159 people showed how they’re using Claude Code for non-coding tasks.

Welcome to ship faster with ai - practical tips and tools to speed up your work.

Hey! I’m Alena, former AI startup CEO ($2M raised), Yandex and Acronis Sr. PM with 10k LinkedIn followers, 57% of my followers are senior leaders from big tech, good company ;)

Following up Lenny’s LinkedIn poll on how people use Claude Code for non-coding tasks. I think it was brilliant conversation and we can learn so much from that thread. Check the original post here.

Teresa Torres (bestselling author) wrote this:

I now write all of my content with Claude Code in VS Code. We iterate on an outline, it helps me improve the hook, it conducts research for me and adds citations to my outline, and it reviews and gives feedback on each section as I write.

Teresa doesn’t code. She writes books. Using a “coding tool” for everything.

So I scraped 159 comments from professionals using Claude Code. Categorized every single one. Let’s take a look which story data tells us:

What’s Claude Code and how to get started

Claude Code is Anthropic’s AI coding assistant that lives in your terminal or VS Code. But here’s the twist - it’s not just for coding. It reads, writes, and organizes any files on your computer. Think of it as having a smart assistant who can see your entire file system and help you work with it.

Getting started takes 3 minutes:

1. Open your terminal and install Claude.

2. Open it anywhere

• In your regular terminal: Just type claude

• In Cursor: Open the terminal tab (inside Cursor) and type claude

• In VS Code: Same thing - open terminal, type claude

3. Log in (first time only)

4. Talk to it

Just describe what you want:

• “what does this code do?”

• “fix this bug”

• “add a login button”

Done! Claude edits your files right there.

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Everyone is talking about context engineering, I believe it’s the easiest way to start use it as your tool.

Who’s using it

🥇 Product Managers: 38 people (24%)

🥈 Founders/Co-founders: 30 people (19%)

🥉 Engineers for non-coding tasks: 24 people (15%)

What they’re building

1. Content Creation, 31 mentions

2. Knowledge Management (aka Second Brain), 28

3. Product Management, 24

4. File Organization, 18

5. Research & Analysis, 17

6. Data Processing, 15

7. Personal Productivity, 14

8. Business Operations, 12

9. Workflow Automation, 11

10. Documentation, 10

Let me know your thoughts below and I’ll feature best comments in the next issue on this topic.

Now let’s dive into exactly how people use each category - with real examples you can implement today.

5 most popular use cases

01 Content Creation & Marketing (31 Users, Highest Engagement)

The Problem

Writing takes forever. Research is scattered. Quality is inconsistent.

Teresa Torres isn’t alone. 38 people shared similar workflows.

12 agents working sequentially from newsletter research to publish-ready article with gated PDF/Notion template...Repurposing all content (published/research) for Twitter, Substack notes, Medium, Reddit through specific agents

Ayush Poddar

Framework based on comments.

Stage 1: Capture → Everything becomes markdown

Stage 2: Process → Specialized agents extract value

Stage 3: Create → Multi-iteration refinement

Stage 4: Multiply → One source → many formats

Stage 5: Compound → Everything feeds back → Next content uses accumulated knowledge

Edgar’s Quality Gate System

• Draft from docs → Rate 1-10 → If <9, loop back with fixes

• Bans overused AI words (”seamless”, “leverage”)

• Publishes directly to Notion with proper formatting

File structure example:

content-system/
├── 00-inbox/              # Raw captures
├── 01-research/           # Sources, transcripts, notes
├── 02-ideas/              # Brainstorms, outlines
├── 03-drafts/             # Work in progress
├── 04-published/          # Final pieces
├── 05-templates/          # Proven frameworks
├── agents/                # Agent instructions
└── analytics/             # Performance data

Your agent team

Core Agents

1. Librarian Agent: Organizes files, creates cross-links

2. Research Agent: Web search, competitor analysis, pattern extraction

3. Writer Agent: Knows your voice, past content, frameworks

4. Editor Agent: Enforces quality rules, scores output

Specialized Agents (task-specific)

• Hook Optimizer: Tests 5-10 opening variations

• SEO Agent: Keyword research, optimization

• Visual Agent: Quote cards, diagrams, thumbnails

• Distribution Agent: Formats and posts to platforms

• Analytics Agent: Tracks performance, identifies patterns

Results

• 4-6 hours → 45 minutes per piece

• Teresa Torres: Iterates on outlines, gets research with citations, receives real-time feedback on each section

• Ayush Poddar: 12-agent pipeline from research to multi-platform distribution

• Edgar: Quality gates ensure consistent 9/10+ output before publishing

But the most valuable source from the discussion is a GitHub repo containing a fully functional LinkedIn content creation machine that you can copy:

https://github.com/mslavov/linkedin

In the next sections, I’ll break down the remaining 4 popular use cases. There’s a special offer – a 60% discount and the LinkedIn Optimization Playbook for free. The methodology has already helped 3,000 professionals increase recruiter visibility up to 8x and land their dream jobs, from new grads to CEOs.

