Founder Content System#

Everything for creating and multiplying content as a solo founder or indie hacker.

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Master Content Creation Workflow (Must Follow)#

Core Principle: Research → Extract → Adapt → Write

Every piece of content must go through this workflow:

Step 1: Research Hot Content (REQUIRED)#

Before writing ANY content, research what is working:

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1. Search for viral/high-engagement posts on target platform
2. Find 3-5 top-performing posts on similar topic
3. Note: hook structure, format, engagement type, tone
4. Identify what makes them work (specifics, emotion, contrarian angle)

Search patterns:

• [platform] [topic] viral
• site:[platform].com [topic] lessons learned
• [topic] founder thread high engagement

Step 2: Extract Winning Patterns#

What to ExtractWhyHook formulaFirst line determines if people readNumber usageSpecifics add credibility ($400 → $180)Emotion triggersWhat makes people react (cringe, saved, wasted)Story arcHow tension and payoff are structuredCTA designWhat drives comments vs likes

Step 3: Adapt with Founder Voice#

Brand Voice Principles:

• 真实 (authentic) — real stories, not theory
• 硬 (sharp) — specific numbers, direct claims
• 带点自嘲 (self-deprecating) — own failures openly
• 不鸡汤 (no fluff) — substance over motivation

Adaptation Rules:

1. Keep the winning hook structure
2. Replace content with real stories from the user
3. Add specific numbers (e.g. $3,000 wasted, saved $1,000+)
4. Include genuine emotion (still cringe, learned the hard way)
5. Avoid: vague claims, motivational fluff, humblebrags

Step 4: Platform-Specific Polish#

PlatformKey AdaptationTwitter/XPunchy, <280 chars, threads for depthLinkedInLonger, professional vulnerability, spaced lines小红书口语化, 情绪词 (亏麻了/稳了), search-optimized titles

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Quick Reference#

TaskSectionWrite posts from scratchBuild-in-Public WorkflowMultiply existing contentRepurposing FrameworkThread formulaThread FormulaVoice rulesVoice RulesPlatform defaultsPlatform Defaults

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Build-in-Public Workflow#

Step 1: Gather Context#

From version control (auto mode):

• Recent commits since last post
• PR titles and descriptions
• Release notes if tagged

From user input (manual mode):

• What shipped (feature/fix/improvement)
• Who it helps
• Why now
• One metric (optional)
• One lesson learned

Step 2: Extract the Story#

Every post answers 5 questions:

1. What changed? (the ship)
2. Who benefits? (the user)
3. Why it matters now? (the context)
4. One proof (metric, example, before/after)
5. One takeaway (lesson or insight)

Step 3: Render for Each Platform#

Twitter/X: Under 280 chars, concise, slightly spicy, one insight + one proof

LinkedIn: 8-20 lines with spacing, narrative + framework + takeaway

小红书: Chinese-first, structure: 背景→步骤→结果→踩坑→总结

Step 4: Quality Check#

• No identical cross-posts
• Each post has a takeaway
• No banned patterns (see Voice Rules below)
• 小红书 passes sensitivity check
• Metrics/proof included where possible

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Repurposing Framework#

Core Principle: One Excellent Piece → 7-10 Platform-Native Derivatives

Step 1: Evaluate Source#

High-Value (prioritize): Evergreen topics, top performers, content with data/frameworks, long-form (>1000 words)

Skip: Trend-based, low performers, thin content

Step 2: Extract Atomic Units#

ElementWhat to ExtractHookOpening line, attention-grabberStatsNumbers, percentages, metricsFrameworksStep processes, modelsQuotesMemorable phrasesStoriesAnecdotes, case studiesTakeawaysKey lessons, actionable tips

Step 3: Apply STEPPS (from Contagious)#

Every derivative needs at least one:

1. Social Currency — Makes sharer look smart
2. Triggers — Connected to daily habits
3. Emotion — Evokes awe, surprise, anger
4. Public — Visible behavior
5. Practical Value — Useful, saves time/money
6. Stories — Narrative that carries message

Step 4: Make It Stick (SUCCESs)#

• Simple — One core idea
• Unexpected — Break patterns
• Concrete — Specific details
• Credible — Proof points
• Emotional — Care about individual
• Stories — People remember stories

Step 5: Schedule Distribution#

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Day 0:    Original published
Day 1-2:  Tease/announcement
Day 3-7:  First wave derivatives
Week 2-3: Second wave
Week 4+:  Evergreen rotation

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Content Pillars#

Suggested pillar mix for technical founders:

1. Technical Build — AI, tools, architecture decisions, tech comparisons
2. Building in Public — Process over results, real learnings, metrics
3. Cross-Cultural Founder — Unique perspective from background/geography
4. Systems Thinking — Workflows, optimization, productivity

Weekly mix: 2-3 posts from pillars 1-2, 1 post from pillars 3-4

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Voice Rules#

Always:

• Include one takeaway per post
• Adapt content per platform
• Use metrics when available
• Keep 小红书 titles search-optimized

Never:

• Motivational fluff ("believe in yourself")
• Humblebragging / name-dropping
• Vague claims ("game-changing", "revolutionary")
• Thought-leader cringe
• Dunking on competitors by name

Voice Summary:

• 真实 (authentic, not performative)
• 硬 (direct, earned confidence)
• 带点自嘲 (self-deprecating humor)
• 细节感强 (specific details)
• 不鸡汤 (no inspirational soup)

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Platform Defaults#

PlatformLanguageCadenceFormatTwitter/XEnglish3-5/week<280 chars, threads rareLinkedInEnglish1-2/week8-20 lines, spaced小红书Chinese primary2/week干货 + 踩坑 mix

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Thread Formula#

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Tweet 1 (Hook): Surprising stat or contrarian take
Tweet 2-5:      One key point per tweet with proof
Tweet 6:        Common mistake / "what most get wrong"
Tweet 7:        The solution/framework
Tweet 8:        CTA + callback to original

Write 10-15 versions of hook before publishing.

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Example Transformation#

Input (commit message):

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feat: connect database MCP to content scheduler
- Added automatic content storage
- Triggers on push
- Posts to 3 platforms

Output:

Twitter/X:

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Just wired up Claude → database → auto-posting pipeline.
Push to repo now triggers content across 3 platforms.
The surprising part: 80% of the work was tone adaptation, not infra.

LinkedIn:

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Shipped: automated "build in public" pipeline

What it does: commits → AI-adapted posts → 3 platforms

What I learned: The hard part isn't automation—it's maintaining authentic voice at scale.

Next: adding human-in-the-loop approval.

小红书:

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标题：用 Claude + 数据库搭了个自动发帖系统

背景：每次提交都想分享，但手动发三个平台太累

做法：Claude 读 commit → 生成三版本 → 数据库存储 → 自动发

踩坑：以为难点是技术，其实是语气适配

总结：自动化不是复制粘贴，是让机器学会"变脸"

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Input/Output Examples#

Example 1: Build-in-Public Update#

User Input:

"We just shipped a feature that lets users generate brand-consistent ad creative in one click."

Expected Output:

Twitter/X:

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Shipped: One-click brand-consistent ad creative.

Before: 3 rounds of "make it more on-brand" feedback.
After: 90% first-pass approval rate.

The hard part wasn't the AI. It was teaching it what "on-brand" actually means.

LinkedIn:

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We just shipped one-click brand-consistent ad creative.

Here's what changed:

Before:
• Designer creates ad
• Brand team reviews
• "Can you make it more on-brand?"
• 3 rounds of revision
• Finally approved

After:
• AI generates with brand memory
• 90% first-pass approval
• Designer focuses on strategy, not iteration

The insight: "On-brand" isn't subjective. It's computable.

We spent 6 months building the math behind brand consistency. Now it takes one click.

What's the most frustrating feedback loop in your creative process?

Example 2: Content Repurposing#

User Input:

"Turn this blog post into social content: [paste 1000-word article]"

Response Pattern:

1. Extract atomic units: hooks, stats, frameworks, quotes, stories, takeaways
2. Apply STEPPS framework to select the most shareable elements
3. Generate platform-native content for each platform

Example Output Structure:

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## Extracted Atomic Units:
- Hook: [most contrarian/surprising claim]
- Stat: [most specific number]
- Framework: [step-by-step model]
- Quote: [most memorable phrase]
- Takeaway: [core lesson]

## Derivatives:

**Twitter Thread (7 tweets):**
1/ [Hook tweet]
2/ [Supporting data]
[continues...]

**LinkedIn Post:**
[Full expanded version]

**小红书:**
标题：[search-optimized Chinese title]
[Full Chinese adaptation]

## Distribution Schedule:
- Day 1: Twitter thread (9 AM local)
- Day 2: LinkedIn (8 AM local)
- Day 3: 小红书 (8 PM local)

Example 3: Cross-Platform Adaptation#

User Input:

"This tweet performed well — adapt it for LinkedIn and 小红书."

Expected Output:

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## Original Tweet Analysis:
- Hook type: Contrarian ("Everyone thinks X, but Y")
- Key element: Specific number ($3,000 wasted)
- Engagement driver: Relatable failure story

## LinkedIn Version:
[Expanded with more context, spaced lines, professional framing, ends with question]

## 小红书 Version:
[Chinese adaptation with 口语化 tone, 情绪词, structured as 背景→经过→结果→教训]

## Adaptation Notes:
- LinkedIn: Added "Here's what I learned" framework
- 小红书: Localized dollar amounts to local currency context
- Both: Kept the core contrarian insight

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Author#

Canlah AI — Run performance marketing without breaking your brand.

• GitHub: github.com/PHY041
• All Skills: clawhub.ai/PHY041