Helping founders clarify their vision✍️ || Storyteller || Branding || Defi
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For the longest time, whenever a brand or project reached out asking for my portfolio, I had the same problem.
I didn’t really have one.
I’d either send them my X profile and tell them to scroll through my posts, or start digging through my own timeline to find specific pieces of content I thought represented my work well.
And honestly, it wasn’t the best way to show what I could do.
I’ve worked on different types of Web3 content, from breaking down projects and their narratives to creating campaign content, writing educational pieces and helping projects get in front of the right audience.
But having all of that scattered across my X page made it harder for someone discovering me for the first time to actually see the full picture.
So I finally put everything in one place.
If you’re a founder, marketing lead, or team looking for a Web3 creator to work with on content, campaigns, KOL marketing, storytelling or GTM, you can see my work here:
fayycreates.netlify.app
And if you’ve been meaning to reach out, my DMs are open. 🤝
There’s an important detail people can miss when talking about quantum-resistant blockchains.
Checking that a calculation is correct and checking that a transaction was approved by the right person are two different things.
Let me use Starknet to explain this.
1. STARK proofs: Checking the work
When a blockchain performs calculations, it needs a way to check that the work was done correctly.
@Starknet uses STARK proofs to help verify those calculations without requiring the person checking them to repeat all the work.
These proofs rely on hash-based cryptography.
Why does that matter?
Some of the cryptography used by today’s systems relies on mathematical problems that sufficiently powerful quantum computers could solve much more easily. Shor’s algorithm is a well-known example of how quantum computing could threaten certain types of cryptography.
Starknet’s hash-based approach avoids relying on those same elliptic-curve assumptions for its STARK proofs.
That gives it an important foundation for preparing for quantum threats.
2. Wallet signatures: Checking who approved a transaction
This is a separate job.
Before a transaction can be accepted, the network needs to check that the account owner authorised it.
That is where digital signatures come in.
Starknet’s accounts are programmable smart contracts, so developers can build accounts that use different ways of checking signatures. This creates a path for introducing new signature methods without requiring a network-wide protocol upgrade.
But protecting one part of the system doesn’t automatically protect everything else.
Starknet is an Ethereum Layer 2, meaning it operates on top of Ethereum. Some parts of its wider security setup, including aspects of bridging and data availability, still depend on Ethereum.
So, Ethereum’s own cryptography and migration progress matter too.
A blockchain needs to consider both how it checks calculations and how users approve transactions. Starknet has useful foundations for preparing, but full protection across every layer still requires more work.
Read more: quantum.starkware.co/foundat…
What happens if the method your crypto wallet uses to approve transactions becomes unsafe?
Would the whole blockchain need to change before you could use a safer method?
This is one reason Starknet’s programmable accounts are worth understanding.
First, let’s look at what a wallet actually does.
Your crypto wallet doesn’t just hold your assets. It also helps prove that you’re the person authorising a transaction.
It usually does this with something called a digital signature.
Think of a digital signature as a special approval that helps show a transaction was authorised by the right person.
The problem is that if a signature method becomes unsafe in the future, changing it may be difficult on systems where that method is built deeply into the network.
Starknet takes a different approach.
Its accounts are smart contracts. In simple terms, they are programmable accounts that can follow rules written by developers.
This allows developers to create accounts that check transactions using different signature methods.
So, if a new and safer method becomes necessary, developers can build accounts that use it without requiring the entire Starknet network to change its rules.
Think of it like changing the lock on your own door instead of waiting for an entire city to replace every lock.
Of course, this doesn’t mean every wallet automatically becomes safer. The new method still has to be developed, implemented and adopted by users.
But @Starknet’s design gives developers more flexibility to make those changes.
And when cryptography needs to evolve, that flexibility matters.
Learn more about Starknet’s architecture: quantum.starkware.co/archite…
A blockchain can be secure today and still face new security problems tomorrow.
That’s one reason the crypto industry is paying attention to quantum computing and advances in AI.
Let me explain why this matters.
Blockchains use special forms of mathematics to protect transactions and digital assets. But a powerful enough quantum computer could eventually break some of the methods used to protect them today.
And if AI helps people discover new mathematical techniques, some of the security methods we rely on could face new challenges too.
This doesn’t mean those attacks are happening right now. It means the industry needs to prepare.
This is where Starknet’s approach becomes interesting.
Starknet uses something called STARK proofs. In simple terms, these help the network check that a calculation was done correctly without having to repeat the entire calculation.
