Tag: ai

Date: 06/26/2025

Watch the Video

Okay, this video is gold for anyone like me who’s been knee-deep in trying to get AI agents to do some serious data fetching. It cuts right to the chase: your basic search tools inside these AI platforms? They’re kinda lame when it comes to actual web scraping. We’re talking simple Google searches, not real content extraction.

What makes this inspiring is the Bright Data MCP server and how it’s implemented inside Flowise. The video shows you exactly how to get past all the typical web scraping headaches—IP blocks, captchas, the works—and pull real-time data from anywhere. Think live product data from Amazon or snagging the latest OpenAI news. It’s not just about getting some data, it’s about getting the right data, reliably.

I can already see this being huge for automating things like competitive pricing analysis, real-time market research, and even dynamic content generation. Imagine feeding your AI agent live data and watching it adapt on the fly! It’s not just theory either, they show how to actually get it working in Flowise with live examples. Honestly, anything that can take the pain out of web scraping and pump data directly into my AI workflows is worth experimenting with. I’m adding this to my weekend project list right now!

Date: 06/25/2025

Watch the Video

Okay, this video is seriously inspiring if you’re like me and constantly looking for ways to level up your dev game with AI. In a nutshell, it shows how Gemini Pro 2.5 can analyze a video of you performing a task, then generate a script for Nanobrowser to automate that task in your browser. Think of it as turning your screen recording into a mini-automation engine.

The real value here, especially for those of us diving into AI-assisted workflows, is the low barrier to entry. Forget wrestling with complex no-code platforms like n8n or Make (which, don’t get me wrong, are powerful, but can be overkill sometimes). If you can record a video, you can potentially automate a process. Imagine onboarding new team members: instead of writing lengthy documentation, just record yourself going through the steps, and boom, an automated workflow is ready to go. Or think about automating repetitive tasks in your CMS, like content updates or image optimization.

Honestly, the “record and automate” concept is just too good to pass up. The idea of building automations from simple screen recordings, analyzed and scripted by Gemini, then executed inside the browser via Nanobrowser – it’s a workflow revolution. I’m already brainstorming how to use this for client demos, internal tool configurations, and even creating personalized training modules. Definitely worth setting aside an afternoon to experiment and see what’s possible!

Date: 06/22/2025

Watch the Video

Okay, so this video is about someone who initially threw a ton of money, $120k to be exact, at a new software idea, which ultimately didn’t pan out. But here’s the kicker – they learned from that experience, applied a bootstrapped, lean approach to their next SaaS idea, and ended up making over $1 million. That’s the kind of real-world lesson that resonates.

Why is this valuable for us as we’re diving into AI coding and no-code? Because it’s a reminder that technology isn’t a magic bullet. Sometimes, having all the fancy tools (or a huge budget) can distract you from the core problem you’re trying to solve. This video highlights the importance of starting small, validating your ideas, and iterating quickly – all things that are amplified when you leverage AI for rapid prototyping and development. Imagine using LLMs to generate initial code snippets, no-code tools to build out UIs rapidly, and then focusing your energy on fine-tuning and iterating based on real user feedback. We can avoid the trap of over-investing upfront in features nobody wants.

Think about it: Instead of sinking $120k into a fully-fledged, unvalidated product, imagine using AI to build a minimal viable product (MVP) for a fraction of the cost and time. You get to test your core assumptions, gather feedback, and pivot as needed. The video’s message of bootstrapping and learning from failure aligns perfectly with the iterative nature of AI-assisted development. It’s a worthwhile watch because it underscores the importance of smart experimentation and resourcefulness, which are even more critical in this rapidly evolving landscape. I am going to watch to find out what that first failed idea was, and what he did differently the second time.

Date: 06/22/2025

Watch the Video

Okay, so Chris is building productivity apps like Mogul, Ellie, Luna, and Lily, and in this video, he’s diving deep into his updated AI coding workflow using Claude Code. He explains why he switched from Cursor and shares his thoughts on the whole AI coding landscape. Crucially, he claims this new setup makes him 20x faster as a developer.

For those of us transitioning into AI-assisted development, this is gold! Chris outlines his 9-step Claude Code workflow and even provides concrete examples where Claude Code outperformed Cursor’s agents. He gets into the nitty-gritty of which model he’s using and explores the downsides of Claude Code – it’s not all sunshine and roses, apparently. He caps it off with who he thinks should be using it. The fact that he switched from one AI tool to another and provides a clear, step-by-step breakdown of his reasoning and workflow is super valuable.

