More powerful machine intelligence is changing how I think about my work

TL;DR: I used ChatGPT o1 Pro to rapidly prototype music-responsive visualizations, showcasing how AI's context management and task automation can boost productivity, creativity, and adaptability.

I splurged and signed up for the ChatGPT plan that provides access to "o1 Pro." This product offers a model that focuses more on reasoning (ways to think) and allocates significantly more compute time (thinking) for generating results. The thing I was trying to accomplish is weird, but what it could do wowed me and offered generalizable insights. Let's get weird, starting with the scenario...

My friend Eric Laughlin invited me to contribute visualizations that respond to music and ambient sounds for one of his creative projects. I use Python's Matplotlib to produce visuals that help me perceive, analyze, and story-tell. This library provides exquisite fine-grain control over the displays, but it can be fussy and requires manipulating Python. I have used GitHub Copilot to make visuals with this toolchain for a while.

I did not have much time to produce the visualizations. I went all-in using AI tools to generate the code (GitHub Copilot, Claude, ChatGPT [o1 and o1 mini]). As the code got more complex (detailed) and complicated (interconnected) I started bumping up against the limits of the model implementations (context and compute). And I was running out of time to deliver.

I acquired ChatGPT o1 Pro and started dumping the existing pieces of disparate prototype code in with high-level instructions like:

I have created two programs that show visualizations. I would like you to integrate them into a single program that can swap between the animation visualizations when I press the 1 or 2 key. I would also like the unified program to provide a common framework that allows me to add new visualizations using the same libraries. 
# PROGRAM 1
<code>
# PROGRAM 2
<code>         

It was very effective taking a combination of high-level instructions and heterogeneous inputs to produce accurate, coherent outputs. Code is functional, and inherently structured, but it has already changed how I will think about information work. Why?

It is expensive for humans to integrate context and switch tasks. By "expensive" I mean:

• it takes us time to shove context in our brains (rate limits);
• we often have to layer abstractions to deal with our working memory (size limits); and
• when we switch tasks, it often degrades context that we then have to rebuild, again (switching costs).

Our lives seem to have a growing number of signal sources begging for our attention. That makes it harder to do depth work that requires uninterrupted thinking time (see Maker's Schedule, Manager's Schedule). Having a more capable technician agent that can maintain granular context and act on high level instructions to execute complex, complicated tasks lets us get more done as a manager. We can literally stay at a higher level of abstraction, and dive into specific things by asking the agent to explain something to us. I just lived this.

I wanted to create these visualizations. I have historically been most productive developing software when I can dedicate a three-hour uninterrupted block of time with fresh cognitive capacity. I had to drop the work into 15-minute blocks of time in the evenings, after my workday. I had to adapt to the constraints.

I gave o1 Pro high-level instructions, walked away while it processed (2-5 minutes), tried the new version, provided more high-level feedback, and kept iterating. I only dived into the code, always asking for explanation by the machine, when something unexpected happened. And then I disciplined myself to adjust the code with feedback to the machine rather than direct intervention. 99% of the code for the project was generated by some kind of machine intelligence. Operating within that constraint gave me insights on how I can further adapt as machine intelligence becomes even more capable.

Augmentation of Human Intelligence

Context Management: The machine's ability to manage and retain detailed context allows humans to focus on high-level abstraction, reducing the cognitive load associated with switching tasks. This augmentation supports more efficient decision-making and problem-solving.

Detail Automation: By handling the minutiae of complex tasks, such as software development or reading contracts, machines enable humans to concentrate on broader strategies and creative thinking, freeing them from routine or detail-oriented tasks.

Rapid Adaptation: Machines adapt quickly to varied tasks, enhancing productivity by facilitating seamless transitions between activities. This reflects the versatility of machine intelligence in diverse domains and need to try machine capabilities in adjacent human tasks to discover how to apply them.

Changes in Human Patterns to Complement Machine Intelligence

Reduced Task-Switching Costs: With machines managing detailed contexts, humans may adopt workflows that involve frequent shifts between tasks without significant cognitive disruptions. This could lead to more flexible and dynamic work patterns.

