标签：#rce

Do you really need a full framework to handle a few API endpoints or webhooks? Laravel and Symfony are excellent tools — for large applications. But when you're building a focused microservice, a webhook receiver, or a lightweight REST API, bootstrapping a full-stack framework means carrying hundreds of files, a massive autoloader, and a dependency tree you'll never fully use. That's the problem webrium/core was built to solve: a minimalist, zero-dependency PHP micro-framework written entirely from scratch, designed to stay out of your way. Installation composer require webrium/core That's it. No configuration files to publish, no service providers to register. The Entry Point Every webrium application starts with the same three lines: <?php require_once __DIR__ . '/vendor/autoload.php' ; use Webrium\App ; use Webrium\Route ; App :: initialize ( __DIR__ ); // ... your routes here App :: run (); App::initialize() sets the root path and loads the global helper functions. App::run() initializes error handling and dispatches the current request through the router. Routing The router supports all standard HTTP methods. Route handlers can be closures, a Controller@method string, or an [Controller::class, 'method'] array. Basic routes: Route :: get ( '/status' , fn () => [ 'status' => 'alive' ]); Route :: post ( '/items' , fn () => [ 'created' => true ]); Route :: put ( '/items/{id}' , fn ( $id ) => [ 'updated' => $id ]); Route :: patch ( '/items/{id}' , fn ( $id ) => [ 'patched' => $id ]); Route :: delete ( '/items/{id}' , fn ( $id ) => [ 'deleted' => $id ]); Route handlers return an array — the framework automatically encodes it as JSON and sends the correct Content-Type header. Dynamic parameters: Route :: get ( '/users/{id}/posts/{postId}' , function ( $id , $postId ) { return [ 'user_id' => $id , 'post_id' => $postId , ]; }); Named routes: Route :: get ( '/users/{id}' , fn ( $id ) => [ 'id' => $id ]) -> name ( 'users.show' ); // Generate the URL elsewhere: $url = rout

Hey developers! I have been working on a side project to help people discover the best AI tools in one place. It is a curated directory designed to be clean, fast, and user-friendly. You can check it out live here: GetNexusAI Tech Stack Used: Next.js / React Tailwind CSS Vercel for hosting Why I Built This: Finding the right AI tool among thousands of options can be overwhelming. I wanted to create a simple dashboard where users can easily filter and find exactly what they need without the clutter. I Need Your Help! Since I just launched it, I would love to get your honest feedback: How is the loading speed and UI/UX? What features should I add next (e.g., user reviews, bookmarking tools)? If you have built an AI tool, let me know so I can feature it! Check the website here: https://getnexusai.tech

When I shipped Trooper , a privacy-aware LLM proxy written in Go, I didn't have a marketing plan. I had GitHub traffic analytics and a habit of checking them obsessively. Seven weeks later, I have something more useful than a viral moment: a ranked table of every traffic spike, what caused each one, and proof that the exact same playbook that worked at launch still works when you have something new to say. What is Trooper? Trooper sits between your app and your LLM provider. When your cloud quota runs out, it automatically falls back to a local Ollama instance with zero code changes on your end. It also tracks session context, so your agents don't go blind between calls. It's not a chatbot wrapper. It's plumbing. Which makes the distribution story more interesting, because plumbing doesn't go viral the way demos do. The Data GitHub gives you 14-day rolling windows for clones and views. I screenshotted them obsessively and tracked every spike. Here's the full ranked table: Rank Date Clones Unique Cloners Views Unique Visitors Driver 🥇 1 May 13 375 173 1,113 ~140 Reddit wave peak 🥈 2 May 10-12 312 137 974 133 Reddit launch spike 🥉 3 Jun 10 289 124 749 101 "Escalate the model" r/ollama post 4 Jun 11 268 112 840 95 Decaying from Jun 10 spike 5 Jun 12 240 99 739 74 Decaying from Jun 10 spike 6 Jun 9 175 102 802 100 Organic 7 Apr 25 174 71 664 113 Early Reddit posts 8 Jun 7 171 110 876 110 Organic recovery 9 Jun 6 163 104 755 102 Organic recovery 10 May 29-30 122 73 610 83 LinkedIn post 11 May 25 76 48 495 53 Claude Code integration chat What I learned 1. Reddit is the only thing that moved the needle, and community fit matters more than size The #1 and #2 peaks were both Reddit-driven. On May 10-11, I posted across r/ollama, r/LocalLLM, r/ClaudeCode, and r/Gemini simultaneously. Total views across those posts: ~7,000. r/ollama alone drove nearly 4,000 of those views. Not r/LocalLLM. Not r/ClaudeCode. r/ollama , the smallest of the four communities. The reason: Trooper so

