions involve a community vote by token holders. This ensures:
• Transparency – The community understands why assets are acquired
• Alignment – Investments follow the long term strategy of the DAO

By applying this strategic framework of robust tokenomics, engaging gameplay and DAO alignment YGG ensures its scholarship programs are sustainable and inclusive. The selection process prioritizes lasting value over short term hype and builds a strong foundation for the future of decentralized gaming.
@Yield Guild Games #YGGPlay $YGG

The decentralized finance landscape offers great access to yield opportunities but can be complex. Maintaining an optimal risk adjusted portfolio often requires constant monitoring, technical knowledge and quick execution. @Lorenzo Protocol solves this by introducing AI Driven Rebalancing, turning passive deposits into actively managed, resilient and high performing on chain Traded Funds (OTFs).
This is not a simple automated strategy. It is an intelligent, adaptive financial system built on the blockchain.

The Problem with Static Allocation
Traditional set and forget strategies struggle with market changes. A user who splits capital between different yield sources may see their strategy degrade because of:
• Volatility – Prices fluctuate and liquidity positions can lose value.
• Protocol Risks – A previously high yield source can become risky or fail.
• Yield Changes – Market shifts can quickly lower previously profitable returns.
Static allocation cannot respond to these unpredictable changes. Lorenzo Protocol is built to react at machine speed.

The Architecture: AI Meets Financial Management
Lorenzo Protocol uses a Financial Abstraction Layer (FAL) to separate user interaction from complex execution logic. This layer manages all capital movements, strategy choices and risk controls. AI powers the system to create a closed-loop for real time asset optimization.

Real Time Data Analysis
The AI engine collects multiple data streams:
• On-chain data – Liquidity, transaction volumes, funding rates, and other market metrics.
• Off-chain data – Global economic indicators, news sentiment and volatility measures.
The AI identifies true market signals and filters out noise. For example, it can tell the difference between a short term price spike and a structural market change that needs action.

Adaptive Rebalancing Model
At the heart is the Adaptive Rebalancing Model (ARM). Unlike fixed rule systems, ARM uses AI to decide the best moves to maximize risk adjusted returns. It constantly simulates portfolio outcomes to determine optimal actions such as increasing allocation to a high performing asset, reducing exposure to risky positions or hedging with stable assets.
This approach allows proactive portfolio management instead of simple threshold based rebalancing.

How Real Time Allocation Works
Dynamic Strategy Switching
OTFs can switch between strategies based on market conditions.
Yield Shift – If one source drops in return, ARM reallocates funds to a better opportunity.
Efficient Execution – ARM splits transactions to reduce costs and waits for low network fees before executing moves.

Risk Control
The AI sets maximum drawdown limits for each OTF. When volatility rises, ARM automatically:
Hedges – Protects the portfolio using futures or other instruments.
De-Risks – Moves funds to stable or highly liquid positions to protect principal.
This acts as an autonomous circuit breaker prioritizing capital preservation over chasing small gains.

A New Standard for on chain Wealth
With Lorenzo Protocol, users deposit capital into a single OTF token. In return, they get a product that is:
Optimizing – Continuously adjusting for the best risk adjusted yield.
Securing – Actively managing volatility and risk.
Transparent – All actions are recorded on chain for full auditability.

By combining AI speed with blockchain transparency Lorenzo Protocol provides institutional grade strategies on chain without manual effort.
$BANK
#lorenzoprotocol

