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An AI can write a perfect email in seconds. Having a real back-and-forth conversation is much harder. Sarah runs a salon. She has an AI assistant that emails her customers when a slot opens up. Last Friday, a customer canceled his booking. The assistant sent an email: "We have an opening tomorrow at 2 PM. Want it?" The customer replied in a minute: "Yes, book it!" The assistant never saw that reply. The slot stayed open. The customer never got a confirmation. This happens more than people realise — not because it's hard to receive email, but because most setups were never wired to close the loop. Sending is easy. Wiring the whole loop isn't. To be fair, receiving and parsing email isn't some unsolved problem — providers like SendGrid, Mailgun, and Postmark have offered inbound email parsing for years. Point your domain at them, and they'll hand you the clean message. But those webhooks only push the message once. There's no inbox to check back later, and no built-in way to link a reply to the right conversation. You have to build that part yourself — and you still can't run any of it on your own servers. There's a second issue too. AI assistants sometimes send a slightly odd reply — nothing harmful, just a little off. Many managed email providers watch for exactly that pattern, and can suspend an account fast. One strange sentence, and Sarah's whole booking system could go dark with no warning. What a real AI assistant needs For an assistant like Sarah's to actually hold a conversation, a few things need to work together: Replies need to land somewhere the AI can read them They need to arrive clean, not messy They need to stay linked to the right conversation The AI needs to reply back from the same email address All of it needs to run on infrastructure you control, not three different vendors This is what we built Reloop for Reloop puts that whole loop in one place, self-hosted. When Sarah's customer replied "Yes, book it!", Reloop caught the reply, cleaned it up,

Most code that calls an AI model works like a conversation: ask, wait a second, get a reply. Fine-tuning doesn't. You hand off a job and walk away, checking back every few seconds to see if it's finished. DSPy is a framework for building programs that call language models. Instead of hand-writing and endlessly tweaking prompt strings, you declare what you want in terms of inputs and outputs, and DSPy turns that into the actual prompt. It can even optimize those prompts for you automatically, so getting a better result doesn't mean rewording things by hand. Here's something I didn't know starting out: DSPy already knows how to talk to almost any AI model, Together AI included. Asking a question and getting an answer back is handled by a shared layer that works for everyone, so no new code is needed there. Fine-tuning (the "hand off a job and walk away" thing from the top) is the exception. Every company does fine-tuning its own way, so DSPy needs a small custom piece, called a Provider, to handle each one. Building the Provider for Together AI is what my PR does. Why does this matter? Together AI is one of the cheaper, more popular places to fine-tune open-source models like Llama, so a lot of people building with DSPy end up wanting to use it. Before this, they had to step outside the framework: fine-tune on Together by hand, then wire the finished model back into their DSPy program themselves. With the provider in place, fine-tuning becomes a first-class option. You point DSPy at your training data, and it handles the upload, the job, the waiting, and hands back a model you can drop straight into the rest of your pipeline. That is the whole point of a framework, taking a fiddly manual process and making it one clean step, and adding a provider is how that gets extended to one more company. The Provider does one job from start to finish: take your training examples and hand back a fine-tuned model. Under the hood, that's five steps: Check your training data is in a

