Looks like an idiot but works like a god of war. This chubby yellow guy dominates AI programming.

The programming guru Matt Pocock, nicknamed the "TypeScript Wizard", described a tool in his latest programming course like this.

"Previously, no matter which model you used for programming, you could only get a semi - finished product. AI could only do some miscellaneous tasks. But this thing is different. You can hand over your entire to - do list to it to solve."

This four - hour course was priced at $375 and had a capacity of 40 people. Matt sold out all the spots in just half an hour.

Programmers who follow Vibe Coding must have been bombarded in the past few days with images of a yellow fat guy with a finger in his nose, a dull expression, sparse hair, and one eye looking one way while the other looks the other way.

This is Ralph.

To put it simply, Ralph is an Agent plugin. After developers input their requirements, they can just sit back and relax.

It will supervise its own operation. By continuously testing the code, it won't stop until it produces results that fully meet the requirements.

In a real - life scenario, you can turn on Ralph before going to sleep, input tomorrow's development tasks, and by the next day, Ralph will have completed all the code, waiting for your final review.

You don't spend a single second on programming. Moreover, you don't have to sit in front of the computer, line by line, correcting the code of an AI that doesn't understand your instructions, as you did with previous Vibe Coding tools.

The only thing you do is have a good sleep.

Since Ralph was uploaded to Github, it has received 7,000 stars in less than two weeks. This speed is extremely impressive. On Github, even top - selling projects rarely break through 7,000 stars in two weeks.

You know, even the main repository of DeepSeek only got nearly 8,000 stars in the same period after its launch.

Ryan Carson, the founder of the online programming education platform Treehouse, even said bluntly, "Everyone is raving about Ralph."

01

The name Ralph comes from a character in the animated series "The Simpsons", Ralph Wiggum. His characteristic is that he's not very smart but persistent.

Ralph's developer is Geoffrey Huntley. However, on Chinese Internet platforms, people prefer to call him by his nickname, "The Aussie Sheep - Herding Uncle".

Because he's not a programmer in the true sense. In fact, he makes a living by herding sheep in Australia. And the Aussie Sheep - Herding Uncle doesn't live in a house but in his own RV.

Getting back to the topic, Ralph isn't a brand - new, independent software product. Instead, it's more like a work philosophy or a set of executable scripts, aiming to allow existing AI programming agents (such as Anthropic's Claude Code, Cursor, etc.) to keep working even when you're away from the computer.

In the Aussie Sheep - Herding Uncle's own words, the core of Ralph is so simple that it can be summarized in one line of code, but its power shouldn't be underestimated, just like "while true".

Its workflow can be broken down into several key steps.

The first step is task decomposition. It requires developers to break down a large - scale functional requirement into a series of small, independently achievable atomic tasks. The key is that each small task must have a clear, machine - detectable "pass" or "fail" standard.

Vague instructions like "make the interface look better" won't work here. The tasks must be specific, such as "add a priority column with a default value of medium".

After the task list is ready, Ralph starts to execute in a loop.

In each iteration, it selects an unfinished task from the task list. It reads the current state of the project, including the git version history, the progress.txt file that records the work progress, and the prd.json product requirement document that contains all the to - do items.

Then, it calls an AI model to write the code needed to implement the task.

After the code is written, the process automatically enters the testing phase. Ralph runs the preset test scripts to verify whether the new code works properly and meets the acceptance criteria of the task.

If the test passes, it automatically commits the code changes to the git version control system and marks the task as completed in prd.json. This successful experience, along with possible problems and solutions, is recorded in progress.txt for reference in subsequent iterations.

If the test fails, it doesn't stop and wait for user intervention. Instead, it records the error information in the errors.log file. At the start of the next loop, the AI reads these error logs, learns from them, and tries different methods to solve the same problem, avoiding repeating previous errors.

This way of working solves a common problem faced by current AI programming tools - the context window limitation.

When developers have a long - term continuous conversation with AI to build a complex function, as the code volume increases and the number of conversation rounds grows, the AI gradually forgets the initial instructions and constraints.

It may start to contradict itself or accidentally break another part of the function when modifying one part of the code. Eventually, developers find themselves acting like caregivers for a forgetful primary school student, constantly reminding and correcting, which greatly reduces efficiency.

The problem lies in the scale of the tasks.

Generally, a function consists of twenty parts. When an AI tries to keep all the details in its short - term memory, it often struggles.

Ralph's strategy is a bit like "eating small meals frequently". It focuses on one tiny task at a time. After completing a task, it solidifies the results through a git commit and then clears its short - term memory (context) to start the next task with a brand - new, clean state.

This means that, in theory, it can build infinitely complex software without being troubled by the ever - growing conversation history.

It doesn't lack context; instead, it transforms the memory into structured files.

1. The git history becomes the AI's long - term memory. Each commit is an accumulation of knowledge.

2. The progress.txt file records the completed work and the current state. It's a chronological record of the project progress.