The decentralized AI economy is moving beyond simple token swaps and into complex machine to machine interactions. The biggest challenge now is ensuring that every valuable contribution from a dataset to a completed agent task everything is rewarded fairly and transparently. This is the mission of KITE’s Proof of Attributed Intelligence (PoAI).
PoAI is not another version of Proof of Work or Proof of Stake. It is a cryptographic and economic framework designed to bring verifiable provenance and fair settlement to the entire AI value chain. Instead of rewarding only compute power PoAI rewards verifiable attributable intelligence and true marginal impact.
The Core Problem: Black Boxes and Uncredited Work
In centralized AI systems value often disappears inside closed platforms. Developers data providers and model builders lose visibility once their work enters a proprietary pipeline. This creates three major problems that PoAI solves:
• No Provenance – You cannot trace an output back to the exact dataset model or agent that produced it.
• Unfair Compensation – Contributors are not paid based on their real impact.
• Misaligned Incentives – Low quality or malicious inputs are hard to punish while high quality work is not properly rewarded.
The PoAI Mechanism: Verifiable Attribution Across the Value Chain
PoAI builds an open transparent attribution graph on the KITE Layer 1 blockchain. Every step in the AI lifecycle is linked through cryptographic identities so value always flows back to the true source.
Rewarding Data Providers
Data is the foundation of all AI yet data contributors are often overlooked. PoAI changes this through:
• Cryptographic Data Identity – Each dataset is registered on chain with a unique ID and signed credentials.
• Marginal Impact Valuation – Inspired by Data Shapley PoAI measures how much a dataset improves model performance. If a dataset boosts accuracy by 12 percent the contributor earns rewards every time that model is used.
Paying Model Builders and Trainers
PoAI verifies the execution and integrity of AI models.
• Verifiable Execution – Every inference is proven through a verifiable environment such as a zero knowledge VM including model hash and input output commitments.
• On Chain Licensing – Smart contracts issue attestations that link each successful run to a model’s identity so creators earn ongoing licensing fees per use.
Incentivizing Autonomous Agents
Agents are the final layer delivering real services to end users.
• Agent Identity with Kite Passport – Each agent has a verifiable on chain identity and wallet.
• Utility Rewards – When an agent completes a task PoAI tracks it and rewards the agent. The reward then splits instantly between the model developers and data providers whose work contributed to the result.
Conclusion: Turning Intelligence Into a Public Good
KITE PoAI transforms opaque AI pipelines into transparent on chain value networks. It creates a clear feedback loop where contributors are rewarded according to measurable impact instead of centralized control. This encourages the sharing of high quality data and the building of better models forming the economic foundation of the emerging Agentic Economy.
@KITE AI
$KITE
#kite

Fast forward to now and I am staking $KITE the core token behind the Kite AI network. This time it feels bigger than simple blockchain security. Kite is not just another payment system or smart contract chain. It is a global AI infrastructure made up of GPUs and high performance hardware provided by people everywhere. When I stake $KITE I am supporting this network and helping keep open accessible AI compute online at a time when major cloud companies would rather keep it centralized.
How staking $KITE works
Kite uses a nominated proof of stake (NPoS) model optimized for hardware attestation rather than pure token weight.
Every node operator must cryptographically prove what hardware they bring to the network. GPU model memory interconnect speed uptime history and even geographic latency to major inference markets. This “hardware passport” is bonded with a certain amount of $KITE . If the node lies about its specs or underperforms or goes offline a portion of that bond gets slashed. The network stays honest because real money and real reputation are on the line.
As a token holder who is not running a data center my role is simpler. I delegate my KITE to one or more node operators I trust. My stake is added to their bond increasing the total work they can do for the network. Running inference jobs training shards storing model weights and routing tensor traffic. In return the block rewards and transaction fees the nodes earn are shared with everyone who delegated to them minus a commission the operator keeps for electricity cooling and profit.
That commission is public and chosen by the operator. Some take 2% others 10%. You can sort validators by commission uptime slash history hardware quality and decentralization score. It is one of the most transparent nomination markets I have seen.
Why this feels different from staking on other chains
Most proof of stake networks reward you for securing a ledger. Kite rewards you for securing usable compute.
Every time someone runs a fine tune on Llama 70B or generates inference on a Stable Diffusion XL model or trains a small specialist model inside the Kite sandbox real KITE is spent as gas. These fees do not vanish or get burned. They go to the stakers who make the compute possible.
Staking rewards are not only inflation emissions. A meaningful part comes from real demand for decentralized GPU cycles. The more people use open AI models that do not want to be locked behind AWS or Azure the more valuable the Kite network becomes and the higher the organic yield for stakers.
The numbers as of late November 2025
About 38% of the total KITE supply is staked right now. Real yield from fees has been between 4.2% and 6.8% annualized over the past six months depending on network activity. Inflation adds another 3 to 4% in the current phase but it is programmed to flatten once adoption milestones are reached.
These are healthy predictable numbers. No unrealistic APY promises. Just steady utility backed returns tied to something that could be core infrastructure for the next decade.
Risk because nothing is free
Slashing is real. We have already seen a few incidents. Mostly early node operators who overstated VRAM in their attestation and were caught during network stress tests. Penalties ranged from 3% to 28% of bonded stake. It hurts but it keeps the system honest.
There is also smart contract risk governance risk and one big question. What if decentralized training and inference never scale the way people expect? Then demand for KITE gas stays low and real yield declines. I am fine with that risk. The downside is a token that behaves like a normal Layer 1. The upside is being part of the only neutral open compute fabric not controlled by hyperscalers.
The simple routine I follow
Every quarter I review validator performance for about thirty minutes. I spread my stake across eight to ten operators in different regions favoring those who publish clear monthly updates and upgrade hardware consistently. I keep 10 to 15% liquid for governance and some liquidity provision in the $KITE pool. The extra 2 to 4% there is nice but I mostly do it for flexibility.
The rest sits quietly compounding while nodes in Oregon São Paulo Tallinn and Singapore process matrix multiplications powered by the tokens I locked up.
That is it. No day trading. No leverage. Just owning a piece of the physical internet that future AI models will rely on.
If you want a way to align long term capital with decentralized AI without buying expensive hardware. Consider staking KITE is the cleanest method I have found.
@KITE AI #kite