GitHub热门项目 | | Stars: 1,130 | 395 stars today | 语言: Rust

用 FROST 家族治理模型，构建你的「AI 第一性原理」 作者 ：神通说 日期 ：2026-07-23 主题 ：双项目联动 | 周四代码教程 阅读时间 ：12分钟 前言：为什么你需要「第一性原理」？ 埃隆·马斯克推崇「第一性原理」思维——从物理学的最基本定律出发，而不是类比他人的做法。 在 AI Agent 开发领域，大多数人都在用 LangChain、CrewAI、AutoGen 这些现成框架。它们很好用，但你是在用别人的「家族结构」，而不是理解为什么需要家族结构。 FROST 的目标是让你从第一性原理理解 AI Agent： 为什么需要治理结构？为什么需要记忆传承？为什么需要层级分工？ 然后，FROST-SOP 帮你把第一性原理变成可运行的系统。 一、从细胞分裂看 AI Agent 本质 想象一个细胞分裂的场景： ┌─────────┐ │ 细胞 │ ← 拥有细胞核（记忆）、蛋白质（能力） └────┬────┘ │ 分裂 ┌────┴────┐ │ 细胞A │ │ 细胞B │ ← 各自独立，但共享DNA └─────────┘ └─────────┘ FROST 的四个原子就是生命的四个基本元素： 原子 生命类比 技术实现 Store 细胞核 记忆容器，持久化状态 Skill 蛋白质 无状态能力单元 Agent 细胞膜 包裹 Store + Skills 的执行单元 SOP DNA 序列 有序的操作指令集 # FROST 的最小可用示例：50行代码理解 Agent 本质 from frost.core import Store , Agent , skill_set , skill_get , skill_del # 1. 创建记忆容器 store = Store () # 2. 定义能力（蛋白质） skills = { " set " : skill_set , # 存记忆 " get " : skill_get , # 取记忆 " del " : skill_del # 删记忆 } # 3. 创建 Agent（细胞） agent = Agent ( " my_cell " , store , skills ) # 4. 定义 SOP（DNA 序列） sop_steps = [ " set " , # 存一个值 " get " , # 读回来 " del " # 删掉 ] # 5. 运行 result = agent . run ( sop_steps = sop_steps , initial_context = { " key " : " name " , " value " : " FROST " } ) print ( result [ " _result " ]) # 输出: "FROST" 这 50 行代码展示了 FROST 的核心： Agent = Store + Skills + SOP 。 二、为什么需要「家族」？从独居细胞到多细胞生物 单细胞生物可以独立存活。但复杂生命需要多细胞协作——肝脏细胞、心脏细胞、神经细胞各有分工，协同维持生命。 AI Agent 也是如此。简单任务一个 Agent 够了，但复杂系统需要多 Agent 协作。 FROST 的家族模型： ┌─────────────────────────────────────────────────────┐ │ 君主（Human Agent） │ │ 最高决策者，只发布任务不看执行 │ └─────────────────────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────┐ │ 祖辈（Ancestor） │ │ 全局编排、宪法定义、任务拆分、资源分配 │ │ ⚠️ 不亲自执行，只做调度 │ └─────────────────────────────────────────────────────┘ │ ┌─────────────┼─────────────┐ ▼ ▼ ▼ ┌──────────┐ ┌──────────┐ ┌──────────┐ │ 斥候 │ │ 斥候 │ │ 斥候 │ │ (侦察) │ │ (侦察) │ │ (侦察) │ └────┬─────┘ └────┬─────┘ └────┬─────┘ │ │ │ ▼ ▼ ▼ ┌──────────┐ ┌──────────┐ ┌──────────┐ │ 府兵 │ │ 府兵 │ │ 府兵 │ │ (执行) │ │ (执行) │ │ (执行) │ └──────────┘ └──────────┘ └──────────┘ │

