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incredibly important to enable a more “grown up” onchain financial system modelable risk = a must have

What we’ve learned from speaking with risk managers as we’ve built @LotusFi_ They want to manage risk, not just play musical chairs with collateral. Tight competition in a compressed yield regime further nudges managers towards collateral with opaque tail risk because that’s where the premium yield shows up. This is in part, symptomatic of the one rate, one risk level market structure that most vaults are allocating to. If you're a serious risk manager who wants to manage a defined profile on @LotusFi_ get in touch, we’re at DAS and ETHcc.

R.I.P. Balancer

Better Vaults need better market structure. With Resolv, a single issuer-side opsec failure was enough to create losses, illiquidity, or emergency actions across a web of downstream vaults, pools, and credit markets. Last year it was the $93m Stream Finance debacle due to a bad balance sheet. LPs aren’t being compensated adequately for the opaque risk they’re taking on in many of these assets through diversified vault allocations. @LotusFi_ doesn’t eliminate collateral risk, but we think our model eliminates much of the incentive to lend against riskier collateral. Lend against blue-chip collaterals like BTC and ETH. Keep liquidity connected. Price credit risk on a curve inside one market.

🎯 Right now we have two approaches: Morpho parameters are immutable. Aave parameters are governed. @LotusFi_ let's the lenders (risk managers) choose the parameters they are willing to lend at without fragmenting liquidity.

Probabilistic vs. Binary Risk Probabilistic: Drawdowns you can model and size. Binary: Tail events where liquidity disappears and positions unwind violently. You’re managing catastrophe, not volatility. Most “high yield” vaults are binary risk in disguise.

I’ve written plenty on it in the past so won’t spend an essay on it today, but when it comes to stablecoins (including those for ETH or other reference assets), hard-coded oracles are brittle. They work until they catastrophically don’t. In contrast, live oracles allow for real-time risk mitigation, but at the cost of punishing users for false positives if liquidity dries up. Both are a legitimate design choice, but need to follow different underwriting processes. Only hard code if you diligence the underlying asset and are confident in its ability to meet redemptions as designed. Both the technical and financial fundamentals must be solid, and assign a risk premium to rates or haircut to LTV as appropriate. Live oracles let you be relatively agnostic about the quality of the asset (beyond technical safety of the smart contracts) and stay focused on the liquidity availability. Let’s also remember that more complex oracle setups are also possible. I personally have advocated for stablecoins to have a hard code when n liquidity is viable in a redemption contract and then switch to market price oracles the moment liquidity falls below n. Ultimately, this is surfacing again a longstanding “credit migration” problem where DeFi relies on curators and risk consultants to manually flag when an asset goes from excellent to good to fair to poor as collateral. That’s far too slow in cases like USR, even if it was 100% accurate. DeFi automates, speeds up, and simplifies (even if it sometimes seems otherwise) finance compared to traditional alternatives. We’re ultimately building financial vending machines, and it’s just a fact that it’s really hard to make a machine that works as intended under all conditions. But there’s clear ways to improve on both oracle design and oracle use, and it’s disappointing to see slow innovation on that front.

Who knew that Illinois had so many Serbians.

I have so much gratitude to people who wrote extremely complex software character-by-character. It already feels difficult to remember how much effort it really took. Thank you for getting us to this point.

Introducing a global lottery built for the internet age 🚨 Liquidity deposits OPEN NOW 🚨 Tickets sales start March 24th

LlamaAI can now do onchain digging

Researchers trained a humanoid robot to play tennis using only 5 hours of motion capture data The robot can now sustain multi-shot rallies with human players, hitting balls traveling >15 m/s with a ~90% success rate AlphaGo for every sport is coming

A missing primitive in DeFi lending: productive debt. Productive debt makes idle capital productive, gives lenders a base rate even at low utilization, and compresses spreads for lower borrowing costs. lotuslabs.net/blog/productiv…

We’re bringing new capabilities powered by Gemini models to @googlemaps. With Ask Maps, get answers to complex questions about any place you want. For example it can help with complex requests like "Find me the best 3-hour family hikes in the Grand Tetons and a spot for a packed lunch”. Will try this next time I'm there:) Rolling out now in the US and India.

We’re also launching Immersive Navigation - our biggest navigation upgrade in over a decade! A new vivid 3D view better reflects your surroundings, with helpful road details like lanes, crosswalks, traffic lights etc. Gemini models analyze real world imagery from Street View and aerial photos to give you an accurate view of landmarks along your route. Starts rolling out in the US today.

Controversial: Writing good code is harder then Hacking it

This is the new thing that Amjad showed me a couple weeks ago. It generalizes the idea of vibe coding beyond what people usually think of as coding.

the most powerful way to use agents today is as black-box optimizers, so one of the highest leverage human skills is being able to find good reward functions.

Lotus brings a risk curve to onchain lending. By connecting liquidity across tranches, vaults can choose different risk profiles on the same collateral. See the tranche mix behind each risk tier in our Interactive Docs 🔗⤵️

check out our interactive docs to learn about the engine behind @LotusFi_