AI Development Projects in MaharashtraAI Development Projects in MaharashtraMarket Pulse 📈
A modern sales and marketing dashboard built to give teams a clear view of performance without the clutter.
The platform brings together campaign analytics, sales reporting, order management, customer growth, engagement metrics, conversion tracking, spending insights, and performance dashboards into a single workspace. Every screen is designed to make it easier to monitor KPIs, identify trends, and make data-driven decisions from one place.
Loved exploring a bold visual direction with this one while keeping the experience clean, intuitive, and easy to navigate.
#DashboardDesign #SaaS #UIDesign I wanted to share an internal dashboard I just wrapped up using the new Retool App Builder.
The project targets a unique niche: Art Cafes & DIY Craft Studios.
These businesses operate on a "Time-as-a-Service" model where customers book a 2-hour window, get basic supplies and a complimentary drink, but get charged for regular cafe snacks, premium canvases, or extra time.
Standard POS platforms don’t natively track live table occupancy time or handle mixed complimentary/paid item orders smoothly. I built this custom portal to handle the heavy lifting.
Here is a quick video walkthrough of the operational flows, live floor tracking, and dashboard settings:
https://www.loom.com/share/7f8c823309ec4a24a392c5d427f21035
Key features built completely in Retool:
Live Studio Floor/Dashboard:
A clean grid layout displaying currently occupied tables, dynamically tracking group size, and color-coded status pills (Active, Completed, Unpaid).
Contextual Orders (Modals):
Clicking any active card triggers a popup modal form allowing front-desk staff to instantly append food items or premium inventory directly to that specific customer's active session tab.
Back-Office Configuration & Reports:
Dedicated tabs for the store owner to track key metrics (Active Sessions, Total Profits, Unpaid Orders), manage global inventory logs, and edit customer profile records.
I’d love some feedback from the community:
Drop your thoughts below or let me know if you want a breakdown of any specific component setups! What your attention heatmap isn't telling you
Everyone's staring at attention heatmaps and calling it "interpretability." Almost nobody's asking whether a single attention map actually tells you what the model used to make its decision.
It doesn't. Not on its own.
A raw attention map from layer 8 shows you what layer 8 attended to. It says nothing about how that signal got mixed, diluted, or overwritten by every layer before and after it. Attention rollout fixes this — and I built a walkthrough to show why it matters.
Here's what makes it more than a "pretty heatmap" demo:
Instead of visualizing one layer's attention, I traced how information actually flows through the full transformer stack.
→ Every layer's attention matrix is extracted, per head, per token
→ Multi-head attention is averaged, then combined with the residual connection (identity + attention) — this is the step most tutorials skip, and it's the one that actually matters
→ The combined matrices are matrix-multiplied layer by layer, rolling attention forward from input to output
→ The result: a single map showing genuine token-to-token influence across the entire network, not just one layer's snapshot
The overlay shows you everything:
→ Per-layer attention vs. rolled-out attention, side by side
→ Token importance scores overlaid directly on the input text
→ A comparison view: which tokens raw attention says "matter" vs. which ones rollout says actually matter
→ Head-level breakdown so you can see which heads specialize vs. which are noise
No black box. No "trust me, the model looked here." Just linear algebra, applied honestly across every layer instead of cherry-picking one.
Built with PyTorch + HuggingFace Transformers + Matplotlib. Runs on any pretrained transformer, fully offline.
⚠️ Important: attention rollout is an approximation, not ground truth. It assumes attention is the primary information pathway, which ignores MLP layers and can still mislead for very deep models. Treat it as a debugging lens, not proof of causality.
