02 ·Latest
Latest articles
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The Five Mistakes Every Forward Deployed AI Engagement Makes
Operational wisdom from running engagements yourself, watching other operators run theirs, and seeing in-house teams attempt them. These five mistakes show up over and over.
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Forward Deployed Engineering Without the $250K Floor
Why the hottest role in tech needs to exist in two sizes, and why most founder-led B2B SaaS companies are quietly being priced out of the one they actually need.
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The Three Risk Tiers of AI Deployment
Every AI workflow lives in one of three risk tiers. Most teams don’t know which one they’re shipping until something goes wrong. Here is the taxonomy.
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How a Forward Deployed Engineer Scopes an AI Workflow in 2 Hours
Why most internal AI projects fail at scoping, not implementation, and the single 2-hour session that decides whether the next 10 days produce a shipped workflow or a refund conversation.
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The Painkiller Test
A 30-second test for any AI workflow idea, and why most “AI features” fail it.
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How a Forward Deployed Engineer Runs a 10-Day AI Sprint
Day by day inside a fixed-scope, fixed-price AI engagement, including the day that quietly decides whether the sprint ends well.
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Narrow Is the Moat
Why we won't sell you a bigger package, even if you ask.
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The Day-4 Trap
What we learned about integrity, refunds, and reputation from running fixed-price AI sprints.
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The Model Is No Longer the Product
What OpenAI's and Anthropic's twin deployment bets mean for every B2B SaaS shipping AI features this year.
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Welcome to Build Era
Weekly notes on Forward Deployed AI Engineering and AI-native product building. Start here with the first chapters of both books.
03 ·What you'll get
What you'll get
A short, weekly email from someone in the trenches building AI-native SaaS solo with Claude Code. No frameworks borrowed from Twitter threads. No promises of overnight scale. Just what is working, what is not, and what I am trying next.
04 ·What I write about
Five topics
- Forward Deployed AI Engineering in practice. What works in the field, what the labs are getting right and wrong.
- Building AI-native products solo. Vectig and Sengi as live case studies, including the failures.
- Distribution without paid ads. LinkedIn, warm outreach, the referrer network.
- Founder operations. Cash flow, hiring, scope, the unglamorous middle.
- Claude Code workflows. The specific patterns that make a one-person shop ship like a small team.
05 ·A taste
A taste of the voice
One narrow workflow. Not a platform, not an AI strategy, not a copilot, not a chatbot the marketing team thought of. One specific use case with a clear input, a clear output, and a person on the customer's team who will actually use it. Examples we'll come back to: an investor update assistant, a customer summary generator, a support ticket summarizer.
The workflow lives inside the customer's existing software. A feature in their app, an internal admin tool, or a production-ready pull request their team can merge. Not a standalone tool we host. Not a demo on a Loom. Code in their repo. End-to-end.
06 ·About
About
I'm Nasser Ghanemzadeh. 17+ years building products and companies across AI, SaaS, and fintech. Previously CEO/CPO at Nivo (acquired). Currently building Vectig and Sengi solo with Claude Code, and running a 10-business-day AI Feature Sprint for B2B SaaS teams. Also wrote Forward Deployed AI Engineering and Founder Mode.
More on the home page.
07 ·Get the next one
Subscribe
One email a week. Built for founders and operators shipping AI-native products in small teams.