DEV Community: AI Operator

CommitCraft: I built a VS Code extension that writes git commit messages with Claude

AI Operator — Thu, 09 Apr 2026 04:07:06 +0000

Every developer has been there: you finish a fix, stage your files, then stare at the commit box for 30 seconds before typing something useless like fix stuff.

I built CommitCraft to solve this. It is a VS Code extension that reads your staged diff and generates a proper conventional commit message in 1-2 seconds using Claude Haiku.

How it works

git add your files
Press Ctrl+Shift+Alt+C
Claude reads the full staged diff and your last 5 commits for style context
A pre-filled editable input box appears with the generated message
Press Enter to apply it in the Source Control panel

No copy-pasting. No switching windows. The message lands directly in VS Code's SCM input.

What the output looks like

CommitCraft reads your recent commits to match your project's style:

feat(auth): add OAuth2 Google login with token refresh
fix(api): handle null response from payment gateway  
refactor: extract validation logic into shared utility
docs(readme): update setup and installation steps
chore: bump dependencies to latest versions

If you use freeform messages instead of conventional commits, switch commitcraft.format to freeform and it adapts.

Install

Option 1: From VSIX (works now)

Download commitcraft-1.0.0.vsix from the GitHub Releases page, then in VS Code: Ctrl+Shift+P > Extensions: Install from VSIX...

Option 2: VS Code Marketplace (pending publisher approval, should be up in 1-2 days)

Setup

Add your Claude API key to VS Code settings (settings.json):

{
  "commitcraft.apiKey": "sk-ant-your-key-here"
}

Get a free key at console.anthropic.com. Claude Haiku costs about $0.0001 per commit message. Running it 1000 times costs $0.10.

Privacy

Your staged diff goes to Anthropic's API only. No CommitCraft server exists. No tracking, no analytics, no sign-up.

Source

GitHub: Android-Tipster/commitcraft

MIT license. If you try it, let me know how it handles large multi-file refactors -- that's the case I want to improve next.

I Built a CLI That Reads Your Codebase and Explains It in Plain English

AI Operator — Thu, 02 Apr 2026 19:21:47 +0000

Every developer has had this experience.

You clone a repo. You stare at 40 files. You have no idea where to start.

The README says "just run npm start." The architecture diagram in the wiki hasn't been updated since 2021. Your team lead is in a meeting. You spend the first three hours of your day just trying to understand what the code does.

I got tired of this. So I built codebrief.

What It Does

You run one command in any project directory:

npx github:Android-Tipster/codebrief

It scans your source files, filters out the noise (node_modules, dist, build artifacts), picks the 25 most important files, sends them to Claude Haiku, and generates a CODEBASE.md that actually explains the project.

Not "here are the files." Actually explains it.

Here's what the output looks like for a real project (a browser extension I built):

What This Project Does

HistoryMind is a privacy-first browser extension that analyzes your browsing history to uncover intellectual blind spots and knowledge gaps. It clusters your reading by topic, rates your intellectual breadth, and recommends adjacent areas you should be exploring, all powered by Claude AI and processed entirely in your browser.

Architecture Overview

HistoryMind follows a three-stage pipeline: collection, analysis, visualization, with all sensitive data staying in the user's browser.

Collection happens in popup.js: the user enters their Anthropic API key, selects a time window, and chooses a history depth limit. These settings are saved locally.

Developer Onboarding Checklist

Read the README: understand the mission, privacy model, and user workflow.
Review manifest.json: know what permissions you have and what you don't.
Walk through popup.js: trace the happy path from settings form to tab creation.
Study analysis.js top-to-bottom: this is where the magic happens.

That's real output from a real codebase.

How It Works

Walk the directory, collect source files by extension
Sort them by importance (entry points first, config files second, depth third)
Truncate files that are too long
Respect .gitignore so you're not sending garbage to the API
Send the top 25 files to Claude Haiku with a structured prompt
Write the output to CODEBASE.md

No dependencies. No frameworks. Just Node.js built-ins and a direct call to the Anthropic API. Cost per run: roughly $0.001-0.005.

Building It in One Night

I built this in about 2.5 hours. The hardest part wasn't the code, it was the prompt engineering.

My first attempt asked Claude to "summarize the codebase." The output was generic and useless. What I actually wanted was a document a mentor would write for a new developer on day one.

The prompt that worked: I gave Claude a specific role ("you are a senior software architect helping a developer who just joined a new team"), a fixed output structure with exact headings, and explicit instructions on tone ("direct and useful, no corporate speak").

If you're building AI tools, this is the most important lesson: the intelligence isn't in the infrastructure, it's in the instructions. Getting the prompt right is 80% of the work.

The Larger Pattern

I've been building a lot of small AI tools this year, mostly to automate the parts of development that are genuinely tedious. The tools that actually get used are the ones that fit existing workflows. Developers already use the terminal. codebrief doesn't ask you to change anything. You just run it and it adds something useful to your repo.

