Jeff Young

If coding agents struggle to work with your code so will humans. Agent friendly code bases, are also human friendly. Human co-pilots use agents to make your code base more agent and human friendly.

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See how Forward Deployed Engineers have helped close the gap on implementing AI agents leading to a 800% spike in FDE job postings between Jan. and Sep. 2025, according to Indeed and the Financial Times:

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I help customers scale some of the largest Generative AI workloads in the world. I've seen a lot of success, and a lot of failure. One common thread across all the most successful generative AI workloads is Evaluations; it is a topic that has been my largest area of focus for the last few years. In my latest blog post, I share some observations on the most common questions and solutions for building awesome generative AI evaluations. https://lnkd.in/gBKc3avW

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Most vibe coders are optimizing the wrong layer. There’s a post going around showing how a “non-technical” prompt and a “senior engineer” prompt produce wildly different results for the same task. The takeaway people grabbed: write longer, more detailed prompts. That’s not the lesson. Here’s what usually happens. A deal team needs to review 6,000 documents in a virtual data room for a mid-market acquisition. Someone asks an AI to flag every contract with a change-of-control provision. They get a script that searches for the phrase, pulls matches, and dumps them into a spreadsheet. Half the results are boilerplate. Key risks in assignment clauses and consent requirements get missed entirely. Nobody knows what the AI skipped or why. An experienced builder doesn’t write a better search query. They ask different questions first. What document types are actually in this data room? Are we looking for explicit change-of-control language, or any provision that could block or complicate closing? Do amendments override the base agreement? Are side letters in the set? What’s the priority: speed to first-pass flags, or completeness for the disclosure schedule? What happens when a clause is ambiguous and needs deal-team judgment? That reframes the problem before a single line of code gets written. The gap between a weak AI output and strong output isn’t verbosity or technical vocabulary. It’s the quality of the constraints. Success criteria. Tradeoff decisions. Knowing what not to build. Vibe coding gets dramatically better when you stop describing the task and start defining the boundaries around it. I Try this: next time you prompt an AI to work through a diligence set, write three sentences before the request. What’s a real risk versus noise? What should get escalated instead of auto-flagged? What’s the consequence of a false negative? That alone will change the output more than any prompt template. Subscribe to PossibLaw. Substack. Podcast. Tools to become a builder. We run custom training for legal teams who are done watching from the sidelines. Let’s talk.

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I made an explorable esoteric research environment with a Custodian named MERIDIAN that can launch its own agent swarms to solve problems. Does isopsephic arithmetic and lookup for any Neoplatonists :-) https://lnkd.in/gYzqVn7m

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