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Hi all, Lately I have been working on creating a package for Multi Agent RL based drone environments with different objectives, all bundled into a single GitHub repository: tau-intelligence/MuJoCo-drones-gym. I am currently trying to organize things for RL community people, with a couple more tools coming soon. But right now, I want to make it useful for the community and hence would love some feedback from different people, about how I could improve it, incorporate more things into it or fix some broken implementation. Also everyone is welcome to raise issues on the repo. Thank you for the support. PS: I have some research publications at RL and ML venues regarding work on RL, though I still want to consider myself as a student of the field and hence would love your help here. submitted by /u/MT1699 [link] [留言]

1. update WSL environment sudo apt update && sudo apt upgrade -y 2. install dependencies If you don't use -hf option, you don't need to install libssl-dev in this step. sudo apt install build-essential cmake git libssl-dev -y If nvidia-smi shows a GPU/GPUs on your terminal, you will need to install the tooklit. This will take some time. sudo apt install nvidia-cuda-toolkit -y 3. clone the repo Build llama-cli and llama-server. This step also will take some time. If you don't plan to use -hf option, you don't need to use -DLLAMA_OPENSSL=ON . git clone https://github.com/ggerganov/llama.cpp cd llama.cpp cmake -B build -DGGML_CUDA = ON -DLLAMA_OPENSSL = ON cmake --build build --config Release # no GPU git clone https://github.com/ggerganov/llama.cpp cd llama.cpp cmake -B build cmake --build build --config Release 4. run the model Run gemma-4-12b-it with cli and server. unsloth/gemma-4-12b-it-GGUF · Hugging Face We’re on a journey to advance and democratize artificial intelligence through open source and open science. huggingface.co ./build/bin/llama-cli -hf unsloth/gemma-4-12b-it-GGUF:UD-Q4_K_XL > hello [ Start thinking] The user said "hello" . The user is initiating a conversation. Respond politely and offer assistance. * "Hello! How can I help you today?" * "Hi there! What's on your mind?" * "Hello! Is there anything I can assist you with?" [ End thinking] Hello! How can I help you today? [ Prompt: 19.5 t/s | Generation: 11.8 t/s ] or run web-ui ./build/bin/llama-server -hf unsloth/gemma-4-12b-it-GGUF:UD-Q4_K_XL --port 8080 optional download model from huggingface mkdir -p models wget -O models/gemma-4-12b-it-UD-Q4_K_XL.gguf https://huggingface.co/unsloth/gemma-4-12b-it-GGUF/resolve/main/gemma-4-12b-it-UD-Q4_K_XL.gguf

Table of Contents The Integration Problem That Broke Industry 4.0 MCP: The Vertical Connection Layer How MCP Connects to Servers, Tools, and Databases MCP in Real World Industrial Automation ACP: The Horizontal Communication Layer How ACP Works Under the Hood ACP in Real World Industrial Coordination The Six Precise Differences How They Work Together: The Complete Stack Decision Framework for Industrial AI Architects 1. The Integration Problem That Broke Industry 4.0 Industry 4.0 promised connected factories, intelligent automation, and seamless data flow between machines, systems, and humans. The technology arrived. The connectivity did not. The reason is a number called N times M. An enterprise manufacturing facility might have 12 AI agents across quality, maintenance, and planning — and 28 data sources including ERP, MES, SCADA, IoT sensors, databases, CAD repositories, and supplier APIs. Without a standard protocol: 12 agents multiplied by 28 data sources equals 336 custom integrations. Each integration is bespoke code. Each breaks when either side updates. Each requires maintenance. Each represents a point of failure and a security surface that must be independently managed. IBM VP Armand Ruiz stated this precisely: "Without a common standard, every integration is costly duct tape." MCP and ACP together replace 336 pieces of duct tape with two standard protocols — one governing how agents connect to systems, one governing how agents connect to each other. The smart manufacturing market is projected to reach 374 billion dollars by 2025 at 11.8 percent CAGR. Over 50 percent of companies in industrial automation are expected to adopt MCP-based connectivity. The integration problem is not theoretical. The solution is being deployed at scale right now. 2. MCP: The Vertical Connection Layer MCP connects agents to tools and data — the vertical integration layer. It handles the connection between an AI agent and everything it needs to interact with in the external worl