Feel free to unsubscribe anytime, no hard feelings!

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02 Your Second Brain: Knowledge Management (28 Users)

The Problem

Information scattered across tools. Can’t find what you need when you need it. No connections between related ideas.

“I built my own ‘Knowledge Management System’ inside of it. The biggest unlock is creating CLAUDE.md and slash commands that allow your knowledge to be ‘distilled and compounded’ back into the system itself.”

Matt Stockton, Delivering Practical Data (5 likes, 6 replies)

Framework based on comments.

Stage 1: Structure → Everything has a place

Most popular storage solutions: Local markdown files (fastest, most control), Notion (team collaboration), Obsidian (advanced linking), or hybrid approach (markdown locally, sync to Notion for sharing).

Stage 2: Capture→ Friction-free input

• Voice → Wispr Flow → Markdown

• Meetings → Granola → Auto-filed

• Ideas → Quick capture → Daily note

• Research → Web clips → Topic folders

• Conversations → Transcripts → Searchable

Stage 3: Connect → Automatic linking

• Automatic backlinking

• Theme clustering

• People → Projects mapping

• Timeline connections

• Contradiction detection

Stage 4: Synthesize → Insights emerge

• Reviews all week’s notes

• Identifies emerging themes

• Surfaces forgotten insights

• Suggests connections

• Creates summary documents

• Gap analysis (intended vs actual)

Stage 5: Evolve → System improves itself

• Workflow optimization suggestions

• Template extraction from successes

• Automatic reorganization

File structure example:

second-brain/
├── areas/              # Life areas
│   ├── work/          # Projects, meetings
│   ├── learning/      # Courses, books
│   └── personal/      # Ideas, goals
├── resources/         # Reference materials
│   ├── templates/     # Reusable formats
│   ├── people/        # Contact notes
│   └── tools/         # How-tos, configs
├── archives/          # Completed/old
├── daily/             # Journal entries
└── agents/
    ├── librarian.md   # Organizes, tags
    ├── connector.md   # Finds links
    └── coach.md       # Challenges thinking

Your agent team

Core Agents:

1. Librarian Agent: Organizes, tags, maintains structure

2. Connector Agent: Finds relationships, creates links

3. Synthesis Agent: Weekly reviews, pattern finding

4. Coach Agent: Challenges assumptions, prompts reflection

Specialized Agents:

• Research Agent: Gathers context, finds sources

• Template Agent: Extracts patterns, creates reusables

• Archive Agent: Moves old content, maintains relevance

• Daily Agent: Creates journal entries, tracks habits

Results

• Matt Stockton: Knowledge “distilled and compounded”

• Fernanda: Entire company OS in markdown

• Jason Cyr: “2,000+ notes organized in 10 minutes”

• Tyler: Complete life tracking system

• Gang Rui: Self-improving feedback loop

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03 Product Management Workflows (18 Users)

The Problem

PRDs get outdated instantly. User feedback lives in 20 different tools. Roadmap decisions lack data backing.

“Markdown files are my all-encompassing data store. PRD, PRFAQ, requirements, QA testing, user feedback, roadmap decisions. Everything lives in markdown. Then I create subagents in Claude Code specialized for different PM functions. PRD Agent knows the full requirements doc. User Research Agent has all customer interviews and feedback. QA Agent owns testing strategy. All in seconds, not days of digging through docs!”

Gavin McNamara, Founder @ Why Not Us Labs (43 likes)

Framework based on comments.

