How to Write a High-Impact Bug Report

Tagging documents, if done yourself, shifts understanding from documents to the tag layer, resulting in a coherent map of both documents and concepts. Automated tagging also organises things if you use an ontology or controlled vocabulary for tags. Without, and you merely replace disorderly documents with disorderly concepts: the tags become the mess. In fact, curating the tags yourself helps you to develop a suitable conceptual model in the first place. You know of what you speak. EIA205

Problem: #RAG systems need to handle "lower-level" questions that reference specific facts found in a single document or "higher-level" questions that distill ideas that span many documents. Handling both types of questions can be a challenge with typical #kNN retrieval where only a finite number of doc chunks are retrieved. 🔵Idea: RAPTOR (@parthsarthi03 et al) is a paper that addresses this by creating document summaries that capture higher-level concepts. It embeds and #clusters documents, and then summarizes each cluster. It does this recursively, producing a tree of summaries with increasingly high-level concepts. The summaries and starting docs are indexed together, giving coverage across user questions.

Debugging a production issue can feel like searching for a needle in a haystack, blindfolded. That's often a clear sign you might be missing true observability, not just monitoring.

Talking in Diagrams: Co-Designing with an LLM A while ago I was using LangChain for orchestration. It was powerful - but the abstractions kept shifting. One minor version bump and everything behaved differently. Prompts grew verbose, debugging opaque. It felt like building on sand. I started wondering: what’s the right level of abstraction for LLM systems? Too high, and you lose control. Too low, and you reinvent plumbing. Somewhere between those extremes, real engineering patterns must exist. When I looked at the OpenAI Agents JS repo, a few minimal examples stood out: Agents-as-Tools, Sequential Pipeline, Planner–Executor. To understand them, I asked ChatGPT to draw them in Mermaid Markdown. That single shift - from text to structured diagrams - changed everything. The model began reasoning visually. I wasn’t just prompting; we were co-designing. What started as frustration with brittle frameworks became a lesson in language: sometimes the clearest way to talk about a system is to draw it. Full post + diagram: https://lnkd.in/gD29FDxh #AgenticAI #SystemDesign #AIArchitecture #LangChain #OpenAI #MermaidJS #GenerativeAI

📌 Day 3 / 50 — Compare the Triplets (HackerRank) 🚀 🔹 Problem: Compare the Triplets 🔹 Category: Arrays / Implementation 👉 Given two arrays (Alice’s and Bob’s scores), compare each element and assign points: 1 point if Alice’s score > Bob’s 1 point if Bob’s score > Alice’s No points if equal 💡 Key Learning: Practiced conditional checks inside loops. Reinforced array traversal and score tracking logic. Simple but builds a habit of writing clean if-else structures.

🌟 Day 84/100 #DrGViswanathanChallenge Problem: 3003 - Maximize the Number of Partitions After Operations Approach: - Used memoized DFS with state (index, canChange, characterMask) - At each position, explored two options: use original character or change to any other letter (if change available) - When current mask exceeds k distinct characters, forced a partition and reset mask - Tracked maximum partitions achievable from each state Key Insight: The optimal strategy involves deciding when to use the character change to create new partitions. By tracking distinct characters with a bitmask and exploring all change possibilities, we find the maximum partitions. Complexity: O(n × 2^26 × 26) time with memoization, but practically feasible due to constraints and state pruning Result: Complex DP with state optimization that maximizes partitions by strategically using the single character change to create additional split points.

Here's Savannah's first feature in Typer 😎 Suggest commands on error 🙋 Available in Typer 0.20.0 just released 🎉 Read the new docs: https://lnkd.in/d5EtK-9P

✨Spotlighting new research! Yunyi(Icy) Zhang, Ruby Jia, Xiaoxuan “Alicia” Cheng, and CourseKata co-founders Ji Son and Jim Stigler recent work shows that three simple changes to R documentation, including front-loading examples, pairing each code snippet with its output, and ordering examples from easy to hard, help novice programmers find the right example in under a minute (versus nearly two) and boost coding-task scores by about one point. Check out the paper here: https://zurl.co/QWhuC

Day 2 of Array Practice 💻✨ Learning step by step and getting better each day! Today I practiced these array questions: 1️⃣ Find the second largest element in an array 2️⃣ Reverse an array without using built-in functions 3️⃣ Move all zeros to the end of the array 4️⃣ Find the sum of all elements 5️⃣ Check if two arrays are equal Feeling more confident with logic and problem-solving 🙌

Today I learned (and understand it) a neat solution for in-order tree traversal. Morris traversal doesn't use additional space to go in-order through the tree; instead, it uses additional pointers from predecessors to the following nodes. The concept is interesting. However, because we're changing (and reverting the changes) the tree, it might not be suitable for all the cases, and if we create a copy of the tree, the optimization might not make sense, but the solutions look pretty interesting. The post: https://lnkd.in/dWKDsuEx The problem: https://lnkd.in/dFXUfE6T #LeetCode #DSATips #DSA #ThinkDSA #ProblemSolving