2025-09-30 Tue

• What is an "Agentic AI CRM"?
  • Agentic CRM & Workflow Platform with No-Code and AI at Its Core | Creatio
• AI/Eval/Tool
  • langwatch
• rulesync/GitHub/Issue/25/09/Brainstorm needed - how to share some rules across repos, but not all i329

  • • Person/codekiln 2025-09-30 Tue here - #Idea about Context Engineering

      • I guess, by resolving #304, your request will also resolved at least partially.
        

      • Yes, I think the --global feature support will help individual developers not be as repetitive, for sure. But in my opinion, the larger problem is that engineering teams need a way to standardize and encapsulate their best practices (rules, commands, etc) across developers.

      • Side rant about context bloat in agentic coding tools

        • Today's AI coding tools work by requiring a developer to pre-load all the context the AI would need to know before starting the AI to work (rules files) or by at least loading an MCP server that connects the agent to additional docs. Each additional rule file and MCP server tool adds to the context burden, which leads to context rot.
        • I have yet to see an agentic coding tool which can onboard and offboard context dynamically based on its runtime needs, but in my opinion, as a practical concern, the effective context limits I'm observing are not universally increasing fast enough to keep up with the complexity of engineering tasks, and I think we may need to try to find ways for an AI to know how to find where it should load rules and commands for a specific situation from.
        • For example, the techniques for doing json logging with OTEL in python under uwisgi have particular difficulties relative to doing json logging with OTEL in typescript under express. It's not that the problems are unrelated, but the challenging parts tend to be at the intersections between the technologies, where the context needs to get really specific. If agentic coding tools could know where your team has documented the AI instructions for these things and could ask you if it could do a brain dump for that problem's solutions ...
        • ... and then later, offload that context so that the local repo isn't filled with bloat that's superfluous to 95% of the coding you're going to do there, it seems like that would be more optimal.
        • I sense that agentic coding tools may temporarily need something like the breakthrough that happened in GIS that led to google maps with the tile pyramid or image pyramid: if you have a problem that requires you to zoom in, you load different, zoomed in context (images, or in our case, rules and commands) that are suitable for that level of detail, and then, when you need to see where you're at, evaluate and change position, you have the AI zoom out, load in a different, zoomed out context (images, or in our case, rules and commands).
        • This is similar to what Lance Martin talked about on a recent Latent Space/Pod:

          • “What worked really well for me was just passing a simple markdown file—basically a list of docs with URLs and short descriptions—to the code agent. Then it can just grab the right doc and read it when needed. I actually use this all the time, and I don’t even bother with vector store indexing anymore. My go-to is llms.txt with a simple search tool and Claude/Code.” -- #Quote from Person/Lance Martin, LangChain/Team, Context Engineering for Agents on latent.space podcast
            

      • How does this impact   rulesync ? One option to consider ...

        • What if rulesync supported local proxy files which were like local symlinks to remote assets, and then during the generate step it could "unmarshall" that locally.
        • In the .rulesync/{commands, rules, subagents}/*.md's yaml frontmatter, you could support a yaml key like remote which could have a pointer to where the command/rule/subagent definition was located, whether that's a webpage on the internet or whether that's a file in another github (or gitlab, or any SCM) repo. Then upon rulesync generate, it would create the file.

      • Side-note - Marshalling ver Unmarshalling

        • Marshalling is the process of transforming an in‑memory object (including its structure, values, and reference relationships) into a data format suitable for storage or transmission so it can cross process/runtime/boundary lines Wikipedia, *Marshalling (computer science)*.
          

        • Unmarshalling is the reverse: decoding that stored or transmitted representation back into a live in‑memory object, restoring types, references (where possible), and structure Wikipedia, *Serialization*.
          

        • Here, the equivalent of "marshalling" would be storing a rule / command / subagent definition in its canonical location, for example, in a knowledge base or canonical repo of best practices for an enterprise, and "unmarshalling" would be making making those pieces of remote context available to the AI coding agent.

      • Towards the future

        • Perhaps rulesync could define and support a particular kind of MCP server which would know about a particular structure of a repository of agentic coding tool definitions, and then it could make that MCP server available to the coding agent, so the agent could name the things it wants in its context and load those things into context.
        • Essentially, static context is dead; we need a river of context that changes with the problem.
• Anthropic/App/Claude Code/claudelog
  • Claude Code Docs, Guides & Best Practices | ClaudeLog
    • Experiments, insights & mechanics about Claude Code by Person/Wilfred Kasekende (https://www.reddit.com/user/inventor_black/), CTO at Command Stick™ and Mod at Anthropic/App/Claude Code/Reddit r/ClaudeAI
• Person/Tom Doerr
  • AI Engineer and open-source contributor known for creating AI-powered developer tools
  • Popular repositories include zsh_codex (1.7k stars), codex-readme (405 stars), and vim_codex (280 stars)
  • GitHub: @tom-doerr
• Person/Lance Martin
  • Software Engineer at LangChain, expert in context engineering for AI agents
  • Created LangChain Academy with 35+ lessons and "RAG From Scratch" course (450k+ views)
  • Popular repositories include auto-evaluator (1.1k stars) and lex-gpt (343 stars)
  • GitHub: @rlancemartin