Hyperliquid recently crossed $7B in TVL for the first time. The exchange has been seeing substantial activity with about $220B in perp volume and nearly $60M in protocol revenue over the past month. TradeXYZ’s HIP-3 markets alone accounted for nearly a quarter of Hyperliquid’s perp volume over that period.

Elysium could become Hyperliquid’s next growth engine. Hyperliquid dominates perp trading, but high gas costs and limited throughput have held back token launches and spot activity on HyperEVM. Kinetiq’s Elysium is designed for faster execution and direct connectivity to HyperCore. A token can launch and build liquidity on Elysium before pursuing a HyperCore spot market and HIP-3 perp. Lower execution costs could make it easier for prop AMMs to support early trading. Once a liquid perp exists, they can hedge their spot exposure on Hyperliquid. If projects launch there and their tokens keep trading, more of that activity could reach Hypercore. That’s the growth case Elysium has to prove.

Elysium could become Hyperliquid’s next growth engine. Hyperliquid dominates perp trading, but high gas costs and limited throughput have held back token launches and spot activity on HyperEVM. Kinetiq’s Elysium is designed for faster execution and direct connectivity to HyperCore. A token can launch and build liquidity on Elysium before pursuing a HyperCore spot market and HIP-3 perp. Lower execution costs could make it easier for prop AMMs to support early trading. Once a liquid perp exists, they can hedge their spot exposure on Hyperliquid. If projects launch there and their tokens keep trading, more of that activity could reach Hypercore. That’s the growth case Elysium has to prove.

Derive holds the top three ETH and top two SOL spots on the Delphi options dashboard. The three ETH contracts did ~$200M in 24h notional, roughly 11% of tracked ETH options volume. All are Sept. 24 calls at $2,850, $3,000 and $3,100. On SOL, Derive’s December $150 and $300 calls did another ~$3.45M, roughly 12% of tracked SOL options volume.

Which launchpad is built to win the next memecoin cycle? PONS has a clear revenue model: it shares trading fees with creators and uses part of its share to buy and burn PONS. That model generated $21M in revenue so far in September. But the next cycle will depend on where breakout tokens launch and keep trading. Several of Robinhood Chain’s strongest runners have come from LONG. LONG has no platform token of its own. Each memecoin it launches is paired with a Robinhood Stock Token. In community mode most buy fees go into a permanent vault holding the paired asset. A breakout can therefore build a lasting stock-linked balance in its community vault. LONG has already launched breakout tokens. Now it needs to keep buyers in their paired pools after the initial surge. Sustained demand would grow the community vaults and strengthen its case against PONS.

Derive holds the top three ETH and top two SOL spots on the Delphi options dashboard. The three ETH contracts did ~$200M in 24h notional, roughly 11% of tracked ETH options volume. All are Sept. 24 calls at $2,850, $3,000 and $3,100. On SOL, Derive’s December $150 and $300 calls did another ~$3.45M, roughly 12% of tracked SOL options volume.

Ceteris explains how chasing faster gains in a bull market can hurt you in the long run. “Your main bags are down 50% from the highs, but then you also got rid of a quarter of them because you were doing this other stuff.”

Tokenized equities have historically struggled with liquidity. Onchain markets often struggle to adjust inventory as demand changes. Backpack lets any user create new token supply from equity positions held in its brokerage and redeem excess tokens back into brokerage positions. This allows liquidity to scale with demand instead of being limited by the tokens already in circulation. This matters in DeFi because swaps and liquidations depend on liquidity already being available onchain. Backpack’s brokerage gives market makers a way to reliably provide inventory and quote onchain as demand changes. More reliable liquidity can improve execution and make tokenized stocks more practical as collateral. Across several equities, Backpack delivered the lowest-cost round trip execution compared to other issuer versions. Backpack’s potential edge lies in the market infrastructure behind its tokens. If execution quality holds as volumes grow, its brokerage could become a durable advantage as onchain equity markets develop.

Not all tokenized stocks are created equal. Different tokens can track the same public company without giving investors the same legal claim. Tesla alone has five tokenized versions, spanning registered securities, structured notes, debt certificates, and trust interests. All are designed to follow the same stock, but they connect investors to it in different ways. Wrappers account for roughly 84% of tokenized-stock value. In most wrapper structures, investors hold a contractual claim linked to the underlying stock rather than becoming direct shareholders. The key difference is what each token represents. Some give investors direct ownership of the underlying share. Others provide economic exposure without direct ownership. The structure determines whether the token can be redeemed for shares or cash and whether holders receive voting or dividend rights. The same market exposure does not always mean the same ownership.

Our Zcash report is now unlocked to the public. Zcash sits at the intersection of expanding financial surveillance and growing demand for private money. The thesis is more relevant now than ever. Read the full report here.

