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Looking for Contributors: Building FaultPlane, a High-Performance System Engine (Good First Issues Open!)

Why FaultPlane Exists Modern low-latency infrastructure demands bypassing heavy disk serialization. FaultPlane addresses this by implementing an eBPF-driven architecture designed for zero-copy memory management. We are building the core engine along with an interactive Next.js operations dashboard, and we need your expertise to accelerate development. Our Technical Stack Core Infrastructure: Go (Golang), eBPF, Kernel-space architecture, PCIe DMA abstractions Operations Dashboard: Next.js, Tailwind CSS, TypeScript Active Challenges (Good First Issues Available) We have organized our current roadmaps into highly accessible, well-documented GitHub issues. Contributors of all skill levels are welcome to claim tasks: Architecture & Documentation: Migration of repository references and architectural namespaces from AgentMesh to FaultPlane. Frontend Engineering: Implementation of a minimalist dashboard grid layout with command palettes, responsive sidebar navigation, and multi-region panel toggles. System Programming: Implementation of lock-free ring buffer memory allocators using sync/atomic flags, and eBPF sockmap TCP stream splicing. How to Get Your First Pull Request Merged Explore our current open tracker list: https://github.com/devloperdevesh/FaultPlane/issues Leave a comment on the issue you wish to claim, and it will be assigned to your profile immediately. If you require setup assistance or technical clarification, start a thread under our discussions tab or leave a query right here in the comments. Join us in building a high-performance open-source system engine.

2026-07-19 原文 →
AI 资讯

Why I Stopped Copy-Pasting Repositories and Started Building My Own Starter CLI

Every developer has a "starter project." Some keep a GitHub template. Some duplicate their previous SaaS project. Some run create-next-app and spend the next two hours installing the same dependencies, configuring the same tools, and recreating the same folder structure. I was in the second group. Every new project started the same way. bun create next-app Then came the checklist. Install Tailwind CSS. Configure Biome. Add shadcn/ui. Organize folders. Set up a UI library. Configure TypeScript. Add environment files. Set up a monorepo. Copy utility functions. Configure path aliases. Install development tools. None of these tasks were difficult. They were just repetitive. After starting enough projects, I realized something: I wasn't building products. I was rebuilding the same foundation over and over again. The Starter Kit Trap Like many developers, I created a "starter repository." Whenever I wanted to build something new, I'd clone it. It worked... until it didn't. Eventually I had multiple starter repositories. One for a monorepo. One for a standalone project. One with authentication. One without authentication. One for experiments. One that was already outdated. Keeping them synchronized became its own maintenance project. Fix a bug in one. Forget to fix it in another. Upgrade Next.js in one repository. Forget the rest. The more starters I created, the less useful they became. Why Existing Starters Didn't Quite Fit There are already fantastic starter kits in the ecosystem. Some focus on minimalism. Others include every feature imaginable. The problem wasn't that they were bad. The problem was that they optimized for someone else's workflow. Every project I build starts with almost the same stack. Next.js TypeScript Bun/pnpm Tailwind CSS v4 shadcn/ui Biome Production-ready project structure I didn't want to answer twenty configuration questions every time I scaffolded a project. I wanted one command. npx create-notils my-app …and be ready to start building. Opini

2026-07-19 原文 →
AI 资讯

A tiny engine for generating file trees

I just tagged 1.0.0 of ts-treegen, a small TypeScript library for describing file structures as data and writing them to disk. If you've ever built a CLI, a scaffolding tool, or anything that needs to generate a bunch of files and folders, you know the usual approach: a pile of fs.writeFileSync calls, manual path joins, and conditional logic scattered everywhere. ts-treegen is my attempt at making that feel less like plumbing and more like just describing what you want. What it looks like import { file , dir , emit , plan } from " ts-treegen/node " ; const files = await emit ( file ( " README.md " , " # My New App " ), dir ( " src " , file ( " index.ts " , " console.log('hello'); " )), ); const p = await plan ( files , { targetDir : " ./output " }); await p . run (); file() and dir() build a tree. emit() resolves it. plan() figures out what needs to be written and gives you a chance to inspect it before anything touches disk. That's the whole API. Conditional files don't need any special syntax either. It's just JavaScript: isProd && file ( " .env.production " , " NODE_ENV=production " ); No template tags, no wrapper nodes to learn. If a value is falsy, it's filtered out. Why I built it this way The goal from the start was to keep the surface area small enough that you could hold the whole API in your head after reading the README once. I went through a few iterations before landing here, and each one was mostly about removing things rather than adding them. Conflict resolution collapsed down to a single overwrite flag. Copy helpers got cut because fs.cp already does the job. Custom error types got replaced with things you'd actually reach for in normal code. Every feature I kept had to earn its place by solving something real, not just being possible to build. Along the way the library also became runtime-agnostic. The core has zero dependencies and works against a small FileSystem interface, so I/O is fully pluggable. ts-treegen/node wires up Node's fs/promises fo

2026-07-19 原文 →
AI 资讯

What encryption actually is, in plain words.

