Dilsher Singh

Problem

Investors are paid from the revenue operators earn with financed machines. Every cycle has to pay out on time, and no cycle can pay out on bad data.

Constraints

• Revenue is reported by three independent sources: IoT telemetry, bank data and operator reports. They can disagree.
• A payout is money. Once it has gone out, it cannot be clawed back casually.

Design

• Three-way reconciliation gates every payout. Nothing is paid until telemetry, bank data and operator reports agree.
• A daily idempotent job replays the previous 24 hours of settlement events against the ingested records and emails an audit report.
• The job sorts discrepancies into three classes and fixes only the actionable one automatically.
• It never lowers a balance automatically, because that would destroy authoritative data. It logs the case and a human decides.
• Corrupted records are repaired by replaying the event log, with the pre-repair state verified first.

Result

• 12,500+ payout cycles over 17 consecutive months, 100% on time.
• Zero ledger-to-settlement discrepancies since April 2025.
• 13 corrupted records repaired by event-log replay, with zero balances changed.

Problem

The marketplace carries revenue, and it was running on aging versions: Next.js 12, React 17 and web3.js.

Constraints

• Trading could not stop. Every step had to ship to the live platform without interrupting it.
• Three major upgrades in one combined release would have been hard to reason about and hard to reverse.

Design

• The work was staged as separate upgrades: Next.js 12 to 15, React 17 to 19, and web3.js to ethers v6.
• Each stage shipped on its own, so a regression could only come from the one change that had just landed.
• One upgrade was deliberately rolled back when shipping safety warranted it, then redone later. Waiting a cycle on an upgrade is cheap; interrupting a live market is not.

Result

• Zero trading interruption across the whole migration.

Problem

Coding agents can produce a lot of code quickly. The goal was to get that speed without lowering the quality bar on systems that move money.

Constraints

• It had to work for the whole team, not for one person.
• It could not weaken review. Every change still has to be understood by a human before it merges.

Design

• Work starts as a PRD and is broken into scoped backlog issues, so an agent always has a clear, bounded task.
• Each repository carries its own agent scaffolding, AGENTS.md and agent skills, describing how that codebase is built and checked.
• Agent-generated code passes the same gates as any other code: strict TypeScript, lint and pre-commit checks.
• A human reviews every merge.
• This is an engineering process, not an AI product.

Result

• Adopted by all 5 engineers across 10 repositories.
• Merge gatekeeper on 435 of 592 client merges.