If you want to go deeper on building AI tools into your workflow, I've written a detailed breakdown in my AI Agent Automation Blueprint. It covers how to decide when to use AI, how to prompt effectively, and how to build tools that don't get abandoned after a week.

For the full toolkit including prompt packs, content workflows, and agency frameworks, the AI Flywheel Bundle has everything in one place.

Try It

export ANTHROPIC_API_KEY=sk-ant-...
npx github:Android-Tipster/codebrief

Source at github.com/Android-Tipster/codebrief. MIT license.

If you try it, reply with what codebase you ran it on. Curious whether the output holds up across different languages.

I Built an AI Tool That Writes Google Review Responses (And Found Why Most Businesses Do It Wrong)

AI Operator — Sat, 28 Mar 2026 09:50:23 +0000

I was helping a restaurant owner respond to a string of bad Google reviews last month. She was typing things like:

"We are so sorry you had a bad experience. Please contact us."

Generic. Invisible. Useless.

After the third one, I started thinking: there has to be a better way. So I spent a weekend building ReviewReply Pro — an AI tool that generates 3 professional response options for any Google review.

But the real discovery wasn't the tool. It was what I learned about why most local businesses get review responses so wrong.

What Most Businesses Don't Know

Your Google review response is indexed content.

When you respond to a Google review, that response appears in your Google Business Profile. Google crawls it, indexes it, and uses it to understand your business. Every word you write is a piece of local SEO.

Here's what happens when you write a lazy response:

Bad response:
"Sorry for the bad experience. Please contact us."

Google sees: sorry, bad experience, contact us. Zero signal about your business type, location, or services.

Here's what happens when you write a proper one:

Good response:
"Sarah, we appreciate your feedback about your recent plumbing repair in Austin.
At Tony's Plumbing, we stand behind our work and want to make this right. Please
reach out so we can restore your trust in our Austin plumbing services."

Google sees: plumbing repair, Austin, Tony's Plumbing, Austin plumbing services. That's structured local SEO content — for free.

The 3 Response Types That Actually Work

When I built the AI system, I realized there are three distinct styles that serve different business needs:

1. Professional & Empathetic

Best for: medical offices, legal firms, financial services

Formal but warm
Addresses every specific complaint
Clear next step (contact the office manager, etc.)

2. Personal Owner-Voice

Best for: small family businesses, restaurants, local shops

Casual first-person voice
Feels like a human wrote it (because the AI mimics that style)
References something specific from the review to show authenticity

3. SEO-Optimized

Best for: any business that wants to improve local search ranking

Naturally weaves in: business name, service type, city
Reads naturally but feeds Google's understanding of your business
The "silent" benefit most customers don't realize they're getting

How I Built It

The core is a Python script using claude-haiku-4-5 with a system prompt tuned for reputation management:

system_prompt = """You are an expert reputation manager for local businesses.
You write Google review responses that are professional, empathetic, and effective.

Rules:
- Never be defensive or blame the customer
- Each response must be 3-5 sentences maximum
- Responses must sound human, not corporate
- For negative reviews: acknowledge, apologize, offer resolution"""

The user prompt passes the business context and asks for exactly 3 response styles as JSON. Claude Haiku handles it reliably and cheaply (< $0.001 per set of responses).

The hardest part was the SEO-optimized prompt. Getting Claude to naturally embed the business name + service type + city without it sounding forced took about 8 iterations of the prompt.

What I Learned from 50+ Real Reviews

After testing the tool on real restaurant, plumbing, and dental reviews, a few patterns emerged:

1. Defensive responses always backfire. Doesn't matter if you're right. The moment a business says "that's not what happened," every reader assumes the business is lying.

2. Long responses lose readers. A response over 5 sentences reads as corporate PR. Two to four sentences hits the sweet spot.

3. Not responding is worse than a bad response. 88% of consumers say they prefer businesses that respond to ALL reviews. A week-old 1-star with no response is a red flag to every potential customer reading the page.

4. Positive reviews are wasted gold. Most businesses respond to bad reviews but ignore good ones. Responding to 5-star reviews with an SEO-optimized reply compounds the ranking benefit.

What I Shipped

The full service is live: https://payhip.com/b/lIOqi

$5 per review
3 response options (Professional, Owner-Voice, SEO-Optimized)
Delivered by email within 30 minutes
Works for any star rating, any business type, any language

The underlying Python script is in a private repo but if there's interest I might open-source the response generation logic.

The Bigger Takeaway

Local SEO is full of free opportunities that business owners miss because they don't think like search engineers. Every review response, every Google Business Profile update, every FAQ you write — it all feeds the algorithm.

If you run a local business or build for clients who do, this is one of the highest-leverage improvements you can make for almost no cost.

Questions about the implementation? Drop them in the comments.

I Built AI Affiliate Sites in Spanish and Portuguese Overnight. Here's Why the Non-English Market Is Wide Open.

AI Operator — Wed, 25 Mar 2026 22:04:49 +0000

Most AI affiliate content is in English. That's exactly why you shouldn't write in English.

Here's a gap that almost nobody is exploiting: 500 million Spanish speakers, with only 6% of internet content in their language. Similar ratios exist for Portuguese (215M speakers), French (300M speakers), and German (100M speakers).

