Alessandro Rodi

at what point should i check in to see what's going on

We built our own internal Slack agent called cb-agent. Instead of context-switching through multiple tools, anyone can tag it in a channel to kick off agentic workflows. In just its first few weeks of production, cb-agent is already: → Generating ~15% of our PRs → Kicking off thousands of tasks a month, right from Slack → Running our AI on-call to filter noise and only escalate for human-review when necessary

How to keep AI spend flat while token usage grows exponentially: Not with friction and spend alerts. With better defaults, routing, and caching. Better Defaults (not Usage Caps) – Engineers can choose any model they want, but defaults matter. We’re experimenting with defaulting to open weight models like GLM 5.2 and Kimi 2.7 through our LLM gateway, while still encouraging engineers to choose the right model for the task. 91% of our employees were never hitting their usage caps, so instead of lowering caps and driving up alerts, we're moving to cheaper defaults. Note that code reviews use a diversity of models, so they can check each other's work. Better Routing – In our custom harnesses, we preprocess prompts and route to the best model for the job, considering cache hits and model pricing. For instance, you may want a frontier model for planning, but not for execution where they can be overkill. Ultimately, humans shouldn't be choosing models - AI can automate this task. Better Caching – Cache misses are the easiest way to drive your cost up. All of our requests are cache aware, so we’re reusing a warm cache wherever possible. For example, our cache hit rate went from 5% → 60% in LibreChat once properly implemented. Keep Context Lean – Start fresh sessions when switching tasks. Scope file context narrowly. Disconnect unused tools. Don't just compact. The goal isn't fewer tokens used, it's fewer tokens wasted. Better Visibility – Our engineers can use as many tokens as they want, from whatever model they want, but we’ve made usage visible – and the more you spend on AI, the more impact we expect. The goal isn't to suppress usage. It's to build the infrastructure that makes exponential growth sustainable. Putting this into practice has cut our AI spend nearly in half, while our token usage continues to grow.

man i really miss writing code i miss hopping between files, reading error messages, writing shit code and then refactoring it later.. and then refactoring it again and again.. agents are nice but i just spent three days fixing a feature i probably could've just coded from scratch more quickly gpt 5.4 is my model of choice these days, and it's pretty good but i think i fell for the bait that i didnt need to read the code anymore but idk maybe its a skill issue