Our AI Future Is Here. We're All Using It Differently. --- Everyday folks are experimenting with AI to enhance their lives

“There is a huge gap between what AI can already do today and what most people are actually doing with it.

 

Closing that gap will take years. Meanwhile, fortunes will be created, not just for giant tech companies, but for the everyday folks who use those companies' AI models to build products and services of their own.

 

The curious among us are already leading the charge. A goatherd (and software developer) in rural Australia discovered a simple but radical new technique to optimize the performance of the leading software-writing AI. An almost 50-year-old horticulture company in Bakersfield, Calif., is rolling out an AI agent that connects its growers with decades of wisdom from professional agronomists. A copywriter who saw her business decimated by her clients' use of AI pivoted to coaching those same clients on building their own AI tools.

 

Technological diffusion happens every day as people adopt innovations to suit their personal or business needs. With AI, there's a fresh twist: Today's generative AI is much more accessible than past technologies, and can be used even by nontechnical people. There is no "right way" to use it.

 

In just over three years, AI usage has gone from almost nil, to something 62% of Americans report using several times a week, according to the Pew Research Center. And while most of that usage is probably relatively basic, awareness of AI has risen to nearly 100%.

 

This isn't a story of AI turning into a superhuman intelligence that replaces workers. AI remains, primarily, a tool that enhances our existing abilities. But as researchers and users grasp the real-world functionality of today's AI, they're seeing a huge amount of room for productivity and economic growth.

 

Two years ago, AI chatbots were too finicky and error-prone to be reliable and broadly useful to most people, says Ram Bala, an associate professor of AI at Santa Clara University. Today, he says, they're ready for prime time, because of advances in reducing hallucinations, and in plugging these AI models into other software systems.

 

Whatever comes next in the development of AI, adoption of existing technologies will snowball well into the next decade.

 

The biggest AI innovations might come from users at work or at home, rather than tech giants and research labs.

 

The companies making AI models know this, and are now promoting applications their own users pioneered. For example, OpenAI this past week introduced ChatGPT Health to demonstrate its ability to analyze medical records, wellness apps and provider bills in order to improve healthcare outcomes.

 

Users of Claude Code, Anthropic's software-writing AI system, recently discovered a way to create finished, bug-free programs without human intervention. (One of the originators was the aforementioned Australian goatherd.) The trick: Write a small program that asks the AI, over and over again, to improve the code it has already written. Named the Ralph Wiggum technique, after the dimwitted but persistently optimistic "Simpsons" character, this simple trick is effective at forcing Claude Code to solve problems on its own [1].

 

This discovery is a great example of "capability overhang," says Ethan Mollick, a professor of innovation and entrepreneurship at the University of Pennsylvania's Wharton School and a leading authority on generative AI. That's his term for the many new things existing AI can do that were unknown until users discovered them.

 

"This is a tool that does programming and also writes documents, and it can also do image editing, and also can read Etruscan, and a bunch of other stuff too," says Mollick. Software projects with a narrow audience but a big potential impact might have been shelved for want of money and talent. Now, they can be built by a handful of people, or even just one, with the help of AI, he adds.

 

Many people building with AI are finding that fusing several AI models can yield capabilities well beyond those of a single one. Meta is pursuing Manus, which makes a software "agent" that can produce deeply researched reports and perform other actions online. While Meta has its own AI models, Manus uses a combination, including those from Anthropic and others.

 

Santa Clara University's Bala, who also heads a company that builds real-world AI applications, is currently working with his team on an app for Sun World, a California developer of new varieties of fruits and vegetables. Farmers who need advice on how best to grow their crops can have natural-language conversations with AI agents preloaded with research and advice from scientific literature and a community of professional agronomists.

 

While the interface is powered by one of the usual top-tier chatbots, the information it is fed has been predigested and enhanced by other AIs in a process called data enrichment.

 

The skills required to make this app for agronomists aren't so different from the ones Bala has been using for years, as a data scientist and software engineer. The difference is that now, AI makes individual engineers much more effective, allowing a small team to do something that before would have been nearly impossible even with many times as many resources.

