AI Systems Engineer. From concept to working system.
My educational background spans mechanical engineering at ENS, virtual reality engineering at ENSAM, and industrial design at Strate.
I conceive, design and build AI-powered systems — connecting models, tools, software and hardware, from the first idea to a working product.
Voice systems at Snips / Sonos. Collaboration prototypes at SAP. Physical interaction research at MIT Media Lab. Today, I bring that experience to local AI, agentic workflows and connected objects.
Selected work
Systems, objects and experiments I conceived and built. Try the interactive studies, then open each project for the full story.
Synapse
A local nervous system connecting voice, tools, memory and inference. Explore the machines behind it.
Explore the systemPucclab
A handheld espresso lab connecting the recipe, measured extraction and taste to the next experiment.
Explore the objectArchivist
A memory system that shows its sources and asks before it rewrites them.
ARCHIVIST / LOCAL KNOWLEDGE WORKSPACE
Read the case study
F1 Race Replay
A quiet, data-native station for following the whole race. Move through the replay and feel the field unfold.
Explore the stationmmento-1
A camera for the sound of a moment. A physical shutter turns a captured scene into a musical memory.
Explore the objectMOTH
Software creatures learning to inhabit a system. Disturb the field and watch their collective behaviour change.
Explore the ecosystemSPrAyCE
A spray-based interface for drawing in mid-air. Leave an open, suspended trace with your own gesture.
Explore the gestureColor Sequencer
Compose with colour. Change the cells, start the loop and turn a visual pattern into a rhythm.
Explore the instrumentWhat I bring to a team
I work across the boundaries where a promising demo becomes a usable system: interaction, orchestration, inference, persistent state, embedded hardware and deployment. I can define an experience, make the technical choices and build the pieces that let someone actually use it.
The projects here originate in my own concepts and design work. I take them through implementation and iteration; professional contexts and team contributions are identified where relevant.
Built in different contexts
- Snips / Sonos — voice and product prototyping. Contributed to the product definition and UX specification of Sonos Voice Control. Conceived and built contextual listening prototypes spanning interaction, lyrics retrieval, sensing and ESP32 firmware.
- SAP — collaboration. Conceived the idea, built the prototype and developed the OS X application within the Chairman’s project team. The concept later evolved into CoScreen.
- MIT Media Lab — physical interaction. Created SPrAyCE, a spray-based interface for drawing in mid-air, connecting a physical gesture to digital creation.
- Independent R&D — AI systems. Design, build and operate the local inference, voice services, agent integrations and interfaces behind the projects in this portfolio.
How I build
Start with a use case. Make the whole loop work. Then find the weak boundaries: ambiguous requests, missing context, unavailable services, unsafe actions and outputs that cannot be verified.
I use models where interpretation or generation helps, and explicit software contracts where correctness matters. That can mean a source-backed answer, a reviewed diff, an event ledger or a hardware control loop that keeps working without the model.
Synapse architecture
Local-first AI, from an interaction to durable work. Specialized models and independently operated services compose the system.
Interfaces
- Telegram
- Web
- Voice
- ESP32
Capabilities and permissions vary by interface.
Decision & orchestration
- Interaction Decision
- Primary
- Task / DSH
Reply, call a service directly, or delegate durable work.
Intelligence & capabilities
- LLM Fabric
- Speech
- Vision
- Content / Knowledge
- Tools
Specialized services
- Home Assistant
- Media / Reading
- Archivist
- Context Observer
- Observability
- Watch
LOCAL COMPUTE
Fabric · model routing & inference access
ia01
ASUS GX10 · DGX Spark-class
NVIDIA GB10 · unified memory
Qwen 3.8 Flash Next
vLLM · single-node inference
ia02
ASUS GX10 · DGX Spark-class
NVIDIA GB10 · unified memory
Nemotron 3.5 Super VL
vLLM · dedicated image inference
ms03
MS-02 Ultra
NVIDIA RTX PRO 4000 Blackwell SFF
Parakeet · Whisper
Kyutai · TitaNet
NeMo · CTranslate2 · Moshi
Workstation
NVIDIA RTX 5090 · 32 GB
Specialized compute & experiments
Qwen 3.8 · 27B
NInfer · on-demand provider