I’ve just released a small desktop tool I built for tracking time against fixed project allocations: Tariff Clock. It’s designed for situations where time is effectively “prepaid” or capped — consulting, research support, internal service work — and you want a simple, auditable way to track usage. Key ideas: - Each project has a fixed time “tariff” - Start/stop behaves like a chess clock (only one running at a time) - Live countdown of remaining time - Automatic session logging + manual adjustments (with reasons) - Full CSV audit trail per project It’s intentionally lightweight, local-first, and transparent. Cross-Platform https://lnkd.in/encZ45wW #ProductivityTools #TimeTracking #Consulting #ResearchSupport  #DataManagement #OpenSource #Python #MacOS #IndieDev #Workflow

Agent Replay Debugger update - now with real agent reasoning Last week I posted v1 of agent-replay-debugger. Since then I've shipped three updates worth sharing. What changed: The original version only showed lifecycle events - pipeline_started, step_passed, step_failed. Useful for status, useless for understanding why the agent did what it did. Now the viewer parses the full transcript: every reasoning block where the agent explains its thinking ("Let me check the existing component structure..."), every tool call with its target (Read src/components/rewards.tsx, Edit src/lib/api.ts, Bash npm test), and the actual user messages that triggered each chain of decisions. A real session now surfaces 56 reasoning events and 138 tool calls - the complete decision chain, not just the outcome. New viewer features:  - Consecutive reasoning events collapse into groups (click to expand) so you're not scrolling past 50 "I'll now..." blocks  - DAG nodes show duration and tool count, and clicking one filters the timeline to just that span  - Automatic PII redaction: emails, API keys, JWTs, tokens, user paths - all scrubbed before any data reaches the viewer Second adapter: Claude Code. Point ARD at any Claude Code session .jsonl and get the full interactive timeline. Two frameworks supported now, more coming. Still zero runtime dependencies. Still one command: ard view ./session.jsonl Try the live demo (real session, auto-scrubbed): https://lnkd.in/gRFB7uWf Code: https://lnkd.in/gPTUt4ue #buildInPublic #AIAgents #LLM #python #openSource #devTools #AIEngineering

𝗬𝗼𝘂𝗿 𝗣𝘆𝘁𝗵𝗼𝗻 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗵𝗮𝘀 𝗳𝗹𝗮𝗸𝗲𝟴, 𝗶𝘀𝗼𝗿𝘁, 𝗯𝗹𝗮𝗰𝗸, 𝗽𝘆𝗹𝗶𝗻𝘁, 𝗮𝗻𝗱 𝗯𝗮𝗻𝗱𝗶𝘁. Five tools. Five configs. Five places for CI to silently drift. Ruff replaces all of them. Written in Rust, it runs in milliseconds on a full codebase. One config in 𝗽𝘆𝗽𝗿𝗼𝗷𝗲𝗰𝘁.𝘁𝗼𝗺𝗹. One command in CI. 🔹 Replaces: flake8, isort, black, pyupgrade, pydocstyle, and 700+ rules from pylint 🔹 10–100x faster than the tools it replaces — no more slow CI lint steps 🔹 𝗿𝘂𝗳𝗳 𝗰𝗵𝗲𝗰𝗸 --𝗳𝗶𝘅 auto-fixes most violations — not just reports them 🔹 One binary, zero inter-tool conflicts, one source of truth This "BIFES UP" ruleset is my way of beefing up code quality while keeping the stakes high on security. It covers 90% of what matters from security to logic. Want 100%? Just extend it with 𝗪, 𝗔𝗦𝗬𝗡𝗖, and 𝗡 — especially essential for ensuring concurrency safety in FastAPI environments. What other rules do you often include in your projects? #Python #DevSecOps #SoftwareEngineering #BackendDevelopment #SoftwareArchitecture

My first version of the Task Manager CLI was embarrassing. Everything in one file. No error handling. Variables named x, temp, data2. It worked — until it didn't. And when it broke, I had no idea where to start debugging. That moment of staring at my own code and not understanding it was one of the most important moments in my development as an engineer. I refactored the entire project. Broke it into modules. Added structured exception handling. Rewrote the validation logic. Debugging effort dropped by 40%. More importantly, I could actually read my own code. The lesson wasn't technical. It was about ego. Bad code is often the result of being in a hurry to "finish" — instead of being willing to do it right. I now build slower and ship cleaner. And I'm better for it. Have you ever looked back at old code and learned something important about yourself? #Refactoring #CleanCode #Python #SoftwareEngineering #LessonsLearned

huggingface_hub v1.12.0 is here, bringing two improvements that make working with Hugging Face Buckets smoother and more scriptable. First, all hf buckets CLI commands now share a unified --format flag. Whether you're running create, list, info, delete, move, or cp, you get the same --format [auto|human|agent|json|quiet] option. This replaces the old mix of --quiet and --format table|json flags, giving you a single, consistent interface across the entire buckets command suite. Quiet mode prints only bucket IDs, JSON mode is ready for scripting pipelines, and agent mode delivers structured output for AI-driven workflows. Second, you can now search buckets by name directly from the API and CLI. Instead of listing every bucket and filtering client-side, pass --search "checkpoint" to hf buckets list. These changes reflect our ongoing effort to make the huggingface_hub CLI and Python API predictable, composable, and pleasant to use in production. Upgrade today with pip install --upgrade huggingface_hub. Related notes: https://lnkd.in/eHn4UJZD

