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66 articles tagged #Production.
P.01Almost every message queue that advertises 'exactly-once' delivery is actually giving you at-least-once delivery plus a mechanism for you to make your own handler idempotent. Here's the real distinction, why true exactly-once is so hard, and how to design for what you actually have.
P.02Two requests try to update the same row at the same time. Pessimistic locking stops one of them from starting; optimistic locking lets both run and catches the conflict at the end. Here's how each works, with real SQL, and how to pick.
P.03Soft delete keeps a deleted_at column and hides rows instead of removing them. It sounds like the safe default, but it quietly breaks unique constraints, foreign keys, and query performance if you don't design for it up front. Here's the real tradeoff.
P.04A full rewrite of a legacy system is the option that fails most often, not because the new code is bad, but because the business can't stand still for two years. The strangler fig pattern routes traffic to new code piece by piece instead. Here's how it actually works.
P.05PostgreSQL 18 shipped in September 2025 and is now on its 18.4 point release. With PostgreSQL 19 still in beta, here's what 18's async I/O, UUIDv7, virtual generated columns, and OAuth support actually mean for a production upgrade decision today.
P.06A hacker claimed to steal 35GB from Accenture in July 2026, including source code, RSA and SSH keys, and Azure access tokens. The source code isn't the risk. The credentials filed next to it are. Here's the audit worth running on your own repos this week.
P.07Chaos engineering means deliberately injecting failure into a running system to find weaknesses before an outage does. Here's what it actually involves, what tools make it practical, and when a smaller team should and shouldn't bother.
P.08Event sourcing stores every change to your data as an immutable event, then derives current state by replaying them, instead of overwriting a row each time something changes. Here's what that actually buys you, and the real operational cost it isn't worth paying in most systems.
P.09CAP theorem gets summarized as 'pick two of three' so often that the summary has replaced the theorem. Here's what it actually says, why the real constraint only bites during a network partition, and how to pick a consistency model for a system you're actually building.
P.10GitHub cut token spend in its agentic CI workflows by up to 62% by pruning unused MCP tools and swapping some tool calls for CLI commands. Here's how the technique works and how to apply it to your own agent workflows.
P.11Webhooks push data to you the moment something happens; polling has you ask repeatedly whether anything changed. Here's how to decide which fits your integration, plus real code for both and the hybrid pattern most production systems actually need.
P.12A distributed lock keeps two processes on different machines from touching the same resource at once, but the naive Redis implementation has a gap that lets it fail silently. Here's how the pattern actually works, and the fencing token that closes the gap.
P.13Round robin isn't wrong, but it's the wrong default more often than teams realize. Here's how the main load balancing algorithms actually behave under uneven traffic, when each one earns its complexity, and a working consistent hashing implementation.
P.14A naive retry loop can turn a brief blip into a full outage by hammering a recovering service the instant it comes back. Here's how exponential backoff and jitter actually prevent that, with working code, not just the formula.
P.15Sharding splits one database across many machines when a single server can't hold or serve your data anymore. The strategy you pick determines whether you get hot spots, painful resharding, or a system that actually scales. Here's how each one works.
P.16Writing to your database and publishing an event are two separate operations, and a crash between them silently loses data. The transactional outbox pattern closes that gap. Here's how it works and how to implement it.
P.17Adding read replicas is the standard fix for a database under read load, but it introduces a gap between when data is written and when it's readable everywhere. Here's what replication lag actually does to your application and how to design around it.
P.18Microsoft's Foundry Agent Service reached general availability with framework-agnostic hosted runtime for production agents. Here's what's actually new, how the sandboxing and protocols work, and whether it changes anything for teams already running LangGraph or CrewAI.
P.19When a business transaction spans multiple services, you can't wrap it in one database transaction anymore. The saga pattern solves this with a chain of local transactions and compensating actions. Here's how it works and when it earns its complexity.
