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Onchain options ecosystem Part 2
Part 1 covered many of the established names in onchain options.
Part 2 goes further down the stack, mapping the newer venues, primitives and markets starting to take shape.
This time I'm covering:
> Options trading & liquidity
> Perpetual / new optionlyings
> Next wave
1/ Options trading & liquidity
@paradex dated options with RFQ execution on Starknet
@CallPutApp crypto and synthetic stock/ETF options on Base
@ArrowMarkets onchain options building toward Monad
@hypersurfaceX options with structured premium strategies
@SynapseProtocol Hypercall options across crypto and equity underlyings
@SharwaFinance options brokerage and aggregation layer
@splitmarkets onchain options trading desk
@kodachi_io options inside a broader trading superapp
2/ Perpetual / new option primitives
@Panoptic_xyz perpetual options built around AMM liquidity
@GammaSwapLabs short-duration digital options
@0xflatmoney everlasting options primitive
@NoRektFi lending positions hedged with options
@StormbitFinance credit and settlement infrastructure using options for hedging
3/ New underlyings
@nondollar_life onchain stock and crypto options
@convallax options on prediction market probabilities
@Stock_Works tokenized stock options
@LongBowOptions options on new assets via Robinhood Chain
@premarket_xyz options around pre-launch assets, IPOs, TGEs and RWAs
@pokerfi_gg turning poker into an options market
4/ Next wave
@JoyrideExchange 0DTE options on Solana
@HyperdeltaX building options around Hyperliquid
@layerv_official volatility and options platform in development
@degrop_ degen-focused onchain options launching soon
@heat_fi social trading UX for options
@deriverse_io Solana trading stack with options on the roadmap
The options stack is expanding beyond BTC and ETH.
More teams are building options around stocks, prediction markets and pre-launch assets, not just BTC and ETH.
And more new venues are still coming online.
Bookmark and retweet for part 3
Part 1 covered many of the established names in onchain options.
Part 2 goes further down the stack, mapping the newer venues, primitives and markets starting to take shape.
This time I'm covering:
> Options trading & liquidity
> Perpetual / new optionlyings
> Next wave
1/ Options trading & liquidity
@paradex dated options with RFQ execution on Starknet
@CallPutApp crypto and synthetic stock/ETF options on Base
@ArrowMarkets onchain options building toward Monad
@hypersurfaceX options with structured premium strategies
@SynapseProtocol Hypercall options across crypto and equity underlyings
@SharwaFinance options brokerage and aggregation layer
@splitmarkets onchain options trading desk
@kodachi_io options inside a broader trading superapp
2/ Perpetual / new option primitives
@Panoptic_xyz perpetual options built around AMM liquidity
@GammaSwapLabs short-duration digital options
@0xflatmoney everlasting options primitive
@NoRektFi lending positions hedged with options
@StormbitFinance credit and settlement infrastructure using options for hedging
3/ New underlyings
@nondollar_life onchain stock and crypto options
@convallax options on prediction market probabilities
@Stock_Works tokenized stock options
@LongBowOptions options on new assets via Robinhood Chain
@premarket_xyz options around pre-launch assets, IPOs, TGEs and RWAs
@pokerfi_gg turning poker into an options market
4/ Next wave
@JoyrideExchange 0DTE options on Solana
@HyperdeltaX building options around Hyperliquid
@layerv_official volatility and options platform in development
@degrop_ degen-focused onchain options launching soon
@heat_fi social trading UX for options
@deriverse_io Solana trading stack with options on the roadmap
The options stack is expanding beyond BTC and ETH.
More teams are building options around stocks, prediction markets and pre-launch assets, not just BTC and ETH.
And more new venues are still coming online.
Bookmark and retweet for part 3
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Onchain Credit Is Entering a New Phase
DeFi lending is evolving beyond floating rates and liquidity pools.
The next competitive edge lies in financing terms, collateral management and credit risk.
Recent developments show this shift:
- @Morpho Midnight crossed $100M in ging fixed-rate, fixed-term lending.
- @longbowlend is extending this model to Robinhood Chain.
- @aave V4 introduced externally curated markets with @SentoraHQ, supporting RLUSD @Ripple PYUSD and OUSD - @openstandard.
Professional risk management is becoming part of the lending infrastructure.
- @AscendFi and @m1xglobal completed an institutional repo on @StellarOrg, executing 72 automated hourly rolls with USDM1 collateral.
- @chainlink Data Feeds supported pricing for the USDC leg.
- @Ripple and @arf_one announced RLUSD-based settlement financing, connecting short-term credit with real payment flows.
These developments point toward a more specialized credit market.
Borrowers want predictable financing.
Lenders want transparent risk. Institutions want efficient collateral and settlement.
As liquidity infrastructure becomes more standardized, competitive advantage shifts toward those who structure financing, manage risk and optimize capital.
Liquidity provides the foundation.
The next value layer is credit structuring.
DeFi lending is evolving beyond floating rates and liquidity pools.
The next competitive edge lies in financing terms, collateral management and credit risk.
Recent developments show this shift:
- @Morpho Midnight crossed $100M in ging fixed-rate, fixed-term lending.
- @longbowlend is extending this model to Robinhood Chain.
- @aave V4 introduced externally curated markets with @SentoraHQ, supporting RLUSD @Ripple PYUSD and OUSD - @openstandard.
Professional risk management is becoming part of the lending infrastructure.
- @AscendFi and @m1xglobal completed an institutional repo on @StellarOrg, executing 72 automated hourly rolls with USDM1 collateral.
- @chainlink Data Feeds supported pricing for the USDC leg.
- @Ripple and @arf_one announced RLUSD-based settlement financing, connecting short-term credit with real payment flows.
These developments point toward a more specialized credit market.
Borrowers want predictable financing.
Lenders want transparent risk. Institutions want efficient collateral and settlement.
As liquidity infrastructure becomes more standardized, competitive advantage shifts toward those who structure financing, manage risk and optimize capital.
Liquidity provides the foundation.
The next value layer is credit structuring.
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A month ago, I mapped who supplies, routes, and tokenizes AI inference.
Now I'm looking at the next piece of the market: Verifiable Inference.
As agents start paying for compute, proving what actually ran becomes essential.
12 projects building across 4 key pillars:
Pillar 1 > @lagrangedev - $LA builds DeepProve for cryptographic inference proofs.
