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Convenience costs you with Plex, but the alternatives require more work.

The acquisition was approved without concessions by the Department of Justice in June.

GitHub热门项目 | The Rust package manager | Stars: 15,176 | 6 stars today | 语言: Rust

GitHub热门项目 | Open source observability platform for logs, metrics, traces, frontend monitoring, pipelines and LLM observability. A sophisticated, simple and highly performant alternative to Datadog, Splunk, and Elasticsearch with 140x lower storage costs and single binary deployment. | Stars: 19,629 | 65 stars today | 语言: TypeScript

GitHub热门项目 | A privacy-first, self-hosted, fully open source personal knowledge management software, written in typescript and golang. | Stars: 44,816 | 42 stars today | 语言: TypeScript

GitHub热门项目 | Self-hosted AI accounting app. LLM analyzer for receipts, invoices, transactions with custom prompts and categories | Stars: 6,364 | 60 stars today | 语言: TypeScript

GitHub热门项目 | Agentic AI Infrastructure for magnifying HUMAN capabilities. | Stars: 16,215 | 31 stars today | 语言: TypeScript

GitHub热门项目 | The uncompromising Python code formatter | Stars: 41,656 | 22 stars today | 语言: Python

GitHub热门项目 | Turn any technical book PDF into a Claude Code skill — ready to study, reference, and use while you work. | Stars: 7,325 | 205 stars today | 语言: Python

GitHub热门项目 | User-friendly AI Interface (Supports Ollama, OpenAI API, ...) | Stars: 143,680 | 171 stars today | 语言: Python

GitHub热门项目 | Japanese Input Method System for Linux, macOS, Neural Kana-Kanji Conversion Engine | Stars: 531 | 29 stars today | 语言: Rust

GitHub热门项目 | Toolkit for linearizing PDFs for LLM datasets/training | Stars: 18,087 | 295 stars today | 语言: Python

Available in select markets thanks to a partnership with Gigs, Motorola phone owners have one less hurdle to clear when signing up for a data-only eSIM before traveling abroad.

Contorium is a local-first system that introduces persistent project cognition into AI-assisted development workflows. Instead of treating AI as a tool that operates on code, Contorium treats the project itself as a structured, evolving system. ⸻ 🧠 Problem Modern AI coding workflows suffer from a structural limitation: Even with tools like: Cursor Claude Code MCP-based agents IDE copilots context is still: fragmented session-based non-persistent weakly structured This leads to: repeated explanations, lost reasoning, and architectural drift ⸻ 🧩 Solution: Project Cognitive Runtime (PCR) Contorium introduces a runtime model where project understanding is persistent and structured. ⸻ Core Components ⸻ PIK — Project Intent Kernel PIK defines the system-level intent of a project: primary goal constraints non-goals priority weighting It acts as a stable semantic anchor. ⸻ CIL — Cognitive Interaction Layer CIL captures reasoning: why decisions were made what alternatives were considered how context influenced outcomes It makes reasoning persistent instead of ephemeral. ⸻ Timeline Layer All system changes are recorded as events: code changes AI outputs tool interactions architectural decisions This enables replay and evolution tracking. ⸻ Drift Detection Layer A continuous alignment system compares: current behavior vs PIK intent It detects: intent drift structural drift behavioral drift And produces measurable deviation signals. ⸻ 🔁 System Loop Contorium forms a continuous loop: PIK defines intent Execution produces behavior Timeline records evolution Drift system evaluates alignment Suggestions guide correction This creates a self-regulating project system. ⸻ 🧠 Key Insight Contorium is not an AI coding tool. It is a: Project Cognitive Runtime (PCR) A system where software projects maintain structured intelligence over time. ⸻ 🚀 Why it matters The bottleneck in AI development is no longer capability. It is continuity of understanding across: time tools agents sessions Conto

