I build tools for the part of AI work that breaks after the demo: fragmented context, changing state, hidden effort, unclear positioning, and the gap between thinking and action.

Now: Founding engineer at Iditor (EchoMemory) — cross-platform AI memory across iOS, web, Chrome extension, and MCP/agent workflows. BU Computer Science '25.

My current focus is simple:

Turn messy human context into memory, judgment, and reusable agent behavior.

AI can generate more output than people can judge.

So the bottleneck moves to:

• what context should be preserved
• what changed since last time
• what evidence supports the answer
• what mode is safe next
• what identity a repeated behavior is reinforcing
• what one sentence makes the work understandable

I am interested in AI systems that do not only answer the current prompt, but help people carry context forward.

Small installable skills that turn personal systems into reusable agent behavior.

The thread: less tracking overhead, more decisive support.

Some repos are public projects; some are private workspaces. I still treat them as part of my GitHub map because they capture how I work, what I am building toward, and what future agents should not miss.

The thread: turn messy personal context into systems that preserve judgment, reduce repeated effort, and make the next action easier.

I work on cross-platform AI memory: capturing real AI conversations and turning them into retrievable context with evidence.

flowchart LR
    A["💬 messy conversations<br/>(ChatGPT · Claude · coding agents)"] --> B["capture"]
    B --> C[("memory<br/>+ evidence")]
    C --> D{"retrieve at<br/>the right time"}
    D --> E["🤖 coding agents / MCP"]
    D --> F["💭 next chat session"]
    D --> G["🎬 content pipeline"]

The interesting part is not storing more text. It is making AI able to answer:

What do we know, why do we know it, when was it true, and what should be reused now?

Current product questions I care about:

• How should AI preserve live context without over-compressing away taste?
• When does a conversation become memory instead of just transcript?
• How can human discussion become implementation context for another agent?
• How should assistants behave when they can actually save, route, and reuse context?
• How do privacy and trust change the design of memory products?

Co-author on two papers from undergrad research at BU:

• Explore Reinforced — equilibrium approximation with reinforcement learning; accepted at GameSec 2025.
• DebiasPI — inference-time debiasing of text-to-image generative models by prompt iteration; presented at an ECCV 2024 workshop.

What research left me with: the habit of breaking a fuzzy problem down until it can be measured — which is most of what evaluation work on memory systems actually is.

I am building toward AI memory, context engineering, agent workflows, and human-centered tools for self-management and communication.

The long-term bet:

The best AI products will not just produce more output. They will help people preserve context, make better judgments, and act with clearer timing.