I am at that point where I need to shell out for more storage. Specificaly database storage. I run my frontend on Vercel and I was running with Supabase for a while, and as I was working on a mickey mouse project I just dumped it when I hit 0.5GB. Now I have ran into the same issue with Neon. Now I have to dig out the wallet. Theres only $5 between Neon and Supabase, but as I have not dug into either much I am surfing the 'wisdom of crowds' I am thinking Supabase is probably the better option, and its a simple switch in Drizzle ORM to swap over but Neon is Postgres and it has years behind it, but also the baggage. Does anyone wish to venture an opinion on which is better then the other and why? feel free to chime in @Brandon Hancock , @Maksym Liamin , @Bastian Venegas

Anyone with experience with Agno or perhaps curious about the framework? I am getting into it and it seems pretty easy to build with relative to other frameworks

Building real-world AI apps fast is one of the most valuable skills an AI developer can have. Yesterday, I released a new video that's already getting amazing feedback, so make sure you don't miss out! In this video, I'm breaking down the exact 3-step workflow I use to build and ship AI apps 100x faster. Here's what you'll learn: ✅ How to turn a rough idea into a clear, actionable development plan. ✅ My method for rapidly creating interactive mockups to iterate quickly. ✅ Essential development tips to accelerate building your AI apps. As a bonus, you'll see how this workflow applies directly whether you're freelancing or working on your own AI side projects. Also, the creator of Presentify (the tool I used to draw on my screen) hooked me up with a few promo codes, so if you want this app for free, here are some free codes (first come, first served): KEWJWARNHT66 NME3XFXKLEPY HM9HM9EJJ6T7 7YAXALKT7P39 RXWHNTJHKW4L This video is loaded with practical tips designed to save you hundreds of hours. If you're serious about building and shipping real AI projects, you can't miss this one.https://youtu.be/OEJK-J0d1XQ Cheers, Brandon Hancock 🧑‍💻

Hey everyone! During last night's community call, we started sharing some awesome Cursor AI tips and tricks, and realized we should capture all this great insight in one place! I decided to create this post to gather all of your best Cursor AI tips, tricks, and workflow hacks. Afterward, I'll compile your contributions into our first-ever community-driven video for the YouTube channel. To get the ball rolling, here are 5 tips I've found incredibly useful: Tip 1: AI Model - Gemini 2.5 pro is the best model I've ever used. It does exactly what you ask most of the time. It doesn't wander off and do random things too often. It can also solve complex problems. Its context window is huge, so you can pass in a bunch of reference files for context. Tip 2: Cursor Rules - If you notice the model keeps making mistakes, update your cursor rules to instruct the model on what it should do. Tip 3: Open multiple chat tabs - When I'm working on a project, I like to go speed mode. While Gemini 2.5 pro is solving one problem, I open an extra tab to tackle a different problem. Usually, I have 2 to 3 tabs running AI simultaneously. It's like managing a team of engineers, constantly reporting back their work for approval or feedback. Tip 4: @docs - When consistently working with a new library or copying code examples from a specific page, I add that web page as an external doc. Just type @web followed by the link, and then you can ask questions about the docs. Tip 5: Create an ai_docs folder - I store my holistic project plan here. This plan outlines my high-level goals, tasks, and the reasons behind them. It’s a living document that I continuously update. When I open a new chat in Cursor, I easily reference this document. For large tasks, I create specific task documents containing detailed goals and subtasks. I create these documents with AI and then we tackle the subtasks together. Now, it's your turn. What are your favorite Cursor AI tip or trick? Drop in the comments below!

Context/Question: How feasible is it to integrate AI Agents or AI features into an existing SaaS-based web application developed using ASP.NET MVC with SQL Server and hosted in Azure? AI’s Role in SaaS Development: 1) Can AI replace existing SaaS applications, or is it more suited for generating new SaaS applications by introducing AI Agents? 2) How feasible is it to integrate AI Agents or AI features into an existing ASP.NET MVC application with SQL Server, hosted in Azure? -------------------------------------------------------------------------------------------------------------------------------------------- AI-Assisted Development & Tooling: 1) Are there any Visual Studio and SQL Server plugins that allow direct AI-assisted coding within the same application? 2) How well does AI handle vast existing codebases with multiple integrations and common utilities? 3) Will AI automatically utilize existing functions while generating new code? -------------------------------------------------------------------------------------------------------------------------------------------- AI’s Reliability & Engineering Role: 1) Does AI hallucinate when generating code, and how can this be mitigated? 2)Do we still need engineers while AI is in place, or will AI play more of an assistant role in the software development lifecycle?