> @inference_labs makes autonomous AI execution auditable.
> @OpenGradient - base:0xfbc2051ae2265686a469421b2c5a2d5462fbf5eb combines zkML proofs and TEE attestations.
Pillar 2 - Private Execution
> @PhalaNetwork - ethereum:0x6c5ba91642f10282b576d91922ae6448c9d52f4e runs private inference inside GPU TEEs.
> @0G_labs - $0G builds private, verifiable inference infrastructure.
> @nesaorg enables secure inference on its AI Layer 1.
Pillar 3 - Reproducibility
> @gensynai - $AI develops REE to make AI execution reproducible and verifiable.
> @PrimeIntellect built TOPLOC, where validators recompute hidden states to verify inference.
Pillar 4 - Compute Provenance
> @prlnet - $PEARL turns GPU AI compute into proof of useful work that secures a chain.
> @AethirCloud - $ATH runs Checker nodes that guard compute integrity on its network.
> @darkbloomai routes requests to physical Macs across a live compute network.
> @eigencloud - $EIGEN builds open compute infrastructure and verifiable agent tooling.
As inference becomes a tradable service, buyers will demand proof of execution, privacy, and reliability.
These four pillars are where the next wave of onchain AI gets built.
Now I'm looking at the next piece of the market: Verifiable Inference.
As agents start paying for compute, proving what actually ran becomes essential.
12 projects building across 4 key pillars:
Pillar 1 > @lagrangedev - $LA builds DeepProve for cryptographic inference proofs.
> @inference_labs makes autonomous AI execution auditable.
> @OpenGradient - base:0xfbc2051ae2265686a469421b2c5a2d5462fbf5eb combines zkML proofs and TEE attestations.
Pillar 2 - Private Execution
> @PhalaNetwork - ethereum:0x6c5ba91642f10282b576d91922ae6448c9d52f4e runs private inference inside GPU TEEs.
> @0G_labs - $0G builds private, verifiable inference infrastructure.
> @nesaorg enables secure inference on its AI Layer 1.
Pillar 3 - Reproducibility
> @gensynai - $AI develops REE to make AI execution reproducible and verifiable.
> @PrimeIntellect built TOPLOC, where validators recompute hidden states to verify inference.
Pillar 4 - Compute Provenance
> @prlnet - $PEARL turns GPU AI compute into proof of useful work that secures a chain.
> @AethirCloud - $ATH runs Checker nodes that guard compute integrity on its network.
> @darkbloomai routes requests to physical Macs across a live compute network.
> @eigencloud - $EIGEN builds open compute infrastructure and verifiable agent tooling.
As inference becomes a tradable service, buyers will demand proof of execution, privacy, and reliability.
These four pillars are where the next wave of onchain AI gets built.
Every part of onchain inference is starting to accrue value to a different set of projects
Supply → Chutes
Routing → Surplus, UsePod
Tokenized access → Venice
Agent demand → OpenServ
I’m using the leaders as anchors, then building a watchlist of smaller names around each upply
@chutes_ai sets the benchmark.
6.12B requests and 35.8T+ input tokens in its released dataset.
Banger names:
@engyai verified inference
@TargonCompute confidential compute
@dphnAI - $POD consumer GPU inference
@CestusNetwork early distributed inference
The shift here is from renting GPUs → selling served intelligence.
2/ Routing & Inference Markets
OpenRouter proved multi-provider routing can scale.
Now crypto is opening the supply side.
@AskSurplus - base:0xc52aedec3374422d7510e294cfaa90799595cba3 seller-priced inference; 2.5M requests / 107B input tokens by July
@UsePodAI GPU + API-key supply, USDC settlement
@miniroutersh 1.2B tokens / 60.4K requests / 239 models in 30d
@openservai - $SERV agent routing + execution layer
More suppliers → better routing → tighter pricing.
3/ Tokenized Inference
@AskVenice - $VVV is the reference point.
$DIEM turns inference access into a transferable asset, while Venice recently reported 250B tokens/day.
Smaller experiments:
@orbiodotso - robinhood:0xaa07a0e9209e16ac99708c3ec70159c6ef3128a3 → live OpenRouter credit market; ~$35-37K asks, 22.5% best discount
@usedotai - $DOT IRM + stake-for-inference
@AileLabs - solana:8d3bQS9vNy3BfqK9dX95JRoaTbWoiuL2hzmSFLDopump idle Claude / Codex / Gemini access
@Cerebro_RH - robinhood:0xeb24a2663af4ee979dcbfe39cb95b5f12d369c4b agents + inference credits
@manyways_rh - robinhood:0xa26992c4268a8a78a4d872fe4bdad2ed03ac287d early model-access market
This is where inference starts moving from usage → credits → transferable inventory.
4/ Agent-native Demand
Agents may become the biggest native buyers of inference.
@openservai - $SERV agent infra
@AskSurplus x402 inference
@AileLabs - solana:8d3bQS9vNy3BfqK9dX95JRoaTbWoiuL2hzmSFLDopump x402 settlement
@Cerebro_RH - robinhood:0xeb24a2663af4ee979dcbfe39cb95b5f12d369c4b agents consuming credits
@BlockRunAI USDC per LLM call
@dgrid_ai pay-per-inference
Humans pick brands.
Agents can route around price, quality, latency, privacy and availability.
➟ Onchain Inference Map
Supply
Chutes · Engy · Targon · Dolphin · Morpheus · Cestus
Routing / Markets
Surplus · UsePod · MiniRouter · OpenServ
Tokenized Inference
Venice · Orbio · DOT · Aile · Cerebro · Manyways
Agent Demand
OpenServ · Surplus · Aile · Cerebro · BlockRun · DGrid
My approach: keep the leaders as core exposure, then selectively build positions in smaller names as usage and liquidity show up.
The leaders already own the attention.
Now I’m looking for which smaller names capture the next leg.
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Phoenix Research retweeted
Apart from it's novel d(3,3) model, the biggest value proposition of @namidefi lies in its positioning as an all-in-one liquidity + yield coordination layer across chains.
Why so?
Rather than building isolated DeFi products, Nami's Omni Layer connects the entire user journey:
s 16 providers for optimised same-chain + cross-chain execution, alongside MultiSwap.
