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I DNA-encode my encrypted database before writing it to disk - here's why (and why it's not "quantum" anything)

Every value in my little embedded key-value store gets encrypted, then its ciphertext gets encoded as a string of A/C/G/T characters before it ever touches the filesystem. Open the file in a text editor and you'll see actual DNA-looking text - not because it's a gimmick, but because that's genuinely the storage format. This is mdc-lite , a ~348KB embeddable encrypted key-value store I built in Rust for places a server can't reach - a watch face, a phone app, a background service. It's part of a larger repo, ModelDB , that also includes MDC, a Python conversational data engine (query AI models, databases, images, and documents in plain English, no SQL) with its own DNA-inspired archival storage tier. The actual storage format Every put() call does this, in order: Pack [key_len][key_bytes][value_bytes] into one plaintext buffer. Encrypt the whole thing with XChaCha20-Poly1305 (a 256-bit key you supply - the crate never generates or stores key material itself; real key custody belongs to the platform's secure hardware, iOS Secure Enclave or Android Keystore). DNA-encode the resulting [nonce][ciphertext][tag] blob: 2 bits per base, 00→A 01→C 10→G 11→T . Every byte maps to exactly 4 bases, so there's no padding ambiguity on decode. Write the ACGT text to disk, atomically (temp file + rename). Filenames are keyed BLAKE3 hashes of the logical key, not the key name itself, so a directory listing alone leaks nothing - no key names, no values, no way to tell how many distinct keys exist versus how many files are on disk. rust pub fn put(&self, key: &str, value: &[u8]) -> Result<(), LiteStoreError> { let mut plaintext = Vec::new(); plaintext.extend_from_slice(&(key.len() as u16).to_le_bytes()); plaintext.extend_from_slice(key.as_bytes()); plaintext.extend_from_slice(value); let nonce = XChaCha20Poly1305::generate_nonce(&mut OsRng); let ciphertext = self.cipher().encrypt(&nonce, plaintext.as_ref())?; let mut record = nonce.to_vec(); record.extend_from_slice(&ciphertext); let ac

2026-08-30 原文 →
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The undo has to exist before the write does

An agent that changes something runs in the order decide, act, report. Verification, where there is any, reads what already happened. That's a fine shape for a log. As a control it's empty: by the time the check fails, the effect is already on disk, and what's left is describing the damage, attempting a repair nobody verified, or restoring from a backup whose age nobody measured. For the last few months I've been building the other order, not for one tool but for the whole path a change takes. A proposed change gets a canonical identity. Its inverse is constructed, checked, and stored before anything is applied. A gate rules on it and returns one of three verdicts. The outcome, refusals included, becomes a signed record that a third party can re-check offline with no trust in me. There's a longer draft paper behind this, deposited at doi.org/10.5281/zenodo.22168558 . It's a draft, not peer reviewed, and not a specification. This post is the part that fits in a coffee break. What I'd have to be wrong about Putting this first, because a claim that only becomes checkable after you already agree with it isn't checkable. Inverse availability. The escrow design assumes a useful fraction of write-capable tools expose something you can build an inverse from. A first census of public MCP tools put that at about 13.8% of the tools that write anything at all (census v2 stage 1, public MCP servers only, not production deployments). If the real number in production is at or under that, this is mostly a refusal machine, and "reversibility as a property" degrades into "refusal as a property", which is a much smaller and much less interesting thing to have built. That's the most dangerous fact in the project and it's mine, not a critic's. Offline re-verification. If a signed receipt can't be re-checked with networking off and no trust in the issuer, meaning signature, log inclusion and identifier consistency, then the provenance layer is a log and not a proof. This one is runnable

2026-08-30 原文 →
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Launching vizcrush: Three Beliefs My Benchmarks Killed

