I'm doing some research on how AI agencies manage the financial side of client work—specifically around API costs (OpenAI, Anthropic, Claude, etc.). Talking to a few agency owners, I keep hearing variations of: - "I don't actually know my true cost per client until the month ends" - "My 'best' client by revenue might actually be my worst by margin" - "I'm still pulling numbers from 3 different dashboards into a spreadsheet" Curious if this resonates with anyone here? If you're running an agency with multiple clients and juggling costs across providers, I'd love to hear how you're handling it. Not selling anything—just trying to understand how people actually deal with this. Happy to do a quick 15-20 min call. Drop a comment or DM me if you're open to chatting. Dale

A couple of days ago, I posted here about an automation I built for blog articles that are SEO optimized. Seeing @Nate Herks LinkedIn post about Claude Code + n8n made me think you all may see value in this video. The workflows I am showing in n8n were created by Claude, not Claude Code, though I should have used Claude Code because of the mcp capabilities. But Claude was able to provide the JSON for the workflows that got me 90% there. What would have taken a week before is now taking just a day. Tools used: - Claude ( I completely forgot to mention Claude in the video. I am old.) - NoCodeDB - NeuronWriter - n8n - Discord - HighLevel

I Built a 7-Workflow SEO Content Machine (Topic → Published Blog in 24 Hours) Spent about 1 day building something I've wanted for a while, a fully automated content pipeline that takes a topic idea and turns it into a published, SEO-optimized blog post with a custom AI image. I know there have been similar projects before, but I really wanted to use NeuronWriter for the SEO optimization. Here's the breakdown: What It Actually Does Input: I type "API cost management for agencies" into a database field and check a box. Output: 5-8 fully written articles scheduled to publish one per day at 9 AM, each with: - SEO-optimized title and meta description - 1,500-2,000 words hitting all the right keywords - Custom AI-generated featured image - Auto-assigned category - Internal links to my existing content My only job: Review each article in NeuronWriter (5-10 min), make edits if needed, click "Done." The system handles the rest. At some point, I may make it fully automated. But I am a control freak. The 7 Workflows 1️⃣ Keyword Strategy Generation - Pulls 150+ keywords from DataForSEO - GPT-4.1mini analyzes search intent, competition, clusters - Outputs 5-8 article recommendations with priority scores 2️⃣ SEO Brief Creation - Creates NeuronWriter queries for each article - Pulls competitor analysis, word count targets, must-use terms - Saves brief to database 3️⃣ Draft Generation - GPT-4.1mini writes the full article following my brand voice - Includes internal links from my content inventory - Converts to HTML, submits to NeuronWriter for scoring 4️⃣ Image Generation - Analyzes article content - Picks optimal image style (diagram, illustration, data viz) - Generates via fal.ai, uploads to HighLevel media library 5️⃣ Human Review Notification - Discord ping + email - Includes NeuronWriter link, SEO score, quick stats 6️⃣ Approval Sync - Polls NeuronWriter every 10 min for "Done" tag - Fetches final edited content - Updates database status → Approved

# Lesson 2: The Intelligence Spectrum ## Why Multiple Tiers Exist You might wonder: why don't AI companies just offer their best model and call it a day? The answer comes down to a fundamental tradeoff that applies to all AI models: **The AI Tradeoff Triangle:** 1. **Intelligence** - How smart/capable the model is 2. **Speed** - How fast it responds 3. **Cost** - How much it costs per query Here's the hard truth: **You can optimize for two, but not all three.** Want maximum intelligence AND speed? It'll be expensive. Want cheap AND intelligent? It'll be slow. Want fast AND cheap? It won't be as smart. This is why every major AI provider offers multiple tiers. --- ## The Major Provider Lineups ### Anthropic (Claude) | Model | Intelligence | Speed | Cost | Best For | |-------|--------------|-------|------|----------| | **Opus** | ⭐⭐⭐⭐⭐ | ⭐⭐ | $$$$ | Complex analysis, research, nuanced reasoning | | **Sonnet** | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | $$ | General purpose, everyday tasks | | **Haiku** | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ | $ | High-volume, simple tasks | ### OpenAI (GPT) | Model | Intelligence | Speed | Cost | Best For | |-------|--------------|-------|------|----------| | **GPT-4o** | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ | $$$ | Complex tasks, multimodal | | **GPT-4o-mini** | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | $ | Most everyday tasks | ### Google (Gemini) | Model | Intelligence | Speed | Cost | Best For | |-------|--------------|-------|------|----------| | **Pro/Ultra** | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ | $$$ | Professional, complex use | | **Flash** | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | $ | Speed-critical applications | ### Meta (Llama - Open Source) | Model | Intelligence | Speed | Cost | Best For | |-------|--------------|-------|------|----------| | **405B** | ⭐⭐⭐⭐⭐ | ⭐⭐ | Self-host | Maximum open-source capability | | **70B** | ⭐⭐⭐⭐ | ⭐⭐⭐ | Self-host | Balanced open-source | | **8B** | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ | Self-host | Runs on consumer hardware | --- ## What Do the Numbers Mean? (Parameter Counts) When you see "70B" or "405B," that's the parameter count in billions.

I started a series on Linkedin to help people better understand AI models. And thought I would share it here as well. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ WELCOME TO THE SERIES! Over the next 8 lessons, we're going to demystify everything about AI models - how they're named, how they differ, and how to choose the right one for your needs. Let's start with something that confuses almost everyone: model naming conventions. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ THE ANATOMY OF AN AI MODEL NAME When you see a model name like "claude-3-5-sonnet-20241022", here's what each part means: 1. THE FAMILY NAME "claude" - This identifies which AI system or company made the model. claude = Anthropic gpt = OpenAI gemini = Google llama = Meta 2. THE VERSION NUMBER "3-5" - This indicates the generation and iteration. The first number (3) = major generation The second number (5) = iteration within that generation Claude 3.5 is an improvement on Claude 3, but not a full new generation 3. THE TIER/VARIANT "sonnet" - This indicates where the model sits on the capability spectrum. We'll cover this in depth in Lesson 2 For now: bigger/smarter vs. faster/cheaper 4. THE DATE STAMP "20241022" - October 22, 2024 - the specific "snapshot" of the model. This is crucial! AI models are constantly being improved. The date "freezes" a specific version. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ WHY DATES MATTER (A LOT) Imagine you build an app using an AI model. It works perfectly. Then one day, it starts behaving differently - giving longer responses, refusing certain requests, or formatting things differently. What happened? The model was updated. Date stamps solve this by letting you "pin" to a specific version: ✅ "gpt-4-turbo-2024-04-09" - Always the same ⚠️ "gpt-4-turbo" - Could change anytime 🚨 "gpt-4-turbo-latest" - Definitely will change FOR BUILDERS: Always pin to a dated version in production. FOR USERS: This explains why ChatGPT or Claude might seem different over time - they're being updated!