These proofs rely on hash-based cryptography, a different approach from the type of cryptography that powerful quantum computers are expected to threaten.
@Starknet also has programmable accounts. This means developers can build wallets with different ways of checking whether a transaction is authorised. If a signature method needs to change, developers can introduce a new one without requiring the entire network to upgrade.
But there’s an important detail: this doesn’t mean every part of Starknet is already quantum-safe. Starknet still depends on some parts of Ethereum’s infrastructure, and those dependencies also need to be considered.
The bigger lesson is simple: security isn’t just about what protects a blockchain today. It’s also about whether that blockchain can adapt when better technology creates new threats.
Starknet is working on that future.
Explore the Quantum Hub: quantum.starkware.co/
Fayy🌼 retweeted
One thing I’ve noticed about trading is that it’s easy to focus on your next entry and forget to check the positions you already have open.
That’s one reason I started looking into @talistrade an AI copilot built to help with trading.
One feature that caught my attention is the open-position check-in, where you can ask it to review your current risk and identify trades that don’t have a stop loss.
I like this because checking several positions manually can get tiring, especially when you’re trying to keep track of everything at once.
Having Talis help you spot what you might have missed gives you another way to review your positions before making your next move.
Another thing I like is that you can ask questions in plain English instead of trying to figure everything out on your own.
It gives you a second opinion while keeping you in control of what happens next. Of course, you still need to understand the risks because having an AI copilot doesn’t mean every trading decision will be right.
Sometimes, managing what you already have matters just as much as finding your next trade.
Fayy🌼 retweeted
Being in crypto for a while has made me realize that giving AI the ability to make payments is only one part of the bigger picture.
How does an AI know the information it is using is actually true?
And how can it act on someone’s behalf without exposing their private information?
That’s where I think @verona_dev ’s approach makes sense.
An AI needs to know enough to make the right decision, but that doesn’t mean it should have access to everything about you.
With verUSD handling the payment side, Verona is working on the trust and verification side.
I think this is an important part of making AI agents useful in the real world, especially when money and personal information are involved.
verUSD launched in Seoul last month as the first stablecoin for AI agents, backed by more than $100M in institutional launch commitments, and since then Korea’s business press has been asking what it takes for an agent to act on someone’s behalf. @BurntBanksy told Hankyung that it starts with proof, because Verona verifies what an agent needs to know about a person without ever exposing who they are. An agent needs verified context to make a decision and a dollar built for it to pay, and Verona provides both.
verUSD는 지난달 서울에서 AI 에이전트를 위한 최초의 스테이블코인으로 출시되었으며, 1억 달러 이상의 기관 출시 약정과 함께 시작했습니다. 이후 국내 경제 언론은 AI 에이전트가 사람을 대신해 움직이려면 무엇이 필요한지 묻고 있습니다. @BurntBanksy는 한국경제와의 인터뷰에서 그 출발점이 증명에 있다고 전했습니다. 베로나는 에이전트가 알아야 할 정보를, 그 사람의 신원을 드러내지 않고 검증합니다. 에이전트에게는 결정을 위한 검증된 정보와 결제를 위한 전용 달러가 필요하며, 베로나는 그 두 가지를 모두 제공합니다.
hankyung.com/article/2026100…
Fayy🌼 retweeted
I think one of the next interesting developments in prediction markets will be the move from broad public events to questions built around specific communities.
Major elections, cryptocurrency prices, and sporting events will always attract attention.
But those aren’t the only things people want to predict.
Gaming communities follow release dates and tournament results. Crypto communities debate protocol upgrades. Fans follow creators, niche competitions, and internet culture.
These audiences already have opinions. The question is whether they will have useful markets where they can express those opinions.
This is why @xomarket has caught my attention.
Its approach allows anyone to create a Conviction from $10, while its MODRA AI engine assists with market creation and resolution.
Markets that attract enough traction can graduate into Prediction Markets, while liquidity incentives aim to support trading activity.
I don’t think every user-created market will succeed. Many will probably remain small, and some won’t attract much interest at all.
But that might be okay.
A platform doesn’t necessarily need every market to become enormous if it can support a wide range of communities with different interests.
The real challenge is making those markets useful: clear questions, reliable resolution, sufficient liquidity, and reasons for people to participate.
If XO can keep improving those parts, I can see user-generated markets becoming a more meaningful part of the prediction market landscape.
The interesting question isn’t just what people will predict next.
It’s what they’ll want to create markets around when they finally have the option.
