YogeshVeeraraghavan

Generative Media Tool

Generating footage used to mean leaving the edit: prompt another app, download, import, cut. The Generative Media Tool keeps that whole loop in Premiere's timeline. Select an empty range and a task bar opens over it. Describe the shot, choose Adobe Firefly or a partner model such as Google Veo, Kling, Runway or Luma, and anchor it to frames from your own footage. The result lands as an ordinary, editable clip.

• Generative task bar. I architected the bar and its contract with the app. It reads its target through a small pull-based interface, so the bar and the timeline evolve independently.
• Range selection. I wrote the timeline range selection behind every generation and took it through architecture review to release.
• Generation lifecycle. Placeholder media while a job runs, the metadata behind it, and linking the finished media when it arrives.
• Reference-frame picker. Sample a frame from the timeline, the Project panel or a file, with a live preview that scrubs like a small player.

Search panel with media intelligence

On a long project, finding the shot can take longer than cutting it. Media intelligence analyzes footage in the background and recognizes what is in each clip: objects, places, lighting, camera angles. Editors search in plain language across visuals, spoken words and metadata at once. The models run locally, so footage and searches never leave the computer.

• Architecture. Co-authored with our lead engineer. I made it clear for the team and built the first working skeleton: the panel's messaging and data model.
• Result thumbnails. I found a fast path by adapting Premiere's existing thumbnail renderer to the panel.
• Hover-scrub playback. Move across a result to play through it. Making that quick on big projects meant working with the playback experts.

The surfaces editors live in

Most of an editor's day runs through a handful of panels. They have to be fast and dependable on every machine, for every format. These are the areas my team owns.

Making sync measurable

Every sync is a round trip. OneDrive detects what changed on disk and sends it to the cloud in a safe order, then pulls down what changed in the cloud and applies it locally in a safe order. Get the order wrong and a file, or its history, can be lost.

I instrumented that entire round trip, stage by stage, on macOS and Windows. The telemetry became how the team measured the engine's correctness and efficiency, and how we found and fixed the problems that mattered.

• SharePoint permissions on Mac. I implemented SharePoint's permission roles in the Mac client, while a peer built the Windows side.
• End-to-end instrumentation. Every stage of the sync cycle shown here, on both platforms, feeding large-scale dashboards.
• Files On-Demand. I contributed to placeholder hydration and dehydration built on the Windows Cloud Files API.
• Data integrity. I built checks aimed at every path to data loss. They exposed rename cases that silently dropped version history, and I fixed them.

Synapse and the Tetrode application

Researchers build an experiment in Synapse by chaining gizmos, modular blocks that filter signals, detect spikes, generate stimuli and run logic. Synapse compiles that design and spreads the work across the parallel DSPs inside TDT's processors.

A tetrode is a bundle of four electrodes. Each nearby neuron reaches the four at slightly different strengths, so plotting spike peaks from two channels against each other separates neurons into clusters. Circle a cluster and the hardware labels that neuron's spikes from then on.

• Gizmo compilation. Key contributor to the engine that turns a chain of gizmos into code on the processor's DSPs.
• Tetrode application. I wrote it: four-channel capture, feature projections and real-time sorting.
• Plotting. Platform-independent OpenGL and GLSL plotting for live neural signals.