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41 articles tagged #Production.
P.01LLM 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.02Three 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.03OpenFeature 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.04Logs 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.05Rails 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.06Getting 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.07MCP 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.08Most 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.09Blue-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.10Local-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.11Row 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.12A hands-on comparison of Typesense, Meilisearch, and Algolia for production SaaS search in 2026 — covering self-hosting, performance, pricing, and real implementation patterns.
P.13A 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.14A 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.15A 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.16How 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.17Both 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.18Password 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.19An 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.20Past 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.21The 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.22JavaScript 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.23Hallucination 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.24Single-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.25Neon 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.26Connection 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.27From .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.28Site 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.29Leaked 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.30Building 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.31Code-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.32Background 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.33Unit 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.34Rolling 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.35AI 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.36ALTER 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.37Async Django is production-ready in 2026, but landmines still exist. Four real projects, benchmarks, and a framework for deciding when async actually helps.
P.38EditorPickWe 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.39EditorPickThe 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.40EditorPickMost AI agents fail in production. Here are the architecture patterns, error handling strategies, and guardrails we use to build agents that actually ship.
P.41EditorPickMulti-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.