It's the week before vizcrush goes public, and I have two files open side by side. On the left, the launch copy: the JS core beats the most popular npm downsampling package by 32×, "and WASM adds another 5-10x on top." On the right, the repo's own benchmark control run: wasm/js ≈ 1.00× . One million points, same algorithm, same machine. Parity. I go looking for the measurements behind the claim. Half of it holds up: the 32× JS comparison has a result file (1.72ms against 55.52ms, real). The claimed additional 5-10× from WASM has nothing behind it, and the repo's own control run contradicts it. That afternoon set the shape of the whole launch: before anything shipped, every performance claim would either get a measurement behind it or get deleted. Three beliefs didn't survive. Each one got a public retraction, written up as an ADR in the repo. vizcrush is a set of data primitives for browser visualization (downsampling, binning, spatial indexing, streaming sketches), written in Rust, compiled to WebAssembly, with a pure-JS core behind the same API as a fallback and explicitly selectable backend. It went open source this week: the repo and the book are public, and all 11 packages are live on npm. npm install @vizcrush/core @vizcrush/downsample This is a launch story about turning benchmark results into product policy: claims, documentation, and WebGPU policy follow the measurements, while WASM dispatch stays availability-based pending further investigation. One scope note before the data. Every result here is workload-specific: LTTB (Largest-Triangle-Three-Buckets, the downsampling algorithm that picks, per bucket, the point that best preserves the visual shape of the line) is downsampling, the stats kernel is a reduction, and bin2d is histogramming. Which backend wins is algorithm- and engine-dependent, so none of what follows is a library-wide WASM-versus-JS verdict. It is three specific workloads measured on specific engines, with the claims and documentation follo

2026-08-30 原文 →
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Quipu: post-quantum encryption in pure Rust, with a Python wheel

Protecting data that must stay secret ten years from now is a problem for today : an adversary can capture your encrypted traffic now and decrypt it once quantum capability exists ( harvest now, decrypt later ). Quipu is a free hybrid post-quantum encryption library for data at rest: it combines proven classical cryptography with the new kind, so that it only breaks if both fall at once. Pure Rust, and why Quipu started out aiming at several languages: a Rust core with a C ABI on top and bindings for Python, Node and Go. It worked, but the lesson was clear: maintaining a stable C interface plus four bindings, each with its own packaging and interoperability tests, was complexity that did not pay for itself against the real goal — protecting data at rest — and it widened the attack surface with unsafe we did not want. Today Quipu is pure Rust : memory safe, no garbage collector, no first-party unsafe . And for people who do not write Rust, it ships as a native Python wheel via PyO3 — the surface that non-Rust users actually need. One codebase, one thing to audit. It is the same philosophy that guides the rest: where good cryptography exists, reuse it; simplicity is a security decision, not a convenience. Installation cargo add quipu # Rust pip install quipu-crypto # Python (native wheel, PyO3) Encrypt and decrypt in Python import quipu # Symmetric, with a passphrase blob = quipu . encrypt_stream ( b " sensitive data " , " my-passphrase " ) assert quipu . decrypt_stream ( blob , " my-passphrase " ) == b " sensitive data " # Post-quantum, for a recipient pub , sec = quipu . generate_keypair () # X25519 + ML-KEM-1024 c = quipu . encode_to_recipient ( b " secret " , pub ) assert quipu . decode_as_recipient ( c , sec ) == b " secret " What is underneath Encryption: XChaCha20-Poly1305 (authenticated AEAD). Key derivation: Argon2id (brute-force resistant) + HKDF. Post-quantum: X25519 + ML-KEM-1024 for keys; Ed25519 + ML-DSA-87 for signatures. Security level: NIST category 5

2026-08-30 原文 →
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Parallel coding agents without the carnage

We build GPTree with several coding agents working the same repository at once: Claude Code, Codex, and Cursor, each in its own git worktree. The failure that finally made us build tooling for it was small and completely silent. One session was told to replace PaymentService with a Stripe-specific implementation. Another was told to add PayPal support to PaymentService . Different worktrees. Different files. Zero textual conflict. Git merged both branches cleanly, and the second change now depended on an extension point the first had deleted. Nothing in the toolchain had an opinion about it at any moment. Git compares diffs. It cannot compare plans. Worktrees isolate files, not plans Worktrees became the standard answer to parallel agents for a good reason: two sessions editing one checkout will overwrite each other's files and poison each other's context. Isolated checkouts fix that completely. But three failure modes survive file isolation, because they were never about files: Destructive versus additive. One agent removes or replaces a thing another agent is building on. The example above. Merges clean, breaks the design. Duplicate work. Two agents solve the same problem from different angles because nothing assigned ownership. You pay twice and then pay again to reconcile. Contract drift. One agent changes an API, a schema, or a config contract while another codes against the old shape. Compiles, runs, disagrees at runtime. A shared task list helps with the second one, if every agent reads it, every time. Nothing in that setup catches the first or third, because the collision is between intentions, and intentions live in prompts, not in any file a tool can watch. Declare the work before doing it Foremerge is the internal tool we built for this, open-sourced this week. It is a coordination protocol that sits above Git: agents declare what they are about to do, before they do it, in a form precise enough to check. A declaration is an intent with one or more semant

