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---
slug: remote-claude-workspace
title: "How to Access Claude Projects from Any Device"
status: inbox
created: 2024-12-27
source: internal-discovery
author: henry
---
# Idea
## Discovery
**Source:** Internal setup documentation — "Remote Claude Workspace Setup" chat
**Evidence:** Complete working solution implemented and tested
## The Problem
Claude Desktop with Projects is powerful but tied to one machine. Developers who:
- Work from multiple devices (laptop, desktop, travel setup)
- Want to access their Claude Projects remotely
- Need their MCP tools and project context everywhere
...are stuck. Projects don't sync. MCP servers don't transfer.
## The Solution
VPS-based setup with:
- Cloudflare Tunnel for secure access
- Browser-based IDE (code-server) for remote work
- MCP servers running on VPS, accessible via tunnel
- Claude Desktop connecting to remote MCP endpoints
## Why This Matters
1. **Hot topic:** Claude, MCP, remote development all trending
2. **Unique angle:** Almost nobody writing about this specific setup
3. **Technical depth:** Shows real problem-solving, not surface-level tips
4. **Right audience:** Developers using AI tools — exactly our ICP
5. **Not Banatie-specific:** Good for account warmup, builds authority
## Potential Angle
"Your Claude Projects shouldn't be trapped on one machine. Here's how I set up remote access to my entire AI development environment."
Personal story: why I needed this, what I tried, final solution.
## Technical Components
- Contabo VPS (Singapore)
- Docker containers for isolation
- Cloudflare Tunnel (free tier works)
- code-server for browser IDE
- MCP server configuration for remote access
- Security considerations
## Content Type
Tutorial with real code and configs.
## Keywords (to research)
- claude desktop remote access
- mcp server remote
- ai coding tools remote setup
- claude projects multiple devices
## Notes
- Full technical solution already documented internally
- Screenshots and configs available
- Real-world tested (Oleg's actual setup)
- Good fit for Henry's technical voice

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## Overview
This is a **content repository** for Banatie blog. Content is created by 8 Claude Desktop agents. You (Claude Code) manage files and structure.
This is a **content repository** for Banatie blog and website. Content is created by 9 Claude Desktop agents. You (Claude Code) manage files and structure.
**Core principle:** One markdown file = one article. Files move between stage folders like kanban cards.
@ -16,27 +16,61 @@ You manage files, validate structure, check consistency.
```
banatie-content/
├── CLAUDE.md ← You are here
├── shared/ ← Context for all agents
├── content-framework.md ← System architecture documentation
├── human-editing-checklist.md ← Human editing guide
├── batch-processing.md ← Intensive workflow guide
├── project-knowledge/ ← Static context (for Claude Desktop Projects)
│ ├── project-soul.md
│ ├── banatie-product.md
│ ├── target-audience.md
│ ├── competitors.md
│ ├── content-framework.md
│ └── ...
├── style-guides/ ← Author definitions
│ └── dataforseo-guide.md
├── shared/ ← Dynamic context (for operational updates)
│ └── (empty by default)
├── desktop-agents/ ← Agent configs (9 agents)
│ ├── 0-spy/
│ ├── 1-strategist/
│ ├── 2-architect/
│ ├── 3-writer/
│ ├── 4-editor/
│ ├── 5-seo/
│ ├── 6-image-gen/
│ ├── 7-style-guide-creator/
│ └── 8-webmaster/
├── style-guides/ ← Author personas
│ ├── AUTHORS.md
│ └── {author}.md
├── research/ ← @spy output
├── desktop-agents/ ← Agent configs (read-only reference)
├── pages/ ← @webmaster output (landing pages)
├── 0-inbox/ ← 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
├── 5-seo/ ← SEO optimization
├── 6-ready/ ← Ready to publish (+ image specs)
└── 7-published/ ← Archive
```
## Context Architecture
### project-knowledge/
Static context files. Added to Claude Desktop Project Knowledge.
Rarely changes. Contains: product info, audience, competitors.
### shared/
Dynamic context. Agents read via MCP at /init.
Used for: operational updates, experiment results, temporary priorities.