P.20Idempotency keys let a client safely retry a request that might have already succeeded, without double-charging a card or double-creating an order. Here's how the pattern works, with a real implementation against Postgres and Redis.
P.21CQRS separates the code path that changes data from the code path that reads it. It solves real scaling and modeling problems, and it's also one of the most over-applied patterns in backend architecture. Here's when it earns its complexity.
P.22An N+1 query bug turns one page load into hundreds of database round trips. Here's what causes it, how to spot it in Django, Rails, and Prisma, and the eager-loading fixes that make it go away for good.
P.23PostgreSQL 19 Beta 1 shipped with a REPACK command that finally makes VACUUM FULL and CLUSTER safe to run without a maintenance window, plus parallel autovacuum and smarter async I/O. Here's what changed and what to test now.
P.24A circuit breaker stops your application from hammering a failing dependency until it recovers. Here's how the pattern works, the three states that make it up, and how to implement one without over-engineering a simple retry.
P.25Firebase's complexity and Supabase's Postgres assumptions do not fit every project. PocketBase, Appwrite, and Convex each solve the backend problem differently. Here is when each one makes sense.
P.26LLM API bills grow faster than usage. These are the concrete techniques that cut costs by 40-80% without degrading quality: prompt caching, semantic deduplication, tiered model routing, and batch inference.
P.27Three tools, three different bets on where complexity belongs. Here's how to choose between BullMQ, Inngest, and Temporal based on what your system actually needs — not what sounds most impressive.
P.28OpenFeature is now a CNCF incubating project with broad SDK support. Here's how to use vendor-neutral feature flags in production — and why the standard matters more than the specific tool you back it with.
P.29Logs tell you what happened. Error tracking tells you what broke, in what context, for which users. Here's how to set up Sentry properly and avoid the alert fatigue that makes it useless.
P.30Rails 8 ships Solid Queue, Solid Cache, Solid Cable, and Kamal 2. Together they make it possible to run a production app on one server, without Redis, without a separate Node process, and without external managed services for the basics.
P.31Getting a language model to return reliably structured data is not just about asking nicely. Here's the pattern that actually works at production scale.
P.32MCP has become the standard for connecting AI models to external systems. Here's how it works, how to implement an MCP server, and what to watch out for before going to production.
P.33Most slow queries come from a small set of fixable problems: missing indexes, N+1 patterns, and over-fetching. This is the practical diagnostic and fix guide.
P.34Blue-green and canary deployments give you a way to release software without taking down your service or discovering a bug when it's already affecting everyone. Here's how they work and when to use each.
P.35Local-first means your app works offline and syncs when connected. The technology (CRDTs and sync engines) is mature enough to use. The question is whether your use case actually needs it.
P.36Row Level Security moves data isolation into the database where it belongs. Here's how to set it up for a multi-tenant SaaS, handle common edge cases, and avoid the traps that break it.
P.37A hands-on comparison of Typesense, Meilisearch, and Algolia for production SaaS search in 2026 — covering self-hosting, performance, pricing, and real implementation patterns.
P.38A practical comparison of PostHog, Amplitude, and Mixpanel for SaaS teams in 2026 — covering pricing, self-hosting, event tracking patterns, and what metrics actually move products forward.
P.39A practical guide to building SaaS billing with Stripe in 2026 — covering subscription plans, the Meter API for usage-based pricing, webhook reliability, and the Customer Portal.
P.40A practical guide to transactional email for web apps in 2026 — comparing Resend, Amazon SES, and SendGrid, with a focus on SPF/DKIM/DMARC setup and the common deliverability mistakes teams make.
P.41How Zod became the standard for runtime type validation in TypeScript apps — API contracts, form validation, environment variables, and the patterns that make schemas a source of truth across your stack.
P.42Both approaches customize LLM behavior for your use case, but they solve different problems. Here is how to decide which one you need, how to know when to use both, and what teams consistently get wrong.
