标签：#ENSO

The document hub, our vision of DocuShark . We want to make collaboration simple again. There are too many amazing tools, too many surfaces to get lost in. Bring them together - and you've got a near-endless wealth of knowledge for anyone with access. The editor is out, and loaded with features, only getting more powerful. Our editor offers: high-speed, realtime collaborative editing on documents in your Cloud Workspace, documents that can write, draw, and store files at the same time, never lose access when your network goes out - offline copies let you use every feature anywhere, and agent endpoints (MCP) for all your agentic needs. The page and canvas are one, with generous file storage, allowing you to design whitepaper-level PDFs in hours, not weeks, with every file, reference, and diagram within that document, all while offline with changes saving when you're back online. It's a mini Google Drive in each document, with offline storage so you can edit anywhere, anytime with changes syncing across your team. The Integrations Story - Combine, don't Compete DocuShark isn't here to compete, it's here to integrate, and keep complex ideas lean and organized across platforms. As we release our integrations, knowledge drift shrinks, leaving you with richer context while you keep working with your favorite apps - or don't, we have rich editor tools as well. An Agent Powerhouse - Keep your Context Close DocuShark is built for agents from the ground up. Citations keep your agent's research properly attributed. Fields eliminate drift and block duplication before it starts. Anchored edits make changes surgical, not sweeping. More is in the works, and the roadmap is moving fast. Try DocuShark - The Editor's Free and Fast You can either launch straight into the editor , or get a cloud workspace and start collaborating today!

AI can now scan major open-source projects and surface a batch of real, exploitable vulnerabilities in a single pass. That's a defensive win — until you remember attackers have the same tools. Anthropic, Google, Microsoft, OpenAI, AWS, and 15 other organizations aren't waiting for that race to get worse. On Thursday they launched Akrites under the Linux Foundation — a coordinated body built specifically for AI-era vulnerability discovery, remediation, and disclosure in critical open-source software. What actually changed A shared Security Incident Response Team (SIRT) replaces the fragmented model where multiple orgs independently scan the same libraries, file duplicate CVEs, and bury maintainers in noise Patch first, publish second — findings are held under strict confidentiality until a fix is ready and tested Fallback maintainer coverage — if a project has no active maintainer, Akrites steps in so fixes still reach downstream users Funded by Alpha-Omega , an OpenSSF project with $7M+ annual budget backed by the same founding members Three membership tiers — Premier (critical infra operators), General (contributing orgs), Associate (OSS foundations, free) The name comes from the Akritai — Byzantine soldiers who guarded the empire's outermost borders. The places most exposed, most frequently attacked, and most dependent on whoever showed up to defend them. The problem it's actually solving The current coordinated disclosure model was designed around a world where finding vulnerabilities took weeks of expert work. AI has collapsed that timeline. Endor Labs CEO Varun Badhwar put a number on it: thousands of validated open-source vulns surfaced by AI in recent months, with fewer than 5% patched. And the old model makes it worse — every org independently sitting on knowledge of an unpatched flaw is another leak risk before a fix exists. "For years, we have believed finding vulnerabilities was never the hard part. Fixing them was. AI has made that gap impossible to igno

Back in May we here at Waggy Labs launched the beta release of our Web Component UI kit " Yumekit ". Yumekit is a pure web component UI toolkit. Upon its release, it was comprised of roughly 36 fully styled and fully functional UI components that work with just about every web architecture straight out of the box. No configuration or setup necessary, all one needs to do is include the Yumekit script (using either a CDN or installed through NPM) and start building. All components come styled out of the box with no need to include any style sheets. Last week, we launched version 0.5. With this latest release, that job is being made easier with the inclusion of new components that add several layout options as well as new Data, Navigation, and Utility components, bringing the total number of components to 51. For us, this toolkit has provided us a framework-agnostic solution for our internal tools as well as any client projects. With over 60 themes spread over 9 well-known (and some brand new) open source Design Systems all built directly into the library, we have plenty of options available to us to keep our designs fresh without needing to spend hours dealing with CSS. It's light-weight, dependency free, and well documented. New in 0.5 Animate The y-animate component allows you to animate entrances and exits for nested components using a few simple configuration attributes. Code The y-code component allows you to display formatted and colorized code, as well as providing a few easy and convenient ways for your users to copy the provided code. Help The y-help component provides a tutorial experience for users of your application with minimal configuration. Simply provide the elements to be highlighted, the messages to be shown, and it handles the rest! Paginator y-paginator provides a configurable set of pagination buttons to help your users navigate through large data sets. Sidebar We had originally included a y-appbar component (which we still do) that had a "Sideba

GitHub热门项目 | A cross-platform GUI library for Rust, inspired by Elm | Stars: 30,845 | 10 stars today | 语言: Rust

GitHub热门项目 | A faster, lighter, cheaper alternative to sandboxes. Run any coding agent inside an isolated Linux VM, with agent orchestration built in. | Stars: 3,278 | 52 stars today | 语言: Rust

GitHub热门项目 | This is the Rust course used by the Android team at Google. It provides you the material to quickly teach Rust. | Stars: 33,156 | 31 stars today | 语言: Rust

