Non-fungible tokens have been used in various use cases since their inception. The ERC-721 standard initially finding its use in art and then mass Pfp collections. A trend started in Jan 2021 with hashmasks and found quick popularity with various subsequent collections. Many agree that NFT narratives dominated the cycle in the latter half of 2021. We saw art blocks, punks, apes and other derivatives so on and so forth.

Where does one buy NFTs? The available marketplaces include Super rare, Zora, Foundation, Rarible, Opensea, Looksrare, and NFTX. The first 3 are marketplaces for 1/1 art pieces, Rarible never found its groove or capitalized on the relatively early mover advantage they had. Lookrare didn’t exist until earlier this year, and while NFTX was a novel product, it didn’t precisely capture the market demand that is usual to marketplaces (which it wasn’t exactly meant to be). Nftx essentially unlocked the index idea for NFTs, wherein you didn’t have to own the entire Nft and could own fractions of it, thereby maintaining exposure to the tokens you like/blue chip Nfts.

Opensea has found its place as the king of marketplaces with its superior discovery and relatively easy-to-use platform and clocks in about a 100 million volume every week, reaching as much as 1.5 billion dollars at its peak, and has had about 2 million unique users and much more. At the end of the day, though Opensea is still a traditional marketplace experience and isn’t anything crypto native, its simplicity and ease of use have found a loyal customer base.

There haven’t been many crypto-native (not just in origins but ideas) solutions to the NFT marketplace problem. The attempts have gone awry in various ways, such as misplaced or hyper incentivization, which has led to false growth of revenue or volume but hasn’t led to sticky usage, or relatively niche-r products which have hit the ceiling on their growth arc.

Temperature check about making $UNI great again.

A short history of uniswap

Initially launched in 2018, from an idea inspired by Vitalik’s post in r/ethereum and a blog post:

or How to make things a little slower.

Recently been reading about randomness in ethereum and blockchains generally.

Randomness on blockchains doesn’t exist in ideal form and is hard to find. The use of randomness has various applications like raffles, lotteries, or perhaps an auction model where the initial minimum bid is randomly chosen from a list. On a protocol level, the protocol can also use this randomness to achieve privacy or select a random block producer in the case of proof of stake consensus.

The closest we come to is using chainlink VRFs which, when provided with a seed, generate a random number published on-chain with proof and can be verified using the oracle’s public key and application seed. VRFs have been used in crypto in various applications and at the protocol level in Cardano (Ouroboros) and Polkadot.

If you think about it we’ve come a long way since defi summer of 2020, almost had an entire cycle at this point, onboarded a variety of users and through NFTs even are reaching the broader culture base of humanity. We are now experiencing a new wave of defi innovation and experiments within NFTs, trying to scale and adjust our products and tools to onboard users to the future of finance and the metaverse.

The other day, A friend of mine was just talking about inflation and how the best banks in his country gave around 1-2% in returns on savings. And when he added the inflation, the real rate was well in the negative. I offered him a few alternatives as to how he could buy index funds or ETFs and DCA into for a year and then maybe the stock market will have done its correction by then. As a final option, I also suggested crypto, the likes of luna providing around 20% and even the safest choices giving around 6-8% which is a few multiples that you’d find in CeFi institutions at the moment. So then he asked me, “Where should I get started with crypto?”

So where does someone who has no idea what wallets are, what a CeX is or what Defi is get started in crypto?

The usual steps let’s assume you are interested in returns on a CEX would involve:

Writing down some notes of what I’ve seen in the market for the last two years.

I’m not quite sure where the market is going, it looks pretty fatigued. When I came back to trading/investing in crypto in summer 2020, I was under the assumption that this cycle would probably end around us topping at 5 trillion dollars. This article is perhaps just a recollection and retrospection with some learnings of what I saw since then with some Dota analogies (bear with me through those).

Note: Someone told me that this work reminds them of Cobie ser’s, Trading the Metagame, So you can probably skip this if you have read that, This is probably just an extensive exploration of that idea dating back to defi summer 2020.

I’m a DOTA 2 player (Defense of the Ancients), and it’s viciously addictive having spent 3000 hours playing the game and even then still being engaged by it, I often wonder what makes me go back to the game? Most modern RPGs are played around for 100 hours to complete the main storyline while 200-300 hours for covering everything. DOTA 2 is an MMORPG (pronounced morph) and something that keeps getting changed due to patches, the heroes are balanced/the map is changed/the items are updated, and that keeps it very interesting.

I think that having an open list of problems is a good reminder of progress for a field, nascent or old, research problems or practical ones. These also provide a benchmark to look towards and glance upon to take stock of the progress it has done over the years.

