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Building Educational Software for Mandarin Chinese and Interlingua IALA Language-learning software is most useful when it makes structure visible. I’m Ian Blas, a developer based in Buenos Aires, Argentina, and I build educational tools around Mandarin Chinese, Interlingua IALA, etymology, morphology, writing systems, and open-source language learning. Two projects, one educational approach My work currently takes two complementary forms. Chety is an educational app for Mandarin Chinese. It approaches characters and words through their structure, etymology, morphology, historical development, and use in context. Schola Interlingua is a free, open-source learning platform for Interlingua IALA. It brings together lessons, readings, review tools, and progress-oriented study on multiple platforms. The languages are different, but the design question is similar: how can software help a learner notice the patterns that make a language readable and memorable? Learning through structure For Mandarin Chinese, a character is not only a unit to memorize. It can open a path into components, historical forms, pronunciation, word formation, and reading. That perspective guides Chety’s tools for exploring characters and vocabulary. For Interlingua IALA, the focus shifts toward transparent vocabulary, reading, morphology, and sustained practice. Schola Interlingua is designed to make that learning path approachable without separating learners from the materials and tools that support it. In both projects, the goal is practical: make language learning more legible. Etymology and morphology are useful when they give learners better ways to connect forms, meanings, and usage. An open educational practice I care about software that can be examined, shared, and improved. Schola Interlingua’s development is available through its GitHub repository , and my broader work can be found on GitHub . I also write and share updates through Medium and Substack . Explore the projects Chety — Chines

Sandboxing Untrusted Code: Meet CubeSandbox As AI agents become capable of writing, compiling, and running code dynamically, a major security issue has surfaced: how to run this code safely . If a coding agent runs a malicious script or makes an error, it could access files on the host computer or break the entire server. Standard software containers are not always secure enough to prevent escape. CubeSandbox is an open-source, high-performance sandbox service designed specifically to solve this problem. Developed by Tencent Cloud and written in Rust, it provides isolated, secure, and ultra-fast environments for running code generated by AI. What is CubeSandbox? CubeSandbox is a lightweight virtualization system. It spins up a tiny, isolated "virtual machine" for each AI agent task. This ensures that the code runs inside its own virtual bubble, completely separated from the main server. Key Features 1. Hardware-Level Isolation Unlike standard Docker containers that share the same kernel, CubeSandbox uses KVM (Kernel-based Virtual Machine) and RustVMM to give each sandbox its own dedicated Guest OS kernel. This prevents untrusted code from breaking out of the container and accessing your primary server. 2. Under 60ms Cold Starts Traditional virtual machines take seconds to boot. CubeSandbox starts in under 60 milliseconds . This speed is crucial for real-time AI agents that need to execute code instantly. 3. High Density (Low Memory) Each sandbox instance has a memory overhead of less than 5MB . This allows developers to run thousands of concurrent, fully isolated sandboxes on a single physical machine without running out of RAM. 4. Drop-in E2B Replacement For developers currently using E2B (the popular cloud sandboxing SDK), CubeSandbox is fully API-compatible. You can migrate your setup to local hosting by simply changing an environment variable, saving you massive cloud subscription fees. How to Get Started Developers can deploy CubeSandbox locally or in a cluster

