Your entire SDLC should be automatable with AI agents. But just like there's no one-stop dev workflow, there's no one-stop agent. That's why we're building amika.dev: infra for every company to build their own software factory, customized to them.

## The Future Codebase After the PR dies, after the Code Review dies, i am seriously wondering if Git needs to die next. roughly 20-40% of code spend is just managing and updating merge conflicts. necessary evil? or legacy "horseless carriage"? cargo culting the past? we don't do line by line merge conflicts when we collaborate with human colleagues - instead we chat, suggest edits, do side comments, and an owner ships it. btw we also don't do CI/CD even collaborating on documents with serious legal/financial implications. maybe the future codebase looks more like a Notion or Linear database than .git objects. It will be less efficient, but more scalable. exactly the Salty Lesson.

We've always told startups to launch early, but I realized there's a powerful new reason to. Before you launch, the speed you can build is now mainly limited by your imagination in what you tell AI. After you launch, the AI can watch your users and make improvements on its own.

Everyone's talking about AI-generated HTML. But have you tried giving your sites a zero-config API for saving data, file storage, AI, websockets, etc? We did this at Shopify. Runs on a single VM that costs $200/month, and it's changed the way we work. We call it Quick 👇🧵

Introducing Cohere's first open-source coding model: North Mini Code Small & efficient, designed for agentic performance and built for community input.

Good take My guess is - demand for intelligence is near infinite - but 80% of workloads will be running on 99% cheaper models within 12-18 months - 20% of workloads will still run on latest gen models where IQ maxing is important (scientific breakthroughs, higher level ochestrator agents?) - rough analogy might be what % of macbooks or gaming PCs sold have the maxed out specs for CPU/GPU, prices are falling much faster than Moore's law here though - this leads me to think the limiting factor will be energy and compute, not better models At Coinbase we're working hard on routing prompts to cheaper models where appropriate, and in some cases have been able to keep costs roughly flat, while token usage continues to grow exponentially.

Here’s your monthly reminder that you shouldn’t be prompting coding agents anymore. You should be designing loops that prompt your agents.

I want some kind of LLM workflow tool. • Ability to manage a set of input files (Markdown or similar), plus other general-purpose context. • With real-time collaboration. (And maybe some concept of snapshots or VCS integration.) • And the ability to create/manage a inference workflows and a stored set of prompts. • Access to general-purpose coding agents (and not just chat models). • Some concept of compiled outputs/inference results (which ideally can be shared externally). Many projects have this feeling: "there is all this stuff, which I want to process/compute over in this iterated way, with some build artifacts being important/worth saving." GNU Autotools x Notion or something. Is anyone building this?