Straight answers from our engineers
Expert, reviewed answers to the questions founders and engineering leaders actually ask - on building with AI, technical leadership, and hiring development teams.
Building with AI
Straight answers on building software with AI - production-readiness, code audits, AI agents, and choosing an AI-assisted partner.
10 questions →Tech Leadership
Architecture, code quality, technical due diligence, and engineering-leadership questions for CTOs and founders.
8 questions →Hiring & Engagement
Hiring, outsourcing, dedicated teams, and engagement models - how to build and scale development teams.
8 questions →CRM
Straight answers on CRM: what founders and teams actually want to know before getting started with planning and building a CRM solutions.
10 questions →Recently answered
When does a business outgrow Salesforce or HubSpot?
You've outgrown them when the platform starts costing you more than it gives back: escalating per-seat fees, expensive tier upgrades, growing reliance on workarounds, and workflows you have to bend to fit the tool. For most teams this hits somewhere past 50 users or when your process becomes too specific for a generic CRM. The signal is friction, not size alone.
Read the answer →What should you validate after a CRM migration?
Before you trust the new CRM, validate five things: record counts match, relationships between records survived, integrations still fire, user permissions are correct, and key reports return the right numbers. The dangerous failures are silent: data that looks present but is subtly wrong. A structured post-migration check catches those before your team makes decisions on bad data.
Read the answer →Can a dedicated team take over a codebase built with AI tools?
Yes, a capable team can, but not every team will, so choose deliberately. Taking over AI-built code starts with an audit to understand how it works, then stabilizing weak spots and extending it. The main variable is how readable the code is. Pick a team experienced with AI-generated codebases and willing to audit before they build, not one that insists on rewriting everything.
Read the answer →How do you vet a dedicated or offshore team’s code quality, especially AI-generated code?
Judge process and proof, not promises. Review a real code sample, ask exactly how they test and review code, check references from similar projects, and run a small paid trial before committing. For AI-generated code specifically, confirm a human reviews every AI suggestion and that quality gates apply equally. A short trial reveals more than any sales call.
Read the answer →Should a non-technical founder trust AI-generated architecture decisions?
For low-stakes, standard choices, mostly yes; AI reflects common best practice well. For decisions that are expensive to reverse, database design, security model, core architecture, no. AI doesn't know your business context, compliance needs, or growth plans, and it states wrong answers with the same confidence as right ones. Have a human expert validate the decisions you can't cheaply undo.
Read the answer →I built my app with Lovable/Cursor/Bolt. Is it production-ready?
Usually not yet, and that's normal. AI tools are built to make an app that works in a demo, not one that's safe for real users, payments, and data. Before you launch, check seven things: authentication, data security, tests, error handling, scalability, code structure, and dependencies. Most vibe-coded apps pass two or three.
Read the answer →