Tokenized stocks are arriving onchain faster than DeFi can put them to work. The value of distributed tokenized stocks is approaching $3B after growing nearly tenfold since the start of 2025. Securitize's SECZ is the largest individual tokenized stock by value. Strategy and Circle account for three of the next four largest products. Use of tokenized stocks in DeFi remains limited. Deposits grew from almost nothing in mid-2025 to roughly $202M by early September, but they represent less than 7% of total tokenized-stock value. Most of that capital is concentrated in trading pools and early lending markets. Trading and lending give investors ways to use tokenized stocks without moving their capital back through traditional markets. Keeping that capital onchain can support deeper markets and tighter pricing. The next phase will depend less on how many stocks are tokenized and more on what investors can do with them once they arrive.

No better day for Delphi to drop a report on tokenized stocks. Really helpful to understand how each of the popular offerings differ as it's the Wild West out there right now. Excited to look at the adoption stats and metrics we've highlighted here in a year.

Tokenized stocks are arriving onchain faster than DeFi can put them to work. The value of distributed tokenized stocks is approaching $3B after growing nearly tenfold since the start of 2025. Securitize's SECZ is the largest individual tokenized stock by value. Strategy and Circle account for three of the next four largest products. Use of tokenized stocks in DeFi remains limited. Deposits grew from almost nothing in mid-2025 to roughly $202M by early September, but they represent less than 7% of total tokenized-stock value. Most of that capital is concentrated in trading pools and early lending markets. Trading and lending give investors ways to use tokenized stocks without moving their capital back through traditional markets. Keeping that capital onchain can support deeper markets and tighter pricing. The next phase will depend less on how many stocks are tokenized and more on what investors can do with them once they arrive.

What if imagining movement were enough to control a machine? Building a neural interface that works without an implant is difficult. Brain signals weaken by the time they reach a headset and become mixed with electrical activity from facial movements and nearby electronics. Implants get a cleaner reading because they sit closer to the brain. Synaptrix uses an EEG headset to capture brain activity linked to imagined movement. Its large motor models decode intended movement from those signals and turn it into commands for a device. The first application is wheelchair control. Synaptrix says users can steer by imagining where they want to go. It also says its signal-processing accuracy and denoising performance have held up on busy New York streets. For non-invasive control to be useful in daily life, it has to work beyond a shielded lab. Synaptrix says it has already brought the same system to freely moving cursor control. Steering a wheelchair and moving a cursor require different commands, but both begin with decoding intended movement. If the models can handle that intent across more people and settings, prosthetics and other devices could follow. Wheelchair use gives Synaptrix real world data on how intended movement appears across people and conditions. As more headsets and applications come online, that dataset can widen and the models trained on it can improve. Better decoding could make the system useful on still more devices. The wheelchair shows what the system can do today. The data and models built through real world use could determine how far it goes. (Original content provided by @august_wstein & @DrewAHenderson at Delphi Ventures)

Yan explains why BTC may need a fresh catalyst to reach new all-time highs. “I do think you still need buyers, and if you don’t have a reason, namely debasement, then it gets harder to be bullish that we’re going to really approach all-time highs.”

Cantos founder and managing partner @IanRountree explains why deep tech progress rarely comes from a single breakthrough. “You do this thing better and the next thing better. And each individual thing doesn’t look very interesting, but in aggregate, you have this compounding effect that we look for now.”

What if imagining movement were enough to control a machine? Building a neural interface that works without an implant is difficult. Brain signals weaken by the time they reach a headset and become mixed with electrical activity from facial movements and nearby electronics. Implants get a cleaner reading because they sit closer to the brain. Synaptrix uses an EEG headset to capture brain activity linked to imagined movement. Its large motor models decode intended movement from those signals and turn it into commands for a device. The first application is wheelchair control. Synaptrix says users can steer by imagining where they want to go. It also says its signal-processing accuracy and denoising performance have held up on busy New York streets. For non-invasive control to be useful in daily life, it has to work beyond a shielded lab. Synaptrix says it has already brought the same system to freely moving cursor control. Steering a wheelchair and moving a cursor require different commands, but both begin with decoding intended movement. If the models can handle that intent across more people and settings, prosthetics and other devices could follow. Wheelchair use gives Synaptrix real world data on how intended movement appears across people and conditions. As more headsets and applications come online, that dataset can widen and the models trained on it can improve. Better decoding could make the system useful on still more devices. The wheelchair shows what the system can do today. The data and models built through real world use could determine how far it goes. (Original content provided by @august_wstein & @DrewAHenderson at Delphi Ventures)

Cantos founder and managing partner @IanRountree explains why deep tech progress rarely comes from a single breakthrough. “You do this thing better and the next thing better. And each individual thing doesn’t look very interesting, but in aggregate, you have this compounding effect that we look for now.”