 

Before the debut of ChatGPT in November 2022, Leanne Shelton made a comfortable living as a freelance copywriter in the suburbs of Sydney. Not long after its debut, like others in her field, she saw her business dry up. So she became an expert in customizing ChatGPT to write voicey marketing copy. She now makes more than she ever did as a copywriter, she says.

 

She and others are discovering the capability overhang of AI for themselves. Her story also illustrates that customizing AIs with your own data doesn't mean you have to be a software engineer like Bala.

 

The intense pressure to adopt AI -- from bosses, peers and, if you're an earlier adopter like me, voices in your head -- is real. So are the seemingly endless options for exploring its existing capabilities.

 

"I think about fields that might get suddenly affected by AI," says Mollick. He thinks we will see sudden innovation, often in unexpected areas, even as other fields and people in some roles fall behind. "The unevenness will be hard to predict."” [2]

 

1. Ralph Wiggum technique (or "Ralph Mode") is a viral AI development methodology that uses autonomous loops to complete complex coding tasks.

Named after the persistent Simpsons character, the core philosophy is "iteration beats perfection"—rather than expecting an AI to get code right in one shot, the technique forces it to fail, learn, and try again until a specific goal is met.

Core Methodology

The technique was popularized by developer Geoffrey Huntley, who famously described it as simply a "Bash loop". It works through a continuous cycle:

 

    Fixed Prompt: An AI agent (typically Claude Code) is given a task and a "completion promise" (e.g., <promise>DONE</promise>).

    Autonomous Execution: The agent attempts the task, running tests and linter checks.

    Stop Hook: When the agent tries to exit, a specialized plugin (the ralph-wiggum plugin) intercepts the exit command. If the completion promise hasn't been met, it re-injects the original prompt.

    Persistent Context: Because progress is saved in git history [5] and modified files, the next iteration "sees" the previous work and error logs, allowing it to self-correct.

 

Use Cases and Successes

 

    Legacy Migrations: Migrating tests (e.g., Jest to Vitest) or upgrading major framework versions while developers sleep.

    Greenfield Projects: Huntley used a 3-month loop to create Cursed, a fully functional programming language based on Gen Z slang.

 

    Contract Work: One developer reportedly completed a $50,000 contract for only $297 in API costs by running Ralph overnight.

 

Implementation and Safety

To use the technique, developers typically install the official plugin in Claude Code:

 

    Command: /ralph-loop "<prompt>" --completion-promise "<text>" --max-iterations <n>.

    Safety: Users are strongly advised to set --max-iterations to prevent "token burning" (infinite loops that waste money) and to run loops in Docker sandboxes to prevent the AI from accidentally damaging the local system.

 

2. EXCHANGE --- Keywords: Our AI Future Is Here. We're All Using It Differently. --- Everyday folks are experimenting with AI to enhance their lives. Mims, Christopher.  Wall Street Journal, Eastern edition; New York, N.Y.. 10 Jan 2026: B1.  

 

3. A "completion promise" is a specific phrase or token that a large language model (LLM) is instructed to output only when it has genuinely completed a given task to the specified requirements.

Function and Context

 

    AI Agent Loops: This technique is primarily used in AI agent development, particularly in methodologies like the "Ralph Wiggum" loop. In this loop, an agent repeatedly works on a task and attempts to "exit" the loop. A stop hook intercepts the exit and re-prompts the agent to continue working until a specific condition is met.

    Exit Condition: The "completion promise" serves as an explicit signal for the agent to terminate the loop. When the agent outputs the exact promised phrase within the specified XML tags (e.g., <promise>DONE</promise>), the system recognizes the task as complete and stops the iterative process.

    Encourages Thoroughness: The phrasing is typically strong and explicit (e.g., "The task is complete. All requirements have been implemented... I have not taken any shortcuts...") to encourage the AI to be thorough and verify its work before attempting to exit. The agent is instructed not to output the promise unless the statement is genuinely true.

