Data-Oriented Design (Or Why You Might Be Shooting Yourself in The Foot With OOP)

There's no reason for software to be slow anymore

The other day, I saw a viral tweet saying that people talking about how LLMs are causing slow, bloated, code are going to eat crow once they re-write everything in super-optimized assembly. We're not quite at the point where we want to write everything in assembly, but some variant of what Nolan Lawson said about testing, you can choose how many bugs you want now, which I less eloquently noted here, is becoming more true for performance.

OODA loop

Make faster decisions with incomplete data.

Devs aren't "lazy" and game updates aren't guaranteed | Opinion

Where did the demand that every game must be continually updated come from – and is there a way to avoid the friction it causes?

Designing Data-Intensive Applications

Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and... - Selection from Designing Data-Intensive Applications [Book]

turbopuffer: fast search on object storage

Inaugural blog post about the development of turbopuffer, a search engine that uses object storage and SSD caching for cost-effective, low latency search. This post describes into the motivation behind its creation, its unique architecture, and how it significantly reduces costs for large-scale vector searches. Discover how turbopuffer is transforming search infrastructure for companies like Cursor and Suno, offering a scalable and reliable solution.

Optimizing Protocol Buffers in Go Applications

If you’ve ever wondered why your Go service is eating memory like it’s at an all-you-can-eat buffet, chances are you haven’t optimized your Protocol Buffers usage. I’ve been there, watching heap profiles with the kind of horror usually reserved for checking your bank account after a night out. But here’s the good news: Protocol Buffers in Go can be wickedly fast and memory-efficient when you know the tricks. Let me walk you through the optimization techniques that transformed my services from memory-hungry monsters into lean, mean, serialization machines.

Trying the Software factory pattern.

One of the interesting challenges of the AI ecosystem in 2026 is that new, effective patterns emerge faster than I can adopt them. I’ll find a handful, get back to work, and realize a month later that I’d missed four or five more. The adoption cycle for Imprint this year has been something like: January: get every engineer onto Claude Code every single day March: ok, let’s also get everyone else onto Claude Code or Claude Cowork every single day April: local development is bottlenecked on checkout and worktree model, instead create ~10 local workspaces which each have an independent checkout of every repository, and operate at the workspace level, not at the repository level, so it can generate cross-repository pull requests across frontend, backend, infrastructure and data monorepos June: oh boy, agent-driven development is heavily constrained by lack of a common task management system with higher visibility and less permission complexity than Jira, so let’s migrate the entire company over to Linear and hard stop on Jira July: yikes, now we have visibility into all these tickets, many of them are trivial but managing them through local development isn’t scaling, let’s roll out an orchestrated harness which internally we call “Agent Fleet”, along the lines of Stripe’s Minions The most recent question for me has been figuring out how to adopt the software factory pattern. (After some light research, the specific AI-context origin of this term is slightly messy to attribute, but I think it might be Justin McCarthy in February 2026’s Software Factories And The Agentic Moment.)

Making Databases Faster with LLM Evolutionary Sampling

Traditional query optimization relies on cost-based optimizers that estimate execution cost (e.g., runtime, memory, and I/O) using predefined heuristics and statistical models. Improving these heuristics requires substantial engineering effort, and even when implemented, these heuristics often cannot take into account semantic correlations in queries and schemas that could enable better physical plans. Using our DBPlanBench harness for the DataFusion engine, we expose the physical plan through a compact serialized representation and let the LLM propose localized edits that can be applied and executed. We then apply an evolutionary search over these edits to refine candidates across iterations. Our key insight is that LLMs can leverage semantic knowledge to identify and apply non-obvious optimizations, such as join orderings that minimize intermediate cardinalities. We obtain up to 4.78$\times$ speedups on some queries and we demonstrate a small-to-large workflow in which optimizations found on small databases transfer effectively to larger databases.

Learning to Continually Learn via Meta-learning Agentic Memory Designs

The statelessness of foundation models bottlenecks agentic systems' ability to continually learn, a core capability for long-horizon reasoning and adaptation. To address this limitation, agentic systems commonly incorporate memory modules to retain and reuse past experience, aiming for continual learning during test time. However, most existing memory designs are human-crafted and fixed, which limits their ability to adapt to the diversity and non-stationarity of real-world tasks. In this paper, we introduce ALMA (Automated meta-Learning of Memory designs for Agentic systems), a framework that meta-learns memory designs to replace hand-engineered memory designs, therefore minimizing human effort and enabling agentic systems to be continual learners across diverse domains. Our approach employs a Meta Agent that searches over memory designs expressed as executable code in an open-ended manner, theoretically allowing the discovery of arbitrary memory designs, including database schemas as well as their retrieval and update mechanisms. Extensive experiments across four sequential decision-making domains demonstrate that the learned memory designs enable more effective and efficient learning from experience than state-of-the-art human-crafted memory designs on all benchmarks. When developed and deployed safely, ALMA represents a step toward self-improving AI systems that learn to be adaptive, continual learners.

Support hotpatching systems · Issue #19296 · bevyengine/bevy

What problem does this solve or what need does it fill? A common complaint from Bevy users is the relatively high iteration time. Hotpatching is a nice solution for this, as it allows code changes ...

The Shape of Memory Benchmarks

Why the familiar memory benchmarks are outdated, how the agent-native work looks today and why design your own.

Designing Data-Intensive Applications

Data is at the center of many challenges in system desi…

Improving My Hardware Workflow With CI and Nix

One thing you might notice if you read my blog is that I do a lot of hardware projects. Generally, these include a board, some software, and possibly some extraneous components (like CAD design, such as a case). Software tooling is quite mature, but I find that hardware had traditionally lagged quite a bit behind. For a while, I just lived with this, but over the last few months I've put in a bunch of effort to improve my workflow.