When starting an Ethereum project, it’s easy to believe smart contracts will be the hardest part to scale. That wasn’t my experience. While working on Ethereum backends—bots, indexers, and supporting services—the contracts themselves were stable. The real issues appeared once traffic increased: RPC rate limits, latency spikes, and unreliable node connections became the primary bottleneck. Running a full node initially seemed like the ideal solution. In practice, it introduced a different set ...

When starting an Ethereum project, it’s easy to believe smart contracts will be the hardest part to scale. That wasn’t my experience. While working on Ethereum backends—bots, indexers, and supporting services—the contracts themselves were stable. The real issues appeared once traffic increased: RPC rate limits, latency spikes, and unreliable node connections became the primary bottleneck. Running a full node initially seemed like the ideal solution. In practice, it introduced a different set ...

When building Ethereum backend systems, things usually work fine—until they don’t. Bots fail silently. Transactions revert unexpectedly. Gas usage spikes without a clear reason. And suddenly, what looked like a simple transaction becomes opaque once it hits the network. Most of the time, the problem isn’t the smart contract itself. It’s the lack of visibility into what actually happened during execution. This is where transaction tracing becomes essential—and also where many teams hit a wall....

When building Ethereum backend systems, things usually work fine—until they don’t. Bots fail silently. Transactions revert unexpectedly. Gas usage spikes without a clear reason. And suddenly, what looked like a simple transaction becomes opaque once it hits the network. Most of the time, the problem isn’t the smart contract itself. It’s the lack of visibility into what actually happened during execution. This is where transaction tracing becomes essential—and also where many teams hit a wall....