The goal is to start the day already briefed — not to spend the first hour becoming briefed. What follows isn't groundbreaking. It's just what pushing my own boundaries looks like in practice. The problem As a Tech Lead of a larger team, my mornings used to look something like this: open email, skim through multiple newsletters I subscribed to for staying current on AI and dev topics, switch to Slack, scroll through everything I missed, try to figure out what actually needs my attention, then check what code went into the repo in the last 24 hours. By the time I was done "catching up," a good chunk of the morning was gone. I knew there had to be a better way. Starting with Claude Cowork Claude's desktop app has a feature called Cowork, and within that, you can set up Scheduled tasks — automated tasks that run on a schedule. I set up two that run every morning: Newsletter digest: This one pulls in all the newsletters I received the day before and summarizes them for me, grouped by topic — AI-related first, then dev, then everything else. Instead of opening each email and scanning for what's relevant, I get a curated briefing in seconds. Slack summary: This gives me a full summary of yesterday's Slack conversations across channels, and more importantly, flags what actually needs my attention. No more scrolling through hundreds of messages trying to separate signal from noise. The only downside? The Claude desktop app needs to be open and running for these to kick in. It's not a dealbreaker, but worth knowing. I'll be honest — the idea wasn't entirely mine. When you set up a new Scheduled task in Cowork, a Daily Brief is literally the example they suggest. I just happened to already be poking around with something similar. A lucky coincidence. Taking it a step further with Claude Code One of the hardest parts of leading a larger team is keeping tabs on everything that changes in code. PRs get merged, features get shipped, bugs get fixed — and it's nearly impossible to

We need to have a serious talk about the Model Context Protocol. Everyone is losing their minds over "vibe coding" right now. You plug an MCP server into Cursor, Claude Code, or VS Code, tell the AI to fix a bug across three directories, and go grab a coffee while it spins up local servers, reads files, and executes terminal commands. It feels like absolute magic. But honestly? It's also completely terrifying. Maybe I’m just paranoid, but it seems like we’ve collectively skipped the part where we ask ourselves if giving a statistical text-prediction engine raw, unvetted access to our local machines is a good idea. Some security folks are already warning that we’re walking directly into a massive remote code execution crisis. Think about it. Most MCP servers run as local subprocesses. They inherit your exact user permissions. If you run your editor as an admin or with access to sensitive environment variables, so does the AI. And the real issue isn't that the AI will spontaneously turn evil. The issue is prompt injection. The Security Void in the Hype I spent some time looking through public MCP servers on GitHub recently, and the sheer lack of input validation is wild. Because developers are rushing to build cool tools, basic security hygiene has completely lagged behind. If an AI agent reads an untrusted string—like a malicious comment in a GitHub issue, an automated email, or a dirty record inside a database—it can easily be manipulated into executing an injection payload. The model doesn't know the difference between your system instructions and the data it's processing. It treats them exactly the same. What happens when a prompt injection tricks a standard filesystem MCP tool into looking for a file named ../../../../../../etc/passwd or pulling your private AWS keys? The tool just does it. It’s a classic path traversal vulnerability, except instead of a malicious hacker typing it into a web form, an automated agent is doing it because a piece of text told it to.

Modern Angular Guards: Architecture, Best Practices & Enterprise Patterns A deep dive into designing lightweight, composable, and maintainable routing guards in modern Angular applications. Table of Contents Introduction Why Guards Exist The Golden Rule of Angular Guards Functional Guards: The Modern Standard CanActivateFn: Authentication Guard CanMatchFn: Permission-Based Route Matching CanDeactivateFn: Unsaved Changes Guard CanActivateChildFn: Nested Route Protection Signals + Guards: Reactive Permission State Feature Flags in Routing Guard Composition Patterns UrlTree Redirects vs Imperative Navigation Async Guards: When and How Permission Service Architecture Role-Based Access Control (RBAC) Permission-Based Access Control (PBAC) Route Data for Configuration Lazy Loading with Guards Standalone Routing with provideRouter Route-Level Providers Guards vs Interceptors Guards vs Backend Authorization Performance Considerations Navigation UX Best Practices Error Handling in Guards Testing Guards Common Mistakes Production Checklist Enterprise Routing Insights Conclusion Introduction In modern Angular applications, routing guards have evolved from class-based monoliths into lightweight, composable functions. This shift isn't just syntactic—it's architectural. As Angular applications become larger and more complex, the routing layer becomes a critical piece of the architecture. Guards are the gatekeepers of your navigation, but they should never become the orchestrators of your application logic. This article is for senior Angular developers, software architects, and team leads who are designing routing strategies for enterprise-scale applications. We won't explain what a route guard is—we'll explore how to architect them properly. Why Guards Exist Guards exist to protect navigation boundaries. They evaluate whether a transition should proceed, redirect, or be blocked. In modern Angular, this is achieved through functional guards that return: boolean — allow or block na

