My AI-Assisted Workflow

This is pretty much a spec driven workflow.

I do similar, but my favorite step is the first: /rubberduck to discuss the problem with the agent, who is instructed by the command to help me frame and validate it. Hands down the most impactful piece of my workflow, because it helps me achieve the right clarity and I can use it also for non coding tasks.

After which is the usual: write PRDs, specs, tasks and then build and then verify the output.

I started with one the spec frameworks and eventually simplify everything to the bone.

I do feel it’s working great but someday I fear a lot of this might still be too much productivity theater.

My workflow is also highly inspired by Matt's skills, but I'm leveraging Linear instead of Github.

/grill-me (back-and-forth alignment with the LLM) --> /write-a-prd (creates project under an initative in Linear) --> /prd-to-issues (creates issues at the project level). I'm making use of the blockedBy utility when registering the issues. They land in the 'Ready for Agent' status.

A scheduled project-orchestrator is then picking up issues with this status leveraging subagents. A HITL (Human in the loop) status is set on the ticket when anything needs my attention. I consider the code as the 'what', so I let the agent(s) update the issues with the HOW and WHY. All using Claude Code Max subscription.

Some notes:

- write-a-prd is knowledge compression and thus some important details occasionally get lost

- The UX for the orchestrator flow is suboptimal. Waiting for this actually: https://github.com/mattpocock/sandcastle/issues/191#issuecom...

- I might have to implement a simplify + review + security audit, call it a 'check', to fire at the end of the project. Could be in the form of an issue.

Congrats! You just rediscovered something called water-fall model.

Here's mine: code to spec until I get stuck -> search Google for the answer -> scan the Gemini result instead of going to StackOverflow.

No kids, don´t put yourself through this suffering. If you have to invest so much deliberate effort to sort of make it work - while you still handle the most tenuous and boring parts yourself, then what is the point? Lets keep the LLM vendors to their word - they promised intelligent machines that would just work so well to the point of causing mass unemployment. Why on earth do we have to work around the LLMs to make them work? What is the point? Where is my nation of datacenter PhDs or a PocketPhd, depending on whose CEOs misleading statement one quotes?

>What is AI actually good at? Implementation. What is it genuinely bad at? Figuring out what you actually want

I've found it to be pretty bad at both.

If what you're doing is quite cookie cutter though it can do a passable job of figuring out what you want.

Why is everyone compelled to write one of these articles? Do they think that their workflow is so unique that they've unlocked the secret to harnessing the power of a pattern generator? Every single one of these reads like influencer vomit.

My workflow hasn't changed since 2022: 1. Send some data. 2. Review response. 3. Fix response until I'm satisfied. 4. Goto 1.

My AI-Results

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