On-chain Maniac▫️Early Alpha ▫️NFA▫️DM FOR COLLAB🔺
Joined February 2022
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8 Websites I use for Airdrop Research in 2026 🪂🪂
1• Cryptorank;
> cryptorank.io
> New Fundraising Alerts
> Early Airdrop opportunities
2• Airdrops io
> airdrops.io
> Trusted for high quality Airdrop updates and early alphas
3. Defillama
> defillama.com/yields
> yield farming updates
4• DappRadar Rewards
dappradar.com/rewards
> Perfect for Web3 & on-chain reward hunts.
5• AirdropAlert
airdropalert.com
> Beginner-friendly airdrop guides.
AirdropBob
6• airdropbob
> airdropsmob.com
> Clean & simple airdrop tracker.
7• certik skynet
> skynet.certik.com
> token unlocks
> security audit reports
8• crt sh
> crt.sh
> get projects domain before public
> verify domains
Sire retweeted
I was murdered at the Ecosystem Call Halloween Party. Who did it?!
My prime suspects are @b1ockbelle and @meiyixing1.
Find out who murdered you on murderboard.fun!
Sire retweeted
Welp, my old account got suspended 😭
We’re starting over.
If you knew me as @aabashweb3, it’s still me.
Let’s rebuild this together. Who’s still here? 🤝🫂
Sire retweeted
Seven companies account for roughly a third of the S&P 500’s market capitalization. Their products run the phones, the cloud and the AI systems most of the world already uses. Ownership of them has stayed concentrated among people with direct access to US capital markets.
zMAG7 holds all seven at equal weight in a single position on Base, minted with USDC from one dollar.
Sire retweeted
AI memory deserves more attention than it's getting.
one thing I keep coming back to in the AI space is how much attention goes into making models smarter, while the infrastructure that helps AI agents retain and retrieve useful information often gets overlooked.
think about an agent working with you on a long-term project.
over time, you establish preferences, make decisions, test different approaches, and learn what works.
all that context can be valuable for future tasks.
but storing information is only part of the challenge.
the agent also needs to find the right information when it matters.
otherwise, having a large collection of memories doesn't necessarily make it more useful.
that's where the memory layer comes in.
@LighthouseWeb3 is tackling this challenge with lighthouse memory.
its approach combines keyword search and vector search to help AI agents retrieve relevant information from their stored memories.
keyword search helps locate specific terms, while vector search can find information based on the meaning of a query.
together, these approaches give agents different ways to find the context they need.
and there's a bigger picture here.
as AI agents move beyond simple conversations and begin handling more complex, ongoing tasks, managing their context becomes increasingly important.
developers need to consider how agents preserve useful information, retrieve it efficiently, and manage memories as their needs evolve.
the challenge isn't simply teaching AI to remember.
it's making that memory useful when it counts.
ofc, there's still plenty to solve around memory quality, outdated information, and retrieval accuracy.
but these challenges also show why dedicated memory infrastructure matters.
that's why I'm keeping an eye on what lighthouse is building.
if you're exploring AI agents and the infrastructure powering them, I'd encourage you to check out lighthouse and see what they're working on.
the next phase of AI won't be defined by intelligence alone.
the infrastructure supporting how agents operate over time will matter too.
lighthouse is actually building towards this.
I'm really bullish on where they're headed.
why do you have to explain the same thing to an AI agent twice?
you explain your goals, establish preferences, make important decisions and work through a problem together.
then you return to continue the work, only to find yourself rebuilding context you've already provided.
the problem isn't necessarily intelligence.
it's memory.
@LighthouseWeb3 is addressing this challenge with lighthouse memory, a persistent memory system designed to help AI agents store information and retrieve it across sessions.
but storing information is only half the challenge.
an agent also needs to find the right information when it matters.
lighthouse memory combines keyword-based search with vector search, supporting the retrieval of information through exact terms and semantic similarity.
its architecture has three main components:
🔹 engine: handles memory operations and search.
🔹 storage: determines where memories are kept.
