Stan Kirdey

Stan Kirdey · AI engineer

I’m back at Inflection AI building full-duplex voice systems. I want a model to hear you start to interrupt while it’s still speaking and know when to let you finish.

Netflix · Finding the lines that explain a failed build

At Netflix, we used distilled transformers to process petabytes of observability data. For an engineer investigating a failure, the useful output could be just a few lines.

For build logs, William High and I used embeddings and locality-sensitive hashing to group log lines by meaning, even when their wording changed. In one build, our report narrowed 6,892 lines to three for an engineer to inspect.

Inflection AI · How do you reward an open-ended answer?

I led post-training and open-source model work at Inflection. We trained Qwen and Llama models with reinforcement learning, using verifiable rewards when an answer could be checked and judgment-based rewards when several answers could be good. Those models achieved our best results on internal evaluations.

In a separate experiment, Andrei Kanavalau and I taught a byte-level language model new languages. We kept its main transformer frozen and retrained just three modules, about 4% of its parameters.

Hark · Giving computer-use agents a place to practice

On Hark’s founding team, I helped build simulations and reinforcement-learning environments where agents could practice using a browser and operating a computer. We also worked on proactivity, including the question of when an agent should take the initiative.

Hark Handoff later recorded 97.7% average task success on the human-evaluated Online-Mind2Web browser benchmark in August 2026.

Clark Labs · A personal agent that keeps working when you close the app

I build Clark for tasks you can hand off from your phone. Ask it to compare prices across a few sites, close the Android app, and come back on the web to a report with the prices and links it found. Clark does the work on its own computer. The Android app has reached 1,000+ downloads on Google Play.

I care about making that affordable for everyday use. For developers, our Managed Agent v1 rate card lists $0.75 per million input tokens and $2.50 per million output tokens for an agent with browser and file tools.

Amplifier Health

I founded Amplifier Health to study what a voice recording can tell us about health. We built foundational audio models to learn from the acoustic signal itself, including information a transcript leaves out.

VoiceRestore

Clean and damaged recordings of the exact same speech are hard to collect. I trained a flow-matching transformer by introducing damage synthetically, so one model could repair noise, reverberation, compression and missing frequencies.

Tenyx

At Tenyx I researched continual learning. The question was how to teach a model a new skill while preserving the things it could already do.

Orbital Command

Play a sci-fi tower defense game in your browser. Defend the base through ten waves, then continue in endless mode.