Focus on Abstract Thinking: Humans might increasingly focus on high-level problem framing, conceptual design, and strategic oversight, while relying on machines for execution and detail management.

Streamlined Collaboration: Collaboration with machines could become more efficient than with humans in specific contexts, due to consistent "style" adherence and tighter feedback loops, shifting human collaboration towards tasks requiring empathy, intuition, or cultural context.

Expanded Task Scope: The ability to maintain productivity across domains (e.g., coding, business analysis, and tutoring) suggests that humans could integrate more diverse activities into their workflows, supported by machine intelligence.

Play to Discover

The most valuable reflection for me might be I needed something to play with outside of my day job to push me to explore and discover (???? Eric). Business imperatives often push us towards incremental efforts aimed at faster/better/cheaper on workloads that are not allowed to degrade or fail. A narrow focus on scenarios that line up with immediate business value to justify investment are important and necessary. Employing this focus exclusively is also limiting and may impair acquiring learning that is necessary for transformative change. We do not have to suffer the tyranny of "or." We can optimize our work, and we can protect time for play, and these can create reciprocal benefits. Doing so requires intentional choice and action.

Born-in-AI firms will compete differently

These are early, rambling thoughts on a product that recently appeared. But I am "wowed" now. A lot more wow is probably coming. We get more wow faster if we open the doors of our minds. This actively catalyzes diffusion of innovation. I have increasing conviction that born-in-AI organizations are going to compete very differently from their legacy analogs because they will have much lower machine intelligence adoption friction (the firm specific diffusion rate) AND deploy their scarce human capital to activities that don't waste the increasingly precious resource that is human time, attention, and cognition (leverage on constrained resources). The lever and fulcrum are already here.

I recorded a demo of the program outputs here. You can see Damien Riehl 's delightful performance of the input track here. The code is below, and yes, it may be trivial for a professional developer to produce. But it signals that the rate and direction of travel of these general-purpose-technologies are compelling because no professional human developers participated, and the interaction pattern may reveal an emerging paradigm shift of how experts across different domains might manage machine intelligence-based agents. I commend Damien and Horace Wu 's Further Comments podcast to legal industry folks looking for thinking that can help you go faster.

Thank you for playing.

import sys
import requests
from io import BytesIO
import numpy as np
import math
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import sounddevice as sd
import queue
import threading
import keyboard  # optional
from scipy.fft import fft, fftfreq
import random
import string
from matplotlib.colors import Normalize
from matplotlib.cm import ScalarMappable
from matplotlib.collections import LineCollection
from matplotlib.transforms import Affine2D

# --------------------------------------------------
#  1) COMMON AUDIO MANAGER
# --------------------------------------------------
class AudioManager:
    """
    A cross-platform AudioManager using sounddevice.
    It continuously records audio from the chosen device
    and stores blocks in a thread-safe queue.
    """
    def __init__(self, device=None, channels=1, samplerate=44100, blocksize=256):
        self.device = device
        self.channels = channels
        self.samplerate = samplerate
        self.blocksize = blocksize

        self.audio_queue = queue.Queue()
        self.stream = None
        self.lock = threading.Lock()
        self.running = False

    def _audio_callback(self, indata, frames, time, status):
        if status:
            print(f"Audio Manager Status: {status}", file=sys.stderr)
        block = indata.copy()
        # Put the newest block into the queue
        self.audio_queue.put(block)

    def start(self):
        if self.running:
            return
        self.running = True
        self.stream = sd.InputStream(
            device=self.device,
            channels=self.channels,
            samplerate=self.samplerate,
            blocksize=self.blocksize,
            callback=self._audio_callback
        )
        self.stream.start()

    def stop(self):
        if not self.running:
            return
        self.running = False
        if self.stream is not None:
            self.stream.stop()
            self.stream.close()
            self.stream = None
        with self.lock:
            while not self.audio_queue.empty():
                self.audio_queue.get_nowait()

    def read_frames(self, num_frames=None):
        """
        Returns a numpy array of shape (frames, channels).
        If no data is available, returns an empty array.

