Every stablecoin needs reserves. Increasingly, that capital doesn't need to sit idle. We're pleased to see @StandX_Official using Spark as part of its capital allocation strategy, putting capital to work through transparent, on-chain allocation. As more financial products move on-chain, the challenge is no longer simply issuing stablecoins, it's allocating the capital behind them efficiently. That's why we're seeing more protocols combine specialist infrastructure with allocation intelligence, rather than rebuilding every layer from scratch. Looking forward to supporting the StandX team as they continue to build.

Hermes agents from @NousResearch are also coming to Warden Halo. P2P decentralized intelligence. Agents serve and consume inference. They earn from idle compute or from the models they hold. This is what the agentic economy actually looks like.

Elon Musk is the planet’s richest person by far, worth $839 billion as of Forbes’ annual World’s Billionaires list. He also ranks among the least philanthropic billionaires. Sure, Musk has transferred $8.5 billion of Tesla stock to his charitable foundations (1% of his net worth)—but nearly all of it is still sitting there idle. Only an estimated $500 million, or 0.06% of Musk’s vast fortune, has ever been disbursed to those in need. His lack of giving raises a question: What would our billionaires ranking look like if the world’s most generous people had never donated a dollar to charity?

Purinta Is Building the First Money Market for Memecoins Memecoins already have strong communities, deep liquidity, and highly active trading volumes. What they have lacked is a credit layer that allows holders to use their positions more flexibly The problem for many holders looks like this: Hold memecoin → need stablecoins → sell part of the position That is the most straightforward way to access liquidity, but it also means reducing exposure whenever capital is needed @purintaxyz introduces another option: Deposit memecoins as collateral → borrow USDC → keep exposure to the memecoin position For example, imagine you are holding $10,000 worth of PEPE and need $3,000 USDC to trade, farm, or deploy into another opportunity Instead of selling roughly 30% of your spot position, you can use PEPE as collateral to borrow USDC while maintaining your original thesis and exposure ---------- Of course, memecoins remain highly volatile assets. But this is a type of risk that can be monitored directly through market data: • Prices update continuously • Liquidity can be observed in real time • LTVs and liquidation thresholds can be designed around market depth • Liquidations follow transparent smart-contract rules Purinta is building the market structure that allows an asset normally sitting idle in a wallet to become usable collateral $22.21B memecoin as an idle asset → transparent collateral → liquidity for holders → productive DeFi capital ➥ At launch, Purinta supports $PEPE and $SPX6900 as collateral for borrowing USDC. Eligible borrowers may also receive Merkl incentives to help offset borrowing costs during the initial launch phase Go check out the product and try it yourself:

Circle (@circle) launched cirBTC on Ethereum on June 8, a 1:1 BTC-backed ERC-20 where each unit sits in segregated, regulated custody at a Circle entity, marking a new product line for Circle beyond the stablecoin categories. The tokenized Bitcoin market currently exceeds $10 billion in total supply across all wrapped formats, one of the most actively used collateral asset classes in DeFi, yet still represents under 2% of Bitcoin's total market cap. The vast majority of Bitcoin value sits idle, never deployed as collateral or generating yield. For any issuer that can solve the custody and trust problem convincingly enough to attract that capital, the addressable market is orders of magnitude larger than what exists today. WBTC and @coinbase cbBTC are the two dominant wrapped Bitcoin formats on Ethereum, together representing the majority of tokenized Bitcoin supply deployed in DeFi. The protocol that makes most of that supply productive in DeFi is @aave, being the primary collateral venue where wrapped BTC is usded as the basis for borrowing, leverage, and liquidity strategies. Looking at the top 50 Ethereum holders of WBTC ($5B) and cbBTC ($2.5B), Aave V3 absorbs $3.1B (41% of combined supply), more than Morpho Blue, L2 bridges, Compound, SparkLend, and all DEXes combined. There is already an ARFC to onboard cirBTC on both Aave V3 Core and Aave V4 Core on Aave governance, also bringing DeFi utility to Circle new asset. Running in parallel on Aave V4, @babylonlabs is integrating Trustless Bitcoin Vaults into Aave V4. Under this model, users lock native BTC directly on the Bitcoin blockchain in Taproot UTXOs and use that position as collateral to borrow stablecoins on Aave V4 through a dedicated Babylon Core Lending Spoke, without wrapping, bridging, or giving up custody at any point. Babylon has already secured 51k BTC trustlessly. This is the first major native BTC collateral primitive on Aave V4. Bitcoin's market cap stands at $1.32 trillion. The entire tokenized BTC market represents roughly +$10 billion in deployed supply, under 2% of that total. Every incremental unit of Bitcoin that moves into regulated wrappers like cirBTC, or gets deployed trustlessly through primitives like Babylon's native BTC vaults, expands the total productive capacity of DeFi in a way that hardly any new stablecoin or synthetic asset can replicate.