🔸 OmniDeposit + OmniShift → Simplifies LP creation + migration across chains, allowing capital to follow better yield opportunities w/o the usual bridging + rebalancing complexities.
🔸 OmniGauge → Aggregates liquidity incentives across chains into unified markets, enabling veNAMI holders to vote once across multiple networks.
🔸 Nami Relayer → Automates voting, reward collection + reinvestment strategies.
imo the bigger picture is how these products reinforce one another.
More efficient execution + mobile liquidity enables deeper markets + trading activity → greater fee generation → stronger incentives for veNAMI voters + ogNAMI stakers.
Coupled with d(3,3)'s adaptive emissions, Nami is essentially building a multichain liquidity economy where capital efficiency, user experience + token value accrual are interconnected.
The interesting part isn't any single feature, but how the entire stack could compound adoption + economic activity across the ecosystem.
Worth a read 👇
Why so?
Rather than building isolated DeFi products, Nami's Omni Layer connects the entire user journey:
s 16 providers for optimised same-chain + cross-chain execution, alongside MultiSwap.
🔸 OmniDeposit + OmniShift → Simplifies LP creation + migration across chains, allowing capital to follow better yield opportunities w/o the usual bridging + rebalancing complexities.
🔸 OmniGauge → Aggregates liquidity incentives across chains into unified markets, enabling veNAMI holders to vote once across multiple networks.
🔸 Nami Relayer → Automates voting, reward collection + reinvestment strategies.
imo the bigger picture is how these products reinforce one another.
More efficient execution + mobile liquidity enables deeper markets + trading activity → greater fee generation → stronger incentives for veNAMI voters + ogNAMI stakers.
Coupled with d(3,3)'s adaptive emissions, Nami is essentially building a multichain liquidity economy where capital efficiency, user experience + token value accrual are interconnected.
The interesting part isn't any single feature, but how the entire stack could compound adoption + economic activity across the ecosystem.
Worth a read 👇
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Follow the money after it enters DeFi.
Across 8 emerging protocols, capital is taking on very different category
That capital is being put to work across three areas:
1/ Yield-Bearing Dollars
@tori_finance | ~$71.5M TVL
Uses delta-neutral trading strategies to back trUSD and DN | ~$60.8M TVL
Uses cross-venue arbitrage to generate returns for USDx holders.
@noon_capital | ~$38.4M TVL
Offers USN with yield generated through managed strategies.
All three compete for capital seeking dollar-denominated returns, but the underlying strategies and risks differ.
2/ Lending & Capital Allocation
@CurrentSUI | ~$58.1M TVL
A lending market on Sui with roughly $78M in active loans.
@infiniFi | ~$68.3M TVL
Allocates capital across strategies with different liquidity profiles. TVL has risen from ~$45M at the end of September.
@TradeNeutral | ~$22.4M TVL
Routes capital into managed quantitative trading strategies.
Capital efficiency depends on borrowing activity, strategy performance and how liquidity is managed.
3/ Risk Coverage & Tokenized Assets
@Firelightfi | ~$68.7M TVL
Uses staked FXRP to back DeFi insurance coverage.
@KAIO_xyz | ~$39.6M TVL
Brings investment products onchain through tokenization infrastructure.
These are two different roles for capital: backing financial risk and providing exposure to investment assets.
TVL measures the assets held or accounted for by a protocol, but it tells us much less about how those assets are used.
The next layer of DeFi growth will be measured by how effectively capital is put to work, not simply how much is deposited.
Across 8 emerging protocols, capital is taking on very different category
That capital is being put to work across three areas:
1/ Yield-Bearing Dollars
@tori_finance | ~$71.5M TVL
Uses delta-neutral trading strategies to back trUSD and DN | ~$60.8M TVL
Uses cross-venue arbitrage to generate returns for USDx holders.
@noon_capital | ~$38.4M TVL
Offers USN with yield generated through managed strategies.
All three compete for capital seeking dollar-denominated returns, but the underlying strategies and risks differ.
2/ Lending & Capital Allocation
@CurrentSUI | ~$58.1M TVL
A lending market on Sui with roughly $78M in active loans.
@infiniFi | ~$68.3M TVL
Allocates capital across strategies with different liquidity profiles. TVL has risen from ~$45M at the end of September.
@TradeNeutral | ~$22.4M TVL
Routes capital into managed quantitative trading strategies.
Capital efficiency depends on borrowing activity, strategy performance and how liquidity is managed.
3/ Risk Coverage & Tokenized Assets
@Firelightfi | ~$68.7M TVL
Uses staked FXRP to back DeFi insurance coverage.
@KAIO_xyz | ~$39.6M TVL
Brings investment products onchain through tokenization infrastructure.
These are two different roles for capital: backing financial risk and providing exposure to investment assets.
TVL measures the assets held or accounted for by a protocol, but it tells us much less about how those assets are used.
The next layer of DeFi growth will be measured by how effectively capital is put to work, not simply how much is deposited.
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Every Uniswap LP knows the feeling: fees come in, then price moves and eats them.
@rangepilotio calls itself an AI Liquidity Desk.
One side earns fees as a Uniswap LP.
The other side holds a hedge on @Lighter_xyz.
The AI desk runs both together.
So the position collects fees while the hedge absorbs the price swing.
$RANGE is now live on Robinhood Chain, with the product live too.
Farm and stake open from day one.
New chains need LPs who stay in the pool through volatility.
Hedged LPing is how that liquidity sticks.
CA: 0x0d92477c8D66f2a6a95B83b9526de4e61be1D733
@rangepilotio calls itself an AI Liquidity Desk.
One side earns fees as a Uniswap LP.
The other side holds a hedge on @Lighter_xyz.
The AI desk runs both together.
So the position collects fees while the hedge absorbs the price swing.
$RANGE is now live on Robinhood Chain, with the product live too.
Farm and stake open from day one.
New chains need LPs who stay in the pool through volatility.
Hedged LPing is how that liquidity sticks.
CA: 0x0d92477c8D66f2a6a95B83b9526de4e61be1D733
$RANGE is live on Robinhood Chain.
Your AI Liquidity Desk: Uniswap LPs + Lighter hedges.
Product live. Farm & Stake starts today.
Stay degen. Stay hedged.
