A 3-step AI coding workflow for solo founders | Ryan Carson (5x founder)

One Sentence Summary:

Ryan Carson demonstrates structured AI-driven product development processes, emphasizing patience, context, and iterative task management.

Main Points:

1. Rushing context to AI hampers effective problem-solving and slows progress.
2. Slowing down and doing two key steps accelerates product building.
3. Hands-on experimentation is essential to mastering AI tools and workflows.
4. Using Cursor with structured rules improves AI coding and product management.
5. Creating clear, specific prompts and rules enhances AI output quality.
6. Breaking down PRDs into manageable tasks streamlines development and reduces loops.
7. Managing context with tools like repo prompt improves accuracy and control.
8. Automating web browsing and database queries with AI reduces toil and increases efficiency.
9. Combining AI tools with human oversight ensures better results and fewer errors.
10. Building with AI allows founders to handle more responsibilities but still requires discipline.

Takeaways:

1. Invest time in crafting detailed prompts and rules for AI to get reliable results.
2. Break complex projects into smaller, manageable tasks to avoid getting lost.
3. Use version control and commit early to manage AI-generated code safely.
4. Leverage tools like MCPs and repo prompts to control context and automate workflows.
5. Embrace patience and iteration as key to mastering AI-assisted product development.

1. Summary

This transcript explains a structured workflow for using Cursor as an AI-assisted development environment to build features more reliably than “vibe coding” alone. The core skill is turning a vague product idea into a reproducible implementation plan using PRDs, task lists, Cursor rules, and controlled context management. The main example is a small CRM tool for a yacht club, where the user wants to add a report showing boat names of members and how many emails they have been sent.

The primary technology is Cursor, a VS Code fork with AI agent capabilities. The workflow also references Claude 3.7 Sonnet, Gemini 2.5 Pro, OpenAI o3, Prisma, Postgres, SQLite, Vercel, Browserbase, Stagehand, MCP servers, Repo Prompt, and Taskmaster.

The main implementation lesson is: do not ask the AI to build large features from vague prompts. Instead, create structured context files, generate a Product Requirements Document, convert that PRD into a task list, then have the AI complete one subtask at a time while the human reviews progress.

2. Detailed Step-by-Step Breakdown

Step 1: Start with a Cursor project

Open the project in Cursor.

The transcript uses a small example project:

Project type: basic CRM-style application Example app: yacht club CRM Feature request: add a report showing:

• member boat names
• number of emails sent to each member

The speaker describes Cursor as a VS Code fork, so the workflow assumes familiarity with a standard code editor layout: file explorer, editor pane, terminal or chat pane, and project folders.

Assumption: Standard/Typical Setup

The transcript does not provide a full installation procedure. A typical setup would include:

• Cursor installed from cursor.com
• an existing codebase opened in Cursor
• project dependencies already installed
• a working local development environment
• Git initialized for change management
• a database layer such as Prisma, Postgres, or SQLite, depending on the project

Step 2: Create a dedicated rules folder

The speaker creates three files inside a Cursor rules folder.

The transcript does not give the exact folder path, but it refers to a “cursor rules folder.” In modern Cursor projects, this is commonly implemented using a folder such as:

Assumption: Standard/Typical Setup

.cursor/rules/

The three rule files described are:

generate-prd
generate-tasks
task-list

The transcript’s names are approximate because the speaker refers to them conversationally. The important part is their purpose:

1. PRD generation rule

   • Teaches the AI how to create a product requirements document.
   • Frames the PRD as suitable for a junior developer to understand and implement.

2. Task generation rule

   • Teaches the AI how to convert a PRD into a detailed implementation task list.

3. Task execution / task list management rule

   • Teaches the AI how to work through the generated task list one subtask at a time.

Step 3: Write the PRD-generation rule

The first rule instructs the AI to create a Product Requirements Document.

Implementation-critical instruction from the transcript:

“Create a PRD suitable for a junior developer to understand and implement this feature.”

This matters because the AI may otherwise over-assume knowledge or skip obvious implementation detail. By specifying junior developer, the prompt biases the output toward clarity, explicitness, and reproducibility.

The PRD-generation rule should instruct the AI to ask clarifying questions before writing the PRD.

The transcript specifically highlights using dot notation for questions:

1.1
1.2
2.1
2.2

This avoids messy multi-question bullets and makes it easier for the human to answer individual questions.

