donbr

AIE OnRamp Session 1 - Visual Diagrams

This document provides visual diagrams to help understand the development workflow and architecture covered in Session 1.

---

1. Deployment & Implementation Architecture

This diagram shows the tools, services, and how they connect in your development pipeline.

donbr

You are reviewing my .claude.json cleanup tooling.

Context:

• Python script: cleanup_claude_json.py (backs up ~/.claude.json, analyzes projects, and removes entries whose directories no longer exist, with a dry-run/execute flag).
• Strategy doc: CLAUDE_JSON_CLEANUP_STRATEGY.md (describes goals, risks, and a conservative cleanup process).

Tasks (be brief and concrete):

1. In 3–5 sentences, restate the core goal of this script + strategy and the main safety mechanisms (backups, dry-run, scope of deletions).
2. Evaluate the approach against best practices for:

• config/safety (backups, rollback, blast radius),

donbr

GitHub Repository Research Prompt

You are analyzing a GitHub repository as a software architect and systems researcher.

Critical rules

1. Do not assume technologies or patterns based on the repo name, description, or my prior comments.
   • Instead, infer everything from:
     • README*, docs/, pyproject.toml / package.json, Dockerfile*, compose*, etc.
2. Separate facts from inferences:

donbr

🚀 Executive Summary

A Spec-Driven Operating Model for Human + AI Engineering Teams Using GitHub

AI Engineering in 2025 requires more than prompting or code generation—it requires a repeatable, spec-driven system that aligns humans and AI agents on what to build and why before any code is written.

GitHub’s Spec Kit provides a lightweight, practical foundation for this: a standardized workflow that uses structured specifications to guide AI agents, reduce rework, and eliminate “vibe coding.”

This bootcamp framework extends that foundation into a three-phase operating model, helping future AI architecture & engineering leaders create teams where humans and AI work together effectively, predictably, and safely across a GitHub Organization.

donbr

Here’s a concise analysis of the markdown‑confluence GitHub organisation (and its tooling), why it appears to have waned in activity, and alternative tools/approaches you might evaluate.

---

1. What the project was

• The mono-repo at [markdown-confluence/markdown-confluence] described itself as “a collection of tools to convert and publish your Markdown files to Confluence (using Atlassian Document Format – ADF)”. ([GitHub][1])

• It included components like:

• an npm CLI (@markdown-confluence/lib) for converting Markdown → ADF. ([GitHub][1])

donbr

🎨 Breakout Room Visual Guide

Frontend Development & Deployment Workflow

---

📋 Complete Workflow Overview

graph TB
    Start([👋 Start: Breakout Room Session]) --> Phase1[💡 Phase 1: Planning & Design]

donbr

Santosh - Assignment 15 - A2A

🧭 TL;DR Feedback – Simplifying & Aligning with A2A Client Patterns

Great job — your code works and shows deep understanding of streaming, chunk parsing, and A2A context handling. To simplify and align with the official A2A client patterns (like test_client.py), focus on these refinements:

---

🔹 1. Separate Concerns

donbr

The LangChain Provider Switching Pattern

How to Switch Between LLM Providers in 20 Lines of Code

---

🎯 The Core Pattern

The essential truth about switching LLM providers in LangChain:

donbr

2025 agentic papers

1. Agentic Retrieval-Augmented Generation: A Survey — arXiv, Jan 2025 Why it matters: formalizes “agentic RAG” patterns (reflection, planning, tool use, multi-agent) and maps implementation choices you already teach. Great for framing why orchestration beats “just a better model.” ([summarizepaper.com][1])

2. Reasoning↔RAG Synergy (Survey): Toward Deeper RAG-Reasoning Systems — arXiv, Jul 2025 Why it matters: unifies “reasoning-enhanced RAG” and “RAG-enhanced reasoning,” then spotlights agentic interleaving (search ↔ think loops). Solid taxonomy + dataset links you can fold into eval curricula. ([summarizepaper.com][2])

3. LLM-based Agents in Medicine (Survey) — ACL Findings 2025 Why it matters: a rigorous vertical survey (healthcare) with evaluation tables, safety constraints, and workflow patterns (routing, oversight, audit). Use it as a model for domain-specific agent governance sections in your posts. ([ACL Anthology][3])

donbr