Articles and case studies.
Notes on building data-heavy products, useful automations, and the decisions behind the work.
Latest writing
25 piecesBuilding an ERP you can talk to
What changed when inventory, invoices, and ledgers became a conversation instead of a collection of forms.
Building a Real-Time Geospatial Monitoring Platform
The architecture decisions that make live operational data useful instead of merely fast.
How I Approach System Design Before Writing Code
A practical sequence for turning an ambiguous product request into a system that can be discussed, tested, and changed.
Designing Resumable Uploads for Large Files
Why upload reliability is mostly a coordination problem between the browser, the API, and object storage.
What I Learned Building Real-Time Data Pipelines
The useful lessons are in ordering, backpressure, and recovery, not in making a demo stream move quickly.
The Engineering Trade-Offs Behind Event-Driven Architecture
Events create useful decoupling, but they also move complexity into contracts, debugging, and operational discipline.
How I Design Idempotent APIs for Reliable Processing
Retries are inevitable; duplicate business effects should not be.
Retry Logic Is Not Enough for Reliable Background Jobs
A durable job system needs a failure policy, visibility, and a safe way to stop trying.
Observability for Backend Systems: Logs, Metrics, and Traces
A useful observability strategy starts with the questions an operator must answer during an incident.
How I Structure Large React Applications for Long-Term Maintainability
Maintainability comes from clear ownership of data, effects, and user-facing state.
Designing Frontends That Stay Responsive Under Heavy Data Updates
High-frequency data is a rendering problem, a data-shaping problem, and a product-priority problem at the same time.
How I Approach Frontend Performance Debugging
A repeatable investigation is more valuable than a list of optimization tricks.
Frontend Error Handling for Real Users, Not Just Happy Paths
Errors are part of the interface; the product should explain what happened and what the user can do next.
Building AI Features That Actually Work in Production
A production AI feature is a bounded workflow with contracts, fallbacks, and a way to learn from mistakes.
Designing Reliable AI Agents for Business Workflows
An agent becomes trustworthy when its autonomy is constrained by explicit tools, approvals, and observable state.
How to Evaluate AI Output Before It Reaches the User
Evaluation becomes practical when quality is defined as a set of observable product behaviors rather than a single score.
RAG, Tool Calling, and Structured Outputs: Choosing the Right Approach
These patterns solve different problems, and combining them without a clear job creates unnecessary complexity.
From Raw Telemetry to a Useful Real-Time Dashboard
A dashboard earns its space by helping someone decide what to do next, not by displaying every available field.
The Engineering Challenges of Streaming Video Alongside Live Map Data
Video and geospatial data have different timing, bandwidth, and failure characteristics; the interface has to respect both.
Building Software for Environments Where Connectivity Is Not Guaranteed
Offline behavior is a consistency policy, a user experience, and an operational promise.
How to Keep Real-Time Data Consistent Across Multiple Clients
Consistency is easier to discuss when the system names its source of truth and its acceptable delay.
The Difference Between a Feature That Works and a System That Scales
The gap appears in retries, permissions, observability, migration paths, and the people who operate the software.
When to Use REST, WebSockets, SSE, and gRPC
Protocol choice should follow communication shape, operational constraints, and how the client needs to recover.
From Monolith to Modular Architecture: When to Split a Backend
A modular monolith can create the boundaries a product needs before it pays the cost of distributed deployment.
Making Engineering Decisions When the Information Is Incomplete
Good decisions do not require perfect certainty; they require explicit assumptions and a cheap way to learn.