60 Cents of Kimi K2 AI to Code One Level of Video Game

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The new open source model from China, Kimi K2, when used with Claude Code can code one level of a Nintendo Mario like video game and create a rich text editor using less than 60 cents worth of compute.

Integrating into Claude Code lets you use the Kimi K2 coding capability and its agentic capability.

Youtuber AI Jason, Jason Zhou, a product designer who shares interesting AI experiments and product shared his experiments with Kimi K2.

David Ondrej Gives More Advanced AI Usage Training Using Claude and Grok

David Ondrej gives five practical workflows for using Claude to build projects, even for beginners. He gives workflows that help save time and boosts productivity for him and a team of developers.

Demonstrates these in tools like Cursor IDE, emphasizing efficiency, collaboration, and scalability for AI startups.

1. Multi-Agent Setup
Description: Set up multiple Claude agents (e.g., three instances) to collaborate on a single project, like creating an interactive 3D mountain terrain game using Three.js.

How it Works:Create MD files: coms.md (for action logs and inter-agent communication) and cloth.md (system prompts for all agents).

Use Grok 4 or Claude to generate initial setups and rules.

Launch agents in separate terminals within Cursor, assign roles (e.g., Agent 1, 2, 3), switch to auto-accept mode, and use Opus model.

Agents read MD files, self-identify, execute tasks sequentially, and log progress in coms.md without stopping until instructed.

Tips: Update Claude globally daily; watch his prior video on multi-agent systems for troubleshooting.

Benefits: Enables parallel work; agents communicate and hand off tasks dynamically.

2. Reviewing Pull Requests (PRs) Description: Use Claude to systematically review GitHub PRs, supplementing human oversight.

A pull request (often abbreviated as PR) is a key feature in version control systems like Git, commonly used on platforms such as GitHub, GitLab, or Bitbucket. It serves as a formal proposal for developers to suggest merging code changes from one branch (typically a feature or bug-fix branch) into another branch (usually the main or master branch of a repository).

Essentially, when a developer completes work on a new feature, fix, or improvement in their own branch, they create a PR to notify the team that the changes are ready for review, discussion, and potential integration into the main codebase.

How it Works (in stages, via an optimized 90+ line prompt):

Stage 1: Run Git commands (e.g., gh pr view) to understand PR (Pull Request) purpose.
Stage 2: Switch to the PR branch.
Stage 3: Manually test changes (e.g., start servers, verify features like character limits).
Stage 4: Paste changed file paths; Claude explains diffs, checks for SOLID principles, and spots bugs.

Tips: Work on feature branches for safety; combine AI’s pattern recognition with human logic for faster, thorough reviews.

Benefits: Saves hours weekly; ideal for teams; handles complex PRs efficiently.

3. Claudia UI for Claude

Description: An open-source alternative to terminal-based Claude usage, providing a simple UI for managing multiple agents.

Claude Code – https://www.anthropic.com/claude-code
Claude Code UI – https://github.com/getAsterisk/claudia

How it Works: Install via GitHub repo (requires Rust); run agents in parallel without IDEs or terminals.

Benefits: User-friendly for non-terminal users; gained 8,000 GitHub stars in three weeks. Note: Anthropic may release an official UI soon.

4. Using MCPs (Model Control Planes?)
Description: Integrate Claude with API-based tools like Vectara for task management.

How it Works:Use /mcp in Claude to manage servers.

Generate API keys in tools like Vectara (Pro/Ultra tiers), install via npm install -g remote, and configure JSON schema.

Delegate tasks from Vectara lists (e.g., MVP development) to Claude.

Tips: Stick to official MCPs (e.g., Notion, Cursor, Linear, GitHub); request a full video in comments.

Benefits: Replaces outdated tools like Todoist; automates task execution.

5. Refactoring Large Files

Description: Break down messy, long files (e.g., 500–1,500 lines) into modular ones without introducing bugs.

How it Works (three-phase “surgical” approach via optimized prompt):
Phase 1: Safety Net: Create tests for 80–100% coverage; benchmark performance on a separate branch.
Phase 2: Surgical Planning: Identify low-risk complexity hotspots (e.g., isolated UI code) for extraction; prioritize 50–150 line blocks.
Phase 3: Incremental Execution: Extract code in small steps, test after each, and stage changes.

Tips: Avoid one-shot refactors; treat it like surgery to preserve functionality. Shorter files improve AI and human efficiency.
Benefits: Essential for scaling startups; maintains readable, scalable codebases beyond “vibe coding.”

Background Pull Request Flow
How Pull Requests Work
Branching: Developers work on changes in a separate branch to avoid disrupting the main code.
Creating the PR: Once ready, they push the branch to the remote repository and open a PR, describing the changes, purpose, and any relevant details (e.g., screenshots or test results).
Review and Collaboration: Team members review the code, leave comments, suggest improvements, or request modifications. This fosters code quality and knowledge sharing.
Merging: After approval (often requiring passing tests or CI/CD checks), the PR is merged, incorporating the changes. If not approved, it can be closed or updated.
Tools Integration: PRs often integrate with automated testing, linting, and deployment pipelines.

Another Pro Guide to Claude Code

Another Kimi K2 Review