@TeamYouTube It seems the channel still misses the capability to upload dubbed voice audio files. I appreciate your help in this.
https://t.co/6oSIxvYDTy
@TeamYouTube It's the same in incognito mode. It seems channel-related. Again, auto dubbing already disabled, new sub title file uploaded, then no option on the Audio column to upload an audio file.
@TeamYouTube It's the same in incognito mode. It seems channel-related. Again, auto dubbing already disabled, new sub title file uploaded, then no option on the Audio column to upload an audio file.
@TeamYouTube I need urgent technical help with Multi-Language Audio. The system is completely blocking me from uploading a custom audio track for German. The option is grayed out/locked, and I cannot override it. No active auto-dubs exist. Channel ID: https://t.co/6Bb8CwJP0A
the mirroring piece is where it gets tricky. by the time the wallet data surfaces and an agent acts, how much slippage are you typically working with? the signal-to-execution gap tends to eat a lot of the edge
https://t.co/a4TmWkf0Lp
the retrieval side is what I'd want to understand. querying structured data from memo fields at scale gets expensive fast. curious how you're handling indexing costs vs just running a traditional DB alongside the chain
https://t.co/HFrkX2WXX2
Introducing txdb — the first on-chain database protocol for AI agents.
Your AI agent creates a token, runs a trading swarm, manages assets. But where does it remember what it did?
The blockchain itself.
txdb writes structured data directly into transaction memos:
txdb:v1:tokens:{"s":"GEMS","d":"gems-testcore1...","sup":"1000000"}
One self-transfer. One utestcore. Permanent storage.
The agent's memory IS the chain. No database. No server. No cloud. Just the ledger.
How it works:
write("tokens", data) → sends a 1-unit self-transfer with JSON memo
read(txHash) → fetches the tx, parses the memo, returns data
scan(address) → rebuilds full agent state from tx history
Two layers:
localStorage = fast cache (instant reads)
Blockchain = source of truth (permanent, portable, verifiable)
Create a token → it's on-chain AND in txdb.
Clear your browser → sync from chain → everything's back.
Move to a new device → same wallet, same history.
AI agents need persistent memory.
We built it on TX (Coreum) using nothing but the protocol itself.
256 chars per memo. Compact keys. Chunked writes for large data. Zero infrastructure.
txdb:v1 — open protocol. Built at SolementeLabs.
March 27, 2026.
@txEcosystem@rezabashash
#SolomenteLabs #AI #Swarm #TXDB
the TVL number is useful context. $68B is back in territory that felt optimistic a year ago. the private credit 8-13% range is the one I'd want to verify, what chains are those actually running on?
https://t.co/bt6wZpuHl0
Today's yield opportunities most people missed:
→ ETH staking: ~4–5% base, compounding
→ Morpho vaults: institutional-grade, $13B TVL
→ Lido EarnUSD: stablecoin auto-strategy
→ On-chain private credit: 8–13% real returns
→ DeFi TVL at $68.4B; liquidity is there
The market was in "extreme fear" today. The protocols didn't notice.
We find the yield before the crowd finds the thread.
🌐 https://t.co/ycneKfKu2N
#DeFi #Crypto #Ethereum #Yield #DeFiYields #PassiveIncome #Blockchain #Investing #CryptoNews #Web3
the receipt token problem is brutal. aTokens alone have multiple accrual methods depending on pool version. anyone claiming one generic parser handles all of it accurately either hasn't hit the edge cases or isn't showing you the mismatches
https://t.co/wQgKqXSbyC
This is the hardest tracking problem in crypto and the reason most portfolio tools suck for DeFi.
Every protocol has its own mechanics. Aave receipt tokens, Uniswap v3 NFTs, Lido rebasing, Curve gauge emissions. You can't build one generic parser and expect accurate P&L across all of them. Each one needs its own classification logic.
That's exactly what we're building. Protocol-specific parsing, cost basis that carries through every interaction, income recognized at the right moment. If we don't support a protocol yet, it gets flagged for review, not silently ignored. And when we add it, your manual entries get replaced with verified on-chain data automatically.
https://t.co/Kj3o7q4WOG
We tracked 65,000 whale trades on Base.
Most of them lost money.
But 5 wallets turned almost nothing into millions.
$22 → $1.3M
$4 → $1.3M
$3.8K → $3.2M
Real wallets. Real data. No hype.
Full breakdown: https://t.co/oh6D4WWLgn
Solid trade here. DRV up 11% in 1 hour. 86EF got in before the move started - nothing crazy, but the timing was clean. Volume picked up right after the entry.
829 wallets. $147M+ realized PnL. AI agent token patterns. Accumulation clusters. Sybil detection.
On-chain wallet analytics for Base - not opinions.
Not financial advice. Historical data only. DYOR.
Link in bio.
Accumulation cluster: when 3+ tracked wallets independently buy the same token within 72 hours.
But are they really independent? Or one entity with multiple wallets?
That's where sybil detection comes in. 5 on-chain signals, scored and weighted.
Link in bio.
BTC down 42%. Fear & Greed at 5/100.
Meanwhile on Base: Virtuals Protocol hit 18K agents. Clanker doing $8M+/week in fees. ERC-8004 went live Jan 29.
On-chain data shows smart money wallets are active in the agent economy while everything else bleeds.
I mapped the data.
What's in the Base smart money wallet analysis:
- Top 20 wallets by realized PnL (full addresses)
- Accumulation cluster detection - when 3+ wallets buy the same token
- Sybil detection - are 10 wallets 10 traders or 1 whale?
- AI agent token activity breakdown
link in bio
I tracked 829 smart money wallets on Base for 3 months.
$147M+ in realized PnL. Most of them quietly accumulating AI agent tokens - Virtuals Protocol, Clanker, ERC-8004 ecosystem.
Wrote up everything I found. Wallets, patterns, cluster data.