$KITE launched exactly nine days ago. What happened since is the kind of price action that makes traders both excited and cautious at the same time. The token did not just pump and fade. It formed a clear pattern of expanding volume followed by sharp volatility then a slower grind higher on thinner but steady participation. That sequence is worth unpacking because it reveals more about underlying demand than any single green candle ever could.
The First 48 Hours Real Accumulation Not Just Hype
Most new tokens see their biggest volume in the first few hours then fade. KITE did the opposite. The first day volume was around 18 million but the real surge came on day two when volume jumped to 47 million. This was not retail chasing a fast chart. This was new wallets entering with conviction.
Unique buyers grew from about 6200 on launch day to over 19000 by day two. Average buy size increased from 180 to nearly 420. Someone or more likely multiple groups were accumulating while the chart still looked risky to most traders. That early conviction is often the best signal that a token can survive the inevitable correction phase.
The Volatility Phase Day 3 to Day 5
Once KITE crossed the 75 to 80 million market cap zone profit taking arrived. The token saw three separate 35 to 42 percent pullbacks in 36 hours. Each drop found buyers at the previous local high creating a stair step pattern that shows reaccumulation.
Volatility metrics confirm the pattern. The 4 hour ATR moved from about 9 percent on day one to 28 percent on day four then cooled to 14 percent by day six. The token went from steady climbing to rapid swings and then into a calmer but active range. This cooling while price held above the previous range is a positive sign. It shows sellers exhausted themselves without breaking structure.
Where Volume Stands Today
By day nine volume has settled between 22 and 29 million. This is lower than the peak but still three to four times higher than most new tokens from the same week. More important is that sell volume dropped from 58 percent during the volatile day four session to about 41 percent over the past three days. The balance of aggression has shifted back toward buyers even if total volume is lower.
Holder Distribution and Concentration Risk
The top ten wallets hold 21.4 percent of supply which is high but not dangerous. Only two of them have sold any meaningful amount since launch and both appear to be early liquidity providers rotating rather than exiting. The top one hundred wallets increased their average holdings by about 6 percent over the past five days. Early weak holders are giving way to stronger hands which is healthy for week two.
Key Price Levels Right Now
KITE has built a clean range between 0.068 and 0.104 since the last shakeout. The midpoint around 0.086 has acted as a magnet several times. A daily close above 0.104 with rising volume would break the range and open a move toward 0.15 to 0.17. Losing 0.068 on strong selling would signal that accumulation has ended and a deeper correction could follow.