This isn’t just theoretical; Chris is building real productivity apps! Imagine applying his workflow to automate tedious tasks in Laravel, generate boilerplate code, or even refactor legacy code. He’s essentially showing us how to leverage LLMs for a significant productivity boost. Honestly, the potential to 20x your output is reason enough to experiment! I’m eager to see how this integrates with my existing Laravel projects, especially with the promise of such a dramatic speed increase. Worth a try, right?

Date: 06/22/2025

Watch the Video

Okay, this video by Chris about his updated AI coding workflow using Claude Code is seriously inspiring, and here’s why. As someone neck-deep in transitioning to AI-enhanced development, seeing a fellow indie developer go all-in and achieve a “20x faster” speed boost is hard to ignore. The video dives into his 9-step workflow using Claude Code, explaining why he switched from Cursor, and highlighting instances where Claude Code outshone Cursor agents. We are talking real-world comparisons between different AI tools in a coder’s real workflow.

The real value lies in Chris’s practical approach. He doesn’t just hype up AI; he breaks down his exact workflow. The examples provided and the time-stamps make it easy to drill down into the most important sections. For someone like me, who’s actively looking for ways to integrate LLMs into Laravel and PHP projects, this is gold. Imagine automating the generation of complex Eloquent queries or scaffolding entire API endpoints with a few well-crafted prompts. I’m really interested in testing some of Chris’s examples in my day to day.

Ultimately, what makes this worth experimenting with is the promise of tangible productivity gains. Chris is upfront about the downsides, which keeps it real. It’s not about replacing developers, but about augmenting our abilities. The video is not just about coding, but about building apps and increasing productivity. Now, if I can carve out a couple of hours this week, I will definitely dive into the same approach.

Date: 06/22/2025

Watch the Video

Okay, so this video is a rapid-fire roundup of some seriously cool AI advancements. We’re talking about everything from 3D model generation (Hunyuan 3D 2.1, PartTracker) to video creation (Midjourney V1) and even AI that can understand and interact with humans in a more nuanced way (InterActHuman, POLARIS). There’s also some interesting stuff on prompt engineering and model editing (LoraEdit). It’s a lot to take in, but that’s what makes it so inspiring.

For a developer like me, who’s been diving headfirst into AI-assisted workflows, this video is gold. It’s not just about flashy demos; it’s about seeing practical applications of these tools that could revolutionize how we build software. Imagine using Hunyuan 3D 2.1 to rapidly prototype 3D assets for a game, or leveraging LoraEdit to fine-tune a model for a specific client’s needs without retraining from scratch. And Midjourney V1 video? Think about creating engaging marketing materials or explainer videos in a fraction of the time. The possibilities for automation and faster development cycles are huge.

Honestly, what makes this video worth experimenting with is the sheer breadth of tools presented. It’s a reminder that the AI landscape is evolving at warp speed. While I might not use every single tool showcased, it’s crucial to stay informed and explore how these advancements can be integrated into my existing Laravel and PHP projects. Plus, the resources and links provided offer a solid starting point for hands-on experimentation. Definitely adding a few of these to my “try this next” list.

Date: 06/21/2025

Watch the Video

Okay, so this video is all about leveling up your Flowise game by building AI “teams” to tackle complex tasks. It walks you through setting up a supervisor system – think of it as a project manager AI – that coordinates specialized AI “workers,” like software engineers and code reviewers, all within Flowise. It dives deep into conditional routing, managing the flow’s state, and structuring outputs, using JSON and enums for validation. This enables your team of agents to hand off tasks and collaborate to solve bigger problems.

Why is this inspiring? Because it’s the next step in moving beyond simple chatbot demos. For me, it’s about orchestrating multiple LLMs to handle entire development workflows. Imagine automating code generation, testing, and even deployment, all orchestrated by a Flowise supervisor. The video’s focus on structured output, conditional routing, and state management are key to building systems that are not just cool demos but are reliable and predictable, a challenge in the world of LLMs. You can take one task and break it down into a series of smaller more manageable tasks for each agent.

