I build systems that fix things. I bring structure to chaos. And drama.
Product & Support Operations leader, 12+ years across Meta and EarnIn. I help organizations turn any growth idea into reality. I own the launch framework that aligns EPD, Marketing, Sales, Finance, and Support on goals, positioning, and metrics. I run the planning cadences and demo forums that keep the company in step, build voice-of-customer loops from GTM and Support back into the roadmap, and shape the PM operating system: planning, tracking, decisions, and the tools PMs live in. Where AI and agents can change how product work actually gets done, that's where I push hardest.
Stack: Hands-on with SQL (Databricks), Amplitude, Braze, Zendesk, Jira, BERT/NLP, n8n, and Claude.
Built once, refined under pressure, reused across product problems.
pzle
Problem: AI is moving faster than most people can absorb. Abstracts feel intimidating, so daily engagement falls off and the field stays opaque.
What it is: A daily 4×4 grouping puzzle with AI-themed words. Solving reveals the hidden theme behind the 16 terms and recommends one paper to read. Daily AI literacy as a ritual. One Claude call per day, edge-cached for millions of plays at ~$1.50/month.
paper-radar
Problem: 200+ ML papers drop on arXiv every day. Abstracts don't tell you which ones are actually being built on or gaining momentum.
What it is: Daily arXiv tracker that scores ML papers by real-world traction: GitHub repos building on each paper, citation velocity, and time-decayed shipping signals. Two-tier LLM pipeline: Haiku classifies 150 papers daily, Sonnet writes narratives for the top 10.
stackmap
Problem: The AI ecosystem changes faster than any static market map. Most maps don't help you decide what to actually ship. They just list companies.
What it is: A living architecture explorer that maps companies as blocks across 12 layers, connected by typed wires, assembled dynamically around the solution someone is shipping. Stays alive via a five-agent pipeline (Scout, Classifier, Comparator, Synthesizer, Auditor), each with its own eval cohort.
digital-twin
Problem: Your knowledge (resume, docs, history, links) lives in 20 silos. Other people can't reach the useful parts, and you can't even chat with your own context.
What it is: An AI alter-ego platform. Upload your resume, docs, and links to build a private knowledge base, then chat with an agent that represents you. Includes PII detection, per-user RLS, and an MCP server so other agents can talk to your twin.
nybf
Problem: Companion chat products are easy to ship as toys but break under real use. They drift out of persona, miss context, or get flagged by safety. Manual QA can't keep up.
What it is: A companion chat product with multiple personas (Partner, Big Sibling, Friend). The interesting half is the autonomous eval loop: simulated user agents stress-test the app, a product-head agent triages findings, a dev agent writes code diffs, a QA agent re-simulates to validate, all in one command.
social-media-pipeline
Problem: Daily social posting is the kind of work that kills momentum. Most automation pipelines stop at generation. A human still has to hit publish, every day.
What it is: Fully autonomous daily social-content pipeline. A scheduled n8n trigger kicks off a Mastra workflow that generates copy with Claude, synthesizes images and videos in Python, then publishes via the Publer API. Zero human touch per post. Runs on a private host, not a Next.js app.
Patterns in data, conversations, and systems that other people walk past. The unnamed thing is usually where the leverage is.
Not a deck. The actual pipeline, classifier, workflow, or metric framework that turns the insight into a behavior change.
Adoption is designed in, not bolted on at launch. Every team that has to live with the system actually uses it.
I run on a Rumsfeld matrix, known unknowns versus unknown unknowns. Three steps that survive every project:
Essays and notes, written in public — mostly on AI-native operations and building systems that fix things.
For any two candidates A and B competing for role R at time T: if A ranks above B on f(R, T), it does not follow that B is bad, nor that B will rank below A at f(R′, T′). Rejection is a partial measurement, not a total verdict.
Career growth used to be linear. AI broke it. Everyone is at a starting line, at every layer, simultaneously. This is a race — not against AI, but against the version of yourself from yesterday. The only way to win is how fast you can unlearn and learn. Every single day.
Choreography, freestyle, and the occasional reel that goes harder than it should.
@narenkris →Training is currently paused, but the logbook is open. Single-engine, VFR.
Most weekend builds below get from idea to shipped in 1-2 sittings. The compression is real.
What I've read, what I'm flipping through now, and what's queued up next.
My take: The belief that focused, emotionally charged thought attracts matching outcomes. The Law of Attraction as a self-reinforcing loop of expectation, gratitude, and visualization.
My take: A timeless playbook for winning conflicts through positioning, deception, and economy of force. The conviction that the supreme victory is the one won before the battle begins.
My take: To learn how three-act structure shapes pitches, decks, and product narratives.
My take: Wealth, happiness, and freedom compound the same way: through specific knowledge, leverage, and the patience to play long games with the right people.
My take: You don't control what happens to you, only your judgment of it, and that single distinction is the source of every freedom worth having.
My take: When everything can be taken from you, one thing remains: the freedom to choose your response, and in that choice lies the meaning of a life.