Rds challenge lab devto post 🗄️🐬 aws #rds #database #tutorial Build Your DB Server and Interact With Your DB INTRO Did a hands-on AWS challenge lab on Amazon RDS. Task: spin up a managed database, connect from a Linux server, and run real SQL — create tables, insert data, join across tables. No hand-holding here, just requirements to figure out myself. Here's the walkthrough. SCENARIO Service: Amazon RDS Role: Cloud/DB Admin Goal: Launch RDS under set constraints, connect via EC2, run SQL (create, insert, select, join) ARCHITECTURE LinuxServer (EC2) sits in the Lab VPC — this is the client RDS instance (Aurora or MySQL) in the same VPC Security group lets LinuxServer talk to RDS Flow: LinuxServer -> MySQL client (port 3306) -> RDS -> tables STEP 1: LAUNCH THE RDS INSTANCE Constraints for this lab: Engine: Aurora (Provisioned) or MySQL — no serverless Template: Dev/Test or Free tier No standby instance (single-AZ only) Instance size: db.t3.micro to db.t3.medium Storage: gp2, up to 100 GB — no Provisioned IOPS Network: Lab VPC Security group must allow LinuxServer access MySQL only: turn off Enhanced Monitoring On-Demand only These limits keep costs in check — Provisioned IOPS and Multi-AZ are the fastest ways to blow up an RDS bill. Noted the master username, password, and endpoint — needed next. STEP 2: CONNECT TO THE LINUX SERVER Downloaded the PEM key, grabbed the LinuxServer address, connected over SSH: chmod 400 labsuser.pem ssh -i labsuser.pem ec2-user@<LinuxServer-address> This box is just the SQL client — it needs network access to RDS, nothing more. STEP 3: INSTALL MYSQL CLIENT AND CONNECT On the LinuxServer: sudo yum install mysql -y Connect using the master credentials from Step 1: mysql -h <rds-endpoint> -u <master-username> -p If it hangs, it's almost always the security group — check port 3306 inbound. STEP 4: CREATE THE RESTART TABLE CREATE DATABASE lab_db ; USE lab_db ; CREATE TABLE RESTART ( StudentID INT , StudentName VARCHAR ( 100 ), RestartCity VA

Over the past few months, I've been exploring how AI can move beyond chatbots and become an active part of business workflows. That journey led me to build SmartStock AI , an inventory management platform that combines modern web technologies with AI agents, Retrieval-Augmented Generation (RAG), demand forecasting, and automation. Instead of only tracking inventory, SmartStock AI helps businesses make proactive decisions. What SmartStock AI Can Do 🤖 Forecast future product demand 📦 Recommend purchasing decisions through AI agents 📚 Answer inventory-related questions using Hybrid RAG with source citations 📄 Process invoices using multimodal AI ⚡ Generate real-time inventory alerts 🔐 Secure the platform with JWT authentication and role-based access control Technology Stack Frontend React 19 Backend Django 5 Django REST Framework Database PostgreSQL pgvector AI LangChain Hybrid RAG AI Agents Prophet Forecasting Infrastructure Celery Redis Docker GitHub Actions What I Learned Building SmartStock AI taught me much more than integrating an LLM into an application. Some of the biggest lessons were: Designing AI features that solve real business problems. Building reliable agent workflows instead of simple chatbot interactions. Combining vector search with structured database queries. Managing asynchronous AI tasks using Celery and Redis. Creating production-ready APIs with Django REST Framework. Deploying and maintaining a modern full-stack application. Demo 🎥 YouTube Demo https://www.youtube.com/watch?v=DQJqs6bgE98 🌐 Live Demo https://smart-stock-dev.vercel.app/ Demo Account Email: viewer@smartstock.ai Password: Viewer123! 💻 GitHub Repository https://github.com/Eng-Ayman-Mohamed/SmartStock-AI Final Thoughts This project was developed as my graduation project during the Information Technology Institute (ITI) Full Stack Web & Generative AI Program. It was an incredible opportunity to explore AI engineering, backend architecture, and modern software development while buildin