3. The prd.json file is the product requirement document, clearly defining all the tasks to be completed.

4. The AGENTS.md file is the operation guide, recording the specific methods for building and testing the project.

5. The errors.log file is used to avoid the same errors in the next cycle.

02

Although we've talked a lot before, the core mechanism of Ralph can be condensed into a simple Bash script:

while :; do cat PROMPT.md | claude - code ; done

The essence of this line of code is an infinite loop. It continuously feeds a prompt file containing instructions to an AI agent, making it iterate repeatedly until external conditions are met and it stops.

This seemingly "clumsy" loop is precisely the innovation of the Ralph model.

Many AI models think the task is "almost done" and exit on their own after one round of output. They may ignore key finishing steps such as testing, building, and fixing errors.

Ralph's mechanism takes over the AI's exit signal. For example, when integrated with Claude Code, a plugin intercepts the AI's exit instruction.

It checks whether the work results meet the preset completion conditions and whether the output contains a specific completion marker.

If the marker isn't found, the plugin doesn't let the AI process stop but forces it to enter the next iteration. This mechanism allows the entire development process to break free from manual "stumbling blocks" and achieve full automation from coding to testing and then to correction. This is the underlying foundation for it to run unattended while developers sleep.

However, the key to hosting isn't simply "letting the AI run continuously" but "letting the AI know when to stop".

Without a clear, machine - understandable completion standard, the AI's own iterative loop becomes a purposeless consumption of resources, and the final output may still be unusable.

Therefore, the Ralph model emphasizes defining clear, quantifiable, and machine - detectable completion conditions before starting. These conditions are encoded in the task acceptance criteria.

At the end of each loop, the AI checks these conditions on its own. If the conditions aren't met, it prevents itself from exiting and triggers the next iteration. This process continues until all the task acceptance criteria are met and the final completion marker appears.

This is equivalent to using a machine to replace humans in the "result acceptance" stage.

This model also offers two main collaboration methods. One is "AFK Ralph". Developers set tasks before going to sleep at night and let it run all night. When they wake up the next morning, new features may have been developed and are waiting for review. This method is suitable for tasks with clear and straightforward requirements.

The other is "Hands - on Ralph". Developers run only one loop at a time. After each loop, developers review the AI's commits and guide and adjust as necessary. This method is more suitable for complex, exploratory functions where developers want more control over the process.

But even in manual mode, due to its structured tasks and clear feedback loop, it's usually much more efficient than traditional Vibe Coding.

03

In addition to the innovation in the work mode, another prominent feature of Ralph is its relatively low operating cost.

According to some developers' sharing, a typical Ralph run with 10 iterations costs about $30 in API calls.

A developer used this model to deliver, review, and test a complete application, with a total cost of less than $300.

In the past, the outsourcing cost for developing a product of the same complexity could be as high as $50,000.

Even if Ralph can only complete 90% of the work and developers only need to spend a small amount of time on the final cleaning and finishing, it's equivalent to compressing days of work into a few hours.

This is actually counter - intuitive. Ralph runs multiple loops but is cheaper than other products.

The reason for Ralph's low cost is related to its unique workflow.

When using other AI models for long conversations, to make the model remember what was discussed half an hour ago, each time a new request is sent, usually the entire previous chat history needs to be resent to it.

As the conversation gets longer, the number of Tokens consumed in each API call increases exponentially, causing the cost to rise sharply.

As mentioned before, the "memory" of the entire project is stored in git and local files, not in the expensive AI model context.

So when Ralph requests the AI each time, it only needs to provide the minimum information related to the current task. After completing the task, this short - term context is discarded. Since the context is short, the cost of a single call is very low.

Some real - life cases also confirm the potential of this model. In a startup hackathon, a team used Ralph to deliver 6 different project prototypes overnight. The Aussie Sheep - Herding Uncle himself is said to have mainly used Ralph to build a complete programming language in less than three months.

Strictly speaking, Ralph has no direct competitors, but Cascade, launched by Codeiu in 2025, can achieve similar engineering results to Ralph.

It has the same goal as Ralph, which is to host development until complex programming is finally completed. But it's not as simple and straightforward as Ralph. Instead, it operates based on the logic of "plan - act - verify".

In terms of code execution, Ralph is an infinite Bash loop + single execution. Before the first run, since there are no logs, it will make mistakes. Then, in the second loop, it corrects the mistakes based on the previous ones.

While Cascade first plans the entire process, then executes it, and finally verifies it.

Due to the initial arrangement and planning, its programming and verification processes take less time than Ralph's.

But the problem is that Cascade can't run in an infinite loop. So there's a threshold. Once the complexity of the project exceeds this threshold, Cascade can't achieve the same level of hosting as Ralph.

Moreover, an ecosystem around Ralph is rapidly taking shape.

Anthropic has included Ralph as one of the official plugins for its AI programming tool, Claude Code. And the laboratory of the front - end development platform Vercel has also released an AI SDK version that supports this model.

These official and semi - official adoptions indicate that the Ralph model has gradually evolved from a project into an effective paradigm recognized by the industry for solving practical problems.

This article is from the WeChat official account "Alphabet AI", written by Miao Zheng and published by 36Kr with permission.