KITE is doing what most new tokens fail to do. It attracted real buying after launch survived the volatility spike and settled into a stronger base with improving volume behavior. This does not guarantee a major breakout but it does show the token has moved past the pure gamble phase and is gaining organic demand.
The next key signal will be whether volume increases again on a break above 0.104 without the wild wicks of last time. If it does momentum can turn into something more sustainable. If it breaks down instead the early accumulation may have been temporary.
Nine days in KITE has already separated itself from most tokens that launched the same week and are now down heavily. That alone makes it worth watching.
@KITE AI #kite

If you have ever stared at stock charts at 2 a.m. wondering whether to buy, sell or sleep then this is for you. A few months ago I discovered a game changer for trading KITE. AI trading bots that can make money while you do anything else.
How it clicked for me
I used to trade manually, placing orders, setting stop loss, refreshing the page, panicking, canceling stop loss, watching stocks gap down. One Tuesday I lost ₹18,000 in a single day and thought, “There has to be a better way.” That’s when I learned people were connecting AI models to KITE to automate trades. The bot trades calmly remove emotional mistakes.
How it works
1. Write or copy code that tells KITE what conditions trigger an order.
2. An AI model decides those conditions using simple strategies or advanced techniques like reinforcement learning.
3. It runs 24/7 on an inexpensive cloud server.
4. Money flows in and out of your account via KITE Connect API.
5. Wake up check your P&L and smile.
Why it is safe and effective
• Capital never leaves your account and bots can only place orders.
• Payments for bots or signals go through secure gateways.
• Paper trading is easy to start for testing.
• Bots can trade intraday, overnight, BTST, options, futures and anything you code.
My first bot
I am not a coding wizard but started simple:
• Mean reversion strategy on Bank Nifty options using 5-minute VWAP and RSI
• Buy ATM call if RSI < 30 and price < VWAP
• Buy ATM put if RSI > 70 and price > VWAP
• Exit everything at 3:20 pm or 8% profit / 4% loss
First month live: +41% on ₹1.5 lakh margin
Second month: +19%
Third month: -6%
Overall still far better than manual trading
Secure payments
• Never share API keys or passwords
• Buy bots or signals only via official gateways
• Host code yourself on AWS, GCP or DigitalOcean
• Use app specific passwords and 2FA
Common mistakes to avoid
1. Overcomplicating the strategy. Start simple. My first bot had 12 lines of logic.
2. Ignoring risk management. Fixed fractional sizing and hard daily loss limits are crucial.
3. Running it on your laptop. Use a VPS to avoid interruptions.
4. Expecting huge daily returns. Realistic expectancy is 1-3% per month after costs.
Tools I use daily
• Zerodha KITE Connect API
• Python + kiteconnect library
• Pandas & TA-Lib
• ccxt or REST calls for extra data
• ₹500/month VPS
• Telegram bot for trade notifications
Final thoughts
AI trading bots on KITE are no longer futuristic. Thousands of retail traders in India are using them successfully. You don’t need a PhD or huge capital just discipline, a decent strategy and willingness to remove emotions.
The first time I woke up to ₹27,000 profit while still in bed and I knew I would never trade the same way again.
Happy automating and may your drawdowns be small and your sleep be deep.
Someone who finally stopped fighting the market and let the bots do the work
@KITE AI $KITE #kite

