hand-metal The Cool Explanation

Hyper Coding is the evolution of Vibe Coding. You'll still vibe it, but with a whole ecosystem ensuring your assistant stays on track.

In essence, Hyper Coding is the practice of Context Engineering automation to steer the AI Agent in the right direction, even when you are not looking.

The Problem
with Vibe Coding

"Oh, you are absolutely right!" - Claude

How many times have read that after pointing out a glaring mistake in an AI-generated response?

Many times the thing being overlooked is so simple and obvious, yet so critical, that you can't help but question in fear:

What else have the Assistant missed?
Did I catch all the critical mistakes?

Here's a non-exhaustive list of things you should be checking for:

  • Security Vulnerabilities
  • Inconsistent code patterns
  • Scope Creep / Over-engineering
  • Poor documentation
  • Lack of Error Handling / Edge cases
  • Context Drift

Through years of practice, an experience developer will learn to avoid many of these pitfalls without even thinking about them. But if the same developer is asked to review code and check for these issues, they will spend a significant amount of time and effort doing so, much more than they would have spent writing the code in the first place.

But why is that so?

Devs vs Agents

There are several reasons for why is it easier to write code than to review it, some internal (in our own brain) and some external (the environment we work in).

Internal Reasons

  • Habit: Experienced developers develop habits and intuition for writing code that meets quality standards. In time, the right way becomes the easier way and we apply it without thinking.
  • Cognitive Cost: Scope creep has a cognitive cost, and that cost will automatically steer humans away from it when they are focused on a task.
  • Peripheral Awareness: While an AI takes the context beforehand, humans have peripheral awareness, which brings to our attention thoughts and context that we did not actively search for.

External Reasons

These can be summarized in one word: tooling.

  • Linters and Formatters remove any effort to keep code style standards and catch errors.
  • IDE Integrations offer autocompletion and real-time feedback.
  • Continuous Integration Pipelines run automated tests, static analysis, and provide more feedback before code is merged.
  • Security Scanners tools like Snyk, Dependabot, and Bandit scan for vulnerabilities in dependencies and code.

The question then becomes:

How can we bring the same advantages to our AI agent?

The Solution
Hyper Coding

Note: This methodology was developed based on the established understanding of how large language models behave.

Measurable validation, benchmarks, and case studies will follow soon.

As mentioned, Hyper Coding is the practice of automating internal and external feedback loops to steer the AI Agent in the right direction.

The word "practice" is important here. Hyper Coding is not a tool (although Hyperdev is a tool that facilitates it), nor a single technique, but rather a methodology that combines several techniques and tools to achieve the desired outcome.

This methodology works by systematically simulating the internal and external advantages that expert human developers naturally possess. Each of the five Hyper Coding principles is a direct answer to one of those advantages, creating a robust framework for guiding AI.

Here's how they map directly:

Let's explore what each of these principles means in practice.

Hyper Coding

Learn the principles that will guide you
through adopting the methodology.

01

Deterministic First

Experienced developers build habits and intuition for writing code that meets quality standards. In time, the right way becomes the easier way. We can simulate this "habitual" correctness by reducing what the AI generates freely.

This means that we should limit the AI's freedom to generate code, and instead provide it with a structure to follow. A simple implementation of this principle is to use code templates.

Using a template, we can ensure that the AI will follow the current project's coding standards and architectural patterns. The AI's task becomes filling in the blanks of a pre-approved structure, rather than creating one from scratch.

book-heart

If it can be done deterministically, you should not use an LLM to generate it.

02

Tools Integration

Just as a human developer relies on an IDE, linters, and scanners, a core tenet of Hyper Coding is to give the AI Agent the same external advantages. The external tooling layer of Hyper Coding includes:
  • Integrated Linting and Formatting: Every generated code snippet is immediately validated against project-specific linting rules.
  • Security Scanning Integration: Real-time security analysis runs on each generated piece of code.
  • API Validation: Automatic verification that all referenced APIs and packages exist and are compatible.
  • Architecture Compliance Checks: Generated code is validated against the project's established design patterns.
book-heart

Give your AI Agent the same external advantages you have.

