Tag: ai

Date: 08/27/2025

Watch the Video

This video is gold for devs like us who are diving into AI. It breaks down how to run massive language models locally using Ollama and quantization. Instead of shelling out for expensive hardware, it teaches you how to tweak settings like q2, q4, and q8 to optimize performance on your existing machine. It also covers context quantization to save even more RAM.

Why is this valuable? Well, think about it. We’re trying to integrate LLMs into our Laravel apps, build AI-powered features, and automate tasks. But running these models can be a resource hog. This video gives you the practical knowledge to experiment with different quantization levels and find the sweet spot between performance and resource usage. You can prototype and test locally without needing a cloud server every time.

Imagine building a customer service chatbot using a 70B parameter model. This video shows you how to get that running smoothly on your laptop first. It’s a total game-changer for iterative development and aligns perfectly with the no-code/AI-coding ethos of maximizing efficiency. It’s worth checking out just to see how much you can push your current setup!

Date: 08/25/2025

Watch the Video

Okay, this video is exactly what I’ve been digging into! It’s a hands-on guide to setting up a local AI server using llama.cpp within an LXC container, leveraging the power of multiple GPUs (quad 3090s in this case!). The video walks through the whole process, from installing the NVIDIA toolkit and drivers to actually building llama.cpp, downloading LLMs from Hugging Face (specifically, models from Unsloth, known for efficient fine-tuning), and running both CPU and GPU inference. Plus, it shows how to connect it all to OpenWEBUI for a slick user interface.

Why is this valuable? Because it tackles the practical side of running LLMs locally. We’re talking about moving beyond just using cloud-based APIs to having full control over your AI infrastructure. This means data privacy, offline capabilities, and potentially significant cost savings compared to constantly hitting cloud endpoints. And the fact that it uses Unsloth models is huge – it’s all about maximizing performance and efficiency, something that’s key when you’re dealing with resource-intensive tasks.

Think about it: you could use this setup to automate code generation, documentation, or even complex data analysis tasks, all within your local environment. I’m particularly excited about experimenting with integrating this with Laravel Forge for automated deployment workflows. Imagine pushing code, and the server automatically optimizes and deploys AI-powered features with this local setup. The video is worth a shot if, like me, you’re itching to move from theoretical AI to practical, in-house solutions. It really democratizes access to powerful LLMs.

Date: 08/24/2025

Watch the Video

Okay, this video is like a rapid-fire update on the latest and greatest in AI, hitting everything from conversational AI with InfiniteTalk to image editing with Qwen and even code generation with Mesh Coder. It’s basically a snapshot of how quickly AI is evolving and infiltrating different areas of development.

For a developer like me, who’s actively trying to integrate AI and no-code tools, this is pure gold. It’s not just about theory; it’s about seeing concrete examples of what’s possible right now. Imagine using DeepSeek V3.1 to optimize complex algorithms or leveraging Qwen Image Edit to quickly iterate on UI assets. The possibilities for automating tedious tasks and accelerating development cycles are mind-blowing. Plus, seeing projects like InfiniteTalk gives me ideas for building more intuitive and human-like interfaces for my applications, something I have been tinkering with using LLMs and no-code front ends, but I never quite knew how to approach the natural language backend.

Honestly, the speed of innovation is what makes this so inspiring. It’s a reminder that we need to constantly experiment and adapt. I’m particularly intrigued by the image editing and 3D manipulation tools – things that used to take hours can now potentially be done in minutes. I am going to try to see if I can speed up my asset creation workflow with something like Nanobanana. Worth experimenting with? Absolutely! It’s about finding those AI-powered force multipliers that let us focus on the higher-level creative and strategic aspects of development.

Date: 08/22/2025

Watch the Video

Okay, this video is seriously exciting! It’s all about leveraging Claude Code, a powerful AI coding assistant, alongside N8N’s API and a couple of neat MCP servers (one for direct API and another for Playwright browser automation) to build complete N8N workflows without ever touching the N8N canvas. We’re talking about AI-powered workflow creation, modification, and even testing! It’s about shifting from dragging and dropping nodes to describing what you need, and letting the AI build it.

