In this article, I want to make the case for a structured way to think about Large Language Model (LLM)-based agentic systems (mostly for coding, but also for knowledge work in general) that fixes some of the greatest pains I (and I sure most of you) have been facing when trying to scale AI-assisted workflows to professional levels.
It’s a system that puts the right constraints in the right places and leaves just enough space for creative exploration (or however you want to call what LLMs do when they hallucinate in your favor). It’s also a system that makes it clear you are in charge. — Read More
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Bad Analogies
I saw an interaction on twitter the other day that I’m not going to share here because these specific people don’t deserve to be singled out because almost everyone is guilty of something similar.
Basically, one person said, “It’s amazing that OpenAI was able to raise $122 billion when they don’t have a single business that works,” and the other person replied, “Yeah they do they have a bunch of different business lines doing billions of dollars in revenue,” and the OP responded, “Yes but none of them is profitable, they’re losing a lot of money,” and the replier replied, “You could have said the same thing about Amazon!”
Amazon’s success has done a great deal of harm to a great number of companies.
Obviously, it’s done more good. AWS is a miracle. But go with me.
Jeff Bezos is a generational entrepreneur who came from a hedge fund and made a very calculated decision to lose money in the short term if it meant making more of it in the long term. — Read More
What Is Claw Code? The Claude Code Rewrite Explained
… On March 31, 2026, security researcher Chaofan Shou noticed something odd in the npm registry. Version 2.1.88 of @anthropic-ai/claude-code had shipped with a 59.8 MB JavaScript source map file attached.
… Within hours of the exposure, mirrored repositories appeared on GitHub. Anthropic began issuing DMCA takedowns. The internet did not wait.
Sigrid Jin (@instructkr) — a Korean developer who had attended Claude Code’s first birthday party in San Francisco in February — published what became claw-code. The repo reached 50,000 stars in two hours, one of the fastest accumulation rates GitHub has recorded.
The important distinction: claw-code is not an archive of the leaked TypeScript. It’s a clean-room Python rewrite, built from scratch by reading the original harness structure and reimplementing the architectural patterns without copying Anthropic’s proprietary source. Jin built it overnight using oh-my-codex, an orchestration layer on top of OpenAI’s Codex, with parallel code review and persistent execution loops.
… The real value here — for builders — isn’t the drama. It’s what the exposed architecture tells us about how production-grade agentic coding systems are actually structured. — Read More
Clouded Judgement 3.20.26 – Digital Twins
Every week I meet with founders building in the agent space. And lately, I keep hearing the same concept come up over and over – digital twins (or some version of this). When a concept starts showing up as frequently as this one, my ears generally perk up. Digital twins are the thing perking up my ears! And I think they’re about to become one of the most important concepts in AI. I think they could become a layer that helps scales AI to the masses (and consumption of AI).
So what actually is a digital twin? The term originally comes from manufacturing. You’d build a digital replica of a physical asset (a jet engine, a factory floor) to simulate and monitor it. With AI it’s the same core concept, but with a totally new application. In the AI era, a digital twin is just representing knowledge (from any source, in any form) digitally, so an agent can act on it. That knowledge could live in a person’s head, across a dozen siloed systems, in years of company history, or in the collective behavior of your customers. The twin is just the bridge between that knowledge and the agent that needs it to do work.
… This is where I think the job displacement narrative gets it wrong. Everyone asks “will AI take my job?” But the better question is “can I build a digital twin of myself before someone else does it for me?” The people who win in this world are generally the ones who move fastest to adopt new technologies. — Read More
Diving into Claude Code’s Source Code Leak
On March 31, 2026, Anthropic accidentally shipped a .map sourcemap file inside a Claude Code npm update. In minutes, this was found and was going viral. The 600k lines of code were mirrored, analyzed, ported to Python and other languages, and uploaded to decentralized servers.
Claude Code is known to be notoriously closed down. Their Agent SDKs provide almost no insight into the internals of Claude Code, and Anthropic themselves do their best to keep the source as closed as possible.
… The legal question nobody has an answer to yet: does a codegen clean-room rebuild violate copyright? — Read More
DefenseClaw
DefenseClaw is the enterprise governance layer for OpenClaw. It sits between your AI agents and the infrastructure they run on, enforcing a simple principle: nothing runs until it’s scanned, and anything dangerous is blocked automatically. — Read More
When agents hit the walls
For decades, structural engineers and IT teams have shared the same testing logic: apply controlled pressure, find where things give way and fix. In IT, that means a server that buckles at scale, a query that times out under load or a process that degrades when pushed past its limits.
Agentic AI could upend the way we approach testing. When an agent stops, there is no bug to fix, no threshold to raise. The agent is at a dead end: a system it can’t reach, an approval with no interface, a data handoff that lived in someone’s morning routine instead of in the architecture. This becomes about not a flaw in what was built, but of what wasn’t.
Humans filled those gaps without anyone noticing until now. An agent can’t. And every place it stops is a precise record of where the enterprise assumed a connection that was never made. These gaps were always load-bearing, patched up and held up by hand. Now you have a blueprint. — Read More
The Great Convergence
Over the last year, a strange thing has happened in tech: very different companies have started moving towards the same product shape, and it feels like everyone is building the same thing.
Linear announced last week that they’re building coding agents. OpenAI is deprecating Sora and focusing entirely on Codex. Anthropic is obviously all-in on claude code and cowork. Notion is building agents for work. So are Google, Microsoft, Meta (Manus), Lovable, Retool and many others.
These companies have different histories, customers and product categories, but they’re starting to converge on the same idea: software that can take a goal, use tools, and do work on your behalf.
This convergence is not hard to explain: the market is enormous. This is so much more than a new feature. The prize is enterprise knowledge work. — Read More
The Feedback Loop Is All You Need
So Claude Code added CRON a few days ago. Recurring tasks, native, built right in. The thing we’ve been dreaming about since the first AI coding demos — schedule an agent, go to sleep, wake up to merged PRs. An engineer that works while you don’t.
And I’m sitting here like… I can’t even use this. Not on the real codebase. Not at work.
… The old loop: write or review code, spot smells by experience, leave comments explaining intent, promise to fix things “later” — which usually meant never.
The new loop: encode rules once, let agents iterate against them, observe what fails, tighten the constraints. Less “remember this next time,” more “this literally cannot happen.”
Agents break the old loop completely. When code can be produced nonstop, manual review becomes the weakest link. — Read More
Google’s Quantum Crypto Paper Tells You Quite a Lot
Last week Google Quantum AI dropped a 57-page whitepaper that should be keeping every blockchain developer awake at night. The headline finding: Shor’s algorithm can break the 256-bit elliptic curve cryptography underpinning Bitcoin, Ethereum, and most of the crypto ecosystem using fewer than half a million physical qubits on a superconducting architecture. Their circuits could execute in about nine minutes–within Bitcoin’s average block time.
… Basically: Google withholds the specific quantum circuit they discovered in the name of responsible disclosure, yet the paper itself constrains the search space so tightly that reproducing comparable circuits is well within reach for any serious quantum algorithms group. Including, I would say, our team at SingularityNET, even though quantum is not our main shtick.
Another point I made to the journalists who asked me about this is: The qubit counts that make these cryptographic attacks feasible are roughly the same qubit counts that make quantum-enhanced AI feasible. So regarding quantum computing, the threat and the capability will arrive on roughly the same time-scale, and if you’re only looking at the threat side, you’re missing half the picture–arguably the more important half. — Read more
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