A practical version of the PRD rule would include instructions like:

You are an expert product manager.

Your goal is to help the user create a Product Requirements Document suitable for a junior developer to understand and implement.

Before writing the PRD, ask clarifying questions using numbered dot notation.

Use this question format:

1.1 Question text
1.2 Question text
2.1 Question text
2.2 Question text

Do not combine multiple questions into one bullet.

After the user answers, create a PRD in Markdown with sections for:
- Overview
- Problem Statement
- Goals
- Non-Goals
- User Stories
- Functional Requirements
- Design Considerations
- Technical Considerations
- Success Metrics
- Open Questions

Step 4: Invoke the PRD rule in Cursor Agent Mode

In Cursor, open the AI chat panel and use Agent Mode.

The speaker tags or includes the PRD rule file in the context window. In Cursor, this is typically done with an @ reference.

Example prompt pattern:

@generate-prd

I'd like to add a report that shows me all the boat names of members and how many emails they've been sent.

The transcript notes that the speaker was using:

• Claude 3.7 Sonnet in Max Mode
• but usually prefers Gemini 2.5 Pro
• the host mentions often defaulting to o3

No exact Cursor configuration values are provided beyond those model names.

Step 5: Answer the AI’s clarifying questions

The AI asks clarifying questions before writing the PRD.

Example questions from the transcript:

1. What problem is this report trying to solve?
2. Who specifically will be using the report?
3. Where should this report be accessible?

Example answers from the speaker:

We're trying to give visibility into how many emails people are getting.

Admins.

You pick.

For the remaining questions, the speaker says:

The rest, make your best judgment.

This is an important workflow pattern: answer the high-value questions, then delegate low-risk decisions to the AI.

Step 6: Generate the PRD Markdown file

The AI writes the PRD into a tasks folder.

The transcript refers to a task folder where the PRD is placed.

Assumption: Standard/Typical Setup

A likely structure would be:

tasks/
  prd-email-report.md

The PRD includes sections such as:

• Functional Requirements
• Non-Goals
• Design Considerations
• likely implementation notes and requirements

The purpose of this file is not just documentation. It becomes structured context for the next AI operation.

Step 7: Generate tasks from the PRD

Next, use the second rule file: generate tasks.

The prompt pattern is:

@generate-tasks

Please generate tasks for @prd-email-report.md

This gives the AI two important pieces of context:

1. the rule explaining how to create a task list
2. the PRD describing the feature

The task-generation rule should instruct the AI to create a step-by-step Markdown checklist.

A practical task format would look like:

# Tasks: Email Report

## Relevant Files

- `prisma/schema.prisma`
- `app/reports/page.tsx`
- `components/email-report-table.tsx`
- `lib/email-service.ts`
- `lib/db.ts`

## Tasks

- [ ] 1.0 Update database model/query support
  - [ ] 1.1 Review existing member, boat, and email-related schema
  - [ ] 1.2 Define or update Prisma query for email counts
  - [ ] 1.3 Confirm required joins or relationships

- [ ] 2.0 Add report backend logic
  - [ ] 2.1 Create report query function
  - [ ] 2.2 Return boat name and email count per member

- [ ] 3.0 Add UI
  - [ ] 3.1 Create report page
  - [ ] 3.2 Render report table
  - [ ] 3.3 Add empty and loading states

- [ ] 4.0 Test and validate
  - [ ] 4.1 Verify counts against database records
  - [ ] 4.2 Run lint/type checks
  - [ ] 4.3 Manually test as admin

The transcript specifically mentions that the generated task list includes:

• relevant files
• parent tasks
• subtasks
• sub-subtasks
• Markdown checkboxes

Step 8: Require the AI to pause before generating subtasks

The task-generation rule uses a checkpoint.

The AI first generates basic tasks, then asks whether to proceed.

The transcript says the AI outputs something like:

Ready to generate the subtasks. Respond with go to proceed.

The user then responds:

go

This is an important control mechanism. It prevents the AI from producing a large implementation plan before the human has validated the direction.

Step 9: Review the generated task file

After accepting the generated file, inspect the task list.

The transcript highlights a Relevant Files section at the top of the task list.

This section is used to keep the AI focused on the implementation surface area. It reminds the model which files matter most for the feature.

