LeetCode Problem Solving Strategy

You don't need LangChain to build AI agents. You need to understand what's inside it. I spent the last month building agents from scratch just Python, Ollama, and the ReAct loop. No frameworks. No abstractions. Nothing I couldn't debug. 6 concepts kept coming back: → Anatomy — the 4 parts of every agent → The loop — how decisions actually happen → Tools — what they really are (not magic) → Memory — the illusion the harness creates → Scaling — what breaks at each stage → Orchestration — when one agent isn't enough I put them in a visual guide. 11 slides. Save it. Open it next time your framework does something weird. Share it with someone building their first agent. This is the reference I wish I had on day 1. 👇 Swipe through. #AIAgents #AIEngineering #LLM #MachineLearning #SoftwareEngineering #Python #BuildInPublic #FirstPrinciples #TechCareers

Just solved “First Matching Character From Both Ends” 🚀 💡 My approach (simple & efficient): Instead of overcomplicating it, I used a two-pointer mindset without explicitly creating two pointers. Loop through the string from the start For each index i, compare: s[i] (from the front) s[n - i - 1] (from the back) The moment both match → return the index If no match → return -1 ✨ This works because we're checking symmetry from both ends in a single pass (O(n)) with O(1) space. Sometimes the best solutions aren’t fancy — they’re just clean and intuitive. 🔥 Consistency > Complexity. Small wins like this build strong problem-solving instincts. #LeetCode #DSA #Python #CodingJourney #ProblemSolving #TechGrowth #CodeDaily #WomenInTech #FutureEngineer #100DaysOfCode #KeepBuilding

Posted a tiny Python demo: atlas-kvd-demo. It models a bounded state update with: K = carried state  V = outside pressure  Δ = local event nudge Small repo, but the idea matters to me: systems should drift, react, and settle instead of snapping all over the place. https://lnkd.in/e8WFcFPv Not a giant framework.  Not “AI solves everything.”  Just one legible state law, in code. #Python #GitHub #Simulation #StateMachines #AI #IndieDev #BuildInPublic

Wild to see Claude Code, one of the most talked-about AI coding tools right now, go viral like this. v2.1.88 was accidentally shipped with a debug/source map file, exposing a large part of the internal TypeScript codebase. Anthropic said it was a release mistake caused by human error, not a hack, and no customer data was leaked. The craziest part? The internet moved so fast that people were already digging through it and rebuilding ideas around it in Python and Rust. Just shows how one bad release can put your product everywhere in a few hours. checkout python implementation https://lnkd.in/gWEV_XKc from instructkr. #ClaudeCode #Anthropic #AI #SoftwareEngineering

✅ Day 97 of 100 Days LeetCode Challenge Problem: 🔹 #1281 – Subtract the Product and Sum of Digits of an Integer 🔗 https://lnkd.in/gxTAZc6U Learning Journey: 🔹 Today’s problem involved extracting digits of a number and performing two operations simultaneously. 🔹 I initialized two variables: one for product (pr) and one for sum (sm). 🔹 Using a while loop, I extracted each digit using n % 10. 🔹 Updated the product by multiplying the digit and updated the sum by adding it. 🔹 Reduced the number using integer division (n //= 10) after each step. 🔹 Finally returned the difference between product and sum. Concepts Used: 🔹 Digit Extraction 🔹 While Loop 🔹 Arithmetic Operations 🔹 Number Manipulation Key Insight: 🔹 Both product and sum can be computed in a single traversal of digits. 🔹 Efficient use of modulus and division avoids converting the number to a string. Complexity: 🔹 Time: O(d) 🔹 Space: O(1) #LeetCode #Algorithms #DataStructures #CodingInterview #100DaysOfCode #Python #ProblemSolving #LearningInPublic #TechCareers

🚀 Day 19 of My Generative & Agentic AI Journey! Today’s focus was on exploring different types of functions in Python and how they are used in real-world programming. Here’s what I learned: ⚙️ Pure vs Impure Functions: • Pure Functions → Always return the same output for the same input and don’t modify external data 👉 More predictable and easier to test • Impure Functions → Depend on or modify external variables 👉 Less predictable, generally avoided in clean code 🔁 Recursive Functions: • A function that calls itself to solve a problem step by step 👉 Example use case: Breaking a problem into smaller parts (like factorial, countdown, etc.) ⚡ Lambda (Anonymous) Functions: • Small, one-line functions without a name • Useful for short operations where defining a full function is unnecessary 👉 Example use case: Quick calculations or transformations 💡 Key takeaway: Understanding different types of functions helps in writing cleaner, efficient, and more maintainable code. Slowly moving towards writing optimized and professional-level Python 🚀 #Day19 #Python #GenerativeAI #AgenticAI #LearningJourney #BuildInPublic

Stop digging through logs. Start fixing the problem. 🛠️ During a technical incident, every second counts. I built the Automated Technical Log Analyzer to handle the heavy lifting. What it does: It takes raw logs from multiple sources and uses native Python logic to strip away the noise. The Brain: I implemented an agentic workflow using GPT-4o-mini to identify root causes and recommend actionable recovery steps. The ROI: This pipeline is designed to reduce Mean Time To Repair (MTTR) by approximately 70%. Check out the repo: https://lnkd.in/dDPyJDNH #Python #DevOps #AI #AgenticWorkflows #GPT4

LLM apps are powerful, but they're also hard to debug and expensive to run. Observability shouldn't make that worse. In our latest blog, we break down how the Python OpenTelemetry SDK impacts performance in LLM workloads and what happens when you rethink the stack. If you're building with LLMs and care about performance, this is worth a read 👉 https://lnkd.in/e_aHurKw #honeycomb #OpenTelemetry #AI #LLMs

🧠 Technical Quiz: Which is more important for a Junior Dev in 2026? A) Memorizing every library function in Python/JS. B) Explaining the complexity and trade-offs of an AI-generated solution. The answer is B. With AI tools generating code in seconds, your value as an engineer lies in your ability to *audit*, *optimize*, and *explain* the code. Syntax is a commodity; architectural logic is the luxury. KodeMaster AI focuses on project-based learning that tests your understanding of system design and complexity, not just your ability to remember syntax. Sharpen your logic: https://kodemaster.ai/ #CodingQuiz #SoftwareArchitecture #CSMajor #EngineeringLife #KodeMasterAI

I published a new article about something many developers will appreciate: how to recover your Claude Code chat history in VS Code without losing the early context that gets compacted away. The key idea is simple: - Claude Code stores history locally in ~/.claude/projects. - The logs are saved as JSONL files. - Context compaction affects the live session, not the stored history. - A Python CLI can help you retrieve and read the transcripts. If you’ve ever lost a good idea because you clicked “yes” too quickly or forgot to save your prompt trail, this workflow can save you time and frustration. Read the article here: https://lnkd.in/epUjEF6f #ClaudeCode #VSCode #Python #DataScience #AI #DeveloperProductivity #Automation #Substack