Tired of squinting at VS Code’s stacked merge editor? MergeForge brings a JetBrains-style three-pane conflict resolver to VS Code and Cursor — and pairs it with an AI assistant that actually reads your repository before it suggests a fix. The problem We’ve all been there. You’re halfway through a rebase. Git stops. Twelve files are conflicted. You open one in VS Code… and get that familiar stacked layout: Incoming, Current, and a result pane that somehow still feels like a puzzle with half the pieces missing. If you ever used WebStorm or IntelliJ, you know how good merge tools can feel: Your side on the left Their side on the right The result in the middle Gutter arrows that just… work In VS Code land, that flow never quite arrived. You click Accept Current, Accept Incoming, Accept Both, and hope nothing important got flattened. Word-level diffs? Authorship? A clear “who wrote this chunk?” signal? Often missing when you need them most. And when AI entered the chat, a lot of tools treated conflicts like isolated text blobs: “Here are the <<<<<<< markers. Good luck.” But real merges need context. What was the branch trying to do? What does the surrounding file look like? Who touched this last? Without that, “AI resolve” is just confident guessing. I wanted the JetBrains merge experience — inside VS Code and Cursor — with an assistant that behaves more like a careful teammate than a slot machine. So I built it. The solution: MergeForge MergeForge is an open-source VS Code / Cursor extension that turns conflicted files into a proper three-pane visual merge. Layout: Left Center Right Yours (local) Result (editable, seeded from the merge base) Theirs (incoming) Panes scroll together. Chunks connect with bands. Gutter controls let you accept, ignore, or blend sides without fighting the UI. When you’re done, Apply writes the result and stages it with git. If you prefer Cursor, you’re covered too. The editor works the same; for AI features you plug in your own provider key (

I'm a scrum master. I was a developer ten years ago. I have enough background to discuss design and trade-offs with an LLM — but three months ago I made a deliberate bet on my solo project: I would never read the code. The specs define the tests. The tests control the code. The code is a black box. I'm not claiming this is what everyone should do. But it's my bet, and it forced a system into existence: when nobody reads the code, the process has to carry the trust that a code-reading human normally provides. I've just published that system as a reference implementation: backlog-as-data — the full writeup, the Claude Code skills translated to English, and the CLI source, verbatim from my daily setup. Here's the short version. The backlog is git data, not a document Most agent task-management tools store tasks in a dedicated place — a tasks.json , a database, a backlog/ folder. My bet is different: the backlog is the YAML frontmatter of my spec files. One file per ticket, and the ticket's status is a field — never a location in a document. --- id : PARSE-07 title : Tolerate CRLF in decklist import type : ticket status : todo priority : should exec : model : sonnet effort : think review : light matured : 2026-07-22 --- # PARSE-07 — Tolerate CRLF in decklist import The spec body: design, contracts, test cases. The ticket file IS the spec. Everything below the frontmatter is the spec — written by the LLM, after it has challenged the need I expressed in conversation. The frontmatter is data — owned by a small CLI, mutated only through it. Same file, so they can never drift apart. Why it matters: "move it to Done" is not an operation. LLMs (and humans) mangle documents when a state change means relocating text. Making status a field makes every transition a one-line, idempotent, testable mutation. The board I look at (a small web page on my server, with GitHub deep links to each spec) and the readable markdown view are generated projections , locked by a do-not-edit sentin

If you've ever had to check code or infrastructure against a compliance framework, you know the drill: someone reads a 100-page PDF, then reads your codebase, then makes a judgment call. It's slow, inconsistent, and it can't be automated. So I built a pipeline to fix that — for real. The problem CMMC Level 1 and NIST SP 800-171 Rev 2 are two of the most common compliance frameworks small defense contractors and government-adjacent companies have to meet. Both exist only as dense regulatory text. There's no official machine-readable version. That means every compliance check is manual. Every AI coding assistant reviewing your infrastructure has zero built-in awareness of these requirements. Every CI/CD pipeline has to skip compliance checks entirely or rely on someone remembering to look. ** What I built** A Python pipeline that: Pulls the real regulatory source data — NIST's official CPRT export for SP 800-171, and the verbatim text of 48 CFR § 52.204-21 for CMMC Level 1 Normalizes it into a structured SQLite schema Generates a JSON rule for every single control, with a machine-actionable instruction attached Here's what one rule actually looks like: \ json { "rule_id": "nist_sp_800-171_rev_2_3.1.1", "framework": "NIST SP 800-171 Rev 2", "control_id": "3.1.1", "title": "ACCESS CONTROL — 3.1.1", "requirement": "Limit system access to authorized users, processes acting on behalf of authorized users, and devices.", "agent_guidance": "When generating or reviewing code/infrastructure, ensure compliance with NIST SP 800-171 Rev 2 control 3.1.1. Flag any implementation that does not satisfy: Limit system access to authorized users, processes acting on behalf of authorized users, and devices.", "generated_at": "2026-07-15T16:42:56.026218+00:00" } \ \ That agent_guidance field is the interesting part — it's written specifically to drop straight into an AI coding agent's system prompt as a compliance guardrail. Three ways to actually use this 1. AI coding agent system prompt