Example:

## Relevant Files

- `prisma/schema.prisma`
- `app/admin/reports/page.tsx`
- `lib/db.ts`
- `components/reports/email-report.tsx`

The speaker says this is useful even when files are manually tagged later, because it helps the LLM “remember” the main implementation files.

Step 10: Use the task-list management rule to execute work

The third rule controls how the AI works through the task list.

It tells the AI:

• work on one subtask at a time
• after completing a subtask, mark it complete
• stop after each subtask
• wait for the user’s approval before continuing

The key behavior is not letting Cursor attempt the full feature in one long autonomous run.

A practical task-list rule would include:

You are managing implementation from a Markdown task list.

Rules:
- Work on only one subtask at a time.
- Do not start the next subtask until the user confirms.
- After completing a subtask, immediately update the task list and mark the checkbox complete.
- Explain what changed.
- Mention any files modified.
- Stop and ask whether to continue.

Step 11: Start implementation from the task list

Prompt Cursor with the task-list rule and the generated task file:

@task-list

Let's start @tasks-email-report.md

The AI begins with the first subtask.

The transcript’s example first subtask is approximately:

Define Prisma schema email campaign

The AI reads the existing Prisma schema and begins implementation.

After completing the subtask, it marks the task complete in the Markdown file:

- [x] 1.1 Define Prisma schema email campaign

Then it asks whether to continue:

Shall I proceed with subtask 1.2?

The user can respond:

yes

or, as the speaker jokes:

y

Step 12: Keep a human in the loop

The transcript strongly emphasizes human review after each subtask.

The speaker notes that AI coding tools can introduce:

• small bugs
• lint errors
• incorrect assumptions
• unwanted implementation paths
• broken frontend behavior

The workflow is intentionally slower than a single huge prompt, but more reliable.

Recommended review after each subtask:

npm run lint

npm run typecheck

npm test

npm run dev

The transcript does not mention these exact commands, but they are typical validation steps for a modern JavaScript or TypeScript project.

Assumption: Standard/Typical Setup

Use the commands appropriate to the project’s package manager:

npm run lint
npm run test
npm run build

or:

pnpm lint
pnpm test
pnpm build

or:

yarn lint
yarn test
yarn build

Step 13: Use Git for change management

The host asks whether the workflow is connected to Git.

The speaker says he typically commits after finishing a parent task if the app is in a workable state.

Commit strategy from transcript:

• complete one or more subtasks
• verify the app is still usable
• commit after a parent task or logical unit
• avoid committing broken intermediate states

Example Git workflow:

git status

git diff

git add .

git commit -m "Add email report data model support"

For larger features, commit after each stable parent task:

git commit -m "Add admin email report page"

git commit -m "Add email count query for members"

git commit -m "Add report table UI"

Step 14: Optionally use Taskmaster as a heavier alternative

The transcript mentions Taskmaster, an open-source CLI tool.

Purpose:

• generate and manage task lists
• provide a more powerful version of the manual PRD/task workflow

The speaker says Taskmaster was “too much” for his preferred workflow. He wanted:

• less power
• more direct control
• simpler Markdown files

Implementation takeaway:

Use Taskmaster when you want a fuller CLI-based task-management system. Use simple Markdown files when you want transparent, editable, low-overhead control.

Step 15: Add MCP servers for tool access

The transcript then moves into MCP, or Model Context Protocol, servers.

The speaker uses MCP to give Cursor access to external tools and systems.

Mentioned MCP use cases:

1. Postgres MCP

   • query database contents from Cursor chat
   • ask whether values exist in rows
   • avoid manually writing SQL for quick checks

2. Browserbase MCP

   • control a headless browser in the cloud
   • navigate websites
   • take screenshots
   • potentially support frontend testing

3. Stagehand

   • referenced alongside Browserbase
   • used for browser automation-style workflows

4. Prisma

   • used in the demo project

5. SQLite

   • used in the play project

Step 16: Configure Browserbase MCP in Cursor

The speaker opens Cursor Settings, then goes to the MCP section.

There he has a Browserbase MCP configured.

The transcript does not provide the exact JSON config, API key names, or install command.

Assumption: Standard/Typical Setup

A typical MCP configuration in Cursor may involve adding a server definition that includes:

• server name
• command
• arguments
• environment variables
• API key

Example conceptual structure:

{
  "mcpServers": {
    "browserbase": {
      "command": "npx",
      "args": ["some-browserbase-mcp-package"],
      "env": {
        "BROWSERBASE_API_KEY": "your-api-key",
        "BROWSERBASE_PROJECT_ID": "your-project-id"
      }
    }
  }
}

This is a generic example only. The transcript does not specify the exact package name or config.