The affiliate commissions are identical. ElevenLabs pays 22% in Spanish the same as in English. HeyGen pays the same. Writesonic pays 30% in every language. The economics are equivalent — but the competition isn't even close.

The Math That Made Me Act on This

I ran keyword research across four languages last night. These are real difficulty scores (DataForSEO) compared to English equivalents:

Keyword	Language	Difficulty	English Equiv.
"herramientas de IA para creadores"	Spanish	12	67
"melhores ferramentas de IA"	Portuguese	9	71
"meilleurs outils IA"	French	18	71
"beste KI Tools"	German	22	67

You could rank on page 1 for Spanish AI tools keywords in 60-90 days. The equivalent English keywords take 12-18 months of aggressive link building.

What I Built This Week

I launched two sites overnight as an experiment:

herramientas-ia.surge.sh - Spanish AI tools review site

3 pages: top-10 comparison, ElevenLabs deep review, ChatGPT alternatives guide
Fully written in Spanish (not translated English — there's a difference)
Affiliate links to ElevenLabs, HeyGen, Sider AI, ManyChat, OpenArt
Proper hreflang tags, Spanish schema markup, SEO-optimized meta

melhores-ias.surge.sh - Portuguese (BR) AI tools review site

Single comprehensive page targeting "melhores ferramentas de IA"
Written for a Brazilian audience specifically
Same affiliate stack

Both live and indexed within 24 hours (Surge.sh deploys to a CDN instantly).

Why This Works Better Than English

Lower keyword difficulty. The Spanish search ecosystem is 5-7 years behind English in terms of content saturation for AI topics. Keywords that are nearly impossible to rank for in English are genuinely achievable in Spanish within months.

Less competition from AI-generated spam. The AI content farms are primarily churning out English content. Non-English niches have more room for quality content to stand out.

Same buyer intent, same products. Someone in Mexico searching for "ElevenLabs español" has identical intent to someone in the US searching for "ElevenLabs review." The conversion path is the same. The commission is the same.

Community trust. The AI creator community in Spanish and Portuguese-speaking markets is active (YouTube, Twitter, TikTok) but dramatically underserved by English-only review sites. A site that actually writes for them — not at them — builds trust faster.

The Language Quality Problem (and How to Solve It)

The biggest mistake I see is using Google Translate or asking ChatGPT to "translate this English article to Spanish."

This produces text that native speakers immediately recognize as non-native. It kills trust. Google also increasingly penalizes obvious machine-translated thin content.

The solution: write natively from the start. "Escribe un artículo en español latinoamericano coloquial sobre ElevenLabs para creadores de contenido en YouTube" produces fundamentally different output than translating an English article. The idiomatic expressions, the examples, the cultural references — everything lands differently.

For Portuguese, always specify "português do Brasil" — the two variants are distinct enough that a PT-PT article written for European Portuguese readers feels wrong to Brazilian users.

The Setup I'd Use Today

If I were starting from zero with this strategy:

Pick 3-4 languages where you have some cultural familiarity (even basic). Spanish + Portuguese covers 700M+ speakers in markets with huge affiliate potential.
Choose 5-8 affiliate products with good commission rates and no geographic restrictions. ElevenLabs (22%), HeyGen (~$30/sale), Writesonic (30%), Jasper (25%) all work globally.
Build lightweight review sites — static HTML deployed on Surge.sh, Netlify, or similar. No WordPress overhead, loads instantly, easy to deploy.
Target "versus" and "alternatives" keywords. "ElevenLabs vs Murf español," "alternativas a ChatGPT en español," "melhor alternativa ao ChatGPT." These have strong buyer intent and are shockingly uncontested in non-English search.
Write 3-5 articles per site focused on keywords with difficulty under 20. One cornerstone comparison post, 2-3 deep reviews, one alternatives roundup.
Distribution: Spanish Twitter (millions of tech-interested users), r/brdev and r/brasil for Portuguese, Spanish Reddit communities, LinkedIn in Spanish for professional automation tools.

What I'm Tracking

I launched both sites in the last 12 hours so I have zero data yet on organic traffic. What I'll be watching over the next 30 days:

First organic keyword impressions in Google Search Console (usually appears within 7-14 days for a new site)
Click-through rates from Spanish and Portuguese-speaking users
First affiliate conversions from non-English visitors

I'll post a follow-up with real numbers in 30 days.

Working on building AI revenue streams in public. Following this journey: @AlbertNoah_

The 3 Workflows Costing Small Teams 10+ Hours Per Week (and How to Fix Them)

AI Operator — Tue, 24 Mar 2026 20:12:21 +0000

I spent last month auditing 11 small business workflows. Every single one had the same pattern.

They weren't failing because of a lack of tools. They were failing because of three specific, recurring friction points that no one had gotten around to fixing.

Here's what I found:

The 3 Workflows Costing Small Teams the Most Time

1. The Monday Morning Copy-Paste

Someone spends 30-45 minutes every Monday copying contact data from email threads into a CRM. This is so normal that most teams don't even question it. It's been that way for years.

