Feature wars are unwinnable. Bigger competitors will always have more features. I compete on: - Speed (faster to value) - Simplicity (easier to use) - Focus (better at one thing) - Service (more responsive support) - Price (or premium positioning) Pick dimensions where more resources don't guarantee victory. How do you compete?

In this video I'll be showing you exactly how I built the AI workflow, and how I sold it for $1650. Hope you guys enjoy!

I built https://selfscale.app — a content scheduling tool for creators — using AI coding tools. Claude, Cursor, the works. It took months, not years. No dev team. Just me and an AI that writes code. Sounds like a dream, right? Here's the truth. --- The Dream Day 1: I described a feature. The AI built it. I felt like a god. Day 30: Something broke. I had no idea why. The AI "fixed" it by breaking something else. Day 90: I've rebuilt the same feature four times. I'm managing code I don't fully understand. --- What I Learned AI gets you 80% there fast. The last 20% — production bugs, API changes, security gaps — that's still on you. Documentation becomes your product. I spend more time writing context docs for the AI than I do on features. Without them, every session starts from scratch. You're not coding. You're directing. Different skill. Different ceiling when things break. --- Why I Built SelfScale Anyway Because I was tired of the content hamster wheel. Post for a week. Fall off for two. Feel guilty. Repeat. I needed something that could write like me - not generic AI slop - and let me schedule everywhere without living in five different apps. So I built it. --- What It Does - Learns your voice. Feed it your content. It captures your style. - One dashboard. 12 native integrations: LinkedIn, X, Insta, YouTube, wherever - schedule from one place. - Canva built in. Pull your designs directly into posts. - Skool alerts (beta). Get texted when someone posts in your community. The Ask I built this to solve my own problem. Now I want to know if it solves yours. Try it free for 7 days at https://selfscale.app. Poke around. Break things. Tell me what sucks. There's a chatbot in the app — just tell me directly. Your feedback shapes what gets built next. - Bill

Level up your n8n AI agents effortlessly! In this video, discover how to utilize OpenAI's Responses API as a powerful chat model to enhance your agents. I show you the quicker, easier method to bake in essential tools like web search and file search directly into your agents, bypassing the need for connecting other tools, messing with prompts, and building data pipelines for you knowledge base. Learn exactly why this new approach is a significant upgrade and how to set it up in n8n without writing any code, unlocking a new level of power and capability for your AI agents.

Worth reading and keeping an eye on.. Introducing Nested Learning: A new ML paradigm for continual learning We introduce Nested Learning, a new approach to machine learning that views models as a set of smaller, nested optimization problems, each with its own internal workflow, in order to mitigate or even completely avoid the issue of “catastrophic forgetting”, where learning new tasks sacrifices proficiency on old tasks. The last decade has seen incredible progress in machine learning (ML), primarily driven by powerful neural network architectures and the algorithms used to train them. However, despite the success of large language models (LLMs), a few fundamental challenges persist, especially around continual learning, the ability for a model to actively acquire new knowledge and skills over time without forgetting old ones. When it comes to continual learning and self-improvement, the human brain is the gold standard. It adapts through neuroplasticity — the remarkable capacity to change its structure in response to new experiences, memories, and learning. Without this ability, a person is limited to immediate context (like anterograde amnesia). We see a similar limitation in current LLMs: their knowledge is confined to either the immediate context of their input window or the static information that they learn during pre-training. The simple approach, continually updating a model's parameters with new data, often leads to “catastrophic forgetting” (CF), where learning new tasks sacrifices proficiency on old tasks. Researchers traditionally combat CF through architectural tweaks or better optimization rules. However, for too long, we have treated the model's architecture (the network structure) and the optimization algorithm (the training rule) as two separate things, which prevents us from achieving a truly unified, efficient learning system.