The story
The story, in three acts
A kid fixing computers on an island. The job everyone wants, and the morning discipline nobody saw. A leap, a long apprenticeship, and then the year the machines arrived. The research thread running underneath has its own page.
- Act I · The island and the floor
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Growing up
San Juan
Puerto Rico
Born and raised on the island. By high school I was running a 200-member youth organization - the activities, the budget and the fundraising, with a fifteen-person board. My first lesson in keeping a system running when every part of it is a volunteer.
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Engineering
At fourteen
Paying customers
Building and programming computers for people in San Juan who paid me for them.
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Engineering
Four years, then one more
Cornell
Engineering, then a master's. Information science is the study of how to represent the physical world in a digital one, which turns out to be a fair description of everything I have done since. I taught calculus to other students and kept the books for the Hispanic engineers' chapter.
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Engineering
Three summers running
Goldman Sachs, as an intern
The same trade-processing team three years in a row, building the tools that check the firm's trade records against its own risk system. Somewhere in there I also started running the mentoring program for the incoming interns.
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Engineering
Then the job itself
The trading floor
Software engineer in Securities technology, on the systems that double-check trades and file the firm's regulatory reports. I built a data-lookup library the whole firm could use, and helped stand up a round-the-clock operation that catches trade errors and fixes them.
- Act II · The leap, and the long apprenticeship
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June 2015
Two names, one month
I gave up the “perfect job.” That same month I started two companies - LazoffTech for client engineering, and a second for a long-running personal research project: that story has its own page. The first has funded the second ever since.
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Research
The next two years
Learning platforms
Senior engineer on a structured-learning platform built with a team in Israel, then a ground-up rebuild of a video-learning platform. Two years of shipping products where the content model - not the code - was the hard part.
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Engineering
Six years
ConsenSys
I came in through the education arm, building its online learning platform, and stayed six years. I left as the senior engineering manager for every website behind metamask.io and consensys.io.
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Engineering
Three and a half years
Chainlink Labs
Contract engineer inside Chainlink - a company that feeds real-world data like prices and events onto blockchains - building the sites and tools its developers rely on. The contract ran through TorahTech LLC: the blockchain income flowed through the Torah company.
- Act III · The machines arrive
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Engineering
2023
A machine in the house
Everything the career taught - connecting information, making it findable, running systems that do not lose things - pointed at one product: private AI on hardware you own, sitting on your own shelf.
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Engineering
2026
The year of the machines
When the AI tools matured, everything I had spent two decades saving became fuel. In one year I shipped more new projects than in every earlier year combined - sites, graphs and agents, built and run from a fleet of machines in my own house. One person, working like a team.
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Research
Now
OpenRabbi, my grounding testbed
A knowledge graph of a classical text corpus, every piece carrying its source - with eight conversational voices on top, each one grounded in the graph. The generation rule is the point: cite a real, checkable source, or say nothing at all. It runs in public, on hardware in my house.
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The rule, measured
Does the rule hold?
It holds. The whole system turns on one constraint, written into the code.
Every answer must name a real source you can go and look up, verified word for word against the actual text - and if no source exists, the system says nothing. Only a model that clears every check ships. The rule is the oldest one on the shelf, and it was always the rule: say who said it, or say nothing.
That discipline - cite or stay silent, measured, in public - is what I want to bring to a team working on AI you have to be able to trust. The research behind it is at jew.tech; the productized version is Digital Twin Pro.