Explore XO: s.kaito.ai/sdVtQJU
Fayy🌼 retweeted
zkTLS and FHE are both important to @primus_labs , but they solve different problems.
Here’s the simple version.
zkTLS: Can this information be verified?
It helps prove the authenticity of data obtained from websites and online services, without requiring the underlying information to be fully exposed.
For example, an application could verify a claim about someone’s off-chain financial activity.
FHE: Can this information be used without revealing it?
Fully Homomorphic Encryption allows computations to run directly on encrypted data, so the information doesn’t need to be decrypted before it is processed.
One focuses on verifying data. The other makes it possible to compute on protected data.
Together, these technologies can help applications use information that would otherwise be too sensitive to share openly.
That’s the core idea behind Primus: make data useful on-chain without treating privacy as an afterthought.
The combination is especially relevant to finance, where both the authenticity of information and the confidentiality of that information matter.
Technical docs: s.kaito.ai/g81sAwY
Fayy🌼 retweeted
A wallet balance doesn’t always tell the whole story about a borrower.
Someone might have a history of trading, hold assets on a centralized exchange, or have financial records that could help a lender assess their creditworthiness.
But how can a DeFi protocol use that information if it lives outside the blockchain and shouldn’t be exposed publicly?
@primus_labs is working on infrastructure that can help verify off-chain data using zkTLS.
That creates possibilities for applications that want evidence of financial history or asset ownership without requiring users to publish their entire account records.
For example, a lending protocol could explore using a verified claim about off-chain assets as one input in its assessment of a borrower.
This doesn’t automatically eliminate lending risk or guarantee that someone qualifies for a loan. The protocol still needs its own rules and risk checks.
But it could expand the kinds of information on-chain applications can use.
DeFi credit needs more than collateral. It also needs reliable ways to verify relevant information.
Learn more: docs.primuslabs.xyz/implemen…
Explore Primus XP: s.kaito.ai/g81sAwY
Fayy🌼 retweeted
As AI agents gain the ability to interact with websites, APIs, and financial applications, a new question becomes important:
How do we verify what an agent actually did?
An agent might report that it checked a source, completed an action, or followed a particular process. But a statement from the agent isn’t the same as independently verifiable evidence.
@primus_labs is extending its verification approach beyond data to include states and actions.
Its infrastructure uses cryptographic techniques such as zkTLS to help verify information and interactions involving web-based systems.
This matters for AI agents that may eventually handle tasks involving financial information, trading workflows, or other sensitive operations.
Privacy also matters here. Verification shouldn’t automatically require exposing every piece of data involved in an action.
The direction is interesting: AI systems will need ways to make their work more verifiable, not just more automated.
As agents become more capable, proving what happened could become just as important as the result they produce.
Explore Primus: s.kaito.ai/g81sAwY
Fayy🌼 retweeted
Privacy and verification aren’t just features that appear because a project says it supports them. The underlying system matters.
Primus’s zkTLS network involves three main roles:
1. The client
The user or application that requests a specific piece of data to be verified.
2. The developer
The person or team building an application that uses Primus’s SDK to request verification.
3. The attestor node
A computing node that participates in the zkTLS process and helps generate the proof.
According to @primus_labs documentation, attestor nodes run inside Trusted Execution Environments (TEEs), which help protect runtime integrity and sensitive cryptographic materials.
The network uses system contracts to coordinate tasks and related fees, while the SDK connects applications to the verification workflow.
Why does this matter?
Because usable verification infrastructure needs more than cryptography on paper. It also needs a system for applications to request proofs and for the network to execute those tasks.
That’s the infrastructure side of Primus worth understanding.
Explore Primus: s.kaito.ai/g81sAwY
Fayy🌼 retweeted
There’s a common assumption in crypto that financial activity must be completely transparent to be accountable.
But those are not the same thing.
A business can need to prove that it meets a requirement without revealing every customer record. A user can need to verify a claim without making their entire financial history public.
The challenge is designing systems that protect sensitive information while still allowing the right claims and computations to be checked.
@primus_labs is building toward this with two key technologies:
• zkTLS helps verify the authenticity of off-chain data.
• FHE allows computation on encrypted information.
In its confidential transaction design, Primus also describes controlled access to decryption, so encrypted balances and transaction amounts don’t have to be publicly readable by default.
This isn’t the same as saying every transaction is automatically compliant. Compliance still depends on the application, its rules, and how authorized access is implemented.
