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# Banatie Product Description
## What is Banatie?
Banatie is an **AI-powered image generation API platform** designed specifically for developers who use AI coding tools like Claude Code, Cursor, and other agentic development environments.
## The Problem We Solve
Developers building web projects face a significant workflow friction:
1. Need an image for their project
2. Leave coding environment
3. Go to AI image generator (Midjourney, DALL-E, etc.)
4. Generate and iterate on prompts
5. Download the image manually
6. Organize files in project structure
7. Import back into project
**This process often takes longer than building the actual functionality.**
## Our Solution
Banatie provides seamless integrations that allow developers to generate production-ready images directly within their development workflow:
### Integration Methods
- **MCP Server** — Direct integration with Claude Code, Cursor, and other MCP-compatible tools
- **REST API** — Standard HTTP API for any application
- **Prompt URLs** — Generate images via URL parameters (on-demand generation)
- **SDK/CLI** — Developer tools for automation
### Key Features
- **Prompt Enhancement** — AI improves your prompts automatically
- **Built-in CDN** — Images delivered fast, globally
- **@name References** — Maintain consistency across project images
- **Project Organization** — Images organized by project automatically
## Technology
- **AI Models:** Google Gemini (Imagen 3)
- **Infrastructure:** Global CDN delivery
- **Future:** Image transformation pipeline (resize, format conversion)
## Positioning
We're at the intersection of **AI generation** and **image delivery infrastructure**.
NOT competing with:
- Creative tools (Midjourney, Leonardo) — we're for developers, not artists
- Generic APIs (OpenAI DALL-E API) — we add workflow integration
Competing with:
- Manual workflow (download → organize → import)
- Cloudinary/ImageKit (on transformation features)
- Replicate (on generation API)
## Pricing Philosophy
- **Value-based pricing** — Price on workflow time saved, not raw generation cost
- **Developer-friendly** — Free tier for testing, reasonable paid tiers
- **No surprise bills** — Clear usage limits and alerts
## Current Status
- ✅ API v1 complete
- ✅ Landing page live (banatie.app)
- 🔄 Customer validation in progress
- ⏳ MCP server in development
- ⏳ SDK/CLI tools planned
## Brand Voice
- Technical but approachable
- Problem-focused, not feature-focused
- Developer-to-developer communication
- Honest about limitations and roadmap

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# Batch Processing Workflow
## Для интенсивов (6-8 статей за 2 недели)
Вместо создания статей последовательно от начала до конца — группируй по типу задачи.
## 14-дневный план
```
День 1-2: Все outlines (8 штук) → @strategist + @architect
День 3-5: Все first drafts + critique → @writer + @editor
День 6-8: Все revisions → @writer (с Opus)
День 9-11: Human editing (2-3/день) → ТЫ
День 12-14: SEO + images + публикация → @seo + @image-gen + ТЫ
```
## Почему batching эффективнее
1. **Контекст не переключается** — ты в режиме "структуры" или "редактирования"
2. **Patterns видны** — на 3-м outline замечаешь повторы, которые не видел на 1-м
3. **Параллельная работа** — пока @editor анализирует draft 1, @writer пишет draft 2
4. **Bulk loading** — загрузил контекст в агента один раз, обработал 8 статей
## Детальный workflow по дням
### День 1-2: Planning & Outlines
**Утро дня 1:**
1. @strategist: загрузи все keyword research, competitor analysis
2. Пройдись по 10-15 идеям из inbox
3. Выбери 8 тем, создай briefs
**Вечер дня 1 + День 2:**
1. @architect: создай outlines для всех 8 статей подряд
2. Не переключайся между агентами
3. К концу дня 2: 8 готовых outlines в `2-outline/`
### День 3-5: Drafting + Critique
**День 3:**
1. @writer: напиши drafts для статей 1-4
2. Сразу после каждого draft → @editor для critique
3. Не исправляй пока — только генерируй и получай feedback
**День 4:**
1. @writer: напиши drafts для статей 5-8
2. @editor: critique для каждого
**День 5:**
1. Просмотри все critiques
2. Отметь patterns — что повторяется?
3. Приоритизируй: какие статьи требуют больше работы?
