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Yeah every same human would use m-dashes in their sentences — it just makes sense
Regardless - the emdash predated LLMs … but that’s going way off topic!
I'm too OCD to not point out this time it was a minus.
great approach with dogfooding! the failure I hit most is not that the agent forgets, it is that it re-opens things the team already closed. So treating ruled-out paths as first-class is the right instinct quick question why mcp instead of cli tool? I'm mostly building the latter (less context burden), but help me understand your own ADR regarding this why not both? also after quick review on your doc I don't see whe…
the mcp is a wrapper around the cli! You can run “rac explorer” if you have installed the extra: pip install 'rac-core[explorer]'
in terms of UX, working on a couple of improvements there but wanted to validate the idea first and get the integrations with common platforms like GitHub sorted first!
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Hi! What I'm not quite getting from a cursory overview of the docs is how does RAC overlay overlapping decisions over each other? My mental model is a Docker overlay filesystem where RAC somehow manages to construct a cohesive view of the entire set of decisions over time, but how does that happen deterministically? Let's say ADR 1 specifies users are unique by e-mail and should be soft-deleted, and ADR 2 says users…
So rac doesn’t merge ADRs or infer that soft-deletes survive… supercession is managed by the status on one ADR saying Superseeded, and the other saying Supersedes. This is enforced by the CI pipelines you can use (GitHub, etc) I think your example is a bit more of a modelling question, and if soft-delete must outlive the uniqueness decision it should be its own requirement that’s persisted as RAC guarantees the graph…
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I wrote this, I just spend enough time with LLMs to sound like one
Yeah every same human would use m-dashes in their sentences — it just makes sense
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Good questions! Lore isn't really competing with CLAUDE.md, it sits under it. A CLAUDE.md (or AGENTS.md, etc) is typically hand-written, untyped, and never checked. Nothing stops it from still telling the agent to do something you reversed six months ago, and nobody validates it in CI. Lore keeps the actual decisions/requirements/designs as typed Markdown in your repo, and rac export --agent-rules generates those rul…
Please stop posting this self-aggrandizing llm drivel, it makes HN a worse place for learning and interesting discussions You're either a bot, in which case piss off, or a bot-augmented human, in which case please tone down the bot augmentation and take the time to write less sloppy, more concise remarks
Bad timing either way epic games release
great approach with dogfooding! the failure I hit most is not that the agent forgets, it is that it re-opens things the team already closed. So treating ruled-out paths as first-class is the right instinct quick question why mcp instead of cli tool? I'm mostly building the latter (less context burden), but help me understand your own ADR regarding this why not both? also after quick review on your doc I don't see whe…
thanks! the mcp is a wrapper around the cli! You can run “rac explorer” if you have installed the extra: pip install 'rac-core[explorer]' in terms of UX, working on a couple of improvements there but wanted to validate the idea first and get the integrations with common platforms like GitHub sorted first!
add rac steward to check and validate "quality" of ADR (freshness, completness, clarity, brewity, etc.)
so expect your tool to help clean it's own database ;)
I have taken a different approach: allow team members to sync all of their Claude Code and Codex transcripts on a project and give them a skill that lets them ask their AI why decisions were made.
The skill I've built, /total-recall is backed by a Swift-based CLI that provides efficient query tooling that coding agents can use however they see fit to arrive at the answer.
The corpus of data contextify queries is a SQL database managed by macOS and Linux clients. These clients ingest the jsonl files in realtime and optionally can sync transcript data through either a hosted or self-hosted server.
This allows any team member to simply invoke the skill: "Why did we switch over to allauth from aws cognito? /total-recall."
My experience is that Claude Code and Codex don't just land "near" a decision, but can assemble it from what is sometimes a winding pathway of research, benchmarking and experimentation.
Rather than codify requirements into a separate spec, Contextify lets agents pair the state of the code with the conversational record.
I have just released the free personal and source available self-hosted version of Contextify, I'd be glad for feedback.
https://contextify.sh/teams/, https://contextify.sh/self-hosted/
Interesting approach. I am curious how this information stacks up over time and how efficient it is at incorporating decision knowledge into active context. I have taken a different approach: allow team members to sync all of their Claude Code and Codex transcripts on a project and give them a skill that lets them ask their AI why decisions were made. The skill I've built, /total-recall is backed by a Swift-based CLI…
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thanks! the mcp is a wrapper around the cli! You can run “rac explorer” if you have installed the extra: pip install 'rac-core[explorer]' in terms of UX, working on a couple of improvements there but wanted to validate the idea first and get the integrations with common platforms like GitHub sorted first!
my 3 cents as I'm working on agentic CRM add rac steward to check and validate "quality" of ADR (freshness, completness, clarity, brewity, etc.) so expect your tool to help clean it's own database ;)