AI

Obelisk is an AI-powered marketing platform that helps teams create content with brand consistency. It orchestrates specialized AI agents for SEO, email marketing, brand voice analysis, and strategy—all within a multi-tenant SaaS architecture with space-level isolation. Tech Stack: FastAPI, LangGraph, PostgreSQL, Vertex AI, Redis, Google Cloud Source: Private (commercial product) · Book a call to discuss The Hard Problems Building production AI systems exposes problems that don’t appear in tutorials: Agent reliability — LLMs hallucinate, get confused, and can be manipulated. How do you build agents that fail gracefully and resist prompt injection? ...

Every few months, something gets released that looks like infrastructure plumbing but turns out to matter more than the flashy launches. Model Context Protocol (MCP) is one of those things. If you’re a developer working with LLMs, MCP will change how you integrate AI into your workflows. Here’s an early-adopter perspective on what it is, why it matters, and how to actually use it. What Problem Does MCP Solve? Today’s AI tools are context-starved. You paste code into ChatGPT, upload files to Claude, manually copy database schemas into prompts. Every session starts from scratch. Every context window is a blank slate. ...

Extended thinking isn’t just “model thinks longer”—it’s a fundamentally different interaction model. If you’re prompting extended thinking models (Claude Opus, o1) the same way you prompt standard models, you’re leaving most of the value on the table. This post is a developer’s mental model for working with these systems: when to use them, how to prompt them, and what trade-offs to expect. How Extended Thinking Actually Works Standard LLMs generate tokens one at a time, each token conditioned on everything before it. The model “thinks” only as fast as it speaks. Ask it to solve a complex problem, and it often commits to an approach in the first few tokens, then rationalizes that approach even if it’s wrong. ...

The graveyard of failed AI features is full of chatbots nobody asked for. Every product team I talk to has the same story: leadership watched a GPT demo, got excited, and mandated “we need AI in the product.” Three months later, there’s a chatbot in the corner of the app that 3% of users have tried and 0.5% use regularly. As CPO at Entropy Labs, I’ve been on both sides of this. I’ve built AI features that users loved and killed features that seemed brilliant in demos but died in production. Here’s what I’ve learned about the difference. ...

Every LangChain tutorial ends right where the real work begins. You see a neat 50-line script that queries a PDF, and you think, “Cool, I’ll ship this by Friday.” Three weeks later, you’re debugging memory leaks, wondering why your chain silently returns empty strings, and questioning every decision that led you here. I’ve shipped LangChain-based features to production at multiple companies. Here’s what I wish someone had told me before I started. ...

I’ve been skeptical of every “game-changing AI release” for the past two years. Every few months, a new model drops and Twitter explodes with claims that AGI is here. Spoiler: it never is. But when Anthropic released Opus 4.5, something actually shifted in how I work. Not because it’s AGI—it’s decidedly not—but because it’s the first model that consistently delivers on complex, multi-step reasoning without falling apart halfway through. ...