Anurag Nagare
I’m an AI & Machine Learning engineer with expertise in deve
One Gate: Voice-controlled Gmail, Calendar, and Drive that can't fire the wrong thing
Every "AI agent" demo shows a voice command turning into a sent email like magic, but almost nobody shows the part that actually matters: what stops it from sending the wrong thing. I built proof: an agent that runs your Gmail, Calendar, Contacts, and Drive by voice reply to a thread, schedule a meeting, archive an email, find a file hands-free end to end, but never fires anything irreversible without you saying so. The honest hard part isn't getting an LLM to sound smart, it's this: a transcript goes to LLM against a strict JSON schema and comes back as an ordered plan, every step tagged reversible or not and exactly one thing in the whole system is allowed to check that flag. Finding a thread, drafting a reply, creating a calendar event: those just run.
The result covers real ground without ever feeling like it's guessing: forward or reply to email with the original quoted underneath, archive/label/trash, resolve a name to a real address through your Contacts first and your mail history as fallback, schedule an event that creates quietly and only emails the invite after a second confirmation, answer "what did John say" or "what's on my calendar Thursday" grounded in content actually fetched from your account not hallucinated. No backend, no server anywhere in the loop
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What every "AI gesture control" demo quietly leaves out
Everyone films a hand waving at a screen and calls it AI. Almost nobody shows what's underneath that there's usually no gesture model at all. A "grab" is one distance crossing a line.
Here's proof. I built a jigsaw puzzle you solve with your bare hands no mouse, no controller, no gesture classifier, no training.
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Everyone's building AR filters and calling it "computer vision magic." Almost nobody's asking what's actually happening underneath — that most of these effects are just clever masking, not detection.
Here's proof. I built an invisibility cloak that runs entirely in the browser, no green screen, no chroma key, no model training.
https://github.com/AnuragNagare/Ghost-frame
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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.
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