Practically speaking, I can see this being used to automate a lot of tedious dev tasks. Think automated API creation, bug fixing based on error logs, or even generating documentation. The possibilities are huge, and the video gives you the tools to experiment and build something truly useful. I think it’s worth experimenting with because it showcases how to move from isolated LLM applications to orchestrated, collaborative systems. It really feels like the future of AI-assisted development.

Date: 06/20/2025

Watch the Video

Okay, this video looks incredibly useful for any developer like me diving headfirst into AI-assisted video creation! It’s all about achieving consistent characters in Google’s Veo 3, which, let’s be honest, is a huge pain point with most AI video generators. The presenter breaks down a workflow using Whisk (for prompt engineering) and Gemini (for prompt optimization) to get more predictable results. Plus, they cover practical post-processing tips like removing those pesky Veo 3 subtitles using Runway or CapCut and even using ElevenLabs for voice cloning.

What makes this valuable is that it tackles a real-world problem: inconsistent characters ruining the flow of a narrative. We’ve all been there, right? Spending hours generating videos, only to have the main character morph into someone completely different in the next scene. The techniques shown—prompt refinement with Whisk and Gemini—are directly applicable to my work in automating content creation for clients. Imagine being able to generate marketing videos with a consistent spokesperson, all driven by AI.

For me, the most inspiring part is the combination of different AI tools to achieve a cohesive final product. It’s not just about generating the video; it’s about refining it, adding voiceovers, and removing unwanted elements. The presenter even shares their full prompt and music sources! I am excited to try these tools with a recent project to create training videos for a client onboarding process. I think this approach could save us a significant amount of time.

Date: 06/19/2025

Watch the Video

Okay, so this video by Sirio Oberati is gold for anyone like us who’s diving headfirst into AI-assisted development. It’s all about creating consistent AI-generated story scenes from a single image, and, more importantly, how to make them good enough that clients will actually pay for them. Think of it as taking AI image generation beyond just cool pictures and turning it into a scalable, commercially viable content creation pipeline. He walks through tools like Enhancor.ai for consistency and realism, Google Imagen4, and even touches on adding sound using AudioX. He even shares a ComfyUI workflow – which is awesome because it gets you started quicker!

What makes this so valuable is the focus on consistency. As developers, we know how crucial consistent APIs, data structures, and workflows are to scaling anything. This video applies that same principle to AI-generated visuals. Imagine using these techniques to create consistent UI elements for a no-code platform, or generating training datasets with controlled variations for a machine learning model. He’s literally showing how to use these AI tools to build repeatable visual content that maintains a coherent “brand look and feel.” That’s huge for automation.

Frankly, seeing someone bridge the gap between cool AI demos and practical, revenue-generating workflows is exactly what I’m looking for right now. He’s sharing the process, not just the output. And let’s be real, who wouldn’t want to experiment with tools that can create visually compelling content with a level of consistency that was previously out of reach? This is the type of stuff that makes me want to carve out some time this week and try building my own LLM-powered content creation service, even if it’s just a proof of concept.

Date: 06/19/2025

Watch the Video

Okay, this video from Sirio is definitely something I’m adding to my weekend experiment list. It’s all about generating cinematic, multi-shot videos using AI from just a single image or prompt. He’s using Enhancor.ai with their new Seedance 1.0 model and claims it’s blowing Google Veo out of the water. As someone knee-deep in trying to automate content creation for marketing campaigns (and maybe even some explainer videos for client projects), this is huge.

Why is this valuable? Well, the idea of creating consistent characters and scenes with fluid camera movements with minimal input is like a holy grail. Forget about spending hours on storyboarding and shooting separate clips – imagine just feeding in a character design and getting a professional-looking video sequence. Sirio even breaks down how to structure cinematic prompts, which is crucial. We’ve all been there, right? Throwing random keywords at an LLM and hoping for the best? This seems way more strategic.

For me, the most inspiring part is the potential for real-world application. Think automated ad creation, personalized video content, even generating cutscenes for games. The comparison with Google Veo and Kling is super interesting because it provides a benchmark. If Enhancor.ai can genuinely deliver better results with less effort, it’s a game-changer. I’m eager to see if it lives up to the hype. The free prompting guide he mentions is a great starting point, and diving into Seedance 1.0 could unlock a whole new level of creative automation.