Ever curious about why incident.io is the most exciting place to build your career in 2025? Let us share our incredible product momentum, ambitious roadmap, and unique engineering culture that's attracting the talent eager to redefine incident response.| incident.io
Working in AI today, I'm seeing the innovator's dilemma play out in real time. While larger companies carefully plan deployments that work for their entire customer base, smaller teams like ours can ship, learn, and improve our AI products through actual usage. This isn't just about moving faster—it's about fundamental advantages in how AI products develop that favor startups, regardless of the resources incumbents can deploy. The dynamics surprised me, and they might surprise you too.| blog.lawrencejones.dev
I've met teams who switched to Python just to build AI features, abandoning their normal stack for the ecosystem. But it's really not worth it! At incident.io we stuck with Go and it's been great - turns out static typing and proper concurrency are exactly what you want when building AI systems, provided you build some nice abstractions to go with it.| blog.lawrencejones.dev
The gap between demo-ready AI products and production-grade systems is much larger than most realise. This post explains the four stages of AI product maturity, what tooling you actually need to build reliable AI systems, and how to recognise if you're stuck in the MVP trap.| blog.lawrencejones.dev