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Robinhood Chain Kept Producing Blocks During Sept. 4 Disruption

Glass Hull data shows Robinhood Chain produced 854,255 blocks through Sept. 4, correcting halt claims, while Walnut found busy-app transactions fell to about one fifth of normal for 40 minutes.

Robinhood Chain kept producing blocks through a roughly 40-minute app disruption
WitnessRobinhood Chain kept producing blocks through a roughly 40-minute app disruptionAI-generated

Outputs

  1. Glass Hull counted 854,255 blocks across all 1,440 minutes of Sept. 4 UTC, with the longest gap between block timestamps at two seconds

  2. Walnut found the median busy app completed about one fifth of usual successful transactions during a roughly 40-minute slump from 12:37 to 13:20 UTC

  3. Two separate Ethereum data-posting gaps totaled 14 minutes but ended before the claimed 12:57 halt and never interrupted block production

Robinhood Chain, the Ethereum layer-2 network built on Arbitrum technology, produced blocks continuously through its Sept. 4 disruption, according to a full-day measurement published Sept. 29 by Glass Hull. The finding corrects an earlier CryptoSlate report from Sept. 5 that claimed block production had halted for at least 14 minutes.

Glass Hull counted 854,255 blocks across all 1,440 minutes of Sept. 4 UTC. The longest gap between consecutive recorded block timestamps was two seconds. In the minute beginning 12:57 UTC — the reported start of the supposed halt — the chain produced 592 blocks.

The original 14-minute figure measured something different: pauses in posting Robinhood Chain transaction data to Ethereum. Glass Hull identified two separate posting gaps, from 12:29:47 to 12:38:23 UTC and from 12:42:47 to 12:48:11 UTC. Both ended before 12:57, and neither interrupted block production. L2BEAT's liveness record shows comparable gaps in Ethereum data submissions. A layer-2 chain can keep making blocks while its separate posting process to the base layer runs behind.

The operational impact on users was real nonetheless. Walnut's Sept. 23 root-cause analysis found that successful traffic through busy Robinhood Chain apps fell sharply from roughly 12:37 to 13:20 UTC. The median busy app completed about one fifth of its usual successful transactions during the slump. Walnut reads delayed oracle updates and smart-wallet transaction failures as signs that attempted submissions were lost before they reached blocks. Public chain data leave the precise point of failure unresolved.

Infrastructure provider QuickNode opened an investigation into increased Robinhood Chain mainnet latency at 13:10 UTC. At 16:20 UTC, the provider warned users they might encounter degraded performance and transactions failing to land while it investigated sequencer-feed connection issues. QuickNode's incident record covers its own service, while Walnut's roughly 40-minute figure measures successful app traffic across the ecosystem.

In a Sept. 4 statement, the @arbitrum account said the chain had no downtime and that direct user transactions experienced no delays. It acknowledged a brief performance impact for some providers relying on the chain's data stream amid a high number of feed subscribers. The statement separates direct submissions from provider services. Walnut's app-traffic measurement and QuickNode's incident log show why that distinction matters to end users, who experienced failures even as the chain itself kept finalizing blocks.

The causes remain contested. Walnut argues that a low batch-poster tip was outbid during an Ethereum fee spike, delaying data posts to the base layer. Glass Hull leaves the posting gaps' cause unresolved. The public record establishes two parallel facts: continuous block production and impaired app traffic. The precise off-chain failure point — whether in a sequencer queue or an RPC layer — and its relationship to the posting delays remain open questions.

For operators assessing Robinhood Chain's reliability, the episode illustrates the operational separation between block production, data posting to Ethereum and application-layer throughput. Until the failure point is identified, verifying app-level liveness rather than block cadence remains the more meaningful uptime signal.

via gh.jetfyul.com (Original)

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Daniel Okafor

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Correspondent covering industry trends and analytics at Mempool Brief.

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