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29 articles tagged #AI Integration.
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.02Comparing LangSmith, Braintrust, and Weights & Biases Weave for LLM evaluation. What each platform does well, where it breaks down, and how to build a minimum viable eval pipeline.
P.03Cursor's rules system lets teams encode their architecture decisions, naming conventions, and coding standards into the AI's context. Here's how to set it up so every engineer gets consistent suggestions.
P.04Both frameworks can build RAG pipelines and agent systems, but they're designed with different priorities. Here's when to reach for each and when to skip both.
P.05Ollama makes it possible to run Llama 3, Mistral, Phi-4, and dozens of other open-weight models on your laptop or server with a single command. Here's what actually works and when local inference makes sense.
P.06When AI tools write 40-70% of a codebase, the usual rules for estimating support costs break down. Here's how to price maintenance work when your team didn't write most of the code.
P.07Workers AI gives you access to a catalog of open-weight models — Llama, Mistral, Whisper, embedding models — running in Cloudflare's network. Here's what's actually useful, what the limitations are, and when it makes sense.
P.08Getting 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.09MCP 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.10Employees are using AI tools IT hasn't approved, and the data leaving through those tools is largely invisible. Here's what the risk looks like and what actually helps.
P.11Clients ask agencies to 'do something with these PDFs' more often than you'd think. Here's how to actually build document extraction pipelines that work in production: OCR, vision models, and structured output.
P.12AI video tools have moved from toy to production-grade in 18 months. Here's what's real, what still fails, and how to have an honest conversation with a client about it.
P.13A growing class of AI agent frameworks can control a browser the way a human does — clicking, typing, navigating. Here's what works in production, what breaks, and when to actually reach for these tools.
P.14Both 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.15Three leading agent orchestration frameworks, three different mental models. Here's when each one earns its place, what each costs you in complexity, and what the choice looks like when you're debugging at 2am.
P.16AI projects introduce IP, liability, and data training questions that standard software contracts don't address. Here's the language clients send and what it actually means for an agency taking the work.
P.17An 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.18When your AI agent needs to run the code it writes, you can't let it touch your production servers. Here's how the main isolation options work and when to use each.
P.19Figma shipped real AI features at Config 2025 and teams have been using them for nearly a year. Here is what actually works, what still doesn't, and how design workflows at agencies have changed.
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.21Bad documentation is usually not a writing problem — it's a maintenance problem. AI tools are changing the equation by making initial doc generation cheap and doc refresh practical at scale.
P.22The Vercel AI SDK has become the default for building AI features in JavaScript apps. Here is what it actually does, how its core primitives work, and where the sharp edges still live.
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.25CodeRabbit, GitHub Copilot code review, and similar tools have changed the first-pass review. Here's what they actually catch, where they fail, and the workflow that combines both effectively.
P.26Add similarity search to your existing Postgres database using pgvector. Real setup, indexing strategies, and when you actually need a dedicated vector database.
P.27Building 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.28Explore Google's latest Workspace update with Gemini AI enhancing Docs, Sheets, Slides, and Drive, empowering users to leverage AI capabilities for more efficient productivity.
P.29Choosing the wrong AI partner wastes 6 months and $50K+. Here is the evaluation framework our CTO clients wish they had before their first engagement — 23 questions across technical depth, process, and culture.