03

Engineered Friction

A human developer naturally avoids over-engineering because it's hard work. This "cognitive cost" creates a preference for the simplest path. An AI, however, feels no such friction; it will gladly build a labyrinth when a straight line would suffice. Since we can't make the AI "tired", we must simulate this cost by making undesirable paths systematically more difficult to travel. This isn't about just telling the agent "no" to the agent. It's about designing a system where doing the "wrong thing", like adding unnecessary complexity, requires the agent to perform additional, difficult tasks, such as formally justifying its deviation from the simpler path. By making the "right" path the "easy" path, we create a powerful, passive guidance system that steers the agent toward the desired outcome even when we're not looking. Remember, this is about probabilities. Sometimes, adding more information to the context can actually be counterproductive, as it increases the chance of context drift.
book-heart

Make simplicity the most probable path, by intentionally increasing the friction for complex solutions.

04

Reactive Context

Humans have "peripheral awareness" that brings relevant thoughts to mind without active searching. We can simulate this by building a system that provides the right context at the right time. Instead of providing every possible piece of documentation for every task, the system recognizes the task's nature—for example, a database operation—and automatically injects only the relevant context, such as security guidelines, schema definitions, or established data access patterns.
book-heart

Automate context engineering to provide the model with the right context at the right time, without overwhelming it.

05

Real-time Feedback

Integrating tools is only half the story. The real power comes from using them to create an immediate, automated feedback loop, which is our final principle. What makes this different from a traditional CI pipeline is the immediacy and context of the feedback. When a validation fails, the AI agent doesn't just receive an error message – it is immediately halted, provided with contextual information about why the validation failed, and tasked with attempting the correction. This prevents errors from compounding and teaches the agent the project's rules through direct, iterative experience.
book-heart

Provide feedback to the agent as soon as possible to avoid compounding errors.

HyperDev Toolkit

Give your Agent superpowers,
without the configuration hassle.

square-chevron-righthyper gen

Code generation engine
for the age of AI.

packageStarter Kits

Project Starter Kits that set your agent up for success.

codeCode Templates

Tailor made templates for consistent and high-quality AI-generated code.

Beyond anything you can do with Yeoman and similar tools, hyper templates support prompt placeholders that allow you to seemingly integrate rule-based deterministic generation that always follows the rules with context-dependent AI-driven generation.

packageStarter Kits

Project Starter Kits that set your agent up for success.

codeCode Templates

Tailor made templates for consistent and high-quality AI-generated code.

pocket-knifehyper tools

Swiss army knife for your Agent.

Takes care of the laborious task of setting up and configuring all the tools for your project. Here's a non-exhaustive list of tools the user will be able to configure:

botAI

  • MCP servers
  • Agent Definitions
  • Claude Code commands

shield-checkSecurity

  • Dependency Scanners
  • Secret Scanners
  • SAST tools

scan-textCode

  • Linters
  • Formatter
  • Type Checkers
  • Tests & Coverage
  • CI/CD Pipelines

send-to-backArchitecture

  • Complexity Scanners
  • PyTestArch, TSArch, etc
  • Plan-adherence Monitor Agent
  • Architectural Decision Records (ADR) Tools

botAI

  • MCP servers
  • Agent Definitions
  • Claude Code commands

shield-checkSecurity

  • Dependency Scanners
  • Secret Scanners
  • SAST tools

scan-textCode

  • Linters
  • Formatter
  • Type Checkers
  • Tests & Coverage
  • CI/CD Pipelines

send-to-backArchitecture

  • Complexity Scanners
  • PyTestArch, TSArch, etc
  • Plan-adherence Monitor Agent
  • Architectural Decision Records (ADR) Tools

maphyper plan

Every journey starts with
deciding where to go.

