JSON Craft: AI-Powered JSON Toolkit for Debugging and Editing

Most people don’t realize this yet: You can turn Claude Desktop into your own custom tool — in a day. I tried it. Built something useful. I created a custom MCP server that lets Claude find and clean duplicate photos — just from a prompt. No extra apps. No terminal. Just: "Find duplicates in D:\megha\Photos" "Move duplicates to a folder" And it handles everything. Why this matters: Most duplicate finder tools are either sketchy apps or scripts non-technical users won’t touch. I wanted AI to be the interface — you describe the task, it executes it. Under the hood: → Perceptual hashing (pHash) — detects visually similar images → Multithreaded scanning — handles 1900+ images smoothly → Async + non-blocking — Claude stays responsive → Safe cleanup — moves duplicates, doesn’t delete → Plug-and-play with Claude via JSON config Big takeaway: Building MCP servers is easier than it looks. The real challenge? Making them non-blocking so Claude doesn’t timeout. Once you solve that, you can turn almost any Python script into an AI tool. Built with: Python, MCP SDK, Pillow, imagehash, asyncio Open source: https://lnkd.in/gs4ExNec Curious — what would you automate if Claude could run your scripts? #ClaudeAI #MCP #AsyncPython #AIEngineering #DevCommunity #NoCode #LowCode #FutureOfWork

Most developers memorize code. Top 1% understand the pattern behind it. 👇 Here's every algorithm type you need to know — by category. 🔖 ━━━━━━━━━━━━━━━━━━━ ⚡ STAGE 1 — Sorting → Bubble Sort — compare & swap adjacent, O(n²) → Selection Sort — find min, place at front, O(n²) → Insertion Sort — build sorted array one item at a time → Merge Sort — divide, sort, merge — O(n log n) → Quick Sort — pivot-based divide & conquer — O(n log n) avg → Heap Sort — uses max-heap, O(n log n) guaranteed → Counting Sort — non-comparison, O(n+k) for integers → Radix Sort — digit by digit sort, O(nk) ━━━━━━━━━━━━━━━━━━━ Don't memorize code. Understand WHEN to use each pattern. That's what separates good devs from great ones. 🚀 📖 Practice: leetcode.com 📖 Theory: https://lnkd.in/g3g6tSU2 📖 Roadmap: https://lnkd.in/gMRaYYW5 💬 Which stage are you currently grinding? ♻️ Repost — every dev preparing for interviews needs this map. #DSA #Algorithms #LeetCode #SoftwareEngineering #CompetitiveProgramming #SDE #TechInterview #Python #CPlusPlus #BuildingInPublic

🚀 FastAPI vs REST API — What’s the Difference? Many developers confuse FastAPI with REST API, but they are not the same thing 👇 🔹 REST API (Architectural Style) REST (Representational State Transfer) is a design pattern for building APIs. It defines how clients and servers communicate over HTTP using methods like GET, POST, PUT, DELETE. ✔️ Language-agnostic ✔️ Widely adopted standard ✔️ Focuses on structure & principles 🔹 FastAPI (Framework) FastAPI is a modern Python framework used to build APIs, often following REST principles. ✔️ Built with Python 🐍 ✔️ High performance (comparable to Node.js & Go) ✔️ Automatic API docs (Swagger UI) ✔️ Async support out of the box ✔️ Data validation using Pydantic ⚖️ Key Difference 👉 REST is how you design APIs 👉 FastAPI is a tool to implement APIs 💡 In Simple Terms: You can build a REST API using FastAPI, Django, Express, or any framework — FastAPI is just one of the fastest and most developer-friendly options today. 🔥 When to Choose FastAPI? - Building high-performance APIs - Working with Python ecosystem - Need auto docs & validation - Creating AI/ML backend services 📌 Final Thought: REST gives you the blueprint 🏗️ FastAPI helps you build it faster ⚡ #FastAPI #RESTAPI #Python #WebDevelopment #BackendDevelopment #API #SoftwareEngineering #Coding #Developers #Tech

