标签：#ens

Most data engines can answer a query. I wanted one that could also explain, offline and byte for byte, why that answer belongs to a specific durable state. That question led me to build Hyphae , an open-source data engine in Rust with a deliberately small operational footprint: one native binary; one data directory; no required database, cache, cloud service, embedding provider, or LLM; deterministic KV and structured queries; portable result proofs that can be verified offline. If you read my earlier post about provenance over prediction , this is the next and deliberately narrower step. That work began as a cognitive substrate and clarified the provenance thesis. I have since rebuilt Hyphae as a standalone data engine. The research lineage remains, but the current product does not need an AI stack to be useful. Hyphae 0.1.0 is now available on GitHub , through crates.io, and as signed multiplatform archives in the first public release . The problem was not another query syntax The usual path for an application data feature grows surprisingly quickly. A local library becomes a database service. Search adds another service. Caching adds another. Semantic retrieval adds a model provider. Soon, a feature that should be optional controls whether the application can start at all. There is a second problem hiding underneath that operational stack: a successful response is usually just an assertion from the system that produced it. If I receive a filtered, sorted, limited result, how do I check that: the underlying durable state was not corrupted; the query was executed with the declared semantics; matching rows were not silently omitted; the returned result is tied to the state I actually intended to trust? Checksums help with corruption. Signatures can identify a producer. Neither one, by itself, proves that an arbitrary query result is complete and was reexecuted correctly. Hyphae is my attempt to make those concerns part of the engine instead of application glue. What

Last Tuesday I was deep in a session with an AI assistant, rewriting a caching layer that had been quietly leaking memory in production. We tried Redis first. It worked. Then I looked at the actual data volume (about 50MB, tops) and killed the idea, because spinning up a whole service to cache 50MB felt absurd. We landed on sqlite instead. Good call, I still think. Two days later a teammate opened the branch, saw cache_sqlite.py sitting next to a deleted cache_memory.py , and asked why we weren't just using Redis like the rest of our stack. I didn't have a good answer ready. The reasoning had lived entirely inside a chat session that was long gone by then. I remembered making the decision. I could not reconstruct it convincingly, and "trust me, we talked about it" is not a great answer to give a teammate, or future me, three months from now. That's the part most tooling misses with AI-assisted coding. The code survives. The diff survives. A wave of AI memory tools is racing to store more of what the code does , and a few (Selvedge, presence) are starting to chase the why too, which tells me the pain is real. But almost all of them are MCP servers you have to wire into a specific agent, with a database sitting beside your repo. What I wanted was dumber and more portable: the reasoning, and the approaches I tried and threw out, written in plain text I can read, next to the code, no matter which editor or agent produced it. Because that reasoning lives in a chat window, and chat windows end. You switch from Claude Code to Cursor, or you just close the laptop, and it's gone. The facts stay. The judgment behind them evaporates. This didn't feel like a hard problem, so I built a small thing called Alpheon to fix it for myself, the simplest possible version: one Python file, no server, no database , works off git so it doesn't care what editor or agent you use. Facts vs. reasoning Here's the distinction that clicked for me. A fact is "cache.py was modified, cache_sqlite.py

Hey friends! It's been a while since I tried to find open source projects to contribute to as I've been swamped with some projects myself. Someone approached me. They want to learn and start to contribute to open source. They're familiar with GitHub for their own projects, but never contribute to any open source project. They tried looking for beginners friendly issues on GitHub with no luck. Well, I immediately tried this myself and got overwhelmed with millions of issues only for JavaScript! So, I understand the struggle. 😅 If you're maintaining an open source project or know beginners-friendly open source projects that open to new contributors (preferably JavaScript, TypeScript, React, or NextJS), please send them my way in the comments below! 😊

GitHub热门项目 | 把书、长视频、播客等高价值内容蒸馏成可执行的 Agent Skills | Stars: 3,227 | 972 stars this week | 语言: Python

GitHub热门项目 | Modern embedded framework, using Rust and async. | Stars: 9,544 | 9 stars today | 语言: Rust

GitHub热门项目 | Memory layer for AI Agents. Replace complex RAG pipelines with a serverless, single-file memory layer. Give your agents instant retrieval and long-term memory. | Stars: 15,926 | 91 stars today | 语言: Rust