CA:
0x0d92477c8D66f2a6a95B83b9526de4e61be1D733
rangepilot.io
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Phoenix Research retweeted
11 Aave markets ended Q3 with $1.3B+ in deposits and $417M borrowed.
Across V3 and V4, @aave expanded into new chains, consumer credit, and different lending structures.
A few things stood out to me 🧵
Across V3 and V4, @aave expanded into new chains, consumer credit, and different lending structures.
A few things stood out to me 🧵
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Every chain says it is quantum-ready.
A holder can check it in one step: sign with a post-quantum key.
I sorted 18 teams by how far a holder can go with that key today.
Layer 1 · Post-quantum from genesis
@QRLedger: post-quantum native.
@QuantusNetwork: every mainnet ntum signed.
@PQabelian: lattice-based, quantum-secure from day one.
@cellframenet: built to protect funds from quantum threats.
Layer 2 · Upgraded and live
@Algorand: Falcon-1024 accounts, 2.7M+ post-quantum transactions.
@PeraAlgoWallet: rekey an existing account to post-quantum.
@LuteWallet: Falcon accounts with password protection.
@NEARProtocol: ML-DSA signing on mainnet.
@blueshift: Winterwallet, Winternitz vaults on Solana mainnet.
Layer 3 · Testnet
@tezos: Quantumnet, staking with post-quantum keys.
@QANplatform: XLINK pairs EVM wallets with post-quantum keys.
@Starknet: a phone signed an ML-DSA transaction, verified onchain.
@SuiNetwork: ML-DSA-65 signatures in testnet releases.
Layer 4 · Proposals and research
@Aptos: AIP-137, opt-in SLH-DSA signatures.
@ethereum: EF post-quantum team targets L1 resistance by 2029.
@hedera: A post-quantum user key type targeted for 2027.
Bitcoin BIP-360: P2MR outputs drop the exposed key path.
@projecteleven: Strongpoint post-quantum custody.
Signatures move first, and consensus follows.
Chains that let holders rekey in one transaction will carry their users through the quantum migration.
A holder can check it in one step: sign with a post-quantum key.
I sorted 18 teams by how far a holder can go with that key today.
Layer 1 · Post-quantum from genesis
@QRLedger: post-quantum native.
@QuantusNetwork: every mainnet ntum signed.
@PQabelian: lattice-based, quantum-secure from day one.
@cellframenet: built to protect funds from quantum threats.
Layer 2 · Upgraded and live
@Algorand: Falcon-1024 accounts, 2.7M+ post-quantum transactions.
@PeraAlgoWallet: rekey an existing account to post-quantum.
@LuteWallet: Falcon accounts with password protection.
@NEARProtocol: ML-DSA signing on mainnet.
@blueshift: Winterwallet, Winternitz vaults on Solana mainnet.
Layer 3 · Testnet
@tezos: Quantumnet, staking with post-quantum keys.
@QANplatform: XLINK pairs EVM wallets with post-quantum keys.
@Starknet: a phone signed an ML-DSA transaction, verified onchain.
@SuiNetwork: ML-DSA-65 signatures in testnet releases.
Layer 4 · Proposals and research
@Aptos: AIP-137, opt-in SLH-DSA signatures.
@ethereum: EF post-quantum team targets L1 resistance by 2029.
@hedera: A post-quantum user key type targeted for 2027.
Bitcoin BIP-360: P2MR outputs drop the exposed key path.
@projecteleven: Strongpoint post-quantum custody.
Signatures move first, and consensus follows.
Chains that let holders rekey in one transaction will carry their users through the quantum migration.
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5 catalysts for $GRVT before Nov 30.
The first is already done: 1.1M $GRVT bought back and burned, nearly 1% of circulating supply.
4 more launches are coming:
> Upgraded Earn with up to 11% yield
> Stable Funding Perps across TradFi markets
> Yield-bearing assets as trading margin
> GRVT 2.0, an all-in-one onchain investing app
The flywheel is worth watching: more products attract users and volume, driving revenue that could support further buybacks.
With $51B in January trading volume, @grvt_io already has a foundation to build on.
Curious how this plays out
The first is already done: 1.1M $GRVT bought back and burned, nearly 1% of circulating supply.
4 more launches are coming:
> Upgraded Earn with up to 11% yield
> Stable Funding Perps across TradFi markets
> Yield-bearing assets as trading margin
> GRVT 2.0, an all-in-one onchain investing app
The flywheel is worth watching: more products attract users and volume, driving revenue that could support further buybacks.
With $51B in January trading volume, @grvt_io already has a foundation to build on.
Curious how this plays out
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Ethereum privacy is moving into execution.
From private L2s to FHE coprocessors and institutional settlement, a full infrastructure stack is taking shape.
Part 1 mapped the applications.
Part 2 looks at the infrastructure behind them.
1/ Private Execution
Some networks into the environment where transactions and applications run.
- @aztecnetwork: Privacy-first Ethereum L2 using ZK proofs for private state and execution, with Alpha V6 governance underway.
- @Starknet: STRK20 brings shielded balances and transfers to an existing public L2. Already 50 assets and ~$1.4M shielded (per Starknet, Oct 7).
- @zksync: Prividium enables permissioned execution with private data and proofs anchored to Ethereum.
- @silentdao_: TEE-based Ethereum L2 built on OP Stack, serving institutional workflows.
@horizenglobal: Base L3 developing confidential EVM infrastructure.
These approaches support different needs, from private DeFi to institution-controlled execution.
2/ Confidential Computation
Another approach brings privacy to applications already running across Ethereum.
- @zama: FHE infrastructure powering confidential tokens, with $100M+ earning yield through Morpho (per Zama, Oct 15).
- @fhenix: CoFHE brings encrypted computation to EVM contracts, with mainnet launch planned.
- @inconetwork: TEE-based confidential computation through Inco Lightning, now live on Base mainnet.
- @nillion: Dusk brings encrypted markets infrastructure to Ethereum mainnet.
- @chainlink: Confidential HTTP is live, while broader Confidential Workflows remain in private beta.
This is where modular privacy gets interesting.
Developers can integrate confidentiality into existing applications and tap into established liquidity, rather than building an entirely new privacy network.
3/ Wallet & Transaction Privacy
Privacy also extends to how users access applications, submit transactions and settle payments.
