🔸Web3 Community Development ||| 🔸Defi Explorer ||| 🔸Strategist ||| 🔸Blockchain ||| 🔸AI
Joined September 2021
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What if you could access liquidity without selling your crypto? 👀
That's the problem @Lendaryasia_ is building around.
Lendary is a Crypto-Backed lending platform that lets eligible users borrow against BTC, ETH or SOL instead of selling their holdings to access liquidity.
Here's why I find the model interesting 🧵👇
4️⃣ My Takeaway 👇
Crypto-Backed lending is an interesting alternative for people who need liquidity but don't want to automatically sell their digital assets.
But borrowing against volatile collateral comes with risks, including changing collateral values and possible liquidation.
I'm exploring how Lendary approaches this model and what it is building around $LRY.
Explore the project: lendary.xyz/
Token details: lendary.xyz/lry
#DeFi #CryptoLending #LRY
3️⃣ Where $LRY fits in 👇
Lendary's $LRY token is intended to add utility to the ecosystem.
The stated use cases include potential Borrowing-Cost discounts, staking rewards linked to lending activity and governance participation.
The important question isn't just what a token promises but how its utility is implemented and sustained as the platform develops.
2️⃣ How @Lendaryasia_ approaches it
According to its website Lendary offers loans backed by BTC, ETH and SOL with borrowers able to access cash or stablecoins.
Its stated model includes fixed rates agreed Upfront, Third-party custody arrangements and no penalty for early repayment.
Loans start at $10,000, so this isn't necessarily designed for every retail user. Check the eligibility requirements before assuming it fits your needs.
1️⃣ The Problem: Liquidity vs. Holding your position
Imagine holding ETH but needing cash for an opportunity or an expense.
Selling your ETH gives you liquidity but it also means giving up that position.
Crypto-Backed lending offers another route 👉 Use eligible assets as collateral to borrow, subject to the loan terms and risks.
Vøid Alpha 〽️〽️ retweeted
@Gate 𝗪𝗖𝗧𝗖 𝗦𝟵 𝗜𝗦 𝗟𝗜𝗩𝗘 𝗔𝗡𝗗 𝗧𝗛𝗘 𝗦𝗧𝗔𝗞𝗘𝗦 𝗔𝗥𝗘 𝗛𝗜𝗚𝗛!
We're talking about a massive 𝟲,𝟬𝟬𝟬,𝟬𝟬𝟬 $USDT 𝗣𝗥𝗜𝗭𝗘 𝗣𝗢𝗢𝗟 across multiple competitions. Whether you're a solo trader or someone who performs better with a team, there's a way to get involved.
Here's how the rewards break down 👇
🏆 𝗟𝗼𝗴 𝗜𝗻 & 𝗪𝗶𝗻: A free launch try and a share of the 𝟳𝟮𝟱𝗞 𝗨𝗦𝗗𝗧 pool.
🏆 𝗦𝗼𝗹𝗼 𝗧𝗿𝗮𝗱𝗲: Compete individually for a share of the 𝟰𝟬𝟬𝗞 𝗨𝗦𝗗𝗧 pool.
🏆 𝗧𝗲𝗮𝗺 𝗕𝗮𝘁𝘁𝗹𝗲: Build your squad and compete for 𝟯.𝟮𝗠 𝗨𝗦𝗗𝗧 in team rewards.
🏆 𝗠𝗮𝗿𝗸𝗲𝘁 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻 𝟭𝘃𝟭 𝗣𝗞: Put your market analysis to the test and compete for a share of the 𝟰𝟬𝟬𝗞 𝗨𝗦𝗗𝗧 pool.
🎁 𝗖𝗮𝗿𝗻𝗶𝘃𝗮𝗹 𝗣𝗲𝗿𝗸𝘀: Unlock opportunities for 𝟭.𝟮𝟳𝟱𝗠+ 𝗨𝗦𝗗𝗧 in bonus rewards.
And the competition is already taking shape, with 𝟱𝟬,𝟬𝟬𝟬+ 𝘁𝗿𝗮𝗱𝗲𝗿𝘀 registered and 𝟱,𝟲𝟲𝟯 𝘁𝗲𝗮𝗺𝘀 formed.
Got a squad? Lead the charge.
Flying solo? You can still compete.
The team competition kicks off October 26 at 08:00 UTC, so start preparing your lineup and check the eligibility requirements before participating.
