At TechCognify , one of the most rewarding experiences for me has been mentoring students from different universities across Pakistan through our Advanced Internship Program — Cohort 01. I started this initiative because during my own learning journey, I realized how difficult it was to learn real-world skills, build production-level projects, and understand how to actually turn those skills into freelancing or career opportunities. Please Visit to know more about the program and the structure of mentorship we followed, https://lnkd.in/dbi6MApD Today, Alhamdulillah, more than 178 interns are enrolled in our completely FREE, remote-based 15-week SaaS Mentorship and Internship Program. The interns have now successfully completed their first month, where the primary focus was building strong web development foundations. During the first phase, interns learned: • Git & GitHub workflows • HTML, CSS & responsive design • Modern JavaScript (ES6+) • REST APIs fundamentals • React.js fundamentals • Component architecture & state management Alongside the learning sessions, interns also worked on practical projects to apply these concepts in real scenarios instead of only following tutorials. This program is designed around real-world execution and mentorship. Every weekend, interns attend live cohort sessions, receive tasks, submit projects, participate in reviews, and continuously improve through structured feedback. We also built the TechCognify Interns LMS to manage the complete cohort experience with attendance analytics, quizzes, community interaction, project reviews, feedback systems, and progress tracking — making the internship feel closer to a real engineering environment rather than a traditional internship. I’ll make a separate post soon sharing more details about the Advanced Internship LMS Portal as well. One of the most beautiful parts of this journey has been reading the interns’ feedback after their first month. Many students shared how the program helped them gain confidence, understand the industry roadmap, improve their practical skills, and finally start building real projects instead of just watching tutorials.

Building great technology shouldn't mean drowning in hiring headaches. At TechCognify, we partner with founders to design, build, and scale reliable software — so you can focus on growing your vision, not managing a tech team.

One thing I’ve started realizing while working on software and AI projects is this:ideas are rarely the real problem — architecture is. A lot of systems work perfectly with 10 users, but the moment growth begins, weak design decisions start to show. Most software doesn’t fail because of bad ideas.It fails because it’s built on weak foundations. When systems aren’t designed with scalability, architecture, and real engineering experience in mind, they break the moment growth hits. At TechCognify, we focus on building software the right way from day one: → Strong architecture → Scalable systems → Real-world engineering practices Because software should support growth — not collapse under it. If you’re building something that needs to scale, build it properly.

What if a clinic could get an AI second opinion on every chest X-ray — instantly? That's exactly what I'm building at TechCognify. MedScan AI is a full-stack AI SaaS that: → Accepts a chest X-ray from any browser → Classifies 14 conditions using EfficientNetB3 → Shows the doctor WHERE the AI is looking (Grad-CAM heatmap) → Generates a structured PDF diagnostic report → Delivers results in under 10 seconds No installation. No expensive hardware. Just a browser. The tech behind it: 🧠 TensorFlow + EfficientNetB3 (112,120 training images) ⚡ FastAPI + async Celery inference pipeline ▲ Next.js 14 SaaS frontend 🗄️ Supabase + Upstash Redis 🚀 Vercel + Render.com deployment This is the exact type of AI SaaS product I build for healthcare, diagnostics, and medical tech clients. If your clinic, hospital, or health-tech startup needs a custom AI solution — let's talk. 📎 Architecture diagrams and full system spec attached in the SRC docs below.

🚨 A Hidden LLM Issue That Can Break Your AI SaaS While auditing an AI-powered eCommerce system (voice-driven with LLM actions), I found a serious problem: A single request hit 37,402 tokens — far beyond the allowed limit. ⚠️ The Problem This wasn’t user input. It was poor system design: Full MongoDB documents returned in tools (get_products, get_orders, etc.) Entire conversation history sent on every request Slightly heavy system prompts Result: Token usage kept compounding → costs skyrocketed → system became unstable 🛠️ The Fix Limited tool responses to essential fields + max 35 records Enabled smarter actions (e.g., create product + quantity in one call) Sent only the last 8 messages to the LLM for the user chatbot, and only 5 messages for Agentic Tasks Reduced prompt size Added clean error handling for TPM / 413 issues 📉 Outcome Controlled token usage Stable performance Predictable billing Production-ready system 💡 Takeaway LLMs don’t become expensive on their own — architecture makes them expensive. If you're building AI systems, control: context, data, and token flow. Open to connecting with teams working on AI products facing similar challenges.