While there is no doubt that yield stacking is impressive and that we can create extensive levels of leverage by stacking things on top of one another. The stick of measure will be solving problems that either extend the ethos of decentralization to every real-world product or rectify the errors that current systems possess or fill the gaps to make the system seamlessly functional, all of this leads to more adoption or reduces the friction/inefficiencies in the ecosystem.

Following is an incomprehensive list of defi problems, I will take stock of these problems in 12 months.

• Automated risk scoring of lending borrowing pools -> Increasingly important problem
  • Possible Solution: Risk assessment without historic data is really hard because it could be so that a pool of users with a good credit history will always pay the loan back. “Credit score” in tradfi.
    • One alternative way of looking at the problem would be, looking at a function for calculating the probability of default given the pool of assets you have.
    • Thinking of risk in the extreme terms of everyone defaulting although useful is an edge case, every time someone defaults (since these are collateralized pools) there'll be a liquidation. So this means that there is an existence of a liquidation price, so one way to view this problem (even without considering the users at all) given that I borrow X amount at Y price, What is the chance that price hits my liquidation price Z (in this case the user will have defaulted and will get liquidated).
    • How do you calculate/model the price action?
      • One naive and simple metric would be a weighted average(weight by asset in a pool of n assets) of historical/implied volatility (because in a highly volatile asset it’s likely that the price reaches your liquidation price) and you can do it on a basis of a monthly epoch (now this is a very sanitized way of thinking about pool risk because an asset can improve its order book/action price becoming less volatile)
      • The added advantage of doing a weighted average across a function (IV/historical volatility) is you can just add another variable modelled through ML or some other way and get a more refined answer
    • The assumption of modelling the problem this way is that a borrower might borrow from a pool with less risky/volatile assets.
• Managing Risk for lenders and distributing risk/ Undercollateralized Loans
  • Defi Lending is not in a place where it can be used by traditional or real word entities because of over-collateralization
  • Most real-world loans are undercollateralized
  • This under collateralization ensures that the lender establishes the borrower’s ability of repayment
  • Tradfi is plagued by NPAs but still ultimately fall back to some sort of credit score establishment [Spectral finance solving this, but still an open problem].
  • But still, most credit score methods would rely on onchain history for credit establishment, we are moving towards privacy-centric defi is this approach extendable to that idea? [Homomorphic encryption could provide a solution]
• Uniswap v3 is one of the most used AMMs, with over a few billion dollars in it, What is the optimal strategy for lending for both passive and active liquidity providers? How can you formulate and model the problem?
  • Most vault managers have failed and only charm finance appears to have some stronghold over this having consistently beaten full range v2 performance

(3,3) is a meme that has taken crypto by storm, and while we might not be in the (3,3) phase of the project anymore, here is a look towards the future.

This article is a “the cup is half-full” look at Olympus, instead of the other side of the coin that seems to be popular. I only think of ohm as far as the decentralized reserve currency experiment is concerned any pivot from this idea moots the arguments that I’ve made in this essay.

There seems to be little love lost between most of CT and the downfall of Olympus. But if there’s one characterization that I don’t entirely agree with that has dominated the common parlance of Olympus talk is calling it a Ponzi, and I think it’s quite sad that the project itself tried to popularize that narrative on some level, you reap the seeds you sow.

Decentralized Exchanges (Dex), which provide a way for market participants to trade pairs of on-chain assets, have this additional feature of acting as an oracle for measuring the relative price for this pair of assets, and because of this can be thought of as Decentralized oracles.

Decentralized oracles are smart contracts that rely on users voting on particular prediction market outcomes. A final outcome is chosen via a social choice function, similar to how majority or weighted majority voting is used to decide outcomes in elections.

Decentralized oracles are becoming increasingly important and sought after sources of information because no one agent/entity/organization, i.e. a centralized source of information, can be trusted for its information completely. While this is not something wholly solved by the de-oracles, their design usually involves a “challenge” phase or/and provide users with rewards for reporting info correctly.

A fundamental problem when it comes to price markets is the “oracle problem”, the ability to have correct prices for an asset, or more generally, in the case of blockchains, the problem of providing external data (i.e. outside of the system) to a blockchain. One concern whenever using an AMM is that the price of an asset should accurately reflect that on a centralized exchange (assuming this to be the global price). AMMs are automated market makers and have no notion of this global price and aren’t adjusted accordingly; this price adjustment is usually left to arbitrage bots or traders who use these products. Why is the validity of this price being close to the global price important, though? Suppose an AMM generally has a negligible error or constantly reflects the same price as the global external prices. In that case, such an AMM’s price feed can be used as ground truth for various purposes and is said to be a “good price oracle”.