DEV Community: SHAIK TAUFEEQ AHMAD

I Built an AI-Native Productivity System Instead of Another AI Wrapper

SHAIK TAUFEEQ AHMAD — Wed, 27 May 2026 04:38:30 +0000

Most productivity apps today feel passive.

They organize tasks.
Track deadlines.
Store notes.

But they rarely help people actually execute.

That idea became the starting point for Momentum AI — an AI-native execution copilot designed to reduce execution friction through contextual workflow intelligence.

Live Demo: https://momentum-ai-eight.vercel.app

The Problem

Traditional productivity systems expect users to manually:

prioritize work,
track follow-ups,
break down goals,
manage context switching,
and maintain momentum.

The more I thought about it, the more it felt backwards.

If AI can understand workflows, context, urgency, and intent — why should productivity systems remain static dashboards?

I wanted to explore a different idea:

What if productivity software behaved more like an AI Chief of Staff than a task manager?

What is Momentum AI?

Momentum AI is an AI-native productivity system focused on:

contextual prioritization,
execution workflows,
adaptive timelines,
recruiter CRM workflows,
and intelligent task orchestration.

Instead of acting like a traditional productivity dashboard, the system continuously surfaces:

execution recommendations,
prioritization reasoning,
recruiter follow-up suggestions,
blockers,
and workflow insights.
Core Features
AI-Native Prioritization

Tasks dynamically reprioritize based on:

urgency,
workload,
deadlines,
and contextual workflow signals.

The system also exposes reasoning behind prioritization decisions instead of behaving like a black box.

Adaptive Execution Timelines

Users can generate roadmap-style execution plans for goals like:

landing internships,
launching portfolios,
preparing for interviews,
or shipping products.

These timelines sync directly into the execution backlog.

Recruiter Workflow CRM

I integrated a lightweight recruiter CRM system that helps track:

applications,
outreach,
follow-ups,
blockers,
and recruiting pipeline movement.

The goal was operational clarity instead of spreadsheet chaos.

Keyboard-First UX

The interaction design was heavily inspired by products like:

Linear,
Superhuman,
Notion AI,
and Arc Browser.

I wanted the product to feel:

fast,
calm,
minimal,
and intentional.

Features include:

command palette navigation,
keyboard shortcuts,
animated transitions,
onboarding flows,
contextual overlays,
and responsive workspace architecture.
Product Thinking > Feature Count

One thing I intentionally avoided was feature bloat.

I didn’t want:

50 tabs,
enterprise complexity,
overloaded dashboards,
or AI features pasted randomly onto workflows.

Instead, I focused on:

interaction quality,
workflow clarity,
visual hierarchy,
and believable AI-native UX patterns.

The hardest part wasn’t building components.

It was designing systems that felt:

useful,
trustworthy,
and cognitively lightweight.
Tech Stack

Built using:

Next.js
Tailwind CSS
Framer Motion
TypeScript
Vercel

The frontend architecture focused heavily on:

responsiveness,
motion polish,
layout systems,
and interaction fluidity.
What I Learned

The biggest insight from building Momentum AI:

AI products become significantly more valuable when they reduce execution friction instead of simply generating content.

Most AI tools today optimize for output.

But workflows break because of:

prioritization,
context switching,
follow-through,
and operational clarity.

That’s where I think AI-native workflow systems become interesting.

Final Thoughts

Momentum AI started as an exploration into how AI could improve execution workflows instead of simply organizing information.

Building it pushed me to think more deeply about:

AI-native interaction design,
workflow orchestration,
prioritization systems,
and calm product experiences.

Still iterating — but this project completely changed how I think about productivity software.

Would love feedback from builders, PMs, and designers exploring similar ideas.

No Dataset? No Problem. How I Curated a Custom AI Dataset From Instagram & Pinterest to Build a Pose Suggester

SHAIK TAUFEEQ AHMAD — Tue, 19 May 2026 14:19:21 +0000

When you start a new Machine Learning project, you pray there’s a clean, ready-to-use dataset on Kaggle or Hugging Face.

But when I decided to build an AI-powered Pose Suggester—a system that analyzes a user's background (like a cafe or a park) and overlays a suggested 2D stick-figure pose skeleton on their camera screen—I hit a massive wall: No such dataset existed.

Nobody had built an open-source dataset mapping lifestyle locations to "good" aesthetic poses.

If I wanted this project to exist, I had to stop looking for a dataset and start building one. Here is exactly how I built, annotated, and augmented a custom computer vision dataset from scratch.

Step 1: Defining the Taxonomy
Before scrolling mindlessly for images, I needed a strict data structure. If your categories are messy, your neural network will learn absolute nonsense. I broke my target universe down into 3 core environments, each with 2 framing subcategories:

Cafe Indoor (Full Body / Waist Up)

Nature/Parks (Full Body / Waist Up)

Urban Street (Full Body / Waist Up)

My target was to source 50 high-quality, distinct anchor images per subcategory, giving me a baseline of 300 reference images.

