BLS G1 MSM exploration Input: 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 Output: 0000000000000000000000000000000011ed5c29653e93c0d6f3e39e82cd916958e091eefc94659b3a08fc2df138e8f1b9eb8ba7f7370d72a66195b8e7cf39f80000000000000000000000000000000012c3e6183bb3094f71b7b8e9ec4b73e4b9f71cf244a41739f148cc5f92e4b8bd7a00a0b29113fe5100d332af974e3ebb Input decoded: Point: 000000000000000000000000000000000f89ff59d6cdf6a80a4bd2ef847d7328cdda8153f9119dc8252cba7cbc065e7ce635d2eb538044387eea8db2219537a9000000000000000000000000000000000a3468224c7a9d4df13ab6a64fe7fd3eb6ecb716156646f0eced5708f69f994061bba0a3a4b741bc4f9c23ba69ef061e Scalar: c1828e413dd0b08fdbb04763c308d2a013df3c193dd49d3a9633745f01c88eb7 Point: 0000000000000000000000000000000014c03a472acfd2837196af16b56a9c1519b443792aa8f14b100feab759c0b6b1938e126127f3d173495086dd50cedbd30000000000000000000000000000000002c20dcc4e6297a90b616c15dacae186a7c1cdd6d8fcd966cb73d02bc05ec242ea6d8aa9bef1af14a147c6a2b267b139 Scalar: 3c40185d7f2594cb94d67ca0b72133e84869482dd62fd960879056142304d42a Point: 0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000 Scalar: e40e1db8fcbf3dbb91727e166ce0ec04bb2f18767b3448bee52e27ea1bababff Point: 00000000000000000000000000000000051d1dab66442184421fcb55a61c20c6923d156554229b4955668c7dffb3a78174e681472f0c162a6567ec5219e60f820000000000000000000000000000000018a1f5c6e281acc223e3f01d3a57831a3385be683887d035902ae6fc7b6e42c995553b7e1c66a43055400b0308c50bf6 Scalar: 9324ac82a05b52103cc501da438c983630b00b5b214f7329bcb332df877a740d Point: 0000000000000000000000000000000013529240d0dd34047826b80c7efe64af519fc3546ea1eaa11ba3cb2d34fcc5599d192d933137562f18852310676b5b3000000000000000000000000000000000056cea5afa7af7fc45a918727e19ed2c09f40352bd975af75cb3e369da2088fa1d596f584399046532401e40b32e0168 Scalar: 4f9f0ab9972051f489993ff43f551124ee6aca7644b3b159d2957a03b3ab8bab Point: 0000000000000000000000000000000006558d2dffd0361e5eb65fbe8b7244d2ecdfb5602a0be94f2dfbd3b5186a2ef105f24289e671d55a88754cdd4acc83b3000000000000000000000000000000001410a4c89d2cd95c2cc3b887bc46d32f779d935b5678234f22212bae81da8e78dfd3b6bc34b1aaaab2e235ee64c76c1f Scalar: 1c61cf76958db68e901b9b53298b684ccd1db78a3e9ff2ada5666b76217c92dd Point: 000000000000000000000000000000000cb01d33935474a2b4ea10afb5c657d2d48f418d81604f33531778300bdecc7140b3c2baa8811aff436576938a227066000000000000000000000000000000000ae4fceb8677d4d7fbdb1a07932648edffe82543fba4caf18b98508e1bc079f65dd066a1efa8baea4fd682c82d223cd6 Scalar: aa39b6b9978d9c743c5333dc3771fe5ce6e48f073b90b62d18ac14b7da956d32 Point: 000000000000000000000000000000000d3306579eb93622e67fef53d8bb839f03651af8c8694bc22b5efbb7529f82dcd25bd30cdea21c489c9ff37490abea8900000000000000000000000000000000095a82b42b7551d947c0c5237d29b268bdc6563fec3adb6b38888c8e29f7e9590fd0e1bfd728c8301be6f47ead55ac53 Scalar: 63fe50f3bc82261da2c96158247eb47b2131608386e7b094eac2e172d9a5fa0c Point: 000000000000000000000000000000000d3aff0f3b2e6f4878f15a81eabab5d8c9f765bc93ae0a2f0da5ed1941b4924bca4516661600c41a74beeb243695b52d0000000000000000000000000000000016ce48c345cce7cd395692cecbb8704bcb29645135c8ac366c4e5c40dead3c218814904df7d4c92cbb9aa956bbb1deab Scalar: c03d1856de9cf1e3403fb4278b85c65685c1723eb5d0a2437769ef423476cb11
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Marius van der Wijden
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A way forward for Holesky

Holesky has not been finalizing for a couple of days now. We need a healthy chain for staking pools to test their setups and it is pretty clear that it will take Holesky longer to finalize again. A solution to this is to do a shadowfork of holesky in order to allow staking pools to test on Pectra. Note that pools can not test their setups on Sepolia, since Sepolia uses a permissioned validator set. Since we are still collecting a lot of useful information from Holesky, we do not want client teams to stop their nodes over there. It is a good experiment to see what happens to the network when so many validators leak out or get mass-slashed. The Plan We do a shadow fork of the chain before the Pectra fork, and gothrough the transition The network would be run by PandaOps, with ~1.5M keys We use the validator set of Holesky before the Pectra fork as the new genesis set and replace the public keys of all client teams with keys owned by PandaOps.

3/4/2025
Future of the Transaction Pool

Summary: With the recent rise in MEV, private mempools, blob txs, AA, the role of the public mempool changes. In this session we discuss what the issues are and what we might be able to about it. Facilitator: Marius Note Taker: TBD, lemme know if you want to take notes! Pre-Reads: https://eips.ethereum.org/EIPS/eip-7702#backwards-compatibility https://github.com/erigontech/erigon/wiki/Transaction-Pool-Design

11/11/2024
Quantum-secure signed transactions

Introduces a new transaction type signed with a quantum secure signature scheme

11/11/2024
BLS gas costs

For comparison: ECRecover on this laptop averages 48 mgas/s, the KZG precompile averages 40mgas/s. name killic (no sg checks) (mgas/s) gnark (mgas/s) gnark (no sg checks) (mgas/s) gas/op Add G1 17-51

4/10/2024
Read more from Marius van der Wijden
Published on HackMD