🔹 embedder: supports semantic search by converting information into representations that can be compared for similarity.
the storage options documented by Lighthouse include IPFS/Filecoin, S3-compatible storage, and local files, giving developers different ways to organise their memory infrastructure.
there's also the question of what happens after information has been stored.
memories can become outdated, lose relevance, or need correction.
lighthouse memory documents operations for listing, editing, forgetting, rebuilding, and recovering memories.
that matters because useful AI memory isn't simply about retaining as much information as possible.
it's also about managing that information over time.
security deserves attention, too.
lghthouse's documentation warns that batched memory blocks and index snapshots may be stored unencrypted, with native encryption described as under development. developers should understand these limitations before storing sensitive information.
yeah, persistent memory doesn't automatically make an agent more intelligent or guarantee perfect recall.
retrieval quality, the accuracy of stored information, and implementation choices still matter.
but as AI agents take on longer-running tasks, the ability to preserve and retrieve useful context becomes increasingly relevant.
think about an agent supporting an ongoing project.
istead of repeatedly reconstructing the same background information, it could retrieve relevant details from previous sessions and use them to inform its next steps.
that's what makes persistent memory an important area of AI infrastructure.
it creates a way for useful context to carry forward between interactions.
if you're building AI agents.
you really need to explore lighthouse memory:
- introduction: docs.lighthouse.storage/memo…
- quick start: docs.lighthouse.storage/memo…
the goal isn't for an AI agent to remember everything, but for the agent to find the right information when it matters.
so what do you think is the hardest part of AI memory?
deciding what to remember, retrieving the right context, or keeping stored information accurate over time?
I'll be in the cs.
Sire retweeted
what if swapping bitcoin to solana didn't require figuring out bridges, networks, and multiple swaps yourself?
here's the catch:
BTC lives on bitcoin, while SOL lives on solana.
you can't simply send BTC across and expect it to arrive as SOL.
but you can swap between them.
I looked at how @RangoExchange approaches this problem and what you should actually compare before choosing a route.
because the cheapest-looking swap isn't always the best one.
walk with me.🧵👇
🤝 Paid partnership
Sire retweeted
why do you have to explain the same thing to an AI agent twice?
you explain your goals, establish preferences, make important decisions and work through a problem together.
then you return to continue the work, only to find yourself rebuilding context you've already provided.
the problem isn't necessarily intelligence.
it's memory.
@LighthouseWeb3 is addressing this challenge with lighthouse memory, a persistent memory system designed to help AI agents store information and retrieve it across sessions.
but storing information is only half the challenge.
an agent also needs to find the right information when it matters.
lighthouse memory combines keyword-based search with vector search, supporting the retrieval of information through exact terms and semantic similarity.
its architecture has three main components:
🔹 engine: handles memory operations and search.
🔹 storage: determines where memories are kept.
🔹 embedder: supports semantic search by converting information into representations that can be compared for similarity.
the storage options documented by Lighthouse include IPFS/Filecoin, S3-compatible storage, and local files, giving developers different ways to organise their memory infrastructure.
there's also the question of what happens after information has been stored.
memories can become outdated, lose relevance, or need correction.
lighthouse memory documents operations for listing, editing, forgetting, rebuilding, and recovering memories.
that matters because useful AI memory isn't simply about retaining as much information as possible.
it's also about managing that information over time.
security deserves attention, too.
lghthouse's documentation warns that batched memory blocks and index snapshots may be stored unencrypted, with native encryption described as under development. developers should understand these limitations before storing sensitive information.
yeah, persistent memory doesn't automatically make an agent more intelligent or guarantee perfect recall.
retrieval quality, the accuracy of stored information, and implementation choices still matter.
but as AI agents take on longer-running tasks, the ability to preserve and retrieve useful context becomes increasingly relevant.
think about an agent supporting an ongoing project.
istead of repeatedly reconstructing the same background information, it could retrieve relevant details from previous sessions and use them to inform its next steps.
that's what makes persistent memory an important area of AI infrastructure.
it creates a way for useful context to carry forward between interactions.
if you're building AI agents.
you really need to explore lighthouse memory:
- introduction: docs.lighthouse.storage/memo…
- quick start: docs.lighthouse.storage/memo…
the goal isn't for an AI agent to remember everything, but for the agent to find the right information when it matters.
so what do you think is the hardest part of AI memory?
deciding what to remember, retrieving the right context, or keeping stored information accurate over time?
I'll be in the cs.