On June 9, Anthropic shipped Claude Fable 5 — the most capable coding model the industry had ever seen. Three days later, the U.S. government ordered it offline for every user on Earth . No warning. No transition period. One directive, and the frontier vanished overnight. 📖 Read the full version with charts and embedded sources on ComputeLeap → The same week, Z.ai (Zhipu AI) released GLM-5.2 — a 744-billion-parameter coding model with a one-million-token context window, MIT-licensed open weights arriving within days. The timing was not lost on the developer community. ℹ️ The message landed clearly on Hacker News: as user Reubend put it, they're "grateful to Chinese labs for being open with their work" — especially after "the Fable 5 fiasco." Open weights aren't just a cost play anymore. They're insurance. This guide walks you through actually running GLM-5.2 on your own hardware — the VRAM you need, the quantization that fits, and the exact commands for llama.cpp, Ollama, and LM Studio. No API keys. No cloud dependency. No one can pull the plug. What GLM-5.2 Actually Is GLM-5.2 is the third major iteration in Z.ai's GLM-5 line, purpose-built for agentic coding and long-horizon software engineering . Here is what you are working with: Spec Value Architecture Mixture-of-Experts (MoE) Total Parameters 744 billion Active Parameters ~40 billion per token Context Window 1,000,000 tokens Max Output 131,072 tokens Training Data 28.5 trillion tokens License MIT (open weights) Thinking Modes High and Max The MoE architecture is the key to local viability. Only ~40 billion parameters fire per token — the rest sit idle. That is what makes aggressive quantization work: you are compressing 744B weights, but inference only touches a fraction of them at any given time. GLM-5.2 supports two thinking-effort presets: High and Max. Z.ai recommends Max as the default for coding work — it produces longer reasoning chains before generating output. The model launched on June 13 on Z.ai's C

We open sourced Centaur last month—a Slack agent we built for our own investing and engineering work. Over the past few months it's grown to 100-150 daily power users across a few organizations, handling both judgment-heavy tasks like investment research and raw horsepower work like searching massive codebases. The interesting part isn't just our internal use. We've been running a small Slack Connect with external orgs using it, and the feedback has been consistent: most SaaS tools don't cut it because companies need too much customization and their critical integrations aren't supported out of the box. Our roadmap is getting clearer as we tackle the tricky parts of multi-org collaboration. We're working on scoping Slackbot access by channel, which would finally let different organizations' agents coexist safely in the same space—almost like an Enterprise Matrixbook. But the real challenge isn't the vision, it's execution. Keeping costs low while staying self-hostable for smaller teams has forced us to rethink everything. The hard problems only become obvious once you're deep in the implementation. That said, I do think Slack has won in one sense: it's the best place for a coworker agent to emerge, rather than a standalone application. Curious whether others are seeing this pattern too.

GitHub热门项目 | AI coding assistant skill (Claude Code, Codex, OpenCode, Cursor, Gemini CLI, and more). Turn any folder of code, SQL schemas, R scripts, shell scripts, docs, papers, images, or videos into a queryable knowledge graph. App code + database schema + infrastructure in one graph. | Stars: 67,157 | 5,478 stars this week | 语言: Python

As developers, we compare files constantly — reviewing pull requests, checking config changes, spotting bugs between versions. But not all diff tools are created equal. Some require installation, some upload your files to remote servers, and some just don't support the formats you need. Here's a rundown of the best free file diff tools available in 2026, so you can pick the right one for your workflow. 1. FileDiffs — Best for Privacy & Format Support If you work with sensitive files or just don't want your data sitting on someone else's server, FileDiffs is the tool you need. What makes it stand out: Supports 60+ file formats — PDF, Word, Excel, code files, JSON, XML, CSV and more Runs entirely in your browser — client-side processing means your files never leave your device 100% private — zero data transfer, zero uploads, zero risk No install, no signup, no hassle — just open and compare It's the go-to tool when you need to compare files quickly without worrying about privacy or compatibility. 2. Meld — Best Desktop Diff Tool Meld is a classic open-source visual diff and merge tool for Linux, Windows, and macOS. It's great for comparing files, directories, and version-controlled projects. Best for: Developers who prefer a desktop app and work heavily with Git. 3. Beyond Compare — Best for Power Users Beyond Compare is a feature-rich diff tool with support for files, folders, FTP, and cloud storage. It's not free (paid after trial) but worth mentioning for its depth of features. Best for: Teams that need advanced folder sync and merge capabilities. 4. Diffchecker — Quick Online Diffs Diffchecker is a simple web-based diff tool for text and code. It's quick and easy but uploads your content to their servers and has limited format support compared to FileDiffs. Best for: Quick one-off text comparisons where privacy isn't a concern. 5. KDiff3 — Best for Three-Way Merges KDiff3 is a free, open-source diff and merge tool that supports three-way comparison. It's a bit dat