📣 SynapseKit v1.4.7 + v1.4.8 just dropped. Back to back. Huge thanks to Dhruv Garg and Abhay Krishna who drove most of this sprint. 🙌 Two themes in these releases: getting data in, and making workflows resilient. Getting data in: 5 new loaders The gap between "I have a RAG pipeline" and "I can actually feed it my company's data" is a loader problem. These close it: 📨 SlackLoader — pull channel messages directly into your pipeline 📝 NotionLoader — ingest pages and databases from Notion 📖 WikipediaLoader — single article or multiple, pipe-separated 📄 ArXivLoader — search arXiv, download PDFs, extract text automatically 📧 EmailLoader — any IMAP mailbox, stdlib only, zero extra dependencies SynapseKit now has 24 loaders. Your data is probably already covered. Better retrieval — ColBERT ColBERTRetriever brings late-interaction ColBERT via RAGatouille. Instead of comparing a single query vector against a single document vector, ColBERT scores every query token against every document token (MaxSim). On long documents the recall improvement is significant- single-vector approaches lose detail in the compression. Token-level scoring doesn't. Resilient graph workflows Subgraph error handling now ships with three strategies — retry with backoff, fallback to an alternative graph, skip and continue. Production workflows break. The question is whether they break gracefully. Where SynapseKit stands today: 27 providers · 9 vector backends · 42 tools · 24 loaders · 2 hard dependencies ⚡ pip install synapsekit==1.4.8 📖 https://lnkd.in/dvr6Nyhx 🔗 https://lnkd.in/d2fGSPkX #Python #LLM #RAG #AI #OpenSource #MachineLearning #Agents #SynapseKit

What if your portfolio could talk back? I built an AI agent that represents me on my website — answers questions about my background, captures leads, and logs anything it doesn't know. All powered by Gemini 2.5 Flash + tool calling. No framework. Pure Python. The two tools it uses: 🔧 𝐫𝐞𝐜𝐨𝐫𝐝_𝐮𝐬𝐞𝐫_𝐝𝐞𝐭𝐚𝐢𝐥𝐬 — captures name + email when someone's interested 🔧 𝐫𝐞𝐜𝐨𝐫𝐝_𝐮𝐧𝐤𝐧𝐨𝐰𝐧_𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 — logs gaps so I can improve it over time Every lead, every unknown question → instant push notification to my laptop/phone via ntfy.sh. The whole agentic loop is just ~50 lines of Python: • Build messages array • Call LLM • If tool_calls → execute → feed back • Repeat until done Frameworks abstract this. But writing it raw makes you actually understand what's happening under the hood. GitHub → https://lnkd.in/eB2VwqDs This is step 1 of building my Personal Concierge Agent in public. Step 2: rebuilding this as a full Next.js + FastAPI web app — proper UI, real deployment. Follow along if you're into agentic AI, Python, and building real things — not just demos. #AgenticAI #Python #BuildingInPublic #LLM #Gemini

Data extraction from modern enterprise websites in 2026 is officially an extreme sport. 🧗♂️ If you’ve tried to pull data from large-scale hospitality or e-commerce systems lately, you’ve likely slammed into a brick wall. The "Standard Stack" (Python Requests + BeautifulSoup) just isn't cutting it anymore. You're probably seeing: ❌ 403 Forbidden errors on the first attempt. ❌ TLS Fingerprinting that identifies your script in milliseconds. ❌ IP Bans after fewer than 5 requests. ❌ Anti-bot walls feel impossible to scale. Standard headers aren't enough when the server is looking at your JA3 fingerprint and HTTP/2 settings. The Good News? There is a way through. 🛠️ Over the past few weeks, I’ve been reverse-engineering to understand the process. I’ve built a production-grade, asynchronous system specifically for hotel booking APIs that uses a methodology to extract the data by providing the required assessment. In my next post, I’ll dive into the exact architecture and the specific Python libraries I’m using to build the system. What’s the toughest challenge you’ve faced down recently? Let's swap war stories in the comments. 👇 #WebScraping #DataEngineering #Python #Backend #SoftwareDevelopment #APIs #SunnyJaiswal

Most tools for working with code do one of two things: - help you search   - help you generate  But neither really helps you understand an unfamiliar codebase. So I built something for that. You give it a repository → ask a question → it answers based on the actual code. Not just “here are some files”, but: - how functions connect   - what depends on what   - where things might be breaking  Under the hood it’s simple: AST parsing → function-level chunks → embeddings → call graph → constrained reasoning What made it interesting wasn’t the pieces, but getting them to work together: - balancing retrieval vs context expansion   - keeping answers grounded (not generic)   - moving expensive work to indexing time  It’s still very much v1. Works best on local Python repos, and there are a lot of open problems left. If you’ve worked on: - code search   - developer tooling   - LLM systems  I’d genuinely value your thoughts. Full breakdown here: 🔗 https://lnkd.in/eUGDgzzG #SoftwareEngineering #AI #LLM #MachineLearning #DeveloperTools