This is the third and final part of my first React project for the Frontend Mentor's Digital Bank Landing Page Challenge . I'm excited to say that I finally finished it. Live Demo: https://bank-landing-page-react-gmtz.vercel.app/ Github Repo: https://github.com/ayra-baet/bank-landing-page-react Learning Component Reusability Beyond Small Elements At first, I thought this final part would mostly involve finishing the Articles and Footer. But while building, I realized something more important: React's reusability isn't limited to small UI elements like buttons or cards; entire sections can be reusable too. Earlier in this project, I reused a single Button component across the header, hero, and footer. This time, I noticed that the Features and Articles sections shared almost the same structure: both had an h2 heading both used a grid layout both wrapped child components The only real difference was that the Features section included a description paragraph. That immediately felt like a perfect use case for a reusable component with conditional rendering. So I created a reusable Section component: function Section ({ backgroundColor , title , description , children }) { return ( < section className = { backgroundColor } aria-labelledby = { ` ${ title } -heading` } > < div className = "container section__container" > < div className = "section__header" > < h2 id = { ` ${ title } -heading` } > { title } </ h2 > { description && < p > { description } </ p > } </ div > < div className = "section__grid" > { children } </ div > </ div > </ section > ); } Then I reused it inside my LandingPage component: function LandingPage () { return ( <> { /* other LandingPage JSX */ } < section id = "features" > < Section backgroundColor = "section--gray-100" title = "Why choose Digitalbank?" description = "We leverage Open Banking to turn your bank account into your financial hub. Control your finances like never before." > < Features /> </ Section > </ section > < section id = "articl

TL;DR — The DIFP protocol was designed to be data-compact and geo-aware from day one. We recently discovered it maps almost perfectly onto the Nostr event format. Here's how, and why it matters for decentralized food infrastructure. Background: What Is DIFP? DIFP (Djowda Interconnected Food Protocol) is an open protocol designed to sync food product data across distributed nodes — compactly, efficiently, and with geo-location awareness built in by default. One of its core design decisions is the PAD system (Preloaded Asset Distribution): Apps ship with a preloaded asset pack — item metadata, compressed images, category structure — all bundled at install time. Only price and availability need to travel over the wire during sync. This means the data footprint per product is tiny. Very tiny. Enter Nostr Nostr is a simple, open protocol for decentralized communication. One of its key specs: events support up to 64 KB of content . When we started exploring Nostr as a potential transport layer, we ran the numbers — and the fit was surprisingly clean. The Math: Products Per Event Baseline encoding A product represented with three fields: { "id" : 500 , "available" : true , "price" : 30000 } At this level of verbosity, a single 64 KB Nostr event can hold approximately: ~1,500 – 2,000 products Already useful. But we can do better. Optimized encoding Two key optimizations: 1. Drop the availability key — If a product entry exists in the JSON, it's available. If it's absent, it's not. No boolean needed. 2. Drop the field names — Instead of {"id": 500, "price": 30000} , just store: 500,30000 Field mapping is handled at the app level, not the protocol level. The device knows position 0 is the product ID, position 1 is the price (in smallest currency unit, e.g. cents). Result ~6,000 – 7,000 products per single Nostr event Possibly more, depending on the price distribution and ID ranges in a given catalog. Geo-Discovery: MinMax99 Cells DIFP uses a geo-cell system called MinMax99 to

The first trillionaire in history won't make their money from banking, oil, or real estate. They'll make it from rockets and algorithms — and the implications of that distinction are genuinely unsettling. The Problem It's Solving (Or Creating) SpaceX is preparing for its IPO. Analysts tracking the raise estimate it will push Elon Musk's net worth past the trillion-dollar threshold, making him not just the richest person on Earth by a wide margin, but something qualitatively different from every billionaire before him. The standard framing treats this as a wealth story. It isn't. A billionaire is powerful because they have money. A trillionaire is powerful because, at that scale, they stop needing permission from anyone — governments, investors, boards, markets. The constraints that keep institutional power in check simply don't apply anymore. How Trillionaire-Scale Power Actually Works There's a clean way to understand the difference. A billionaire can fund political candidates, buy media, lobby aggressively. Another billionaire can fund the opposition. It's expensive, but the system has a counter. A trillionaire doesn't have a counter. They are the counter. They can simultaneously build the communications infrastructure (Starlink), the transportation layer (SpaceX), the compute stack (through xAI), and the political attention economy (via platform ownership). No single democratic institution was designed to regulate someone who owns the pipes that the institution runs on. Arnab Ray's piece in today's Times of India puts it directly: a trillionaire's thoughts and algorithms will shape planetary outcomes. That's not hyperbole. When Musk eventually lands people on Mars, the governance frameworks, the property rights, the social contracts of that colony — those will be engineered by him and his companies, not negotiated through any existing democratic process. What Societies Are Actually Unprepared For Most of the policy debate around billionaires focuses on tax rates