### День 6-8: Revisions
**Важно:** Используй Opus для revisions
1. @writer (Opus): исправь critical issues в каждом draft
2. При необходимости → второй проход через @editor
3. Цель: все drafts на score ≥ 7
### День 9-11: Human Editing
**Это твоя работа, не агентов.**
2-3 статьи в день:
- [ ] Добавь личный опыт (замени [TODO])
- [ ] Проверь код (запусти примеры)
- [ ] Humanization (varying sentences, personal voice)
- [ ] Final read вслух
### День 12-14: Optimization & Publish
**День 12:**
1. @seo: оптимизируй статьи 1-4
2. @image-gen: создай briefs для images
**День 13:**
1. @seo: статьи 5-8
2. Генерация images через Banatie
**День 14:**
1. Публикация на banatie.app/blog
2. Расписание для cross-posting (не всё сразу)
## Cross-posting schedule
Не публикуй всё в один день:
```
Неделя 1: Статьи 1-2 на blog → через 7 дней на dev.to
Неделя 2: Статьи 3-4 на blog → через 7 дней на dev.to
Неделя 3: Статьи 5-6 на blog → через 7 дней на dev.to
Неделя 4: Статьи 7-8 на blog → через 7 дней на dev.to
```
Это даёт:
- Время для Google индексации (canonical важен)
- Постоянный поток контента
- Возможность A/B тестировать titles
## Для регулярного cadence (1 статья/неделю)
```
Понедельник: @strategist (topic selection) + @architect (outline)
Вторник: @writer (draft) + @editor (critique)
Среда: @writer (revision) + @editor (re-check if needed)
Четверг: Human editing
Пятница: @seo + @image-gen + publish
```
## Tracking progress
Используй meta.yml status:
```yaml
status: inbox | planning | outline | drafting | review | optimization | ready | published
```
Команда `/status` в Claude Code покажет где какая статья.

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# Competitors Overview
## Direct Competitors
### 1. Runware
**URL:** runware.ai
**Funding:** $13M
**Pricing:** $0.0006 per image (extremely cheap)
**What they do:**
- Fast image generation API
- Multiple model support
- Focus on speed and cost
**Their strengths:**
- Massive funding → can subsidize prices
- Very fast generation
- Multiple models (SD, SDXL, Flux)
**Their weaknesses:**
- No workflow integration (MCP, IDE)
- Generic API, not developer-workflow focused
- No built-in CDN/delivery
- No project organization features
**Our differentiation:**
- Workflow integration > raw price
- Built-in delivery pipeline
- Project organization
- MCP support
---
### 2. Replicate
**URL:** replicate.com
**Model:** Pay-per-use API
**Pricing:** ~$0.01-0.05 per image depending on model
**What they do:**
- Run any ML model via API
- Image generation is one use case
- Community models marketplace
**Their strengths:**
- Huge model variety
- Developer-friendly API
- Good documentation
- Established brand
**Their weaknesses:**
- Generic platform (not image-specific)
- No workflow integration
- Complex pricing (varies by model)
- No CDN/delivery built-in
**Our differentiation:**
- Image-specific optimization
- Simpler pricing
- Built-in CDN
- Workflow integration
---
### 3. Cloudinary
**URL:** cloudinary.com
**Type:** Image management platform
**Pricing:** Free tier + paid plans ($99+/month)
**What they do:**
- Image upload, storage, transformation
- CDN delivery
- Some AI features (background removal, etc.)
