AI Engineer · Software Architect · Open-source builder
LinkedIn · Turin, Italy
I help product and engineering teams turn useful AI ideas into dependable products: private when they need to be, efficient enough to run in the real world, and clear enough for people to understand and trust.
Imagine a team building an assistant for sensitive documents. It should keep private work on the right device, choose an expensive model only when a smaller local one is not enough, survive an approval or restart without doing the same work twice, avoid loading the same model in every browser tab, and leave a readable record of what it decided and changed.
My projects explore each part of that journey as an independent, replaceable building block. They can be used separately: the goal is not one closed platform, but practical foundations that let teams change models, providers, policies, and infrastructure without rebuilding everything.
| Project | Real-world impact | Maturity |
|---|---|---|
| myMoE | Uses smaller local AI models for suitable work, keeps stronger models available, and escalates only when policy and evidence justify the extra cost. | Installable alpha (v0.4.0-alpha.1). Its published local-assistant compatibility check was inconclusive, so broader tool capability remains disabled; live route canaries also stay off because no paired evaluation has yet proved savings. |
| AgenticStrata | Turns an AI run into a checkable record of what was requested, allowed, changed, and observed; its optional Sigstore verifier can confirm that a trusted signer signed that exact record. | Executable alpha reference architecture. Unsigned records prove consistency, not identity; each deployment still owns its trust roots, revocation, and production policy. |
| SemWitness | Measures token-saving transformations while preserving protected instructions, code, schemas, and reproducible evidence. | Experimental alpha; it does not prove natural-language equivalence or authorize cache hits. |
| PauseMesh | Lets a long-running assistant survive approval waits, disconnects, restarts, and duplicate callbacks without continuing twice. | Alpha runtime; external effects still need the documented idempotency contract. |
| TabLoom | Lets browser tabs share one on-device AI runtime instead of loading a separate model in every tab. | Alpha library; the real WebLLM path is verified on Chrome/WebGPU only. |
| IMPOSBRO Search | Gives applications one reliable search API across separate data clusters; indexing survives restarts and partial failures stay visible. | Self-hosted alpha with live multi-service recovery gates; a tagged public release is still pending. |
- StageFabric plans each AI step across browser, local, edge, or cloud locations while blocking forbidden data movement. It is an experimental alpha planner, not a globally optimal scheduler.
- LocusMesh checks whether a distributed AI route stays within an operator-approved boundary. It is experimental alpha and does not yet prove that a peer performed the claimed computation.
- IntentABI measures whether differently worded requests can converge on the same typed intent before anyone enables semantic caching. It is alpha, shadow-only, and never serves a cached answer.
- WITShift turns a narrow TypeScript MCP tool into a reviewable WebAssembly Component candidate and compares its behavior with the original. It is alpha and intentionally rejects tools outside its bounded source subset.
- Semantic Junkyard connects knowledge in files, databases, and Git while keeping the original sources authoritative. It is a local-first reference product, not a production multi-tenant platform.
- MoveBeta helps indoor climbers review one measurable movement signal on-device and compare a focused repeat without uploading video. The PWA is a private beta; its pose signals are not coaching or medical advice.
- FreeJoy turns phones into up to four Windows game controllers through one QR code. Host and controller links stay separate, and each phone receives a revocable reconnect lease, so a refresh can recover its slot without gaining room administration. It is a CI-tested Windows/Ryujinx prototype; a real PC-and-phone latency and reconnect session is still required before calling it consumer-ready.
- Configuration over hardcoding — models, providers, policies, and infrastructure should be replaceable without redesigning the product.
- Explicit contracts — clear boundaries and verifiable outcomes make complex systems safer to change.
- Local-first where it matters — privacy, cost control, reproducibility, and offline use are product capabilities.
- Evidence before claims — tests, evaluations, and operational records should support every important guarantee.
- Useful to people — technical novelty matters only when its real-world benefit can be explained plainly.
My background spans full-stack development, microservices, distributed systems, data engineering, cloud-native architecture, and applied AI. I also have a research background in security and privacy for IoT systems.
Current interests: Agentic AI · Local AI · Distributed Inference · MCP · Data Platforms · Cloud Native · Python · TypeScript · Java / Spring · React
The best place to reach me and follow my professional work is LinkedIn.