1/ Ethereum's state grows far faster than it churns. Most of it goes dormant and never gets used again, while real activity sits on a tiny set of active applications.
Here's a deep dive into what 10 years of mainnet access data shows about how Ethereum's state is actually used. 🧵👇
1/ Ethereum’s protocol roadmap is shaped by many teams, researchers, and contributors.
At the Berlin Ethereum Day, @parithosh_j from the @ethPandaOps team will share perspectives on the strawmap, and the path forward.
Register or get involved! 🔗👇
We built Panda, a CLI/MCP tool that lets agents work directly with our observability data.
We found that if you give the agent a code sandbox and a library for your stack, things start to click together very nicely.
Post below 👇
We built a simulator for the fast confirmation rule, and replayed a years worth of blocks and attestations on Mainnet.
Across 800,000 mainnet slots, roughly 96 out of every 100 slots would have been fast-confirmed within 12 seconds.
Zero false confirmations.
Read more below!
New in The Lab: Validator Report.
Paste validator indices, pick a range, scan performance, and drill from day -> hour -> slot to find exactly what happened.
Catch missed proposals and head-vote drops faster.
https://t.co/kiviTuGkZJ is live.
See how long Ethereum transactions wait on average to land on-chain.
Let me provide some additional context: how the website works, what Glamsterdam (the next hardfork), in particular ePBS (EIP-7732), changes, and what this means for different stakeholders.
With 12s slots, the average inclusion delay is ~6s. Shortening slot time reduces it.
This number will increase by ~2s with ePBS, which is an unfortunate downside, degrading UX.
Under ePBS, builders should wait until the beacon block committing to their payload has enough attestations before releasing it.
Thus, ePBS has a similar effect to increasing slot time to 16s, and Ethereum would need ~8s slots just to get back to today’s inclusion delay.
A similar effect applies to zkEVM provers: if provers are separate from builders, they effectively lose ~2s of proving time, delaying real-time proving and slowing down the rollout of zkEVMs. With shorter slots (e.g. 8s), this means provers would need to be ~25% faster.
The data is sourced from @ethPandaOps’ Xatu nodes, comparing when transactions are first seen in the mempool vs when they are included in a block. It filters for transactions that can afford the base fee and pay at least $0.01 in priority fees.
If you're a dApp builder, infra provider, zkEVM prover, exchange, power user, etc., I'd be curious how much every second of inclusion delay matters.
Drop your thoughts in the comments or reach out via dm.
New in Xatu: Execution trace data.
Per-opcode gas consumption for blocks/txs. 9 new tables covering call frames, opcode gas, and daily/hourly aggregations.
Blog post + schema docs: https://t.co/jQdeG2a1uV
BPO2 landed last week. It bumped the max blobs per block to 21. We've been going through the data.
Initial analysis doesn't look good! Have a look at this scary chart. But there might be more to this story..
Link below!
I got two new tools:
https://t.co/NAQl5qffna shows the MEV-Boost auction live, including bids, their timing, and dynamics that are usually opaque.
https://t.co/g4rIdfWxtW shows private/exclusive order flow at the block level.
Both are built on data from @ethPandaOps. I had rough prototypes and the ideas for over a year, but never the bandwidth to finish them.
The trick: implement the core logic and data preprocessing by hand, then let AI take over the rest.
What used to take weeks now just ships.
The first devnet for EIP-7928, Block-level Access Lists, is live.
This marks a big step toward major L1 scaling improvements next year.
Excited for where this leads!
Big shoutout to everyone involved — especially @fselmo2, @raxhvl, @stefan_star and all the client teams involved!