→ 𝗝𝗢𝗜𝗡 𝗚𝗔𝗧𝗘 𝗪𝗖𝗧𝗖 𝗦𝟵 𝗡𝗢𝗪: gate.com/competition/wctc-s9…
#GateWCTCS9 #GateBooster
Vøid Alpha 〽️〽️ retweeted
the old creative loop was too slow. this skips the blank prompt part @avastudio_
Vøid Alpha 〽️〽️ retweeted
What happens when an AI agent needs context from a previous session? 🧵
AI agents can be useful but without persistent memory useful information can become fragmented across sessions and applications.
Information learned in one session may not be available in another and switching to a different AI tool can mean losing access to previously established context.
@LighthouseWeb3 aims to solve this problem by giving AI agents Persistent, Portable long term memory backed by decentralized storage.
Instead of keeping useful context tied to one conversation, AI agents can save information and retrieve it when needed
I explored its documentation to understand how it works and what developers should know.
What happens when an AI agent needs context from a previous session? 🧵
AI agents can be useful but without persistent memory useful information can become fragmented across sessions and applications.
Information learned in one session may not be available in another and switching to a different AI tool can mean losing access to previously established context.
@LighthouseWeb3 aims to solve this problem by giving AI agents Persistent, Portable long term memory backed by decentralized storage.
Instead of keeping useful context tied to one conversation, AI agents can save information and retrieve it when needed
I explored its documentation to understand how it works and what developers should know.
Final thoughts
@LighthouseWeb3 provides a way to separate an agent's long term context from any single conversation or application.
Its "remember", "recall" and "forget" operations form the basic interface, while SDK and MCP integrations make that functionality available to different software environments.
The real value is not simply storing more information. It's making useful context persist, remain retrievable and become available to the right applications when needed.
For anyone exploring AI agents, decentralized storage and portable context, @LighthouseWeb3 is worth understanding.
5️⃣ An important security consideration
Persistent memory is useful but it should not be treated as a secure vault for secrets.
Batched storage and index snapshots can contain plaintext.
Memwal uses SEAL-encrypted Walrus blobs but optional public IPFS mirrors can expose plaintext copies.
Deletion also requires care cause with the batched engine, A forgotten record may remain readable in a stored batch or an earlier snapshot.
Avoid storing passwords, private keys, API keys or other sensitive credentials as ordinary agent memories.
Official docs: docs.lighthouse.storage/memo…
4️⃣ What happens behind the scenes?
@LighthouseWeb3 supports different engines, including BatchedEngine and Memwal.
BatchedEngine buffers memories and periodically writes them to storage in batches. It maintains a local search index, which can include memories that haven't yet been flushed.
Memwal sends memories through a relayer that handles embedding, SEAL encryption, storage and search. It has a different retrieval and recovery model.
These differences matter because the selected engine affects persistence, retrieval, encryption and deletion behavior.
3️⃣ How can one agent's memory help another?
Imagine this workflow:
🔸Agent A saves useful context through Lighthouse Memory.
🔸 The information is persisted in the configured memory system.
🔸Agent B connects to the appropriate memory tools.
🔸Agent B retrieves the relevant context using a recall operation.
🔸Agent B uses that information to respond more appropriately.
This is the potential benefit of portable memory (useful context doesn't have to remain confined to one AI session or application.)
However, sharing is not automatic simply because two agents use MCP. They need access to the appropriate memory store and compatible configuration. Authentication, namespace, network and storage settings can affect what is available.
The exact Cross Agent setup should be verified for the intended deployment rather than assumed.
2️⃣ How do agents access Lighthouse Memory?
There are two main approaches.
🔸SDK Integration
Developers can integrate Lighthouse Memory directly into an application using its TypeScript packages. This provides programmatic control over the memory engine, storage, and retrieval configuration.
🔸MCP integration
The Model Context Protocol (MCP) allows compatible AI clients to interact with memory functionality through exposed tools. Instead of implementing each memory operation directly in the application, the client can call tools such as "memory_remember" and "memory_recall".
The hosted Memory MCP endpoint documented by Lighthouse is:
"memory-api.lighthouse.storag…"
This is distinct from Lighthouse's general storage MCP endpoint. They should not be confused.
Official docs: docs.lighthouse.storage/memo…
1️⃣ How Lighthouse Memory works
Lighthouse Memory provides three core operations:
- "remember" — stores information for future use.
- "recall" — searches stored memories for relevant information.
- "forget" — removes a memory subject to the storage engine's deletion behavior.
For example, an agent could save:
"The user is learning blockchain fundamentals and prefers concise explanations."