Reading a public website and changing something inside a signed-in account are not the same kind of job. I wanted an AI agent to collect the first 10 Hacker News stories, explain how it gathered them, and place that work inside my DEV editor. Save the article as a draft. Do not publish it. The hard part was not finding the stories. It was giving the agent enough access to finish inside my signed-in account without giving it permission to publish. That instruction contained two jobs with different access needs: Read public data from Hacker News. Change private account state by creating a draft in my signed-in DEV account. Using a full browser for the first job would add machinery and access that the result did not need. The public fetch path had neither the authenticated context nor the authority required for the second job. The useful question was not “Which browser tool should do everything?” It was “Where does this task actually cross into a browser?” Disclosure: I work on Unchained, SearchAgentSky, and Unbrowser. This article was prepared with AI assistance and reviewed before publication. The public step needed only fetch I initially opened Hacker News in a lightweight page session and queried its story links. It worked—but that did not mean it was necessary. Hacker News has a public API . The same result could be produced with ordinary HTTP: const base = " https://hacker-news.firebaseio.com/v0 " ; const ids = await fetch ( ` ${ base } /topstories.json` ). then ( r => r . json ()); const stories = await Promise . all ( ids . slice ( 0 , 10 ). map ( async ( id , index ) => { const story = await fetch ( ` ${ base } /item/ ${ id } .json` ). then ( r => r . json ()); return { rank : index + 1 , title : story . title , url : story . url ?? `https://news.ycombinator.com/item?id= ${ id } ` }; })); console . log ( JSON . stringify ( stories , null , 2 )); At 2026-07-19T03:19:04Z , the page query returned 30 Hacker News story anchors. At 2026-07-19T03:35:29.670Z , fetch

Try asking this in your next team meeting: "Who on our team is the best at using AI?" There will probably be a moment of silence. Someone thinks of the person who uses the tools the most. Someone thinks of the person who's good with prompts. Someone thinks of whoever recently produced something impressive-looking with AI. But none of these answers feels convincing. There's a gut sense that something is off — and yet nobody knows what the right criterion would be. Nearly every company is standing in front of this wall right now. AI competence is rising to the top of hiring and performance criteria, and there's no yardstick to measure it with. This essay proposes one. The short answer is a set of five axes I call ABCD2. But before that, we need to fix the question itself. The very idea of "good at using AI" is wrong The title of this essay says "good at AI" — but that phrasing is itself the trap. The word "using" carries a hidden frame: AI is a tool, and the person is a user. It puts AI on the same shelf as Excel and Photoshop — something you get skilled at operating. On top of that frame, evaluation naturally becomes a measure of tool proficiency. How many features do you know? How fluently do you operate it? But AI is not a hammer. AI is an engine that judges and executes. It's less like a tool and more like a junior colleague you can delegate work to. And once you think of a junior colleague, the frame flips. Behind every junior who performs well, there is invariably a senior who makes them perform well — someone who explains the context of the work, lays out the criteria for judgment, makes it safe to ask about ambiguities, and verifies the output. That senior isn't "using" the junior. They are making the junior do the work well . AI is exactly the same. The person who truly gets the most out of AI is not the person who is good at using AI, but the person who helps AI do the work well. And what do they help with? Two things, broadly. One is knowledge building — pr

Bluetooth may seem the obvious next step in the remote's evolution, but there are reasons that infrared hasn't died out.

