Pork Better

AI at work. Real teams, junior hiring & human judgment. Honest conversations with developers and founders.
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Give a team three months to adopt AI. What changes—and what doesn’t?

CTO Jason on workshops, mentoring, junior hiring, and letting candidates use AI in interviews.

Welcome to Pork Better. AI at work. Human judgment.

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A weekend experiment became a much bigger rewrite.

Jason recalls rebuilding roughly three-quarters of an existing product with AI and a different codebase. He corrects the timeline to three days.

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#AICoding #LegacyCode

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Which model changed how you worked?

For Jason, Claude 3.5 Sonnet was the turning point. A personal retrospective, with the model's limits kept in view.

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#Claude #AICoding

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More code is not the whole scorecard.

Jason looks at commits, finished features and post-release issues. In the full interview, he stresses that numbers are only one input.

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#TechLeadership #DeveloperProductivity

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Nobody fired after the first three months. Later team changes are a different part of the story.

Jason discusses subsequent staffing decisions and his view of performance.

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#AI #EngineeringLeadership

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What worries you about AI agents outside a sandbox?

A question about agents and security leads into Jason's background. This clip raises a concern; it does not demonstrate an escape.

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#AIAgents #Cybersecurity

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Are your estimates still based on life before AI?

An MVP estimate made Jason question his team's assumptions about the work: scope, planning and new tools.

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#AI #MVP

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Jason lets candidates use AI in interviews. Copying the answer isn’t enough: he asks them to challenge it, iterate and check the result.

Would you allow AI in your hiring process?

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#TechHiring #AI

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When did AI become useful to you?

Jason remembers the shift from a funny chatbot to scripts and tasks he'd put off. Useful did not yet feel revolutionary.

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#AI #DeveloperTools

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Two jobs, predictable results: does exclusivity matter?

Jason shares his view on outside work and delivery. One CTO's management position, not a rule for every workplace.

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#TechLeadership #DeveloperCareer

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When does keeping the old system become the bigger obstacle?

Jason explains why difficult-to-maintain products pushed his team to rethink its development process.

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#LegacyCode #SoftwareEngineering

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Before the next prompt, make the problem smaller.

Jason explains why breaking big tasks into smaller pieces matters even more with AI. What would you split first?

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#AIAgents #ProblemSolving

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Code is only part of the job.

From reverse engineering and QA to CEO/CTO: Jason explains why understanding people matters alongside understanding systems.

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#SoftwareEngineering #DeveloperCareer

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Built with AI in five days. Could the team maintain it?

Jason describes a working implementation and team architecture review, not a system hardened for millions of customers.

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#AICoding #SoftwareEngineering

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What leaves your team when you send a prompt?

Jason describes a privacy gateway for remote AI models. It aims to reduce exposure; this is not proof that leaks are impossible.

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#AI #DataPrivacy

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Would you let a candidate use AI in an interview?

We ask CTO Jason how he evaluates the way candidates work with it. What would you ask next?

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#AI #TechHiring

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The product works. Where are the users?

Jason's side-project stories cover the work code cannot do for you: distribution, customer acquisition and support.

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#BuildInPublic #ProductDevelopment

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Your AI made the mistake. You still own the result.

Jason explains why using agents means checking the output and understanding the fundamentals.

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#AIAgents #SoftwareEngineering

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If your best AI model disappeared, could you still build?

Jason argues that engineering discipline, patience and creative use of tools still matter.

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#AI #SoftwareEngineering

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Three months to adopt AI. A workshop wasn't enough.

Jason describes continued mentoring. After the initial three months, nobody had been fired. Adoption was only the starting point.

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#AI #EngineeringLeadership