Step 2: Sourcing via Pinterest & Instagram
Where do you find the best examples of people posing naturally in everyday environments? Instagram and Pinterest.

I spent hours reverse-engineering what makes a photo "good" for these platforms, looking for distinct body compositions. However, building a dataset this way comes with strict engineering rules:

Avoiding Bias: I couldn't just download photos of the same 5 influencers. The model needed to learn diverse body types, heights, and clothing to ensure the pose estimation wouldn't fail in the real world.

Background Variety: "Nature" couldn't just mean a green lawn; it needed to include forests, beaches, and hiking trails so the Scene Classifier wouldn't overfit to a single shade of green.

Clarity: Every image needed a clear, unobstructed view of the primary subject so the pose landmarker wouldn't get confused by crowded backgrounds.

Step 3: Automated Labeling (The MediaPipe Hack)
Manually labeling coordinates for 17 to 33 skeletal joints across hundreds of images sounds like a nightmare. To save my sanity, I engineered an automated pipeline.

Instead of hand-labeling, I ran MediaPipe’s Pose Landmarker over my curated directory.

Python

The core logic behind the automation pipeline

import mediapipe as mp

MediaPipe processes the curated image and auto-extracts joint coordinates

detection_result = landmarker.detect(mp_image)
keypoints = serialize_to_json(detection_result.pose_landmarks)
The script automatically detected the human subjects, extracted their coordinates, normalized the vectors relative to the torso scale, and saved them into a neat pose_library/ folder as JSON annotations. Boom. Zero manual coordinate labeling required.

Step 4: Multiplying Data with Smart Augmentation
300 images are great for a prototype, but a deep CNN like MobileNetV2 will overfit and fail on a dataset that small. I needed to scale my 300 images up to roughly 1,800 training samples.

Using the albumentations library, I applied strategic augmentations to simulate real-world conditions without breaking the underlying pose logic:

Brightness & Contrast Jitter: To simulate poor lighting inside dim cafes or harsh sunlight outdoors.

Blur & Noise: To prepare the model for shaky, low-quality phone cameras.

Horizontal Flips: To double the dataset instantly.

The Tricky Part: When you flip an image horizontally, your labels break! A person's left hand becomes their right hand. My augmentation script had to explicitly intercept the MediaPipe keypoints and swap the left/right joint indices so the data remained perfectly accurate.

Key Takeaways from Building Data
Building your own dataset teaches you things a clean Kaggle download never can:

Data Quality > Model Complexity: A lightweight model like MobileNetV2 trained on tightly curated, high-quality data will effortlessly outperform a massive model trained on garbage data.

Think Like a Product Manager: Sourcing data forces you to think about how your users will actually use the app. Defining "Egocentric" (first-person/glasses) vs "Exocentric" (third-person/laptop) views early completely changed how I filtered my images.

Now that the dataset is verified and locked, Phase 2 is officially underway: fine-tuning the scene classifier and training the K-Nearest Neighbors (KNN) engine to match a user's background with the perfect pose.

Have you ever had to build a dataset from scratch for a passion project? What did your pipeline look like? Let me know in the comments!

Demo Day: Showcasing Shruthi Bandhu & Power Grid Digital Twins at the AI for Next-Gen Education Conclave

SHAIK TAUFEEQ AHMAD — Sun, 17 May 2026 14:50:55 +0000

Building technology in a lab is one thing, but standing at a booth explaining your architecture to government officials, top scientists, and educators is a completely different ballgame.

Yesterday (May 16, 2026), my team and I had the incredible opportunity to set up exhibition stalls and present our research at the "AI for Next-Gen Education Conclave". The event was hosted by C R Rao AIMSCS in collaboration with JNTU-H, aiming to demystify artificial intelligence and build real-world awareness for educators and professionals across the state.

We owe a massive thank you to our Director, Dr. S. Venkataraman, for giving us the platform, support, and opportunity to represent student innovation at this scale.

Being part of the official "Innovation Showcase," we got to put up stalls for two very distinct, deep-tech projects we’ve been pouring our hours into:

Shruthi Bandhu: The AI-Powered Smart Glass Fresh off our recent win at the Vishwakarma Awards, we brought Shruthi Bandhu to the conclave—but with a focus on its hardware-software integration.

We demonstrated how the platform transitions from an exocentric view (standard laptop camera for meetings) to an egocentric view via smart glasses designed for daily, first-person communication. Demonstrating an assistive sign-language tool to school teachers and faculty highlighted exactly why digital inclusion matters in next-gen education. Seeing educators realize the potential of AI to bridge classroom communication gaps was incredibly rewarding.

Digital Twin of the Power Grid Moving from computer vision to real-time industrial simulation, we also showcased our Digital Twin project for a power grid infrastructure.

This project tackles a completely different engineering challenge: processing high-frequency data streams to monitor, simulate, and predict anomalies in electrical grid dynamics. We got to demonstrate how a virtual twin can simulate load failures, map network behaviors, and trigger instant safety breaches before they physically happen. Discussing grid telemetry and real-time dashboarding with seasoned tech architects and scientists gave us invaluable feedback on how to scale our backend architecture.