        - Discard older blocks if multiple accumulate, so only the newest is used.
        """
        with self.lock:
            while self.audio_queue.qsize() > 1:
                self.audio_queue.get_nowait()

            if self.audio_queue.empty():
                return np.empty((0, self.channels), dtype=np.float32)

            block = self.audio_queue.get_nowait()
            if num_frames is None:
                return block
            else:
                if block.shape[0] > num_frames:
                    block = block[:num_frames, :]
                return block


# --------------------------------------------------
#  2) BASE VISUALIZATION CLASS
# --------------------------------------------------
class VisualizationBase:
    def __init__(self, fig, audio_manager):
        self.fig = fig
        self.audio_manager = audio_manager
        self.active = False
        self.ax = None

    def activate(self):
        self.active = True
        if self.ax:
            self.ax.set_visible(True)

    def deactivate(self):
        self.active = False
        if self.ax:
            self.ax.set_visible(False)

    def update_frame(self, frame):
        pass


# --------------------------------------------------
#  3) TITLE SCREEN CLASS
# --------------------------------------------------
class TitleScreen(VisualizationBase):
    """
    Displays a background image and usage instructions.
    """
    def __init__(self, fig, audio_manager, image_url):
        super().__init__(fig, audio_manager)
        self.image_url = image_url

        self.ax = self.fig.add_subplot(111)
        self.ax.set_visible(False)
        self.ax.axis('off')

        r = requests.get(self.image_url)
        img_data = BytesIO(r.content)
        self.img = plt.imread(img_data)

        self.img_height, self.img_width = self.img.shape[:2]
        self.aspect_ratio = self.img_width / self.img_height

        self.cmap = plt.get_cmap("turbo")
        self.color_index = 0.0
        self.instruction_text = None

    def activate(self):
        super().activate()
        self.ax.clear()
        self.ax.axis('off')

        self.ax.imshow(
            self.img,
            extent=[0, self.aspect_ratio, 0, 1],
            aspect='equal',
            alpha=0.5
        )
        self.ax.set_xlim(0, self.aspect_ratio)
        self.ax.set_ylim(0, 1)

        top_y = 0.85
        self.ax.text(
            self.aspect_ratio / 2.0, top_y,
            "AUDIO VISUALIZER",
            color="white", ha="center", va="center",
            fontsize=24, fontweight='bold',
            zorder=10
        )

        self.instruction_text = self.ax.text(
            self.aspect_ratio / 2.0, top_y - 0.30,
            "Press 1 for Dancing Polar Visualizer\n"
            "Press 2 for 3D Wireframe Visualizer\n"
            "Press 3 for Propeller Arms Visualizer\n"
            "Press 4 for Frequency Bar Chart\n"
            "Press 5 for Waveform Line Plot\n"
            "Press '0' to come back here\n"
            "Press 'q' or 'Esc' to quit\n\n"
            "Press a letter key (A..Z) on this screen to select an input device\n"
            "(See device list in terminal)",
            color="yellow", ha="center", va="center",
            fontsize=16,
            zorder=10
        )

    def update_frame(self, frame):
        if not self.active:
            return
        self.color_index += 0.01
        if self.color_index >= 1.0:
            self.color_index = 0.0
        color = self.cmap(self.color_index)
        if self.instruction_text:
            self.instruction_text.set_color(color)


# --------------------------------------------------
#  4) FIRST VISUALIZATION (POLAR DANCING FFT)
# --------------------------------------------------
class DancingPolarVisualizer(VisualizationBase):
    def __init__(self, fig, audio_manager):
        super().__init__(fig, audio_manager)
        self.sample_rate = audio_manager.samplerate
        self.min_freq = 20
        self.max_freq = 6000
        self.height_scale = 4
        self.polar_radial_distance_scale = 30.0
        self.polar_marker_size_scale = 2000.0
        self.background_color = 'white'
        self.current_pos = [0.5, 0.5]
        self.target_pos = [0.5, 0.5]

        self.ax = self.fig.add_subplot(111, projection='polar')
        self.ax.set_visible(False)
        self.ax.axis('off')
        self.fig.patch.set_facecolor(self.background_color)

        self.polar_plot = self.ax.scatter(np.zeros(1024), np.zeros(1024))
        self.ax.set_ylim(0, 100)

