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7 Low-Risk Land Investment Strategies That Could Generate Thousands Monthly   Have you ever dreamed of owning your own slice of land? A place to escape when the world gets chaotic, yet still generates income when you're not there? Most people assume land ownership requires hundreds of thousands—or even millions—of dollars. But what if that assumption is wrong? What if, for just a few thousand dollars and some creative financing, you could buy land, earn significant cash, and even get others to help pay for it? Let me show you how this is entirely possible. The Hidden Opportunity in Land Investment My friend Kate discovered this opportunity early. She purchased a piece of land near Joshua Tree for just 10,000 (yes, incredibly affordable) and transformed it into a low-maintenance business using platforms like HipCamp. Within her first 90 days, she was already earning 1,500 monthly—with projections for much more. Inspired by Kate's success, I decided to purchase my ow...

Fine-Tuning LLMs with UNS Sloth: 5x Faster with 70% Less Memory In the rapidly evolving landscape of AI development, efficient fine-tuning of large language models has become a critical bottleneck. Today, I'm excited to introduce you to **UNS Sloth**, a groundbreaking tool that's revolutionizing how we fine-tune models like Mistral, Gemma, and LLaMA 2. ## Why UnSloth.ai Is a Game-Changer UNS Sloth offers several impressive advantages over traditional fine-tuning methods: - **5x faster** fine-tuning process - ** 70% less memory* * consumption - ** Zero loss in accuracy ** compared to standard methods - ** Cross-platform support ** for Linux and Windows (via WSL) - ** Flexible quantization options**: 4-bit, 16-bit, QLoRA, and LoRA fine-tuning - **Outperforms Hugging Face** by 2x in benchmarks across multiple datasets   Step-by-Step Tutorial: Fine-Tuning Mistral-7B In this tutorial, I'll walk you through how to fine-tune the Mistral 7B parameter model using the OI...

Hardened BSD: A Security-Enhanced FreeBSD Fork Welcome to this week's BSD Synergy article, where we dive into Hardened BSD - a security-focused fork of FreeBSD that brings much-needed security features to the BSD ecosystem. The Origin Story Hardened BSD began in 2013 as a project with a simple goal: to implement Address Space Layout Randomization (ASLR) in FreeBSD. The initiative was spearheaded by Oliver Pinter and Shawn Webb, who initially hoped to contribute these security enhancements back to the FreeBSD project. However, their attempt to upstream ASLR into FreeBSD faced significant resistance and controversy. After reviewing the archived email threads and forum discussions surrounding this effort, it's clear there was considerable drama within the community about implementing this security feature. Eventually, the developers made the decision to stop trying to upstream their work and instead devoted their efforts to creating Hardened BSD as a standalone securit...

A Practical Introduction to OpenBSD's Built-in Tools OpenBSD is known for its security, simplicity, and minimalist design philosophy. Unlike many other operating systems that require you to install numerous additional packages to get work done, OpenBSD comes with a surprisingly comprehensive set of tools right out of the box. In this post, I'll explore what's included in a fresh OpenBSD installation and demonstrate some of its built-in capabilities.   Coding in OpenBSD: The Basics OpenBSD ships with everything you need to write, compile, and debug code without installing any additional software. Let's start with a simple C program using the classic Ed editor. Ed is considered the classic Unix editor, and while it's not as user-friendly as modern alternatives, it's a part of Unix history. Here's how to use it to write a simple "Hello World" program: ``` $ ed a #include <stdio.h> int main() {     printf("Hello world\n");  ...

Mervin Praison (YouTube) https://www.youtube.com/user/MervinPraison Why do multi agent systems FAIL Link should be is post OR QR Code ON picture. https://arvix.org/abs/2503.13657

The Ultimate Guide to Vibe Coding: Building Software with AI Like a Pro In the rapidly evolving landscape of software development, a revolutionary approach called "Vibe Coding" is changing how we build applications. Pioneered by some of the brightest minds in AI, this method is making software development accessible to everyone — not just experienced programmers. Let me share how this approach works and why it might be the future of coding.   What is Vibe Coding? Vibe Coding is a new way of building software using AI. Rather than focusing on writing code yourself, you let AI tools handle the technical details while you focus on the vision and features. The term was coined by Andre Karpathy, co-founder of OpenAI and former Tesla self-driving team leader, who described it as: > "A new kind of coding where you fully give into the vibes, embrace exponentials, and forget that the code even exists." This approach has enabled entrepreneurs like David Andre t...

 Understanding and Solving Underflow in Hidden Markov Models: The Log-Sum-Exp Trick When implementing Hidden Markov Models (HMMs) on non-trivial data sets (around 100 data points or more), you'll inevitably encounter a critical computational challenge: underflow. This blog post explains what underflow is, why it happens in HMMs, and how to solve it elegantly with the log-sum-exp trick. ## The Underflow Problem Underflow occurs when a number becomes too small for your computer or programming language to represent accurately. While some languages can handle arbitrarily small numbers, many cannot. When a number becomes extremely small, these languages simply set it to zero, leading to incorrect computational results. This problem is particularly common in algorithms that multiply many small probabilities together, such as HMMs. In the forward algorithm of HMMs, we calculate the probability of observing a sequence of data points, which becomes vanishingly small as the seque...

