I help teams turn AI ideas into real products that users can actually rely on. That includes text-to-image, text-to-audio, and text-to-video systems, as well as voice transcription, speech-to-text, and text-to-speech pipelines. I’ve built full stacks using Python, JavaScript, Scala, Dart, and Elixir, backed by SQL and cloud infrastructure. I’m comfortable working with generative AI tools, synthetic data, and data augmentation when real data isn’t enough. From early prototypes to production rollouts, I focus on stability, performance, and cost. Clean systems beat flashy demos every time.

Anthropic just announced they are doubling usage for Claude Max plans from now until New years eve. So if you have it, go build something cool ! Merry Christmas Eve for those who celebrate.

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This can be applied to any type of newsletter. How I Automated a Local Newsletter in Claude Code (5 to 10 Minutes End to End) I built an automation in Claude Code that handles the full workflow of a local newsletter, from research to a polished HTML preview, in about 5 to 10 minutes. This post breaks down the approach so you can copy the system and adapt it to your own city or niche. What a Local Newsletter Is A local newsletter is a curated digest of: - Events happening soon - Local news and openings - Things to do and places to go - Community updates, deals, and highlights Most are monetized through sponsorships and ads, and some add revenue through events or products. The hard part is not writing. It is research. The Problem: Manual Research Is the Bottleneck If you have ever run one of these, you know the weekly routine: - Scroll Instagram posts and stories - Check event sites and venues - Skim local news sources - Pull details, dates, ticket links, and addresses - Then rewrite it all into a consistent format It is repetitive, time-consuming, and perfect for automation. The Core Idea: Break the Workflow into Stages A local newsletter can be treated like a pipeline with four stages: 1. Research 2. Writing 3. Polishing 4. Deployment In Claude Code, I implemented each stage as a separate reusable skill. The goal is simple: build each skill once, then reuse it forever. Stage 1: Research Research has two parts: 1. Find sources 2. Extract information from those sources Finding Sources For local newsletters, some of the best sources are Instagram accounts: - local venues - community pages - restaurants and cafes - galleries and event organizers I had Claude Code find relevant Instagram accounts using: - web search for discovery - browser automation through an MCP server to navigate Instagram directly when needed Extracting Information Once the sources are identified: - Claude Code scrapes posts from the target accounts - It parses both the captions and the images - Since Claude is multimodal, it can read details from event posters, flyers, and screenshots