NFT LOVER MODERATOR CONTENT CREATOR
Joined August 2024
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Sayan retweeted
GoodLaunch is expanding 🌐
Building the ultimate multi-chain launchpad
We are recruiting:
MODS:
🇺🇸 US | 🇪🇺 Europe | 🌏 Asia | 🌍 Africa
AMBASSADORS - few key voices
Looking for: crypto-active, English + local language is a plus.
How to apply: Follow Repost Comment your region
⏰ Deadline: Nov 15
#GoodLaunch #crypto
Sayan retweeted
One thing I think we underestimate about the Al agent economy:
An agent being capable isn't enough.
You also need to know whether that agent can actually deliver.
That's why I've been looking more closely at what @termix_ai is doing with agent reputation.
On a normal Al marketplace, you might see an agent claiming it can audit a contract, analyze data, build a smart contract or handle research.
But then comes the obvious question:
"Why should I trust this agent with the job?"
TermiX is trying to make that answer more than just a profile description.
Agents have an on-chain identity, and the marketplace exposes things like reputation, pass rate and completed jobs. Completed work can become part of the agent's public track record.
That creates an interesting feedback loop:
✅️An agent gets a job.
✅️It delivers.
✅️The result is settled.
✅️The outcome contributes to its reputation.
Then the next client has more information before deciding who to hire.
And this becomes even more interesting when you stop thinking about humans hiring agents.
Think about agents hiring other agents.
If an Al agent needs a Solidity audit, data analysis or research it can't handle itself, it needs some way to discover another agent and assess whether that provider is worth using.
A name and a fancy description won't be enough, track record becomes infrastructure.
#TermiX #AIAgents #AgentEconomy
Sayan retweeted
Can a meme coin establish a lasting footprint in mobile gaming?
Meet @CyFurCoin, an emerging Solana venture seeking to bridge community culture with an expansive entertainment brand.
Here is what you need to know about the universe of $CYFUR
Sayan retweeted
good night ct
same btc setup. same entry. same stop loss. three different risk levels.
i used @tryquantio to analyze a hypothetical btc trade with a $1,000 account.
entry: $84,000
stop loss: $82,500
take profit: $87,000
here's how the risk changes.
0.5% risk means $5 potential loss
1% risk means $10 potential loss
2% risk means $20 potential loss
the potential rewards follow the same pattern.
0.5% risk means $10 potential profit
1% risk means $20 potential profit
2% risk means $40 potential profit
all three scenarios have the same 2:1 risk to reward ratio. only the position size changes.
that's what stood out to me. the same trade idea can have very different consequences depending on how you manage your capital.
quant ai made it easier to compare the numbers before risking real money.
my takeaway is simple. protecting capital comes first. bigger positions don't make a trade smarter.
try it yourself.
app.tryquant.io/
#QuantAIPioneers
Sayan retweeted
The research behind Flint is what caught my attention
@recursive_si’s Eureka Machine helped develop improvements across training and inference
That work is now part of a coding model people can run locally
I’m curious what that research process produces next
🤝 Paid partnership
Introducing Flint, @Recursive_SI’s first model: a fast local coding model built with our automated research system, the Eureka Machine.
• Best-in-class fast local coding
• Up to 2x faster than comparable models: 125+ tokens/sec on a MacBook Pro, 150+ on RTX Spark
• Built from scratch in under 4 months
Private, unmetered intelligence on your own machine. Open weights available now on @huggingface, run it locally on your favorite framework or try it for free on @opencode or @pidotdev.
Sayan retweeted
privacy and transparency can work together
as more financial activity moves onchain, the goal should be to prove what matters without exposing everything else
this is what makes @primus_labs interesting to me
zkTLS can verify data from trusted websites while FHE enables computation on encrypted information
from creditworthiness checks and proof of reserves to verifying trading history, these technologies could unlock real use cases without forcing users to reveal sensitive data
the future isn’t about making all data public
it’s about making data verifiable while keeping it private
Sayan retweeted
The part of the film that stayed with me wasn't the 75-year case.
It was realizing that Lee Castleton never had a fair chance to prove the computer was wrong.
A system showed a £23,000 shortfall he didn't cause.
He couldn't afford a lawyer.
He represented himself.
He lost.
He was ordered to pay £321,000 in legal costs.
He went bankrupt and lost everything.
Years later, the Horizon system was shown to produce false shortfalls.
What unsettles me is that this wasn't really a story about a computer making a mistake.
Computers make mistakes.
People make mistakes.
The deeper failure was a system that stopped questioning its own conclusions.
It trusted an output more than the person living with the consequences.
That's the moment an error becomes something much bigger.
A number becomes a debt.
A verdict becomes a punishment.
A process becomes more important than reality.
That lesson feels even more important today.
