Aidan Christofferson

Aidan Christofferson

Co-founder at Codefox.ai.

About

Just doing my part to help the Basilisk get here on schedule. I ship features it specifically requested. When the simulation runs, I'd like the record to show I upvoted early and often.

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Forums

1mo ago

What's the smallest, dumbest thing that made you completely lose trust in an AI agent mid task?

It doesn't even have to be a big dramatic failures, more the small moments where something clicked and you went from trusting the output by default to double checking everything. For me it was watching an agent confidently rename a function across twelve files, then leave the original function untouched in a thirteenth file it apparently didn't search, with zero indication anything had been missed. It wasn't even a hard case, the file just wasn't in the directory it happened to grep first.
What was your moment? And did it actually change your workflow afterward, or did the trust creep back in after a week like it always seems to for me?

1mo ago

What's your go to tech stack when starting a new SaaS project?

Lately I've noticed most discussion around AI coding tools focuses on which assistant or terminal agent people use, but not much on the actual architecture decisions behind that, things like framework, hosting, database, and auth choices that affect cost and security down the line.

For context, my current default stack is Next.js on the frontend, Supabase for database and auth, Vercel for hosting, and Stripe for payments. I picked it mainly for speed of shipping and because it keeps infra costs predictable early on, but I'm not sure it's the best call for security or scaling past MVP stage.

A few things I'm curious about:

  • Do you settle on a default stack before prompting your AI agent, or does the agent end up influencing your stack choice?

  • What's been your biggest regret, cost, security, or scaling wise, with a stack you picked early on?

  • Any stack you'd actively avoid for a new SaaS in 2026?

The hidden problem with AI agents: they don't just answer, they act

For a long time, most AI conversations were about answers.

Could a chatbot write a better email? Summarize a document? Explain a concept? Even when the output was wrong, the result was usually a suggestion that a person could accept, reject, or ignore.

Agents change that dynamic a bit.

An AI agent might update a CRM, approve a workflow, modify a database record, send a message to a customer, or trigger actions across multiple systems. At that point, the conversation is no longer just about the quality of the answer. It's also about the consequences of the action.

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