AI

Metadata Then & Now: A Decade of Machine Understanding Ten years ago, I wrote a blog post for Dalet about metadata and online video advertising. In 2015, digital video was accelerating fast, budgets were moving from TV to online, ad-blocking was spiking, and programmatic was scaling, so we focused on the unglamorous layer that made it all work: metadata. Back then, my main argument was that metadata shouldn’t be entirely human or entirely automatic. Machines could process at scale, but humans understood nuance. That idea sounds self-evident now, but in 2015 it bordered on heresy. ...

Introduction For years, I wanted a place to share my thoughts on product management, operations, AI, software development, and the small “weekend projects” I build from time to time. I always wanted to, but never actually did. Why? No good reason. At first, I tried Twitter. Too short, not a good fit for full ideas. Then I moved to LinkedIn — better, but quickly chaotic. Posts disappear into the feed, long-form posts feel clumsy, and it’s hard to revisit or organize old ideas. The more I wrote, the more I felt the need for a home, a place where my work, thoughts, and experiments could live together. ...

Summary CVMatch matches candidate CVs to role descriptions using minimal infrastructure and pragmatic AI. Focus: signal over noise, reproducibility, and low operational overhead. Problem People waste time on manually checking if their CV matches job descriptions or are busy copying and pasting from/their AI chat. LinkedIn’s “AI” tool, thought to be a great promise, is cumbersome and not a good fit. Approach Embedding-based similarity for CVs ↔ role text. Lightweight prompt ranking with guardrails. Deterministic pipelines, versioned artifacts. Privacy first. Strict token budget management to reduce costs. BYOA - Bring Your Own AI Architecture Ingestion: parse PDFs/Docs to normalized text. Vectorization: embeddings store. Scoring: hybrid rank (semantic + keyword boosts). Review UI: simple shortlist with rationales. MLOps Reproducible runs (seeded). Config-as-code (YAML). Eval set and regression checks. CI for lint + smoke tests. Results Faster shortlist creation. Clear rationales for ranking. Easy to extend with domain rules. Try It Install CVMatch directly from the Chrome Web Store: 👉 CVMatch on Chrome Web Store

Summary image2sound is an artistic experiment that converts visual information into audio. Each pixel’s brightness, hue, and position are mapped to pitch, volume, and rhythm — allowing you to hear the hidden patterns within an image. Concept This project explores synesthesia through data — bridging vision and hearing using simple yet expressive Python-based tools. Features Converts PNG or JPG images into WAV files. Maps RGB values to frequency ranges. Customizable tone generation and envelope shaping. Supports batch conversions for entire folders. Try It 📦 PyPI Package: View on PyPI ...