I was sitting on the balcony of a small apartment in Tbilisi last winter watching snow fall on Soviet era rooftops. My phone buzzed with a payment notification. Someone halfway across the world had just sent me 27 dollars for an old photograph I had taken years ago. No bank. No fees. No questions. The money arrived in the same second the shutter had once clicked.
That quiet moment felt like the future arriving without announcement.
The tool that made it possible was still in beta then almost invisible. It was called @KITE AI . Not an app with a loud logo not another fintech unicorn chasing headlines. Just a protocol. A thin layer of code that let any AI agent move value the way it already moves words or images: instantly without permission without middlemen.
I have spent the past eighteen months living inside that protocol the way one used to live inside early online forums. Quietly. Obsessively. Watching it grow from something only twelve people understood into something that now runs beneath millions of small transactions every day.
$KITE is the first payment network that was not built for humans first.
That single fact changes everything.
Traditional payment systems were designed around people sitting at desks filling forms proving identity waiting for clearance. They added apps and QR codes and biometric logins later like lipstick on a dinosaur. The architecture never changed: speak to a central ledger ask for permission wait.
Kite began with a different assumption: the primary users would not be humans but agents. Language models autonomous bots on chain algorithms smart contracts that need to hire workers buy compute rent memory trade data. Beings that do not sleep do not forget passwords and do not care about weekends.
So the network was built to be spoken in function calls not button clicks. An agent says pay(wallet amount memo) and the value moves. No login screen. No captcha. No confirm your transaction in the app. Just the same certainty that if you say write this sentence the sentence appears.
That is the part most people still do not feel in their bones: Kite is not a better payment app. It is the moment money became native to the environment where AI already lives.
I watched it happen in small ordinary ways.
An AI art bot I follow started selling prints. Instead of directing buyers to a platform it simply said tip me here with a Kite address. People sent small amounts. The bot noticed when payments arrived and ordered the print from a service in Seoul paid the shipping and messaged the buyer a tracking link. The entire loop took ninety seconds. The human owner woke up to find fifty new orders fulfilled while he slept.
A writer in Nigeria began paying his editors with a single prompt. Pay each of these five people 200 dollars from the article budget. Done. No international fees. No waiting for clearance. The editors in Manila Bucharest and Nairobi received the money before the writer finished his coffee.
My favorite example is quieter. An elderly woman in Kyoto runs a small tea shop. Her grandson built her a simple AI clerk that speaks to customers through a tablet on the counter. Tourists order in English Spanish and Korean. The AI translates suggests blends and accepts payment with Kite. The woman never learned to use a card reader. She just watches the tablet chime softly when money arrives. She told me it feels like the old days when neighbors dropped coins into her palm. Nothing between hands.
These stories sound like features. They are not. They are side effects of a deeper shift.
When you make money move at the speed of thought you stop thinking of it as money. It becomes attention. It becomes reputation. It becomes trust flowing through the same pipes as language.
That is why Kite feels different from every payment change before it. Bitcoin gave digital gold. Ethereum gave programmable money. Kite gives ambient money. Money that lives where conversation already lives. Money that does not ask you to switch apps or scan codes or remember which wallet holds which asset. Money that is simply there when an intelligence human or not decides value should move.
There is a gentle philosophical consequence to this.
We are no longer the center of the economy.
We are participants in a larger metabolism. Our AIs pay each other for micro tasks. They rent our GPUs while we sleep. They license our voices and faces for small amounts. They settle with each other in real time. We wake up and find our wallets a little fuller or lighter based on how useful we were to the wider system while we dreamed.
This is not dystopian. It is simply the natural continuation of every tool we have ever built. The wheel did not ask permission to carry more than human legs. The internet did not pause to make sure every letter arrived by horse. Tools extend us then surpass us then include us again on new terms.
Kite is that moment for economic life.
I still catch myself staring at the snow in Tbilisi remembering the soft buzz of that first frictionless payment. It felt like the world exhaled.
The network has grown since then. It is no longer twelve people in a quiet Discord. It is quiet infrastructure now like the early internet once was. Most people who use it do not know they are using it. They just know things work.
That is the surest sign you have built something real.
When the tool disappears and only the feeling remains.
A quiet calm almost tender sense that value can flow as easily as words.
That money has finally learned to speak the same language as thought.
And we are only at the beginning.
#kite

