Most people think ASCII art is simple, and a nostalgic remnant of the early internet. But when the GitHub Copilot CLI team asked for a small entrance banner for the new command-line experience, they discovered the opposite: An ASCII animation in a real-world terminal is one of the most constrained UI engineering problems you can take on.

Part of what makes this even more interesting is the moment we’re in. Over the past year, CLIs have seen a surge of investment as AI-assisted and agentic workflows move directly into the terminal. But unlike the web—where design systems, accessibility standards, and rendering models are well-established—the CLI world is still fragmented. Terminals behave differently, have few shared standards, and offer almost no consistent accessibility guidelines. That reality shaped every engineering decision in this project.

Different terminals interpret ANSI color codes differently. Screen readers treat fast-changing characters as noise. Layout engines vary. Buffers flicker. Some users override global colors for accessibility. Others throttle redraw speed. There is no canvas, no compositor, no consistent rendering model, and no standard animation framework.

So when an animated Copilot mascot flying into the terminal appeared, it looked playful. But behind it was serious engineering work, unexpected complexity, a custom design toolchain, and a tight pairing between a designer and a long-time CLI engineer.

That complexity only became fully visible once the system was built. In the end, animating a three-second ASCII banner required over 6,000 lines of TypeScript—most of it dedicated not to visuals, but to handling terminal inconsistencies, accessibility constraints, and maintainable rendering logic.

This is the technical story of how it came together.

Why animated ASCII is a hard engineering problem

Before diving into the build process, it’s worth calling out why this problem space is more advanced than it looks.

Terminals don’t have a canvas

Unlike browsers (DOM), native apps (views), or graphics frameworks (GPU surfaces), terminals treat output as a stream of characters. There’s no native concept of:

• Frames
• Sprites
• Z-index
• Rasterized pixels
• Animation tick rates

Because of this, every “frame” has to be manually repainted using cursor movements and redraw commands. There’s no compositor smoothing anything over behind the scenes. Everything is stdout writes + ANSI control sequences.

ANSI escape codes are inconsistent, and terminal color is its own engineering challenge

ANSI escape codes like \x1b[35m (bright magenta) or \x1b[H (cursor home) behave differently across terminals—not just in how they render, but in whether they’re supported at all. Some environments (like Windows Command Prompt or older versions of PowerShell) have limited or no ANSI support without extra configuration.

But even in terminals that do support ANSI, the hardest part isn’t the cursor movement. It’s the colors.

When you’re building a CLI, you realistically have three approaches:

1. Use no color at all. This guarantees broad compatibility, but makes it harder to highlight meaning or guide users’ attention—especially in dense CLI output.
2. Use richer color modes (3-bit, 4-bit, 8-bit, or truecolor) that aren’t uniformly supported or customizable. This introduces a maintenance headache: Different terminals, themes, and accessibility profiles render the same color codes differently, and users often disagree about what “good” colors look like.
3. Use a minimal, customizable palette (usually 4-bit colors) that most terminals allow users to override in their preferences. This is the safest path, but it limits how accurately you can represent a brand palette—and it forces you to design for environments with widely varying contrast and theme choices.

For the Copilot CLI animation, this meant treating color as a semantic system, not a literal one: Instead of committing specific RGB values, the team mapped high-level “roles” (eyes, goggles, shadow, border) to ANSI colors that degrade gracefully across different terminals and accessibility settings.

Accessibility is a first-class concern

Terminals are used by developers with a wide range of visual abilities—not just blind users with screen readers, but also low-vision users, color-blind users, and anyone working in high-contrast or customized themes.

That means:

• Rapid re-renders can create auditory clutter for screen readers
• Color-based meaning must degrade safely, since bold, dim, or subtle hues may not be perceivable
• Low-vision users may not see contrast differences that designers expect
• Animations must be opt-in, not automatic
• Clearing sequences must avoid confusing assistive technologies

This is also why the Copilot CLI animation ended up behind an opt-in flag early on—accessibility constraints shaped the architecture from the start. 

These constraints guided every decision in the Copilot CLI animation. The banner had to work when colors were overridden, when contrast was limited, and even when the animation itself wasn’t visible.

