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OGDC announced Management Trainee Program Last Date: April 27, 2026 Link: https://lnkd.in/daDQ6R4z

Google Stitch recently introduced 𝗗𝗘𝗦𝗜𝗚𝗡.𝗺𝗱. A plain markdown file your AI agent reads to generate consistent UI: No Figma exports. No JSON schemas. Nothing to configure. Just add it in your project root, and any AI coding agent instantly understands your colors, typography, spacing, and component style. Someone put together a collection of 40+ ready-to-use 𝗗𝗘𝗦𝗜𝗚𝗡.𝗺𝗱. files extracted from real websites: → Stripe → Apple → Spotify → Airbnb → Lovable → Notion → Figma → Claude → ...and more Each one shows how top products structure design rules in plain text so AI coding agents can follow them. Drop a 𝗗𝗘𝗦𝗜𝗚𝗡.𝗺𝗱. into your project and AI agent builds the rest. Link to the repo: https://lnkd.in/d7SdxSv8 -- P.S. We’re building the best AI English tutor in the world. Try it to improve your speaking - it’s 15× cheaper than a human one → GetFluently.app -- ♻️ Find it useful? Leave a reaction and share with your network!

We’re Hiring at syslab.ai Join us on our mission to build impactful AI-driven solutions transforming education, assessments, and enterprise systems. We are looking for passionate individuals for full-time, on-site roles: 🔹 Product Manager: Lead cross-functional teams, align product strategy with business goals, and ensure exceptional customer experience for our growing client base. 🔹 MERN Stack Developer: Design, develop, and maintain scalable full-stack applications using the MERN stack. 🔹 Business Associate: Manage social media presence, support business development initiatives, and conduct market research and analysis If you’re driven, innovative, and ready to grow with a fast-evolving tech company, we’d love to hear from you. Send your resume to: hr@syslab.ai All positions are full-time on-site. Be a part of something meaningful. Be a part of syslab.ai. #Hiring #TechJobs #ProductManagement #MERN #Careers #AI #Syslab

𝐕𝐢𝐬𝐮𝐚𝐥 𝐛𝐥𝐨𝐠 on Vision Transformers is live. https://lnkd.in/g38zZtgC Learn how ViT works from the ground up, and fine-tune one on a real classification dataset. 𝐑𝐞𝐩𝐨𝐬𝐭 𝐭𝐡𝐢𝐬 𝐩𝐨𝐬𝐭 𝐬𝐨 𝐭𝐡𝐚𝐭 𝐦𝐨𝐫𝐞 𝐩𝐞𝐨𝐩𝐥𝐞 𝐜𝐚𝐧 𝐥𝐞𝐚𝐫𝐧 𝐚𝐛𝐨𝐮𝐭 𝐕𝐢𝐓 CNNs process images through small sliding filters. Each filter only sees a tiny local region, and the model has to stack many layers before distant parts of an image can even talk to each other. Vision Transformers threw that whole approach out. ViT chops an image into patches, treats each patch like a token, and runs self-attention across the full sequence. Every patch can attend to every other patch from the very first layer. No stacking required. That global view from layer one is what made ViT surpass CNNs on large-scale benchmarks. 𝐖𝐡𝐚𝐭 𝐭𝐡𝐞 𝐛𝐥𝐨𝐠 𝐜𝐨𝐯𝐞𝐫𝐬: - Introduction to Vision Transformers and comparison with CNNs - Adapting transformers to images: patch embeddings and flattening - Positional encodings in Vision Transformers - Encoder-only structure for classification - Benefits and drawbacks of ViT - Real-world applications of Vision Transformers - Hands-on: fine-tuning ViT for image classification The Image below shows Self-attention connects every pixel to every other pixel at once. Convolution only sees a small local window. That's why ViT captures things CNNs miss, like the optical illusion painting where distant patches form a hidden face. The architecture is simple. Split image into patches, flatten them into embeddings (like words in a sentence), run them through a Transformer encoder, and the class token collects info from all patches for the final prediction. Patch in, class out. Inside attention: each patch (query) compares itself to all other patches (keys), softmax gives attention weights, and the weighted sum of values produces a new representation aware of the full image, visualizes what the CLS token actually attends to through attention heatmaps. The second half of the blog is hands-on code. I fine-tuned ViT-Base from google (86M params) on the Oxford-IIIT Pet dataset, 37 breeds, ~7,400 images. 𝐁𝐥𝐨𝐠 𝐋𝐢𝐧𝐤 https://lnkd.in/g38zZtgC 𝐒𝐨𝐦𝐞 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 Dr Sreedath Panat Videos on ViT ViT paper dissection https://lnkd.in/gekCFhTr Build ViT from Scratch https://lnkd.in/gGdYFE67 Original Paper https://lnkd.in/gZCDjpsp Next up: demystifying Low-Rank Adaptation (LoRA) in PEFT! Follow me Mayank Pratap Singh along for more deep learning insights, cool fine-tuning projects, and updates from the upcoming blog posts.

Eid Mubarak from all of us at syslab.ai May this blessed occasion bring peace, happiness, and prosperity to you and your loved ones. Wishing you joy, success, and countless blessings this Eid. iParhai ProctorParhai Eyecon AI

If you're an AI enthusiast or just starting out; here are some absolutely free courses you can enroll in right away! #DigitalNationPakistan #EthicalAi #AiForGood

Innovation grows stronger when diversity leads the way. This International Women’s Day, we celebrate the women who are shaping the future of technology, education, and leadership. Their ideas, resilience, and vision are helping build a smarter and more inclusive world. At syslab.ai, we remain committed to supporting environments where talent thrives—regardless of gender. #InternationalWomensDay #WomenInAI #WomenInTech #Innovation iParhaiProctorParhaiEyecon AI

AI is already transforming education, recruitment, security, and enterprise operations. From adaptive learning and AI-driven testing to real-time anomaly detection and intelligent analytics, innovation is no longer optional, it’s strategic. Syslab.ai delivers scalable AI solutions built for forward-thinking organizations. #AIInnovation #TechDriven #SmartEnterprises #EdTechSolutions #ComputerVisionAI #FutureReady ProctorParhai | iParhai | Eyecon AI

Syslab.ai wishes you and your loved ones a blessed Ramadan. 🌙 ✨ May this holy month bring peace, prosperity, and success to all. Ramadan Mubarak. 🤍 ProctorParhai | iParhai | Eyecon AI