Documentation is often the last thing developers think about—until a broken link frustrates users or a README sends someone to a 404 page. I wanted a simple way to automatically verify links in Markdown documentation during CI, so I built urldn-link-check. It's an open-source GitHub Action and CLI that scans your documentation and reports issues before they're merged. Features ✅ Detect broken links (404/500) ↪️ Detect redirect chains 🔒 Detect insecure HTTP links 📏 Find overly long URLs 📄 Scan Markdown & MDX files ⚡ Fast concurrent scanning 💬 GitHub PR summaries 📊 JSON & Markdown reports Installation npm install -D urldn-link-check or npx urldn-link-check . GitHub Action uses: urldn/link-check@v1 That's it. Every push or pull request can automatically verify your documentation. Why I Built It While maintaining documentation, I noticed that broken links often go unnoticed until someone reports them. Instead of checking them manually, I wanted a lightweight tool that integrates directly into GitHub Actions and fits naturally into a CI workflow. Open Source The project is MIT licensed and contributions are welcome. ⭐ GitHub: https://github.com/urldn/link-check 📦 npm: https://www.npmjs.com/package/urldn-link-check https://www.npmjs.com/package/urldn-link-check This is the first developer tool in the URLDN ecosystem, with more open-source projects planned in the future. If you have ideas or feedback, I'd love to hear them.

Java is everywhere in backend systems, enterprise applications, and production environments. But when it comes to numerical computing, data manipulation, and scientific-style operations, Python's NumPy ecosystem has become the standard. I wanted a similar experience in Java: a lightweight, dependency-free library for working with multidimensional arrays and linear algebra. That idea became NumPy4J. What is NumPy4J? NumPy4J is an open-source numerical computing library for Java inspired by NumPy. It provides: Multidimensional arrays (NDArray) NumPy-style broadcasting Array creation utilities Reshaping and slicing Element-wise operations Linear algebra operations Example: NDArray A = NDArray . of ( new double []{ 1 , 2 , 3 , 4 }, 2 , 2 ); NDArray B = NDArray . ones ( 2 , 2 ); NDArray C = A . add ( B ); Matrix operations: NDArray result = LinearAlgebra . matmul ( A , B ); Solving equations: NDArray x = LinearAlgebra.solve(A, b); Why build another numerical library? There are already excellent Java math libraries available. The goal of NumPy4J is different: Provide a NumPy-like API experience Make multidimensional arrays a first-class concept in Java Keep the API simple and approachable Create a foundation for future scientific computing features Testing approach To make sure behavior stays consistent, NumPy4J uses Python NumPy as a reference implementation. Test cases are generated with NumPy and validated against the Java implementation, covering: Broadcasting Matrix operations Reshaping Transpose Linear solving Element-wise calculations What's next? The roadmap includes: More NumPy-compatible operations Matrix decompositions (QR, LU, Cholesky) Eigenvalue computation More statistics functions Performance improvements Try it out If you work with Java and need NumPy-style numerical operations, I would love for you to try NumPy4J, provide feedback, and contribute ideas. GitHub: https://github.com/darius1973/numpy4j Documentation: https://darius1973.github.io/numpy4j/inde