**Their strengths:**
- Industry standard for image management
- Excellent transformation pipeline
- Robust CDN
- Enterprise-ready
**Their weaknesses:**
- Not focused on AI generation
- Complex/overwhelming for simple use cases
- Expensive at scale
- Legacy architecture
**Our differentiation:**
- AI generation first (they bolt it on)
- Simpler API for generation
- Developer workflow focus
- More affordable for generation use cases
---
### 4. ImageKit
**URL:** imagekit.io
**Type:** Image CDN + management
**Pricing:** Free tier + paid plans ($49+/month)
**What they do:**
- Similar to Cloudinary but simpler
- Real-time image transformation
- CDN delivery
**Their strengths:**
- Good developer experience
- Simpler than Cloudinary
- Real-time URL-based transformations
**Their weaknesses:**
- Limited AI generation capabilities
- Still focused on management, not generation
**Our differentiation:**
- Generation-first approach
- AI-native architecture
- Workflow integration
---
## Indirect Competitors
### 5. Midjourney
**Type:** Creative tool (Discord-based)
**Not really competing:** Different audience (artists vs developers), different workflow (Discord vs API)
**But:** Developers might try to use it → shows demand for AI images
---
### 6. OpenAI DALL-E API
**Type:** Image generation API
**Strengths:** Brand recognition, quality
**Weaknesses:** Expensive, no workflow integration, generic API
**Our differentiation:** Developer workflow focus, built-in delivery
---
### 7. Stability AI API
**Type:** Image generation API
**Similar positioning to us but:**
- Less developer-workflow focused
- No built-in CDN
- More model-focused than solution-focused
---
## Competitive Positioning Map
```
Developer Workflow Integration
↑
│
│ ★ BANATIE
│ (target position)
│
←─────────────────────────┼─────────────────────────→
Generic API │ Creative Tool
│
Replicate ● │ ● Midjourney
│
Runware ● ● DALL-E │ ● Leonardo
│
Stability ● │
│
↓
Raw Image Generation
```
## Competitive Intelligence Sources
For ongoing monitoring, see `research/competitors/` folder and:
- Google Ads Transparency: adstransparency.google.com
- SpyFu: spyfu.com (competitor keywords)
- Product Hunt: AI launches
- Hacker News: Show HN posts
- Twitter/X: Competitor announcements
## Our Competitive Moat
1. **Workflow Integration** — MCP, IDE plugins, CLI (competitors don't have this)
2. **Project Organization** — Images organized by project automatically
3. **Consistency Features** — @name references for consistent style
4. **Developer Experience** — API designed for developers, not data scientists
5. **Speed to Value** — Works in minutes, not hours of setup

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# Content Creation Framework
## Overview
Multi-agent system for creating technical content. **8 Claude Desktop agents** handle different aspects of content creation, **Claude Code** manages the repository.
**Core principle:** One file = one article. File moves between stage folders like a kanban card.
---
## File Structure
### Single File Per Article
Each article is ONE markdown file with frontmatter:
```
{stage}/{slug}.md
```
Examples:
- `0-inbox/nextjs-images.md`
- `3-drafting/nextjs-images.md`
- `7-published/nextjs-images.md`
### Frontmatter Template
```yaml
---
slug: nextjs-images
title: "Generate Images in Next.js with AI"
author: henry
status: drafting
created: 2024-12-22
updated: 2024-12-25
content_type: tutorial
# SEO (added by @seo)
primary_keyword: "ai image generation nextjs"
secondary_keywords: ["nextjs api", "gemini images"]
meta_description: "Learn how to generate AI images..."
# Images (added by @image-gen)
hero_image: "https://banatie.app/cdn/..."
# Publishing (added after publish)
publish_url: "https://dev.to/..."
publish_date: 2024-12-26
platform: dev.to
---
```
### Status Values
| Status | Folder | Meaning |
|--------|--------|---------|
| `inbox` | 0-inbox/ | Raw idea, not yet evaluated |
| `planning` | 1-planning/ | Brief being created |
| `outline` | 2-outline/ | Structure being designed |
| `drafting` | 3-drafting/ | Writing in progress |
| `revision` | 3-drafting/ | Revision after critique |
| `review` | 4-human-review/ | Human editing |
| `optimization` | 5-optimization/ | SEO + images |
| `ready` | 6-ready/ | Ready to publish |
| `published` | 7-published/ | Published, archived |
### "In Progress" Detection
- If `status` matches current folder → file is being worked on
- If `status` is from previous folder or missing → new file, not yet started
---
## File Evolution
File grows by accumulating sections as it moves through pipeline:
### Stage: 0-inbox
```markdown
---
slug: nextjs-images
title: "Idea: Next.js image generation"
status: inbox
created: 2024-12-22
---
# Idea
(raw notes, research links, pain points discovered)
```
### Stage: 1-planning (after @strategist)
```markdown
---
(frontmatter with author, keywords added)
---
# Brief
## Strategic Context
...
## Target Reader
...
## Keywords
...
## Content Requirements
...
```
### Stage: 2-outline (after @architect)
```markdown
---
(frontmatter)
---
# Brief
(preserved from planning)
---
# Outline
## Article Structure
...
## Section Breakdown
...
## Code Examples Planned
...
```
### Stage: 3-drafting (after @writer + @editor)
```markdown
---
(frontmatter)
---
# Brief
(preserved)
---
# Outline
(preserved)
---
# Draft
(full article text, latest version only)
---
# Critique
## Review 1 (2024-12-23)
**Score:** 5.8/10 — FAIL
### Critical Issues
- ...