Stage 1: Capture → Every input becomes structured data

• All running initiatives and core features documented

• Team info including dev strengths, knowledge matrices

• Daily Granola meeting notes auto-sorted and tagged

• Cross-functional todos tracked and chased

Daily update workflow:

• Morning: Pull calendar, review priorities

• During meetings: Granola captures everything

• Post-meeting: Auto-sort into feature folders

• Evening: 3-round consistency check

Stage 2: Synthesize → Patterns emerge from chaos

• Read all week’s meeting notes

• Extract feature requests and pain points

• Identify contradictions in requirements

• Surface hidden dependencies

• Generate insight reports

Stage 3: Decide → Data-driven prioritization

Derek DeHart’s Evidence System:

• Evidence scoring from customer calls

• Cross-reference with analytics (via MCP)

• Weight by strategic alignment

• Output: Prioritized backlog with reasoning

Stage 4: Document → Living specs that update themselves

• Latest customer quotes

• Current implementation status

• Test results and edge cases

• Acceptance criteria validation

Stage 5: Communicate → Stakeholder updates on autopilot

File structure example:

product-brain/
├── initiatives/          # Active projects
│   ├── feature-x/       # PRDs, specs, research
│   └── experiment-y/    # Hypotheses, results
├── meetings/            # Granola captures
│   ├── 2024-01/        # By date
│   └── _processed/     # Sorted by feature
├── evidence/           # Customer insights
│   ├── calls/          # Transcripts
│   ├── feedback/       # Support tickets
│   └── analytics/      # Data pulls
├── team/               # People context
│   ├── strengths.md    # Who knows what
│   └── capacity.md     # Current load
└── agents/
    ├── prd-agent.md    # Spec maintenance
    ├── research.md     # Pattern finding
    └── standup.md      # Daily updates

Your agent team

Core Agents:

1. PRD Agent: Maintains living requirements docs, ensures consistency

2. Research Agent: Analyzes customer feedback, identifies patterns

3. QA Agent: Owns testing strategy, validates acceptance criteria

4. Standup Agent: Generates daily updates, tracks cross-functional todos

Specialized Agents:

• Evidence Scorer: Prioritizes features based on customer data

• Dependency Mapper: Flags hidden dependencies across initiatives

• Consistency Checker: Runs 3-round validation on requirements

• Stakeholder Agent: Generates tailored updates for different audiences

Results

• Gavin: “All in seconds, not days of digging”

• Inna: 3-round consistency check catches contradictions

• JD: Kanban dashboard with dozen integrations

• Megan: PRDs write themselves from conversations

• Manav: “It pushes back on vague criteria”

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04 File Organization & Data Management (22 Users)

The Problem

Digital hoarding. Downloads folder chaos. Can’t find anything. Duplicates everywhere.

“You can give Claude Code access to a local folder and it will iteratively automate the sorting of the files in that folder. Not just by date or name... but by the content of the files.”

Justin Parnell (9 likes)

Framework based on comments.

Stage 1: Audit → Know what you have

Initial Scan:

• File type distribution

• Size analysis

• Duplicate detection

• Naming pattern identification

• Date range mapping

Stage 2: Categorize → Smart filing rules

• Screenshot_2024* → Projects/Screenshots/[Date]/

• Invoice_* → Financial/[Year]/[Vendor]/

• Download(*).pdf → _Inbox/ToReview/

Stage 3: Clean → Intelligent deduplication

• Keep newest in primary location

• Archive older versions with timestamps

• Delete true duplicates

• Log everything for recovery

Stage 4: Organize → Everything finds its home

• Active vs Archive separation

• Date-based hierarchies

• Project-based clustering

• Content-aware placement

Stage 5: Maintain → Ongoing sanity

• Process Downloads folder

• Archive completed projects

• Update file index

• Clean desktop

• Compress old logs

File structure example:

life-organized/
├── Active/              # Current work
│   ├── Projects/       # In progress
│   └── Daily/          # Today’s files
├── Archive/            # Completed
│   ├── 2024/          # By year
│   └── _Old/          # Legacy items
├── Admin/              # Life stuff
│   ├── Financial/      # Invoices, receipts
│   ├── Contracts/      # Agreements
│   └── Taxes/         # By year
├── Resources/          # References
└── _Inbox/            # To be processed

Your agent team

Core Agents:

1. Auditor Agent: Scans folders, identifies patterns, flags duplicates

2. Organizer Agent: Applies filing rules, creates folder structure

3. Cleaner Agent: Handles deduplication, archiving, compression

4. Maintenance Agent: Daily processing of inbox, desktop cleanup

Specialized Agents:

• Financial Agent: Sorts invoices, receipts by vendor and date

• Screenshot Agent: Organizes by project and timestamp

• Archive Agent: Moves completed projects, maintains structure

• Index Agent: Maintains searchable file catalog

Results

• Martin: Tax invoices organized perfectly

• Justin: “Cognitive load” eliminated

• Preet: Gmail receipts → organized expenses

• Taimoor: Large CSVs split, PDFs merged

• Everyone: “Game changer for reducing mental clutter”

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05 Research & Analysis Deep Dives (17 Users)

The Problem

Information overload. Can’t connect dots across sources. Insights buried in noise.