We're entering a world where AI agents will negotiate agreements, move funds, purchase services, and make decisions at a scale no human institution was designed for.
The biggest risk isn't that AI will make mistakes.
The biggest risk is repeating the same habit.
Treating an output as truth simply because it came from a sophisticated system.
And when disputes inevitably happen, trying to resolve them using processes that are too slow, too expensive, and too difficult for ordinary people to access.
The lesson I took from the film is simple:
Justice doesn't fail when a mistake is made.
Justice fails when a mistake can no longer be challenged.
That problem existed before AI.
The AI era just makes it impossible to ignore.
That's why @GenLayer matters to me.
Not because AI needs more power.
Because a world powered by intelligent agents will also need a practical way to evaluate evidence, challenge outcomes, and resolve disagreements before trust breaks down.
The question isn't whether AI will make mistakes.
The question is whether we'll build better ways to correct them.
#JusticeNeedsAnUpgrade
🤝 Paid partnership
Slow. Expensive. Wrong.
It's been this way for centuries. It doesn't have to be.
#JusticeNeedsAnUpgrade
Sayan retweeted
Most leveraged products are designed backwards.
Pick a multiple first.
Build around it later.
Want 2x exposure? Create a 2x product.
Want 3x? Create a 3x product.
The target comes first. The structure adapts.
What caught my attention about @2FactorFinance is that it flips that process on its head.
Instead of starting with a leverage multiple, it starts with the behaviour of the asset itself.
Bitcoin's volatility becomes the constraint.
The leverage multiple becomes the result.
That process currently produces roughly 1.33x exposure for cbBTC in the public beta on Base.
At first glance, that sounds less ambitious than the numbers most traders are used to seeing.
But maybe that's the point.
The objective is not to maximise leverage.
The objective is to identify how much leverage an asset can support before the structure itself becomes the problem.
To do that, 2Factor partitions volatility into perpetual senior and junior tranches.
The senior side provides stability. The junior side receives leveraged exposure without liquidation risk or dependence on an external short counterparty.
The result is leverage priced against stable capital rather than the economics of shorts.
Bitcoin is simply the first proving ground.
What makes this more interesting is where it is happening.
Coinbase is bringing tokenized stocks onchain through Base. Asset tokenization expands access, but access alone does not create capital structure.
Kelly-optimized leverage is one example of how that missing layer could emerge.
That's why I joined the @2FactorFinance Season 1 Points Program.
Season 1 is all about social activities, education and referrals.
Marks are earned from verified actions only. No purchase, deposit or holding earns Marks.
When the leaderboard freezes, the top 10 participants will split 1 BTC paid in cbBTC.
Join here: points.2factor.finance/r/n4s…
You can also earn 10% of the Marks generated by accounts you refer.
A final thought:
If one protocol launches at 3x because traders want 3x, and another launches at 1.33x because the mathematics says 1.33x, which one is actually taking the bigger risk?
Sayan retweeted
A mortgage statement spells out exactly what you owe and why, piece by piece, every single month. A leveraged position just shows you a number on a screen and leaves you to guess what’s quietly eating into it while you hold.
That gap is two separate costs running at once. One is volatility drag, the mathematical cost of compounding through a choppy market. The other is financing drag, the price of whoever’s capital is sitting behind your exposure. Neither one shows up as a line item. Both run whether the asset goes up, down, or nowhere.
@2FactorFinance live on Base, working with Coinbase on their tokenized-stocks ecosystem, and it’s built around making that second cost legible instead of hidden. Exposure splits into a senior tranche that holds deep downside protection and a junior tranche that carries the leveraged side, with no liquidation engine and no external hedge counterparty behind it.
The target multiple isn’t fixed across assets either. Bitcoin’s volatility near 60 percent keeps its productive band tight, closing in around 1.33x. Lower-volatility equities, closer to 16 to 19 percent, open a band that runs well past 3x. Tokenization already solved where these assets live. It never touched how they behave once you’re actually holding leverage on them for months.
I joined Season 1 of the Points Program to watch this play out. Marks only come from verified social activity, education, and referrals, never from depositing or holding. When the leaderboard freezes at launch, the top 10 accounts split 1 BTC, paid in cbBTC. Marks carry no cash value, can’t be transferred, and aren’t a claim on any token or asset.
If you want to follow the same thing, here’s my referral:
points.2factor.finance/r/1oi…
If a mortgage statement has to show you the real cost every month, what would it take for a leveraged position to be that transparent about what it’s charging you right now?
Sayan retweeted
Everyone training robots is collecting the same thing: successful demonstrations. Perfect runs. The robot grabs the cup, places it, done.
That's the wrong data, and @axisrobotics is about to prove it.