🔍 Solved: Search in a 2D Matrix II (LeetCode 240) Today I worked on an interesting problem that looks simple at first but really tests your understanding of matrix properties. Instead of going with brute force or applying binary search on each row, I used the elimination approach to optimize the search. 💡 Key Insight: The matrix is sorted both row-wise and column-wise. So, starting from the bottom-left corner, we can eliminate either: 1. an entire row (if current element > target), or 2. an entire column (if current element < target) This reduces the search space efficiently at every step. ⚡ Approach Highlights: 1. Start from bottom-left 2. Move up if the value is too large 3. Move right if the value is too small ⏱️ Time Complexity: O(m + n) 📦 Space Complexity: O(1) This approach is much more efficient than checking every element and is a great example of how recognizing patterns in sorted data structures can lead to optimal solutions. #DataStructures #Algorithms #DSA #ProblemSolving #CodingInterview #LeetCode #LeetCodeDaily #CodingPractice #SoftwareEngineering #Programming #Coding #Developer #Tech #ComputerScience #CodeNewbie #Cpp #LearningInPublic #100DaysOfCode #CodeEveryday #KeepLearning #TechJourney #SelfImprovement

Day 32/50 – LeetCode Challenge 🧩 Problem #416 – Partition Equal Subset Sum Today’s problem focused on determining whether an array can be divided into two subsets with equal sum, which is a classic Dynamic Programming (Subset Sum) problem. 📌 Problem Summary: Given an array of integers, check if it can be partitioned into two subsets such that their sums are equal. 🔍 Approach Used ✔ First calculated the total sum of the array ✔ If the sum is odd → not possible to divide equally ✔ Converted the problem into: Can we find a subset with sum = total / 2 ? ✔ Used a set (DP approach) to store all possible sums ✔ Iteratively built new sums by adding each number ✔ Checked if the target sum exists ⏱ Time Complexity: O(n × target) 📦 Space Complexity: O(target) 💡 Key Learning ✔ Converting problems into Subset Sum pattern ✔ Using Dynamic Programming with sets ✔ Thinking in terms of possibility instead of brute force ✔ Recognizing important DP patterns This problem helped me understand how to simplify complex problems into familiar patterns. Consistency builds strong problem-solving intuition 🚀 🔗 Problem Link: https://lnkd.in/gCRhSyaz #50DaysOfLeetCode #LeetCode #DSA #DynamicProgramming #SubsetSum #ProblemSolving #CodingJourney #FutureAIEngineer #Consistency

🚀  Day 29 LeetCode Problem Solved: Longest Consecutive Sequence (128) Today I solved an interesting Data Structures & Algorithms problem on LeetCode. 💻 🔹 Problem: Given an unsorted array of integers, find the length of the longest consecutive elements sequence in O(n) time complexity. 🔹 Example: Input: [100, 4, 200, 1, 3, 2] Output: 4 👉 The longest consecutive sequence is [1,2,3,4]. 🔹 Approach: Instead of sorting the array (which takes O(n log n)), I used a HashSet to achieve O(n) time complexity. ✔ Store all numbers in a HashSet ✔ Identify the start of a sequence (num - 1 not present in the set) ✔ Expand the sequence forward (num + 1, num + 2...) ✔ Track the maximum length 🔹 Complexity: ⏱ Time Complexity: O(n) 📦 Space Complexity: O(n) 💡 Key Learning: Using HashSet efficiently can help optimize problems that involve searching and sequence detection. Excited to keep learning and improving problem-solving skills! 🚀 #leetcode #coding #java #datastructures #algorithms #softwaredeveloper #programming #codingjourney

Stop wasting time on manual tasks. 🐍⚡ I’ve put together a 12-slide Python Automation Roadmap to help you turn repetitive work into clean, scalable scripts. No fluff—just the core libraries and patterns you actually need. What’s inside: ✅ File Ops: Mastering os, shutil, and pathlib. ✅ Web & APIs: Scraping with Selenium and robust requests handling. ✅ Production Ready: Scheduling, Logging, and CLI design. ✅ Advanced: Asyncio, decorators, and a full real-world project. Whether you're looking to clean up your filesystem or build a headless web bot, this guide has you covered. 👇 Download the PDF below and start automating. #Python #Automation #Programming #SoftwareEngineering #PythonRoadmap

🚀 Day 85 of #100DaysOfCode Today, I solved LeetCode 79 – Word Search, a classic problem that combines backtracking with grid traversal. 💡 Problem Overview: Given a 2D grid of characters and a word, the task is to determine if the word exists in the grid by forming it through sequentially adjacent cells (horizontal or vertical), without reusing any cell. 🧠 Approach: ✔️ Applied DFS with backtracking ✔️ Explored all possible paths starting from matching characters ✔️ Marked cells as visited during traversal and backtracked when needed ✔️ Ensured each cell is used only once per path ⚡ Key Takeaways: Backtracking is essential for exploring multiple possibilities DFS helps in exploring paths deeply Proper state management (visited/unvisited) is crucial 📊 Complexity Analysis: Time Complexity: O(n × m × 4^L) (L = length of word) Space Complexity: O(L) (recursion stack) Building strong recursion and backtracking skills 🚀 #LeetCode #100DaysOfCode #DSA #Backtracking #DFS #Recursion #ProblemSolving #CodingJourney #SoftwareDevelopment #InterviewPrep