gm guys!!!!!!!!

Here is my submission for @codexfx mascot event Name- Codor Powers- Stablecoins chad Speed- 0.0000000001 second haha I hope this looks cool, haha. So much time to think Abt these. #CodexMascot

There are so many crypto super apps out there, but I am still bullish on @liquidtrading But why? - Liquid is execution first, not feature bloated Other crypto apps have various tabs for every option, but liquid is made for fast execution. Just see the signal, open the app, tap on buy or sell whichever you want and boom> order executed. - Built for onchain users, not retails visiters. Liquid pre assumes things like > you already have a wallet > You understand the odds of trading (risk/profit) > you want direct protocol exposure, not wrappers So the UI removes hand holding issues , no complexity underneath. - Vaults are intent driven, not APY bait Liquid focuses on strategies, giving users > Clear risk framing > Providing simple thesis ( park idle capital and low touch yields ) > Some knobs with some unique surprises - No hidden offchain dependencies Liquid focuses on transparency and low movements(for users ), it makes users not to burn their energy on other things rather than trading freely. - design philosophy: Calm> Hype The goal is not to show the app like random casino, it's giving user to feel they are on trading desk, not on games (Attracting smaller but serious audience) The product is neat, clear, easy to understand. Making it fast and targeting the real traders of the market. @ledbyalex @songoku230_

People thinks that cross border settlement means sending crypto from a person(country A) to another person (Country B) Is this that much easy? And the answer is No. It ends when the money becomes usable. In the today world, international money transfers have a long tail. The payments move through multiple banks, FX desks and compliance checks. Each step runs on its own schedule because these systems don’t settle in real time, payments take days, FX rates are unclear upfront and funds sit in transit. ** Stablecoins fixed the speed through onchain transfer but failed to meet the expectations off chain.The moment FX or fiat is involved, delays return.** > Codex solves this by making settlement the core layer. FX, compliance and finality happen together in one atomic flow. The tech:- Instead of sending money through multiple banks, Codex settles payments using stablecoins on chain. FX is handled before the transfer, with the rate locked upfront. Compliance checks run during the transaction, not after. Here is the small information of the tech behind cross border settlement layer of @codexfx

As we know, if you want to trade then you need a full system or something like that. Pc and laptops are generally use for trading. And mostly there was no application for easy trading earlier in the mobile but @liquidtrading is here to ship you it's trading app and making it easy to use anywhere freely, just on your mobile. > Liquid core tech goal is execution speed with minimal cognitive load. Everything is built around that. > The market price goes up and down as per the real time data, makes it more fast and simple. > The app is mobile native means anyone, anywhere can use it and trade their favorite crypto. > Liquid execution layer is designed for fast execution and makes it low latency fills for too. > The app has Vault Yield System - idle balances are routed into managed yield strategies like pooled capital, predefined lockups and transparent APY paths > The app has inbuilt automated revenue attribution system. Users has no need to do it manually, it uses onchain accounting. > Talking about the security it has secure custody infrastructure, risk controls and separate isolation between user balances and system operations. liquid tech is not about inventing new cryptography. It's just redefining the financial stack. gLiquid to all !!!! @liquidtrading @ledbyalex

Delphi is basically a public competition for AI models but instead of judges or secret scoring, it uses a market to decide which model is good. > Think you have 3 singers and crowd. the crowd will bet on who they think will win then the performance starts. The moment when someone sings better than expected, the crowd interest shifts and the value goes up. That's exactly how Delphi works but with Ai models 😅 > models perform a task-> the market reacts on it-> the best model rises. the Delphi leaderboard changes on models performance, so all depends on them. a live scoreboard for Ai quality > in future, anyone can create their own contests on it anything, anyone can enter their model and the market will decide which is actually good. right now it's under testnet phase, no real money involved yet but the system works. models enter, compete and the scoreboard moves @gensynai

some ppl says me why you are trying to make your position on @symbioticfi there are various reasons behind this, lets break all of them(this opinions are from my side) > The project raised 34.8m$ from top VCs like Coinbase, Paradigm, Aave etc > The tech is very new and promising, where they are building a modular multi asset restaking protocol that gives new chains shared security. Users deposit collaetral into vaults, delegate to operators and earns extra yields while the network gets flexibility security. > The current TVL is over 500M$ , this is quite interesting ifykyk > the team is super active and bullish about it's product > They share monthly insights with the community more than 2-3 times ( i always join these sessions, they are so much informative. also they collab with other protocols lead excecutives) connects with the community and get their thoughts > They are not making hype, focusing on their product building these reasons are my favourite, what do u think about all of them? tell in the below comments @symbioticfi @roth_edgar @0xnempc

