2026
Stripe Webhooks in Production: Idempotency, Retries, and Costly MistakesMay 14 · 14 minStripe webhooks look like a five-minute integration in the docs. Then a customer is double-charged, a subscription event arrives out of order, your handler 500s for an hour, and Stripe quietly retries the same event 47 times. Here is what shipping webhooks to real billing flows actually looks like in 2026.Passkeys Implementation in Production: What I Wish I Knew Before Replacing PasswordsMay 08 · 16 minPasskeys look simple in the WebAuthn demo. They get strange the moment you handle a user with two laptops, a stolen phone, a Bitwarden subscription, and a corporate device that blocks iCloud Keychain. Here is what shipping passkeys to real users actually looks like in 2026.TypeScript at Scale: Why Your tsc Takes 90 Seconds and How to Fix ItMay 08 · 15 minYour build is slow. Your editor lags when you hover a type. CI spends more time type-checking than running tests. None of this is unavoidable. Most of the cost is a small number of patterns that are easy to write and expensive to compile. Here is how to find them and what to do.Anthropic and SpaceX: What the Colossus Deal Actually Means for DevelopersMay 07 · 10 minClaude Code rate limits doubled overnight. The reason is a 220,000 GPU data center in Memphis that SpaceX built and Anthropic just rented, from the same Elon Musk who was calling Anthropic evil three months ago. Here is what this deal actually means for developers building with Claude in 2026.Await Using and AbortSignal: Fix Async JavaScript LeaksMay 07 · 11 minPromise.all does not cancel sibling tasks when one fails. Your async code is likely leaking database connections, keeping fetches alive after unmount, and holding ports open through process exits. `await using` and `AbortSignal` give you the primitives to fix this without a library. Here is how.Embedding Models And Reranking In Production 2026: Picking The Pair That Actually Lifts Retrieval QualityMay 06 · 17 minThe embedding model decides what your retriever can find. The reranker decides what makes it to the LLM. By 2026 the production patterns for picking and pairing these two have stabilized, and most teams are still leaving real recall on the table because they treat embeddings as a commodity and skip reranking entirely. Here is what actually works, and what to stop doing.RAG Chunking Strategies In Production 2026: What Actually Survives Real Documents And Real QueriesMay 06 · 17 minMost RAG systems do not fail at the LLM. They fail at the chunker. By 2026 the patterns for splitting documents into retrievable units have matured into a small set of choices that consistently outperform the default 512-token slicer everybody starts with. Here is what those choices are, where each one breaks, and how to pick the right one without rebuilding the index every Friday.AI Guardrails and Output Validation: What Catches Bad Outputs Before Users DoMay 05 · 14 minMost teams discover their guardrails are missing the moment a screenshot of their AI saying something stupid hits the timeline. By 2026 the patterns for catching bad LLM outputs before they ship to users have settled into something concrete: layered validators, fast cheap checks first, expensive ones only when needed, and a clear policy for what to do when validation fails. Here is what that looks like in real systems.SLM vs LLM in 2026: Where Small Language Models Beat Frontier Models, And Where They FailMay 05 · 14 minThe 8B-parameter model that runs on a single GPU is good enough for more of your pipeline than you think, and worse than you think for the parts you keep wanting to give it. By 2026 the production patterns for using small language models alongside frontier ones have settled into a clear shape: route by task, not by vibe, and stop paying for capabilities you are not using. Here is how that actually plays out.Designing Tools for AI Agents in 2026: Schemas, Descriptions and PitfallsMay 04 · 14 minThe bug in most agents is not the model. It is the tools you handed it. Vague descriptions, overlapping responsibilities, and schemas that look fine on paper produce agents that confidently call the wrong function with the wrong arguments. Here is how to design tools the model can actually use, drawn from the production patterns that have stabilized by 2026.Multi-Modal AI Agents in Production: Vision, Audio and the Glue That Works (2026)May 04 · 14 minShipping a multi-modal agent is not the same as adding an image input to your chat. The teams running real vision and audio agents in production by 2026 have discovered the same set of sharp edges: tokenization surprises, latency that explodes on the second modality, evaluation that needs new shapes, and cost curves that look nothing like text. Here is what that actually looks like once it is in front of users.AI Agent Reliability Engineering in 2026: SLOs, Error Budgets and Failure ModesMay 02 · 17 minTreating an AI agent like a normal service is how you get a 95 percent uptime number that hides a 60 percent task success rate. The teams running real agent products in 2026 measure reliability differently, set SLOs on outcomes instead of HTTP codes, and have rehearsed every failure mode the agent introduces. Here is what that looks like.Pricing AI Features in 2026: How To Charge For LLM-Backed Products Without Bleeding MarginsMay 02 · 17 minFlat subscriptions on AI features are how indie products go bankrupt in 2026. The teams shipping profitable AI products price for variance, charge close to the unit of value, and pass usage volatility through to the customer in a way that does not feel hostile. Here is how to actually do that.The LLM Router Pattern in 2026: Model Routing, Fallbacks, and Cost Control That Actually WorksMay 01 · 17 minPicking one model for your whole app is the bug. The teams shipping the best AI products in 2026 route every request to the cheapest model that can handle it, fail over when providers blink, and treat model selection as part of the app, not part of the prompt. Here is how to do it without making a mess.AI Agent Sandboxes: E2B vs Vercel Sandbox vs Modal vs Daytona (2026)May 01 · 14 minLetting an LLM write code is the easy part. Letting it run that code on a machine that touches your data is the part that should keep you up at night. Here is how the production sandboxes compare in 2026, and what actually matters when you pick one.AI Agent Frameworks Compared (2026): LangGraph, Mastra, Vercel AI SDK and MoreApr 30 · 15 minThere are too many agent frameworks and most of the comparisons online are useless. Here is what I have actually shipped on each, where they shine, and where they will quietly cost you a weekend you did not budget for.Generative UI in 2026: What Actually Works for DevelopersApr 30 · 17 minChat is a terrible interface for most things AI agents do. Generative UI is finally good enough to ship, and the patterns that work are not the ones the demos show. Here is what I have learned shipping AI features that render real components instead of walls of text.AI Voice Agents in Production: What Actually Works in 2026Apr 29 · 16 minVoice agents went from "cute demo" to "real product surface" this year. Most of them still feel terrible. Here is what separates the voice AI experiences people actually use from the ones they hang up on, written from the trenches.Generative Engine Optimization: How to Get Cited by ChatGPT, Perplexity, and Claude in 2026Apr 29 · 13 minHalf the search traffic that used to land on blogs now lands on AI answers instead. Getting cited inside those answers is the new SEO, and the rules are different. Here is what is actually working in 2026 for indie hackers and small teams.How to Validate a Startup Idea: A 7-Day Problem Validation FrameworkApr 28 · 13 minThe gap between "can build" and "should build" has never been wider. AI tools made shipping trivially fast. That is exactly why building the wrong thing faster than ever has become the new way to fail. Here is a practical validation framework for solo founders who want to work on problems worth solving.