LLM-written bibliographies do not stop at arXiv preprints. Law review drafts invent reporter cites; multilingual papers mangle Chinese titles. citesure 0.2 extends the integrity gate into those failure modes. US case law via CourtListener References that look like court cases — @jurisdiction entries, Plaintiff v. Defendant titles, or reporter strings such as 347 U.S. 483 — are resolved against Free Law Project CourtListener. Ranking prefers an exact reporter cite over companion orders, so Brown lands on 347 U.S. 483 rather than a later procedural listing. @jurisdiction { brown1954 , title = {Brown v. Board of Education} , year = {1954} , howpublished = {347 U.S. 483} , } citesure check examples/packs/us-case-law.bib citesure warm-cache cases.bib Optional COURTLISTENER_TOKEN for higher rate limits. Law-review CI: templates/journal/law-review.yml . CJK-aware matching NFKC + fullwidth folding; character-level similarity for CJK-heavy titles; CJK bigrams in claim scoring so Chinese claims are not silently empty. Evidence Integrity bench 242/242 (US cases + Chinese titles + multi-domain set) Claims mini-bench 29/29 Eight domain packs including us-case-law Install pip install "git+https://github.com/SybilGambleyyu/citesure.git[pdf]" Source: github.com/SybilGambleyyu/citesure · Demo: workers.dev

I wanted to use web-based AI chatbots — Claude, Gemini, ChatGPT, Qwen — for actual development work, not just Q&A. The free tiers are generous, and I didn't want to be locked into a single coding agent or pay for API access just to get an assistant to touch my code. But the moment you try to actually use a web chat for real dev work, you hit the same wall every time: you either paste in your whole codebase manually every session, or you give up and reach for a paid extension with an API key behind it. So I built AI Bridge — a CLI tool that bridges a local codebase and any browser-based AI chatbot. No API keys, no extensions running in the background, no vendor lock-in. You pack your code, paste it into whichever AI chat you're using, and apply the changes back with a command. Why not just use Copilot, Cursor, or an API key? Coding agent apps and API-based tools work, but they come with tradeoffs I wanted to avoid: Web-based AI chat plans are usually more generous on the free tier than API usage I'm not locked into one provider — I can switch models mid-project depending on which one is handling a task better There's no background agent or extension — it's just a CLI and whatever chat tab I already have open The tradeoff is that browser chats don't have direct filesystem access. AI Bridge closes that gap without turning it into full manual copy-pasting. How it works There are two modes, depending on project size. Simple Mode is for projects small enough to fit in a single prompt. You pack the codebase, upload it along with a couple of prompt templates, and apply the AI's response back to your files: dotnet tool install --global Tools.AIBridge cd /path/to/your-project ai-bridge init ai-bridge pack # Upload ai-bridge/1-SimpleMode/*.md + the generated context files to your AI # Copy the AI's response, then: ai-bridge apply --paste Advanced Mode is for larger codebases, where uploading everything every time burns tokens and adds noise. Instead, you generate a one-time in

Workflows is a Rust library crate (not a hosted service; the crate name on crates.io/GitHub is tinyflows) that models an automation as a WorkflowGraph: a directed graph of typed nodes and edges. You build or generate that graph, it gets structurally validated, compiled into an opaque CompiledWorkflow, and lowered — once per run — onto tinyagents, a state-graph execution engine, via engine::run. model::WorkflowGraph -> validate -> compiler::compile -> engine::run (typed graph) (structural) (validated handle) (lowers onto tinyagents, drives to done) Run state is a single JSON value shaped like { "run": { "trigger": … }, "nodes": { "": { "items": [ … ] } } }. A merge reducer folds each node's output under its own id, so independent branches never collide — which is what keeps parallel fan-out deterministic. The node catalog Kind What it does trigger Entry node that starts the workflow (exactly one per graph); firing mode is host-driven agent Runs an LLM agent turn, with optional chat-model / memory / tool / output-parser sub-ports tool_call Invokes one specific integration action deterministically, no LLM involved http_request Outbound HTTP request code Sandboxed user code (JavaScript or Python) output_parser Parses/validates an upstream agent's output into a structured shape sub_workflow Runs another workflow as a nested sub-graph and returns its output condition Two-way IF, emits on true/false switch Multi-way branch keyed by an expression result merge Fan-in barrier — waits for every wired predecessor before running split_out Fan-out — emits one item per element of a list transform Pure, expression-based field mapping over the run state Data flows between nodes as arrays of items shaped { json, binary?, paired_item? } — closer to n8n's item-based model than a plain function-composition DAG — and node config can reference the run scope with =-prefixed expressions like =item.name. The part I actually want to talk about: host-agnosticism Every place this engine would n