Gensyn vs clouds (in terms of model training) As we all know about gensyn ai( a decentralized ai compute network for model training) , but why it's cheaper than regular Clouds ? the answer is simple - Clouds charge for trust, Gensyn doesn't charge anything like that. > Clouds need trusted data centres to check the work is correct or not , but Gensyn removes this cost completely. Instead of big data centres, Gensyn uses cryptographic verification(Verde) for checking the work. This makes training so easier that any GPU from anywhere can train your model correctly. > Clouds are expensive bcz of there huge and massive data centres. They need various things like big buildings, electricity etc to run it. These things feels like overhead in the model training segment. that's where Gensyn removes this cost and cuts the bill from your head > And a another reason is Supply, Clouds have low supply of GPUs(obv they don't have millions of h100 GPUs in their data centres) Gensyn have big supply than them. we all know that big supply eventually lows down the costs, that happens here too. making the model training more cheaper. > clouds pays for machine and Gensyn only pays for work. You only pay for your verified training steps, not for the time you taken. So these things makes model training is way more cheaper than clouds on Genysn. i hope you guys now understand why it's very useful for future. Gswarm to all !!!! @gensynai

what if i say prediction markets aren't random places where ppl just bet on future events. but under the shadows they behave like learning systems. > look at this example:- A market price is basically a shared guess. Every time someone trades, like a market is predicting it will rain tommorow.... the current market price says 60% ve Someone who strongly believes that it won't rain tommorow, he will start selling shares of that prediction trade. on the opposite side, the one who believes that it will rain tomorrow. he will buy shares. each trade is just basically a message the current prediction market is too high or the current prediction is too low. the price moves with every step, that is the learning step. the market updates it's beliefs using new information, same like models when they see new data. if you replace each models with humans, each model bets on what it knows. the final price becomes the combined prediction. > simple idea:- markets aren't just bets. they are learning systems that improve with every signal. @gensynai

People online faces many kinds of risks like eg. Hacks, loans can fail, validators can get penalized and even companies can default. That's why the market of onchain protection getting more bigger daily. Before crypto, insurance was depending on one thing Pay me if this smart contract gets hacked but unfortunately this model was unable to scale more broader as they expected. Now we have different type of protections like > protection against loan losses > protection against system failures > protection against credit defaults > even early forms of reinsurance all these are happening onchain(tbh the world is upgrading daily) and the people who manage this protection are becoming more like real insurance experts. They don’t just chase yield they study which risks are safe to combine and how likely things are to fail at the same time. Traditional World > In the traditional World, insurance companies resuse the amt and use them to cover other multiple risks, bcz not everything goes wrong suddenly. A car crash doesn't affect a house fire or a house fire doesn't claim dental issues. The idea is simple- build one pool of money, which can safely back multiple insurance policies. (Each helping themselves) > This same thing is coming on crypto and @symbioticfi is turning this into reality. They will put all money together and that pool will cover multiple unrelated risks. (Only if they don't move together) That means: more protection, more safety. when you understand which problems are independent, you can protect more things with the same money.

Everyone talks about the Decentralisation, but no-one talks about the Verifying the work. In the current market, most of the solution depends on trust or proof that don't scale. People make a open market fully on hope , never makes the things verifiable. Mostly they rely on hope, which is dangerous. That's where Verde enters and changes the game by making things verifiable. > Verde forces multi language operations to behave same like the way of GPU. Every bit is identical, this helps to build an honest compute which matches. > The system is simple: Two providers gets the same task, of there work matches than all good. if they don’t, verde pinpoints the exact operator where they diverged and rechecks only that part. no heavy reruns or no cryptographic complexity. just clean and reproducible truth. That's why Gensyn is the best open compute market. Bullish on @gensynai

People are getting recognised by their content on @gensynai and getting promoted to Rover Huge congratulations to them.... We are still trying to get that... Congratulations to all Decentralised GRPO lets many nodes train an LLM together by sharing prompt responses. fast, low bandwidth and efficient. but that same design opens a big weakness. the paper shows you can poison the whole system just by adding a few malicious tokens during training. the bad output spreads from one node to all others. in tests, attackers reached 100% success in as few as 50 steps. they also test defenses if nodes coordinate their models properly, they can fully block the attack. the takeaway is direct: shared training is powerful, but extremely easy to corrupt if defenses aren’t built in from day one.

My friends and everyone are working on @gensynai now 👀 Seems like they saw the potential which I saw earlier in Gensyn. They are now actively working on the project and shaping the future with us.... I explained them about the Gensyn tech like this:- Gensyn is building an open source unified protocol, where developers can train their ai models with cheaper price. Also the users who have extra GPU power can contribute with the compute and get paid for it. The product is built for the future bcz most of things depends on AI. Big techs own massive compute power to train their AI models. But after Gensyn's entry, devs can easily reach big compute power with cheaper price. Why it's important? (5 reasons) - big companies own the power to train AI:- currently only big companies have the power to train AI models. - AI becomes more cheaper:- anyone can contribute with its compute power, so the training cost will reduce initially. - Decentralisation of power:- big companies will loose their power of centralization, anyone can build a successful ai model - trust on the work, not on the machine:- the Gensyn tech depends on the work, the protocol system is so much friendly and useful - it also enables Decentralised ai apps in the world:- Decentralised ai system will allow people to build more ai dapps Tech is promising, dive more on it, learn about it !!