In a later session the agent could recall that information when asked to continue teaching blockchain.
The important distinction is that storing a memory does not mean the agent automatically remembers it in every future interaction. The agent must have access to the relevant memory system and retrieve the information when needed.
Official docs: docs.lighthouse.storage/memo…
Vøid Alpha 〽️〽️ retweeted
Web3 Foundations 🧵 | Post 3
Imagine I send you $10,000 digitally.
You receive it but I still have a copy of the same digital money.
What stops me from sending that same $10,000 to someone else?
That’s the double-spending problem.
Unlike physical cash, digital information is a data that can be copied perfectly. A digital money system must therefore prevent the same value from being spent more than once.
Traditional payment systems solve this through trusted intermediaries that maintain records, verify transactions and update balances.
But Bitcoin proposed a different approach. 👇
Instead of relying on one central institution to maintain the transaction history, Bitcoin uses a peer-to-peer network where independent participants can verify transactions.
But verification alone isn't enough.
What happens if conflicting transactions appear? How does the network agree on which transactions belong in its shared history?
Bitcoin combines transaction validation with Proof of Work to help the network converge on a common transaction history and make double-spending increasingly difficult.
That’s one of the key ideas behind Bitcoin.
It wasn't just about sending money digitally.
It was about solving a difficult coordination problem without depending on a single trusted authority.
Next...we'll break down how Bitcoin's network, nodes and miners work together to make this possible. ⚡
#Bitcoin #Web3
Web3 Foundations 🧵 | Post 3
Imagine I send you $10,000 digitally.
You receive it but I still have a copy of the same digital money.
What stops me from sending that same $10,000 to someone else?
That’s the double-spending problem.
Unlike physical cash, digital information is a data that can be copied perfectly. A digital money system must therefore prevent the same value from being spent more than once.
Traditional payment systems solve this through trusted intermediaries that maintain records, verify transactions and update balances.
But Bitcoin proposed a different approach. 👇
Instead of relying on one central institution to maintain the transaction history, Bitcoin uses a peer-to-peer network where independent participants can verify transactions.
But verification alone isn't enough.
What happens if conflicting transactions appear? How does the network agree on which transactions belong in its shared history?
Bitcoin combines transaction validation with Proof of Work to help the network converge on a common transaction history and make double-spending increasingly difficult.
That’s one of the key ideas behind Bitcoin.
It wasn't just about sending money digitally.
It was about solving a difficult coordination problem without depending on a single trusted authority.
Next...we'll break down how Bitcoin's network, nodes and miners work together to make this possible. ⚡
#Bitcoin #Web3
Vøid Alpha 〽️〽️ retweeted
Web3 Foundations 🧵 | Post 2
So, why decentralisation?
To understand that, we need to go back to 2008.
The global financial crisis shook confidence in parts of the traditional financial system. Risky lending, excessive leverage and failures in risk management helped turn a housing market collapse into a worldwide crisis.
But here's the question that got me thinking:
What if sending money online didn't require a central institution to verify every transaction and maintain the ledger?
That was the kind of problem Bitcoin set out to address.
On October 31, 2008 A paper titled Bitcoin: A Peer-to-Peer Electronic Cash System proposed a new approach to digital payments one designed to prevent double spending without relying on a trusted central financial institution.
This wasn't just about creating another digital currency.
It was about rethinking trust, verification and who gets to participate in maintaining the financial ledger.
And that's where our journey into Bitcoin truly begins.
Next: "The double-spending problem" why digital money is harder to solve than it looks. ⚡
#Bitcoin #Web3
Web3 Foundations 🧵 | Post 2
So, why decentralisation?
To understand that, we need to go back to 2008.
The global financial crisis shook confidence in parts of the traditional financial system. Risky lending, excessive leverage and failures in risk management helped turn a housing market collapse into a worldwide crisis.
But here's the question that got me thinking:
What if sending money online didn't require a central institution to verify every transaction and maintain the ledger?
That was the kind of problem Bitcoin set out to address.
On October 31, 2008 A paper titled Bitcoin: A Peer-to-Peer Electronic Cash System proposed a new approach to digital payments one designed to prevent double spending without relying on a trusted central financial institution.
This wasn't just about creating another digital currency.
It was about rethinking trust, verification and who gets to participate in maintaining the financial ledger.
And that's where our journey into Bitcoin truly begins.
Next: "The double-spending problem" why digital money is harder to solve than it looks. ⚡
#Bitcoin #Web3