Seeing Claude Code coordinate 12 parallel agents is marvelous! But to actually achieve good results, you need a good plan. Also, how could we "keep the agents on track", without defining where the track is.

There are several proposed ways for doing this. There's the taskmaster.ai, Kiro, and Github's Spec-ify. But we are not about to tell you that ours is better. Planning is hard and you'll probably be more efficient using the structure that helps you create a mental model of the problem you are trying to solve. The AI can adapt to it.

So instead the hyper tool lets you to pick any standard you want and configure it, mapping your planning format to each of the 4 basic concepts below. You can even use Github's Spec-ify, the TaskMaster's PRD, or Kiro's, and the commands will follow the chosen format. No matter your choice, we'll help you manage all the plans you make and keep up with their progress.

text-selectDefine

Translates high-level business goals into an executable product definition, clearly outlining the what, the why (user journey), and success metrics for the feature.

square-dotDesign

Creates a detailed technical plan and architectural blueprint. This sets the rules for the agents by specifying the stack, outlining architectural constraints, and ensuring compliance with existing system standards before any code is written.

layout-templateDecompose

Break down the technical design into a list of small, actionable work units (user stories or tasks), with dependencies and acceptance criteria. This granular breakdown is crucial for orchestrating multiple, parallel agents and enabling reliable, incremental development.

square-chevron-rightDevelop

Orchestrates AI agents to execute the decomposed task list, generating code, tests, and documentation task-by-task. It shifts review from large, ambiguous code dumps to focused, targeted changes that are easily validated against the initial definition.

text-selectDefine

Translates high-level business goals into an executable product definition, clearly outlining the what, the why (user journey), and success metrics for the feature.

square-dotDesign

Creates a detailed technical plan and architectural blueprint. This sets the rules for the agents by specifying the stack, outlining architectural constraints, and ensuring compliance with existing system standards before any code is written.

layout-templateDecompose

Break down the technical design into a list of small, actionable work units (user stories or tasks), with dependencies and acceptance criteria. This granular breakdown is crucial for orchestrating multiple, parallel agents and enabling reliable, incremental development.

square-chevron-rightDevelop

Orchestrates AI agents to execute the decomposed task list, generating code, tests, and documentation task-by-task. It shifts review from large, ambiguous code dumps to focused, targeted changes that are easily validated against the initial definition.

tower-controlhyper watch

The alaways-on agent whisperer.

brain-circuitKnowledge Capture

Everything the Agent learns from your interactions is captured and stored locally in a versioned vector database.

package-plusReal-time Monitoring

Monitor agent activity, task progress, and system health in real-time.

brain-circuitKnowledge Capture

Everything the Agent learns from your interactions is captured and stored locally in a versioned vector database.

package-plusReal-time Monitoring

Monitor agent activity, task progress, and system health in real-time.

brain-circuithyper dash

Peek into your Agent's brain.

A dashboard for real-time monitoring and oversight of multi-agent workflows.

brain-circuitKnowledge Capture

Everything the Agent learns from your interactions is captured and stored locally in a versioned vector database.

package-plusReal-time Monitoring

Monitor agent activity, task progress, and system health in real-time.

brain-circuitKnowledge Capture

Everything the Agent learns from your interactions is captured and stored locally in a versioned vector database.

package-plusReal-time Monitoring

Monitor agent activity, task progress, and system health in real-time.

book-heart
Hyper Coding

Learn the
principles.

Hyper Dev
terminal

See how the toolkit can help you

01

Deterministic First

Experienced developers build habits and intuition for writing code that meets quality standards. In time, the right way becomes the easier way. We can simulate this "habitual" correctness by reducing what the AI generates freely.

This means that we should limit the AI's freedom to generate code, and instead provide it with a structure to follow. A simple implementation of this principle is to use code templates.