P.43Password resets, invoices, and notification emails are infrastructure. Most developers treat them as an afterthought until a client asks why their welcome emails are disappearing. Here is the full picture.
P.44An agent that forgets everything when the session ends is a limited tool. Here are the practical patterns for building different kinds of memory into your agents.
P.45Past the chatbot hype, AI is genuinely improving specific e-commerce outcomes. Here are the implementations producing measurable results, and the ones that still mostly disappoint.
P.46The queries that destroy production Postgres databases share a small set of root causes. Here is how to find them, understand what EXPLAIN ANALYZE is telling you, and fix them without guessing.
P.47JavaScript error handling is broken by default — exceptions are untyped, async errors are easy to miss, and side effects are invisible in function signatures. Effect TS fixes all three. Here is how it works.
P.48Hallucination is not a bug that gets patched in the next model release. It is a property of how language models work. Here are the patterns that actually reduce it in production systems, and what they cost.
P.49Single-provider AI dependencies are a reliability risk. Routing layers like LiteLLM and OpenRouter let you fall back across providers, cap costs, and try smaller models first. Here is the architecture and when it actually matters.
P.50Neon separates storage from compute to make Postgres behave like a serverless resource. The branching feature is what actually changes the development workflow — here's how it works and when it's worth the migration.
P.51Connection exhaustion is one of the most common production failures for apps that scale. Here's how pooling works, which tool fits which setup, and the configuration decisions that matter.
P.52From .env files to Vault to AWS Secrets Manager: a practical guide to storing credentials, API keys, and certificates without waking up to a breach notification.
P.53Site reliability engineering was designed for teams of hundreds. Most of its principles translate directly to teams of 5. Here is what a practical incident response process looks like when nobody has 'SRE' in their title.
P.54Leaked credentials are the most preventable category of security breach. Here is an honest look at when you need a dedicated secrets manager, which tool to pick, and what to do if you're still on .env files.
P.55Building an AI feature is only half the work. Once it's in production, you need to know when it's drifting, what it's costing, and where it's failing. Here's how to instrument LLM applications properly.
P.56Code-first API development produces documentation as an afterthought. OpenAPI-first flips that: you write the spec, generate the server stubs and client SDKs, and enforce the contract at every layer. Here's how it works in practice.
P.57Background jobs that crash mid-execution lose all their state. Temporal solves this by making workflows durable state machines that survive process restarts, deploys, and outages. Here's what it looks like in TypeScript and Python.
P.58Unit tests tell you if your code does what you wrote. They don't tell you if your AI feature does what users need. Here's how to build an evaluation pipeline that catches the failures that matter before users do.
P.59Rolling back a bad API endpoint takes seconds. Rolling back a bad LLM integration is harder — the damage may already be in your logs, your users' inboxes, or your clients' feeds. Feature flags are how you ship AI features without betting everything on launch day.
P.60AI features ship fast. Then the monthly API bill arrives. Here's a systematic approach to understanding and reducing LLM costs without breaking the product.
P.61ALTER TABLE locks your database. Your migration takes longer than expected. Users get errors. Here's how to handle schema changes that don't interrupt production traffic.
P.62Async Django is production-ready in 2026, but landmines still exist. Four real projects, benchmarks, and a framework for deciding when async actually helps.
P.63EditorPickWe migrated three production projects from Celery to Django's new Tasks framework. Two went smoothly. One was a disaster. Here is everything we learned.
P.64EditorPickThe network latency between your Django app and your FastAPI ML service is probably longer than inference itself. Here is how to serve models from Django directly.
P.65EditorPickMost AI agents fail in production. Here are the architecture patterns, error handling strategies, and guardrails we use to build agents that actually ship.
P.66EditorPickMulti-agent systems sound great in demos but break in production. Here's how to architect, orchestrate, and monitor AI agent teams that reliably handle complex workflows — patterns from real deployments.