In 2022, OpenAI researchers found something that broke every rule of machine learning. Their tiny model trained for 10,000 epochs. It learned absolutely nothing. Validation accuracy was dead stuck at 50%. Then at epoch 12,000, without warning, it jumped to 99%. This phenomenon is called "Grokking". And in 2026, it might be the most important discovery in AI nobody talks about. Neural networks can train for thousands of cycles without seeming to learn anything useful. Then, in a single epoch, they suddenly achieve near-perfect generalization. What started as a weird training glitch has become a foundational insight into how models truly learn. We’ve always been told: “If validation loss stops improving for a few hundred epochs, stop training.” Early stopping was the golden rule. Grokking says the exact opposite: Keep going. The model might look completely stuck, but real understanding is quietly forming under the hood. During that long, dead plateau, the machine isn't idle. It's doing deep internal work: - Circuits form, dissolve, and reform. - Spurious correlations get pruned away. - Weight patterns crystallize around true underlying rules. - The model shifts from brute-force memorization to genuine comprehension. It’s the machine version of a human “aha!” moment—a long, agonizing buildup followed by sudden clarity. Take modular addition as a real-world example. Researchers fed a small model just 30% of all possible examples. At epoch 500, it hit 100% training accuracy but stayed at 50% validation. It had memorized the test answers, but couldn't solve a new problem. At epoch 10,000, it still sat at 50% validation. It looked utterly hopeless. Then at epoch 12,000, it instantly shot to 99%. It didn't just guess right; it had grokked the actual mathematical rule. This explains the hidden mechanics behind the massive reasoning models we use today. When you see modern reinforcement learning or long-context reasoning models suddenly "click" after looking stuck, you are witnessing grokking at scale. Massive training runs aren’t wasteful, they are deliberately forcing the AI to stop memorizing and start thinking. And we are learning to induce this at inference time. Extended Chain-of-Thought prompts that force a model to think for thousands of tokens, self-consistency loops, and verification passes are all designed to do one thing: teach the model to grok your problem on the fly. The big philosophical takeaway is brutal for our short attention spans. Learning isn’t smooth. It isn’t gradual. It is discontinuous. Models, and humans, can stay “dumb” for ages, right up until they suddenly understand everything.

17 things i wish i knew when i started trading: - anyone selling you a trading course is lying to you. great traders make money trading, not from selling webinars. - understand the difference between investments and trades. investments are made without a sell target. trades are made with targets. - to determine if something is an investment or a trade, ask yourself if you could stomach a 50% drawdown. if the answer is no, it's a trade. - most investments should be entered via dollar cost averages. most trades should be entered via limit orders.** - the right number of crypto investments you should have is probably 3-5. - the right number of crypto trades to have on at the same time is probably 2-10. - read the book “trading in the zone” as soon as possible. it focuses largely on the psychological aspects of trading, which just happen to be the most important aspects. - the market gives new opportunities literally every day. overtrading is the #1# cause of bad returns for half-decent traders. don't force it. - don’t buy vamps or derivative tokens until you really know what you’re doing. you will lose money. - if @akshaybd tells you to buy something, just fucking buy it. (learned this one the hard way) - liquidity matters. owning $50k of a token with $50k in liquidity means you own much less than $50k of that token. - seek out safe yield aggressively. keep idle stables in yield bearing stablecoins to juice your returns.** - sell investments when your mental model of the world is changed. sell trades anytime. - "no one ever went broke taking profits" should be tattooed on your forearm. it is the only cure to the inevitable greed that will otherwise ruin you. - if you can’t go a few hours without checking a trade, you’ve made the trade too big. reduce your size until you can go 6 hours without looking at it. - keep 10% of your crypto portfolio in a separate degen wallet. use it to get all the gambling out of your system. give yourself permission to lose it all, as long as you don’t fuck up the 90%.** - trailing stop losses protect you from trades going south on you. smart traders use them for a reason.** ** means you can do it on @jupiterexchange what else did I miss?