Ingeniería de Datos aplicada a la Biodescodificación: Presentando Bio-Mapping Engine 🧬 ¿Es posible aplicar ingeniería de datos de alta fidelidad a campos de conocimiento no estructurados? La respuesta es un rotundo sí. Hoy quiero presentarles Bio-Mapping Engine , un framework diseñado para resolver un problema clásico de la extracción de información: convertir literatura densa y desorganizada en una base de conocimientos semántica, estructurada y totalmente navegable. El Problema: El caos de la información no estructurada En campos como la Biodescodificación , la información suele residir en libros o archivos PDF donde los conceptos (síntomas, emociones, zonas anatómicas) están entrelazados de forma narrativa. Para un investigador o un desarrollador de herramientas de salud alternativa, extraer relaciones precisas entre un síntoma físico y su conflicto emocional mediante métodos tradicionales es una tarea manual, lenta y extremadamente propensa a errores. La Solución: Bio-Mapping Engine Bio-Mapping Engine no es un simple scraper . Es un motor de segmentación semántica y mapeo topológico. Su propósito es transformar un PDF bruto en un grafo de conocimiento estructurado en formato JSON, permitiendo realizar consultas multidimensionales con precisión quirúrgica. 🚀 Características Principales Segmentación Semántica Avanzada: Implementa un parsing topológico que distingue inteligentemente entre encabezados de síntomas, contenido emocional y el "ruido" estructural (como índices o números de página). Mapeo Relacional Multidimensional: Realiza una extracción de alta fidelidad a través de tres vectores fundamentales: Síntomas Canónicos: Estandarización de la nomenclatura de síntomas y condiciones. Jerarquía Anatómica: Mapeo inteligente que escala desde Sistemas $\rightarrow$ Regiones $\rightarrow$ Órganos. Arquetipos Emocionales: Extracción estructurada de modelos mentales y conflictos (ej. "Causa probable" , "Bloqueo emocional" ). Consultas Multi-Eje (CLI): Una potente inte

Open source has always been at the heart of what we do at Omnikon. Today, we're excited to share a milestone that means a lot to our entire community. Our repository, maintained by Sourabh, has been officially selected for GirlScript Summer of Code (GSSoC) 2026. For us, this isn't just another achievement—it's a step toward building a stronger open-source ecosystem where students and developers can learn, collaborate, and create meaningful software together. About Omnikon Omnikon is a student-led open-source organization focused on building high-quality developer tools, educational resources, and community-driven projects. Our mission is simple: Build impactful open-source software. Help new contributors get started. Create projects that solve real problems. Foster a welcoming developer community. Every repository we build is designed with collaboration in mind, making it easier for contributors of all experience levels to participate. What GSSoC Means GirlScript Summer of Code is one of India's largest open-source programs. Every year, thousands of contributors participate by solving issues, improving documentation, fixing bugs, and implementing new features across selected repositories. Being selected means our project will become part of this collaborative ecosystem, giving contributors an opportunity to make meaningful contributions while learning industry-standard development workflows. A Special Thanks This achievement wouldn't have been possible without Sourabh, who maintained and prepared the repository throughout the selection process. A huge thank you to everyone who contributed ideas, reviewed code, reported issues, improved documentation, and supported the project. Open source is never the work of one person—it grows because of a community. What's Next? We're preparing the repository for contributors by: Organizing beginner-friendly issues. Improving documentation. Creating contribution guides. Enhancing project structure. Mentoring new contributors thro

There's a pattern I kept seeing. A team gives an agent real capability, like moving money, shipping a change, or resolving a ticket that touches a customer's account. For a while it's great. Then the agent does one thing nobody can explain or defend after the fact, and the entire program snaps back to a human clicking approve on everything. The blocker was almost never the model. It was that there was no clean way to do two things at once. You couldn't bound what the agent was allowed to do before it acted, and you couldn't prove what it did after, in a form that survives contact with an auditor, a regulator, or a customer dispute. You can assemble that from parts today. Use a policy engine to authorize, and an audit log to record. The problem is they're two systems, and two systems drift. Six months later, when someone is actually asking "was this action allowed, and can you prove it," the policy engine and the log disagree about what the policy even was at the time. Now you're reconstructing intent from two sources that were never the same object. That's the gap. Not authorization by itself, and not observability by itself. The thing that authorizes an action and the thing that proves it should be the same object, bound to the exact policy version in force when the decision was made. The primitive Two verbs, one primitive. Control before. You mint a capability, which is a policy scoped to one agent: a spend cap, a counterparty allowlist, an expiry, whatever the action needs. Every consequential action the agent takes gets checked against the committed policy state and returns an allow or deny in the request path. An over-budget or out-of-policy action is refused before it happens, not flagged after. Refused is the operative word. The enforcement point commits no state change for a denied action, no matter how the agent reasons, how it's prompted, or whether it's been compromised. You've turned unbounded irreversible harm into bounded irreversible harm. Prove after

