Walking Devs

The conversation centers around the exploration of development environments, challenges with code editors, discussion on LM providers, cost considerations of LM usage, exploring new programming languages, Rust and Go as development languages, improving legacy tooling with Rust, and AI development and complexity.

We're back after a quick break and talk about the differences in the JS ecosystem on the backend side. Also, a new category: bug of the week, and we talk a bit about a project we hacked on to put an agent in a sandbox.Links:https://void.cloud/ https://agentclientprotocol.comhttps://linuxcontainers.org/incus

We start the episode and talk about Oban for Python, sharding in Postgres with PgDog, and Next.js by Cloudflare.And then we dig deeper about career ladders and promotions.Linkshttps://oban.pro/articles/bridging-with-obanhttps://pgdog.dev/https://blog.cloudflare.com/vinexthttps://www.modular.com/blog/the-claude-c-compiler-what-it-reveals-about-the-future-of-software

The main topic of this weeks episode is a conversation about the impact of AI coding agents on development collaboration and how we think the software development life cycle (planning, code review, and CI/CD) will change.

They talk about AI's strain on developers, from agent-driven multitasking to late-night prompting spirals. They discuss exhaustion caused by constant context switching and the rising flood of cheap code that chokes reviews. They explore job uncertainty, changing developer identities, and ideas for new collaboration tools to track agent work.

They unpack surprise model releases and the rush of simultaneous launches. They test a new Codex app and debate sandboxing and orchestration for agents. They compare strategies for running multiple agents and tiny, fast coding assistants. They argue about using AI during interviews, redesigning interview formats, and flaws in traditional hiring practices.

In this pilot episode, Nicolas and Philipp discuss the rise of OpenClaw and MoltBook, the democratization of coding, and the impact of AI on software engineering. They also explore the security implications of AI tools and the slow pace of innovation in big tech companies.