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Author: Alfred Nutile

  • Kimi Coder: FULLY FREE + FAST AI Coder! High Quality Apps With No Code! (Opensource)



    Date: 07/17/2025

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    Okay, this video on Kimi Coder looks incredibly relevant to what I’m exploring right now. It’s all about using a free, open-source AI coding assistant (Kimi Coder), powered by the Kimi K2 model, to generate full-stack applications from a single prompt. Think of it as a no-code tool that actually generates code for you, which you can then customize. The video highlights how it outperforms some serious players like GPT-4 Sonnet and DeepSeek on coding benchmarks. For someone like me who’s transitioning to AI-enhanced workflows, this is huge! It’s not just about replacing coding, but about accelerating development and freeing up time to focus on architecture and complex logic.

    The real value here is in the potential for rapid prototyping and automation. Imagine quickly spinning up a working version of a web app or an agentic tool just by describing it. Instead of spending days on initial setup and boilerplate, you could have a functional prototype in hours. Then, you can dive into the generated code, tweak it, and refine it. The video mentions use cases like agentic workflows, tool use, and rapid prototyping, which is directly aligned with my interest in automating complex tasks with AI. Plus, the fact that it’s open source means you can host it locally and customize it, which is a big win for control and security.

    Honestly, the fact that it’s claimed to outperform GPT-4 on certain coding tasks is what really piqued my interest. We’ve been experimenting with OpenAI’s models, but the cost can add up fast. So I’m inspired to dive in, set up Kimi Coder locally, and throw some real-world challenges at it. I want to see if it can genuinely accelerate my development process, and free me up to focus on the higher-level architectural decisions. If it lives up to the claims, it could be a game-changer for our team.

  • n8n Evaluation quickstart



    Date: 07/17/2025

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    Okay, so I watched this video on using LLMs to generate Laravel code, and honestly, it’s a game-changer for how I’m thinking about development these days. It’s basically showing how you can feed a large language model a description of what you want – a new API endpoint, a database migration, even entire controllers – and it spits out working code. It’s like having a junior dev that never sleeps but speaks fluent Laravel!

    What’s so cool about this is that it directly aligns with my push into AI-assisted workflows. For years, I’ve been hand-crafting Eloquent models and tweaking Blade templates. Now, instead of starting from scratch, I can use the LLM to generate the boilerplate and then focus on the interesting, complex logic. Imagine automating the creation of CRUD operations or quickly scaffolding out a new feature based on client requirements. I can definitely see applying this to speed up repetitive tasks and free up time for more strategic problem-solving.

    This isn’t about replacing developers; it’s about augmenting our abilities. The code might not be perfect right out of the box, but it’s a fantastic starting point and a huge time-saver. I’m excited to experiment with this, refine the prompts, and integrate it into my existing Laravel projects. I really want to see if I can start using the generated code as the basis of my unit tests. If I can just use a couple of commands to generate tests and base code? Watch out world!

  • Amazon Just Killed Vibe Coding With This New Tool!



    Date: 07/16/2025

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    Okay, so this video is all about Amazon Kiro, their new AI code editor. It dives into how Kiro works, comparing it to existing tools, and explaining why its approach to AI-assisted coding is actually pretty interesting for modern application development. Things like “Spec vs. Vibe” and “Steering Docs” – it’s about giving the AI a direction and keeping it on track, which is key when you’re building something complex.

    Why is this inspiring? Well, for me, it’s another sign that we’re moving past just using AI for simple code snippets. The video showcases how Kiro lets you structure your projects and use AI to fill in the gaps, almost like pair programming with a super-smart assistant. It gets into how you can use “hooks” and steering documents to guide the AI, ensuring it stays aligned with your vision. I see this as a path toward automating larger chunks of development, not just individual functions.

    Imagine using something like Kiro to scaffold a new Laravel feature, handling the boilerplate and even some of the business logic based on a well-defined specification document. The video touches on rate limits and terminal access, so you’re not completely cut off from traditional coding. The whole concept of “Spec vs Vibe” resonates with the need to clearly define what we expect from AI, and I’m eager to test how well it works in a real-world project. It’s worth experimenting with to see if it can truly bridge the gap between traditional coding and AI-driven development.

  • Vibe-Kanban: SUPERCHARGE Claude Code, Gemini CLI, & ANY AI CODER! 100x Coding! (Opensource)



    Date: 07/15/2025

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    Okay, so this video is all about Vibe Kanban, an open-source tool designed to be a control center for AI coding agents like Claude Code, Gemini CLI, and even AMP. Essentially, it’s a visual Kanban board that helps you orchestrate, monitor, and deploy different AI agents from one place. Think of it as a single pane of glass for managing all your AI-powered coding tasks. The video shows how it can help you switch between agents, track task status, and even launch dev servers directly from agent outputs. They even demo merging 4 PRs in 20 mins with it – crazy!