TLDR; I got tired of babysitting N terminal tabs of five different coding-agent CLIs. So I built agentproto — one daemon that drives Claude Code, Codex, Hermes, opencode, and Mastra through the same lifecycle, and actually supervises them. Why I built a daemon to drive every AI coding agent from one interface I have a confession: at any given moment I have Claude Code, Codex, and Hermes running in parallel terminal tabs, and I cannot remember which flag spawns which, which one eats --prompt , which one needs --cwd vs cd , and which one will hang forever if I close the laptop lid. simonw described the feeling on Hacker News recently — "Today I have Claude Code and Codex CLI and Codex Web running, often in parallel" — and called it a real jump in cognitive load compared to a year ago. aantix asked, also on HN: "how does everyone visually organize the multiple terminal tabs open for these numerous agents in various states?" I didn't have a good answer. So I built one. It's called agentproto . It is one daemon and one CLI that drives any coding-agent CLI — Claude Code, Codex, Hermes, opencode, Mastra, and a few more — through the same start / prompt / monitor / kill lifecycle, so you stop memorizing five different CLIs. On top of that lifecycle it adds the supervision layer people keep hand-rolling by hand: durable policy gates, nested orchestration, and multiplexed fan-in monitoring. MIT, no paid tier, the daemon itself is an MCP server. This is the story of why it exists. The hand-rolled watchdog The sharpest signal while I was building this came from other people independently re-inventing the same primitives in tmux scripts. On r/ClaudeAI, Confident_Chest5567 posted a writeup of orchestrating agents via tmux panes with a watchdog that resets dead sessions — "a swarm of agents that can keep themselves alive indefinitely." In the same thread, IssueConnect7471 (18 upvotes) described wiring a Redis pub/sub heartbeat plus dead-letter respawn between tmux panes, and arriv

TL;DR Three unrelated repos, one recurring theme: derive from a single source of truth instead of duplicating it. Shipped a registry-driven sidebar section switcher (public), converged a multi-system password flow, and pushed on a customer-data identity engine. Details on the first two live in their own posts today. 1. Registry-driven sidebar switcher (public) Added a section switcher to the kickoff starter kit. The sidebar, the switcher, and breadcrumbs all read the same config/menu.php list, and the active section is picked by longest URL-prefix match — so a detail page like /admin/roles/42/edit keeps its parent selected. Full write-up in the focused post. 2. One canonical password flow Converged two apps that each rolled their own password-change/reset logic onto a single shared engine, with a fixed order (directory → external DB → local app) and no config-toggle to skip backends. A password that syncs to some systems is worse than one that fails outright, so partial success is now impossible by construction. Also fixed a subtle status bug — an unreachable backend reports skipped (a runtime fact), not disabled (a config state that no longer exists) — and added an audit log so "did it sync?" is a query, not a guess. Separate post today goes deeper. 3. Identity resolution engine (customer data work) Steady progress on a CDP-style identity layer: an idempotent, header-versioned ingest endpoint that queues incoming records, then a resolution engine that can resolve, merge, unmerge, and quarantine profiles. Two things I care about here: PII handling: sensitive identifiers are encrypted at rest with a blind index for lookups, and masked in audit trails — you can search on a value without storing it in the clear. Right-to-erasure: an erasure cascade plus an erasure log, so a deletion request actually propagates and leaves a defensible record that it did. ingest -> queue -> resolve -> profile | +-> merge / unmerge / quarantine No code from this one here — it's teaching t

