I settled on Substack for a couple of reasons, popularity, ease of use and discoverability. I’m already active on the platform and I figured it was a great place to start this journey.

For Maestro’s prologue I decided to put the prospective AI tools to the test by creating the branding for a Substack newsletter.

Overview

• The Tech
• Base Prompts

Gerggely Orosz of Pragmatic Engineer dove into Ayhan Fuat Çelik’s analysis of and speculated that coding assistants like Copilot, Cody, ChatGPT are partly to blame. He speculates that engineers are reaching for AI chatbots for code questions, and it’s hurting Stack Overflow’s traffic.

Something I’m very excited about is the ability to run AI, LLM, etc. on local hardware. It addresses a couple of disadvantages to cloud based AI: security & privacy, lack of customization, and high cost of renting GPU cycles.

Stability AI just announced StableCode which is intended to do for code generation what Stable Diffusion did for image creation. Not only can you run it locally, but the model supports a longer context window (16,000 tokens) which allows for more detail instructions.

The present challenge is with hardware as AI requires a very beefy GPU and Nvidia has a lock on most of the market with its proprietary CUDA parallel computing programming model. It is possible to hack your way into running AI on AMD hardware, but it isn’t for the faint of heart.

I’ve been experimenting with Stable Diffusion locally w/ my AMD GPU (RX 580 8GB) for a bit. It’s not the fastest, but b/c I’m since not renting Google Colab I can leave it running long batches without a cost concern. If StableCode is similar it could address some of the privacy concerns about co-mingling code and data inside a public LLM.