What Ollama actually is Ollama is an open-source runtime for large language models that runs on your own computer — Mac, Windows, or Linux. Think of it as the “Docker for LLMs”: instead of wrestling with Python environments, model weights, and GPU drivers, you type one command and a model is running. The pitch is simple: keep your data on your machine, pay nothing per token, and work offline. When you run ollama run gemma4, Ollama downloads the model, loads it into your GPU’s memory (or system RAM if you don’t have a GPU), and drops you into a chat prompt. That’s it. Behind that simplicity, Ollama is doing a lot of work for you: Model management — pulling, versioning, and storing models from its registry, the way a package manager handles software. Quantization — automatically using compressed (GGUF) versions of models so a 27-billion-parameter model fits in consumer memory. GPU layer allocation — deciding how much of the model lives on your GPU versus CPU, based on the VRAM you have. Context and KV-cache management — handling the memory that grows as a conversation gets longer. A REST API — exposing everything on http://localhost:11434 so your own apps can talk to it. How it works under the hood Ollama is not itself an inference engine. It’s an experience layer wrapped around one. Under the hood it uses llama.cpp, the C++ engine that does the actual math of running a quantized model efficiently on CPUs and GPUs. As of v0.19 (March 2026), Ollama also uses Apple’s MLX backend on Apple Silicon — a change that delivered enormous speedups (on an M5 Max running Qwen 3.5, decode throughput nearly doubled). The workflow looks like this: You run a command — ollama run qwen3 from the terminal, or a request to the API. Ollama resolves the model — if it isn’t already downloaded, it pulls the GGUF weights from the registry. It loads the model into memory — splitting layers between GPU and CPU based on available VRAM. It serves responses — either interactively in your terminal o

This story was shared by a fellow developer on DEV who asked to remain anonymous. If you've got a story to tell — come find me. Your name won't appear anywhere. Based on real microservice security design patterns. About an engineer whose PR got blocked by an AI security system — he thought he was fixing a vulnerability. Turns out, someone had a vested interest in that vulnerability staying open. 1. $1,400,000 All-hands meeting. CTO James stood at the front, a number on the screen: $1,400,000 "This is what we're spending on security this year." He pointed at the number. "The biggest piece — right here." He clicked the remote. VoidSentinel's architecture topology appeared on screen. "VoidSentinel — an AI security platform. Integrated into our CI/CD pipeline. Starting today, every PR involving internal service-to-service calls — it reviews them automatically." The CEO didn't show up today. James didn't mention it. He looked straight at Mark — VP of Security. Mark took the mic. "VoidSentinel has been running in our pre-production environment for three weeks. It's caught 47 high-risk patterns. Zero false positives." He paused. " — Of course, some people might feel uncomfortable when their PR gets blocked. But this isn't personal. This is the security standard. " He wasn't looking at me. But I knew who he was talking about. 2. High Risk. Denied. The story started three weeks earlier. We had a payment service and a user service that talked to each other internally. They shared an old API key — one key across thirty-plus services, unchanged for five years. It wasn't that nobody knew. It just never made it to the top of the backlog. On Day 1, I opened a PR: add independent service-to-service auth between the payment and user services. Not much code — a new token exchange module, three call sites modified. Five minutes later, VoidSentinel's automated comment hit: "High-risk alert: Unauthorized internal access pattern change detected. This PR has been automatically rejected. C