“I had it pull in interview transcripts, survey responses, and web research, then draft a PRD, ICP, product roadmap, and feature prioritization, all backed by actual evidence. It connects dots across sources, flags contradictions, and builds coherent artifacts you can actually use.”

Michael Ulin, 3x AI Founder (2 likes)

Framework based on comments.

Stage 1: Collect → Gather all sources

• Customer call transcripts (Fireflies)

• Support tickets (Linear)

• Product docs (Notion)

• Survey responses

• Competitor analysis

Stage 2: Extract → Surface key information

• Pattern recognition across transcripts

• Quote mining with attribution

• Statistical extraction

• Contradiction flagging

Stage 3: Synthesize → Connect the dots

• Theme clusters

• Evidence chains

• Gap analysis

• Insight documents

• Research questions

Stage 4: Analyze → Deep pattern recognition

• Cross-source validation

• Pattern identification

• Outlier detection

• Trend analysis

• Hypothesis testing

Stage 5: Deliver → Actionable outputs

• Executive summaries

• Product strategies

• Research briefs

• Competitive reports

• Feature prioritization

File structure example:

research-ops/
├── sources/            # Raw inputs
│   ├── interviews/     # Transcripts
│   ├── surveys/        # Responses
│   ├── competitors/    # Intel gathering
│   └── literature/     # Papers, articles
├── analysis/          # Processing
│   ├── themes/        # Clustered insights
│   ├── patterns/      # Cross-source
│   ├── evidence/      # Supporting data
│   └── contradictions/# Conflicts
├── outputs/           # Deliverables
│   ├── strategies/    # Product docs
│   ├── briefs/        # Research summaries
│   └── reports/       # Deep dives
└── agents/
    ├── collector.md   # Source gathering
    ├── extractor.md   # Information mining
    └── synthesizer.md # Pattern finding

Your agent team

Core Agents:

1. Collector Agent: Gathers sources from multiple platforms

2. Extractor Agent: Mines insights, quotes, and data points

3. Synthesizer Agent: Connects themes across sources

4. Analyst Agent: Validates patterns, tests hypotheses

Specialized Agents:

• Contradiction Detector: Flags conflicting information

• Evidence Chain Builder: Links insights to supporting data

• Competitive Intelligence Agent: Tracks competitor moves

• Hypothesis Tester: Validates assumptions against data

Results

• Michael Ulin: “Catches patterns I would’ve missed”

• Derek DeHart: “Hub for ongoing product research”

• Sumant: Automated competitive intelligence

• Gang Rui: “Information compounds into competitive advantages”

• 80% faster than manual analysis

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Best practices across the board

1. Everything becomes markdown - The universal format

2. Agents specialize by function - Not by tool

3. Knowledge compounds - Today’s output feeds tomorrow’s context

4. Automation handles repetition - Humans handle creativity

5. Local-first wins - Your data, your control

Quick Start Guide

Pick your use case and start small:

Drowning in content creation? Start with a simple content-system folder. Create one agent that helps you outline. Add research capabilities next week. Build your content engine one piece at a time.

Drowning in meetings? Start with Product Management workflow. Let Granola capture one meeting. Have Claude sort the notes. Add consistency checking when you’re ready.

Can’t find anything? Start with File Organization. Pick one messy folder (Downloads, Screenshots, Desktop). Let Claude audit it first. Watch it organize by content, not just filename.

Research takes forever? Start with the Analysis system. Dump your last 5 customer calls in a folder. Ask Claude to find patterns. Be amazed at what you missed.

The key: Start with today’s urgent problem, create your first simple building block. Let it prove value. Then expand systematically. Your system grows with you.

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Comment with your experience or your thoughts if you want to be featured in the next analysis - your insights make everyone better.

Before you go…

Work with me:

- 📅 Schedule intro call for 0→1 AI Product Management advising

- 📊 Schedule intro call for AI adoption audit sprint

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- Reply and tell me what you’re struggling with - I read everything

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With gratitude for your curiosity,

Alena