    Customizable: The exact text of the promise can be customized, but meaningful phrases are recommended over vague ones like "DONE" to prevent premature exits.

 

Example of Use

A user might run a command in an AI coding environment:

/ralph-loop "Your task description" --completion-promise "<promise>TASK_VERIFIED</promise>"

The AI would then iteratively work on the code until it determines the task is done, at which point it outputs the required <promise>TASK_VERIFIED</promise> to stop the loop.

 

4. Linter checks are automated code analysis performed by tools (linters) to find syntax errors, stylistic issues, and potential bugs like unused variables or security vulnerabilities, enforcing coding standards, improving readability, and ensuring consistency across projects before code is even run, saving time in manual reviews and CI/CD pipelines. They analyze code structure (often using an Abstract Syntax Tree, AST) against defined rules, flagging problems like missing semicolons, inconsistent indentation, or risky patterns, and are crucial for maintainable, high-quality code. 

Common types of linter checks:

 

    Syntax Errors:

    Catches fundamental language rule violations, like unclosed brackets or missing semicolons.

 

Style & Formatting:

Ensures consistent indentation, spacing, line length, and naming conventions (e.g., camelCase vs. snake_case).

Best Practices:

Flags inefficient, outdated, or risky code patterns (e.g., using == instead of === in JavaScript).

Error-Prone Code:

Identifies potential bugs like unused variables, unreachable code, or uninitialized variables.

Type Checks:

Verifies variable and function types, catching type mismatches.

Security:

Detects common vulnerabilities, like SQL injection risks or use of insecure functions, often by checking against standards like OWASP.

 

Key benefits:

 

    Early Bug Detection:

    Catches issues in the editor or during pre-commit, before they reach testing or production.

 

Code Consistency:

Enforces a unified style, making code easier for teams to read and maintain.

Faster Reviews:

 

Automates simple checks, allowing human reviewers to focus on logic and architecture.

 

Learning Tool:

Helps developers learn language-specific best practices and complexities.

 

Examples of Linters:

 

    ESLint: (JavaScript/TypeScript)

    Ruff: (Python)

    Stylelint: (CSS/SCSS)

    Biome: (JavaScript/TypeScript)

    Gosec: (Go security)

 

5. Git history refers to the detailed record of every change (commits, branches, merges) made to a project over time. It is a core feature of the Git version control system, used for tracking progress, debugging, and understanding the evolution of a codebase.

The history is stored in a hidden .git directory within your project, and Git provides several powerful command-line tools to view and manage it.

 

Key Commands for Viewing History

The primary command for viewing the history is git log. By default, it lists commits in reverse chronological order.

Here are some common ways to use git log:

 

    git log: Shows the full commit history, including the commit SHA-1 checksum (often referred to simply as the commit hash or SHA, is a unique, 40-character hexadecimal string that serves as the unique identifier for every commit in a Git repository), author, date, and commit message.

    git log --oneline: Provides a concise, single-line summary for each commit, which is useful for a quick overview.

    git log -p or --patch: Shows the specific changes (diffs) introduced in each commit.

    git log --author="Name": Filters the history to show only commits made by a specific author.

    git log --since="2 weeks ago": Limits the output to commits made within a specific time frame.

    git log path/to/file: Shows the commit history of a specific file.

    git log --graph --oneline --all: Visualizes the branch history with an ASCII graph, showing how different branches and merges occurred.

 

History Management

Git history can be altered or simplified using commands for management, but these should be used with caution as they rewrite project history:

 

    git reset: Used to move the current branch head to a different commit, which can effectively discard subsequent history (soft or hard reset).

    git revert <commit>: Creates a new commit that undoes the changes of a specified previous commit, preserving the project history.

    git rebase: Used to move or combine a sequence of commits to a new base commit, often used to create a linear history.

    git cherry-pick <commit>: Applies the changes introduced by an existing commit from another branch.