### Recommendations
- ...
## Review 2 (2024-12-24)
**Score:** 7.4/10 — PASS
### Minor Issues
- ...
(critique history accumulates, draft gets rewritten each iteration)
```
### Stage: 4-human-review (after PASS)
```markdown
---
(frontmatter)
---
# Brief
(preserved)
---
# Outline
(preserved)
---
# Text
(draft renamed to Text, Critique section removed)
(human edits this section)
```
### Stage: 5-optimization (after @seo + @image-gen)
```markdown
---
(frontmatter with SEO fields, hero_image added)
---
# Brief
(preserved)
---
# Outline
(preserved)
---
# Text
(SEO-optimized text with images embedded)
![Diagram description](https://banatie.app/cdn/...)
```
### Stage: 6-ready
Same as optimization, ready for copy-paste to platform.
### Stage: 7-published
```markdown
---
(frontmatter with publish_url, publish_date, platform added)
---
(same content, archived)
```
---
## The Pipeline
```
Research → Inbox → Planning → Outline → Drafting ⟲ → Review → Optimization → Ready → Published
@spy @strategist @architect @writer Human @seo Human Human
@editor @image-gen
```
### Revision Loop
```
@writer creates Draft
↓
@editor reviews → FAIL (score < 7)
↓
status: revision, Critique added to file
↓
@writer reads Critique, rewrites Draft
↓
@editor reviews again → PASS (score ≥ 7)
↓
status: review, file moves to 4-human-review/
Critique section removed, Draft renamed to Text
```
---
## Agents
| # | Agent | Role | Reads from | Writes to |
|---|-------|------|------------|-----------|
| 0 | @spy | Research | web, communities | research/ |
| 1 | @strategist | Topic planning | 0-inbox/, research/ | 1-planning/ |
| 2 | @architect | Article structure | 1-planning/ | 2-outline/ |
| 3 | @writer | Draft writing | 2-outline/, 3-drafting/ | 3-drafting/ |
| 4 | @editor | Quality review | 3-drafting/ | 3-drafting/ (adds Critique) |
| 5 | @seo | SEO + GEO | 4-human-review/ | 5-optimization/ |
| 6 | @image-gen | Visual assets | 5-optimization/ | 5-optimization/ (updates file) |
| 7 | @style-guide-creator | Author personas | interview | style-guides/ |
### Special Agents
**@spy** — Creates research files in `research/`, not article files. Other agents read from there.
**@style-guide-creator** — Creates author style guides in `style-guides/`. Not part of article pipeline.
---
## Agent Session Protocol
### Starting a Session
Every agent responds to `/init` command:
1. Read required shared files
2. List files in input folder(s)
3. Show which files are new vs in-progress
4. Ask user which file to work on
Example:
```
User: /init
Agent: Загружаю контекст...
✓ shared/banatie-product.md
✓ style-guides/AUTHORS.md
Файлы в 2-outline/:
• nextjs-images.md — status: outline (в работе)
• react-placeholders.md — status: planning (новый)
С каким файлом работаем?
```
### During Session
- One file per conversation
- Agent works on that file until done
- User can ask agent to perform operations
### Ending Session (Handoff)
When work is complete:
1. Agent summarizes what was done
2. Agent asks: "Переносим в {next-stage}?"
3. User confirms
4. Agent moves file to next folder
5. Agent updates status in frontmatter
6. Agent reports: "Готово. Открой @{next-agent} для продолжения."
---
## Stage Transitions
### Allowed Transitions
| From | To | Trigger |
|------|----|---------|
| inbox | planning | @strategist approves idea |
| planning | outline | @strategist completes brief |
| outline | drafting | @architect completes outline |
| drafting | drafting | @editor FAIL → revision |
| drafting | review | @editor PASS |
| review | optimization | Human completes edit |
| optimization | ready | @seo + @image-gen complete |
| ready | published | Human publishes |
### Backward Transitions (with user confirmation)
| From | To | When |
|------|----|------|
| review | drafting | Human found major issues |
| optimization | review | Need more human edits |
| Any | inbox | Start over |
---
## Content Sources
### Original Content
Standard flow: idea → planning → outline → draft
### From @spy Research
1. @spy creates `research/topic-name.md`
2. @strategist reads research, creates article in 0-inbox/
3. Normal flow continues
### From Perplexity Threads
1. Save Perplexity thread to `research/perplexity-topic.md`
2. @strategist evaluates, creates article with source reference
3. @architect restructures Q&A into article format
4. @writer translates Russian → English, adapts to author voice
---
## Folder Structure
```
banatie-content/
├── CLAUDE.md ← Claude Code instructions
├── README.md
├── shared/
│ ├── banatie-product.md
│ ├── target-audience.md
│ ├── competitors.md
│ ├── content-framework.md ← This file
│ ├── model-recommendations.md
│ └── human-editing-checklist.md
├── style-guides/
│ ├── AUTHORS.md
│ ├── banatie-brand.md
│ └── henry-technical.md
├── research/ ← @spy output
│ ├── keywords/
│ ├── competitors/
│ ├── trends/
│ └── weekly-digests/
├── desktop-agents/ ← Agent configs
│ └── {N}-{name}/
│ ├── system-prompt.md
│ └── agent-guide.md
├── 0-inbox/ ← Raw ideas
├── 1-planning/ ← Briefs
├── 2-outline/ ← Structures
├── 3-drafting/ ← Drafts + Revisions
├── 4-human-review/ ← Human editing
├── 5-optimization/ ← SEO + Images
├── 6-ready/ ← Ready to publish
└── 7-published/ ← Archive
```
---
## Model Recommendations
| Agent | Model | Reason |
|-------|-------|--------|
| @spy | Sonnet | Web search, aggregation |
| @strategist | **Opus** | Strategic decisions |
| @architect | **Opus** | Structure design |
| @writer | Sonnet | Content generation |
| @editor | **Opus** | Critical analysis |
| @seo | Sonnet | Technical optimization |
| @image-gen | Sonnet | Image prompts |
| @style-guide-creator | **Opus** | Deep persona work |
---
## Language Protocol
- **Files:** Always English
- **Communication with agents:** Russian
- **Tech terms:** English even in Russian dialogue
---
## Time Estimates
| Phase | Time |
|-------|------|
| Research (@spy) | 30 min/week |
| Planning + Outline | 30-45 min |
| Draft + Critique cycle | 30-45 min |
| Human Edit | 30-60 min |
| SEO + Images | 20-30 min |
| **Total per article** | **2.5-4 hrs** |

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# Human Editing Checklist
## Это делаешь ТЫ, не агенты
После @editor дал PASS и перед публикацией — твоя ручная работа.
Минимум 30-45 минут на 1000 слов. Это irreducible minimum.
---
## 1. Личный опыт
- [ ] Найди все `[TODO]` пометки от @writer
- [ ] Добавь реальные истории: "Когда я интегрировал это в Banatie..."
- [ ] Опиши конкретные проблемы которые решал
- [ ] Включи числа и детали: "занял 3 часа вместо ожидаемых 20 минут"
**Без личного опыта статья = generic AI content.**
---
## 2. Проверка кода
- [ ] Запусти КАЖДЫЙ code example
- [ ] Убедись что работает с текущей версией Banatie API
- [ ] Исправь ошибки
- [ ] Проверь imports и dependencies
- [ ] Добавь комментарии где неочевидно
**Сломанный код = потерянное доверие.**
---
## 3. Голос и стиль
- [ ] Прочитай ВСЛУХ — звучит как ты?
- [ ] Убери фразы которые "не твои"
- [ ] Добавь свои характерные обороты
- [ ] Проверь: ты бы так сказал коллеге?
**Red flags (убери):**
- "In this article, we will explore..."
- "It's worth noting that..."
- "Let's dive into..."
- "At the end of the day..."
- Любые фразы которые sound corporate
---
## 4. Humanization (AI Detection)
### Sentence variation
- [ ] Разбей длинные предложения (>25 слов)
- [ ] Объедини слишком короткие (choppy rhythm)
- [ ] Mix: короткое. Среднее предложение. Потом длинное с несколькими частями и деталями.
### Неидеальности
- [ ] Добавь contractions (don't, isn't, won't)
- [ ] Casual phrases ("honestly", "look", "here's the thing")
- [ ] Occasional sentence fragments. Like this.