I’ve read the word “encrypted” on more apps than I can count, and most of the time it tells you almost nothing. Here’s what it really means, the way I’d explain it to a friend. Every app you use will tell you your data is encrypted. It’s on the login screen, the pricing page, the little padlock in the corner of the browser. And because it’s on everything, it’s stopped meaning much. I’ve spent more time on that one word than I’d care to admit, building a notes app where it actually has to be true, so here’s how I think about it. No maths. No padlock pictures. Underneath, encryption is an old and simple idea. You take a message, turn it into nonsense nobody can read, and make sure only the right person can turn it back. That’s the lot. Everything after that is detail about how good the nonsense is, and who’s allowed to undo it. The whole thing in one sentence Encryption takes something readable and mixes it into a mess that means nothing on its own. A matching key turns the mess back into the original. No key, and the mess stays a mess. The readable version is called plain-text. The mixed version is called cipher-text. That’s the entire vocabulary you need to know. Encryption turns plain-text into cipher-text, decryption is turning it back. A note is just text until you scramble it Say you’ve got a note on your phone, “dentist Thursday, 3pm”. Stored as it is, anyone who gets at the file reads it straight off. A thief with your unlocked phone. An app you handed too many permissions to. A company keeping a copy on its servers. All of them see “dentist Thursday, 3pm”. Encrypt it and that same note might sit on the disk as 9f2ac1b0e7..., a run of characters that means nothing. The appointment is still in there, in the sense that the right key brings it back, but on its own it tells a snoop nothing. Not the time, not the day, not that it was ever about a dentist. People reach for a padlock to explain this and I’ve never liked it. A padlock just stops you getting to the thi

2026-07-18 原文 →
AI 资讯

I built a workflow that forces AI agents to teach you the code they write

As a developer, I got a bit scared and fed-up of blindly merging massive pull requests and losing context on my own codebase, so I built FluencyLoop . https://github.com/baokhang83/fluencyloop It is an open-source, local-first workflow plugin for Claude Code and Codex that ensures your code and your codebase fluency are produced together. Instead of letting an agent dump raw code, it tracks your technical familiarity locally and pauses to explain complex architectural choices or rejected alternatives only on topics you do not know yet. It also produces documentation and tracks the decisions it makes along the way. Try it out ! It feels really good to get the AI caring for my understanding 🤣

2026-07-18 原文 →
AI 资讯

Retrieval-Augmented Self-Recall — What the Comments Taught Me (RE-call v0.3)

A follow-up to Part 1: the self-recall thesis — the series runs through Part 6 . Code: RE-call — everything below is measured and reproducible ( make eval ), full study in docs/ENTAILMENT_SUPERSESSION_STUDY.md . I published a thesis post about agent memory and got five comments that were better than the post. Two of them didn't just critique the design — they described, precisely, why it would fail and what would fix it. So I did the only reasonable thing: I turned both into experiments, ran them on the same eval harness the series is built on, and shipped what survived. That's RE-call v0.3 , and this post is the receipt. I want to be explicit about why I'm writing it this way. The point of publishing this series was never broadcast — it was error-correction . A design you keep in a drawer accumulates conviction; a design you publish accumulates objections , and objections are the cheapest high-quality signal you will ever get. The comment section of Part 1 did more for this codebase than any week of solo iteration. This post exists to pay that back with the thing commenters almost never receive: evidence that someone listened, measured, and changed the code. Comment 1: "A similarity score is not a confidence score" Vinicius Pereira put it in one line I've been quoting since: Proximity is a candidate; entailment is the evidence. His argument: the near-misses that hurt most are high-similarity and wrong — memos semantically adjacent to the query that don't answer it. A threshold-based gap_warning (Part 3, Part 5) waves them straight through by construction , because their similarity clears any threshold you could calibrate. The abstention signal cannot be the retriever's own score. You need a separate check that the retrieved memo actually entails an answer. He was right, and measurably so. I built a held-out challenge set of 10 near-miss queries — each names a strongly on-topic memo that does not contain the asked-for fact ("how much did the cache reduce memory usag

2026-07-18 原文 →
AI 资讯

I tried to trick my own AI-skill signing tool. Here's what happened.