        self.cmap = plt.get_cmap('turbo')
        self.norm = Normalize(vmin=self.min_freq, vmax=self.max_freq)
        self.scalar_map = ScalarMappable(norm=self.norm, cmap=self.cmap)

    def _get_new_target_position(self):
        padding = 0.2
        return [
            random.uniform(padding, 1 - padding),
            random.uniform(padding, 1 - padding)
        ]

    def _update_plot_position(self, speed):
        dx = self.target_pos[0] - self.current_pos[0]
        dy = self.target_pos[1] - self.current_pos[1]
        distance = np.sqrt(dx**2 + dy**2)
        if distance < 0.01:
            self.target_pos = self._get_new_target_position()
        else:
            self.current_pos[0] += dx * speed
            self.current_pos[1] += dy * speed

    def update_frame(self, frame):
        if not self.active:
            return
        audio_data = self.audio_manager.read_frames(num_frames=1024)
        if audio_data.shape[0] < 1:
            return

        mono = audio_data[:, 0]
        block_len = len(mono)

        fft_data = np.abs(np.fft.fft(mono))
        half_len = block_len // 2
        fft_data = fft_data[:half_len]
        freqs = np.fft.fftfreq(block_len, 1 / self.sample_rate)[:half_len]

        if fft_data.size == 0:
            return
        dominant_freq = freqs[np.argmax(fft_data)]

        speed = np.clip(dominant_freq / self.max_freq, 0.01, 0.1)
        self._update_plot_position(speed)

        size_factor = 1 - (dominant_freq / self.max_freq) * 0.5
        self.ax.set_position([
            self.current_pos[0] - size_factor / 2,
            self.current_pos[1] - size_factor / 2,
            size_factor,
            size_factor
        ])

        marker_sizes = fft_data * self.polar_marker_size_scale
        radial_positions = fft_data * self.polar_radial_distance_scale
        polar_colors = self.scalar_map.to_rgba(freqs)

        phases = np.angle(np.fft.fft(mono))[:half_len]

        self.polar_plot.set_offsets(np.c_[phases, radial_positions])
        self.polar_plot.set_sizes(marker_sizes)
        self.polar_plot.set_color(polar_colors)


# --------------------------------------------------
#  5) SECOND VISUALIZATION (3D WIREFRAME FFT)
# --------------------------------------------------
class WireframeFFTVisualizer(VisualizationBase):
    def __init__(self, fig, audio_manager):
        super().__init__(fig, audio_manager)
        self.samplerate = audio_manager.samplerate
        self.CHUNK = 1024
        self.FREQ_LIMIT_LOW = 20
        self.FREQ_LIMIT_HIGH = 16000
        self.HISTORY_SIZE = 100
        self.MAX_ROTATION_SPEED = 20.0
        self.MIN_ROTATION_SPEED = 2.0

        self.z_axis_scaling = 0.5
        self.current_rotation = 0.0

        self.n_freqs = 64
        self.x = np.linspace(-6, 6, self.n_freqs)
        self.y = np.linspace(-3, 3, self.HISTORY_SIZE)
        self.x, self.y = np.meshgrid(self.x, self.y)
        self.z = np.zeros((self.HISTORY_SIZE, self.n_freqs))
        self.last_fft = np.zeros(self.n_freqs)

        self.ax = self.fig.add_subplot(111, projection='3d')
        self.ax.set_visible(False)
        self.ax.set_facecolor('white')
        self.ax.axis('off')

    def _get_dominant_frequency(self, fft_data, freq_axis):
        if len(fft_data) == 0 or np.all(fft_data == 0):
            return 0
        return freq_axis[np.argmax(fft_data)]

    def _frequency_to_rotation_speed(self, freq):
        if freq == 0:
            return self.MIN_ROTATION_SPEED
        norm = (freq - self.FREQ_LIMIT_LOW) / (self.FREQ_LIMIT_HIGH - self.FREQ_LIMIT_LOW)
        norm = np.clip(norm, 0, 1)
        speed = self.MIN_ROTATION_SPEED + (self.MAX_ROTATION_SPEED - self.MIN_ROTATION_SPEED) * norm
        return speed