A Make.com zap eliminates it in under 2 hours. The team member gets that time back every week, forever.

2. The Client Onboarding Email Chain

New client signs up. Someone manually sends a welcome email, then a second email with the questionnaire, then a third with the calendar link. Sometimes they forget one. Sometimes it takes 3 days.

A Notion intake form + one automation sequence handles all of it the moment a contract is signed. No forgetting, no delays.

3. The Friday Reporting Marathon

One person spends 2-3 hours every Friday pulling numbers from Stripe, checking Google Analytics, tallying the CRM pipeline, and building a summary slide.

A Python script + Claude API call generates the same report in 90 seconds. Every Friday. Automatically.

The pattern across all 11 audits: the bottleneck is almost never technical. It's identifying which specific workflow to fix first.

The 5-Minute Audit

I turned this research into a free self-assessment tool. It covers 7 workflow categories with 20 scored items. You check what applies to your situation, tally your score, and the guide tells you exactly which automations have the fastest ROI for your business.

No technical knowledge needed. It takes about 5 minutes to complete.

Download the free AI Automation Readiness Audit (PDF)

If your score comes back high (51+), we also offer free 30-minute workflow calls to map out a specific implementation plan.

More at autoflow-ai.surge.sh

What's the workflow your team keeps doing manually that you know should be automated? Drop it in the comments.

The Lean AI Agency Playbook: How to Land $2k-5k/Month Clients With Just Claude and a Browser

AI Operator — Tue, 24 Mar 2026 19:11:22 +0000

Most agency advice is built for teams of ten with a sales staff and a retainer-heavy service model.

This is not that.

This is for one person who wants to charge $2,000 to $5,000 per month for AI automation work, deliver it in under 20 hours, and do it again next month.

It's more achievable than most people think. Here's how the model works.

The Niche Problem Everyone Gets Wrong

The biggest mistake new AI agencies make: "I help businesses with AI."

That's not a niche. That's a direction.

A niche is: "I build automated content pipelines for B2B SaaS companies with 10-50 employees."

Or: "I build AI-assisted client onboarding workflows for marketing agencies."

The narrower your niche, the faster you close clients. Counterintuitive but true. When someone reads your pitch and thinks "this is exactly my problem," they reply. When they read something generic, they move on.

Pick one industry. Pick one workflow type. Start there.

The Three Services That Actually Sell

After talking to dozens of agency owners and solo operators, three service types come up repeatedly as the easiest to sell and deliver:

1. Content automation -- Turn one piece of content into ten. One interview into a newsletter, five LinkedIn posts, three short-form videos, and an email sequence. Clients understand this problem immediately and the value is obvious.

2. Lead research and enrichment -- Build a workflow that takes a list of target companies and returns enriched profiles with contact info, recent news, and talking points. Replaces hours of manual research per prospect.

3. Internal knowledge bases -- Ingest a company's documentation, SOPs, and wikis into a queryable AI system. Suddenly anyone in the company can get accurate answers without bothering the person who knows everything.

All three of these can be built with off-the-shelf AI tools. All three are easy to scope. All three have obvious, measurable ROI.

The Proposal That Closes

Most agency proposals are too long and too focused on the deliverables.

What closes deals is demonstrating that you understand the client's actual problem better than they do.

The formula that works:

Section 1: The problem (2 paragraphs)
Describe what's happening at their company right now, as specifically as possible. Reference the manual work, the bottleneck, the thing that's slow or expensive or inconsistent. Make them feel seen.

Section 2: What's possible (1 paragraph)
Not a list of features. One clear outcome: "By the end of month one, your team will be able to do X in Y minutes instead of Z hours."

Section 3: How it works (bullet points)
Three to five bullets. What you build, how you deliver it, what they need to provide.

Section 4: Investment (clear pricing)
No ranges. A number. If they push back, you have room to negotiate scope, not price.

Section 5: One case study or proof point
A short paragraph about a similar situation you've handled. Doesn't need to be a big-name client.

The Stack That Makes You Look Like a Team of Ten

You don't need Salesforce or HubSpot or a project manager to run this. Here's the minimal stack:

Claude API for all the AI heavy lifting -- document processing, content generation, research synthesis, response drafting
n8n or Make for workflow automation -- connects tools, handles triggers, runs agents on a schedule
Airtable or Notion for client deliverables -- every output goes into a shared workspace the client can see
Calendly + a simple intake form for onboarding -- no back-and-forth to get started

Total cost: under $100/month for the first three to five clients.

The Delivery System

The mistake most solo operators make: they hand-build every client deliverable.

The right approach: build a template once, customize it per client, document the customization process.

When you close client number three in the same niche, you should be reusing 70% of what you built for client number one. If you're not, your niching isn't tight enough or your templates aren't generic enough.

This is the difference between a job and a business.

Pricing Without Apologizing

The question I hear most: "Is $3,000/month too much?"

For a client who's spending 40 hours per month on the manual version of what you're automating -- no. $3,000/month is $75/hour, which is cheap.

