Evaluating the architecture of a system can be a very complicated process, with a lot of different possibilities for tools and techniques. In this video, we describe a mental model for evaluating architecture that helps us make sense and make the right decisions.

Longtime Linux developer David Woodhouse sent out a patch series today to 'deprecate legacy IP' support within the Linux kernel

More than half of breaches occur after fixes are available, revealing an execution gap as delayed patching turns known vulnerabilities into ongoing enterprise risk.

The Simple Text Oriented Messaging Protocol

Syncing reading progress across devices sounds simple until you try it. Discovery, sync, and conflict resolution — with and without a cloud.

I went to the movies the other day, and while standing in line, I caught myself mentally designing the system the ticket controller was…

If someone asked me to set up a team in charge of software documentation, I would not hire for specific roles or cookie-cut job descriptions. Professions tied to knowledge buckets are bound to shrink or disappear. Instead, I would hire people that could move freely between four quadrants, each defined by the proximity to a focus pole and its skills. The poles in this team setup would be the following: Product Vision, Knowledge Design, Engineering Depth, and Delivery Strategy.

Valentin Geffrier, Tanguy Cornuau

When it fails to do your thinking for you

Durable agent-to-agent messaging using a filesystem: inspectable, remote, replayable, and SQL-queryable with db9 fs9.

Intra-domain delegation enables an agent to grant constrained authority to subagents without expanding the system’s trust boundary. In the OpenClaw + Cedar demo, delegation is modeled as data and enforced by a stable policy set, preserving policy integrity while allowing dynamic scoping of power.

If you have built APIs, then by now, you are already familiar with the practices of how we typically build APIs:

The Sequel Nobody Asked For (But AI Agents Demanded)

For the next paper in my Paper Implementation series, I picked the Dynamo 2007 paper by Amazon. I had a really good time with this one. Large scale distributed systems always fascinate me , how do you handle that much data, across that many machines, and keep it working when things inevitably break? The Dynamo paper has a clear answer, and it keeps repeating it in case you miss it: eventual consistency.

Modern verification systems force individuals to rely on institutions to prove facts about themselves, creating a

A practical, SQL-first workflow: file artifacts + Postgres state + fs9 (files as relations) for retrieval, governance, and debuggability.

The famous article Choose Boring Technology lists two problems with using innovative technology: There are too many "unknown unknowns" in a new technology,...

Privacy-first email infrastructure for fast growing companies. Run agents and automations securely on your email and proprietary data.

The following article originally appeared on Q McCallum’s blog and is being republished here with the author’s permission.Generative AI agents and rogue

I deployed a standard ECS architecture for Lush Aural Treats and didn't watch the bill. NAT gateways and ALBs quietly ate $1,000. Here's how I redesigned the whole thing to cost almost nothing.

Raft is a consensus algorithm that allows a group of servers to agree on the order of a sequence of commands. The objective of this article is not to re-explain RAFT, as this work has already been done in several other places, and the RAFT paper gives enough details to implement it. Instead, my focus will be on two problems that I faced when I voluntarily studied the open distributed systems course from MIT (course code 6584, I didn’t enroll in the course officially, and I wasn’t a student of MIT).

I introduce Memory-Continuous Architecture (MCA), a runtime model that decouples deployment boundaries from memory boundaries in function composition. KubeFn implements MCA across JVM, Python, and Node.js runtimes, demonstrating 4-100x latency reduction in full HTTP benchmarks.

Learn how to use AI as an intent layer in Phoenix apps to build reliable, structured filtering without replacing your existing UI.