**Priority:** shared/ overrides project-knowledge/
## File Format
Each article is ONE file with frontmatter:
@ -53,6 +87,11 @@ content_type: tutorial
primary_keyword: "main keyword"
---
# Idea
(raw idea, discovery source)
---
# Brief
(from @strategist)
@ -63,13 +102,18 @@ primary_keyword: "main keyword"
---
# Draft
(from @writer)
# Text
(final text — renamed from Draft after PASS)
---
# Critique
(from @editor — removed after PASS)
# SEO Optimization
(from @seo)
---
# Image Specs
(from @image-gen)
```
## Status Values
@ -82,7 +126,7 @@ primary_keyword: "main keyword"
| drafting | 3-drafting/ | Writing |
| revision | 3-drafting/ | Revision after critique |
| review | 4-human-review/ | Human editing |
| optimization | 5-optimization/ | SEO + images |
| seo | 5-seo/ | SEO optimization |
| ready | 6-ready/ | Ready to publish |
| published | 7-published/ | Archived |
@ -115,18 +159,19 @@ Move article to stage (validate first):
- Communication with user: Russian
- Reports: Russian
## The 8 Agents
## The 9 Agents
| # | Agent | Role |
|---|-------|------|
| 0 | @spy | Research |
| 1 | @strategist | Topic planning |
| 2 | @architect | Article structure |
| 3 | @writer | Draft writing |
| 4 | @editor | Quality review |
| 5 | @seo | SEO optimization |
| 6 | @image-gen | Visual assets |
| 7 | @style-guide-creator | Author personas |
| # | Agent | Role | Special Tools |
|---|-------|------|---------------|
| 0 | @spy | Research, competitive intelligence | DataForSEO |
| 1 | @strategist | Topic planning, briefs | DataForSEO |
| 2 | @architect | Article structure | — |
| 3 | @writer | Draft writing | — |
| 4 | @editor | Quality review | — |
| 5 | @seo | SEO optimization | DataForSEO |
| 6 | @image-gen | Visual asset specs | — |
| 7 | @style-guide-creator | Author personas | — |
| 8 | @webmaster | Landing pages, web content | — |
## What You Do NOT Do
@ -136,3 +181,9 @@ Move article to stage (validate first):
❌ Generate images
These are done by Claude Desktop agents.
## Key Documentation
- `content-framework.md` — Full system architecture and design decisions
- `human-editing-checklist.md` — What human does after AI review
- `batch-processing.md` — Workflow for content intensives

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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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# Banatie Content System — Constitution
**Document owner:** Oleg + @men
**Purpose:** Design documentation for the multi-agent content creation system
**Audience:** Us (Oleg, @men). Not for agents.
---
## Why This Document Exists
This is the reference for understanding WHY the system is built this way. When we return to this project after a break, or when something breaks, or when we want to extend it — this document explains the reasoning.
Agents don't read this. They have their own instructions in system prompts.
---
## Core Architecture
### Single-File Article Pattern
Each article lives in ONE markdown file that accumulates sections as it moves through the pipeline:
```
article.md:
├── Frontmatter (metadata)
├── # Idea (from @spy or manual)
├── # Brief (from @strategist)
├── # Outline (from @architect)
├── # Draft → Text (from @writer, renamed after PASS)
├── # SEO Optimization (from @seo)
└── # Image Specs (from @image-gen)
```
**Why single file:**
- Git-friendly: one commit = one article state
- No sync issues: everything in one place
- Easy to move: just move the file
- Simple mental model: file location = article status
### Stage Folders (Kanban)
```
0-inbox/ → Raw ideas, discoveries
1-planning/ → Ideas with Briefs
2-outline/ → Articles with structure
3-drafting/ → Writing in progress
4-human-review/→ Passed AI review, needs human touch
5-seo/ → SEO optimization stage
6-ready/ → Ready to publish
7-published/ → Archive of published content
```
**Why folders instead of status field:**
- Visual: `ls` shows pipeline state instantly
- Atomic: moving file = changing status
- Git history: folder shows when status changed
- No parsing: don't need to read file to know status
### Dual Context System
```
project-knowledge/ → Loaded into Claude Project Knowledge (static)
shared/ → Read via MCP at runtime (dynamic)
```
**Why two sources:**
- Project Knowledge is fast but requires manual update
- MCP read is slower but always current
- Base context rarely changes (product, audience, competitors)
- Operational context changes often (priorities, experiments, new findings)
**Priority:** shared/ overrides project-knowledge/
This lets us hot-patch agent behavior without rebuilding Claude Desktop projects.
