Category: Try

Date: 08/12/2025

Watch the Video

Alright, buckle up fellow devs, because this video about Open-SWE is seriously inspiring! It’s all about a free and open-source alternative to tools like Jules, which, let’s face it, can get pricey. Open-SWE leverages LangGraph to function as an asynchronous AI coding agent. That means it can dive deep into your codebase, plan out solutions, write, edit, and even test code, and automatically submit pull requests, all without you having to constantly babysit it. You can run it locally or in the cloud and connect it to your API key, even free APIs like OpenRouter or locally with Ollama.

Why is this a game-changer for those of us exploring AI-enhanced workflows? Well, first off, the “free” part is music to my ears. More importantly, it demonstrates how we can integrate AI agents into our existing development pipelines without being locked into proprietary systems. Think about automating those tedious tasks like bug fixing, writing unit tests, or even refactoring larger codebases. Imagine setting it off to run and self-review, while you get back to designing new features!

From my perspective, what makes Open-SWE worth experimenting with is that it empowers us to build genuinely custom AI assistants tailored to our specific project needs. I could see this being useful for automating repetitive tasks, freeing me up to tackle more complex challenges. It’s about adding another AI engineer to your team but without that monthly bill. Plus, the fact that it’s open-source means the community can contribute, evolve, and improve it. I’m already thinking about how I can integrate this into my workflow and automate some of the more mundane aspects of my projects. The flexibility to use it locally with models hosted on Ollama is really interesting and a big win. I’d recommend giving it a whirl if you have any interest at all in AI assisted coding and have looked into tools like Jules!

Date: 08/11/2025

Watch the Video

Okay, so this video is all about using Eddie AI, a virtual assistant editor, to streamline video production, specifically for filmmakers. It demonstrates how AI can automate tedious tasks like logging footage, organizing media, and even creating rough cuts. It’s basically showing how to use AI to massively speed up the editing workflow.

This is gold for someone like me (and maybe you!) who’s diving into AI coding and no-code solutions because it’s a concrete example of AI tackling a real-world creative problem. We’re always looking for ways to automate the boring stuff so we can focus on the actual development, right? Well, imagine applying these AI-powered transcription and organization techniques to code documentation, bug reporting, or even generating initial code structures from project descriptions. Think about feeding meeting recordings into an AI to automatically generate action items and code changes!

What really makes this video worth checking out is seeing Eddie AI in action, especially the rough cut mode. It provides a glimpse into how LLMs can assist creative processes, not just replace them. Plus, the video acknowledges the limitations, which is crucial. It’s not about blindly trusting the AI, but about leveraging it as a powerful assistant. I am all in to test this in my personal video editing projects and see where it fits in my workflow!

Date: 08/06/2025

Watch the Video

Okay, these videos on building an AI agent that queries relational databases with natural language are seriously cool and super relevant to what I’ve been diving into lately. Forget those basic “AI can write a simple query” demos – this goes deep into understanding database structure, preventing SQL injection, and deploying it all securely.

The real value, for me, is how they tackle the challenge of connecting LLMs to complex data. They explore different ways to give the AI the context it needs: dynamic schema retrieval, optimized views, and even pre-prepared queries for max security. That’s key because, in the real world, you’re not dealing with toy databases. You’re wrestling with legacy schemas, complex relationships, and the constant threat of someone trying to break your system. Plus, the section on combining relational querying with RAG? Game-changer! Imagine being able to query both structured data and unstructured text with the same agent.

Honestly, this is exactly the kind of workflow I’m aiming for – moving away from writing endless lines of code and towards orchestrating AI to handle the heavy lifting. Setting up some protected views to prevent SQL injection sounds like a much better security measure than anything I could write by hand. It’s inspiring because it shows how we can leverage AI to build truly intelligent and secure data-driven applications. Definitely worth experimenting with!

Date: 08/03/2025

Watch the Video

Okay, so this video is a rapid-fire rundown of some seriously cool AI advancements – everything from Tencent’s Hunyuan World (a generative world model!) to GLM-4.5, which boasts improvements over GPT-4, and even AI-powered motion graphics tools. It’s basically a buffet of what’s new and shiny in the AI space.

Why is this useful for us, moving towards AI-enhanced development? Well, first, it’s about awareness. We need to know what’s possible. Seeing things like X-Omni (for AI-driven UI/UX) and the FLUX Krea dev tool (AI-powered image generation) immediately sparks ideas about how we can automate front-end tasks, create dynamic content, or even rapidly prototype interfaces. Imagine using something like Hunyuan World to generate realistic test environments for our applications. The key is to keep our minds open to how these tools could be integrated into our existing workflows, potentially saving us hours on design, testing, and even initial coding.

Honestly, staying on top of this stuff can feel like drinking from a firehose, but that’s why these curated news roundups are so valuable. It’s worth experimenting with a couple of these tools – maybe that Hera motion graphics tool for spicing up our UI or diving into GLM-4.5 to see if it can streamline our code generation. The goal isn’t to replace ourselves with AI, but to find those 20% of tasks that AI can handle, freeing us up to focus on the higher-level problem-solving and architecture that makes development truly rewarding. Plus, keeping our skills current means we can deliver more value to clients and stay ahead of the curve.