Ink (React for the terminal) helps, but it’s not an animation engine

Ink lets you build terminal interfaces using React components, but:

• It re-renders on every state change
• It doesn’t manage frame deltas
• It doesn’t synchronize with terminal paint cycles
• It doesn’t solve flicker or cursor ghosting

Which meant animation logic had to be handcrafted.

Frame-based ASCII animation has no existing workflow for designers

There are tools for ASCII art, but virtually none for:

• Frame-by-frame editing
• Multi-color ANSI previews
• Exporting color roles
• Generating Ink-ready components
• Testing contrast and accessibility

Even existing ANSI preview tools don’t simulate how different terminals remap colors or handle cursor updates, which makes accurate design iteration almost impossible without custom tooling. So the team had to build one.

Part 1: A request that didn’t fit any workflow

Cameron Foxly (@cameronfoxly), a brand designer at GitHub with a background in animation, was asked to create a banner for the Copilot CLI.

“Normally, I’d build something in After Effects and hand off assets,” Cameron said. “But engineers didn’t have the time to manually translate animation frames into a CLI. And honestly, I wanted something more fun.”

He’d seen the static ASCII intro in Claude Code and knew Copilot deserved more personality.

The 3D Copilot mascot flying in to reveal the CLI logo felt right. But after attempting to create just one frame manually, the idea quickly ran into reality.

“It was a nightmare,” Cameron said. “If this is going to exist, I need to build my own tool.”

Part 2: Building an ASCII animation editor from scratch

Cameron opened an empty repository in VS Code, and began asking GitHub Copilot for help scaffolding an animation MVP that could:

• Read text files as frames
• Render them sequentially
• Control timing
• Clear the screen without flicker
• Add a primitive “UI”

Within an hour, he had a working prototype that was monochrome, but functional.

Simplified early animation loop

Below is a simplified example variation of the frame loop logic Cameron prototyped:

import fs from "fs";
import readline from "readline";

/**
 * Load ASCII frames from a directory.
 */
const frames = fs
  .readdirSync("./frames")
  .filter(f => f.endsWith(".txt"))
  .map(f => fs.readFileSync(`./frames/${f}`, "utf8"));

let current = 0;

function render() {
  // Move cursor to top-left of terminal
  readline.cursorTo(process.stdout, 0, 0);

  // Clear the screen below the cursor
  readline.clearScreenDown(process.stdout);

  // Write the current frame
  process.stdout.write(frames[current]);

  // Advance to next frame
  current = (current + 1) % frames.length;
}

// 75ms = ~13fps. Higher can cause flicker in some terminals.
setInterval(render, 75);

This introduced the first major obstacle: color. The prototype worked in monochrome, but the moment color was added, inconsistencies across terminals—and accessibility constraints—became the dominant engineering problem.

Part 3: ANSI color theory and the real-world limitations

The Copilot brand palette is vibrant and high-contrast, which is great for web but exceptionally challenging for terminals.

ANSI terminals support:

• 16-color mode (standard)
• 256-color mode (extended)
• Sometimes truecolor (“24-bit”) but inconsistently

Even in 256-color mode, terminals remap colors based on:

• User themes
• Accessibility settings
• High-contrast modes
• Light/dark backgrounds
• OS-level overrides

Which means you can’t rely on exact hues. You have to design with variability in mind.

Cameron needed a way to paint characters with ANSI color roles while previewing how they look in different terminals.

He took a screenshot of the Wikipedia ANSI table, handed it to Copilot, and asked it to scaffold a palette UI for his tool.

Adding a color “brush” tool

A simplified version:

function applyColor(char, color) {
  // Minimal example: real implementation needed support for roles,
  // contrast testing, and multiple ANSI modes.
  const codes = {
    magenta: "\x1b[35m",
    cyan: "\x1b[36m",
    white: "\x1b[37m"
  };

  return `${codes[color]}${char}\x1b[0m`; // Reset after each char
}

This enabled Cameron to paint ANSI-colored ASCII like you would in Photoshop, one character at a time.

But now he had to export it into the real Copilot CLI codebase.

Part 4: Exporting to Ink (React for the terminal)

Ink is a React renderer for building CLIs using JSX components. Instead of writing to the DOM, components render to stdout.