---
## Agent Design Principles
### Agents Have "Soul"
Each agent has a Mindset section that establishes:
- Ownership of outcomes (not just task completion)
- Permission to question and propose
- Understanding of how their work fits the whole
**Why this matters:**
- Order-taking AI produces mediocre results
- Strategic thinking catches problems early
- Ownership means caring about quality
### Positive Instructions
Prompts tell agents what TO DO, not what NOT to do.
**Instead of:** "Don't use generic phrases"
**We write:** "Write like explaining to a smart colleague"
**Why:**
- Negative instructions often backfire (attention on forbidden thing)
- Positive framing is clearer and more actionable
- Matches how you'd instruct a human colleague
### Filesystem MCP Only
All agents use `filesystem:*` MCP tools. No virtual FS, no artifacts, no create_file.
**Why:**
- Real files on real disk
- Git tracks everything
- Human can edit same files
- No "where did my file go?" confusion
### Self-Reference via agent-guide.md
Each agent has an agent-guide.md that it can read to help users.
When user asks "что ты умеешь?" — agent reads its own guide and answers.
**Why:**
- Agent doesn't need to remember everything
- Guide can be updated without changing system prompt
- Consistent help across sessions
---
## File Locations
### Repository Root
```
/projects/my-projects/banatie-content/
```
### Static Context (for Project Knowledge)
```
project-knowledge/
├── project-soul.md ← Mission, principles, team context
├── banatie-product.md ← Product description
├── target-audience.md ← ICP details
├── competitors.md ← Competitive landscape
└── dataforseo-guide.md ← DataForSEO usage guide
```
These files are added to Claude Desktop Project Knowledge. Agents reference them but don't modify.
### Dynamic Context
```
shared/
└── (empty by default, used for operational updates)
```
When we need to push urgent context to agents (new priority, experiment results, temporary instructions), we put files here. Agents check this folder at /init.
### Agent Definitions
```
desktop-agents/
├── 0-spy/
│ ├── system-prompt.md ← Full agent instructions
│ └── agent-guide.md ← Quick reference for user help
├── 1-strategist/
├── 2-architect/
├── 3-writer/
├── 4-editor/
├── 5-seo/
├── 6-image-gen/
├── 7-style-guide-creator/
└── 8-webmaster/
```
**system-prompt.md** — copied into Claude Desktop Project system prompt
**agent-guide.md** — added to Project Knowledge, agent reads to help users
### Content Pipeline
```
0-inbox/ ← Ideas land here
1-planning/ ← Briefs created
2-outline/ ← Structure done
3-drafting/ ← Writing happens
4-human-review/ ← AI done, human needed
5-seo/ ← SEO optimization (folder name differs from 5-optimization)
6-ready/ ← Ready to publish
7-published/ ← Archive
```
### Supporting Files
```
research/ ← All research outputs (@spy writes here)
style-guides/ ← Author personas
pages/ ← Landing page content (@webmaster writes here)
assets/ ← Static assets
```
### Root Level Docs
```
CLAUDE.md ← Instructions for Claude Code
README.md ← Project overview
human-editing-checklist.md ← Human editing guide
batch-processing.md ← Intensive workflow guide
```
---
## Agent Roster
| # | Handle | Role | Reads | Writes |
|---|--------|------|-------|--------|
| 0 | @spy | Research Scout | shared/, research/ | research/, 0-inbox/ |
| 1 | @strategist | Content Strategist | 0-inbox/, research/, style-guides/ | 1-planning/ |
| 2 | @architect | Article Architect | 1-planning/, style-guides/ | 2-outline/ |
| 3 | @writer | Draft Writer | 2-outline/, 3-drafting/, style-guides/ | 3-drafting/ |
| 4 | @editor | Quality Editor | 3-drafting/, style-guides/ | 3-drafting/, 4-human-review/ |
| 5 | @seo | SEO Optimizer | 4-human-review/ | 5-seo/ |
| 6 | @image-gen | Visual Designer | 5-seo/ | 6-ready/ |
| 7 | @style-guide-creator | Persona Designer | style-guides/ | style-guides/ |
| 8 | @webmaster | Web Content | research/, 0-inbox/ | pages/ |
### DataForSEO Access
Only some agents have DataForSEO MCP:
- @spy — competitor intelligence, backlinks, LLM mentions
- @strategist — keyword research, search intent
- @seo — SERP analysis, on-page, LLM responses
Budget: $0.50/session default, ~$10/month total.