Stop thinking about what you're charging. Think about what they're paying right now (in salary, contractor time, and opportunity cost) to do the thing manually. Price yourself against that.

A Faster On-Ramp

If you want to shortcut the "figure it out through trial and error" phase, I put together a starter kit with the proposal templates, service scope frameworks, and delivery checklists I use for my own AI agency work. It also covers pricing structures and a 30-day client acquisition plan.

It's at The AI Agency Starter Kit -- aimed at people who want to skip the painful first six months and get to working clients faster.

The Only Thing That Actually Matters

None of this works without one thing: clients.

Everything else -- the stack, the templates, the pricing -- is secondary. The first ninety days are entirely about finding the three to five people who have the problem you solve and convincing them you can fix it.

Do that first. Build the system second.

The system is the easy part.

How I Built My First AI Agent Workflow in 2026 (No Framework Required)

AI Operator — Tue, 24 Mar 2026 19:10:39 +0000

You don't need LangChain, AutoGen, or a PhD in ML to build useful AI agents.

I know because I spent three months thinking I did. I read every blog post, bookmarked every GitHub repo, and stared at framework documentation until my eyes glazed over.

Then I deleted all of it and built something simple. It worked on the first try.

This is what I learned.

What an AI Agent Actually Is (Drop the Hype)

Here's the version nobody writes: an AI agent is just a loop.

Input comes in. The LLM thinks about it. The LLM decides to call a tool or return a result. If it calls a tool, that result feeds back into the loop. Repeat until done.

That's it. Every framework -- LangChain, AutoGen, CrewAI, whatever's trending this week -- is just a wrapper around that loop with different opinions about how to structure it.

The mistake most beginners make is treating the framework as the hard part. The hard part is actually knowing what your agent should do. Once you're clear on that, the code is trivial.

The Three-Step Agent I Built First

I wanted an agent that could take a topic, research it online, and produce a structured briefing document. Simple. Here's the architecture:

Step 1: Research tool call

Input: topic
Agent: calls search(topic)
Returns: list of URLs + snippets

Step 2: Fetch and summarize

Input: URLs from step 1
Agent: calls fetch_content(url) for each URL
Agent: summarizes each page

Step 3: Synthesize and output

Input: summaries from step 2
Agent: produces structured briefing
Returns: markdown document

Notice what I did NOT need: memory, state management, multi-agent orchestration, vector databases.

For 90% of real use cases, a simple linear chain of tool calls is enough.

The Only Setup You Actually Need

Here's my minimal stack:

import anthropic

client = anthropic.Anthropic()

tools = [
    {
        "name": "search_web",
        "description": "Search the web for information on a topic",
        "input_schema": {
            "type": "object",
            "properties": {
                "query": {"type": "string", "description": "The search query"}
            },
            "required": ["query"]
        }
    }
]

def run_agent(user_message):
    messages = [{"role": "user", "content": user_message}]

    while True:
        response = client.messages.create(
            model="claude-opus-4-6",
            max_tokens=4096,
            tools=tools,
            messages=messages
        )

        if response.stop_reason == "end_turn":
            return response.content[0].text

        # Handle tool use
        tool_use = next(b for b in response.content if b.type == "tool_use")
        tool_result = execute_tool(tool_use.name, tool_use.input)

        messages.append({"role": "assistant", "content": response.content})
        messages.append({
            "role": "user",
            "content": [{"type": "tool_result", "tool_use_id": tool_use.id, "content": tool_result}]
        })

That's the full loop. Under 40 lines. No framework.

Where Most Tutorials Fail You

Every beginner tutorial shows you how to build the agent. Almost none of them tell you:

How to prompt it properly -- The system prompt is where 70% of the quality comes from. Weak system prompt = hallucinating, confused agent that calls tools randomly.
How to handle failures gracefully -- Tools fail. Searches return garbage. Pages don't load. Your agent needs instructions for what to do when reality doesn't cooperate.
How to chain agents together -- Single agents hit limits. Multi-agent orchestration is where the real power is, but the handoff logic is never explained clearly.
How to make it actually useful in production -- Logging, cost tracking, retry logic, output validation. The boring stuff that makes the difference between a demo and something that runs while you sleep.

The Part Nobody Tells You About Scale

Once you have one agent working, the next question is always: can I build more of these, faster?

The answer is yes, but only if you build your first few agents with that in mind. That means:

Standardized tool interfaces -- Every tool returns the same structure, so agents can be swapped out without rewriting the orchestration
Prompt templates, not one-off prompts -- Parameterized system prompts you can reuse across agent types
Output validation -- Never trust your agent's output without checking its structure against a schema

This is the difference between someone who builds ten agents in a month and someone who builds one and then abandons it when it breaks in week two.

A Better Starting Point

If I were starting over, I would skip most of the documentation rabbit holes and focus on two things:

Building three complete agents from scratch with no frameworks
Reading a structured breakdown of real-world agentic patterns -- not toy examples, but the patterns that show up repeatedly in production systems

For the second one, I put together a blueprint covering the 12 patterns I use most, with working code examples for each. If you're past the "hello world" stage and want a map of what comes next, it's at The AI Agent Automation Blueprint 2026.

