Welcome to the Out of The Box Series — where I test how far curiosity and AI can take you in 30, 60, or 90 minutes using today’s best no-code and low-code tools. No studio. No production team. No advance training. Just exploration to see what we can do — right out of the box. 🎧 Finished Song*: I Got Your Six Little Girl 🎬 This Episode: Suno.com – AI Song Creation 🕒 Time Limit: 90 Minutes 📂 Category: AI Music & Personal Creativity 🎶 What Is Suno? Suno is an AI music generation tool that can create songs from prompts, lyrics, and style direction. In this case, Suno did the musical composition. I uploaded my original lyrics. 🎧 Finished Song*: I Got Your Six Little Girl Because rights and ownership matter, I started with lyrics I had written myself and kept the words original. With Suno Pro, you can publish what you create, so I wanted to be thoughtful about what I uploaded and refined. 📝 Backstory In February 2020, I wrote a poem for my daughter called I Got Your Six Little Girl. It was written from the perspective of a father looking back on all the firsts: - first heartbeat - first breath - first steps - first bike ride and moments in between The poem was already written. But I cannot sing. I cannot play instruments very well. I was never in the band. So I wanted to see if I could use AI to help turn the poem into a song to give her as a graduation present. ⏳ What I Built in 90 Minutes: Within one focused session, I: 🎼 Uploaded my original lyrics into Suno 📝 Converted the poem format into a song lyric format 🎚️ Used Suno’s interface presets to guide the style 🔁 Generated multiple versions 🎧 Listened for tempo, transitions, hooks, and continuity 🎵 Created a strong working version of the song 🎧 Finished Song*: I Got Your Six Little Girl The prompt was less of a traditional instruction and more of a music style descriptor.

I was commenting on a great question posed by @Girish Mohan, and I found myself thinking about it long after I responded.🤔 That reflection led to this post about the future of AI in a practical, real-world sense. The essence of the question: Is there a risk in becoming too dependent on one AI company, product, or tool set? I thought that was a smart question, because there is some real tension there. At this early stage of AI adoption, there is always a risk in overcommitting too soon. We have seen this before. During the eCommerce boom, a lot of companies looked like they were going to dominate, and many of them did not last. Early markets move fast. Leaders change. Sometimes you pick the wrong horse. 🐎 At the same time, over-diversifying creates its own problem. If you keep jumping from one tool to the next, you can lose the benefit of synergy. Some tools work better together. 🔗 Gemini and NotebookLM are a good example. When tools are designed to complement each other, the combined value can be better than chasing ten separate platforms that do similar things. There is also a practical reality that matters. One person cannot learn every AI tool coming to market. There are too many. At some point, each of us has to decide where we want depth, where we want breadth, and what kind of workflows actually fit the way we work. 🎯 That means some specialization is going to matter. People will need to find their niche instead of trying to master everything. But for me, the bigger point sits above all of that. We are moving into a very different communication model. 1) AI is shifting toward natural language. 2) More of the work will be handled through machine-to-machine interaction at machine speed, 3) All this be done without the user interface we think of today. 🛍️ My shopping AI may eventually interact with a retailer’s concierge AI. 🤖 Your scheduling assistant may work directly with mine. 🔄 Business systems will increasingly pass tasks, context, and decisions across platforms without the same kind of manual navigation we deal with today.

Day: 129 Level: Intermediate Edition: Claude Code Category: Costs & Usage AI Learning Path: Real-World Usage API Use refers to the specific interactions with Claude Code that trigger charges through Anthropic's API. Every time Claude Code processes your requests, generates responses, or accesses models, it's making API calls that count toward your usage and billing. AI Terms in Series: Tokens → Models → Subscription Level → API Use → Extra Usage 🪄 Simple Definition: The specific Claude Code actions that trigger charges through Anthropic's API.→ "What you do that costs money." 🌟 Expanded Definition: API Use encompasses all the billable interactions between Claude Code and Anthropic's servers. Each time you interact with Claude Code, it makes API calls that consume your subscription allowance or generate charges. 🔨 What Counts as API Use: Direct Interactions • Sending prompts to Claude Code • Receiving responses and generated code • Asking for code explanations or reviews • Requesting file modifications or creations Context Processing • Claude Code reading your project files for context • Processing large codebases or documentation • Analyzing existing code before making changes Model Operations • Switching between different Claude models • Using higher-capability models (Opus vs Sonnet vs Haiku) • Accessing new features or advanced capabilities 🔨 Why API Use Matters Understanding API use helps you: • Predict costs before starting large projects • Choose appropriate models for different tasks • Optimize your workflow to minimize unnecessary calls • Stay within your subscription limits • Avoid surprise charges from heavy usage API Use is essential for: • Budgeting Claude Code projects accurately • Understanding what drives your costs • Making informed decisions about model selection • Optimizing your development workflow • Tracking and controlling your spending ⚡ In Action: "You ask Claude Code to refactor a large Python file. This triggers multiple API calls: reading the existing file (input tokens), analyzing the code structure (processing), generating the improved version (output tokens), and showing you the diff (additional processing). A simple request becomes several billable API operations."

I have recently started publishing AI Terms for Claude Code (and I will do others soon). I plan to continue with my normal post rotation of Live Sessions, AI Quirks, AI in Real Life, etc. However, I want to make sure that I am not posting to much. What is your preferred frequency and format for AI Terms? As a side note, I do publish the daily dose on LinkedIn each day.

I spent a little time testing the new ChatGPT Images 2.0 for a branding use case. I wanted a new visual for my LinkedIn “The Daily Dose” series that felt clean, modern, professional, and aligned with my AI Bits & Pieces branding. My prompt was simple and straight forward: "I need to create a LinkedIn thumbnail for a post I'm doing called "The Daily Dose". I'm doing the Claude Code edition, and I need my logo in there for AI Bits and Pieces." In one shot, I had the base image: I uploaded my logo, it came back with a great design, it matched my colors, and was not overly produced. From there, I took the image into Canva to add my headshot, and I was done. It saved me hours today. ChatGPT gave me the creative acceleration. Canva gave me the final polish. That is becoming a very practical workflow for content creation: generate fast with ChatGPT → refine visually with Canva → publish confidently ChatGPT Images 2.0 helped me create the foundation. Canva helped me finish the job. Results below: 1) Tile shot 2) Original image from ChatGPT Image 2.0 3) Final image - After applying Magic Layers, offsetting the word "Daily" and adding my headshot in a frame it was done