Now in early beta for SuperGrok Heavy subscribers — Grok Build is a new coding agent that runs right from your terminal.

Today we're launching an early beta of Grok Build, a powerful new coding agent and CLI for professional software engineering and complex coding work.

Okay so the Flutter and Dart teams just shipped something quietly cool — official Agent Skills repositories, and they work with basically every major AI coding assistant out there.

Here's the quick rundown. There are two official repos now:

`flutter/skills` — layouts, routing, JSON serialization, integration tests, overflow fixes

`dart-lang/skills` — unit test gen, pub dependency resolution, static analysis fixes Install them into your project with one command:

npx skills add flutter/skills --skill '*' --agent universal
npx skills add dart-lang/skills --skill '*' --agent universal

That dumps everything into `.agents/skills` and your agent (Claude Code, Cursor, Copilot, Antigravity, whatever you're using) just... picks them up automatically.

So why is this actually interesting and not just more AI slop??

Instead of cramming a wall of instructions into a rules file and hoping the agent remembers them, skills use progressive disclosure — the agent reads just the metadata first, then pulls in the full instructions only when it needs them for a specific task. Context window stays lean, and the agent gets laser-focused guidance exactly when it matters.

Think of it like giving your AI a proper runbook instead of just vibes.

And it goes further — the `skills` CLI on pub.dev can pull skills directly from your dependency tree, meaning packages could eventually ship their own skills alongside their code. Imagine adding a package and your AI agent automatically knows how to use it correctly.

Are you already using agent skills or custom rules files in your Flutter projects? Has AI-assisted Flutter dev actually clicked for you yet, or does it still feel like more trouble than it's worth? And which AI assistant are you reaching for most — Claude Code, Cursor, something else?

AI Product Management Software Development

One-shotting is everywhere right now. Write a single prompt, maybe drop in a screenshot, and watch a full app materialize while you keep your fingers crossed. It’s a great demo. It’s a terrible workflow.

The problem isn’t that AI can’t generate a lot. It can. The problem is that a massive all-in-one output gives you no seams to work with. If something is wrong, which is less likely with frontier models but never impossible, you’re either redoing the whole thing or accepting the mess. There’s no middle ground.

One-shotting also doesn’t scale with complexity. A simple landing page, sure. A real product with multiple pages, components, navigation, visual consistency? The output starts degrading fast. You end up with something that looks right in a screenshot and breaks in production.

This gets worse in a team. If everyone is one-shotting independently, there’s no shared reference point. Components drift. Decisions contradict each other. A spec gives the team a common language. It also gives the AI a common language across multiple sessions. It’s the difference between everyone improvising and everyone playing from the same sheet.

I’ve been through this, and I think I’ve figured it out.

Last week a colleague and I were presenting the AI coding setup we’d built. The full show: fine-grained instruction files, custom agents with scoped responsibilities, reusable skills, and to top it all off, an orchestrator agent tying it all together. It looked impressive.

Then someone in the audience asked: “That’s cool, but do we always need this? If we skip all that and keep it simple, what do we actually lose?”

Over the past year, I changed my perspective on AI in software development. I used to be skeptical, but after watching Burke Holland's content on Copilot agents, I realized that I had to give it another try. From using AI as a simple code completion tool, to a tool to generate tests or simple isolated enhancements and smaller tasks, I started to see it as a system that's capable of doing much more. Fast forward to today, I'm using it more and more to create new features from scratch.

Over the last month I used Agentic AI to rewrite a project entirely from scratch, and the results are amazing. The speed boost is incredible, and the quality of the code is good, not perfect, but good enough in my opinion. In this blog post, I want to share my experience and thoughts on AI in software development, and why I think it's a game-changer for software development.

How I actually use AI in software development today, why context matters more than hype, and why this gets much harder in SAP.

Every developer has a workflow they've spent years refining.

Mine lives almost entirely in the terminal. I'm SSH'd into a development machine. I organize my sessions with tmux, edit code in Vim, and use git worktrees to juggle multiple tasks in parallel.

When people talk about AI-native development, the conversation sometimes assumes developers will converge on the same new workflow. A new IDE, a new interface, a new way of building software.

I've been saying the world of engineering has changed. However what would you say if I told you, its not automated.

Developers who want to be super productive are all using AI, but each has their own AI workflow. There's no shared playbook and limited consistency in what gets produced. ⏳

🐸 LLMs are leapfrogging each other every few months, so any process tied to a single tool is already at risk. And autonomous coding without a human in the loop can lead to AI drift, where small unchecked decisions compound into real problems.

👉 EXIT83's methodology, AI Skill-Driven Engineering, empowers engineers to make decisions while staying 10x productive. Instead of ad-hoc prompting, your team works from structured, portable workflows that standardize how AI agents are directed across the entire development lifecycle. The human orchestrates. The AI executes from specs. Quality is built in, not bolted on.

📝 EXIT83 Consulting has developed a remote training workshop for your engineering team where we teach your team how it can best leverage Skill Driven Engineering. We teach the methodology, run live demos on real code, and get them productive in days.

Our methodology was developed after much trial and error on real projects. If you want to learn more and inquire about this training for your team, just type the word 'Skill' in the comments below.

Skill

#ai #softwaredevelopment #software #agents

The future of AI productivity is not just better prompts. It is better context, better systems, and a clearer sense of who you are trying to become.

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Photo by Compagnons on Unsplash

Most people use AI like a vending machine.

They type a prompt, get an answer, and move on.

That works for quick tasks. It does not work well for building a serious body of work, a stronger personal brand, or a higher-performance life. If you are trying to become more intellectually sharp, more operationally effective, and more strategic over time, the real edge is not only in prompting. It is in context.

The breakthrough in my workflow came when I stopped treating AI as a collection of isolated chats and started treating it as a context-aware operating environment.

That shift changed everything.

Platforms are increasingly building around memory, project context, shared files, and persistent workspaces. ChatGPT’s Projects are built around project memory and can use prior chats and files inside a project as working context. OpenAI has also expanded project-only memory options for some plans, which makes it possible to keep one stream of work separate from the rest of your broader AI usage. Claude positions itself as a tool for problem solving and collaborative thinking, with Artifacts giving users a dedicated space to iterate on documents, code, and visual outputs. Gemini has been pushing in a similar direction through Gems, Canvas, Deep Research, file uploads, and tight integration with Google Workspace.

This matters because context is how AI stops being a novelty and starts becoming leverage.

...

Over the past few months, I’ve been experimenting with a structured development workflow in Cursor.

Not prompt hacking.
Not random AI-assisted coding.

A deliberate system to maintain project-level context while building feature-by-feature — without losing direction halfway through.

Technically, it worked.

But financially?

That’s where things got interesting.💰

This article breaks down:

What workflow I used

What worked surprisingly well

Where the hidden issue was

What the token data revealed

All based on actual usage data — not assumptions.