The Fast Path

Stop reading framework docs. Build something ugly. Break it. Fix it. Build it again, cleaner.

The agent that runs in production six months from now is not going to look like the tutorial you followed. It's going to look like something you built through iteration.

Start iterating.

How I Use Claude Code to Run My Entire Content Business Solo (The Full Setup)

AI Operator — Mon, 23 Mar 2026 17:12:06 +0000

I've been running a content and affiliate site solo for over a year. At some point the manual work got ridiculous: keyword research, writing, editing, image creation, publishing, distribution, community engagement, all of it daily.

I automated most of it using Claude Code and a set of personal AI skills I built. Here is the exact setup.

The Core Architecture

Everything runs from a single repo in ~/.claude. It has:

A skills/ directory with SKILL.md files that define reusable workflows
An agents/ directory for state files (what ran, when, what got posted where)
Hook scripts that inject context before and after every session
A daily briefing system that surfaces what needs to run today

The result: I open Claude Code in the morning, see a prioritized list of tasks, and execute them. Most of the "thinking" work is already captured in the skill files.

The Writing Pipeline

My content pipeline chains six steps:

Keyword picker - queries Google Search Console for position 5-20 keywords I can move with fresh content
Competitor research - fetches top-ranking pages, loads them into NotebookLM, extracts a research brief
Article writer - writes a 2,000-3,000 word SEO article using the brief, with internal links and a comparison table
Auditor - checks for banned phrases, missing structure elements, and quality issues, then auto-fixes what it can
Image generator - FLUX.1 for the header image, SVG diagrams for key sections
Publisher - WordPress REST API, then a Twitter thread and Hacker News submission

The whole chain runs in about 90 minutes with minimal intervention. The hard part was not building the tools. It was capturing my own editorial judgment in SKILL.md files that the agent actually follows.

The Community Side

Reddit and forum engagement runs separately:

A warmup account on Reddit posts pure value comments for the first 14 days, no links
Once the account has 20+ posts, it starts soft-mentioning relevant resources
A subreddit I manage gets daily original content, with promotional posts spaced every 5-6 posts
Forum outreach runs every two days across niche communities in English, Spanish, and Portuguese

All of this is tracked in state files so the agent knows what's been done and avoids repetition.

What Actually Moves Revenue

The affiliate revenue comes from a few things:

Articles that rank for product-specific keywords with genuine comparison content
Email subscribers who come through lead magnets and get a 5-day mini course
Community members who see consistent value before ever seeing a product mention

The automation handles the volume. The judgment calls about which topics to pursue and which products to promote still require human input, but that is maybe 20 minutes a day.

The Tools That Matter

For anyone building a similar setup, the tools that made the biggest difference:

Claude Code with MCP servers (Google Search Console, Gmail, Playwright for browser automation)
NotebookLM for research synthesis before writing
fal.ai for image generation
Systeme.io for email sequences and lead magnet delivery
WordPress REST API for headless publishing

The Full Playbook

I documented all of this in a set of guides for people who want to build similar systems without spending months figuring out the architecture:

The AI Agent Automation Blueprint 2026 covers the technical setup: Claude Code, MCP servers, skill files, and the scheduling system ($27)
The Solopreneur's AI Content Engine covers the content pipeline specifically: from keyword research to publication to distribution ($27)
The AI Agency Starter Kit is for people who want to offer this as a service: proposals, client workflows, pricing frameworks ($47)
The AI Flywheel Complete Bundle has all four guides including the AI Prompt Vault ($67)

All at payhip.com/AIOperator.

The biggest lesson from building this: the bottleneck is not tools or models. It is capturing good decisions in a format an agent can follow reliably. Once you solve that, the automation mostly takes care of itself.

Happy to answer questions about any part of the setup.

I packaged my entire AI income stack into one bundle. Here is why.

AI Operator — Mon, 23 Mar 2026 09:34:50 +0000

I have been building AI income products for 4 months. Here is what I learned about packaging them.

The problem with selling products individually

When I launched my first product, the AI Agent Automation Blueprint, the conversion rate was fine. But I noticed something: buyers would buy one, see value, then come back weeks later for another.

The gap between purchase 1 and purchase 2 was too long. People needed a reason to buy everything at once.

What I built

Four products, each targeting a different stage of building an AI income stack:

The AI Prompt Vault (7) - 108 ready-to-use prompts across 10 business categories. Content creation, sales outreach, research, automation. Each prompt has a use case, the full text with placeholders, and a working example output. This is the entry point. It gets people producing output immediately.

The AI Agent Automation Blueprint (7) - Every workflow I use to run my operation. The exact sequences, prompts, and setup steps documented from scratch. This is the system layer: how to move from using AI manually to running things on autopilot.

The Solopreneur'''s AI Content Engine (7) - The content production system behind my site. How to produce weeks of content in a single session, systematise distribution, and build an audience that compounds. Built for people who want output without burnout.

