Hi everyone! I am new to the group and loving the energy in this group and how helpful collaboration has been for AI devs. I’m currently exploring a model portability layer that helps developers use a consistent workflow across different LLMs (same prompts/embeddings/fine-tuning data, same integrations, less switching). The goal is to reduce friction and cognitive load when moving between models or tools. If you’re working with multiple LLMs or looking into migrating from one provider to another and want to test an early concept, I’m looking for beta users to validate the idea and provide feedback. If interested, feel free to DM me. Thanks!

Hey everyone 👋 I’m really glad to be part of AI Developer Accelerator — a space where developers, builders, and AI creators support each other to grow skills and turn ideas into real applications. I’ve seen posts about debugging workflows, full‑stack AI projects, and ways to monetize AI dev skills — and that’s exactly the kind of collaboration that helps all of us level up faster. Here’s something I’ve realized as I’ve been working on my own AI dev journey: Trying to figure everything out alone slows you down — but when you share, ask questions, and get feedback, progress accelerates. This group already does that — people help with bugs, share templates, brainstorm features, and refine ideas. That kind of collaboration is gold. Why community matters in AI dev: ✨ You get real feedback on your code and logic✨ You learn practical tools & workflows faster✨ You avoid time‑wasting blind alleys✨ You build networks — not just apps How I can support or contribute I help with things like:✍️ Structuring how you write technical content or documentation🚀 Organizing launch plans or MVP outlines📣 Helping you explain your project clearly so others can test and use it🤝 Brainstorming features, flows, or integration methods If you’re:• working on an AI project but stuck on next steps• unsure how to explain or share your progress• need feedback on your idea or presentation• or just want someone to test a concept with you …drop a comment or share what you’re building👇 Let’s make development smoother and more strategic together. 🚀🤖

Remember the early days of large language models (LLMs)? It felt like a single, powerful oracle at our fingertips, ready to answer coding questions and debug tricky problems. Now, we're bombarded with a dizzying array of models, each with its own strengths, weaknesses, and quirky personalities. While choice is generally good, this explosion of LLMs is starting to feel less like a helpful toolkit and more like… well, a digital form of ADHD for developers. We're calling it "AI ADHD" – the constant distraction and context switching caused by the sheer number of LLMs available and the pressure to know which one is "best" for any given task. Here's how this overload is quietly hurting the programming experience: **1. Decision Fatigue Sets In (Before You Even Write Code):** Before you even type your first line, you're faced with a choice: Which LLM should I ask? Do I need something specifically tuned for Python? Should I use the one known for creative code generation or the one better at factual explanations? This initial decision-making process, repeated multiple times throughout the day, is surprisingly draining. It's like having to choose from a hundred different screwdrivers for a single screw – most of them will *kind of* work, but you're wasting time trying to figure out the *optimal* one. **2. Context Switching Becomes a Constant Headache:** Each LLM has its own prompt engineering nuances, its own preferred input formats, and its own unique ways of interpreting requests. Switching between models for different tasks means constantly shifting your mental gears. You might have just gotten used to crafting prompts for Model A when you realize Model B is better for your current problem, forcing you to relearn how to effectively communicate with it. This constant context switching breaks flow and hinders deep work. **3. The Fear of Missing Out (FOMO) is Real:** There's a nagging feeling that you're not using the "right" tool. Did that other LLM have a more up-to-date knowledge base? Would it have generated cleaner code? This FOMO can lead to second-guessing, re-running requests in different models, and ultimately, more wasted time chasing an elusive "perfect" answer.

Sometimes, our carefully crafted prompts work superbly with one model but fall flat with another. This can happen when we’re switching between various model providers, as well as when we upgrade across versions of the same model. For example, Voiceflow found that migrating from gpt-3.5-turbo-0301 to gpt-3.5-turbo-1106 led to a 10% drop on their intent classification task. (Thankfully, they had evals!) Similarly, GoDaddy observed a trend in the positive direction, where upgrading to version 1106 narrowed the performance gap between gpt-3.5-turbo and gpt-4. (Or, if you’re a glass-half-full person, you might be disappointed that gpt-4’s lead was reduced with the new upgrade) Thus, if we have to migrate prompts across models, expect it to take more time than simply swapping the API endpoint. Don’t assume that plugging in the same prompt will lead to similar or better results. Also, having reliable, automated evals helps with measuring task performance before and after migration, and reduces the effort needed for manual verification. Article link: https://applied-llms.org/#prompting