Most people use Claude Code as a smarter autocomplete. I wanted to see what happens if you treat it as the operating system for how you study, rather than a tool you call when you are stuck. The setup: study plans and review schedules live as plain files that the model reads and updates. It does the organizing I would otherwise skip. I do the thinking it cannot. Written up here: hackernoon.com/how-i-turned-… I am not claiming this beats a good textbook and a quiet room. I am claiming the bottleneck in self-study is rarely the material. It is the system around the material, and that part is automatable. Where does this break down for you? Specifically, what part of studying do you believe a model should never touch?

Passed the TOGAF exam today.

If you are studying something difficult and not using AI as a learning companion, I think you are leaving a lot on the table. Not as a shortcut. As a way to ask better questions, test your understanding, build examples, generate exercises, review your notes, and turn confusion into a concrete next step. My Claude Code workflow for studying has become one of the most useful learning systems I have ever used. It does not replace the work. It makes the work sharper.

A lot is lined up. The CISSP exam sits 38 days out, and it is priority 1 until then. That is harder to write than it sounds. I have spent the last year building everything that should make the next phase work. Content pipelines. Review cycles. The longer-form project I have been pacing toward for months. Tools that are still cooking. A sprint format I have designed but not yet started. None of it is moving right now. Some days the discipline feels easy. Some days it feels like watching a built engine sit cold while I study. The hardest part is not the studying. It is the waiting. But there is value in this kind of waiting. It forces clarity about what actually deserves to ship and what was just busywork dressed as ambition. Summer is when this list starts moving. The CISSP is between me and that. So I sit with it.

I feel like I currently live in two worlds at once. In my day job, I work in cybersecurity, and I keep AI on the edge of my workflow by choice. In my private life, research, writing, and side projects, I am deep in it. AI is part of how I think, build, explore, draft, test ideas, and move faster. And it splits me in two. On one side, I love what I am doing. AI enhances it. It helps me go deeper, connect dots faster, and turn vague ideas into something concrete. For research, writing, and learning, it often feels like having a second brain next to me. On the other side, it is strangely refreshing to keep some distance from it in my day job. There is value in doing the work yourself. There is value in being slower, more deliberate, more skeptical. Especially coming from cybersecurity and cryptography, I still deeply believe in being in the driver seat. But then the conflict comes back. I get bothered by tasks where I know AI could help. Repetitive writing. Summaries. Structuring thoughts. Drafting documentation. Searching through complexity. The kind of work where I can almost feel the wasted time because I know another mode of working exists. At the same time, not all AI output is satisfying. Speed is not the same as quality. Assistance is not the same as understanding. And using AI everywhere can slowly blur the line between amplification and dependence. That is the tension I am sitting with. I do not want to reject AI. I also do not want to outsource my judgment, my craft, or my responsibility. I want to use it well. Deliberately. With taste. With control. With security in mind. But I am still figuring out what that actually means in practice. Do you separate AI-heavy work from AI-light work? Do you feel the same conflict? Or have you already found a healthy way to integrate it without feeling like you are giving something up?

Yesterday we wrapped up our 14th edition of the zkSummit. Hosted in Rome, it was honestly one of our best yet. Thanks to everyone who came out, to the speakers, sponsors and team! 💜🇮🇹 All videos are coming throughout the coming week! #zk14

Already received some great feedback. Would love to have more eyes on it to refine the book. If you are interested or know someone who is, please let me know. And please share to reach the right people :) Thank you!

🎙 For ep 400, @AnnaRRose brings back @danboneh — Stanford professor & one of the sharpest minds in crypto. They cover Google's quantum algorithm announcement, why rushing PQ transitions might be riskier than quantum itself, algebraic vs hash-based signatures, hybrid sig schemes, new ZK advances, and the Ethereum Foundation's $ 1M Proximity Prize. A milestone episode. zeroknowledge.fm/podcast/400…

One thing I have learned from working in cybersecurity. Security works best when the right thing is the easy thing. If secure defaults require five extra approvals, three wiki pages, and someone remembering a policy from last year, people will work around them. But if the secure path is built into the workflow, documented clearly, automated where possible, and easier than the insecure shortcut, people usually take it. A lot of security is not about making people care harder. It is about designing systems where doing the right thing feels natural, and doing the wrong thing becomes inconvenient.