Step 17: Use Browserbase from Cursor chat

The speaker starts a new Cursor chat in Agent Mode and asks:

Navigate to ChatPRD and take a screen grab.

Cursor uses the Browserbase MCP to control a cloud browser.

Then the speaker asks:

Navigate to pricing.

The browser moves to the pricing page.

Implementation use case:

• automated frontend QA
• screenshot capture
• reproducing browser bugs
• checking navigation flows
• reducing manual browser tab switching
• allowing the AI to interact with the app through a browser-like interface

Step 18: Use Postgres MCP for database inspection

The speaker uses Postgres MCP most often.

Example natural-language prompt:

Use the Postgres tool and tell me if this value is in the database.

Practical use cases:

• confirm whether a row exists
• inspect a specific field
• verify test data
• debug whether frontend state matches backend data
• avoid manually switching to a SQL client
• reduce tab/context switching

The transcript says the startup uses Postgres on Vercel.

Step 19: Use Repo Prompt for precise context control

The speaker then demonstrates Repo Prompt, a Mac application.

Purpose:

• manually select files from a repo
• calculate token count
• package selected files into a prompt
• send controlled context to a model like o3 or o1 Pro
• reduce uncertainty about what context the AI actually sees

The speaker contrasts this with Cursor, where context management can feel like a black box unless files are explicitly tagged.

Step 20: Select files in Repo Prompt

The transcript describes selecting too much context first:

Whole repo: 395,000 tokens

Then reducing it:

324,000 tokens

After excluding generated content:

12,000 tokens

The speaker selects relevant directories/files such as:

app/
components/
lib/
prisma/schema.prisma
scripts/

The exact files are not fully enumerated, but Prisma schema, components, lib, and scripts are explicitly referenced.

Step 21: Compose a higher-level architecture prompt

The speaker enters a prompt such as:

How can I improve the maintainability of this code?

Then includes a stored prompt called something like:

architect

The architect prompt tells the model to reason more like a software architect than a developer.

Step 22: Copy the packaged prompt into a model

Repo Prompt copies:

• saved prompt
• selected files
• user instructions

It packages the context using XML-style tags.

The transcript describes sections like:

<user_instructions>
How can I improve the maintainability of this code?
</user_instructions>

<meta_prompt>
...
</meta_prompt>

<file_contents>
  <file path="app/...">
    ...
  </file>
</file_contents>

The speaker pastes this into o3 or previously o1 Pro for heavy reasoning.

Step 23: Use music as part of the coding environment

The speaker jokingly says an important part of the stack is listening to Tiësto’s live set from New York City while coding.

This is not an implementation requirement, but it reflects the working environment.

3. Key Technical Details

Main tools and technologies mentioned

Cursor Primary AI coding environment. Described as a VS Code fork with AI agent capabilities.

VS Code Referenced because Cursor is based on or forked from it.

Claude 3.7 Sonnet Used in Max Mode during the demo.

Gemini 2.5 Pro The speaker’s usual preferred model for Cursor work.

OpenAI o3 Mentioned by the host as her default model.

o1 Pro Previously used by the speaker for heavy reasoning with carefully selected context.

ChatGPT Used frequently by the speaker, including with his children and for AI coaching.

ChatPRD / Chat PRD An AI product-management copilot mentioned by the host. Used for PRDs, product docs, coordination work, and strategic product coaching.

PRD Product Requirements Document. Used as the first major planning artifact before coding.

Markdown Used for PRDs and task lists.

Cursor rules Custom project-level instruction files that define repeatable AI behavior.

Taskmaster Open-source CLI tool for task management and AI-assisted task breakdown.

MCP / Model Context Protocol Mechanism for connecting Cursor/AI tools to external capabilities such as browsers and databases.

Browserbase Cloud browser automation backend used through MCP.

Stagehand Referenced as another browser automation-related MCP/tool.

Postgres Database used by the speaker in his startup.

Vercel Platform where the speaker uses Postgres for the startup.

Prisma Database ORM/schema tool used in the play project.

SQLite Used in the play project.

Repo Prompt Mac app for selecting repository files and packaging precise context for LLM prompts.

Git Used for change management and commits after stable implementation units.