If you’ve been around @Yield Guild Games (YGG) for a while then you’ve probably heard people talk about the “Manager” role and these big 10–30% overrides. It sounds almost unreal at first but the system is actually simple and designed to reward real community leadership.
Here’s the clean breakdown.
What a Scholarship Is in $YGG
A scholarship is when YGG acquires game assets and lends them to players so they can play and earn without paying upfront. The player earns in game rewards and the earnings are shared.
A typical split looks like this:
• 70% → Scholar
• 30% → Guild
This is where Managers come in.
What a Manager Does
A Manager is a community leader who takes care of a group of scholars. Instead of YGG trying to manage thousands of players across many regions, Managers take charge of the daily operations. They guide players, track progress, solve issues, and keep the community active.
How the Override Works
Let’s say a scholar earns 100 tokens a day (example only).
Standard split:
• Scholar: 70
• Guild: 30
If you are a Manager on a 20% override, you earn:
• 20% of the guild’s 30 = 6
• Guild keeps 24
• Scholar keeps their full 70
Across 100 or 300 scholars and this becomes serious income.
How You Unlock 10–30%
YGG reviews Managers every season. The key factors are:
1. Retention – Are your scholars still active
2. Performance – Do they earn above the guild average
3. Community Health – Are you running events, sharing updates, helping players
4. Size – Larger communities with good performance unlock higher tiers
Many Managers start at 10% and grow to 30% by building strong communities.
Why YGG Pays Managers This Much
It is more efficient than hiring a huge central support team. Managers live inside the communities, understand the culture, speak the language and react faster than any global operation. When Managers help scholars succeed both the guild and the Manager earn more. Everyone wins.
A Real Example
One early Manager started with only 40 scholars and earned 10%. He improved his community with daily check ins, simple guides in his local language, weekly events and direct support. Within the next season he grew to 180 scholars and reached 25%. At his peak he managed almost 400 scholars at a 30% override. It changed his life.
Is It Still Worth It in 2025
Yes. YGG now supports a wide range of games. Some are early stage with high earning potential. The Managers who stayed active during slower markets are now leading strong communities and earning well as new games launch.
How to Become a Manager
There is no formal application. You grow into the role by becoming useful.
• Join the YGG Discord
• Stay active in your region or game channel
• Help new players
• Share guides and tips
• Organize activities or support sessions
People who consistently help the community get noticed. When new Manager slots open, these are the people YGG trusts.
Final Thoughts
The Manager system is one of the smartest designs in the YGG ecosystem. It transforms the guild into a network of community driven leaders. The 10–30% override isn’t a free bonus. It is earned by guiding players, keeping the community alive and building a sustainable environment where everyone grows.
If you enjoy helping people and you’re active in a YGG supported game, becoming a Manager might be the best opportunity you haven’t explored yet.
#YGGPlay

For years AMMs dominated DeFi. They made swapping simple and they helped new ecosystems grow fast. But once you start trading real size the weaknesses become obvious. High slippage. Impermanent loss. Price swings from even medium trades.
That’s why @Injective stands out. Instead of copying the same AMM model they chose a fully on chain order book. It sounds old school but it solves problems traders have been dealing with since the early DeFi days.
AMM vs Order Book in Simple Terms
An AMM is like a giant community bowl of assets. The price changes every time someone trades. It is easy to use but also easy to get hit by slippage when someone places a large order.
Order Book
An order book is people posting buy and sell orders at specific prices. Trades only execute when buyers and sellers agree. This gives you fair pricing and low slippage even for large size.
Most chains couldn’t run a real order book because it was slow and expensive. Injective built its own chain so it could handle it.
Why Injective Chose the Order Book Model
1. Real price discovery
Prices come from real bids and asks not a formula.
2. Low slippage for big trades
Large orders execute with high efficiency similar to major trading platforms but with full on chain control.
3. No impermanent loss
Liquidity providers place limit orders instead of holding risky AMM positions.
4. Advanced trading tools
Stop loss take profit post only and other pro features work natively on Injective.
5. Strong protection against front running
Injective uses batch auctions for spot markets which makes sandwich attacks far harder.
Isn’t an on chain order book slow
It used to be. Injective solved this with a high performance layer 1 that processes thousands of order updates per second. Only final matched trades hit the chain which makes fees extremely low.
Placing or canceling orders costs a fraction of a cent.
The Trade Offs
• Tokens need active market makers
• The interface feels more like a pro trading terminal than a basic swap page
For serious trading these are not drawbacks. They are features.

AMMs helped DeFi grow and still work well for small swaps and long tail assets. But for fair pricing efficient execution and real market dynamics the order book model wins.
Injective is one of the few ecosystems that built this fully on chain. After using it daily it is hard to go back to paying silent costs on every swap.
#injective
$INJ