For someone like me, knee-deep in exploring AI-enhanced workflows, this is gold. I’ve been manually building N8N workflows for years, and the thought of being able to simply describe a complex automation and have it built for me is game-changing. Imagine automating the creation of chatbot workflows with memory and web search, all driven by voice commands! The video shows how to set up these MCP servers, configure API keys, and, most importantly, how to engineer prompts to get Claude Code to generate efficient and functional workflows.

The potential for real-world development is huge. Think about rapidly prototyping complex integrations, automating repetitive tasks, or even enabling non-technical users to contribute to workflow design. The ability to automate workflow testing via the API is also a massive win. It’s worth experimenting with because it promises to significantly reduce development time, improve accuracy, and unlock new possibilities for automation. I’m particularly keen to explore how this approach can accelerate the creation of custom CRM integrations. Plus, the link to cheaper N8N hosting is a welcome bonus!

Date: 08/22/2025

Watch the Video

Okay, so this video is all about Alibaba’s new AI IDE called Qoder, positioned as a free alternative to Cursor and Windsurf, emphasizing “agentic coding.” It highlights features like programming via conversation, a “Quest Mode” to delegate tasks to AI agents, and a context-aware engine that learns from your codebase.

Why is this exciting? Well, as someone knee-deep in integrating AI into my workflows, the idea of an IDE that truly understands context across the entire project is a game-changer. We’re talking about less time copy-pasting context into prompts and more time letting the AI actually architect solutions. The Quest Mode is particularly appealing – imagine delegating entire features to an agent that handles the planning, coding, and testing asynchronously! That’s a HUGE leap toward autonomous development and frees you up to focus on the higher-level architecture and business logic.

Think about it: Instead of spending hours debugging a complex Laravel Eloquent query, you could delegate it to an agent in Qoder, which has already ingested your model definitions and database schema. It tests the solution, and you review the results. Or, imagine automating the creation of API endpoints based on business requirements you outline conversationally. It’s worth experimenting with because it represents a shift towards AI not just as a code completion tool, but as a true collaborative partner, potentially unlocking significant productivity gains. The fact that it’s free in preview? No brainer!

Date: 08/21/2025

Watch the Video

Okay, this video is right up my alley! It’s all about building a local AI server using a beefy quad 3090 setup to run the new DeepSeek 3.1 model. The video provides a detailed parts list (mobo, CPU, RAM, GPUs, cooling, PSU, etc.) and links to tutorials for setting up the software stack: Ollama with OpenWebUI, llama.cpp, and vLLM. This means you can run powerful LLMs locally, which is HUGE for development.

Why is this valuable for us? We’re moving into a world where AI coding assistants are becoming essential. This video helps us take control by setting up our own local inference server. Instead of relying solely on cloud-based APIs (which can be expensive and have data privacy concerns), we can leverage our own hardware for faster iteration, customized models, and offline capabilities. Imagine being able to fine-tune DeepSeek 3.1 on your own codebase and then using it to generate code, refactor legacy systems, or even automate documentation – all without sending sensitive data to external services!

This isn’t just theory; it’s about real-world automation. Think about the time savings. Instead of waiting for API responses or dealing with rate limits, we can have an AI coding assistant responding in real-time. Yes, the initial setup might be a bit of a project (building the server, configuring the software), but the payoff in terms of productivity and control over our AI workflows is immense. I’m definitely adding this to my weekend experiment list – the potential for integrating this into my Laravel workflow is just too exciting to pass up!

Date: 08/19/2025

Watch the Video

Okay, this video on Perplexity Comet is seriously sparking my interest, and I think it should be on your radar too. It’s basically a walkthrough of how the presenter is leveraging this AI tool to automate a bunch of everyday tasks – think auto-applying promo codes, digging through YouTube for specific clips, parsing analytics data, generating spreadsheet formulas, and even finding files buried on websites. It’s a real-world showcase of how an LLM can become a serious productivity booster.

Why is this gold for us? Because it’s showcasing practical, immediately usable examples of what we’re all trying to do: integrate AI into our existing workflows. We’re constantly looking for ways to offload repetitive tasks, extract insights from data faster, and generally make our development lives easier. This video isn’t just theory; it’s a demonstration of how Perplexity Comet is achieving that right now. Imagine using it to automatically analyze server logs, generate API documentation stubs, or even craft SQL queries based on natural language descriptions. That’s the kind of automation we’re after!