Stepping Out of the Sandbox
The highlights of the day weren't just the demos, but the conversations. The conclave kicked off with an inaugural address featuring prominent leaders including Dr. Yogita Rana, IAS, Smt. A. Sridevasena, IAS, and JNTU Vice-Chancellor Dr. T. Kishen Kumar Reddy.

During the Technology Exhibition walkthrough, we had the privilege of walking attendees through our codebases, data pipelines, and hardware setups. Later in the afternoon, we officially presented our work during the Student Research Innovation Showcase. Listening to talks from pioneers like Dr. Seshagiri Rao (Former Distinguished Scientist, ISRO) on the distinction between foundational ML and applied AI reinforced exactly how we need to approach our future engineering decisions.

Gratitude to the Team
Events like these are a marathon of setting up hardware, debugging live edge-cases on the spot, and pitching consistently for hours. Huge shoutout to my amazing teammates for holding down the stalls and executing a flawless demo day.

We walked away from the conclave not just with certificates, but with a refined perspective on how our models can step out of the terminal and into public infrastructure and classroom environments.

The journey from a local script to a public showcase continues. Next stop: turning these prototypes into production-ready deployments!

Building Shruthi Bandhu: How We Engineered an AI Gesture Tool for the Deaf-Mute Community (And Won the Vishwakarma Awards)

SHAIK TAUFEEQ AHMAD — Fri, 15 May 2026 14:50:45 +0000

Some wins take time. Over the past year, I’ve walked out of innovation halls with more lessons than trophies. Every post I made was about participation, never victory. Each time, I clapped for others while swallowing the frustration of my own near-misses.

But building Shruthi Bandhu was different.

At IIT Indore, our team won 1st Prize in the HealTech Category at the Vishwakarma Awards 2025 for our AI-powered gesture communication tool. Hosted by the Maker Bhavan Foundation, the competition brought together over 3,600 STEM students from India and SAARC nations. After nine months of prototyping and mentoring, we made it from the top 1,000+ teams down to the final 12—and ultimately took home the win.

But this post isn’t just about the trophy. It’s about the six months of prototyping, the roadblocks we hit, and the engineering decisions we made to build a platform that bridges the communication gap for the deaf and mute community.

The Bottleneck: The Data Drought
When we set out to build an AI that could translate Indian Sign Language (ISL), we hit a massive wall almost immediately: the unavailability of a robust ISL dataset.

You can't train a reliable model without quality data. Since off-the-shelf datasets for ISL were either non-existent or heavily fragmented, we realized we couldn't rely on open-source repositories. We had to build it ourselves.

We didn't just scrape the web; my teammates and I actually sat down and learned Indian Sign Language. We then recorded and manually curated a custom dataset of over 1,000+ videos. It was a tedious, brute-force approach, but it gave us the clean, high-quality foundational data our models desperately needed.

The Architecture: Two Perspectives for Real-World Use
Sign language translation isn't a one-size-fits-all problem. How a user interacts with the world daily is very different from how they interact in a digital workspace. To solve this, we engineered two distinct approaches:

Egocentric View (Daily Communication): We designed this for a spectacles/smart-glasses point-of-view. This allows for real-time translation as the user navigates their physical environment, capturing gestures from a first-person perspective.
Exocentric View (Digital Workspaces): We optimized this for standard laptop webcams. This approach is specifically tailored for virtual meetings and conferences, capturing the user from a standard front-facing angle to ensure accessibility in professional environments.

**
**
As engineers, it is dangerously easy to build solutions in a vacuum, assuming we know what the user wants. We knew early on that if Shruthi Bandhu was going to be truly accessible, we couldn't just guess at the community's needs from behind our laptops.

We stepped out of the lab and conducted extensive field visits at local deaf and mute schools and specialized clinics. We sat down with educators, clinicians, and the students themselves to observe their daily friction points and understand the nuances of how they communicate.

This wasn't just research; it was strict product validation. We aligned our engineering goals with their real-world needs. By the end of our prototyping phase, this user-centric approach allowed us to secure formal Letters of Intent (LOIs) from several of these institutions, proving that we weren't just building a cool project—we were building a product they genuinely wanted to deploy.

The Village Behind the Build
Seeing hardware-first and assistive tech innovation valued at this scale was validating. Receiving this award in the presence of industry leaders like Hemant Kanakia, Prof. Suhas Joshi (Director, IIT Indore), and Gautam Khanna reminded us that accessibility deserves serious engineering attention.

You don't build something like this alone. A massive thank you to:

My incredible co-founders and teammates: Shivaraj Gollapally, Varun Chiguru, and Subbarayudu Bolisetty. We solved technical bugs, logistical nightmares, and motivational slumps together.

This win isn’t a finish line—it’s a starting point. Competitions end on a stage, but impact begins in the real world.

If you are working in assistive tech, computer vision, or multimodal AI, I’d love to connect. Have you ever had to brute-force a dataset from scratch? Let me know in the comments!