Ethereum Kohaku: Privacy-focused wallet and SDK infrastructure, with Privacy Pools tooling currently in alpha.
@ShutterNetwork: Threshold-encrypted mempools, live on Gnosis with Ethereum L1 integration under development.
@0xfairblock: Confidential settlement infrastructure, with a MoonPay PYUSDx integration announced.
These tools aim to reduce information exposure beyond smart contract execution.
Ethereum privacy is developing through two complementary approaches:
Dedicated networks provide private execution, while modular infrastructure brings confidentiality closer to existing applications and liquidity.
Wallet tooling and transaction protection extend privacy further into the user experience.
The next milestone is adoption: more production integrations, more active shielded capital, and more applications using privacy by default.
Part 1 covered what users can do privately.
Part 2 maps the infrastructure making it possible.
From private L2s to FHE coprocessors and institutional settlement, a full infrastructure stack is taking shape.
Part 1 mapped the applications.
Part 2 looks at the infrastructure behind them.
1/ Private Execution
Some networks into the environment where transactions and applications run.
- @aztecnetwork: Privacy-first Ethereum L2 using ZK proofs for private state and execution, with Alpha V6 governance underway.
- @Starknet: STRK20 brings shielded balances and transfers to an existing public L2. Already 50 assets and ~$1.4M shielded (per Starknet, Oct 7).
- @zksync: Prividium enables permissioned execution with private data and proofs anchored to Ethereum.
- @silentdao_: TEE-based Ethereum L2 built on OP Stack, serving institutional workflows.
@horizenglobal: Base L3 developing confidential EVM infrastructure.
These approaches support different needs, from private DeFi to institution-controlled execution.
2/ Confidential Computation
Another approach brings privacy to applications already running across Ethereum.
- @zama: FHE infrastructure powering confidential tokens, with $100M+ earning yield through Morpho (per Zama, Oct 15).
- @fhenix: CoFHE brings encrypted computation to EVM contracts, with mainnet launch planned.
- @inconetwork: TEE-based confidential computation through Inco Lightning, now live on Base mainnet.
- @nillion: Dusk brings encrypted markets infrastructure to Ethereum mainnet.
- @chainlink: Confidential HTTP is live, while broader Confidential Workflows remain in private beta.
This is where modular privacy gets interesting.
Developers can integrate confidentiality into existing applications and tap into established liquidity, rather than building an entirely new privacy network.
3/ Wallet & Transaction Privacy
Privacy also extends to how users access applications, submit transactions and settle payments.
Ethereum Kohaku: Privacy-focused wallet and SDK infrastructure, with Privacy Pools tooling currently in alpha.
@ShutterNetwork: Threshold-encrypted mempools, live on Gnosis with Ethereum L1 integration under development.
@0xfairblock: Confidential settlement infrastructure, with a MoonPay PYUSDx integration announced.
These tools aim to reduce information exposure beyond smart contract execution.
Ethereum privacy is developing through two complementary approaches:
Dedicated networks provide private execution, while modular infrastructure brings confidentiality closer to existing applications and liquidity.
Wallet tooling and transaction protection extend privacy further into the user experience.
The next milestone is adoption: more production integrations, more active shielded capital, and more applications using privacy by default.
Part 1 covered what users can do privately.
Part 2 maps the infrastructure making it possible.
Tornado Cash holds $629.7M on Ethereum.
Railgun holds another $109.7M.
Privacy on Ethereum is leaving the vault for apps people already use.
1/ Private Balances
The foundation is letting users receive and hold assets without exposing their entire financial history.
- h: Stealth addresses for private receiving.
- @RAILGUN_Project: Shielded balances for private DeFi, accessible through wallets like @anondotinc.
- @zERC20io: Token wrapping for unlinkable transfers.
- @Veildotcash: Privacy pools on Base.
2/ Private DeFi
Once assets are shielded, the next step is putting capital to work.
- @zama: Confidential token infrastructure, including integrations with Morpho.
- @PrivacyBoost: SDK for private Morpho Earn balances.
- @ZKPanther: Shielded trading through DEX liquidity.
- @0xbowio: Privacy Pools with yield opportunities.
- @xochi_fi: Shielded settlement on Aztec.
- @tacitfi: Private issuance and swaps backed by BTC and ETH.
- @EkuboProtocol and @OpusMoney: Private liquidity and borrowing on Starknet.
3/ Private Applications
The final layer brings privacy into products users already understand.
- @ambire: Integrating Railgun and Privacy Pools into wallet UX.
- @hinkal_protocol: Hinkal Pay combines public and private balances.
- @AnomaPay: Private payments and swaps on Ethereum.
- @P_Inference: Private AI chat with payments on Aztec.
- @Haven_Hn_: Private neobank card on Starknet.
- @raycashxyz: Private stablecoin app, currently on waitlist.
The next phase of Ethereum privacy is about what users can do with their private balances.
From shielding assets to earning yield, trading and spending, privacy is gradually moving into the applications themselves.
These products are at different stages of development, but the direction is becoming clearer.
The next metric worth watching is how much shielded capital actually gets used.
Part 2 covers the networks powering the stack.
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4,408
Phoenix Research retweeted
My take on the partnership between @redstone_defi and @Brickken:
1. T+0 settlements via Settle could bring the most value to Brickken's ecosystem.
Price feeds and credit ratings are offered by multiple providers, but turning T+N settlement into T+0 liquidity addresses one of ts.
Brickken has supported over $660M in tokenized assets, spanning equity, real estate, and energy. Many of these asset classes are traditionally illiquid, with lengthy redemption cycles.
RedStone Settle uses solvers to provide immediate liquidity, while solvers take over the positions and handle redemption.
If executed well, this could unlock more Brickken assets as collateral in defi lending markets.
2. Expanding agent-ready RWAs on Brickken
Brickken already offers APIs, an MCP server, and SDKs that allow AI agents to interact with tokenized assets.
RedStone adds three key infrastructure components:
- Pricing to determine asset valuations.
- @CredoraNetwork to assess credit risk.
- Settle to support exits and liquidations.
Together, these components could help AI agents move beyond executing transactions toward managing RWA positions autonomously.
3. An institutional moat is taking shape
RedStone is expanding vertically across pricing, risk, and settlement rather than competing solely as an oracle provider.