**🙄 فى كتاب chapter الصدمة دي هي بالظبط اللي حسيت بيها وأنا بقرأ أول “operating system: three easy Pieces “ ‘OSTEP’. 🖤اهلا بيكم ! يارب تكونوا بخير 💯 حابه اشارك معاكم رحلتى مع قراءة الكتاب ونستفيد منه سوا قبل ما نعرف هو !!!! ولا لا OS is virtual machine 🤔 !!!!دا يعنى ايه ووظيفته ايه OS تعالوا الاول نعرف ال موجودة فى الكمبيوتر ولا لا ؟؟؟؟ RAM وال CPUزى ال HARDWARE وهل هو قطعة ::هنبدأ من الاخر موجود على الكمبيوتر SW هو عبارة عن HW يسيدى مش قطعة OSال .على الجهازINSTALL ولازم يبقى ووظيفته ايه؟؟؟ OS طب يعنى ايه ‘OS’ اختصار “OPERATING SYSTEM”. .“يعنى نظام تشغيل “ .👁‍🗨👁‍🗨 تعالوا نعرف معنى ” نظام تشغيل” ونفسرها نظام تشغيل يعنى مسئول عن 1_ “HWالنظام “من غيره مش هيبقى فيه نظام خصوصا لقطع ال . يعنى بناخد منه الاذن للعمليات HW ببعضه وكمان بيدير ال HWيعنى هو بيربط ال 👏 . (Order)من الاخر بيخلق “النظام” 2_.”التشغيل “هو المسئول عن تشغيل البرامج Learn about Medium’s values هو اللي بيتحكم في دورة حياة البرنامج بالكامل؛هو اللي بيبدأ تشغيله في الذاكرة،OSيعني الـ وبيحدد هو محتاج إيه من إمكانيات الجهاز عشان يشتغل بكفاءة وبيفضل مراقبه ويحميه طول ما هوشغال ، ولحد ما تقفليه بنفسك 🌹 .ويقوم هو بتنظيف الذاكرة وتوفير المساحة لغيره 🧐::وظيفته بقا بيخلي البرنامج يشتغل بسهولة:1️⃣ لأنه بيخلق للبرنامج بيئة وهمية مريحة، وبيخفي عنه كل التعقيدات والأسلاك بتاعة الهاردوير عشان يشتغل بسلاسة 2️⃣: بيسمح للبرامج إنها تشارك الذاكرة بين كل البرامج بالعدل RAM بيشتغل كعسكري مرور بيوزع مساحة الـ . وبيحمي كل برنامج في مساحته الخاصة 3️⃣: بيمكن البرامج إنها تتعامل مع الأجهزة بيعمل كـ “وسيط” أو مترجم بيوصل كود البرنامج بالقطع الحقيقية (زي الشاشة أو الهارد) .عشان ينفذ أوامره فوراً بيعمل التلات حاجات دول إزاي في نفس الوقت وبمنتهى السلاسة؟ ؟؟؟OS طب الـ بيعملهم يسيدى عن طريق أكبر خدعة سحرية في عالم الكمبيوتروهي الـ Virtualization👌 كنت حابة جداً أكلمكم المرة دي عن خدعة الـ “Virtualization” 💎 😌👁👁اللي اتكلمنا عليها في العنوان، بس لقيت البوست هيبقى طويل أوي عليكم ✔🔥!!وعشان متعبكمش معايا.. هنخليها للبوست الجاي إن شاء الله 💯✅!!بيعمل الخدعة دي إزاي OSاستنوا البوست الجاى عشان نعرف الـ 💋.دمتم بخير **

If you're a .NET developer looking to break into AI engineering, agents are the single best place to start. They're the point where "calling an LLM API" turns into "building a system that reasons, uses tools, and takes action" — and Azure AI Foundry Agent Service, paired with .NET, makes this surprisingly approachable. In this post, I'll walk through exactly how to stand up your first agent end-to-end — from the Azure side setup to the actual C# code — and share the full walkthrough in video form as well. 🎥 Watch the full hands-on video here: https://youtu.be/mrsEsculrNg Why Agents, and Why Now Most of us started our AI journey with a simple chat completion call — send a prompt, get text back. That's fine for Q&A, but it falls apart the moment you need the model to do something: run code, search documents, call an API, or hold a multi-turn conversation with real state. That's exactly the gap Foundry Agent Service closes. An agent in Foundry is: Durable — it lives as a resource in your Foundry project, not in your app's memory Tool-aware — it can invoke built-in tools (like a code interpreter) or your own custom functions Stateful — conversations persist and carry context across turns And the best part for us .NET folks: the entire thing is callable from clean, typed C# — no wrestling with raw REST payloads. What You'll Need Before writing any code, set up the Azure side: An Azure AI Foundry project with a chat model deployed (e.g., gpt-4o-mini ) The Foundry User RBAC role assigned to your account at the resource/resource-group scope — this is the single most common blocker people hit (a silent 403 when calling the SDK), so don't skip it az login run locally, so your code can authenticate without hardcoding any keys If you've worked with Cognitive Services roles before, note that agent management needs this separate Foundry-specific role — that trips up a lot of people coming from plain Azure OpenAI usage. Setting Up the .NET Project dotnet new console -n FoundryAgen

In 2017, I bought an old Magnus chord organ off Craigslist for $10. It's one of my favorite music gear purchases. Electric chord organs let you play full chords with just a press of a button, making them perfect for aspiring singer-songwriters or fooling around at home. But chord organs largely fell out of favor […]

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Clock drift, deadline mismatches, retries, and TTL behavior keep causing real incidents. Network companion: https://blog.gaborkoos.com/posts/2026-07-01-Beyond-Happy-Path-Engineering-the-Network/ submitted by /u/OtherwisePush6424 [link] [留言]

"Everybody knows the Greeks are inside."

I might be the only SRE on Earth with his own bowling center. It's a more in-depth gig than you'd think. My family and I bought an abandoned 8-lane bowling center in the rural mid-west. In our small town there weren't many recreation options for families. You've heard of a food desert? This is an R&R desert. It had been abandoned for a good reason. The roof leaks, the electrical system was constantly surging, and my 70-year-old bowling equipment (still) doesn't work perfectly. The system that ke

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Prevent product drift in AI-written code Discussion | Link