🤝 Paid partnership
Sire retweeted
US stock market sees an average daily trading volume of ~$1T, or around $22.8T per month. Yet, the market is open for only 6.5 hours a day, five days a week
On the other hand, onchain tokenized stocks can be traded 24/7. That means you can now trade your favorite stocks even on weekends
But the sector's transfer volume over the last month was only $11.09B, just 0.05% of the US stock market's monthly volume
However, tokenized stock holders grew 37% last month, reaching 5M
So, where does @base stand in all this?
Tokenized stocks issued on Base have surpassed $2.13B in cumulative trading volume. So far, there have been 4.08M trades, around 30K holders, and an average daily volume of $80M+
In my opinion, the more people understand the benefits of 24/7 trading and the more institutions start participating, the faster adoption will grow
And with its low transaction fees and various launchpads, Base has a good opportunity to stay ahead in this race
Sire retweeted
gQuip 🟣
The future of compute isn’t about more machines sitting idle. It’s about putting available power to work.
That’s the vision @quipnetwork is building toward.
🤝 Paid partnership
Sire retweeted
zMAG7 is live.
One stablecoin deposit now holds AMZNc, AAPLc, NVDAc, GOOGLc, METAc, MSFTc and TSLAc at equal weight, with real shares in regulated custody behind every token and the token itself in your own wallet.
Built on @coinbase Tokenized Stocks on @base, powered by Zoth’s zVaults.
Available to eligible users outside the US and other restricted jurisdictions.
Deposit now: app.zoth.io/zvault/zmag7
🤖 Made with AI
Sire retweeted
Zoth has been featured in the @CBinsights x @money2020 TradFi x DeFi Market Map 2026.
The only stablecoin native neobank on @base, bringing real-world yield, payments, and treasury into one account, powered by @coinbase infrastructure.
This is the category we set out to define. Good to be recognized among the names shaping it.
Read the report: cbinsights.com/reports/CB-In…
Sire retweeted
what if you could find the next project before ct found the narrative?
not by guessing.
by having a better way to discover what deserves your attention, research it properly, and build your own thesis.
that’s what caught my attention about @HeyResearch
🧵👇
Sire retweeted
Zoth Magnets are converting to Zocta Points 🧲
Magnets were rewarded to our community for participating in and engaging with Zoth. Zocta Points have since replaced Magnets as our rewards system, and Zocta Points are what will convert into ZOTH token. Converting your Magnets now keeps your path to ZOTH token intact.
How to claim:
~ Join our Discord: discord.gg/E5aJkQy5w
~ Raise a ticket with your wallet address and Magnet details
~ Our team takes it from there
Conversions begin today. Your ticket is how we process your conversion, so get it in early.
This is a one-time window for our loyal holders.
Sire retweeted
imagine a supermarket where the shelves move closer to the busiest aisle.
that’s the simplest way I’d explain @PharaohExchange
it’s a dex on avalanche built around concentrated liquidity and an incentive system called x(3,3).
in very simple words, here's what it means.
🧵👇
Sire retweeted
unfortunately as @waleswoosh stopped trading memes & deleted his fomo account.
we lost an huge exit liquidity from the market, thus the market is being slow now.
we would humbly request wale to come back in the trenches so that we can dump on him & flex some pnl on the timeline.
Sire retweeted
Compute shouldn’t sit idle.
@quipnetwork is building a network where unused compute can become useful, verifiable work.
That idea alone is worth watching.
gQuip 🟣
🤝 Paid partnership
Sire retweeted
That's the main reason behind the dumped price of '𝐂𝐫𝐞𝐝𝐢𝐭𝐬'
Jack Butcher’s 𝐒𝐭𝐚𝐭𝐞𝐦𝐞𝐧𝐭𝐬 𝐍𝐅𝐓
Top offer: ~$1.4K
Floor: ~$1.8K
To get one, you need to burn 80 Credits.
So according to this rn
Credits Top offer - 25$ x80 = 2000$ you need to create one proper '𝐒𝐭𝐚𝐭𝐞𝐦𝐞𝐧𝐭𝐬 𝐍𝐅𝐓'
>> Credits opensea.io/collection/credit…
>> Statements
opensea.io/collection/statem…
Statement price will be pumped for sure but always ( DYOR )