Through Brickken, RedStone gains an opportunity to integrate all three layers with asset issuers from the tokenization stage.
As issuers and lending markets adopt the full stack, switching costs could rise, making the infrastructure harder to replace.
imo, that's where RedStone's long-term institutional moat could emerge, with Settle as its strongest differentiator.
1. T+0 settlements via Settle could bring the most value to Brickken's ecosystem.
Price feeds and credit ratings are offered by multiple providers, but turning T+N settlement into T+0 liquidity addresses one of ts.
Brickken has supported over $660M in tokenized assets, spanning equity, real estate, and energy. Many of these asset classes are traditionally illiquid, with lengthy redemption cycles.
RedStone Settle uses solvers to provide immediate liquidity, while solvers take over the positions and handle redemption.
If executed well, this could unlock more Brickken assets as collateral in defi lending markets.
2. Expanding agent-ready RWAs on Brickken
Brickken already offers APIs, an MCP server, and SDKs that allow AI agents to interact with tokenized assets.
RedStone adds three key infrastructure components:
- Pricing to determine asset valuations.
- @CredoraNetwork to assess credit risk.
- Settle to support exits and liquidations.
Together, these components could help AI agents move beyond executing transactions toward managing RWA positions autonomously.
3. An institutional moat is taking shape
RedStone is expanding vertically across pricing, risk, and settlement rather than competing solely as an oracle provider.
Through Brickken, RedStone gains an opportunity to integrate all three layers with asset issuers from the tokenization stage.
As issuers and lending markets adopt the full stack, switching costs could rise, making the infrastructure harder to replace.
imo, that's where RedStone's long-term institutional moat could emerge, with Settle as its strongest differentiator.
NEW: @Brickken, a tokenization platform with 150+ clients and $660M in tokenized assets, has selected RedStone as its data infrastructure partner.
Brickken-issued assets can now access the full RedStone stack: feeds, T+0 settlement via Settle, and @CredoraNetwork risk ratings, e work in DeFi for RWA's.
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3,631
Tornado Cash holds $629.7M on Ethereum.
Railgun holds another $109.7M.
Privacy on Ethereum is leaving the vault for apps people already use.
1/ Private Balances
The foundation is letting users receive and hold assets without exposing their entire financial history.
- h: Stealth addresses for private receiving.
- @RAILGUN_Project: Shielded balances for private DeFi, accessible through wallets like @anondotinc.
- @zERC20io: Token wrapping for unlinkable transfers.
- @Veildotcash: Privacy pools on Base.
2/ Private DeFi
Once assets are shielded, the next step is putting capital to work.
- @zama: Confidential token infrastructure, including integrations with Morpho.
- @PrivacyBoost: SDK for private Morpho Earn balances.
- @ZKPanther: Shielded trading through DEX liquidity.
- @0xbowio: Privacy Pools with yield opportunities.
- @xochi_fi: Shielded settlement on Aztec.
- @tacitfi: Private issuance and swaps backed by BTC and ETH.
- @EkuboProtocol and @OpusMoney: Private liquidity and borrowing on Starknet.
3/ Private Applications
The final layer brings privacy into products users already understand.
- @ambire: Integrating Railgun and Privacy Pools into wallet UX.
- @hinkal_protocol: Hinkal Pay combines public and private balances.
- @AnomaPay: Private payments and swaps on Ethereum.
- @P_Inference: Private AI chat with payments on Aztec.
- @Haven_Hn_: Private neobank card on Starknet.
- @raycashxyz: Private stablecoin app, currently on waitlist.
The next phase of Ethereum privacy is about what users can do with their private balances.
From shielding assets to earning yield, trading and spending, privacy is gradually moving into the applications themselves.
These products are at different stages of development, but the direction is becoming clearer.
The next metric worth watching is how much shielded capital actually gets used.
Part 2 covers the networks powering the stack.
Railgun holds another $109.7M.
Privacy on Ethereum is leaving the vault for apps people already use.
1/ Private Balances
The foundation is letting users receive and hold assets without exposing their entire financial history.
- h: Stealth addresses for private receiving.
- @RAILGUN_Project: Shielded balances for private DeFi, accessible through wallets like @anondotinc.
- @zERC20io: Token wrapping for unlinkable transfers.
- @Veildotcash: Privacy pools on Base.
2/ Private DeFi
Once assets are shielded, the next step is putting capital to work.
- @zama: Confidential token infrastructure, including integrations with Morpho.
- @PrivacyBoost: SDK for private Morpho Earn balances.
- @ZKPanther: Shielded trading through DEX liquidity.
- @0xbowio: Privacy Pools with yield opportunities.
- @xochi_fi: Shielded settlement on Aztec.
- @tacitfi: Private issuance and swaps backed by BTC and ETH.
- @EkuboProtocol and @OpusMoney: Private liquidity and borrowing on Starknet.
3/ Private Applications
The final layer brings privacy into products users already understand.
- @ambire: Integrating Railgun and Privacy Pools into wallet UX.
- @hinkal_protocol: Hinkal Pay combines public and private balances.
- @AnomaPay: Private payments and swaps on Ethereum.
- @P_Inference: Private AI chat with payments on Aztec.
- @Haven_Hn_: Private neobank card on Starknet.
- @raycashxyz: Private stablecoin app, currently on waitlist.
The next phase of Ethereum privacy is about what users can do with their private balances.
From shielding assets to earning yield, trading and spending, privacy is gradually moving into the applications themselves.
These products are at different stages of development, but the direction is becoming clearer.
The next metric worth watching is how much shielded capital actually gets used.
Part 2 covers the networks powering the stack.
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Investors put $29M into Solana privacy engines.
Users put about $2M into the privacy apps.
That 14x gap is the clearest open lane in Solana privacy.
Follow the money, then the users, then the partners a founder needs.
Where did the money go?
Into the lower layer.
Three for other teams:
> @Arcium: MPC across many nodes. Mainnet alpha since Feb, $9M raised.
> @magicblock: Private Ephemeral Rollups in secure hardware. $10.5M raised, Private Payments API in mainnet beta since March.
> @inconetwork: encrypted compute, in beta on Solana devnet. $9.5M raised across two rounds.
Where are the users today?
Deposits sit with a handful of consumer apps.
