Optimizing Claude Code for Confluence Markdown Conversions with Python

Best way to understand how Claude Code operates internally in your machine is to create applications around it. So what about creating an Claude Code Action Observability System? The moment your agentic workflows get slightly complex with multi-step prompts, context-heavy inputs and iterative refinements, you will start seeing outputs but not the actual Claude Code execution path that produced them. So, I tried building a lightweight Agent Observability layer - It logs every interaction as a structured event and store as append-only .JSONL files - Each workflow run is treated as a session with UUID, step-level linkage (parent-child) and metadata (status, timings). - Uses SQLite to keep it local, queryable, and with zero overhead - The backend is in Python (as the Orchestration Layer) that wraps Claude Code CLI, captures and structures events, builds execution traces and exposes APIs for React based UI. - Execution Graph UI is with React Flow - Each graphical node shows: 1) input + context 2) output preview 3) tokens + latency 4) success/failure Click any node and the full trace will get displayed. With this it becomes easy to: - Debug prompts with precision - See exactly what context influenced the output - Identify token-heavy steps - Compare runs (why did this work yesterday but fail today?) - Replay specific steps For a bad answer it will be very easy to know exactly which step, context, and decision caused it

LinkedIn: 📣 SynapseKit v0.6.9 is live. Two graph features in this release that I think matter more than they look. approval_node(): gates your graph on a human decision. The workflow hits a node, pauses, waits for a human to approve or reject, then continues. No polling, no hacks. One function call. dynamic_route_node(): routes to completely different subgraphs at runtime based on whatever logic you write. Sync or async. Your graph decides where it goes next while it's running. Together these two make human-in-the-loop workflows actually practical to build. Not a demo. Production. Also shipped: 💬 SlackTool [Slack]— send messages via webhook or bot token 📋 JiraTool— search, create, comment on issues via REST 🔍 BraveSearchTool [Brave]— web search via Brave API All three stdlib only. Zero new dependencies. Where we stand: 32 tools · 15 providers · 18 retrieval strategies · 795 tests · 2 dependencies. ⚡ pip install synapsekit 🔗 https://lnkd.in/d2fGSPkX #Python #LLM #RAG #OpenSource #AI #MachineLearning #Agents #SynapseKit

I thought contributing to Streamlit meant 𝗳𝗶𝘅𝗶𝗻𝗴 𝘀𝗺𝗮𝗹𝗹 𝗨𝗜 𝗯𝘂𝗴𝘀. I didn’t expect to 𝗿𝗲𝘃𝗲𝗿𝘀𝗲-𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿 its entire architecture. While working on a component, I kept asking: “𝗪𝗵𝗲𝗿𝗲 𝗱𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗯𝘂𝘁𝘁𝗼𝗻 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗴𝗼?” I became so curious that I kept learning more and more about it 👇 User Python Code ↓ Elements API (button.py) ↓ DeltaGenerator ↓ Protobuf ↓ Runtime / Session Manager ↓ Tornado WebSocket ↓ React (TSX) ↓ Browser UI Unlike 𝗗𝗷𝗮𝗻𝗴𝗼 𝘁𝗲𝗺𝗽𝗹𝗮𝘁𝗲𝘀 or 𝗦𝗽𝗿𝗶𝗻𝗴 𝗕𝗼𝗼𝘁 𝗥𝗘𝗦𝗧 𝗔𝗣𝗜𝘀, Streamlit flows like this: 🧠 Python → Protobuf → WebSocket → React → Rerun Model Every click reruns the script. State lives in the session manager. UI updates happen through delta patches over a persistent WebSocket. That’s when it clicked, Streamlit isn’t just client–server. It’s reactive execution architecture, closer to 𝗥𝗲𝗮𝗰𝘁 + 𝗝𝘂𝗽𝘆𝘁𝗲𝗿 + 𝗲𝘃𝗲𝗻𝘁-𝗱𝗿𝗶𝘃𝗲𝗻 systems. The lesson? If a framework feels “𝘀𝗶𝗺𝗽𝗹𝗲,” it’s usually hiding something sophisticated. Don’t stop at using it - dissect it. Understanding the layers transforms you from a user into a builder. How do you usually break down a complex system to understand it completely? #CodingJourney #DeveloperCommunity #Streamlit #OpenSourceContributing

📖 A Small Lesson That Changed How I Think About Code✨✨ ✍️Imagine two developers 👩💻👨💻 trying to find a number in a list of 1,000,000 items. ⭐Developer A checks each number one by one until they find it. 🔍 ✍️Developer B does something smarter. They keep dividing the list in half, quickly narrowing down where the number could be. ⚡ Both developers solve the problem.✍️ But one takes far longer than the other.😞 This is where Big O Notation comes in. 📊 Big O helps us understand how efficient an algorithm is as the data grows.🤗 For example: 🔹 O(1) – Constant Time ⚡ 🔹 O(log n) – Logarithmic Time 🚀 🔹 O(n) – Linear Time 📈 🔹 O(n²) – Quadratic Time 🐢 As I continue my journey learning Python 🐍 and algorithms, concepts like Big O remind me that great programs are not just built to work — they are built to scale efficiently. 💡 Every line of optimized code brings us closer to building better technology. #Python #Algorithms #BigONotation #TechLearning #Programming #ContinuousLearning