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OpenSuperWhisper 评测：macOS 上最被低估的开源语音转文字工具？ 30秒结论 ：OpenSuperWhisper 是一个基于 OpenAI Whisper 模型的 macOS 原生听写（dictation）应用。如果你受够了 macOS 自带听写的间歇性抽风，或者不想每月交钱给 Otter.ai，这个免费开源项目值得一试。 但别期待开箱即用 ——你需要自己配置模型、处理依赖，而且目前只支持 macOS。 适合人群：macOS 重度用户、需要离线语音转文字、对隐私敏感、愿意折腾配置的开发者。 不适合：Windows/Linux 用户、不想碰终端的人、需要实时流式转写（目前不支持）。 核心功能：代码实操 1. 安装部署 # 克隆仓库 git clone https://github.com/Starmel/OpenSuperWhisper.git cd OpenSuperWhisper # 安装依赖（需要 Python 3.10+） pip install -r requirements.txt # 直接运行 python app.py 坑点1 ： requirements.txt 里没写版本号，我踩了 numpy 版本冲突的坑。建议手动指定： pip install numpy == 1.26.0 torch == 2.1.0 whisper == 20231117 坑点2 ：macOS 14 Sonoma 上需要手动授权麦克风权限。第一次运行会 crash，因为没处理 PermissionError 。workaround：在 System Settings > Privacy & Security > Microphone 里手动勾上终端或 Python 的权限。 2. 基本使用 启动后会在菜单栏出现一个小图标（类似 macOS 原生听写）。快捷键是 Option + Space （可自定义）。 核心逻辑：按下快捷键 → 录音 → 松开 → 调用 Whisper 转写 → 结果写入当前光标位置。 代码层面 ，核心函数在 whisper_handler.py 里： # 简化版核心逻辑 import whisper import sounddevice as sd import numpy as np class WhisperHandler : def __init__ ( self , model_size = " base " ): self . model = whisper . load_model ( model_size ) self . sample_rate = 16000 def transcribe_from_mic ( self , duration = 5 ): # 录音 recording = sd . rec ( int ( duration * self . sample_rate ), samplerate = self . sample_rate , channels = 1 ) sd . wait () audio = recording . flatten (). astype ( np . float32 ) # 转写 result = self . model . transcribe ( audio , language = " zh " ) return result [ " text " ] 实测 ：默认 model_size="base" 时，中文准确率约 85%。换成 "large-v3" 能到 92%，但首次加载要 2GB 内存，转写一条 10 秒语音需要 8-12 秒（M1 Pro 芯片）。 3. 自定义快捷键 config.yaml 里可以改： hotkey : modifier : " option" key : " space" model : size : " base" # 可选: tiny, base, small, medium, large-v3 device : " cpu" # 或 "mps" (Apple Silicon) output : paste_delay : 0.3 # 转写后粘贴延迟，防止焦点丢失 注意 ： device: "mps" 在 macOS 14.2 上会报 MPS backend not available 。需要安装 PyTorch 的 MPS 版本： pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cpu 性能测试 测试环境：MacBook Pro M1 Pro (

tags: hardware, iot, opensource, electronics As software developers, many of us reach a point where writing code inside a virtual environment isn't quite enough—we want to manipulate the physical world. Whether it's blinking an LED via an ESP32, visualizing audio frequencies on a desk display, or building custom bench tools, hardware hacking is easily one of the most rewarding rabbit holes to fall down. At NextPCB , we’ve spent the past few years supporting the open-source hardware community by sponsoring independent creators, makers, and embedded engineers to help turn their digital schematics into real, physical circuit boards. If you’re looking for inspiration for your next weekend project, here are four curated roundups of real-world projects featuring open-source files, schematics, and design breakdowns. 1. Retro Tech & Nostalgic Geek Culture Builds 🎮 There’s something uniquely satisfying about recreating classic tech using modern hardware components. From custom hand-held arcade consoles to retro synth modules and glowing mechanical displays, retro builds combine aesthetic nostalgia with serious embedded engineering. These projects aren't just for show—they showcase clever power management, compact multi-layer PCB routing, and custom display interfaces. 👉 Check out the project breakdowns & schematics: 8 Retro Geek Culture PCB Projects: Open-Source Gerbers & Schematics 2. Smart Audio & Interactive Visual Displays 🎵 Audio reactive electronics bridge the gap between digital signal processing (DSP) and hardware UI/UX. Think custom spectrum analyzers, RGB LED matrix drivers, and tactile smart knobs that update in real-time. Building custom audio hardware requires paying extra attention to noise isolation, clean power delivery, and signal integrity—making these projects fantastic learning material for intermediate hardware devs. 👉 Explore the audio & display designs: Smart Audio & Interactive Display PCBs: Open-Source Design Guide 3. DIY Power & Precision Lab Equipm