I feel like the pyth wheel was better when it was running on Base and Monad.
Now, on Hyperliquid, regular chain users like me have to pay around $0.15 on every spin, on top of buying HYPE and bridging it. this doesn’t feel as user friendly as before.
I’d like to hear everyone’s opinion on this “Would it make sense to move the Pyth Wheel back to Base or Monad starting next month?”
I think this could reduce the burden on new users and students who are just getting started with on chain activities and are using a small portion of their savings to gain experience.
What do you guys think? Should we bring the Pyth Wheel back to Base or Monad?
You raise an interesting point. I have to admit I was also surprised to see how expensive a spin on HyperEVM can be from a user’s perspective.
The Entropy protocol fee itself is regularly reviewed and adjusted by the Pythian Council, with a target of roughly $0.02 per request. The latest adjustment was made through OP-PIP-134. Interestingly, HyperEVM was left unchanged at 0.0004 HYPE (roughly $0.033 at the time of the proposal), so the significant difference in the final cost per spin compared with Base or Monad appears to be largely chain-related rather than coming from the Entropy protocol fee itself.
That said, I haven’t personally dug into the technical details of the gas economics on HyperEVM yet, so I’d be very interested to hear from someone with a deeper understanding of the different cost components.
It would be useful to understand this better so the Community Council and the community can properly evaluate whether the current setup still makes sense for the Wheel, and what variables should be considered when choosing a chain for future deployments or migrations.
I’m definitely not an expert on the subject, so please educate me!
Given the price difference between $HYPE and $ETH (Even before the recent pump), calculations have been performed well in advance to account for this. If you care to look at prize tiers and amounts, you will see the restructure is quite significant (compared to previous month’s) in order to increase the equalise the median prize amount per user.
Based off the current snapshot (00:00 UTC), assuming a $PYTH price of $0.055, and a HYPE spin cost of $0.11. so far for September:
394 unique wallets
2,941 total spins
$2.65 average profit per user
$1.06 median profit per user
Total profit across all users $1042.47
361 users in profit
2 break even
31 in loss
Highest net loss = 33c
All wallets are in profit overall on PythWheel used over the past 3 unique blockchains. Anyone that wishes to check the validity of this statement can check the contract address against each wallet and their PYTH winnings at the end of every month. I understand the recent increase in spin cost (due to $HYPE pump), however, the data shows the objective numbers: Pythians are still winning.
Given the amount of custom support given during each chain switch, it is evident that changing chains is still a skill worth teaching at this stage.
Similar to this: anyone can check the same contract address to see where funds go. PythWheel takes ZERO fees, and is proven simply by using a blockchain explorer. There was one weird bug where a user accidentally deposited $ETH to the CA, but that amount was fully sent back. This error was not able to be replicated during testing, and has not occurred since, but we believe it has been patched.
I made a Gas fee video back when users were paying too much on BASE L2 in order to explain how gas fees work and hopefully teach Pythians useful skills:
I hope it is clear that gas fees on these EVM chains is not static. It is a bidding system to get your transaction on the blockchain. This means that ALL proves elevate when demand surges. Therefore the price to use Entropy is not static. This is also briefly explained in my video which was linked and referenced at the announcement
The same principles apply, but this time we are using $HYPE on HyperEVM . Again, this was in response to quite a number of users complaining about the cost of spinning on BASE. Anectodal evidence suggests these users didn’t understand how gas on EVM works: and why this affects the cost of Entropy use during periods of peak activity.
PythWheel is more than simply an entropy-based reward mechanism. It is an education tool, which repeatedly justifies itself in this second degree with situations like this. This PythWheel system is a complement to the existing Impact Awards system.
We will be continuing on HyperEVM for at least this month, and continue to restructure the system to ensure that Pythians are learning while earning.
September has been by far the most successful in terms of total spins for the first week of any month, and the most fair in terms of prize distribution (given a recent upgrade to probabilistic-weighting of prize category on PythWheel side).
Rest-assured: any adjustments that need to be made in order to improve the PythWheel: will be made. The longer-term median should be net-positive.
Furthermore, I will personally see to it that if any negative net value is experienced (based off a minimum gas spend), it will be rectified. So far the total sum of all losses combined (based off minimum gas fee) is just under 66 $PYTH at current prices, (~$3.58). This is easily equalised at the close of the month, if required.
PythWheel will continue to improve its mechanics and fairness over time. Pythians will vote with their feet (Either they will spin or not spin). At the moment, the numbers indicate that PythWheel on HyperEVM will be the most popular so far. Admittedly a single week is not a reliable dataset, but the numbers are overwhelming positive when compared with the previous 6 months, on the previous 2 chains. Feel free to compare the CA utilisation across HyperEVM, BASE, and Monad.
I am personally committed to the practical education that PythWheel provides, as well as ensuring rewards are fair and equitable.
I do hope users now pause and analyse before approving transactions to ensure the points covered in my video are adhered to when using Entropy on ANY chain. It was a great prompt for me to try and help educate, or at least show where to look for answers.
As it stands: on HyperEVM users overwhelming spin for $0.11 (0.001271 $HYPE).
However the most expensive transaction has cost $0.72 (0.008121 $HYPE).
Clearly more education is required as this user above spent almost 7x more than they should have. This kind of FOMO could be far more costly in future if lessons are not learned at this stage. Which is actually a blessing in disguise.
I’m very familiar with these mechanics, but I’m glad you took the time to explain them in more detail, as I think it’s useful for users to better understand how gas and Entropy costs work across EVM chains.
My personal view is that dealing with gas costs and chain activity can indeed be a good opportunity to learn something new. At the same time, I think it’s important to recognize that it also has an impact on how convenient and predictable PythWheel feels to use.
For example, I’ve personally encountered several situations where the transaction simulation was significantly higher than the expected/target cost. I’ve attached a screenshot from a few minutes ago as an example.
Obviously, users should check what they are approving, but from a user-experience perspective, having the expected cost fluctuate this much does add friction.
I also think we should be careful when interpreting the September numbers. It’s encouraging to see the increased activity and the improved prize distribution, but I’m not sure the current dataset is enough to conclude that HyperEVM itself is driving that increase.
There are quite a few variables at play here. PythWheel is now much more established than it was when it launched on Base or Monad, so comparing activity during the first week of those periods with the current first week of September may not be entirely apples-to-apples. As the Wheel becomes more known, I would also expect contract activity to naturally increase regardless of which chain it is deployed on.
I’m not saying the current results are not encouraging — quite the opposite. I just think it would be interesting to see the numbers over a longer period before drawing conclusions about which chain provides the best environment for PythWheel.
@KemarTiti also raised an interesting question that I don’t think has really been addressed yet: why not make PythWheel multichain? Is running the Wheel on multiple chains technically or economically prohibitive, or are there other reasons why a single-chain deployment is preferred?
That actually leads me to a few questions I’d be interested in understanding better:
What are the main reasons for moving PythWheel from one chain to another rather than maintaining it on multiple chains?
What is the process for selecting the next chain? Is this primarily based on gas/Entropy costs, ecosystem considerations, user activity, incentives, technical considerations, or some combination of these?
What are the specific objectives of a chain migration? For example, is the goal primarily to reduce the cost for users, increase adoption/activity, provide exposure to a particular ecosystem, or serve an educational purpose?
I think having more visibility into that decision-making process would help users better understand the rationale behind the migrations, and would also make it easier to evaluate whether a particular chain is actually an improvement over the previous one.
Again, I appreciate the effort that goes into the education side of this. I’m mainly trying to separate the educational value of understanding gas mechanics from the question of whether the overall user experience and economics are actually improved by moving between chains.