Introduction A traditional database. That is what many who have not really interacted with Excel to a great extent would define it as in its most basic form. Not that they are wrong, only that is the scope their utilization of Excel covers. Mostly record keeping, basic operations, and data representation. But for those whose utilization scope of Excel is broader, we definitely know better. This underestimation of Excel is a grave mistake for anyone considering themselves as tech-oriented, especially for anyone dealing with data operations, be it simple record keeping or complex concepts involving data. What is Excel A spreadsheet program or tool that facilitates data organization, analysis, and visualization through mathematical operations, chart creation, and building financial models. Real-world application of Excel in Data Analytics Reporting and visualisation Excel facilitates data representation in the form of charts(bar charts, pie charts, line graphs) and dashboards. Businesses and organisations utilize this to get an organised, more insightful, and simplified view and report of their raw data. Financial Accounting Excel's provision for mathematical operations, functions, and formulas in analysis facilitates financial accounting. Balance sheets and income statements preparation, budgeting, and expense tracking are just some of the ways Excel can be used in accounting. Decision-Making Businesses and organisations heavily rely on analysis to support their decision-making. Excel helps in the analysis through different data metrics comparisons, e.g., sales across seasons and locations, forecasting, and tracking key performance indicators. This helps businesses make the best decisions based on the insights gathered from the analysis. Beginner Excel Features and Formulas for Data Analysis Learnt so far Sort and Filter By applying the Filter feature for each column, data in specific columns can not only be sorted from newest to oldest, but also be filtered based on

Imagine your smartwatch detects an irregular heart rhythm at 3 AM. Instead of just waking you up with a frantic "beep," an AI agent immediately analyzes your historical health data, searches for the best cardiologist nearby, and prepares a calendar invite for a consultation. This isn't science fiction—it's the power of Healthcare Automation driven by AI Agents . In this tutorial, we are diving deep into LangGraph , the cutting-edge framework for building stateful, multi-agent applications. We’ll explore how to use State Machines to orchestrate a complex medical workflow, moving from an "Abnormal Heart Rate Alert" to a "Specialist Appointment" using the Tavily API for research and Twilio for urgent notifications. By the end of this guide, you’ll understand how to manage non-linear LLM workflows that require reliability and precision. The Architecture: Why LangGraph? Traditional LLM chains are linear. But medical emergencies are not. They require loops, conditional branching (e.g., "Is this an emergency or a routine check-up?"), and state persistence. LangGraph allows us to define a graph where each node is a function and edges define the transition logic. Data Flow Overview The following diagram illustrates how our agent processes a heart rate alert: graph TD A[Start: Heart Rate Alert] --> B{Severity Triage} B -- Emergency --> C[Twilio: Alert Emergency Services] B -- High Risk --> D[Tavily API: Find Best Specialist] B -- Normal/Review --> E[Log to Health Records] D --> F[Google Calendar: Draft Appointment] F --> G[Twilio: SMS Patient Confirmation] C --> H[End] G --> H E --> H Prerequisites 🛠️ To follow along with this advanced tutorial, you'll need: Python 3.10+ LangGraph & LangChain : The orchestration engine. Tavily API Key : For searching local medical specialists. Twilio Account : For SMS/Voice alerting. An OpenAI API Key (GPT-4o is recommended for medical reasoning). Step 1: Defining the Agent State In LangGraph, the State is a shared schema that evolves as it m

Every weekday, we averaged 10 groups. Every weekend, 15. Same karaoke bar. Same staff. Same songs. For a long time, I just accepted that gap as "normal." Weekends are busier. That's just how hospitality works, right? Wrong. It took me years to realize I wasn't looking at a staffing problem. I was looking at a funnel problem — and I had no idea what a funnel even was. The moment I noticed something was off One Tuesday afternoon, a group of four walked past the front door, looked at the menu board outside, and kept walking. I watched from the counter. I had open rooms. Competitive prices. Cold drinks. Everything they needed. But they left anyway. That one moment stuck with me. Why did they walk in? Why did they look? Why did they leave? I started tracking these moments obsessively. Not with software — just a notebook and a lot of attention. Here's what I found over six weeks: Weekdays : About 40 people walked past who paused at the sign. Of those, maybe 15 came to the door. Of those, 10 groups actually came in and paid. Weekends : About 90 people paused. 30 came to the door. 15 groups booked a room. The conversion rate was almost identical — roughly 25% from "stopped to look" to "became a customer." The difference wasn't that we were worse at converting on weekdays. We just had fewer people at the top. That's a funnel. I didn't know the term at the time. But what I was describing is exactly what marketers call a marketing funnel : Awareness — people notice you exist Interest — they stop to look Consideration — they walk to the door, check the price Action — they book a room and pay Most businesses obsess over the bottom of the funnel. Better sales scripts. Discount campaigns. Loyalty cards. I did the same. I ran Tuesday specials. I trained staff to upsell drinks. I rearranged the menu. None of it closed the gap. Because the gap wasn't at the bottom. It was at the top. On weekdays, I simply had fewer people aware we existed. What I tried instead Once I framed it as a f