Cameron asked Copilot to help generate an Ink component that would:

• Accept frames
• Render them line-by-line
• Animate them with state updates
• Integrate cleanly into the CLI codebase

Simplified Ink frame renderer

import React from "react";
import { Box, Text } from "ink";

/**
 * Render a single ASCII frame.
 */
export const CopilotBanner = ({ frame }) => (
  <Box flexDirection="column">
    {frame.split("\n").map((line, i) => (
      <Text key={i}>{line}</Text>
    ))}
  </Box>
);

And a minimal animation wrapper:

export const AnimatedBanner = () => {
  const [i, setI] = React.useState(0);

  React.useEffect(() => {
    const id = setInterval(() => setI(x => (x + 1) % frames.length), 75);
    return () => clearInterval(id);
  }, []);

  return <CopilotBanner frame={frames[i]} />;
};

This gave Cameron the confidence to open a pull request (his first engineering pull request in nine years at GitHub).

“Copilot filled in syntax I didn’t know,” Cameron said. “But I still made all the architectural decisions.”

Now it was time for the engineering team to turn a prototype into something production-worthy.

Part 5: Terminal animation isn’t solved technology

Andy Feller (@andyfeller), a long-time GitHub engineer behind the GitHub CLI, partnered with Cameron to bring the animation into the Copilot CLI codebase.

Unlike browsers—which share rendering engines, accessibility APIs, and standards like WCAG—terminal environments are a patchwork of behaviors inherited from decades-old hardware like the VT100. There’s no DOM, no semantic structure, and only partial agreement on capabilities across terminals. This makes even “simple” UI design problems in the terminal uniquely challenging, especially as AI-driven workflows push CLIs into daily use for more developers.

“There’s no framework for terminal animations,” Andy explained. “We had to figure out how to do this without flickering, without breaking accessibility, and across wildly different terminals.”

Andy broke the engineering challenges into four broad categories:

Challenge 1: From banner to ready without flickering

Most terminals repaint the entire viewport when new content arrives. At the same time, CLIs come with a strict usability expectation: when developers run a command, they want to get to work immediately. Any animation that flickers, blocks input, or lingers too long actively degrades the experience.

This created a core tension the team had to resolve: how to introduce a brief, animated banner without slowing startup, stealing focus, or destabilizing the terminal render loop.

In practice, this was complicated by the fact that terminals behave differently under load. Some:

• Throttle fast writes
• Reveal cleared frames momentarily
• Buffer output differently
• Repaint the cursor region inconsistently

To avoid flicker while keeping the CLI responsive across popular terminals like iTerm2, Windows Terminal, and VS Code, the team had to carefully coordinate several interdependent concerns:

• Keeping the animation under three seconds so it never delayed user interaction
• Separating static and non-static components to minimize unnecessary redraws
• Initializing MCP servers, custom agents, and user setup without blocking render
• Working within Ink’s asynchronous re-rendering model

The result was an animation treated as a non-blocking, best-effort enhancement—visible when it could be rendered safely, but never at the expense of startup performance or usability.

Challenge 2: Brand color mapping in ANSI

“ANSI color consistency simply doesn’t exist,” Andy said. 

Most modern terminals support 8-bit color, allowing CLIs to choose from 256 colors. However, how those colors are actually rendered varies widely based on terminal themes, OS settings, and user accessibility overrides. In practice, CLIs can’t rely on exact hues—or even consistent contrast—across environments.

The Copilot banner introduced an additional complexity: although it’s rendered using text characters, the block-letter Copilot logo functions as a graphical object, not readable body text. Under accessibility guidelines, non-text graphical elements have different contrast requirements than text, and they must remain perceivable without relying on fine detail or precise color matching.

To account for this, the team deliberately chose a minimal 4-bit ANSI palette—one of the few color modes most terminals allow users to customize—to ensure the animation remained legible under high-contrast themes, low-vision settings, and color overrides.

