Crypto Dev Peesh Chopra

The indexer worked perfectly during testing. Blocks were processed on time, queries were fast, and nothing looked fragile. Production proved otherwise. The first sign of trouble wasn’t an outage. It was a quiet delay that slowly widened until downstream systems stopped trusting the data altogether. Everything Looked Fine Until It Didn’t At the beginning, nothing appeared broken: the service was running logs looked clean dashboards stayed green But users started seeing inconsistencies. Balances lagged. Activity appeared out of order. The indexer wasn’t down. It was falling behind silently . What Actually Went Wrong The failure didn’t come from one big mistake. It came from several small assumptions: backfills were treated as routine work queues were allowed to grow without limits ingestion and querying shared the same resources Under real traffic, those decisions collided. Once the indexer slipped behind, recovery became harder with every passing hour. T...

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 ...