We needed a pipeline that ingests hundreds of job postings from ATS platforms (Greenhouse, Lever, Ashby), enriches each posting with structured data from their public APIs, and then classifies whether a job is a fully remote EU position — all running on Cloudflare's edge with zero GPU costs.

This article walks through the architecture and implementation of process-jobs, a Cloudflare Python Worker that combines langchain-cloudflare with Cloudflare Workers AI, D1, and Queues to build a production classification pipeline.

This article documents a production implementation of OpenAI's Text-to-Speech (TTS) API with automatic chunking for long-form content and seamless upload to Cloudflare R2 storage.

Introduction​

In the rapidly evolving landscape of Large Language Model (LLM) applications, prompt engineering has emerged as a critical discipline. As teams scale their AI applications, managing prompts across different versions, environments, and use cases becomes increasingly complex. This is where LangSmith's prompt management capabilities shine.

A comprehensive guide to implementing Langfuse features for production-ready AI applications, covering prompt management, tracing, evaluation, and observability.

Overview​

This guide covers:

• Prompt management with caching and versioning
• Distributed tracing with OpenTelemetry
• User feedback and scoring
• Usage tracking and analytics
• A/B testing and experimentation

This article documents the complete OpenRouter integration implemented in Nomadically.work, using DeepSeek models exclusively through a unified API.

This page documents an AI-first enrichment pipeline that turns a company website into a clean, structured company profile you can safely persist into your database and expose through GraphQL.

This bulk processor extracts structured skill tags for job postings using an AI pipeline that combines:

This article documents an evaluation harness for a Remote EU job classifier—but the real focus is AI observability: how to design traces, spans, metadata, scoring, and run-level grouping so you can debug, compare, and govern LLM behavior over time.