🚀 𝗦𝘁𝗼𝗽 𝗪𝗿𝗶𝘁𝗶𝗻𝗴 𝗕𝗼𝗶𝗹𝗲𝗿𝗽𝗹𝗮𝘁𝗲: 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝗬𝗼𝘂𝗿 𝗠𝗼𝗱𝗲𝗹𝘀 𝘄𝗶𝘁𝗵 𝗣𝘆𝗱𝗮𝗻𝘁𝗶𝗰 If you’ve ever stared at a JSON schema or OpenAPI spec and thought: "Do I really have to type all these models by hand?" — you’re not alone. The good news? You don’t have to. With 𝗣𝘆𝗱𝗮𝗻𝘁𝗶𝗰 𝗠𝗼𝗱𝗲𝗹 𝗖𝗼𝗱𝗲 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗼𝗿𝘀, you can turn schemas into type‑safe Python models instantly. ✨ Why it matters: - Save 𝗛𝗢𝗨𝗥𝗦 of repetitive typing - Eliminate 𝗘𝗥𝗥𝗢𝗥𝗦 - Stay in sync with evolving APIs - Keep your codebase 𝗖𝗟𝗘𝗔𝗡 and maintainable In my latest blog, I break down: 👉 Why code generators are game‑changers 👉 How they work (with real code examples) 👉 When not to use them 👉 Best practices for CI/CD automation 📖 Read the full article here: [https://lnkd.in/dynFYK8E] Let’s stop typing boilerplate and start building smarter systems. Your future self will thank you. #Python #Pydantic #CodeGeneration #APIDevelopment #SoftwareEngineering

A ~550-word AGENTS.md reduced agent runtime by 28.64% and token usage by 16.58% on SWE-bench Verified. The trick wasn’t more context — it was less ambiguity. I tested these ideas while refactoring agent docs for a production Python/FastMCP monorepo at NOS. What stuck with me: 𝗔𝗚𝗘𝗡𝗧𝗦.𝗺𝗱 𝘄𝗼𝗿𝗸𝘀 𝘄𝗵𝗲𝗻 𝗶𝘁’𝘀 𝗲𝘅𝗲𝗰𝘂𝘁𝗮𝗯𝗹𝗲 𝗼𝗻𝗯𝗼𝗮𝗿𝗱𝗶𝗻𝗴. Setup + test commands beat prose (Lulla et al.). 𝗔𝗚𝗘𝗡𝗧𝗦.𝗺𝗱 𝗶𝘀 𝗯𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝘁𝗵𝗲 𝗶𝗻𝘁𝗲𝗿𝗼𝗽𝗲𝗿𝗮𝗯𝗹𝗲 𝗱𝗲𝗳𝗮𝘂𝗹𝘁. 4,860 context files across GitHub; `.cursorrules` is basically legacy (Galster et al.). 𝗦𝗵𝗼𝗿𝘁 𝗯𝗲𝗮𝘁𝘀 𝗰𝗼𝗺𝗽𝗿𝗲𝗵𝗲𝗻𝘀𝗶𝘃𝗲. Most files are <500 words; medians cluster around ~335–535 words (Chatlatanagulchai et al.). 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 𝗶𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻𝘀 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗵𝗶𝗴𝗵𝗲𝘀𝘁-𝘀𝗶𝗴𝗻𝗮𝗹 𝘀𝗲𝗰𝘁𝗶𝗼𝗻. They show up in ~75% of high-quality files. 𝗔𝘂𝘁𝗼-𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲𝗱 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗰𝗮𝗻 𝗯𝗮𝗰𝗸𝗳𝗶𝗿𝗲. LLM-generated files dropped success by ~3% on average while raising cost >20% (Gloaguen et al.). 𝗙𝗶𝗹𝗲 𝗹𝗼𝗰𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝘄𝗵𝗲𝗿𝗲 𝗮𝗴𝗲𝗻𝘁𝘀 𝗳𝗮𝗶𝗹 𝗳𝗶𝗿𝘀𝘁. If they edit the wrong file, everything downstream collapses (ContextBench). What I did with this: one canonical AGENTS.md (~550 words, every snippet verified), CLAUDE.md + Copilot instructions as thin pointers, deleted `.cursorrules`, and 4 path-scoped instruction files that auto-inject context per folder. Takeaway: context engineering is mostly negative space — remove contradictions, name the right files, and make “run tests” unmissable. Sources: https://lnkd.in/eM-HnnGs https://lnkd.in/eN7pUsfY https://lnkd.in/eHAarmSC https://lnkd.in/e9Fx6UC7 https://lnkd.in/eJM2EHkh https://lnkd.in/eTqgZZqK https://lnkd.in/egk_dX8U #ContextEngineering #AICoding #CodingAgents #SoftwareEngineering #MCP #LLMs #DeveloperTools

