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Introduction to Git

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

2026-06-15 原文 →
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Turn any PHP host into a gateway to your local network with host2gateway

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

2026-06-15 原文 →
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Hillock: A brain-inspired, CPU-bound memory gate for local LLMs

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

2026-06-15 原文 →
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Making a fleet of self-hosted LLM agents trustworthy

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

2026-06-15 原文 →
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Why I Built a New Memory Plugin for Hermes Agent

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

2026-06-15 原文 →
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