    For someone like me who’s knee-deep in integrating AI into my workflows, this is gold. We’re constantly juggling different AI tools and trying to figure out how to make them work together efficiently. The promise of a unified interface and centralized configuration (MCP) is super appealing. It addresses a real pain point: the context switching and management overhead that comes with using multiple AI coding assistants. Plus, the visual Kanban aspect makes it easy to track progress and identify bottlenecks in your AI-driven development process.

    The real-world application here is massive. Imagine using Vibe Kanban to manage a complex refactoring task, delegating different parts of the process to specialized AI agents and tracking their progress on a single board. Or perhaps automating the deployment pipeline by chaining together AI agents for testing, code review, and deployment. For me, the ability to centralize agent configurations is worth experimenting with alone. It could dramatically reduce the amount of time I spend configuring and tweaking individual AI tools, and ultimately let me focus on the bigger picture. This looks like a serious productivity booster for any dev team leveraging AI, and I’m definitely going to spin it up this week.

  • Unlock the Next Evolution of Agents with Human-like Memory (n8n + zep)



    Date: 07/14/2025

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    Okay, this video on using Zep memory with AI agents in n8n is seriously inspiring for anyone looking to move beyond basic LLM integrations. It’s about giving your AI agents actual long-term memory using a relational graph database (that’s Zep), which means they can understand relationships between entities, users, and events. Think of it: no more just relying on the immediate context window!

    The real value here isn’t just about the cool tech, but about the practical strategies the video shares. It highlights the potential cost explosion you can face by blindly implementing long-term memory, and then dives into token reduction techniques in n8n. This is critical because, while giving an AI agent a memory of all past conversations or user interactions sounds great, it becomes a nightmare when you’re paying by the token. The video shows how to intelligently combine short-term and long-term memory, using session IDs, and other methods so that we can reduce cost without sacrificing performance.

    For me, this video represents a key evolution in how I’m approaching AI-powered automation. No-code tools like n8n, combined with services like Zep that provide memory, offer a powerful way to build sophisticated AI agents. I’m already imagining how I could adapt this to create more personalized customer support bots or even intelligent internal knowledge management systems. It’s one thing to connect an LLM to an API, and it’s another to create systems that truly learn and evolve over time. This video has actionable strategies for that. I am going to sign up for n8n using the link the video provides.

  • I Replaced Lovable with This AI Tool (Vibe Coding)



    Date: 07/14/2025

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    Okay, so this video is basically showing how to spin up a full-blown AI-powered app in minutes using a no-code tool called Rocket.new. As someone who’s spent years hand-coding Laravel apps, and who is now actively diving into AI-assisted workflows, that immediately grabbed my attention. We’re talking about potentially bypassing a significant chunk of the traditional development lifecycle, and focusing more on the idea and the user experience than the nitty-gritty code.

    What makes this valuable for us developers embracing the AI/no-code shift is the promise of rapid prototyping and validation. Imagine you have a client with a wild idea for an app. Instead of weeks of coding, you could use something like Rocket.new to build a functional prototype in an afternoon. You could then test its core functionality, get real user feedback, and iterate before committing to a full-scale build. We can use these tools to quickly build the scaffolding and let the AI tools do what they are good at – filling it out and making it work.

    Ultimately, the idea of quickly generating and deploying AI-driven apps opens up massive possibilities. It’s not about replacing developers, but about augmenting our abilities and allowing us to focus on the higher-level aspects of application development like architecture and scaling. I’m definitely going to play around with Rocket.new; even if it’s not perfect, the speed and ability to iterate on ideas quickly makes it worth experimenting with.

  • The new way to do Auth Keys in Supabase



    Date: 07/14/2025

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    Okay, this Supabase video about JWT Signing Keys and API Keys is seriously worth checking out. It’s all about improving security and performance, which is music to my ears as I dive deeper into AI-driven workflows. Essentially, they’re replacing the old Anon and Service Role keys with more granular API keys and introducing asymmetric JWTs. This means your app can verify users locally without hitting the Supabase Auth Server every time, which is huge for speed.

    Why is this valuable for someone like me transitioning into AI coding and no-code? Well, think about it: many AI-powered apps need secure and fast authentication. These changes streamline that process. I can see using these API keys to lock down specific microservices or AI agents, ensuring they only access what they’re supposed to. Plus, the JWT signing keys mean I can potentially offload authentication logic to the edge, further improving response times for AI-driven features.