Welcome to Git Mastery , a series where we'll learn Git from the ground up, starting with the absolute basics and gradually moving toward advanced workflows, Git internals, hooks, automation, and professional development practices. Whether you're a student, hobbyist, open-source contributor, or professional developer, Git is one of the most important tools you'll ever learn. Let's begin. What Is Git? Git is a distributed version control system (DVCS) — a tool that tracks every change made to your files over time, so you always know what changed, when it changed, and who changed it. But that definition alone doesn't really capture what Git feels like to use. A better way to understand it is through a problem every developer has run into. You start a project. Things are going well. Then you make a change that breaks everything. You try to undo it manually, but you can't remember exactly what you had before. So you do what most people do without a version control system — you start creating backup folders: project-final project-final-v2 project-final-v2-fixed project-final-v2-final project-final-v2-final-final Within a week, you have ten folders, no idea which one is actually the latest, and a growing sense of dread every time you open the project. Git solves this completely. Instead of managing folders manually, Git lets you take a snapshot of your entire project at any meaningful moment — a snapshot called a commit . Each commit is stored safely, labeled with a message you write, and linked to every commit before it. Your project's history becomes a clean, navigable timeline rather than a pile of duplicated folders. And because Git is distributed , every developer working on a project has a full copy of that entire history on their own machine. There is no single point of failure. No central server going down means everyone loses their work. Why Do We Need Version Control? Code changes constantly. Features get added, bugs get fixed, experiments get tried and sometime

Ever wanted to turn a simple PHP host into a gateway for your local network? I built host2gateway to do exactly that. ProfiDE / host2gateway Uses a PHP host or web server to create a gateway that securely allows access to clients through it. host2gateway host2gateway is a tool designed to provide access from a web server (Gateway) to a client without requiring static IP addresses, port forwarding, changing firewall rules, or other complex configurations . It is written in PHP and can be deployed on most hosting provider environments. Features No need for static IP or port forwarding: There is no requirement to modify your firewall or router settings. Platform-independent: Works anywhere PHP 8.2 or higher is supported, making it suitable for most shared hosting services. Lightweight and simple: Minimal dependencies and easy deployment. Strong encryption built-in: Uses a powerful encryption mechanism that secures all communication, even if SSL/TLS is not available on the hosting provider. Your data is protected at all times, regardless of your environment. How It Works The client establishes an outbound connection to a Gateway server that is accessible from the internet (a PHP-enabled web host). Both sides communicate… View on GitHub 🔥 What is host2gateway? It's a lightweight tool that transforms any server running PHP into a gateway that can route traffic, manage requests, and act as a bridge between your local network and external services. No heavy dependencies. No complex configs. Just PHP, Cron and a network interface. 🧠 Why I built this Most gateway solutions are bulky, written in Go or Rust, and require root access and system-level changes. But what if you only have: A shared hosting account A basic VPS with PHP enabled A Raspberry Pi running a PHP server host2gateway fills that gap. It gives you gateway-like capabilities using the tools you already have. 🛡️ Use cases Use Case Description Local network bridge Connect isolated parts of your network Traffic inspe

Hi everyone, I've been hacking on a local personal memory system called Hillock . Honestly, it's very much a work in progress and it isn't some flawless breakthrough, but I wanted to see if we could build a lightweight, completely offline memory layer for local LLMs without the overhead of running a heavy neural vector database or wasting precious VRAM. The project is named after the biological Axon Hillock —the exact gatekeeper region of a human neuron that sums up incoming electrical charges and decides whether to fire (open the gate) or remain silent (block). How the architecture works: The Ground Truth (SQLite) : Stores hard facts as simple database triples (Subject-Predicate-Object) so the system has a solid symbolic foundation. The Synapses (Hebbian Plasticity) : Tracks which concepts co-occur during a conversation to dynamically build gradient-free associative weights. The Context (Hyperdimensional Computing) : Maintains a 10,000-dimensional leaky context vector that rolls, binds, and accumulates history. This helps the system resolve pronouns (like "he/she") and decide when to block a query to prevent hallucinations. The Honest Benchmarks (Yes, it breaks!) I wrote a tough, 30-sentence scientific benchmark with complex sentence structures and hard negatives (like asking what Einstein discovered when the text only mentions Curie discovering radioactivity and Einstein working with her). Running Qwen 1.5B locally on my computer, here is how it actually did: Extraction Precision : 10.6% Extraction Recall : 22.7% Retrieval Accuracy : 30.0% Gate Accuracy : 30.0% Why are these scores low? Because a tiny 1.5B model completely trips over complex English grammar during ingestion (it gets confused and creates weird predicates). However, the actual HDC vector-matching itself is incredibly stable. I enforce a Constant-Component-Count of exactly 3 components per fact, which balances the vector norms and keeps retrieval highly reliable once the facts are actually in the dat