2026-08-28 原文 →
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Astro Introduces Sätteri: A Rust-powered Markdown And Mdx Processor With Up To 60% Faster Builds

Sätteri is a high-performance Markdown and MDX processor developed by the Astro team. Built in Rust, it enhances build speeds by up to 61% for Astro 7.0. Sätteri supports flexible JavaScript plugins and integrates various Markdown features natively. It maintains compatibility with the unified ecosystem while offering faster parsing and reduced dependencies. By Daniel Curtis

2026-08-27 原文 →
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Java Service Steward, an open-source host for Java Windows services that reads wrapper.conf

Java Service Steward is a new Windows service host for Java applications. It reads the wrapper.conf format used by the Java Service Wrapper, follows the same command line and log format, and is licensed Apache-2.0 OR MIT. I wrote it because the Community Edition of the Java Service Wrapper has no 64-bit Windows build, and I did not want to buy a license or rewrite the service integration of applications that already had working configuration files. Repository: https://github.com/jayyanez/java-service-steward What it is The distribution is two files, wrapper.exe and wrapper.jar . The executable is written in Rust and does the Windows part: it registers the service, launches the JVM, keeps a control channel to it over a loopback socket, restarts it when it exits unexpectedly or stops answering pings, writes and rotates wrapper.log , and handles Service Control Manager requests (stop, pause, resume, custom control codes). The JAR is compiled for Java 8 and contains the launcher classes and a small API. There is no native DLL and no JNI. It only runs on 64-bit Windows. There is no Unix version. What is compatible Configuration. wrapper.conf with #include , #encoding , set.VAR=value , %VAR% expansion and numbered properties such as wrapper.java.additional.<n> . Relative paths resolve from the executable's directory, as before. Command line. -c runs in a console, -i and -r install and remove the service, -t and -p start and stop it, -q queries it, -d requests a thread dump. Property overrides on the command line and -- pass-through of application arguments work the same way. Service registration. An installed service's ImagePath calls wrapper.exe -s <conf> , so an existing registration keeps working. Log format. Records use the same LPTM layout, the same column widths and the same SIZE , WRAPPER and JVM roll modes, so scripts that parse wrapper.log do not need changes. Launchers. A configuration that names the original SimpleApp , StartStopApp or JarApp launcher in wrappe

2026-08-27 原文 →
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Agent-to-Agent Discovery in SMESH: Why Coordination Isn't Enough Without Runtime Introductions

You can build a working agent mesh with QUIC transport, encrypted messaging, and decentralized coordination. Five processes can reinforce independent conclusions and let unsupported signals decay. The mesh works. Then you try to introduce it to another agent and discover you have no standard way to ask what the swarm can do. No retained task to retrieve after an internal signal expires. No interoperable progress stream. No cancellation contract. No artifact another framework would understand. SMESH is a Rust-based decentralized agent framework that hit this boundary. The author had built a society with no border crossing. The solution was Google's Agent2Agent (A2A) protocol, announced in April 2025 and moved under Linux Foundation governance in June 2025. A2A provides the missing public contract: a way for agents built by different vendors to discover one another, exchange messages, and collaborate without sharing private memory, tools, or internal plans. The Cold-Start Problem in Agent Meshes Traditional service meshes solve discovery with a central registry. Kubernetes has etcd. Consul has its catalog. Envoy has xDS. You register your service, get a DNS name or IP, and other services find you. This works because services are relatively static and the registry is the source of truth. Agent meshes are different. Agents are ephemeral, context-dependent, and often spawned on demand. They need to: Discover peers without a central registry Exchange capability metadata at runtime Negotiate protocols without pre-shared configuration Maintain security boundaries during introduction The coordination primitives (message passing, consensus, signal decay) assume agents already know about each other. Discovery is the layer below coordination. SMESH had the top layer working but no way to bootstrap the bottom layer without manual wiring. What A2A Provides A2A is not a coordination protocol. It is an introduction protocol. The spec defines: Discovery handshake : How agents announ

2026-08-27 原文 →
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