AI wrote the prose. I do not want to hide that.
The sentences you are reading were made with the latest generation of artificial intelligence models. I dictated ideas, supplied context, argued with the drafts, redirected the structure, cut what felt false, and kept going until the essay said what I was trying to say.
I am not interested in pretending that the machine was merely a spell-checker. It did real work. It found language, built transitions, proposed structures, clarified claims, and sometimes showed me a connection I had not yet made explicit. The writing itself is a collaboration.
But collaboration does not erase authorship. The questions are mine. The convictions are mine. The choice of what belongs, what does not belong, what must be challenged, and what I am willing to publish under my name is mine. Most important, the responsibility is mine.
That is the simplest disclosure I can offer: the thoughts are mine, the expression is made with AI, and I take responsibility for the result.
I can think. I have never been able to translate thought into writing.
I am dyslexic, and I am very bad at writing. I do not mean that as a performance of humility. I mean it literally. I never learned how to make prose do what I wanted it to do.
When I write on my own, I write from inside my head. I skip the bridge between one idea and the next because the bridge is already visible to me. I assume the reader knows the subject, remembers the previous argument, understands the reference, and can feel the direction before I have explained it. If you are already inside the topic, you may know what I mean. If you are not, the writing can feel like arriving in the middle of a conversation.
For most of my life there has been a painful distance between what I could sense and what I could express. I could have an intuition, an argument, even an entire structure in my mind, but when I tried to put it on the page, the thought became smaller, flatter, and less precise.
These models are the first tools that have made me feel that I can actually express myself. I can speak the thought in the order it arrives. I can give the model the missing context. I can say, “No, that is too clean,” or “You lost the tension,” or “Return to the Jewish source,” or “This sounds like a machine.” I can keep steering until the language begins to hold the shape of what I meant.
For the first time, the distance between thought and expression feels crossable.
The models did not create the interior library.
I have spent decades reading. Some books I have read once. Some I have returned to many times. The sources range from Tanakh, Talmud, and the Jewish philosophical tradition to Rav Soloveitchik, Abraham Joshua Heschel, science, technology, history, and general philosophy. There are hundreds of books behind this project, along with years of conversation, work, argument, and ordinary life.
My memory is not especially good. I often cannot summon the exact sentence, page, or citation when I want it. But books leave something behind even when the details become difficult to retrieve. They alter your instincts. They change which questions feel serious, which answers feel too easy, and which tensions you refuse to resolve cheaply.
I sometimes think of this in the language of neural networks. My own weights have been trained. Every book, teacher, failure, conversation, and rereading adjusts something. What remains is not a perfect database. It is more like an intuition for the shape of an argument and the moral weight of a question.
That intuition is the raw material I bring to the model. The machine helps me unfold it. It does not explain where it came from.
I know these systems closely enough to be astonished by them, and cautious around them.
I am a cofounder of Triple Whale. Our work places us close to the frontier of applied AI. We work with models from OpenAI and Anthropic, benchmark their capabilities, and help a wide range of clients use these systems in the real world. That work has given me a deep practical understanding of what the models are becoming good at, where they fail, and how much the surrounding system matters.
The model is only one part of the result. The harness around it matters. Context engineering matters. The sequence of prompts matters. The source material matters. The ability to preserve a long argument across many rounds matters. So does knowing when the model has produced something fluent but empty, elegant but false, or confident but unsupported.
I am also learning with the models. I do not arrive with a finished essay hidden in my head and ask the machine to type it. I arrive with a question, a set of intuitions, fragments of sources, and a direction. The model responds. I react. It challenges or extends the frame. I push back. We go backward and forward.
Sometimes I dictate most of the idea. Sometimes the model helps discover the real structure. Sometimes a draft reveals that my argument is incomplete. Sometimes the model is simply wrong. The useful work happens in the movement between those states.
Every essay is built through a loop, not a single prompt.
There is no magic sentence that produces a finished piece. The process is closer to a workshop. It is iterative, demanding, and often surprisingly slow.
Begin with a live question
I start with something I actually care about, usually a tension I have carried for years. The question must have enough pressure to survive the writing process.
Dictate the interior argument
I speak freely, often in fragments. I explain what I think, what bothers me, what sources are present in the background, and what easy answer I want to avoid.
Build the context
I give the model source material, prior essays, conceptual constraints, tone, audience, and the larger project. Good context changes the quality of the thought that can be expressed.
Argue with the draft
I read, reject, redirect, expand, and compress. I ask for counterarguments. I restore ambiguity when the prose has become falsely certain. I remove language that sounds impressive but says nothing.
Read before publishing
For every article published here, I read at least one complete edition, in English or Hebrew, before it goes out. I make the final decision that this version belongs in the archive.
Fast sentences do not mean fast thought.
It is easy to look at AI writing and imagine that the work has disappeared. In one sense, a great deal of work has disappeared. The mechanical struggle to turn a dyslexic stream of thought into clean sentences is no longer the barrier it once was.
But the sentence is only the visible end of a much longer process. The reading happened over tens of years. The questions came from life. The intuitions were formed slowly. The editing requires attention. The bilingual editions, source checks, imagery, video, design, and technical systems all take time.
The speed of production can conceal the slowness of formation. AI compresses the last mile. It does not replace the road that came before it.
It is also important to say what these essays are not. They are not rabbinic rulings. They are not a substitute for the books and sources they discuss. AI models can make mistakes, invent certainty, flatten disagreements, and produce beautiful language around a weak claim. Readers should keep their judgment awake. I try to do the same.
When I say the thoughts are mine, I do not mean that they appeared from nowhere. They are indebted to Torah, teachers, books, thinkers, friends, work, and generations of argument. “Mine” means that I have chosen them, wrestled with them, and accepted responsibility for presenting them here.