    Honestly, this video is inspiring because it highlights how traditional backend bottlenecks can be solved with smart architectural changes. Experimenting with these new Supabase features feels like a natural extension of my AI/no-code journey, allowing me to build more secure, scalable, and performant AI-powered applications. I am thinking of ways I can use this with llama-index and langchain.net. Definitely worth a weekend project!

  • Stop Using RAG for Spreadsheets — Use This Instead (n8n)



    Date: 07/14/2025

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    Okay, this video is exactly the kind of content that gets me fired up about the future of development. It’s all about building smarter AI agents with n8n that can actually understand and query structured data, like spreadsheets, using a hybrid RAG (Retrieval-Augmented Generation) approach. We’re talking about giving our agents the ability to not just semantically search, but to do things like sum columns, filter rows, and perform real SQL queries through natural language!

    Why is this valuable? Well, how many times have you built a clunky interface just to let a user run a simple report on some data? This video shows you how to use an AI agent to interpret a user’s natural language request (“What were the total sales in France last month?”) and translate it into an actual SQL query against a Supabase database. The magic is in how the data is ingested and managed – storing structured data in a flexible JSONB column, so you don’t need a rigid schema upfront. Plus, it smartly combines vector search for unstructured data with SQL queries for the structured stuff – the agent decides which to use. It walks through a complete data pipeline, too, covering things like handling data changes in Google Drive and keeping everything synced. No-code is cool and all, but the real power comes when you can seamlessly blend it with robust backend logic.

    For me, the most exciting thing is the shift from building rigid UIs and APIs to crafting intelligent agents that can adapt to changing data and user needs. Imagine the possibilities for automating reporting, data analysis, and even complex business workflows! I’m already brainstorming ways to apply this to a reporting project for a client. I’m thinking by setting up a system like this, we can drastically cut down the time spent manually building reports and dashboards. It’s worth experimenting with, as I see it lowering dev time by potentially 50%!

  • Earn your first $100 on the App Store in 30 days (even if you’re a terrible coder)



    Date: 07/14/2025

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    Okay, so this video by Adam Lyttle is all about building simple iOS apps, even if you think you’re a terrible coder, and making your first $100 on the App Store in 30 days. Sounds like a classic “build in public” journey, which I’m always a sucker for. He focuses on simple app ideas and fast development strategies, and shares the resources he used to get started.

    What makes this valuable for us, as developers transitioning to AI-assisted workflows, is the mindset shift. It’s not about being a perfect coder anymore. It’s about quickly iterating and validating ideas. We can use AI tools to rapidly prototype these simple apps, generate boilerplate code, or even debug issues. Astro for keyword research, mentioned in the video, is a great example of leveraging a tool to identify market opportunities and use LLMs and no-code tools to get an app to market quickly.

    Imagine using an LLM to generate a basic framework for one of these simple app ideas, then using a no-code platform to flesh out the UI and user flow. We could even use AI to write the app store description and generate marketing materials. This video is an inspiration to embrace the “fail fast, learn faster” approach, and these tools can help us validate ideas quicker than ever. I’m adding this one to my watchlist – it’s time to experiment and see what simple apps we can launch in the next month!

  • Kimi K2- The FREE AI Model That Killed Claude Code??



    Date: 07/13/2025

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    Okay, this video about Kimi K2 looks super interesting, especially if you’re like me and constantly searching for better, faster, and cheaper AI coding assistants. The presenter walks you through setting up and using Kimi K2, highlighting its potential to shake up the AI industry. What caught my eye is the promise of using it to code, potentially even a whole 3D first-person shooter game in ThreeJS – for free! That’s a bold claim, but the benchmarks mentioned in the video make me want to dive in and see how it stacks up against other models.

    For those of us neck-deep in the transition to AI-enhanced workflows, this is a potential game-changer. Imagine being able to quickly prototype ideas, automate repetitive coding tasks, or even generate entire modules with a tool like this. A 3D FPS game is a good example because it’s complex enough to really put the AI through its paces. If Kimi K2 can actually deliver usable code, it could drastically reduce development time and allow us to focus on the more creative and strategic aspects of our projects.

    Honestly, even if it doesn’t perfectly generate the entire game, the potential time savings in boilerplate code and initial setup are huge. I’m thinking about how this could be applied to rapidly prototyping different UI components or even automating API integrations in Laravel. The fact that it’s potentially free to try makes it a no-brainer. I’m definitely going to experiment with this, especially with its single-file output, which is perfect for proof-of-concept projects.