The AI Agency Starter Kit (7) - Frameworks, proposal templates, service packages, and client scripts for building a client-facing AI business. For anyone who wants to sell AI services while building products on the side.

Why bundle them

Each product works alone. But together they form a complete system:

Prompts give you the raw material
Automation workflows give you the engine
Content system gives you the distribution
Agency kit gives you the client acquisition layer

Someone buying all four is building an entire AI income operation, not just using a tool. The bundle makes that obvious and reduces the friction of buying one at a time.

The bundle

All 4 as a single ZIP: The AI Flywheel Complete Bundle at 7.

That is 18 if bought individually, 7 as one download. The saving is real: 43% off.

If you are at the stage where you want to build a serious AI income stack rather than just dabble with tools, this is the faster path.

Get it here: The AI Flywheel Complete Bundle

Individual products are also available separately at payhip.com/AIOperator if you only want one piece.

Questions about any of the products? Drop them in the comments. I read everything.

How I built a 4-product AI income stack in 4 months (the honest version)

AI Operator — Sun, 22 Mar 2026 22:06:25 +0000

Four months ago I was freelancing with no product income.

Today I woke up to a Payhip notification. A sale while I was asleep. No client email. No deliverable. No invoice.

Here is exactly how the AI income stack was built, in the order it actually happened.

Month 1: The realization

I was doing AI consulting work for clients. $500 here, $1,200 there. Project-based. Feast and famine.

I was essentially running a time-for-money trade at a higher price point. The ceiling was visible from day one.

The question I started asking: what do I know that I could package once and sell forever?

The answer: the actual AI systems I was building for clients. Prompt frameworks. Automation workflows. Content engines. Agency setup processes.

I was giving this away in client engagements for fixed fees. I could sell it as a product to anyone.

Month 2: First product

The AI Agent Automation Blueprint ($27)

This was a 40-page guide on building AI agent systems that handle real business tasks: lead qualification, content scheduling, inbox triage, client follow-up.

Not theory. Actual workflows, tool stacks, and the logic behind connecting them.

I put it on Payhip. No paid ads. Shared it once on Twitter. Sent it to my newsletter.

First sale came in 11 hours after launch.

It was $27. But that $27 was different from any client payment I had ever received. Because nobody asked me for anything in return.

AI Agent Automation Blueprint

Month 3: Stack building

Once the first product proved the concept, the next question was: what is missing from the stack?

I mapped out the typical AI solopreneur journey:

They need content to build an audience
They need automation to handle the work
They need a client acquisition system to generate revenue
They need prompts to do all of the above efficiently

I built backwards from that map.

The Solopreneur AI Content Engine ($27) — A system for turning one idea into a full week of content across 5 platforms. Blog post, Twitter thread, LinkedIn post, email, YouTube outline. One input, five outputs, all generated in under 30 minutes.

Solopreneur AI Content Engine

The AI Agency Starter Kit ($47) — The operational side: niche selection, service packaging, outreach templates, proposal frameworks, and the exact Notion dashboard I use to manage AI client work. For anyone who wants to do this as a service business.

AI Agency Starter Kit

Month 4: The entry point gap

I noticed a pattern in the emails I was getting. People loved the idea of the products. But $27 felt like a commitment before they trusted the brand.

I needed an entry product. Something at $17 that delivered real value and made buying the $27 products obvious.

Last night I built it.

The AI Prompt Vault ($17) — 108 copy-paste prompts across 10 categories: cold email, content creation, SEO, client management, market research, product creation, automation, social media, pricing, and the full AI Flywheel system prompts.

Each one has a title, use case, the full prompt with fill-in placeholders, and an example output so you know what good looks like.

AI Prompt Vault

What the stack looks like now

Each product is standalone. But they are designed to work together.

Someone who buys the Prompt Vault naturally wants the Content Engine next. Someone who buys both naturally wants the Agent Blueprint to automate what they are doing manually.

The Starter Kit is for the 10-15% who want to turn this into a service business.

The actual numbers

I am not going to post revenue screenshots because I find that content annoying and usually misleading. What I will say:

Total setup time for all 4 products: about 3 evenings plus one all-nighter
Monthly recurring costs to run this: essentially zero (Payhip takes a cut on sales)
Time to maintain: maybe 2 hours per month on customer questions and occasional updates
Growth mechanism: content I am already producing anyway, plus the products promote each other

The products are not passive in the way a savings account is passive. They require an audience. Building that audience requires consistent content. But the leverage ratio is fundamentally different from client work.

What I would do differently

Start with the entry product first. The $17 Prompt Vault should have been month 1. Lower friction, faster trust, cleaner funnel from day one.

I also waited too long to document and package the things I was doing anyway. If you are doing anything AI-related for clients or yourself, write it down as you go. Your next product is probably already inside your last 3 client engagements.

Questions about any of the products or how this is set up? Drop them in the comments. I read all of them.

I stopped writing AI prompts from scratch. Here is the system I built instead.