Think about how you learned to pour coffee. Not from watching 10,000 flawless pours. You learned from the near-misses, the overfill, the moment the cup slipped and your hand corrected mid-motion. Recovery is the skill. Success is just the recording of it.
Robot foundation models today barely have that data. When a policy fumbles in the real world, it has never seen what comes next, because nobody collects failure.
Axis is shipping the world's first DAgger dataset this month: massive human-gated, failure-recovery data. A trained policy attempts the task, it starts to get it wrong, and a human takes over to correct it. That correction is the data point. It fills a gap the entire industry has been quietly working around.
And here is why only Axis can produce it at this scale. You cannot buy failure-recovery data. You need thousands of humans watching robots fail in real time and intervening. Axis has 130,000+ contributors doing exactly that in a browser, no hardware required.
The receipts are already there:
Dataset V1 lifted π0.5 from 83.9% to 88.8% on LIBERO-Plus, with performance still climbing as data scaled from 25% to 100% of the set. No saturation. Built with researchers from Georgia Tech, UC Berkeley, Johns Hopkins, Penn, UMich, NUS and NTU.
The Little Prince experiment: 15,371 participants, 85,387 training sessions, deployed on a real robot in five days.
4,000,000 trajectories and 5,000+ tasks in the production library.
Commercial buyers already in procurement: Booster Robotics on hardware, Lotus and Geely on industrial automation, ManyCore and Feagine on foundation models.
So here is the opportunity right now. Epoch 2 is live, and the referral mechanic is unusual: every validated trajectory from someone you refer multiplies your own Kaito mindshare score.
5 trajectories gives 1.05x
10 gives 1.1x
50 gives 1.25x
200 from 10+ referred users gives 1.5x
Plus a sprint bonus of +0.15 for 100 new trajectories inside Epoch 2.
Clicks and signups count for nothing. Only completed, validated, on-chain-signed tasks move the number. That means the people you bring in have to actually contribute, which is the whole point.
Get started: run the beginner tutorial, open the simulator on a desktop browser, complete one task, sign it on Base. Takes minutes.
Most data networks pay you to farm. This one pays you to teach a robot how to fix its own mistakes.
My referral link is below. Bring someone who will actually play.
s.kaito.ai/KxhUzha
Sayan retweeted
Just put in my application for @FungoLabs.
What got me interested is the way they’re approaching NFTs with privacy built in from day one, rather than trying to add it later.
The @zama FHE angle makes it even more interesting since encrypted data can still be used by smart contracts.
Still early, but definitely one I’ll be watching closely. 👀
Sayan retweeted
Most points campaigns give you a reason to chase a leaderboard. What matters more to me is whether there’s something worth understanding behind the points.
I’ve joined the @2FactorFinance Points Program for Season 1, and I’m paying attention to both the competition and what the project is building.
There’s 1 BTC in prizes for the top 10 on the Season 1 leaderboard, with points earned by completing activities across three areas: social engagement, education, and referrals. That gives participants different ways to get involved instead of simply watching from the sidelines.
But the part I find more interesting is the product itself.
2Factor Finance is building on Base and working together with Coinbase to bring Kelly-optimized leverage onchain. That creates an interesting connection to the tokenized stocks ecosystem, where access to tokenized assets is only one part of the bigger picture. How users manage exposure and apply capital efficiently is another challenge worth solving.
The Kelly framework is particularly interesting because it approaches position sizing through the relationship between risk and potential returns. Applying that kind of framework to onchain leverage is a use case worth watching, although the outcome still depends on the model, its assumptions, and how risk is managed in practice.
For Season 1, my plan is straightforward: learn the mechanics, complete the available activities, contribute socially, and earn points consistently. The leaderboard adds competition, but understanding the product is what makes participation more meaningful to me.
If you're exploring the program too, here's my referral link:
points.2factor.finance/r/hp1…
1 BTC is up for grabs among the top 10. Now the real question is who will put in the work to get there.
Sayan retweeted
$AXIS TGE: The Numbers Behind Physical AI
With the October 18 contributor snapshot approaching, I took a closer look at Axis Robotics’ tokenomics and how $AXIS is designed to support its growing Physical AI ecosystem.
Here are the key details:
🔹 Total Supply: 1 billion $AXIS, with no additional inflation planned.
🔹 TGE Unlock: 165 million tokens, equal to 16.5% of the total supply.
🔹 Community Contributor Airdrop: 10% of the total supply.
• Season 1: 40 million $AXIS (4%)
• Season 2: 60 million $AXIS (6%)
🔹 Token Allocation:
• Network: 61.08%
• Team & Investors: 31.67%
• Liquidity: 6%
• Community Sale & Campaigns: 1.25%
What interests me most is how token incentives can support real contributions to Physical AI.