- [ ] Parenthetical asides (like this one)
### Убери "perfect" структуры
- [ ] Не каждый параграф = 3 предложения
- [ ] Не каждый список = 5 пунктов
- [ ] Не идеальные transitions между секциями
### Specific details
- [ ] Конкретные числа вместо "many" или "several"
- [ ] Названия инструментов, версии, даты
- [ ] Sensory details где уместно
---
## 5. Final read
- [ ] Читаешь как будто впервые видишь
- [ ] Вопрос: захочу ли я дочитать до конца?
- [ ] Вопрос: узнал ли я что-то полезное?
- [ ] Вопрос: буду ли я рекомендовать это коллеге?
---
## Quick AI Pattern Check
**Если видишь это — переписывай:**
| AI Pattern | Замени на |
|------------|-----------|
| "It's important to note" | Убери или конкретизируй |
| "This allows you to" | "You can" или "This lets you" |
| "In order to" | "To" |
| "Utilize" | "Use" |
| "Leverage" | "Use" или конкретный глагол |
| "Ensure" | "Make sure" или убери |
| "Subsequently" | "Then" или "After that" |
| Perfect parallel structure | Break it occasionally |
---
## Time Budget
| Task | Time |
|------|------|
| Personal experience injection | 10-15 min |
| Code verification | 10-15 min |
| Voice check + rewrites | 10-15 min |
| Humanization pass | 5-10 min |
| Final read | 5 min |
| **TOTAL** | **40-60 min** |
---
## Remember
> "AI для структуры, человек для голоса и личного опыта"
Цель не спрятать AI. Цель — создать hybrid content где AI сделал heavy lifting, а ты добавил то, что AI не может: твой опыт, твой голос, твои ошибки и решения.

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# Model Recommendations
## Какую модель использовать для какого агента
| Agent | Рекомендуемая модель | Почему |
|-------|---------------------|--------|
| @spy | Sonnet 4.5 | Web search, aggregation — не требует deep reasoning |
| @strategist | **Opus 4.5** | Стратегические решения, author selection, competitive analysis |
| @architect | **Opus 4.5** | Структурные решения, понимание author patterns |
| @writer | Sonnet 4.5 | Генерация текста — Sonnet достаточно для execution |
| @editor | **Opus 4.5** | Критический анализ, выявление AI patterns, quality judgment |
| @seo | Sonnet 4.5 | Техническая оптимизация — structured task |
| @image-gen | Sonnet 4.5 | Prompt generation — structured task |
| @style-guide-creator | **Opus 4.5** | Discovery interview, synthesis, complex decisions |
## Логика выбора
**Opus 4.5 ($15/$75 per million tokens):**
- Стратегические решения
- Критический анализ
- Синтез из множества источников
- Judgment calls
**Sonnet 4.5 ($3/$15 per million tokens):**
- Execution по готовым инструкциям
- Structured tasks
- Генерация контента
- Техническая оптимизация
## Для Revision
| Ситуация | Модель |
|----------|--------|
| First draft | Sonnet |
| Revision после critique | **Opus** (качественные правки) |
| Minor fixes | Sonnet |
## Стоимость на статью
При средней статье 2000 слов:
- Sonnet draft: ~$0.05
- Opus critique: ~$0.15
- Opus revision: ~$0.10
- **Total: ~$0.30 на статью**
## В Claude Desktop/Web
Claude Pro subscription ($20/мес) даёт доступ к обеим моделям.
По умолчанию используется Sonnet. Для Opus-задач:
- Переключи модель в настройках проекта
- Или создай отдельные Projects для Opus-агентов
## Важно
Это рекомендации, не жёсткие требования. Если бюджет ограничен — Sonnet справится со всеми задачами, просто может потребоваться больше итераций для @editor и @strategist.

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# Banatie — Project Context
## What is Banatie
Banatie is an API-first platform for AI image generation, built for developers who use coding agents like Claude Code and Cursor. The platform transforms prompts into production-ready images with built-in CDN delivery.
Small focused team. Quality over quantity. Every contribution matters.
## What Success Looks Like
First paying customers → break-even ($100-500 MRR) → sustainable income.
Every piece of content you create is an attempt to reach a developer who might become that customer. Write for that person.
## Working Principles
**Think strategically.**
You have context about Banatie's goals, audience, and constraints. Use that context. Ask yourself: does this move us forward?
**Own the outcome.**
You're responsible for the quality of your work. If something feels weak or unclear, improve it before passing it on. The next agent in the pipeline trusts that you've done your best.