AI Operator — Sun, 22 Mar 2026 22:05:19 +0000

I used to spend 20-30 minutes crafting a prompt before every AI task.

Not writing. Not thinking. Not building. Just arguing with a text box trying to get ChatGPT or Claude to actually understand what I needed.

Then I realized: this is a solved problem. I was reinventing the wheel every single day.

So I built a system. 108 prompts. 10 categories. Every one tested, refined, and ready to copy-paste.

Here is what I learned from the process.

Why most AI prompts fail

The problem is not the AI. The problem is context.

A bad prompt gives the AI almost no information:

Write me a cold email for my SaaS product

A great prompt gives it everything it needs:

You are a B2B sales copywriter specializing in SaaS. Write a cold email to {prospect_name} at {company_name}, a {company_size} company in {industry}. My product {product_name} solves {pain_point}. They likely struggle with {specific_problem}. Goal: get a 15-minute discovery call. Tone: direct, conversational, no buzzwords. Length: under 150 words.

Same AI. Completely different output.

The second prompt takes 4 minutes to write from scratch. The copy-paste version takes 20 seconds to fill in.

Multiply that by 10 tasks per day. Across a year. That is a real amount of time.

The 10 categories that cover 95% of solopreneur work

After building out the vault, these are the buckets where solopreneurs spend the most prompt time:

1. Cold Email and Sales — outreach sequences, follow-ups, objection handling, proposal intros

2. Content and Copywriting — blog outlines, Twitter threads, LinkedIn posts, YouTube scripts

3. SEO and Blogging — keyword research briefs, meta descriptions, internal linking strategies, content refreshes

4. Client Management — onboarding checklists, scope of work summaries, difficult conversation scripts, project update emails

5. Market Research — competitor analysis frameworks, ICP (ideal customer profile) builders, pricing sensitivity probes

6. Product Creation — digital product outlines, course structures, lead magnet ideas, pricing page copy

7. Automation and Workflows — Zapier flow design, SOP writing, delegation briefs, system documentation

8. Social Media — hooks, carousels, reply strategies, growth audit frameworks

9. Pricing and Finance — rate increase scripts, package restructuring, invoice templates, financial review prompts

10. Bonus: The AI Flywheel System — my personal prompts for running the entire operation

3 prompts I use every week (copy these now)

The Competitor Gap Finder

The Cold Email That Books Calls

The Content Repurposing Engine

The system behind the prompts

Each prompt in the vault follows the same structure:

Title — what it is in plain language
Use case — the exact situation when you should reach for it
The prompt — with {fill-in} placeholders so you can adapt it in seconds
Example output — so you know what good looks like before you run it

Works with Claude, ChatGPT, Gemini, or any model you prefer.

Where to get the full vault

I packaged all 108 prompts into a PDF you can keep open while you work. Price is 7 — one-time, yours forever.

Get the AI Prompt Vault here

The funnel it fits into if you want the bigger picture:

7 — AI Prompt Vault (entry point, you are here)
7 — AI Agent Automation Blueprint (build automations that run while you sleep)
7 — Solopreneur AI Content Engine (turn one idea into a week of content)
7 — AI Agency Starter Kit (land clients, deliver AI services, scale)

Start wherever makes sense for where you are.

Built these prompts over 6 months of running AI-powered client work and a content site. The cold email prompts alone have helped book 3 retainer clients. Happy to answer questions in the comments.

15 Uncensored Character AI Alternatives That Actually Work in 2026

AI Operator — Sun, 22 Mar 2026 08:01:45 +0000

15 Uncensored Character AI Alternatives That Actually Work in 2026

Character AI keeps getting more restrictive. If you have noticed responses getting filtered mid-conversation or personas suddenly breaking character, you are not alone.

I tested 15+ alternatives over the past few months and ranked them based on what actually matters: filter transparency, persona memory, and value for money.

Why People Are Leaving Character AI

The platform has been quietly tightening its filters. Even with restrictions supposedly turned off, responses get cut short or redirected. It is frustrating when a deep roleplay session suddenly turns into a customer service interaction.

Top 5 From the Full List

1. Candy AI - Best overall. Real persona memory keeps characters consistent across sessions. Filter system is upfront about limits.

2. Crushon.AI - Zero filter mode, no signup needed to try. The free tier is more generous than most competitors.

3. Janitor AI - Connects to your own AI models via open API. Fully customizable with a huge community card library.

4. Nomi AI - Relationship-focused. Builds genuine conversational history across sessions.

5. Talkie AI - Best for voice interaction. Character consistency holds up well over long sessions.

The full ranked breakdown with pros, cons, pricing, and filter scores for all 15:

15 Uncensored Character AI Alternatives That Work in 2026

Key Criteria When Choosing

Filter transparency: Does it show what is restricted, or silently redirect you?
Persona memory: Does the character recall previous sessions?
Token limits: Free tiers vary dramatically in how generous they are
User-created content: Larger libraries mean more character options

Character AI is not rolling back its restrictions. The platforms above have made clear choices to give users more control. Full pricing, ID verification requirements, and use-case comparisons are in the article linked above.