Using a template, we can ensure that the AI will follow the current project's coding standards and architectural patterns. The AI's task becomes filling in the blanks of a pre-approved structure, rather than creating one from scratch.

book-heart

If it can be done deterministically, you should not use an LLM to generate it.

square-chevron-righthyper gen

Code generation engine
for the age of AI.

packageStarter Kits

Project Starter Kits that set your agent up for success.

codeCode Templates

Tailor made templates for consistent and high-quality AI-generated code.

Beyond anything you can do with Yeoman and similar tools, hyper templates support prompt placeholders that allow you to seemingly integrate rule-based deterministic generation that always follows the rules with context-dependent AI-driven generation.

packageStarter Kits

Project Starter Kits that set your agent up for success.

codeCode Templates

Tailor made templates for consistent and high-quality AI-generated code.

02

Tools Integration

Just as a human developer relies on an IDE, linters, and scanners, a core tenet of Hyper Coding is to give the AI Agent the same external advantages. The external tooling layer of Hyper Coding includes:
  • Integrated Linting and Formatting: Every generated code snippet is immediately validated against project-specific linting rules.
  • Security Scanning Integration: Real-time security analysis runs on each generated piece of code.
  • API Validation: Automatic verification that all referenced APIs and packages exist and are compatible.
  • Architecture Compliance Checks: Generated code is validated against the project's established design patterns.
book-heart

Give your AI Agent the same external advantages you have.

pocket-knifehyper tools

Swiss army knife for your Agent.

Takes care of the laborious task of setting up and configuring all the tools for your project. Here's a non-exhaustive list of tools the user will be able to configure:

botAI

  • MCP servers
  • Agent Definitions
  • Claude Code commands

shield-checkSecurity

  • Dependency Scanners
  • Secret Scanners
  • SAST tools

scan-textCode

  • Linters
  • Formatter
  • Type Checkers
  • Tests & Coverage
  • CI/CD Pipelines

send-to-backArchitecture

  • Complexity Scanners
  • PyTestArch, TSArch, etc
  • Plan-adherence Monitor Agent
  • Architectural Decision Records (ADR) Tools

botAI

  • MCP servers
  • Agent Definitions
  • Claude Code commands

shield-checkSecurity

  • Dependency Scanners
  • Secret Scanners
  • SAST tools

scan-textCode

  • Linters
  • Formatter
  • Type Checkers
  • Tests & Coverage
  • CI/CD Pipelines

send-to-backArchitecture

  • Complexity Scanners
  • PyTestArch, TSArch, etc
  • Plan-adherence Monitor Agent
  • Architectural Decision Records (ADR) Tools
03

Engineered Friction

A human developer naturally avoids over-engineering because it's hard work. This "cognitive cost" creates a preference for the simplest path. An AI, however, feels no such friction; it will gladly build a labyrinth when a straight line would suffice. Since we can't make the AI "tired", we must simulate this cost by making undesirable paths systematically more difficult to travel. This isn't about just telling the agent "no" to the agent. It's about designing a system where doing the "wrong thing", like adding unnecessary complexity, requires the agent to perform additional, difficult tasks, such as formally justifying its deviation from the simpler path. By making the "right" path the "easy" path, we create a powerful, passive guidance system that steers the agent toward the desired outcome even when we're not looking. Remember, this is about probabilities. Sometimes, adding more information to the context can actually be counterproductive, as it increases the chance of context drift.
book-heart

Make simplicity the most probable path, by intentionally increasing the friction for complex solutions.

maphyper plan

Every journey starts with
deciding where to go.

Seeing Claude Code coordinate 12 parallel agents is marvelous! But to actually achieve good results, you need a good plan. Also, how could we "keep the agents on track", without defining where the track is.

There are several proposed ways for doing this. There's the taskmaster.ai, Kiro, and Github's Spec-ify. But we are not about to tell you that ours is better. Planning is hard and you'll probably be more efficient using the structure that helps you create a mental model of the problem you are trying to solve. The AI can adapt to it.

