Thanks so much for your time and feedback!
Thanks so much for sharing Matt Pocock as a reference. I didn't know about him, and so I spent a couple of hours looking at his materials this morning — as you say there are a lot of versions of this out there, but this is particularly well done as far as I could tell, and I'm really grateful you helped cut through the noise for me! I am going to continue looking at this, but in the interest of giving you something tangible back:
From what I could tell Pocock uses ADRs in their typical form, and only after a decision has been made.
In ADRs for AI, the record is written during deliberation: The model structures an initial braindump (from a file or chat audio dump for instance) into a record with readback and ambiguities surfaced through questions with the ORRCF format (Options, Recommendation, Rationale, Confidence, Falsifier) and a human approves the recommendation or overrules it with more detail. There is a lot of tooling (MCP server, plugin, CLI, extension, iOS) because the record is a parsable, and analyzable, which, allows for its granular referencing and upkeep.
I'll work on your other questions. Thank you again for the reference and feedback.
PS: I think the other thing is: Part of my target audience are people who have never programmed before. This is scaffolded to be "vibe coding but with longevity." But it requires a different kind of effort — it's deliberative programming because for best results it requires you to be able to articulate the why behind your decisions. But that's something people who don't code can oftentimes be good at, for instance.
Thanks so much for sharing Matt Pocock as a reference. I didn't know about him, and so I spent a couple of hours looking at his materials this morning — as you say there are a lot of versions of this out there, but this is particularly well done as far as I could tell, and I'm really grateful you helped cut through the noise for me! I am going to continue looking at this, but in the interest of giving you something tangible back:
From what I could tell Pocock uses ADRs in their typical form, and only after a decision has been made.
In ADRs for AI, the record is written during deliberation: The model structures an initial braindump (from a file or chat audio dump for instance) into a record with readback and ambiguities surfaced through questions with the ORRCF format (Options, Recommendation, Rationale, Confidence, Falsifier) and a human approves the recommendation or overrules it with more detail. There is a lot of tooling (MCP server, plugin, CLI, extension, iOS) because the record is a parsable, and analyzable, which, allows for its granular referencing and upkeep.
I'll work on your other questions. Thank you again for the reference and feedback.
PS: I think the other thing is: Part of my target audience are people who have never programmed before. This is scaffolded to be "vibe coding but with longevity." But it requires a different kind of effort — it's deliberative programming because for best results it requires you to be able to articulate the why behind your decisions. But that's something people who don't code can oftentimes be good at, for instance.