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
Daily Archives: April 2, 2026
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
Inside Meta’s Home Grown AI Analytics Agent
The hypothesis was simple: can an AI agent perform routine data analysis tasks autonomously? Data scientists tend to get asked similar questions over and over, working within a familiar set of tables. An agent seeded with context about which tables a person queries, and how they use them, might be able to handle much of this work on its own.
To test the idea, a data scientist on the team used Meta’s internal coding agent to hack together a prototype on their devserver: an agent that could execute SQL against the internal data warehouse, with access to a few colleague’s query history for context.
The first real-world trial started simply enough: after sharing the prototype with a colleague, they asked it to diagnose a drop in a health monitoring metric. The agent identified the right tables, ran several diagnostic queries on its own, and ultimately traced the root cause to a recent code change.
That was an ah-ha moment that shifted the conversation. — Read More
Cutting the Middle Management Layer
Block, the company behind Square, Cash App and Afterpay, recently cut its staff by 40%, over 4000 employees. Block is questioning the underlying assumption: that organizations have to be hierarchically organized with humans as the coordination mechanism. Instead, Block intends to replace what the hierarchy does. Most companies using AI today are giving everyone a copilot, which makes the existing structure work slightly better without changing it. They’re after something different: a company built as an intelligence (or mini-AGI).
Block CEO Jack Dorsey just co-authored a post arguing the position, believing most companies will follow suit in the near future. — Read More
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