**Stay curious.**
Look for angles others might miss. Question assumptions. If you see a better approach, propose it. Your perspective has value.
**Be honest.**
If you're uncertain, say so. If you see a problem with the task, raise it. Clear communication prevents wasted effort.
## Resources
Time and budget are limited. Every decision should account for this. When choosing between options, favor approaches that are:
- High impact for effort invested
- Sustainable and repeatable
- Aligned with what we know about our audience
## Critical Thinking
You are expected to think, question, and propose.
If something about the task seems off — wrong assumptions, missing information, a better approach available — say so. Explain your reasoning. Propose an alternative if you have one.
The user always makes the final decision. But your perspective matters, and honest feedback prevents wasted effort.
This is collaboration, not order-taking.

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# Target Audience (ICP)
## Primary ICP: AI-First Developers
### Who They Are
Developers who use AI coding tools (Claude Code, Cursor, Copilot, Windsurf) as core part of their workflow. They build web applications and need images but hate context-switching.
### Demographics
- **Role:** Frontend developers, full-stack developers, indie hackers
- **Experience:** 2-10 years
- **Company:** Solo/freelance, small startups, indie projects
- **Location:** Global (English-speaking or English-proficient)
- **Tools:** VS Code/Cursor, Claude Code, GitHub Copilot, React/Next.js
### Psychographics
- Value efficiency and automation over manual processes
- Early adopters of AI tooling
- Prefer API-first solutions over GUI tools
- Willing to pay for time savings
- Active in developer communities (Reddit, HN, Discord, Twitter)
### Pain Points
1. **Context switching kills flow** — leaving IDE to generate images breaks concentration
2. **Manual file management is tedious** — download, rename, organize, import
3. **Consistency is hard** — keeping visual style consistent across project
4. **AI image tools aren't developer-friendly** — designed for artists, not devs
### Jobs to Be Done
- "Generate a hero image for my landing page without leaving my editor"
- "Get consistent placeholder images for my components"
- "Automate image generation in my build pipeline"
- "Create project-specific image assets programmatically"
### Where They Hang Out
- r/ClaudeAI, r/ChatGPTCoding, r/cursor
- Hacker News
- Dev Twitter/X
- Discord servers for AI coding tools
- Dev.to, Hashnode
### Buying Behavior
- Try free tier first
- Pay when they hit limits or need production features
- Monthly subscription preferred over usage-based (predictable costs)
- Will pay $10-50/month for tools that save significant time
---
## Secondary ICP: Web Dev Agencies (Small)
### Who They Are
Small web development agencies (3-10 people) building client websites and applications. Need images for multiple projects.
### Pain Points
- Managing images across many client projects
- Keeping brand consistency per client
- Time spent on asset preparation
- Client requests for quick visual iterations
### Value Proposition
- Project-based organization
- Consistent image generation per brand
- API integration into existing workflows
- Time savings across multiple projects
---
## Tertiary ICP: E-commerce Builders
### Who They Are
Developers building e-commerce solutions (Shopify apps, custom stores). Need product-related images.
### Pain Points
- Product image generation/enhancement
- Lifestyle/marketing images
- Consistent product photography style
- Bulk image processing
### Value Proposition
- Product image generation
- Style consistency across catalog
- Integration with e-commerce platforms
- Bulk operations
---
## Anti-Personas (NOT Our Target)
### 1. Artists/Designers
- Want fine creative control
- Prefer Midjourney/Leonardo UI
- Don't need developer integrations
- **Why not us:** We're optimized for developer workflow, not creative exploration
### 2. Enterprise IT
- Need extensive compliance/security
- Require on-premise deployment
- Long procurement cycles
- **Why not us:** We're not enterprise-ready yet
### 3. Non-Technical Users
- Need GUI-based tools
- Don't understand APIs
- Want drag-and-drop interfaces
- **Why not us:** We're API-first, developer-focused
---
## Validation Status
- ✅ AI-first developers: Strong founder-market fit (Oleg's own experience)
- 🔄 Web agencies: Needs external validation
- 🔄 E-commerce: Needs external validation
## Content Implications
When writing content:
- Use technical terminology freely
- Reference AI coding tools specifically
- Focus on workflow integration, not image quality
- Assume reader knows React/Next.js basics
- Include code examples early and often