    def update_frame(self, frame):
        if not self.active:
            return

        audio_data = self.audio_manager.read_frames(num_frames=self.CHUNK)
        if audio_data.shape[0] < 1:
            return

        mono = audio_data[:, 0]
        block_len = len(mono)

        fft_data = np.abs(np.fft.fft(mono))
        half_len = block_len // 2
        fft_data = fft_data[:half_len]
        freqs = np.fft.fftfreq(block_len, 1 / self.samplerate)[:half_len]

        mask = (freqs >= self.FREQ_LIMIT_LOW) & (freqs <= self.FREQ_LIMIT_HIGH)
        masked_fft_data = fft_data[mask]
        masked_freqs = freqs[mask]

        if masked_fft_data.size == 0:
            return

        if masked_fft_data.size > self.n_freqs:
            masked_fft_data = masked_fft_data[: self.n_freqs]
        else:
            temp = np.zeros(self.n_freqs)
            temp[: masked_fft_data.size] = masked_fft_data
            masked_fft_data = temp

        dom_freq = self._get_dominant_frequency(masked_fft_data, masked_freqs)
        rotation_speed = self._frequency_to_rotation_speed(dom_freq)

        self.current_rotation += rotation_speed
        if self.current_rotation >= 360:
            self.current_rotation -= 360

        smoothing_factor = 0.7
        smoothed_fft = smoothing_factor * self.last_fft + (1 - smoothing_factor) * masked_fft_data
        self.last_fft = smoothed_fft

        fft_max = np.max(smoothed_fft)
        if fft_max > 0:
            smoothed_fft = smoothed_fft / fft_max

        smoothed_fft = np.log1p(smoothed_fft) * self.z_axis_scaling * 4

        db_spectrum = 20 * np.log10(smoothed_fft + 1e-6)
        db_spectrum = np.clip(db_spectrum, -80, 0)
        db_spectrum = (db_spectrum + 80) / 80
        db_spectrum *= self.z_axis_scaling * 4

        self.z = np.roll(self.z, -1, axis=0)
        self.z[-1, :] = db_spectrum

        self.ax.clear()
        self.ax.axis('off')

        z_min, z_max = np.min(self.z), np.max(self.z)
        if z_min == z_max:
            normalized = np.zeros_like(self.z)
        else:
            normalized = (self.z - z_min) / (z_max - z_min)
        colors = plt.cm.turbo(normalized.ravel())

        self.ax.plot_wireframe(
            self.x,
            self.y,
            self.z,
            rcount=self.HISTORY_SIZE,
            ccount=self.n_freqs,
            linewidth=2.0,
            colors=colors
        )
        self.ax.view_init(30, self.current_rotation)
        self.ax.set_xlim(-6, 6)
        self.ax.set_ylim(-3, 3)
        self.ax.set_zlim(0.9, 1.9)


# --------------------------------------------------
#  7) THIRD VISUALIZATION (PROPELLER ARMS)
# --------------------------------------------------
class PropellerArmsVisualizer(VisualizationBase):
    """
    A radial "propeller" style visualization with 12 arms.

    - The arms rotate around the center at a speed based on the dominant frequency.
    - Each arm's arc length depends on the volume (amplitude).
    - The color of each dot cycles outward with the turbo colormap at a rate based on freq.
    - The radius of each dot has a sine-wave "undulation".
    - The dot size increases with distance from the center.
    - The overall radial extent is increased by 50%.
    """

    def __init__(self, fig, audio_manager):
        super().__init__(fig, audio_manager)

        self.samplerate = audio_manager.samplerate
        self.num_arms = 12
        self.max_freq = 8000
        self.min_freq = 20

        self.background_color = 'white'
        # We'll track rotation, color, and sine wave phases.
        self.rotation_angle = 0.0
        self.color_phase = 0.0
        self.sine_phase = 0.0

        # Create a polar Axes
        self.ax = self.fig.add_subplot(111, projection='polar')
        self.ax.set_visible(False)
        self.ax.axis('off')
        self.fig.patch.set_facecolor(self.background_color)