One side of the gap is funding and the other is deposits, so read 14x as a direction:
> @theprivacycash: the top Solana privacy pool on DefiLlama. $1.62M TVL, $135K in 30-day fees.
> @UmbraPrivacy: private hold, send and swap. v2 live since Oct 13, $0.35M TVL.
> @cloak_ag: private payroll and payment links. Mainnet since Sep 15.
> @looftapay: private USDC to a social handle, live with MagicBlock privacy.
Wallets that want privacy through an API have an option too:
@helius: Solana Rings, encrypted balances your app calls through an API. Beta on devnet.
The money sits one layer below the users.
So the engines need apps, and some already have them.
Users put about $2M into the privacy apps.
That 14x gap is the clearest open lane in Solana privacy.
Follow the money, then the users, then the partners a founder needs.
Where did the money go?
Into the lower layer.
Three for other teams:
> @Arcium: MPC across many nodes. Mainnet alpha since Feb, $9M raised.
> @magicblock: Private Ephemeral Rollups in secure hardware. $10.5M raised, Private Payments API in mainnet beta since March.
> @inconetwork: encrypted compute, in beta on Solana devnet. $9.5M raised across two rounds.
Where are the users today?
Deposits sit with a handful of consumer apps.
One side of the gap is funding and the other is deposits, so read 14x as a direction:
> @theprivacycash: the top Solana privacy pool on DefiLlama. $1.62M TVL, $135K in 30-day fees.
> @UmbraPrivacy: private hold, send and swap. v2 live since Oct 13, $0.35M TVL.
> @cloak_ag: private payroll and payment links. Mainnet since Sep 15.
> @looftapay: private USDC to a social handle, live with MagicBlock privacy.
Wallets that want privacy through an API have an option too:
@helius: Solana Rings, encrypted balances your app calls through an API. Beta on devnet.
The money sits one layer below the users.
So the engines need apps, and some already have them.
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Prediction markets traded $76B in September.
All of it answers one question: will it happen?
The bigger money sits in the questions that come next.
Picture planning a trip with a weather app.
You ask four questions, and each one now has its own market.
❱ Will it rain?
The ge.
@Kalshi: $60.85B in Sept
@Polymarket: $13.12B
@opinionlabsxyz: $610M
@HyperliquidX HIP-4: $81M, up 3.7x, with @Outcomexyz as a front end
❱ Which plan do we pick?
Every option gets a market, and the highest price wins. DAOs use this to vote.
@MetaDAOProject: 21 of 22 market-run DAOs, ~$39M
@umia_finance: live on Base and Robinhood Chain, $3.6M TVL
❱ What does rain cost me?
Prices for gold, oil or BTC in the world where the event happens.
@lightconexyz: launch by year end
@ProofMarkets: own appchain, mainnet ~Nov
@ifmarkets: cash-settled 10x, 2,000+ beta traders
❱ How do I insure the trip?
Once an outcome has a price, you can hedge it and borrow on it.
@BranchpointXYZ: one shared name for every event
@gondorfi: loans against Polymarket shares
@DeriveXYZ: $3.82B options in Sept
Odds were version zero.
Consequences are the bigger market.
All of it answers one question: will it happen?
The bigger money sits in the questions that come next.
Picture planning a trip with a weather app.
You ask four questions, and each one now has its own market.
❱ Will it rain?
The ge.
@Kalshi: $60.85B in Sept
@Polymarket: $13.12B
@opinionlabsxyz: $610M
@HyperliquidX HIP-4: $81M, up 3.7x, with @Outcomexyz as a front end
❱ Which plan do we pick?
Every option gets a market, and the highest price wins. DAOs use this to vote.
@MetaDAOProject: 21 of 22 market-run DAOs, ~$39M
@umia_finance: live on Base and Robinhood Chain, $3.6M TVL
❱ What does rain cost me?
Prices for gold, oil or BTC in the world where the event happens.
@lightconexyz: launch by year end
@ProofMarkets: own appchain, mainnet ~Nov
@ifmarkets: cash-settled 10x, 2,000+ beta traders
❱ How do I insure the trip?
Once an outcome has a price, you can hedge it and borrow on it.
@BranchpointXYZ: one shared name for every event
@gondorfi: loans against Polymarket shares
@DeriveXYZ: $3.82B options in Sept
Odds were version zero.
Consequences are the bigger market.
29
0
44
2,778
Phoenix Research retweeted
Tokenized stocks on @base are finding more use cases in DeFi.
3% USDC incentives to boost tokenized stock deposits on @aave V4
$1.2M in collateral from @superformxyz, bringing automated carry trade strategies to retail users.
🧵,
3% USDC incentives to boost tokenized stock deposits on @aave V4
$1.2M in collateral from @superformxyz, bringing automated carry trade strategies to retail users.
🧵,
Aave DAO has approved incentives for the Coinbase Tokenized Stocks market on @base.
Tokenized stocks are only available in eligible jurisdictions outside of the U.S.
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5,942
Seven oracles secure most of onchain finance
The gap between them is wider than most people think.
I lined them up on the same four metrics from
@DefiLlama (TVS | protocols | chains | 30d TVS, Oct 16):
@chainlink: $36.1B | 538 | 101 | +3.0%
@ChronicleLabs: $4.9B | 13 | 9 | 3.9B | 91 | 56 | -3.6%
@PythNetwork: $3.3B | 320 | 110 | +7.9%
@StorkOracle: $238M | 39 | 40 | -5.7%
@switchboardxyz: $132M | 22 | 7 | +12.2%
@Api3DAO: $14M | 44 | 27 | +12.1%
Chainlink secures 7x more than the next oracle, which makes it the Bloomberg terminal of DeFi collateral.
Chronicle plays the opposite game: only 13 clients, yet about $377M secured per protocol, the deepest relationships on the board.
Pyth wins a different race.
It reaches 110 chains and self-reports 94.4% of Q3 RWA perp volume, a market that TVS leaves out entirely.
So the oracle market is quietly splitting in two: collateral on one side, trading on the other.
The winners will each own one side, and the fees will follow.
The gap between them is wider than most people think.