Breaking the Boundaries: Test Time Reinforcement Learning (TTRL) in AI Development In a landscape where AI research continually pushes for greater capabilities, a fascinating new methodology called Test Time Reinforcement Learning (TTRL) has emerged, claiming significant performance improvements. This approach shifts reinforcement learning from training time to inference time, creating what researchers describe as a "self-improving" system. But does it truly represent the breakthrough many hope for? Understanding TTRL: A New Approach to AI Improvement TTRL, developed by researchers at Tsinghua University and Shanghai AI Lab, represents a shift in how we apply reinforcement learning to language models. Rather than using reinforcement learning during training, TTRL applies it during inference (or "test time"). The process works as follows: 1. When given a prompt, the model generates multiple potential answers 2. The model evaluates these answers through ma...

From Overfitting to Generalization: Understanding the "Grokking" Phenomenon Grokking is a fascinating neural network behavior where models suddenly achieve perfect generalization after extended training, well beyond the point of overfitting. This phenomenon, identified by researchers at OpenAI, challenges conventional wisdom about machine learning and offers new insights into how neural networks learn. Let me break down this intriguing concept. What is Grokking? Grokking occurs when a neural network: 1. Initially overfits training data completely (100% training accuracy) 2. Shows poor validation performance for an extended period 3. Suddenly "snaps" into perfect generalization after continued training This behavior was observed on algorithmic tasks involving binary operations, where models were trained to learn rules like modular addition or permutation composition.   The Key Observation The most striking feature of grokking is visualized in the paper...

Revolutionizing Work: How to Deploy AI Agents Using AnythingLLM The business landscape is undergoing a profound transformation as AI agents redefine productivity across industries. This comprehensive guide explores how to harness the power of AnythingLLM – a free, open-source platform for running AI agents without coding skills, monthly fees, or security concerns. What is AnythingLLM? AnythingLLM is a powerful, open-source application for running AI agents with several standout features: - **Model agnostic**: Choose from the latest LLM models like DeepSeek R1 or use familiar models that best fit your needs - **Easy deployment**: Desktop applications with single-click installation for Windows, Mac, and Linux - **Flexible hosting options**: Run locally, connect to cloud options, or deploy via Docker in your own infrastructure - **Open-source flexibility**: MIT license allows modification and customization - **Enterprise options**: Multi-user solutions and white-label options ...

Open Source: A Strategic Approach to Business, Not Just a Price Tag In the ever-evolving landscape of technology, open source is often misunderstood as merely a cost-effective alternative. However, it encompasses a deeper philosophy centered around control, transparency, and liberation from vendor lock-in. This blog post delves into the strategic advantages of embracing open source, featuring insights from Tom of Orange Systems and highlighting the exemplary approach of Vates, the company behind XCPNG and Zen Orchestra. The Philosophy of Open Source Open source is more than a price tag; it's a design philosophy. It empowers businesses to avoid the pitfalls of vendor lock-in, where the whims of a vendor can dictate your operational future. By leveraging open source, companies gain control over their destiny, transparency in their tools, and the freedom to innovate without constraints.   Open Source in Action Tom from Orange Systems exemplifies this philosophy, utilizing ...

Title: How I Tried to Make $800 in 4 Hours Using Google Maps (And What I Learned)  --- Introduction: The Challenge My team recently dared me to make $800 as fast as possible—with a catch. I couldn’t use any existing skills or resources, and the method had to be something *anyone* could replicate. With just four hours on the clock, I rolled up my sleeves and got to work. Here’s how it went down.   --- Step 1: Brainstorming a Viable Business Idea Physical products like dropshipping were out—too time-consuming for sourcing and shipping. Print-on-demand? Still required quality checks and setup time. Then it hit me: **service-based businesses** were the answer. But what service?   The Google Maps Lightbulb Moment    I’d always noticed untapped potential in Google Business Profiles (formerly Google My Business). Many small businesses have unoptimized or unclaimed listings, costing them visibility. Helping them fix this? No inventory, no upfront c...

A Path Towards Autonomous Machine Intelligence: Understanding Yann LeCun's JEPA Architecture In the quest for creating truly intelligent machines, several approaches have emerged in recent years. One of the most interesting proposals comes from Yann LeCun, one of the godfathers of deep learning and a Turing Award winner. In his position paper titled "A Path Towards Autonomous Machine Intelligence," LeCun outlines a comprehensive framework aimed at addressing some of the most pressing limitations of current deep learning systems. The Current Limitations of Deep Learning Current deep learning systems face several fundamental challenges that LeCun's paper aims to address: 1. ** Data Hunger **: Deep learning models typically require enormous amounts of data to learn effectively. 2. ** Limited Reasoning and Planning**: Many current systems struggle with complex reasoning tasks. 3. ** Lack of Multi-level Abstraction **: Most models can't represent percepts ...