OpenAI, Anthropic, and Google expose different APIs, message formats, tool-calling conventions, error types, and response structures. That difference is manageable while an application uses only one provider. It becomes more expensive when the application needs retries, circuit breakers, fallback routes, observability, and recovery across several providers. Without a shared abstraction, resilience logic tends to be implemented repeatedly: OpenAI integration ├── request conversion ├── retry logic ├── error classification ├── circuit breaker └── tool-call recovery Anthropic integration ├── request conversion ├── retry logic ├── error classification ├── circuit breaker └── tool-call recovery Google integration ├── request conversion ├── retry logic ├── error classification ├── circuit breaker └── tool-call recovery I did not want to build resilience three times. I wanted to define the recovery policy once and apply it across every provider supported by the application. That became llm-api-resilience , a Python library for retries, ordered failover, circuit breakers, and checkpoint recovery across multiple LLM providers. Quick start pip install llm-api-resilience Once the provider adapters are configured, an ordered fallback plan takes only a few lines: from llm_api_resilience import RecoveryPlan , ResilientLLM , Route llm = ResilientLLM ( RecoveryPlan ( [ Route ( " openai-primary " , openai_adapter ), Route ( " anthropic-backup " , anthropic_adapter ), Route ( " google-last-resort " , google_adapter ), ] ) ) response = llm . chat ( [{ " role " : " user " , " content " : " Explain circuit breakers briefly. " }] ) print ( response . selected_route ) print ( response . content ) GitHub: llm-api-resilience Provider adapter: llm-api-adapter The library is built on top of llm-api-adapter , which provides one interface for OpenAI, Anthropic, and Google. Because provider-specific differences are handled by the adapter, the resilience layer can operate on a shared contract inst

It started with the dumbest problem in computing: I'm in bed, a movie is playing on my laptop across the room, and pausing it requires physically getting up . Every existing fix annoyed me in some way. Remote desktop apps are overkill and route through someone's cloud. Remote-control apps want accounts, subscriptions, or a native app install. I just wanted my phone to poke my laptop over my own Wi-Fi. So I built LapDeck : one Node.js process on the laptop, a PWA on the phone. Scan a QR code once and your phone becomes an app launcher, touchpad, keyboard, live screen viewer, and media/power remote. MIT licensed, plain JavaScript, no build step. This post is about the four problems that turned out to be more interesting than I expected. The architecture in one line Phone (PWA) ── WebSocket + MJPEG over Wi-Fi ──► Node.js agent (Windows) The agent serves the PWA, exposes a WebSocket for commands, and streams the screen as MJPEG. The protocol is deliberately dumb JSON envelopes — no protobuf, no RPC framework — so a native Android client or a CLI script can speak it in an afternoon. Windows-specific glue (volume, brightness, power, capture) is isolated in src/win/ , so a macOS/Linux port only has to reimplement that layer. Problem 1: mobile keyboards lie to you The obvious way to build a remote keyboard is to listen for keydown and forward key codes. On mobile, this collapses immediately: swipe typing, autocorrect, and IME composition don't emit per-key events. Android will happily tell you every key is keyCode 229 . The fix: stop listening to keys entirely. I keep a hidden input, and on every input event I diff the field's value against the last known state — compute the common prefix, then emit "delete N chars, type this string" to the laptop. Swipe-type a whole word and the laptop receives one clean text insertion. IME composition, autocorrect rewrites, emoji — all just become diffs. The lesson generalizes: on mobile web, treat the text field as the source of truth, n