So instead the hyper tool lets you to pick any standard you want and configure it, mapping your planning format to each of the 4 basic concepts below. You can even use Github's Spec-ify, the TaskMaster's PRD, or Kiro's, and the commands will follow the chosen format. No matter your choice, we'll help you manage all the plans you make and keep up with their progress.

text-selectDefine

Translates high-level business goals into an executable product definition, clearly outlining the what, the why (user journey), and success metrics for the feature.

square-dotDesign

Creates a detailed technical plan and architectural blueprint. This sets the rules for the agents by specifying the stack, outlining architectural constraints, and ensuring compliance with existing system standards before any code is written.

layout-templateDecompose

Break down the technical design into a list of small, actionable work units (user stories or tasks), with dependencies and acceptance criteria. This granular breakdown is crucial for orchestrating multiple, parallel agents and enabling reliable, incremental development.

square-chevron-rightDevelop

Orchestrates AI agents to execute the decomposed task list, generating code, tests, and documentation task-by-task. It shifts review from large, ambiguous code dumps to focused, targeted changes that are easily validated against the initial definition.

text-selectDefine

Translates high-level business goals into an executable product definition, clearly outlining the what, the why (user journey), and success metrics for the feature.

square-dotDesign

Creates a detailed technical plan and architectural blueprint. This sets the rules for the agents by specifying the stack, outlining architectural constraints, and ensuring compliance with existing system standards before any code is written.

layout-templateDecompose

Break down the technical design into a list of small, actionable work units (user stories or tasks), with dependencies and acceptance criteria. This granular breakdown is crucial for orchestrating multiple, parallel agents and enabling reliable, incremental development.

square-chevron-rightDevelop

Orchestrates AI agents to execute the decomposed task list, generating code, tests, and documentation task-by-task. It shifts review from large, ambiguous code dumps to focused, targeted changes that are easily validated against the initial definition.

04

Reactive Context

Humans have "peripheral awareness" that brings relevant thoughts to mind without active searching. We can simulate this by building a system that provides the right context at the right time. Instead of providing every possible piece of documentation for every task, the system recognizes the task's nature—for example, a database operation—and automatically injects only the relevant context, such as security guidelines, schema definitions, or established data access patterns.
book-heart

Automate context engineering to provide the model with the right context at the right time, without overwhelming it.

tower-controlhyper watch

The alaways-on agent whisperer.

brain-circuitKnowledge Capture

Everything the Agent learns from your interactions is captured and stored locally in a versioned vector database.

package-plusReal-time Monitoring

Monitor agent activity, task progress, and system health in real-time.

brain-circuitKnowledge Capture

Everything the Agent learns from your interactions is captured and stored locally in a versioned vector database.

package-plusReal-time Monitoring

Monitor agent activity, task progress, and system health in real-time.

05

Real-time Feedback

Integrating tools is only half the story. The real power comes from using them to create an immediate, automated feedback loop, which is our final principle. What makes this different from a traditional CI pipeline is the immediacy and context of the feedback. When a validation fails, the AI agent doesn't just receive an error message – it is immediately halted, provided with contextual information about why the validation failed, and tasked with attempting the correction. This prevents errors from compounding and teaches the agent the project's rules through direct, iterative experience.
book-heart

Provide feedback to the agent as soon as possible to avoid compounding errors.

brain-circuithyper dash

Peek into your Agent's brain.

A dashboard for real-time monitoring and oversight of multi-agent workflows.

brain-circuitKnowledge Capture

Everything the Agent learns from your interactions is captured and stored locally in a versioned vector database.

package-plusReal-time Monitoring

Monitor agent activity, task progress, and system health in real-time.

brain-circuitKnowledge Capture

Everything the Agent learns from your interactions is captured and stored locally in a versioned vector database.

package-plusReal-time Monitoring

Monitor agent activity, task progress, and system health in real-time.

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