Originally published at llmkube.com/blog/making-self-hosted-llm-agents-trustworthy . Cross-posted here for the dev.to audience. Running a single local LLM node is a solved problem. You write an InferenceService, the operator schedules it, llama.cpp or MLX serves it, and you get an OpenAI-compatible endpoint. We have been doing that for months. Running a fleet of them is where it stops being easy. My fleet is heterogeneous on purpose: CUDA pods in the cluster, and Apple Silicon Macs sitting off-cluster on the homelab network, each one running two separate agents (one for inference, one for the agentic coding harness). The day I shipped 0.8.4 to that fleet, I learned exactly how it does not scale. I updated each Mac by hand. The control plane had no idea what version any agent was running. And the launchd reload I used to restart an agent was a silent no-op on an already-loaded service, so the old binary kept running while I believed I had updated it. I found that out by hand-inspecting a process tree. Three machines made it annoying. Thirty would make it impossible, and the whole pitch for sovereign, on-prem AI is that you run a lot more than three. So the last stretch of work on LLMKube was not about a faster runtime or a bigger model. It was about making the fleet trustworthy : able to update itself safely, and unable to lie to the control plane about its own state. Here is what that took. Helm and brew for the edge The fix is a new cluster-scoped CRD, AgentRelease , and a self-update path in the agents themselves. You describe the release you want once, the operator rolls it out, and the agents pull and apply it. The design borrows directly from prior art that already solved this for Kubernetes nodes: Rancher's system-upgrade-controller, k0s autopilot's per-platform SHA-256 staging, and Teleport's outbound-only poll model. The properties that make it safe to leave running: Declarative and approved. An AgentRelease names the agent, the version, and the per-platform

Hermes Agent already has memory, and that matters. It keeps local context, it improves over time, and it works without forcing you into a cloud service. It also supports several external memory providers. I still built hermes-mempalace , because none of the existing options fit my setup quite right. I wanted something: local-first isolated by Hermes profile verbatim, not just extracted facts easy to inspect on disk simple enough to trust over time That last part is the important one. I did not want a memory layer that turns conversations into an opaque pile of embeddings or summaries you cannot really audit. I wanted actual transcripts, mined into a readable structure, with no hidden server in the middle. Why the existing options were not enough Hermes already gives you a few paths: built-in memory and session context external providers for different use cases enough flexibility to adapt, if you are willing to bend your workflow around them And to be clear, some of those options are good. But ... I run Hermes on a headless machine at home. And I use separate profiles for different contexts. And I do not want conversation content depending on a cloud API or a separate service unless there is a very good reason. So, the best fit had to check a few boxes: [x] no API key [x] no external server [x] no extra runtime I did not already want/install [x] storage isolated by HERMES_HOME [x] memory we can actually read later That let to MemPalace , or https://mempalaceofficial.com/ (hopefully, that's the right one!) What hermes-mempalace does hermes-mempalace wires MemPalace into the Hermes memory provider interface. It follows the same lifecycle as the rest of Hermes memory providers: system_prompt_block() adds a short memory reminder to the prompt. prefetch() can run a MemPalace search before the first model call. sync_turn() buffers completed turns without slowing the chat loop. on_session_end() writes buffered turns to markdown and mines them into the palace. shutdown() flu

GitHub热门项目 | Build fast web applications with Rust. | Stars: 20,899 | 6 stars today | 语言: Rust

GitHub热门项目 | ⚒️ The open source dark software factory for expert engineers. | Stars: 1,189 | 54 stars today | 语言: Rust

GitHub热门项目 | A collection of essential TypeScript types | Stars: 17,197 | 4 stars today | 语言: TypeScript

GitHub热门项目 | Hydra Launcher is an open-source gaming platform created to be the single tool that you need | Stars: 15,930 | 32 stars today | 语言: TypeScript

GitHub热门项目 | Open Source Wealth Management Software. Angular + NestJS + Prisma + Nx + TypeScript 🤍 | Stars: 8,698 | 21 stars today | 语言: TypeScript

GitHub热门项目 | JavaScript API for Chrome and Firefox | Stars: 94,572 | 13 stars today | 语言: TypeScript

GitHub热门项目 | uBlock Origin - An efficient blocker for Chromium and Firefox. Fast and lean. | Stars: 65,471 | 32 stars today | 语言: JavaScript

GitHub热门项目 | A self-hosted web application that lets you stream Live TV, Movies, and Series from your Xtream Codes or M3U provider directly in your browser. It's built with performance in mind and handles large libraries smoothly. | Stars: 1,333 | 49 stars today | 语言: JavaScript