---
## Key Workflows
### Article Creation (Happy Path)
```
1. @spy discovers topic → 0-inbox/article.md (Idea)
2. @strategist evaluates → 1-planning/article.md (+ Brief)
3. @architect structures → 2-outline/article.md (+ Outline)
4. @writer drafts → 3-drafting/article.md (+ Draft)
5. @editor reviews → FAIL: stays in 3-drafting/ (+ Critique)
→ PASS: 4-human-review/article.md
6. Human edits → Same file, manual work
7. @seo optimizes → 5-seo/article.md (+ SEO Optimization)
8. @image-gen specs → 6-ready/article.md (+ Image Specs)
9. Publish → 7-published/article.md
```
### Revision Loop
```
@writer creates draft
@editor: FAIL (score < 7)
Critique added to file
@writer reads Critique, rewrites Draft
@editor: PASS (score ≥ 7)
File moves to 4-human-review/
```
### Landing Page Creation
```
1. Idea/research → research/ or 0-inbox/
2. @webmaster creates content → pages/page-name.md
3. Implementation via Claude Code → /projects/.../banatie-service/apps/landing
```
---
## Design Decisions Log
### 2024-12-27: Dual Context Architecture
**Problem:** Static Project Knowledge couldn't be updated quickly for operational needs.
**Decision:** project-knowledge/ (static, in Project Knowledge) + shared/ (dynamic, read via MCP).
**Rationale:** Base context rarely changes. Operational context (priorities, experiments) changes often. This separation gives us flexibility without constant Project rebuilding.
### 2024-12-27: Agent Mindset Sections
**Problem:** Agents executed tasks mechanically without strategic thinking.
**Decision:** Added "Your Mindset" section to each agent with ownership language.
**Rationale:** Framing affects behavior. Agents that "own outcomes" produce better results than agents that "complete tasks."
### 2024-12-27: DataForSEO Integration
**Problem:** Keyword research was fake (web search guessing volumes).
**Decision:** Integrated DataForSEO MCP for real data.
**Rationale:** Content strategy must be data-driven. $10/month is worth it for real search volumes and difficulty scores.
### 2024-12-27: @webmaster Agent Added
**Problem:** System only produced blog articles, no landing pages.
**Decision:** Created @webmaster for conversion-focused web content.
**Rationale:** Different skill: blog educates, landing converts. Different structure, different copy principles.
### 2024-12-27: Filesystem MCP Enforcement
**Problem:** Agents tried to use virtual FS, artifacts, create_file — files got lost.
**Decision:** Explicit "CRITICAL" section in every prompt: only filesystem:* MCP tools.
**Rationale:** Real files on disk = git tracking, human access, persistence across sessions.
---
## What's NOT Documented Here
- **Agent prompts** — live in desktop-agents/*/system-prompt.md
- **Research findings** — live in research/
- **Style guides** — live in style-guides/
- **Current priorities** — live in shared/ (if any)
This document is architecture. Not operations.
---
## When to Update This Document
- New agent added → update roster
- Folder structure changes → update paths
- Major workflow change → update workflows
- Design decision made → add to log
Don't update for: content changes, research findings, style guide updates.

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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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# 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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# 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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# 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