TL;DR I merged several training courses into one monorepo and gave every course the same numbered folder shape : 00-planning → 04-post-training . A _TEMPLATE/ you copy to start the next one, an _ARCHIVED/ you park old stuff in, and a CLAUDE.md that makes the convention machine-readable. The trick that makes it stick: templates use <angle-bracket-slots> , and "done" is a grep that returns nothing. I had course material scattered across repos — planning notes here, marketing copy there, facilitator run-sheets in a third place. Every new course started from a blank page, and I re-invented the folder layout each time. So I collapsed everything into one monorepo with a single rule: every course has the identical shape. The lifecycle, as folders The insight isn't "use folders" — it's that a course has a lifecycle , and numbered folders make that lifecycle the primary axis instead of file type. Plan it, sell it, teach it, run it, follow up. Five stages, always in order: Folder Stage What lives here 00-planning/ Prepare Syllabus (source of truth), specs 01-marketing/ Sell Positioning master + channel assets 02-content/ Teach Modules, one file each 03-delivery/ Run Run-sheet, pre-flight checklist, fallback plan 04-post-training/ Follow up Feedback form, follow-up email, post-mortem The numeric prefix isn't decoration. It forces sort order to match the actual workflow, so the folder listing reads like the process itself. Same reason migration files are timestamped — order is information. <course>/ 00-planning/ -> derives everything below 01-marketing/ 02-content/ 03-delivery/ 04-post-training/ --. post-mortem findings | ^------------------' feed back into 00..03 That loop at the end matters: the post-mortem doesn't sit in a graveyard folder. Its findings flow back into the syllabus, the run-sheet, the marketing. The structure runs the same feedback loop it's supposed to teach. _TEMPLATE/ : a course starts as a copy Starting a new course is one line: cp -r _TEMPLATE my-new-cou

why-we-open-sour-c1f013bf.webp alt: Building Digital Identity Tools - Why We Open-Sourced Our SSI SDK relative: false Self-Sovereign Identity (SSI) is a framework that allows individuals and organizations to control their own digital identities and share verified credentials without relying on a central authority. This paradigm shift empowers users with greater privacy and control over their personal data, while also providing robust mechanisms for verifying the authenticity of credentials. What is Self-Sovereign Identity (SSI)? SSI is built around the concept of decentralized identifiers (DIDs) and verifiable credentials. DIDs are unique identifiers that are controlled by the entity they represent, enabling them to manage their own identity data. Verifiable credentials are digital assertions that can be issued by one party and verified by another, ensuring the authenticity and integrity of the information shared. Why did we open-source the SSI SDK? Open-sourcing the SSI SDK was a strategic decision driven by several factors. First, fostering innovation within the community is crucial for advancing the field of digital identity. By making our SDK available to everyone, we encourage collaboration and experimentation, leading to new ideas and improvements. Second, promoting transparency is essential for building trust in digital identity systems. Open-source projects allow others to inspect the codebase, understand how it works, and identify potential vulnerabilities. This transparency helps build confidence in the security and reliability of the SDK. Finally, enabling a broader community to contribute to and benefit from secure digital identity solutions aligns with our mission to democratize access to these technologies. By lowering the barriers to entry, we hope to empower more developers and organizations to adopt and improve upon our work. What are the key features of the SSI SDK? The SSI SDK provides a comprehensive set of tools for building digital identity app

Troops received an email informing them that they were rapidly depleting their AI tokens.

We’ve compiled an overview of some of the top alternative browsers available today aiming to challenge Chrome and Safari.

GitHub热门项目 | A hive mind communication platform | Stars: 3,079 | 2,720 stars this week | 语言: Rust

GitHub热门项目 | Real-time token, cost, and AI limits widget with multi-device sync for Claude Code, Codex, OpenCode, Hermes, OpenClaw, Cursor, Antigravity and more. | 为 AI Tools 打造的即时Token、成本与限额监控桌面组件，支持多设备同步 | Stars: 904 | 199 stars this week | 语言: JavaScript