Asana MCP Mentioned as something the speaker considered but decided was more overhead than simple Markdown task files.

Notion AI Mentioned in sponsorship context, especially AI meeting notes, enterprise search, and research mode.

Linear, Confluence, Google Drive, Slack, v0.dev, Dev Mentioned as integrations for ChatPRD.

Files and folders referenced or implied

The transcript explicitly refers to:

Cursor rules folder
tasks folder
PRD markdown file
task list markdown file
Prisma schema
app folder
components folder
lib folder
scripts folder
generated folder

Common practical approximation:

.cursor/
  rules/
    generate-prd.md
    generate-tasks.md
    task-list.md

tasks/
  prd-email-report.md
  tasks-email-report.md

prisma/
  schema.prisma

app/
components/
lib/
scripts/

Prompting patterns shown

PRD generation:

@generate-prd

I'd like to add a report that shows me all the boat names of members and how many emails they've been sent.

Delegating unanswered details:

The rest, make your best judgment.

Task generation:

@generate-tasks

Please generate tasks for @prd-email-report.md

Proceeding after task outline:

go

Starting implementation:

@task-list

Let's start @tasks-email-report.md

Continuing one subtask:

yes

or:

y

Browserbase MCP example:

Navigate to ChatPRD and take a screen grab.

Navigate to pricing.

Postgres MCP example:

Use the Postgres tool and tell me if this value is in the database.

Repo Prompt architecture prompt:

How can I improve the maintainability of this code?

Important model behavior observations

The transcript characterizes LLMs as very capable but prone to missing simple obvious context.

The practical solution is to:

• provide explicit rules
• narrow scope
• define expected output format
• give file context
• require checkpoints
• keep the human in the loop
• avoid asking the AI to do too much at once

4. Pro Tips

Use “junior developer” as a clarity constraint

Tell the AI the PRD should be suitable for a junior developer. This forces the model to avoid hand-wavy assumptions and produce clearer implementation detail.

Example:

Create a PRD suitable for a junior developer to understand and implement.

Force dot notation for questions

Require question numbering like:

1.1
1.2
2.1
2.2

This makes it easier to answer questions selectively and prevents the AI from bundling multiple questions into a single bullet.

Let the AI decide low-risk product details

When the AI asks too many questions, answer the important ones and say:

For the rest, make your best judgment.

This prevents planning from becoming a bottleneck.

Use Markdown task lists instead of overbuilding workflow automation

The speaker considered using tools like Asana MCP, but decided that plain Markdown was easier.

Benefits of Markdown task files:

• visible
• editable
• easy for the AI to update
• easy to review in Git diffs
• does not require extra project management infrastructure
• keeps implementation close to the codebase

Add a “Relevant Files” section to task lists

This helps the AI focus.

Example:

## Relevant Files

- `prisma/schema.prisma`
- `app/admin/reports/page.tsx`
- `lib/db.ts`
- `components/email-report-table.tsx`

Make the AI stop after every subtask

This is one of the most important implementation controls.

Required behavior:

Complete one subtask.
Mark it done.
Stop.
Ask for permission to continue.

This reduces runaway code generation and makes debugging easier.

Commit only when the app is in a stable state

The speaker commits after parent tasks or when the app is workable.

A practical rule:

Do not commit broken intermediate states unless intentionally saving a debugging branch.

Use MCP to reduce tab switching

Database lookups, browser navigation, and screenshots can be performed directly from Cursor when MCP servers are configured.

This reduces engineering toil because the developer does not have to constantly switch between:

• code editor
• browser
• database console
• SQL client
• project management tool
• terminal

Use Repo Prompt when context must be exact

Cursor’s context management can be convenient but opaque. For high-stakes reasoning, use Repo Prompt to explicitly include only the files that matter.

Best use cases:

• architecture review
• maintainability analysis
• large refactors
• debugging cross-file issues
• preparing context for o3 or o1 Pro

Watch token count aggressively

The transcript shows why this matters:

395,000 tokens
324,000 tokens
12,000 tokens

The goal is not to include the entire repo. The goal is to include the smallest sufficient set of files.

5. Potential Limitations/Warnings

Do not rush context creation

The biggest mistake identified in the transcript is rushing through context.

Bad pattern:

Build this feature.

Better pattern:

Create a PRD.
Ask clarifying questions.
Generate tasks.
Implement one subtask at a time.