By the team at MailTester Ninja — a real-time email verification API that stores nothing. We verify a lot of email for a living. So we pointed our infrastructure at a representative panel of 50,000 of the world's most-linked domains and measured how email is actually configured in 2026 — MX providers, SPF and DMARC. Pure DNS, aggregate only, no personal data . Here's what the internet's mail setup looks like right now. Email is still (almost) everywhere 79.9% of these domains are mail-enabled (they publish MX records). Email isn't going anywhere. Authentication: adopted, but not enforced 75.8% publish an SPF record 64% publish a DMARC record …but only 22.6% actually enforce it with p=reject That last number is the real story. Of the domains that bother to publish DMARC, only 35.2% are on p=reject — the rest sit on p=none (37.2%, monitoring only) or quarantine (27.6%). Most of the web announces a policy it doesn't enforce. That's a deliverability and spoofing gap hiding in plain sight. Who runs the world's inboxes? Other / self-hosted — 32.6% Google Workspace / Gmail — 28.2% Microsoft 365 / Outlook — 22.5% Proofpoint — 5.5% Mimecast — 3.1% Tencent QQ — 2% Namecheap — 1.3% Cisco IronPort — 0.9% Self-hosted and the two hyperscalers (Google Workspace and Microsoft 365) dominate, but the long tail of providers is very real — which is exactly why deliverability is hard: every provider blocks, greylists and reputation-scores differently. Why we publish this We built an open, daily-updated dataset and a live dashboard because deliverability decisions should be based on data, not folklore. It's CC BY 4.0 — use it, cite it, build on it. Want to check a specific domain? Our free analyzer shows any domain's MX / SPF / DMARC in one click — no signup, nothing stored. Methodology: Live DNS scan (MX/SPF/DMARC). Aggregate only — no email sent, no personal data. Sample updated Wed, 01 Jul 2026 12:31:00 GMT.

It's the end of an era for the PlayStation store on PS3 and PS Vita, with Sony now planning to shut down its digital distribution service on both consoles. The PlayStation store on PS3 will close in select markets later this year, including Mexico, Honduras, and Nicaragua starting in August, with "additional Latin American and […]

most auto-apply tools have a dirty secret: they only autofill the form. they drop your details in and stop. some press submit. almost none read the confirmation the applicant tracking system sends back afterward, which means they cannot actually tell a click from a landed application. so they show you "applied" and hope. we read that confirmation. it is the whole point of what we build. and the side effect of reading it is that we have a status most tools do not: failed . a column that says, out loud, this one did not go through. having that column means we can be wrong out loud too. today we were. our apply agent clicked submit on a real Greenhouse form. the form went through. then, about half a second later, a downstream network blip threw an error, and the old code took that to mean the whole run had failed. it stamped a real, registered application as failed . a false negative on the one signal that matters most. the fix (in submitter.ts ) is a gate we now call submitClickIssued . once the agent has actually clicked submit, a later transport error can no longer produce a hard failed . it resolves to requires_human_review with a "likely landed, confirm this one" disposition instead. a blip after the click can no longer fake a failure. worst case, we ask you to double-check one, instead of lying to you in either direction. it is not a glamorous ship. no new feature, no screenshot. but a tool that never fails is a tool that never tells you, and the boring reliability days are the actual product. building this in public. no fabricated numbers, just the log.