Everyone learns stacks. But very few understand where they actually matter. Take a simple problem: Checking if brackets are balanced. Most people think it’s about counting. It’s not. It’s about order. Here’s what really happens behind the scenes: → You scan the expression left to right → Every opening bracket goes into a stack → Every closing bracket tries to match the last opening one If it matches → remove it If it doesn’t → the entire structure breaks That’s the moment you realize: Stacks aren’t just data structures. They are decision systems. They enforce rules like: Last In → First Out And that’s exactly how: • Code editors validate syntax • Compilers detect errors • Browsers manage navigation history A simple example: [(a+b)] → Valid ✔ [(a+b] → Invalid ❌ Same characters. Different structure. That’s the difference between working code and broken logic. The lesson? In programming — and in systems — structure beats quantity. Always. #DataStructures #Python #ProblemSolving #CodingJourney #AIThinking

Why I’m Trading "Drag-and-Drop" for Python in Optimization Workflows =================================== In simulation-driven design, the real bottleneck isn't usually the solver—it’s the workflow. Tools like iSight are undeniably powerful. They offer a structured environment and a "plug-and-play" feel for DOE and optimization that can be a lifesaver for standard CAE tasks. But, as many of us have found, that convenience often comes with a "walled garden" tax. The Friction Points ----------------------- Proprietary platforms require a specific kind of "tool-fluency." When you hit a wall, you aren't just solving an engineering problem; you're debugging a closed ecosystem. Limited documentation and a smaller user base mean that troubleshooting often feels like a solo mission. Why Python Wins for Me ------------------------------- This is where Python transforms the experience from "configuring a tool" to "building a solution." Using libraries like SciPy, Optuna, and PyDOE offers a level of granularity that GUI-based tools just can't match. * Version Control: You can actually git commit a script; try doing that effectively with a proprietary project file. * Community Power: If you’re stuck, a solution is usually one Stack Overflow thread or GitHub Issue away. * Future-Proofing: It’s much easier to bridge your optimization results into an ML framework or a custom dashboard when you're already in a coding environment. For teams that live in scripting and automation, Python isn't just an alternative—it’s a faster, more scalable, and frankly, more enjoyable way to work. It turns a rigid process into a creative one. #CAE #CFD #DesignOptimization #EngineeringSimulation #Python #SimulationDrivenDesign #EngineeringAutomation #MachineLearning #DigitalEngineering

Topic 1/100 🚀 🧠 Topic 1 — Metaprogramming Ever wondered how frameworks like Django feel “magical”? That’s because they use metaprogramming. 👉 What is it? Metaprogramming means writing code that can modify or generate other code at runtime. 👉 Use Case: Frameworks dynamically create models, APIs, and admin panels without you writing everything manually. 👉 Why it’s Helpful: Reduces repetitive code Makes systems flexible Powers automation in large applications 💻 Example: def add_method(cls): def new_method(self): return "Hello from metaprogramming!" cls.new_method = new_method return cls @add_method class MyClass: pass obj = MyClass() print(obj.new_method()) 🧠 What’s happening here? We are dynamically adding a method to a class — at runtime. ⚡ Pro Tip: If you understand this, you’ll finally understand how frameworks actually work under the hood. 💬 Follow this series for more Topics #Python #BackendDevelopment #100TopicOfCode #SoftwareEngineering #LearnInPublic

🚀 LeetCode Practice 📌 Problem: Number of Steps to Reduce a Number in Binary Representation to One 🔗 LeetCode Problem #1404 🧠 Problem Statement Given a binary string s, return the number of steps required to reduce it to "1" using: ✅ If the number is even → divide it by 2 ✅ If the number is odd → add 1 It is guaranteed that we can always reach "1". 🔎 Example Input: s = "1101" Output: 6 Explanation: 13 (1101) → +1 → 14 14 → /2 → 7 7 → +1 → 8 8 → /2 → 4 4 → /2 → 2 2 → /2 → 1 💡 Key Insight The length of s can be up to 500 bits, so converting directly to an integer might not be ideal in some languages. Instead, we: Traverse from right to left Simulate division and addition Maintain a carry variable Count operations efficiently ⚡ Optimized Approach (Greedy + Carry Handling) 🔥 Core Observations If last bit is '0' → number is even → 1 step (divide) If last bit is '1' → number is odd → 2 steps (add 1 + divide) Handle carry propagation carefully 🧑💻 Python Implementation (O(n) Time | O(1) Space) class Solution: def numSteps(self, s: str) -> int: steps = 0 carry = 0 # Traverse from right to left (ignore MSB) for i in range(len(s) - 1, 0, -1): bit = int(s[i]) # If bit + carry == 1 → odd if bit + carry == 1: steps += 2 carry = 1 else: steps += 1 return steps + carry 📊 Complexity Analysis ⏱ Time Complexity: O(n) 📦 Space Complexity: O(1) Where n is the length of the binary string. #LeetCode #ProblemSolving #Python #DSA #CodingInterview #BitManipulation #TechGrowth