Over the last few months I’ve noticed a pattern emerging across AI tools. Whether it’s Claude Skills, Cursor, Codex, or custom agent frameworks, we’re increasingly giving AI agents “skills”—packages containing instructions, documentation, and sometimes scripts. The problem is… A skill is usually just a Markdown file (plus some assets). Nothing tells you: Who created it. Whether it has been modified. Whether the version your AI is executing is the same one you reviewed yesterday. Whether someone quietly injected new instructions into it. As AI agents become capable of executing increasingly powerful workflows, that becomes a real supply-chain problem. So I built Skillerr. ⸻ What is Skillerr? Skillerr is an open-source protocol and CLI that adds trust and verification to AI skills before they’re executed. Instead of treating a skill as “just another folder,” Skillerr treats it as a verifiable package. It focuses on three things. Package Integrity Every packaged skill receives a unique content-derived identifier along with cryptographic SHA-256 hashes. If any file changes after packaging—even a single character—Skillerr detects it immediately. No silent modifications. ⸻ Structured Contracts Instead of relying on long paragraphs that an AI has to interpret, a Skill contains a structured contract describing: required inputs permissions forbidden actions expected outputs whether a human has actually reviewed it This makes skills easier for both humans and AI agents to reason about. ⸻ Optional Public Provenance Authors can cryptographically sign their skills. Optionally, the package digest can also be anchored into Sigstore’s transparency log, making it independently verifiable without trusting Skillerr itself. Importantly: Only cryptographic identifiers are published. No prompts. No documentation. No knowledge base. No proprietary content. ⸻ I tried to break my own tool Before releasing it, I intentionally attacked it. First I packaged and signed a simple CSV processing s

2026-07-18 原文 →
AI 资讯

CSV is plain text until a spreadsheet guesses: preflight risky cells with PlainCell

A CSV file can preserve every byte you exported and still look different after somebody opens and saves it in a spreadsheet. The problem is not always a broken parser. Spreadsheet applications deliberately interpret text during import. Depending on the application, version, locale, and import path, that can remove leading zeros, round long integer-like values, treat E notation as scientific notation, parse compact letter-and-digit tokens as dates, or reinterpret decimal and thousands separators. I released PlainCell to make those possible interpretations reviewable before the file is opened and saved. PlainCell is a zero-dependency, local, read-only Node.js CLI and browser workspace. It reports the exact source cell, physical line, column, original text, reason, and an explicit import recipe. It does not rewrite the CSV or claim to know what the value was intended to mean. A small example Consider this export: record_id,account_code,sample,reference,amount 1,00123,JAN1,1234567890123456,"1.234,56" 2,00007,12E5,9876543210987654,"19,95" Install PlainCell from the public Codeberg npm registry: npm install --global plaincell@0.1.0 \ --registry = https://codeberg.org/api/packages/automa-tan/npm/ Then run the preflight: plaincell risky.csv The bundled fixture reports eight possible interpretations across four columns. Those findings cover several separate risks: 00123 and 00007 may lose leading zeros if imported as numbers; 16-digit integer-like references may exceed the precision preserved by ordinary spreadsheet numeric cells; 12E5 may be interpreted as scientific notation; JAN1 may be interpreted as a compact date-like token; decimal-comma and mixed grouping/decimal text depend on the chosen import locale and separators. A finding means “review this cell and the import settings,” not “data loss definitely occurred.” PlainCell cannot infer whether a value is an identifier, a measurement, a date, or a typo. Provenance instead of a generic warning “Be careful opening CSV i

2026-07-18 原文 →
AI 资讯

variant-confidence v0.1.0: a calibrated confidence layer for variant-effect pathogenicity scores

variant-confidence v0.1.0: a calibrated confidence layer for variant-effect pathogenicity scores State-of-the-art variant-effect models are accurate in cross-validation but their scores are poorly calibrated on temporal data. variant-confidence adds an auditable calibration layer on top of existing predictors — it does not train a new model. The problem: accuracy is not trust Protein variant-effect predictors (AlphaMissense, ESM-1v, EVE) report pathogenicity scores, but a clinician or researcher needs to know how much to trust the number , not just its rank. The gap is calibration, not accuracy: AnnotateMissense (2026) reports MCC 0.94 in cross-validation, dropping to 0.76 on temporal ClinVar, accuracy 0.8798. A raw score near 0.9 may not mean 90% probability. Acting on an uncalibrated score is a risk. What it does variant-confidence wraps an existing predictor's score and produces a calibrated, uncertainty-aware output: Probability calibration (AC1): Platt scaling or isotonic regression over a separate holdout. Selectable, not hardcoded. Conformal prediction (AC1b): coverage 1−α intervals, split or Mondrian by gene. ECE (AC2, AC9): Expected Calibration Error reported before/after calibration, with bootstrap CI and per-bin counts. Bins with too few samples are flagged as low-reliability. Leakage-free split (AC3): temporal split by ClinVar release date with gene isolation — the same gene never appears in both train and test. This is unit-tested. Missing-score handling (AC4): works with AlphaMissense or ESM-1v alone; emits an explicit warning instead of failing silently. Non-deceptive reporting (AC7): every result includes interval/ECE + method + threshold, never a bare calibrated score. Verification (clean clone, no network) Built under a three-party governance loop: implement → independent audit in a clean clone → merge approval. ruff check . → All checks passed. pytest tests/ → 28 passed in 8.90s (offline fixture). An honest bug we caught in audit The first ECE tes

2026-07-18 原文 →
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