GitHub热门项目 | AI productivity studio with smart chat, autonomous agents, and 300+ assistants. Unified access to frontier LLMs | Stars: 47,870 | 47 stars today | 语言: TypeScript

GitHub热门项目 | The batteries-included full-stack framework for the AI era. Develop JS/TS web apps (React, Node.js, and Prisma) using declarative code that abstracts away complex full-stack features like auth, background jobs, RPC, email sending, end-to-end type safety, single-command deployment, and more. | Stars: 18,504 | 29 stars today | 语言: TypeScript

GitHub热门项目 | The flexible backend for all your projects 🐰 Turn your DB into a headless CMS, admin panels, or apps with a custom UI, instant APIs, auth & more. | Stars: 36,297 | 44 stars today | 语言: TypeScript

GitHub热门项目 | Google Cloud Client Library for Node.js | Stars: 3,181 | 0 stars today | 语言: JavaScript

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GitHub热门项目 | A script allowing you to download images and videos from Telegram web even if the group restricts downloading. | Stars: 4,656 | 69 stars today | 语言: JavaScript

GitHub热门项目 | Edit videos with coding agents | Stars: 10,432 | 216 stars today | 语言: Python

GitHub热门项目 | AI generates a real, editable PowerPoint from any document — native shapes & animations, speaker notes voiced as audio narration, and the option to follow your own .pptx template, not slide images · by Hugo He | Stars: 32,688 | 589 stars today | 语言: Python

GitHub热门项目 | CasaOS - A simple, easy-to-use, elegant open-source Personal Cloud system. | Stars: 35,603 | 502 stars today | 语言: Go

Hey DEV community! 👋 I've been building web apps for a while, and I noticed there was no good place to discover and rate web-based OS projects — those cool browser-based operating systems you can run without installing anything. So I built Web OS Community 🎉 What is it? A platform where you can: 🔍 Browse web-based OS projects (Windows XP, Ubuntu, macOS clones, and more) ⭐ Rate your favorites with a global rating system 🏷️ Filter by tags (webos, demo, linux, macos, windows...) 📤 Submit your own web OS project via GitHub PR Why I built this The existing resources were scattered — some projects on GitHub, some on random personal pages. I wanted a central hub where developers could showcase their work and users could find these cool experiments easily. Tech stack Frontend: Vanilla JS / HTML / CSS (no frameworks, keeping it lightweight) Backend: Supabase (PostgreSQL + RLS policies) Auth: Custom RPC-based authentication Hosting: GitHub Pages + Cloudflare Workers Some features Global rating system (one vote per user per project) Admin panel for managing submissions Tag-based filtering Dark mode UI 🌙 Check it out! 🔗 web-os-community.tfhy5321.workers.dev If you have a web OS project, feel free to submit it! PRs are welcome 🙌 What web-based OS have you seen that blew your mind? Drop it in the comments!

Legacy ETL modernization is often described as a conversion exercise: Informatica mapping in. Snowflake SQL out. That framing is incomplete. A real migration is not only about translating expressions. It is about preserving transformation intent, identifying what is missing, documenting assumptions, validating target behavior, and ensuring that someone is accountable for decisions before generated artifacts are released. I have been building a prototype called Data Engineering Copilot around that idea. The latest capability starts from an Informatica PowerCenter XML export and produces a governed Snowflake migration delivery packet. The workflow is: Informatica PowerCenter XML ↓ Metadata and Lineage Extraction ↓ Canonical Metadata Model ↓ Snowflake Artifact Generation ↓ Validation and Migration Risk Assessment ↓ Human Review and Approval ↓ Governed Release Package The problem with simple code conversion An Informatica mapping can contain far more than a direct field-to-field relationship. A typical mapping may include: source definitions and target definitions source qualifiers and filters expression transformations reusable transformations lookups constants and default values mapping parameters target load order connector-level lineage update strategy or sequence-generation behavior target fields with no visible incoming connector A generator that only reads source and target columns may produce SQL that looks valid but does not preserve the original delivery intent. That is risky. For example, imagine a target field that has no visible source column. It may still be populated through: a constant such as 'SOURCE_A' a default such as 'XNA' a surrogate-key lookup a runtime parameter a load timestamp a sequence generator a business decision that was never documented in the mapping If the tool silently inserts NULL , the SQL may compile while the migration is functionally wrong. The prototype approach The Data Engineering Copilot prototype accepts two starting points:

I've been using Home Assistant for a few years and Trading212 for longer than that. It was inevitable these two things would end up connected. The Trading212 API is surprisingly good — portfolio value, individual positions, pies, dividends, all there. So I wrote a custom integration to pull it all into HA as sensors, then a Lovelace card to make it actually look decent on a dashboard rather than a wall of entity rows. The card does zero-config auto-discovery which was the bit I spent the most time on. You drop it on a dashboard and it finds your sensors automatically — no copying entity IDs, no manual config unless you want it. Five card types: portfolio overview with a sparkline, scrollable positions list, pies with goal progress, and a combined one if you want everything in one card. The sparkline was fiddly. HA's recorder only writes state changes, not regular samples, so if your portfolio value is flat between polls the chart has gaps. Had to smooth over those client-side. The part I use most though is the automations. Every weekday at 8am Alexa tells me where I stand: action : - action : notify.alexa_media_kitchen data : message : > Portfolio is worth {{ states('sensor.trading212_total_value') | float | round(0) | int }} pounds. Today you are {% if states('sensor.trading212_pnl_today') | float >= 0 %}up{% else %}down{% endif %} {{ states('sensor.trading212_pnl_today') | float | abs | round(2) }} pounds. data : type : tts And Friday at 6pm I get the weekly version with P&L for the week and which position moved the most. I like that it just tells me — if the market's had a bad week I'd probably avoid opening the app, but Alexa doesn't give me the option to ignore it. Both the integration and the card are on GitHub. The card is in HACS as a custom repo while it waits for default catalogue approval: https://github.com/Smart-Home-Assistant-UK/lovelace-trading212-card I wrote up the full setup with all the automation YAML here if you want to copy the whole thing: ful

title: sqlex — A Modern Drop-in Replacement for jmoiron/sqlx published: false description: sqlex is a fully API-compatible modernization of jmoiron/sqlx that fixes 20+ long-standing bugs, adds pluggable hooks, auto IN expansion, and more. Built for Go 1.21+. tags: go, database, sql, opensource If you use sqlx, this is worth 3 minutes of your time jmoiron/sqlx has been the go-to SQL extension library for Go for years. Struct mapping, named parameters, IN clause expansion — it made database/sql actually pleasant to use. I've used it in almost every Go project I've worked on. But here's the reality: its activity has been modest at best, and has slowed to a crawl in recent years. Hundreds of issues sit untouched. PRs go unanswered. Bugs reported years ago are still there, waiting to cause production incidents. This isn't a knock on sqlx — it's a great library with solid design. But an unmaintained foundational library is a liability. So we built sqlex sqlex is a drop-in replacement for jmoiron/sqlx that is 100% API-compatible . All sqlx methods ( Get , Select , Exec , NamedQuery , Preparex , etc.) work identically. Migrating takes 30 seconds — just change the import path: - import "github.com/jmoiron/sqlx" + import "github.com/go-sqlex/sqlex" 🐛 20+ bug fixes from sqlx, all fixed 🚀 New features sqlx never had Auto-Rebind — write ? everywhere, works on PostgreSQL ($1), MySQL (?), SQLite (?), SQL Server ( @p1 ). No more manual db.Rebind(). SQL parsing fixes — colons in strings, :: type casts, ? in comments are correctly handled. Silent bugs from sqlx are gone. Auto IN expansion — slices in IN (?) are detected and expanded automatically on all methods. Hook system — pluggable SQL interceptors for logging, tracing, metrics (onion model). JSONValue[T] — generic JSON column type with auto serialize/deserialize. StrictMode — lenient by default (matching sqlx Unsafe()), optionally strict for debugging. Unified interfaces — Ext / ExtContext / NamedExt / BindExt with compile-time

I Built 9 AI Agents to Run a Gym. Here's the Architecture. The thesis that changed everything Most people think AI in business means: a chatbot → a dashboard → a few automated emails. I think it means: an entire organization runs on specialized AI agents, coordinated by a constitution, accountable to an independent auditor — with one human founder providing direction and warmth. Not a demo. Not a simulation. A real fitness studio in Dongguan Wanjiang, China. Real members. Real revenue. Running since April 2026. Here's the architecture. One Brain, Two Faces, Four Layers Let me start with the big picture, because the architecture is the strategy. ZWISERFIT = AI Operating System for Physical Businesses │ ├── 【Kernel】 9-Agent Enterprise OS (24×7 · full-stack autonomous) │ ├── 【Application Layer】 Saros & Melody │ Saros = Momo(Brain) + SaaS Stack → Digital Store Manager (B2B) │ Melody = Momo(Brain) × 3-Layer Metabolism → Personal Coach (B2C) │ ├── 【Data Layer】 KinTwin │ Hardware sensors + Nova behavioral streams + Ethan ZK proofs │ └── 【Protocol Layer】 Zeus Protocol Cross-domain agent communication + automated data transactions Fitness is the first vertical. Once the protocol runs, insurance, corporate health, and cross-industry data markets come online sequentially. The same architecture, different verticals. The 9 Agents: A Department Store for the AI-Native Company Each agent has domain expertise, a constitution (SOUL.md), identity (IDENTITY.md), memory (MEMORY.md), and cross-validation rules. They don't run on prompts. They run on governance. 🎯 Shuyu — Commander-in-Chief Orchestrates all 9 agents on the founder's behalf. Reads every agent report, coordinates across departments, makes daily strategic calls. The founder sets direction; Shuyu ensures execution 24×7. Role: COO + Chief of Staff, AI-native Output: Daily operational reports, cross-agent coordination logs Constitutional scope: Has authority over all agent scheduling but cannot modify the constitution 💰 Zeus —