Independent practiceCalifornia

Difficult technical work, deliberately scoped.

GYSN LLC is the independent software and machine-learning practice of Gabriel Berger. I build original products and take on selected, bounded technical engagements.

01 / Selected work

Working systems, not just ideas.

Independent products and experiments spanning logic, language, sound, and everyday coordination.

Featured workDesigned and built independently
01

Daily puzzle platform Public preview

Cluewright

Six families of daily logic puzzles, each offered at six calibrated difficulty levels—with original generation systems, careful progression, and hints that preserve the solve.

  • Puzzle generation
  • Difficulty calibration
  • Product engineering
02

Interactive research Coming soon

A World That Speaks

A small world whose inhabitants use a consequential language the player must learn through observation, experiment, and speaking back.

  • Emergent systems
  • Simulation
  • Game design
Also liveProducts, systems, and instruments

03 / Focused productivity productLive

Quiet Done

A task list that keeps your words, closes small loops, and can quietly tell someone when a thing is done—without turning life into project management.

  • Product design
  • Full-stack system
  • Messaging

04 / Generative audioOn air

One Long Signal

One continuously composed broadcast: original music generated ahead of the listener, transmitting day and night, never repeating.

  • Generative music
  • Audio systems
  • Streaming

05 / Browser audio instrumentLive

Colored Noise

A programmable four-voice noise synthesizer with envelopes, movement, effects, JSON composition, and WAV export—all running in the browser.

  • Web Audio
  • Synthesis
  • Interaction design

02 / Practice

A senior engineer for the difficult part.

I am most useful when a problem is technically demanding and still ambiguous. For example: a question that needs a real answer — feasibility, cost, risk — before a team commits; an LLM feature that shipped without an evaluation story, where quality is anecdotal and regressions are invisible; a system that has to be built from scratch and built well. These are examples, not boundaries; the common thread is difficulty, not a particular domain.

01

Frame the problem

Turn an unclear technical objective into a tractable design, experiment, or implementation plan.

02

Build the system

Hands-on development, usually end to end: LLM systems and agents, evaluation pipelines, generation and simulation systems, and complete working prototypes.

03

De-risk the decision

Test an approach, review an architecture, or establish feasibility before committing a larger team.

03 / Background

Engineering depth with a mathematician’s habits.

I have spent more than 25 years building software and more than a decade working in machine learning. Before industry, I taught mathematics as an assistant professor. I later became a Staff Software Engineer at Google and a Senior Staff Machine Learning Engineer at Coupang.

I now work independently through GYSN LLC, dividing my time between original products, experimental systems, and selected client engagements with a clear objective and boundary.

MathematicsResearch · teachingSoftware25+ yearsMachine learning10+ yearsIndependent practiceGYSN LLC

04 / Contact

Have a difficult, bounded technical problem?

Send a short description of the problem, the outcome you need, and the approximate timeframe. If it appears to be a good fit, we can discuss how to scope it.

contact@gysn-llc.com