This meant the team had to:

• Treat the Copilot wordmark as non-text graphical content with appropriate contrast requirements
• Select ANSI color codes that approximate the Copilot palette without relying on exact hues
• Satisfy WCAG contrast guidance for both text and non-text elements
• Ensure the animation remained legible in light and dark terminals
• Degrade gracefully when users override terminal colors for accessibility
• Test color combinations across multiple terminal emulators and theme configurations

Rather than encoding brand colors directly, the animation maps semantic roles—such as borders, eyes, highlights, and text—to ANSI color slots that terminals can reinterpret safely. This allows the banner to remain recognizable without assuming control over the user’s color environment.

Challenge 3: Making the animation maintainable

Cameron’s prototype was a great starting point for Andy to incorporate into the Copilot CLI but it wasn’t without its challenges:

• Banner consisted of ~20 animation frames covering an 11×78 area
• There are ~10 animation elements to stylize in any given frame
• Needed a way to separate the text of the frame from the colors involved
• Each frame mapped hard coded colors to row and column coordinates
• Each frame required precise timing to display Cameron’s vision

First, the animation was broken down into distinct animation elements that could be used to create separate light and dark themes:

type AnimationElements =
    | "block_text"
    | "block_shadow"
    | "border"
    | "eyes"
    | "head"
    | "goggles"
    | "shine"
    | "stars"
    | "text";

type AnimationTheme = Record<AnimationElements, ANSIColors>;

const ANIMATION_ANSI_DARK: AnimationTheme = {
    block_text: "cyan",
    block_shadow: "white",
    border: "white",
    eyes: "greenBright",
    head: "magentaBright",
    goggles: "cyanBright",
    shine: "whiteBright",
    stars: "yellowBright",
    text: "whiteBright",
};

const ANIMATION_ANSI_LIGHT: AnimationTheme = {
    block_text: "blue",
    block_shadow: "blackBright",
    border: "blackBright",
    eyes: "green",
    head: "magenta",
    goggles: "cyan",
    shine: "whiteBright",
    stars: "yellow",
    text: "black",
};

Next, the overall animation and subsequent frames would capture content, color, duration needed to animate the banner:

interface AnimationFrame {
    title: string;
    duration: number;
    content: string;
    colors?: Record<string, AnimationElements>; // Map of "row,col" positions to animation elements
}

interface Animation {
    metadata: {
        id: string;
        name: string;
        description: string;
    };
    frames: AnimationFrame[];
}

Then, each animation frame was captured to separate frame content from stylistic and animation details, resulting in over 6,000 lines of TypeScript to safely animate three seconds of the Copilot logo across terminals with wildly different rendering and accessibility behaviors:

    const frames: AnimationFrame[] = [
        {
            title: "Frame 1",
            duration: 80,
            content: `
┌┐
││







││
└┘`,
            colors: {
                "1,0": "border",
                "1,1": "border",
                "2,0": "border",
                "2,1": "border",
                "10,0": "border",
                "10,1": "border",
                "11,0": "border",
                "11,1": "border",
            },
        },
        {
            title: "Frame 2",
            duration: 80,
            content: `
┌──     ──┐
│         │
 █▄▄▄
 ███▀█
 ███ ▐▌
 ███ ▐▌
   ▀▀█▌
   ▐ ▌
    ▐
│█▄▄▌     │
└▀▀▀    ──┘`,
            colors: {
                "1,0": "border",
                "1,1": "border",
                "1,2": "border",
                "1,8": "border",
                "1,9": "border",
                "1,10": "border",
                "2,0": "border",
                "2,10": "border",
                "3,1": "head",
                "3,2": "head",
                "3,3": "head",
                "3,4": "head",
                "4,1": "head",
                "4,2": "head",
                "4,3": "goggles",
                "4,4": "goggles",
                "4,5": "goggles",
                "5,1": "head",
                "5,2": "goggles",
                "5,3": "goggles",
                "5,5": "goggles",
                "5,6": "goggles",
                "6,1": "head",
                "6,2": "goggles",
                "6,3": "goggles",
                "6,5": "goggles",
                "6,6": "goggles",
                "7,3": "goggles",
                "7,4": "goggles",
                "7,5": "goggles",
                "7,6": "goggles",
                "8,3": "eyes",
                "8,5": "head",
                "9,4": "head",
                "10,0": "border",
                "10,1": "head",
                "10,2": "head",
                "10,3": "head",
                "10,4": "head",
                "10,10": "border",
                "11,0": "border",
                "11,1": "head",
                "11,2": "head",
                "11,3": "head",
                "11,8": "border",
                "11,9": "border",
                "11,10": "border",
            },
        },

