GLM-4.7 + KingMode + Frontend Skill: This SIMPLE TRICK makes GLM-4.7 - A BEAST!

GLM 4.7 Enhanced Coding Workflow: Leveraging Combined Prompts for Production-Ready Results

Key Concepts:

• GLM 4.7: An open-weight language model demonstrating strong performance, particularly in visual tasks, and offering a cost-effective alternative to proprietary models.
• System Prompt/Project Rules: Instructions provided to the model defining its role, behavior, and desired output style.
• King Mode Prompt (Gemini Ultrathink Logic): A prompt designed to encourage deep reasoning, eliminate fluff, and prioritize performance over speed in model responses. Includes the “ultrathink” trigger.
• Clawed Front-End Design Skill: A set of rules defining a specific aesthetic (brutalism, minimalism, editorial typography) for UI design, preventing generic or undesirable visual outputs.
• Tailwind CSS: A utility-first CSS framework used for rapid UI development.
• Framer Motion: A production-ready motion library for React.
• Superbase: An open-source alternative to Firebase.
• Context Window: The amount of text a model can process at once. GLM 4.7 has a large context window.

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1. Introduction & GLM 4.7’s Potential

The video focuses on maximizing the capabilities of GLM 4.7, an open-weight language model previously identified as a top performer, surpassing Sonnet 4.5 in some benchmarks and excelling in visual tasks. While initially showing promise, previous testing revealed limitations in complex application development, specifically syntax errors and logical disconnects in a Spelt Conbon app. The core argument presented is that these limitations aren’t inherent to the model itself, but rather stem from insufficient instruction through system prompts.

2. The Hybrid Prompt Workflow: Combining King Mode & Front-End Skill

The presenter details a new workflow involving layering two distinct prompts within GLM 4.7’s system prompt area:

• Gemini King Mode Prompt: This prompt, originally developed for the Gemini model, is designed to eliminate “laziness” in the model’s responses. It achieves this by:
  • Forcing concise, zero-fluff output.
  • Prohibiting generic, unhelpful phrases like “I hope this helps.”
  • Implementing the “ultrathink” trigger, compelling the model to analyze requests through both psychological and technical lenses, prioritizing performance.
• Clawed Front-End Design Skill: This prompt, a markdown file, enforces a strict aesthetic standard, rejecting “AI slop” (generic designs) and demanding intentional minimalism, editorial typography, and a brutalist aesthetic.

The combination aims to imbue GLM 4.7 with the reasoning capabilities of a senior backend engineer (King Mode) and the design sensibilities of a high-end designer (Front-End Skill).

3. Demonstrating the Workflow: Movie Tracker Benchmark

The effectiveness of this hybrid approach is demonstrated using a movie tracker benchmark. When prompted to “build a scalable movie tracker with a Superbase backend, using the front-end design skill for a brutalist aesthetic,” GLM 4.7’s output differed significantly from previous tests.

• Reasoning Block: Instead of immediately generating code, the model first output a reasoning block, analyzing data relationships and discussing the need for a join table to optimize query performance. This demonstrates the “ultrathink” trigger in action.
• Logical Coherence: The model exhibited improved logic, utilizing memorization and setting up proper error boundaries – addressing the previous issues observed in the Spelt Conbon app.
• Brutalist UI: The generated UI adhered to the brutalist aesthetic, featuring a raw, high-contrast list view with monospaced typography and thick black borders. It avoided generic design elements.
• Tailwind CSS & Customization: The model utilized Tailwind CSS effectively, leveraging the configuration file to create custom spacing variables, demonstrating a level of sophistication typically seen in agency-quality code.
• Framer Motion Implementation: The model flawlessly implemented orchestrated entry animations using Framer Motion, showcasing its strong grasp of code structure.

4. Backend Task & King Mode Alone

The presenter also tested the workflow on a backend-focused task: a Python script to analyze large CSV files. Using only the King Mode prompt, GLM 4.7 demonstrated nuanced understanding, discussing the trade-offs between using Pandas dataframes and streaming lines, and proposing a chunking solution to prevent memory errors.

5. Addressing Open Model Limitations

The video identifies two primary challenges with open-weight models and how this workflow addresses them:

• Architectural Weakness: Open models can struggle with complex application architecture. The King Mode prompt mitigates this by forcing deep reasoning.
• Lack of Aesthetic Sense: Open models often produce visually unappealing or generic designs. The Front-End Skill provides a strict design system, ensuring a high-quality aesthetic.

6. Cost & Accessibility

The presenter highlights the cost-effectiveness of GLM 4.7, noting that an annual subscription to the GLM coding plan ($288) is comparable to one month of Claude’s Mac subscription. This makes GLM 4.7 a viable alternative for users seeking high-quality code generation without the expense of proprietary models. The large context window of GLM 4.7 allows for pasting extensive prompt files without limitations.

7. Conclusion & Future Outlook

The presenter concludes that this hybrid workflow – combining the best prompts from commercial models within the best open model – represents a potential “meta” for coding. It offers the speed, privacy (if running locally or on a secure API), and quality of more expensive systems. The presenter encourages viewers to experiment with the provided prompts (links in the description) to experience the improved output quality firsthand.

Notable Quote:

“It basically allows you to get that agency quality code that we usually only see from Claude 4.5 Opus, but you are getting it from an open model that costs a fraction of the price.” – Presenter, describing the quality of code generated with the enhanced workflow.

This workflow effectively "upgrades" the model, making it capable of handling complex applications and delivering visually compelling results.