Crypto Dev Peesh Chopra

Nobody warns you about decision debt I didn’t break production with a bad commit. I broke it slowly, with decisions that felt safe. The pressure to move fast is real Early on, every choice feels reversible. “Let’s ship first.” “We’ll clean this up later.” Production remembers everything. Small shortcuts stack quietly A missing metric here. A retry loop without limits. An indexer nobody truly owns. None of these cause incidents alone. Together, they do. The hardest part isn’t fixing systems It’s admitting why they ended up this way. Most production issues aren’t technical failures. They’re decision failures , repeated long enough to feel normal. I later distilled these experiences into a more structured, system-level breakdown of how production decisions quietly break blockchain systems. You can read the professional analysis here: 👉 Peesh Chopra on Production Decisions That Break Blockchain Systems What I do differently now I write decisions down. I slow down wher...

I used to think on-call was about reacting quickly. Production taught me it’s about thinking clearly when clarity is scarce . When Alerts Don’t Explain Anything The first few incidents were frustrating. Alerts fired, metrics spiked, logs filled up. None of them explained what users were actually experiencing. I realized something uncomfortable: I didn’t fully understand my own system under stress. Pressure Changes Decision-Making Under pressure: You avoid risky fixes You repeat familiar actions You delay structural changes None of that is irrational. It’s human. But systems designed without this reality in mind quietly fail their operators. The Moment That Changed My Approach During one incident, we stabilized the system without ever understanding the root cause. That felt like success. It wasn’t. It meant we had postponed learning. What I Do Differently Now After enough nights like that, I changed how I build: I design for diagnosis, not just uptime I reduce m...

 At the time, the decision made sense. It simplified the system. Reduced coordination. Helped us ship faster. Everyone agreed it was “good enough for now.” Production changed that perspective. Why It Didn’t Feel Risky at First Traffic was low. Failure was rare. The system behaved politely. The architecture wasn’t wrong, it was untested . I mistook early stability for correctness. The First Signs I Ignored Small issues appeared: Manual restarts Edge-case inconsistencies “Rare” retries that weren’t rare anymore Each one felt manageable. None felt urgent. Together, they were a warning. When Change Became Expensive Once users depended on the system: Refactors became risky Downtime had real cost Workarounds replaced fixes The decision I made early quietly limited every future choice. What That Experience Taught Me Now, I assume: Every shortcut will be stressed Every assumption will be violated Every design choice has a production cost The goa...

Most discussions around blockchains focus on whitepapers, architecture diagrams, and ideal assumptions. My understanding of blockchain systems, however, was shaped less by theory and more by what actually broke once real users arrived. This page documents how my thinking evolved while building and operating blockchain systems in production, where reliability, observability, and trade-offs matter far more than clean designs. Where It Started: Learning the Hard Way My early work with blockchain systems followed the same path many engineers take: build quickly trust testnets assume systems will behave the same in production They didn’t. Indexers fell behind silently. RPC nodes degraded under burst traffic. Assumptions I believed were safe turned out to be fragile. These early failures forced me to stop treating production as an afterthought. Production Changed Everything Once real users depended on the system, the problems shifted: latency mattered more than throughpu...

Everything looked normal on the surface. APIs responded. Dashboards were green. Queries returned results. No alerts fired. But something felt off. The Subtle Drift From Reality Data started lagging—seconds at first, then minutes. Users didn’t complain immediately. They simply trusted the system less. Numbers stopped lining up. Confidence eroded quietly. That’s when I realized the worst failures aren’t outages, they’re misalignments . The Mistake I Didn’t See Coming I had optimized for query speed, not ingestion truth. The indexer wasn’t broken. It was falling behind gracefully , and we treated that as success. Reprocessing later revealed how far off we’d drifted. Debugging After the Damage By the time we investigated: Backlogs were massive State assumptions were invalid Fixes required historical replay The system had been lying politely for days. What I Changed After That Experience After this incident: I tracked freshness, not just latency I treated indexing ...

Everything worked perfectly in staging. Transactions flowed, APIs responded, and latency stayed within limits. I assumed RPC was the least of our worries. Production proved me wrong. When the System Didn’t Break—But Users Did There was no outage. No node crash. No dramatic alert. Users simply experienced slow, inconsistent behavior. Requests timed out sporadically. Wallet actions felt unreliable. Dashboards looked “mostly fine.” The Mistake I Didn’t Know I Was Making I had treated RPC as plumbing. Something stable. Something external. Something “handled.” In reality, our application load had turned RPC into a shared choke point —and we had no visibility into how bad it was getting. Debugging Without a Clear Signal We spent hours chasing symptoms: Retrying requests Scaling nodes Adjusting timeouts The real issue wasn’t failure—it was silent saturation . That was the moment I realized RPC reliability isn’t about uptime. It’s about behavior under stress . What I C...

  For a long time, I trusted our dashboards. They were clean. Metrics looked healthy. Alerts were configured. On paper, everything was “production-ready.” Then came the incident. When Users Felt Pain Before Metrics Did Users started reporting delayed confirmations. Support tickets arrived before any alert fired. At first, I assumed this was edge-case noise. The dashboards showed normal throughput and acceptable latency. Nothing looked broken. That assumption cost us time. The False Comfort of Green Metrics What I didn’t realize then was simple: our monitoring reflected infrastructure health, not user reality . Blocks were still being produced. Nodes were still online. But the system was no longer behaving the way users expected. By the time alerts triggered, we were already in damage control. The Moment My Mental Model Broke Sitting there during the incident, I stopped trusting the graphs. We were debugging from logs, tracing requests manually, trying to understand where ...