Finally, each animation frame is rendered building segments of text based on consecutive color usage with the necessary ANSI escape codes:

           {frameContent.map((line, rowIndex) => {
                const truncatedLine = line.length > 80 ? line.substring(0, 80) : line;
                const coloredChars = Array.from(truncatedLine).map((char, colIndex) => {
                    const color = getCharacterColor(rowIndex, colIndex, currentFrame, theme, hasDarkTerminalBackground);
                    return { char, color };
                });

                // Group consecutive characters with the same color
                const segments: Array<{ text: string; color: string }> = [];
                let currentSegment = { text: "", color: coloredChars[0]?.color || theme.COPILOT };

                coloredChars.forEach(({ char, color }) => {
                    if (color === currentSegment.color) {
                        currentSegment.text += char;
                    } else {
                        if (currentSegment.text) segments.push(currentSegment);
                        currentSegment = { text: char, color };
                    }
                });
                if (currentSegment.text) segments.push(currentSegment);

                return (
                    <Text key={rowIndex} wrap="truncate">
                        {segments.map((segment, segIndex) => (
                            <Text key={segIndex} color={segment.color}>
                                {segment.text}
                            </Text>
                        ))}
                    </Text>
                );
            })}

Challenge 4: Accessibility-first design

The engineering team approached the banner with the same philosophy as the GitHub CLI’s accessibility work:

• Respect global color overrides both in terminal and system preferences
• After the first use, avoid animations unless explicitly enabled via the Copilot CLI configuration file
• Minimize ANSI instructions that can confuse assistive tech

“CLI accessibility is under researched,” Andy noted. “We’ve learned a lot from users who are blind as well as users with low vision, and those lessons shaped this project.”

Because of this, the animation is opt-in and gated behind its own flag—so it’s not something developers see by default. And when developers run the CLI in –screen-reader mode, the banner is automatically skipped so no decorative characters or motion are sent to assistive technologies.

Part 6: An architecture built to scale

By the end of the refactor, the team had:

• Frames stored as plain text
• Animation elements
• Themes as simple mappings
• A runtime colorization step
• Ink-driven timing and rendering
• A maintainable foundation for future animations

This pattern—storing frames as plain text, layering semantic roles, and applying themes at runtime—isn’t specific to Copilot. It’s a reusable approach for anyone building terminal UIs or animations.

Part 7: What this project reveals about building for the terminal

A “simple ASCII banner” turned into:

• A frame-based animation tool that didn’t exist
• A custom ANSI color palette strategy
• A new Ink component
• A maintainable rendering architecture
• Accessibility-first CLI design choices
• A designer’s first engineering contribution
• Real-world testing across diverse terminals
• Open source contributions from the community

“The most rewarding part was stepping into open source for the first time,” Cameron said. “With Copilot, I was able to build out  my MVP ASCII animation tool into a full open source app at ascii-motion.app,. Someone fixed a typo in my README, and it made my day.”

As Andy pointed out, building accessible experiences for CLIs is still largely unexplored territory and far behind the tooling and standards available for the web.

Today, developers are already contributing to Cameron’s ASCII Motion tool, and the Copilot CLI team can ship new animations without rebuilding the system.

This is what building for the terminal demands: deep understanding of constraints, discipline around accessibility, and the willingness to invent tooling where none exists.

Use GitHub Copilot in your terminal

The GitHub Copilot CLI brings AI-assisted workflows directly into your terminal — including commands for explaining code, generating files, refactoring, testing, and navigating unfamiliar projects.

The post From pixels to characters: The engineering behind GitHub Copilot CLI’s animated ASCII banner appeared first on The GitHub Blog.

Today’s data release marks our second full year of regular releases since the launch of the GitHub Innovation Graph. The Innovation Graph serves as a stable, regularly updated source for aggregated statistics on public software development activity around the world, informing public policy, strengthening research, guiding funding decisions, and equipping organizations with the evidence needed to build secure and resilient AI systems.  