Large autonomous AI coding runs can go off track

Letting the AI implement an entire feature in one pass can lead to:

• wrong architecture
• excessive code changes
• broken assumptions
• hard-to-review diffs
• hidden bugs
• lint/type errors
• difficult reverts

Cursor context can be opaque

Unless files are explicitly tagged, the user may not know exactly what Cursor has included in context.

For precise work, use:

• explicit @ file references
• Relevant Files sections
• Repo Prompt
• smaller scoped prompts

MCP setup details are not provided in the transcript

The transcript demonstrates Browserbase MCP, Postgres MCP, Stagehand, Prisma, and SQLite, but it does not provide exact installation commands, API keys, environment variables, or Cursor config JSON.

Any setup beyond the conceptual workflow requires consulting the official documentation for those tools.

Model choice affects results

The speaker prefers Gemini 2.5 Pro, demonstrates Claude 3.7 Sonnet Max Mode, and the host prefers o3. The transcript implies that different models have different strengths, and developers should learn the behavior of the model they use most.

Max/reasoning modes can be expensive

The speaker estimates spending roughly several hundred dollars per month on premium AI coding usage. This is not a required cost, but advanced models and Max Mode can increase usage costs.

Human review remains essential

The transcript explicitly says the human-in-the-loop part is important. Even with a task list, the AI may introduce small problems.

Recommended checks:

git diff
npm run lint
npm run typecheck
npm test
npm run build

Use equivalents for your stack.

The demonstrated app is a toy project

The yacht club CRM is used as a demo. The workflow generalizes, but production systems need stricter review, testing, security validation, and deployment controls.

6. Recommended Follow-Up Resources

Cursor documentation

Use the official Cursor docs to learn:

• Agent Mode
• rules
• model selection
• Max Mode
• file tagging
• context handling
• MCP configuration

Model Context Protocol documentation

Study MCP to understand how AI coding environments connect to tools such as:

• databases
• browsers
• file systems
• APIs
• test runners
• project management systems

Browserbase and Stagehand documentation

Useful for:

• cloud browser automation
• screenshot-based testing
• frontend QA
• automated navigation
• AI-controlled browser sessions

Prisma documentation

Relevant for implementing schema changes, migrations, and database queries in the kind of app shown in the transcript.

Focus areas:

• schema.prisma
• relations
• migrations
• query optimization
• generated client usage

Postgres documentation

Relevant for the database-inspection workflow described through MCP.

Focus areas:

• querying rows
• indexes
• joins
• aggregation
• connection security
• hosted Postgres on platforms like Vercel

Repo Prompt documentation

Useful for learning:

• file selection
• token budgeting
• prompt packaging
• XML-style context formatting
• architecture-review workflows

Git workflow references

The transcript relies on Git as a safety mechanism.

Focus areas:

• small commits
• reviewing diffs
• reverting changes
• feature branches
• commit boundaries around stable parent tasks

7. Suggested Books (5)

1. Designing Data-Intensive Applications — Martin Kleppmann This book is highly relevant to the transcript’s database-heavy workflow involving Postgres, Prisma, reporting, and data visibility. It helps developers reason about schemas, queries, consistency, data modeling, and system reliability when AI-generated code starts touching real application data.

2. Refactoring — Martin Fowler The transcript repeatedly emphasizes maintainability, stepwise implementation, and avoiding uncontrolled AI-generated changes. This book provides practical techniques for safely improving existing codebases, reviewing diffs, restructuring logic, and keeping AI-assisted development from turning into messy, fragile code.

3. Clean Architecture — Robert C. Martin This is useful for the Repo Prompt architecture-review workflow shown in the transcript. It helps developers evaluate whether generated code has appropriate boundaries between UI, business logic, database access, and external integrations, especially when building features through iterative AI tasks.

4. The Pragmatic Programmer — David Thomas and Andrew Hunt This book supports the transcript’s core engineering mindset: use tools thoughtfully, control complexity, automate repetitive work, and maintain human judgment. It is especially relevant to combining Cursor, Git, MCP, Markdown task files, and manual review into a disciplined development workflow.

5. Software Engineering at Google — Titus Winters, Tom Manshreck, and Hyrum Wright The transcript focuses on scaling from quick AI-assisted coding toward reliable engineering practice. This book deepens understanding of code review, maintainability, testing, ownership, and long-term code health, which are essential when using AI agents to generate large volumes of production code.