What gets me really excited is the potential for no-code integrations. The presenter highlights how he’s using Comet to interact with services like Apple Podcasts and LinkedIn. This is where the rubber meets the road! Suddenly, we can create mini-applications or automations without writing a single line of code, leveraging the LLM to bridge the gap between services. For instance, you can trigger a build process in Laravel Forge from a Slack message using a tool like Zapier and the LLM as a “translator” and data manipulator. Sounds cool right? I am definitely setting aside some time this week to give Perplexity Comet a try and see where I can slot it into my existing projects. I suspect this is a glimpse into a future where we’re orchestrating AI agents instead of writing every single line of code.

Date: 08/19/2025

Watch the Video

Okay, this video is right up my alley! It’s a head-to-head comparison of Ollama and LM Studio, two tools that let you run AI models locally. One’s a CLI-driven powerhouse (Ollama), the other a slick GUI (LM Studio). As someone knee-deep in integrating LLMs into my Laravel apps, this is gold.

Why? Because it’s about bridging the gap between traditional coding and AI. Ollama, with its “Docker for LLMs” approach, speaks directly to my desire to automate model deployment and integrate AI into my existing workflows. Imagine scripting your model deployments and chaining them into your CI/CD pipeline! LM Studio is intriguing too, it’s a fantastic starting point for quickly experimenting with different models without diving into code, and that’s invaluable for rapid prototyping.

This kind of local AI setup has huge implications. Think about building a customer service chatbot that uses a locally hosted model, giving you complete data privacy and control. Or an internal documentation system powered by AI, all running on your own infrastructure. For me, the Ollama CLI approach is definitely something I want to explore for its automation potential. LM Studio seems like a great way to rapidly test ideas and experiment. I reckon I’ll be spinning up both this weekend, starting with LM Studio to get a feel for the models, then migrating over to Ollama for proper integration testing.

Date: 08/18/2025

Watch the Video

Okay, so this video basically tackles how to build a SaaS business that can actually survive in today’s crazy competitive market. Think of it like this: everyone’s pumping out software faster and cheaper, kinda like the “fast fashion” industry but for apps. It’s a real threat, and the video talks about strategies to avoid getting crushed by the “Temu Effect” – that race to the bottom on price.

As someone actively exploring AI-enhanced workflows, this hits home. We can’t just keep churning out the same old code. This video is valuable because it forces you to think about differentiation. How do we build SaaS that’s not just cheap, but truly valuable and hard to replicate? That’s where AI, no-code, and LLMs come in. We can use these tools to build unique features, automate personalized experiences, or even create entirely new product categories. Instead of competing on price, we can compete on innovation and specialized value.

Honestly, what I find most inspiring is the idea of using new tech to leapfrog the competition. Imagine leveraging LLMs to build hyper-personalized onboarding experiences, or using AI to predict user needs and proactively offer solutions. That’s not just cheaper or faster; it’s a whole new level of value that’s hard to copy. It’s absolutely worth experimenting with because it’s about building defensible, future-proof SaaS, and that’s the name of the game.

Date: 08/18/2025

Watch the Video

Okay, this video on building an AI-powered LinkedIn job application bot is exactly the kind of thing that gets me excited about where development is heading. In a nutshell, it walks you through using n8n (a no-code workflow automation platform) and Airtop (remote browser) to create an agent that tirelessly searches and applies for jobs on LinkedIn, all without your computer needing to be on. Think of it: a 24/7 job-hunting assistant!

For someone like me, who’s been deep in traditional PHP and Laravel for years but is actively exploring AI coding and no-code solutions, this is gold. We’re talking about automating a tedious process (job applications) with AI tools that anyone can learn, regardless of their coding background. This isn’t just about saving time; it’s about fundamentally changing how we approach work. Imagine adapting this to scrape competitor pricing, automate social media posting, or even manage customer support inquiries – all through a similar workflow. The video even tackles secure credential management and scheduling, which are crucial for real-world applications.

Honestly, the most inspiring part is the potential for real-world impact. Instead of manually clicking through hundreds of job postings, a system like this can free up time to focus on skills development, networking, or even just taking a break! Plus, seeing how these technologies integrate – n8n for workflow, Airtop for remote browsing, and GPT-4 for decision-making – is a fantastic example of how AI can augment our abilities. I’m definitely adding this to my weekend experiment list. The idea of a tool agent working for me 24/7 is too good to pass up.