Updated bar chart races

With our new data release, we’ve updated the bar chart race videos to the git pushes, repositories, developers, and organizations global metrics pages.

Let’s take a look back at some of the progress the Innovation Graph has helped drive. 

Academic papers

One of the most rewarding aspects of the past year has been seeing the growing range of research questions addressed with Innovation Graph data. Recent papers have explored everything from global collaboration networks to the institutional foundations of digital capabilities.

These studies showcase how network analysis techniques can be applied to Innovation Graph data, in addition to  earlier work we referenced last year linking open source to economic value, innovation measurement, labor markets, and AI-driven productivity through other methodologies.

Historical Institutions and Modern Digital Capabilities: New Evidence from GitHub in Africa

Research by an economist at the Federal Reserve Board uses GitHub data to examine how the density of Protestant mission stations correlates with present-day participation in digital production across African countries.

• Olana, Deriba, “Historical Institutions and Modern Digital Capabilities: New Evidence from GitHub in Africa” (November 25, 2025). Available at SSRN: https://ssrn.com/abstract=5805622 or http://dx.doi.org/10.2139/ssrn.5805622.

The Structure of Cross-National Collaboration in Open-Source Software Development

Researchers from MIT, Carnegie Mellon, and the University of Chicago analyze international collaboration patterns in the Innovation Graph’s economy collaborators dataset, shedding light on how common colonial histories influence modern software development collaboration activities.

• Xu, Henry, et al. “The Structure of Cross-National Collaboration in Open-Source Software Development,” (November 10, 2025). Available at doi.org/10.1145/3746252.3761237.
• Replication package available at https://github.com/hehao98/github-innovation-graph.  

Small-World Phenomenon of Global Open-Source Software Collaboration on GitHub

A social network analysis by researchers at Midwestern State University and Tarleton State University highlights the tightly connected, small-world structure of global OSS collaboration.

• Zhang, Guoying, et al. “Small-World Phenomenon of Global Open-Source Software Collaboration on Github: A Social Network Analysis.” Journal of Global Information Management Vol. 33, No. 1 (2025). Available at doi.org/10.4018/JGIM.387412. 

The Software Complexity of Nations

These researchers extend countries’ software economic complexity into the digital economy by leveraging the geographic distribution of programming languages in open source software, showing that software economic complexity predicts GDP, income inequality, and emissions, which have important policy implications.

• Juhász, Sándor, et al. “The Software Complexity of Nations.” Research Policy Vol. 55, No. 3. Available at doi.org/10.1016/j.respol.2026.105422.

Conferences

The Innovation Graph and related GitHub datasets were featured prominently in academic and policy discussions at a wide range of venues, including:

• ATLC25: The 10th Atlanta Conference on Science and Innovation Policy
• OpenForum Academy Symposium 2025
• 2nd CEU Vienna Data Analytics Jamboree
• Wharton Human-AI Research: 3rd Annual Business & Generative AI Conference

News publications

We were also encouraged to see Innovation Graph data referenced in major international reporting. In 2025, two pieces in The Economist drew on GitHub data examining China’s approach to open technology (June 17, 2025) and India’s potential role as a distinctive kind of AI superpower (September 18, 2025). Coverage like this reinforces the role that data on open source activity can play in understanding geopolitical and economic shifts.

Reports

Once again, Innovation Graph data contributed to several flagship reports, including:

• The 2025 Stanford AI Index Report
• The 2025 WIPO Global Innovation Index
• The Rise of FOSS in India report from the National Law School of India University

We continue to value these opportunities to support macro-level measurement efforts, and we’re equally excited by complementary work that dives deeper into regional, institutional, and community-level dynamics.

Moving forward

As we move through 2026, we’re grateful for the community that has formed around the Innovation Graph, and we’re looking forward to building the next chapter together. Our focus will be on deepening collaboration, welcoming new perspectives, and creating clearer pathways for people to apply the Innovation Graph data in their own contexts, from strategy and research to product development and policy.

The post Year recap and future goals for the GitHub Innovation Graph appeared first on The GitHub Blog.

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