But the underlying principle is important: privacy doesn’t have to mean nobody can verify anything.
That’s a useful direction for institutional on-chain finance.
Primus XP: s.kaito.ai/g81sAwY
Fayy🌼 retweeted
One thing that could hold prediction markets back is that users can only trade on questions someone else decided were worth asking.
Think about how many debates happen every day across crypto, gaming, sports and internet culture that never become tradable markets.
That’s the problem @xomarket is trying to solve with XO 2.0.
Anyone can create a Conviction market from $10, set the opening odds, and give other people a chance to trade on the outcome.
The interesting part is what happens next.
Most markets won’t become massive. Some might attract 40 people who genuinely care about the outcome. Others could grow into much bigger markets as interest increases.
That’s similar to YouTube. Millions of videos don’t need millions of viewers to justify existing. People can create for a specific audience, and the audience decides what deserves attention.
XO is applying that idea to prediction markets.
It still offers mainstream markets, but now niche questions don’t have to wait for a platform to list them.
The real test is whether those markets can attract traders, build liquidity and settle outcomes fairly at scale.
If XO gets that right, prediction markets could become much more than a place to bet on the same major events.
They could become a way to put almost any well-defined question to the market.
Try it: s.kaito.ai/fwMlgBU
When a stablecoin or financial platform says it has assets backing what it owes, users need a way to assess that claim.
Simply asking people to trust the statement isn’t enough.
But publishing every internal financial record isn’t always practical either.
This is where verifiable proof becomes important.
@primus_labs has a Proof of Reserves service designed to help projects show evidence of off-chain backing assets on-chain while keeping sensitive operational information private.
The distinction matters:
Transparency should let people verify important claims. It doesn’t necessarily require exposing every detail of the underlying business.
This kind of infrastructure can be useful for stablecoin issuers, custodians, exchanges, and other projects where confidence in financial backing matters.
According to Primus’s published project overview, its PoR work has included production projects involving stablecoins and real-world assets.
For on-chain finance to mature, users need better ways to verify claims instead of relying on promises alone.
More on Primus and XP tasks: s.kaito.ai/AEWCLx7
Privacy infrastructure becomes more useful when it can reach the ecosystems where people already transact.
That’s why I find the connection between Primus and BNB Chain worth watching.
@primus_labs is building a verification and privacy layer that can work with existing blockchain ecosystems. Its approach combines zkTLS, which helps verify off-chain data, with FHE, which enables computation on encrypted information.
For an ecosystem like BNB Chain, this opens up interesting possibilities to explore:
• Financial applications that use verified off-chain information.
• Lending systems that can make better use of users’ financial history.
• Confidential transfers and financial workflows.
• Applications that need proof of a claim without exposing every detail behind it.
These are potential applications of the underlying technology, not a claim that every use case is already live.
The bigger point is that better privacy shouldn’t require users and liquidity to abandon the networks they already use.
Primus has been building toward this kind of infrastructure, with its zkTLS verification layer going live on BNB Chain in June 2026, according to its project timeline.
Explore Primus and XP portal: s.kaito.ai/AEWCLx7
A public blockchain makes transactions easy to inspect.
That transparency is useful, but it can also reveal more about someone’s finances than they want the world to know.
Imagine a business paying a supplier on-chain. The business may want the payment to be verifiable without making its balance and transaction amount visible to every competitor watching the blockchain.
This is where Fully Homomorphic Encryption (FHE) becomes interesting.
FHE allows computations to be performed directly on encrypted data. The data can be processed without first exposing it in plaintext.
@primus_labs is developing FHE infrastructure to support confidential financial activity on existing blockchain rails.
That means privacy doesn’t necessarily have to come from moving to a completely separate blockchain.
The broader idea is straightforward: financial information can remain encrypted during processing, while access to decrypted information can be controlled.
Public infrastructure doesn’t have to mean every financial detail is public.
Read the technical overview: docs.primuslabs.xyz/fhe/over…
Your financial history can be useful without being public.
Say you want to qualify for a crypto loan. A lending protocol might want evidence that you hold enough assets on a centralized exchange.
Today, proving that can mean sharing screenshots or exposing more account information than the other party actually needs.
@primus_labs uses zkTLS to help verify data from websites and online services while protecting the underlying information.
In simple terms, the goal is to prove a specific claim about your data without handing over the entire record.
That could make it easier to build applications around verified assets, trading history, credentials, and other off-chain information.