I run a transparent OPNsense bridge between a UniFi Dream Machine Pro and the rest of my LAN. It is deliberately boring at Layer 3: the UDM keeps routing, DHCP, DNS, firewall policy, WAN handling, and VLAN definitions. OPNsense sits inline as a Layer 2 bump in the wire. The interesting part is that both sides of that bump use LACP . I already wrote the build/configuration guide for this setup here: Building a Transparent LAGG (LACP) Bridge with OPNsense, UDM, and UniFi - A Practical Guide . That article explains how the bridge was built, how the LAGG devices were configured, and why I wanted the firewall to remain transparent. This article is the other half of the story: what happens when that kind of setup fails in a non-obvious way. Not a clean outage. Not a single "the network is down" moment. Just enough instability to make everything feel wrong. 1. Topology and Failure Surface The topology looked like this: +----------------------+ | UniFi Dream Machine | | kantharos-udm-pro | +----------+-----------+ | LACP aggregate, 2 x 1G | OPNsense lagg0 "ingresslagg" igc1 + igc2, LACP | +----------v-----------+ | OPNsense bridge0 | | "laggbridge" | +----------+-----------+ | OPNsense lagg1 "egresslagg" igc4 + igc5, LACP | LACP aggregate, 2 x 1G | +----------v-----------+ | UniFi USW-Lite-16 | | downstream LAN | +----------------------+ On OPNsense, the relevant interfaces were: igc1 + igc2 -> lagg0 -> ingresslagg -> toward UDM igc4 + igc5 -> lagg1 -> egresslagg -> toward USW lagg0 + lagg1 -> bridge0 -> laggbridge The bridge is a FreeBSD bridge. The aggregates are FreeBSD lagg(4) interfaces using LACP. OPNsense exposes those through its Interfaces > Devices UI. The expected healthy OPNsense state is: laggproto lacp status: active laggport: igcX flags=<ACTIVE,COLLECTING,DISTRIBUTING> laggport: igcY flags=<ACTIVE,COLLECTING,DISTRIBUTING> Those three member states matter: ACTIVE : the member is participating in the LACP bundle. COLLECTING : the member may receive traffic. DIS

We live in an era where scientific breakthroughs are published faster than we can read them. For the biohacking community, the gap between a new PubMed study on NAD+ precursors and actually knowing what dose to take is a chasm of manual research. What if you could build an LLM Agent that monitors research papers, processes them through a RAG (Retrieval-Augmented Generation) pipeline, and maps findings to your specific health profile? In this tutorial, we are building Paper-to-Action , a state-of-the-art agentic workflow using LangGraph , ChromaDB , and Mistral-7B . This isn't just a simple bot; it's a multi-stage reasoning engine designed to turn raw academic data into actionable health interventions. If you've been looking to master AI agents and personalized medicine automation, you’re in the right place. 🚀 The Architecture: From Raw Paper to Personalized Habit Traditional RAG pipelines are linear. To handle the nuance of medical research, we need a "looping" logic. We use LangGraph to manage the state of our agent, allowing it to decide if a paper is relevant before attempting to extract a protocol. System Flow graph TD A[Start: Keyword Trigger] --> B[Search PubMed/Arxiv API] B --> C{Relevance Filter} C -- No --> B C -- Yes --> D[Store in ChromaDB] D --> E[RAG: Extract Intervention Protocol] E --> F[Cross-Reference with User Profile] F --> G[Generate Personalized Action Plan] G --> H[End: Push to Health Checklist] Prerequisites To follow this advanced guide, you'll need: LangGraph : For the agentic state machine. ChromaDB : As our high-performance vector store. Mistral-7B : Running via Ollama or vLLM for local, private inference. Python 3.10+ Step 1: Defining the Agent State In LangGraph, everything revolves around the State . We need to track the fetched papers, the extracted data, and the final recommendation. from typing import Annotated , List , TypedDict from langgraph.graph import StateGraph , END class AgentState ( TypedDict ): keywords : List [ str ] user

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Back in 2015, the two-person studio Metanet released N++, a brutally hard 2D platformer that was a decade in the making, building off of previous releases dating back to the freeware Flash title N. At the time, cofounder Raigan Burns issued some famous last words: "We hope it's not another 10 years before we come […]