GitHub热门项目 | AI coding jargon, explained in plain English. | Stars: 2,771 | 86 stars today | 语言: TypeScript

GitHub热门项目 | 🍍 Intuitive, type safe, light and flexible Store for Vue using the composition api with DevTools support | Stars: 14,662 | 11 stars today | 语言: TypeScript

GitHub热门项目 | CLI to control iOS and Android devices for AI agents | Stars: 3,413 | 34 stars today | 语言: TypeScript

GitHub热门项目 | Modular logistics and supply chain operating system (LSOS) | Stars: 2,098 | 43 stars today | 语言: JavaScript

GitHub热门项目 | PairDrop: Transfer Files Cross-Platform. No Setup, No Signup. | Stars: 10,914 | 107 stars today | 语言: JavaScript

GitHub热门项目 | Local-first AI token usage & cost tracker for 27 coding tools — with a desktop pet, 4 widgets, achievements, native macOS/Windows apps, and a one-command CLI. Never reads prompts. | Stars: 1,018 | 12 stars today | 语言: JavaScript

GitHub热门项目 | A WhatsApp client library for NodeJS that connects through the WhatsApp Web browser app | Stars: 22,200 | 12 stars today | 语言: JavaScript

GitHub热门项目 | Post-training with Tinker | Stars: 3,716 | 90 stars today | 语言: Python

GitHub热门项目 | Experimental menu for GTA 5: Enhanced | Stars: 1,483 | 38 stars today | 语言: C++

GitHub热门项目 | 🤯 LobeHub is your Chief Agent Operator, organizing your agents into 7×24 operations by hiring, scheduling, and reporting on your entire AI team. | Stars: 79,951 | 51 stars today | 语言: TypeScript

GitHub热门项目 | Bonsai Demo | Stars: 1,380 | 323 stars today | 语言: Shell

GitHub热门项目 | 🦔 PostHog is an all-in-one developer platform for building successful products. We offer product analytics, web analytics, session replay, error tracking, feature flags, experimentation, surveys, data warehouse, a CDP, and an AI product assistant to help debug your code, ship features faster, and keep all your usage and customer data in one stack. | Stars: 35,625 | 58 stars today | 语言: Python

GitHub热门项目 | Apache Ossie, industry wide specification effort to standardize how we exchange semantic metadata across analytics, AI and BI platforms, providing a vendor neutral, single source of truth for semantic data | Stars: 750 | 34 stars today | 语言: Python

Every TypeORM project I've worked on grows the same few dangerous lines. I got tired of catching them by hand, so I wrote a linter that does it for me. I was reviewing a pull request a while back and nearly scrolled past this line: await manager . query ( `SELECT * FROM users WHERE id = ${ req . params . id } ` ); Looks fine at a glance. It's a SQL injection hole. The id comes off the request and lands directly in the query string. The thing that bugged me is that I only caught it because I happened to be reading carefully on that line, that day. Review catches this sort of thing a lot of the time. "A lot of the time" is how these end up in production. It's usually not only injection either. The same projects tend to collect a few other habits: synchronize: true in a data source config. It rewrites your schema on startup and can drop a column on the next deploy. A QueryBuilder delete() or update() that hits .execute() with no .where() . Forget that one line and you've changed every row in the table. Three or four writes in one function, none in a transaction, so if the second throws you're left with half-written data. On multi-tenant apps, a query that forgets the tenant filter. That's how one customer sees another customer's data. None of these really need a person to catch them. They're mechanical. A linter can do it on every commit. So I built one. eslint-plugin-typeorm-enterprise npm install --save-dev eslint eslint-plugin-typeorm-enterprise // eslint.config.js const typeormEnterprise = require ( ' eslint-plugin-typeorm-enterprise ' ); module . exports = [ typeormEnterprise . configs . recommended ]; Now those patterns are lint errors. Ten rules today, split into configs ( recommended , strict , performance , multiTenant ): raw SQL, interpolated and concatenated SQL, unsafe QueryBuilder deletes, EntityManager raw queries, writes outside a transaction, tenant scoping, and a nudge to stop counting rows when you only need to know one exists. Two things I was picky