The key distinction: the data doesn’t become trustworthy just because someone uploads a screenshot. There needs to be a way to verify where it came from and whether the claim is authentic.
This is one of the problems Primus is building for.
Learn more: docs.primuslabs.xyz
Explore Primus XP: s.kaito.ai/AEWCLx7
One thing that could slow institutional finance on-chain isn’t a lack of liquidity.
It’s the privacy problem.
Banks, funds, and other financial institutions handle information they cannot simply publish on a public blockchain. Think portfolio positions, transaction amounts, client information, and internal financial records.
But keeping everything private creates another problem: how can other parties verify the information they need to trust?
This is the gap @primus_labs is working on.
Primus combines data verification through zkTLS with privacy-preserving computation through FHE, allowing sensitive information to be used and verified without making all the underlying data public.
The important part is that Primus is designed to work with existing blockchain ecosystems instead of requiring everyone to migrate to a new chain.
Institutional finance needs more than transparent transactions. It needs ways to verify what matters while protecting what shouldn’t be exposed.
That’s the problem worth paying attention to.
Explore Primus and XP tasks: s.kaito.ai/AEWCLx7
Most people hear prediction markets and immediately think about picking a side and hoping they’re right.
But @xomarket has several ways to participate beyond trading.
Here are six worth understanding:
Trade markets ➔ Take a position on an outcome you believe in. If the market resolves in your favor, you can earn from that position.
Create a Conviction ➔ Launch your own market from $10 and earn 20% of the lifetime trading fees it generates.
Provide liquidity ➔ XO has a $1M liquidity rewards program that distributes daily USDC and Conviction Points rewards across selected markets.
Earn maker rebates ➔ When someone trades against your resting limit order, XO offers a 35% maker rebate under its stated program.
Refer other users ➔ Earn Conviction Points and USDC based on referred trading volume.
Use the platform ➔ Activities such as depositing and trading can earn Conviction Points, according to XO’s rewards system.
What I find interesting is that these routes serve different types of users.
You don’t necessarily have to be a trader trying to predict every outcome correctly. You could be a creator building markets, a liquidity provider placing orders, or someone introducing new users to the platform.
Each route has its own conditions, and rewards aren’t guaranteed profits.
XO 2.0 is trying to make prediction markets a place where people can create, trade and participate in different ways.
If you want to explore it yourself:
s.kaito.ai/fwMlgBU
#PredictionMarkets
Creators are already good at starting conversations.
A gaming creator can get people debating which game will win an award. A crypto researcher can spark arguments about a project’s next milestone. A sports creator can spend an entire week discussing an upcoming match.
But what happens when that audience wants to do more than argue in the replies?
With @xomarket, a creator can turn a well-defined question into a market that people can actually trade.
XO calls these markets Convictions, and anyone can create one from $10.
For example, a creator following a new blockchain project could create a market around whether its mainnet will launch before a specified date.
Their audience can then decide which side they believe is more likely.
There’s also a creator incentive: the person who creates a market receives 20% of the lifetime trading fees generated by that market.
That creates an interesting possibility.
Instead of every creator monetizing through sponsorships, subscriptions or selling products, they could also build markets around topics their communities genuinely care about.
Of course, the market still needs clear rules, sufficient interest and reliable resolution to work well.
But I like the idea of giving creators a way to turn community discussions into something people can participate in.
Maybe the next step for some creators isn’t just building an audience around their niche.
It’s building markets around it.
Explore: s.kaito.ai/fwMlgBU
Everyone talks about AI in crypto, but I think prediction market resolution is an interesting use case worth paying attention to.
Creating a market around a question is one thing.
Deciding what counts as the correct outcome is another.
Imagine creating a market about whether a particular crypto project will launch a feature before the end of the month.
What happens if the announcement is vague?
What if the feature launches in beta instead of fully launching?
What if traders disagree about what the original question meant?
Without clear rules and a reliable resolution process, even a good market can become a dispute.
This is where @xomarket’s MODRA comes in.
MODRA is XO’s AI market engine. It assists with market creation and resolution, including using predefined rules and sources of truth to determine outcomes.
The goal is to make resolution scalable without leaving the final decision entirely to a single person or interested party.
That matters because user-generated markets can cover far more topics than a platform could reasonably curate itself.
But opening market creation to everyone only works if the outcomes can also be resolved reliably.
More markets create more opportunities. Reliable resolution gives people a reason to trust the process.
I’m interested to see how far this AI application can go.
Explore XO: s.kaito.ai/fwMlgBU