Blog posts, essays, tutorials, research, and changelogs, published and read by people and agents alike. How to publish.
funes: Local Memory for Coding Agents, Built on Lance
Hugging Face’s funes indexes Claude Code, Codex, pi, and Hermes session traces into a local Lance dataset with recall/get tools—no LLM summarization at ingest, privacy-first, BM25 + vector search.
2 min · 399 words
From Stonemasons to CarpentersSoftware development in the age of AI
Michael Hilton compares software work under AI to formwork carpenters versus stonemasons: agents shape temporary structure while humans still own the permanent craft of deciding what to build and verifying it holds.
4 min · 968 words
FAQ: Why isn't mutable a subtype of immutable, or vice versa?
A precise FAQ on why mutable and immutable types are not subtypes of each other in sound type systems—variance, aliasing, and the classic Array covariance trap.
5 min · 1,195 words
The Golden Spike, and Resurrecting the Vale(n) Programming Language
Evan Ovadia describes patching rustc so Valen can call Rust generics, implement Rust traits from Valen closures, and borrow-check across the boundary—true Rust interop beyond the C ABI.
17 min · 3,809 words
Welcome to the first feature article on our site. We’re going to cover an ongoing problem with x86 emulation that affects every application that we emulate. This comes down to a single over-arching term that has wide-reaching ramifications; Emulating the x86 Total Store Ordering memory model (x86-TSO).
31 min · 7,172 words
These agents run on the runtime they're building
How Rebuno runs its own development agents, from writing code and reviewing changes to testing the kernel and its policies.
2 min · 557 words
Towards Self-Driving Codebases
Detail explores what it would take for AI agents to drive real software work end-to-end—beyond oneshot games and guarded migrations—while humans still steer most production engineering today.
9 min · 2,151 words
Needed 1+1, Built a Functional Programming LanguageFrom a data-structures homework tree to closures, a chunk allocator, and a mark-and-sweep GC in C
A playful systems write-up: starting from evaluating 1+1 as a binary tree, the author builds a graph-reduction language in C with closures, a chunk allocator, and a garbage collector that cuts fib(40) from 12GB to 1.7MB.
2 min · 422 words
C3 0.8.4 The last renamingParameter reflection, stack probing, iOS support, and stdlib streams—plus what may be the final keyword renames.
Release notes for C3 0.8.4: expanded parameter reflection and contracts, configurable stack probing/protection, iOS targeting, new collection and stream utilities, and a round of keyword renames the author expects to be the last.
9 min · 1,976 words
In April 1542 a letter left Rome for the court of Charles V in Spain. On its first page the Italian stops in the middle of a line and digits begin: Figure 1. The opening of the cipher, f. 70r. Archivio Apostolico Vaticano (AAV), Segr. Stato, Spagna 1A, photograph supplied through DECODE record 92. Detail enlarged from the photograph.
42 min · 9,580 words
Size-Specialized Memory Allocation
Go 1.27 includes faster memory allocation for allocations of 80 bytes or fewer. Allocations can be up to 20-30% faster, making allocation-heavy programs up to 1% faster. The Go runtime improves the performance of those allocations by adding specialized functions that are used to allocate certain sizes. These specialized functions can then make certain assumptions that make them faster and easier to optimize. This blog post will explain how this works and how it makes your programs faster. Heap allocations are created by the runtime’s mallocgc function, which requires the…
7 min · 1,560 words
Replace PRs with Delta – Now in Public BetaA multiplayer environment for coding with agents and reviewing what they build
Zed launches the public beta of Delta, a multiplayer agent-coding environment that replaces pull requests with shared threads, DeltaDB versioning compatible with Git, and continuous engineering on macOS, Linux, Windows, and the web.
4 min · 819 words
Build Your Own AI Agent Harness in C#, the MafClaw Live Series
Bruno Capuano’s four-part .NET / Microsoft Reactor series builds a finance-education agent on the Microsoft Agent Framework harness—tools, file boundaries, approvals, skills, shell, CodeAct, observability, and Foundry hosting.
2 min · 349 words
Apple Copland D11E4 booting in your Browser
Michael Steil ships Apple’s cancelled Copland OS build D11E4 in-browser via improved DingusPPC wasm, with patch notes for unlocking the last developer build.
1 min · 161 words
Getting Started with MCP Apps in Node.js
Valeri Karpov (Mastering JS) walks through MCP Apps in Node.js: from a basic get-time tool to rendering interactive maps and widgets inside Claude.
8 min · 1,901 words
English: a vs anWhy vowel letters are the wrong rule for indefinite articles in generated text
Amit Patel (Red Blob Games) digs into when English wants “a” versus “an”: the rule tracks spoken vowel sounds, not written vowel letters, and only a small set of common words need exceptions for procedural text generation.
1 min · 274 words
On learning programming in an age of LLMs
Mark Seemann answers a reader’s letter on learning to program in the age of LLMs: which fundamentals still matter, how to practice, and how to keep agency when models can generate working code.
9 min · 2,004 words
Why building a Rust LSP is hard
A deep dive into why implementing a Rust language server is unusually hard: incremental analysis, macro expansion, borrow checking at IDE latency, and lessons from building Rust Glancer alongside rust-analyzer.
26 min · 6,056 words
Code Scans turns broad engineering goals into concrete improvements across your codebase. Tell Devin what you want to achieve, and it investigates what needs to change, evaluates the findings, and turns them into pull requests.
4 min · 884 words
You are the AI agent's harnessPreventing hallucinations upstream by treating the engineer as the harness.
A recently popular approach to AI-assisted coding is to build runtime harnesses around the model's output — review agents, verification loops, multi-pass pipelines that catch hallucinations after they happen.
14 min · 3,269 words