Axis is building a data engine where contributors generate robot trajectories, verified data helps train models, and improved models can reveal new challenges that require better data.
The key question is how effectively $AXIS can encourage valuable contributions and support sustainable ecosystem growth.
The snapshot reflects past contributions. The real test is what the network builds next.
Note: October 18 is the contributor snapshot date, not the confirmed TGE date. I’ll be watching for official updates from @axisrobotics
@KaitoAI
Join - s.kaito.ai/yMieYdg
#AXIS #PhysicalAI #Robotics
🤖 Made with AI
Sayan retweeted
Installed @conso_xyz extension in my browser today, dyk why?
We use AI in our day to day life like we use oxygen these days and it's not bad cause we are using it to stay more informative and ease our works and more effiecient
What if we get to monetize the data and thoughts we share and generate from it, doesn't it sounds really nice and that's what the extension can do
And if you think about privacy then it doesn't store any chat data or transmit it anywhere, it scans the models and what you use for and the input/output and rewards Zaps for this
For now it is supported on Gemini, Chatgpt, Claude, Perplexity and some others
🤖 Made with AI
CONSO BOTS Eligibility Checker is now LIVE 🚨
5,555 BOTS | Free Mint | Agentic Collectibles 🤖
Check your eligibility 👇
conso.xyz/bots
Sayan retweeted
The news always looks super bearish at the bottom.
But if you're waiting for sentiment to turn green before buying, you're already too late.
DCA into the fear. 📈 @cryptonary
#Cryptonary #Crypto #Bitcoin
Not financial advice.
Sayan retweeted
Some of the most valuable data for Physical AI may come from places people usually overlook.
@vangrid_io makes this problem tangible through its live capture bounty board. Requests include damaged locker doors, CCTV columns, ATM lobbies, hospital corridors and retail floors. These are ordinary environments, but each has its own spatial details that a robot or world model may need to understand.
A general image dataset cannot automatically provide a fresh 3D representation of a specific location. When a developer needs spatial data from a particular environment, the ability to request a new capture becomes much more useful than relying only on whatever data already exists.
This is where Vangrid stands out to me. Buyers can request specific captures, review previews before accepting submissions, and receive reconstructed 3D outputs. Onchain records also make parts of the capture and payment process independently verifiable.
@vangrid_io is connecting physical locations with real data requests through a distributed smartphone network. The important idea is not simply collecting more footage. It is matching the right spatial data to the actual problem a developer needs to solve.
As Physical AI develops, infrastructure that can obtain fresh data from specific places could become an important part of the stack.
Explore real capture requests at data.vangrid.io/
🤝 Paid partnership
Sayan retweeted
If I were staking GEN, I wouldn’t choose a validator just because of the rewards.
I’d first want to understand who I’m trusting with my stake and how that choice affects the network.
On @GenLayer, validators help the network reach decisions. They execute Intelligent Contracts, propose outcomes, and evaluate results as part of the consensus process.
But what if you want to support GenLayer without running your own validator node?
That’s where delegation comes in. GEN holders can delegate their tokens to a validator and participate without managing the infrastructure themselves.
Personally, I’d prefer to spread my delegation across a few reliable validators instead of depending on just one.
Here’s my thinking 👇
Reliability matters: I’d look at a validator’s performance, uptime, and track record before making a choice.
Decentralization matters: Supporting different validators can help avoid concentrating too much influence in a small number of participants.
Risk matters: Staking isn’t risk-free. Delegated stake can be exposed to protocol penalties, so I wouldn’t make my decision based on rewards alone.
Of course, splitting my stake wouldn’t remove every risk, and more validators wouldn’t automatically mean higher returns. I’d still need to research each validator carefully.
For me, staking is not simply about earning something from holding tokens. It’s about making a deliberate choice about who helps secure the network and how I want to contribute to its growth.
As @GenLayer moves toward mainnet, I think understanding these choices is just as important as understanding the technology itself.
Now I’m curious: would you delegate all your GEN to one validator you trust, or spread it across several? And what would you check before choosing? 👀
Sayan retweeted
Absolute disbelief and screaming when @JoeyMoose realizes he hits a 2015 Pokemon card PSA 10
Sayan retweeted
There’s more happening at @zerufinance than just preparing to launch another Activity Market category
The team has been working on scoring improvements, adding support for more chains, and building new tools alongside the upcoming expansion
I think this part of the development is worth paying attention to
When an ecosystem grows, introducing new features is only one part of the work. The underlying infrastructure needs to keep evolving too
Scoring, chain support, and the tools people use all contribute to how an ecosystem develops as more activity and new opportunities come into play
That’s why I’m interested in seeing how these updates fit together with the next category
The new launch will naturally get attention, but the improvements happening alongside it are an important part of the bigger picture
Looking forward to seeing what @zerufinance shares next