Date: 08/01/2025

Watch the Video

This video showcasing Ollama’s new ChatGPT-style interface is incredibly inspiring because it directly addresses a pain point I’ve been wrestling with: simplifying local AI model interaction. We’re talking about ditching the terminal for a proper UI to download, run, and chat with models like Llama 3 and DeepSeek R1 – all locally and securely. Forget wrestling with command-line arguments just to experiment with different LLMs! The ability to upload documents, analyze them, and even create custom AI characters with personalized prompts opens up so many possibilities for automation and tailored workflows.

Think about it: I could use this to build a local AI assistant specifically trained on our company’s documentation, providing instant answers to common developer questions without exposing sensitive data to external APIs. Or maybe prototype a personalized code reviewer that understands our team’s coding style and preferences. Plus, the video touches on optimizing context length, which is crucial for efficient document processing. For anyone who, like me, is trying to move from traditional coding to leveraging local AI, this is a game-changer.

It’s not just about ease of use, though that’s a huge plus. It’s about having complete control over your data and AI models, experimenting without limitations, and truly understanding how these technologies work under the hood. The video makes it seem genuinely straightforward to set up and start playing with, which is why I’m adding it to my “must-try” list this week. I’m especially keen on testing DeepSeek R1’s reasoning capabilities and exploring how custom system prompts can fine-tune models for very specific tasks. This could seriously accelerate our internal tool development!

Date: 07/31/2025

Watch the Video

Okay, this video on building AI agent teams with Claude and a multi-agent framework? Seriously inspiring stuff for anyone like us diving headfirst into AI-enhanced development.

Here’s the gist: it’s not just about firing off prompts to an LLM anymore. The video shows how to use Claude Projects (from Anthropic) alongside a multi-agent framework to create a team of AI agents that tackle complex tasks collaboratively. We’re talking about automating everything from social media content creation (with tailored mentions!) and lead qualification right out of Gmail, to even designing thumbnails. And the coolest part? It connects directly to Zapier, unlocking a world of integrations. Imagine your agents updating databases, sending emails, triggering other automations – all on their own.

Why is it valuable? Because it gives us a glimpse into a future where we’re orchestrating AI, not just coding every single line ourselves. Instead of spending hours on repetitive tasks, we could define the high-level goals, set up the agent team, and let them handle the grunt work. Think about applying this to automating API integrations, generating documentation, or even testing. This isn’t about AI taking our jobs; it’s about AI amplifying our abilities. I’m definitely experimenting with this; the idea of having AI agents handle tedious tasks while I focus on the bigger architectural challenges? Sign me up.

Date: 07/29/2025

Watch the Video

Okay, so this video is basically a head-to-head comparison between OpenAI’s ChatGPT Agent and DeepAgent by Abacus AI. It puts them through real-world scenarios like generating PowerPoints, automating tasks, and evaluating the quality of their responses. The surprising part? DeepAgent seems to come out on top, showcasing its ability to build apps, write reports, and even generate dashboards autonomously.

For someone like me, who’s knee-deep in transitioning from traditional Laravel development to AI-enhanced workflows, this is gold. We’re talking about potentially replacing tedious tasks – like building basic dashboards or generating reports – with AI. Imagine automating all that boilerplate code and freeing up time to focus on the core logic and innovation! The video highlights how DeepAgent could be that “general-purpose AI agent” we’ve been waiting for, and seeing a direct comparison to ChatGPT Agent gives me a clearer picture of what’s possible.

What really makes this video worth checking out, though, is the potential for real-world application. I’m already brainstorming how I could use a tool like DeepAgent to automate API integrations, generate documentation, or even build simple CRUD interfaces for internal tools. It’s about moving beyond just using AI for code suggestions and embracing a future where AI agents handle entire development workflows. I’m definitely going to experiment with DeepAgent to see if it can streamline some of my projects and ultimately, deliver more value to my clients.

Date: 07/29/2025

Watch the Video

Okay, so this video is all about leveling up your AI video game with visual prompting and Runway’s new “Aleph” model. Basically, instead of just typing prompts, you’re drawing and annotating directly on images to guide AI video generation. Think adding speech bubbles to characters or drawing motion paths for dragons to follow. It also gives you a sneak peek at Runway’s new in-context video model, Aleph, for video-to-video edits and object removal.

This is gold for us developers diving into AI-enhanced workflows! We’re always looking for ways to get more granular control over AI tools, and visual prompting seems like a natural evolution. Imagine using this to prototype animations for a client, rapidly iterating on camera angles, or even generating complex visual effects with more precision.

The coolest part? The video shows how to get started with free tools like Adobe Express. It’s a practical guide to experimenting with these cutting-edge techniques today. I’m particularly excited to explore how visual prompting can streamline the creation of marketing materials for my own projects, and even integrate it into some of the no-code automation workflows I’ve been building with tools like Zapier. Definetly time to start experimenting with visual prompting!