I lined them up on the same four metrics from
@DefiLlama (TVS | protocols | chains | 30d TVS, Oct 16):
@chainlink: $36.1B | 538 | 101 | +3.0%
@ChronicleLabs: $4.9B | 13 | 9 | 3.9B | 91 | 56 | -3.6%
@PythNetwork: $3.3B | 320 | 110 | +7.9%
@StorkOracle: $238M | 39 | 40 | -5.7%
@switchboardxyz: $132M | 22 | 7 | +12.2%
@Api3DAO: $14M | 44 | 27 | +12.1%
Chainlink secures 7x more than the next oracle, which makes it the Bloomberg terminal of DeFi collateral.
Chronicle plays the opposite game: only 13 clients, yet about $377M secured per protocol, the deepest relationships on the board.
Pyth wins a different race.
It reaches 110 chains and self-reports 94.4% of Q3 RWA perp volume, a market that TVS leaves out entirely.
So the oracle market is quietly splitting in two: collateral on one side, trading on the other.
The winners will each own one side, and the fees will follow.
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4,072
>> Crypto data alpha here <<
AI labs are paying billions for data.
The edge moves to whoever owns the inputs.
Mercor runs at $2B a year. micro1 set aside $1B to buy company data.
Google bid $10M for a bankrupt airline's files.
Every dataset goes through 7 steps before a model can useams on each step.
Save this map:
❱ Collect
Step 1: raw input enters the pipeline.
@grass, @teneo_protocol, @PublicAI_
❱ Physical
Step 2: robots learn from the real world.
@psdnai, @BitRobotNetwork, @frodobots, @PrismaXai, @reborn_agi
❱ Label and rate
Step 3: raw data becomes training-grade.
@codatta_io, @PerleLabs, @reppo, @FractionAI_xyz, @opentensor
❱ Own
Step 4: users set the price and the permission.
@vana, @DIMO_Network, @oceanprotocol
❱ Provenance
Step 5: every byte carries its source and rights.
@datafdn, @OpenledgerHQ, @numbersprotocol
❱ Store
Step 6: datasets and models live in verifiable storage.
@WalrusProtocol, @Filecoin, @ArweaveEco, @0G_labs, @irys_xyz
❱ Query
Step 7: models pull answers with proof attached.
@spaceandtime, @graphprotocol, @ChainbaseHQ
The market already picked its first winners.
GRASS +94% and WAL +89% in 30 days.
Collection and storage got paid first.
Inference made AI cheap in 2026.
Data decides who wins in 2027.
AI labs are paying billions for data.
The edge moves to whoever owns the inputs.
Mercor runs at $2B a year. micro1 set aside $1B to buy company data.
Google bid $10M for a bankrupt airline's files.
Every dataset goes through 7 steps before a model can useams on each step.
Save this map:
❱ Collect
Step 1: raw input enters the pipeline.
@grass, @teneo_protocol, @PublicAI_
❱ Physical
Step 2: robots learn from the real world.
@psdnai, @BitRobotNetwork, @frodobots, @PrismaXai, @reborn_agi
❱ Label and rate
Step 3: raw data becomes training-grade.
@codatta_io, @PerleLabs, @reppo, @FractionAI_xyz, @opentensor
❱ Own
Step 4: users set the price and the permission.
@vana, @DIMO_Network, @oceanprotocol
❱ Provenance
Step 5: every byte carries its source and rights.
@datafdn, @OpenledgerHQ, @numbersprotocol
❱ Store
Step 6: datasets and models live in verifiable storage.
@WalrusProtocol, @Filecoin, @ArweaveEco, @0G_labs, @irys_xyz
❱ Query
Step 7: models pull answers with proof attached.
@spaceandtime, @graphprotocol, @ChainbaseHQ
The market already picked its first winners.
GRASS +94% and WAL +89% in 30 days.
Collection and storage got paid first.
Inference made AI cheap in 2026.
Data decides who wins in 2027.
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4,827
Off-the-shelf AI is a gym membership
Custom agents are a personal trainer who already knows your routine
The gap is your company's context: the processes, the past work, the tribal knowledge
@workmate pairs agents with forward-deployed engineers who capture exactly that
Context is the moat of the agent era
Custom agents are a personal trainer who already knows your routine
The gap is your company's context: the processes, the past work, the tribal knowledge
@workmate pairs agents with forward-deployed engineers who capture exactly that
Context is the moat of the agent era
Today, we’re introducing the Workmate Workforce.
A team of custom, collaborative AI agents built around how your company already works.
Workmates work just like remote colleagues. They're supported by our flexible, model-agnostic AI platform and the @workmate team of ers.
We turn your repeatable processes, past work, and institutional knowledge into long-running AI teammates that handle the legwork so your people can focus on what they do best.
The leaders that define the next era will be agent first. The question is… will that be you?
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Wall Street already runs onchain.
@Broadridge processed $7.5T of repo on @CantonNetwork in September.
CT scrolled past it.
Canton works like a members-only trading floor.
Each party sees only its slice.
Regulators get a seat at the table.
Four jobs, examples only, DYOR:
mple_ny @ekidenfi @Kora_Protocol @TitanExchange_
> Borrow, lend and earn: @Kintsu @AlpendHQ @Canalfinance_
> Settle treasuries and repo: @CantonNetwork (Broadridge DLR)
> Move dollars in and out: @circle (USDCx)
The floor is already busy:
> 490+ firms building on it
> $345B of real-world assets parked there
> $359B of repo moving on an average day
Pros: institutional volume, privacy by default.
Cons: permissioned, few retail apps.
TradFi brought the volume first.
Crypto-native apps now bring the yield.
Canton becomes the private back office of onchain finance.
@Broadridge processed $7.5T of repo on @CantonNetwork in September.
CT scrolled past it.
Canton works like a members-only trading floor.
Each party sees only its slice.
Regulators get a seat at the table.
Four jobs, examples only, DYOR:
mple_ny @ekidenfi @Kora_Protocol @TitanExchange_
> Borrow, lend and earn: @Kintsu @AlpendHQ @Canalfinance_
> Settle treasuries and repo: @CantonNetwork (Broadridge DLR)
> Move dollars in and out: @circle (USDCx)
The floor is already busy:
> 490+ firms building on it
> $345B of real-world assets parked there
> $359B of repo moving on an average day
Pros: institutional volume, privacy by default.
Cons: permissioned, few retail apps.
TradFi brought the volume first.
Crypto-native apps now bring the yield.
Canton becomes the private back office of onchain finance.
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1
13
1,195