6 data sources. 1 python script. $0 per month. I built an insights engine that pulls from PostHog, dub.co, Waitlister, Beehiiv, and - cross-references everything - and tells me exactly which channel drives signups. here's why this matters more than most founders realize: for 3 weeks I was running TikTok ads, posting on X, sending DMs, scheduling content - and had zero idea which one actually converted. all channels looked "active." none had proper attribution. then I set up ?ref= tags on every single link. every channel, every bio, every PostHog tracks the full funnel: visit to CTA click to signup. first insight after turning it on: 170 visits from X had no ref tag because the bio link was untagged. I was blind to my biggest channel. second insight: relatively high bounce rate. 56% leave within 5 seconds. third insight: 15% visitor-to-signup conversion for people who actually stay. meaning the product message works - the first impression doesn't. attribution is so important at this stage, so you can iterate fasrt

Just discovered Claude Tips for the CLI – and it's a total game-changer! 🚀 No more context switching. Pipe code, docs, or commands directly to Claude from your terminal. ✨ Instant code reviews ⚡ Quick documentation 🔄 Real-time responses 💡 Batch analysis Stay in flow state. Ship faster. Who else is using Claude CLI? What's your favorite use case? 👇 #AI #Productivity #CLI #DeveloperTools #Claude 🎯 Super Interesting Claude CLI Tricks: 1. Pipe & Chain Commands cat messy_code.py | claude "refactor this and explain the improvements" Instantly get optimized code without opening an editor. 2. Batch File Processing cat error.log | claude "analyze all errors in these logs" Process massive logs and get actionable insights in seconds. 3. Real-time Code Debugging python script.py 2>&1 | claude "why is this failing?" Stream errors directly to Claude for instant fixes. 4. Documentation Generation claude "create API docs from this code" < api.js Convert raw code into professional documentation automatically. 5. Multi-file Context Analysis claude "find security vulnerabilities" < *.py Analyze entire projects for issues in one command. 6. Git Diff Review git diff | claude "review this PR and flag risks" Automated code review before pushing to production. 7. Convert Between Formats cat data.csv | claude "convert to JSON with descriptions" Transform data formats on the fly. 8. Write Tests Instantly claude "write comprehensive unit tests for this function" < function.js Auto-generate test cases from your code. The Real Magic: Stream mode shows Claude thinking in real-time. You stay in your terminal. No tabs. No delays. Pure velocity. ⚡

🚀 Efficient Duplicate Detection with Hash Sets | LeetCode Today, I tackled the Contains Duplicate problem. While the brute force approach is often the first instinct, optimizing for time complexity is where the real fun begins! 💡 The Problem: Given an integer array nums, return true if any value appears at least twice in the array, and return false if every element is distinct. ⚡ My Approach: I utilized a Hash Set to track elements as I traversed the array. This allows for near-instantaneous lookups compared to nested loops. 👉 The Logic: Initialize an empty set seen. Iterate through the array once. For each number, check: "Have I seen this before?" (Is it in the set?) If Yes → Return True immediately. If No → Add the number to the set and keep moving. 🔥 Complexity Analysis: ⏱ Time Complexity: $O(n)$ – We only pass through the list once. 📦 Space Complexity: $O(n)$ – In the worst case (all unique elements), we store all $n$ elements in the set. 🏆 The Result: ✔️ Accepted: All 77 test cases passed. ✔️ Performance: 9 ms runtime, beating 73.44% of Python3 submissions! 📌 Key Takeaway: Using a Set turns a potential $O(n^2)$ search into a sleek $O(n)$ operation. Choosing the right data structure isn't just about passing tests; it's about writing scalable, "production-ready" code. 💻 Tech Stack: #Python | #DataStructures | #Algorithms #leetcode #dsa #coding #programming #softwareengineering #100DaysOfCode #pythonprogramming #tech #growthmindset