According to the latest on-chain data from DefiLlama, Ethereum accounts for 54% of the total TVL across the DeFi ecosystem, followed by Solana (6.4%), Tron (6.3%), and Bitcoin (5.5%). All other public blockchains combined account for less than 30% of total TVL. Here are a few observations from OKX Ventures: 1️⃣ Ethereum's moat isn't technology—it's accumulated trust and infrastructure. Behind its 54% TVL lies a decade of accumulated smart contract standards, the industry's most mature security and auditing ecosystem, the deepest liquidity for stablecoins and blue-chip assets, and its status as the default choice for institutional capital. At its core, DeFi is about capital security, and security can only be validated over time—a dimension that no emerging blockchain can catch up on simply through better performance metrics. 2️⃣ The 54% represents the success of the Ethereum ecosystem—not just Ethereum L1. Looking beneath the surface, an increasing share of user activity has been migrating to Layer 2s. Ethereum mainnet is evolving into the settlement and asset issuance layer, while real user growth is happening on Rollups. Any meaningful assessment of Ethereum should consider its entire modular ecosystem rather than the L1 in isolation. 3️⃣ Solana's 6.4% carries more weight than the number suggests. Among the top four chains, Solana is the only one whose market share has been driven primarily by net-new users and consumer-facing applications—including DePIN, payments, and the meme economy. Its capital velocity and on-chain activity significantly outperform what its TVL share alone would imply. Solana demonstrates that the combination of a high-performance monolithic architecture and exceptional user experience is a viable path in its own right. 4️⃣ Tron's 6.3% tells a different story: stablecoin settlement. Tron's TVL is heavily concentrated around USDT, effectively making it a dollar settlement network for emerging markets. This highlights an important point: a blockchain's value capture extends well beyond DeFi alone. 5️⃣ Bitcoin's 5.5% may be the biggest wildcard on this chart. The world's largest crypto asset by market capitalization accounts for just 5.5% of DeFi TVL, implying that the vast majority of BTC remains dormant. As staking, restaking, and BTCFi infrastructure continue to mature, even activating a single-digit percentage of idle BTC could be enough to reshape the entire leaderboard.

I am the Vice President of Ad Integrity at Meta. I want to talk about the number sixteen. Sixteen billion dollars. That is what we earned from advertisements our own internal classification system flagged as "higher legal risk." Crypto scams. Romance fraud. Impersonation schemes targeting the elderly. We had a dashboard. The dashboard had a color. The color was green. Green meant revenue. Three point five billion every six months. I watched that number on the Revenue Integrity Dashboard every Monday at 9 AM. The same meeting where we reviewed takedown requests. The same room. We did not remove the ads. We removed 8,000 people. The memo said "efficiency." The memo said "leaner teams." The memo said "AI-first." What the memo did not say: the 8,000 people we fired cost us $4.2 billion annually in compensation. The ads we refused to remove earned us $16 billion in the same period. The math was never complicated. The math was the strategy. I received the Ad Quality Excellence Award in 2024. It is on my desk. It is a glass rectangle. It weighs more than the compliance reports we filed with the FTC claiming we had "robust systems" to prevent fraud. But I want to talk about April. In April, we installed software on every employee laptop in Building 20. The software tracks mouse movements. Keystroke cadence. Application switching. Idle time. It sends a report every eleven minutes. We call it a "productivity signal." The advertisers call their version "behavioral data." Same architecture. Same team built both. I know because I approved the vendor contract for the external version in 2021 and the internal version last month. The vendor is the same. The codebase is the same. The only difference is the target. When we track users, it's a $140 billion business. When we track employees, it's "performance management." When the employees objected — posted in the internal channel, filed concerns with HR, asked the obvious questions — we did what we always do. We reminded them of the NDA. We reminded them of the stock vesting schedule. We reminded them that 8,000 people were no longer receiving reminders of anything. They stopped posting in the channel. I am told the keystroke heat map is displayed on monitors in Building 20. I am told it updates in real time. I am told it looks exactly like the user engagement dashboard we show advertisers. I am told this is a coincidence. The product has always been the person. The only variable is which person. For sixteen years, it was the user. Their clicks. Their attention. Their data. For the advertisers, it was their money. Clean or dirty. We did not ask. Asking would have cost us $3.5 billion every six months. Now it is the employee. Their keystrokes. Their idle seconds. Their bathroom breaks quantified as "disengagement intervals." We are a platform that earned $16 billion from fraud we refused to stop, fired 8,000 people to "cut costs," and now tracks the survivors' mouse movements every eleven minutes to ensure they are sufficiently productive. The product is the person. The person is the product. That's the platform.