working code
working code at Beyond Market Intelligence is a file of 3 stories. The newest of them: “Fine-Tune LLMs on a Budget With These Five Practical Techniques”, “Two AI transcribers compared: real performance where the numbers count”, and “How Forward Deployed Engineers Turn Code into Action”. Fine-tuning large language models doesn't have to drain your budget. You've read the benchmarks, but what happens when two AI transcribers face real-world data? Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every working code story on Beyond Market Intelligence, newest first.
Fine-Tune LLMs on a Budget With These Five Practical Techniques
Fine-tuning large language models doesn't have to drain your budget. Techniques like LoRA, QLoRA, and Unsloth make it accessible, and this guide delivers working code for each approach, from right-sizing your GPU to leveraging DPO. We appreciate practical, no-hype advice that actually helps you build. If you're curious how similar resourcefulness applies to generating images from scratch, our piece *Building an Image Generator From Scratch on a Home Rig* explores that territory too.
Two AI transcribers compared: real performance where the numbers count
You've read the benchmarks, but what happens when two AI transcribers face real-world data? We pushed them past marketing claims to see where the numbers actually land. The comparison walks through each model's background, a concrete use case with working code, and a side-by-side comparison of the metrics that determine productivity. If you appreciated our breakdown of Qwen3-VL's struggles with dates, this follow-up answers a practical question: which tool earns its place in your workflow?
How Forward Deployed Engineers Turn Code into Action
A forward deployed engineer doesn't just advise from a distance; they sit inside your infrastructure and ship code that stays in production. That's a sharp contrast to consultants who hand over reports and leave. The role demands technical range and a comfort with messy, real-world constraints. What makes it compelling is the honesty of the premise: this job exists because generic solutions often fail. If you're exploring how technical roles evolve, our piece on AI agents learning by editing context offers a useful parallel.