        # Create a single scatter plot
        self.scatter_plot = self.ax.scatter([], [])
        # Increase the total radial limit to 1.5 (instead of 1.0) => 50% bigger
        self.ax.set_ylim(0, 0.5)

        self.cmap = plt.get_cmap('turbo')
        self.norm = Normalize(vmin=0, vmax=1)
        self.scalar_map = ScalarMappable(norm=self.norm, cmap=self.cmap)

    def _get_dominant_frequency(self, fft_data, freq_axis):
        if len(fft_data) == 0 or np.all(fft_data == 0):
            return 0
        return freq_axis[np.argmax(fft_data)]

    def update_frame(self, frame):
        if not self.active:
            return

        # Read up to 512 frames from the queue
        audio_data = self.audio_manager.read_frames(num_frames=512)
        if audio_data.shape[0] < 1:
            return

        mono = audio_data[:, 0]
        block_len = len(mono)

        # Standard FFT logic
        fft_data = np.abs(np.fft.fft(mono))
        half_len = block_len // 2
        fft_data = fft_data[:half_len]
        freqs = np.fft.fftfreq(block_len, 1 / self.samplerate)[:half_len]

        if fft_data.size == 0:
            return

        # 1) Dominant freq
        dom_freq = self._get_dominant_frequency(fft_data, freqs)

        # 2) rotation speed
        rotation_speed = np.clip(dom_freq / self.max_freq, 0.01, 0.2)
        self.rotation_angle += rotation_speed

        # 3) color cycle speed
        color_speed = np.clip(dom_freq / self.max_freq, 0.02, 0.2)
        self.color_phase += color_speed

        # 4) wave speed for the sine undulation
        wave_speed = np.clip(dom_freq / self.max_freq, 0.02, 0.3)
        self.sine_phase += wave_speed

        # 5) amplitude => sets the maximum arm radius
        amplitude = np.sum(fft_data) / fft_data.size
        arc_radius = 0.5 + np.clip(amplitude / 200.0, 0, 0.5)

        # We'll define how many points per arm
        points_per_arm = 30
        total_points = self.num_arms * points_per_arm

        angles = np.zeros(total_points)
        radii = np.zeros(total_points)
        color_vals = np.zeros(total_points)
        sizes = np.zeros(total_points)

        # Sine wave parameters
        wave_ampl = 0.08
        wave_stride = 0.5

        index = 0
        for i in range(self.num_arms):
            base_angle = 2.0 * np.pi * i / self.num_arms
            for j in range(points_per_arm):
                frac = j / (points_per_arm - 1)  # 0..1
                angles[index] = base_angle + self.rotation_angle

                # base radius
                base_r = frac * arc_radius
                # add sine wave
                r_wave = wave_ampl * np.sin(self.sine_phase + j * wave_stride)
                final_r = base_r + r_wave
                radii[index] = final_r

                # color cycles outward with color_phase
                cval = (self.color_phase + frac) % 1.0
                color_vals[index] = cval

                # Dot size grows with radius from center:
                # e.g. base=20, scale=120 => bigger difference
                sizes[index] = 20.0 + 240.0 * max(math.pow(1 + final_r, 4), 0.0)

                index += 1

        # Convert color_vals to RGBA
        colors = self.scalar_map.to_rgba(color_vals)

        # Update the scatter
        self.scatter_plot.set_offsets(np.c_[angles, radii])
        self.scatter_plot.set_color(colors)
        self.scatter_plot.set_sizes(sizes)

# --------------------------------------------------
#  8) UPDATED VISUALIZATION (FREQUENCY BAR CHART)
#     *No axes/ticks
#     *Mirrored horizontally & vertically
#     *Lowest freq in center, highest freq outward
# --------------------------------------------------
class FrequencyBarChartVisualizer(VisualizationBase):
    """
    A mirrored bar chart with the lowest frequencies at the center.
    Frequencies fan out left & right. Each set of bars is also mirrored
    above and below the x-axis. No axes or ticks displayed.
    """
    def __init__(self, fig, audio_manager):
        super().__init__(fig, audio_manager)
        self.samplerate = audio_manager.samplerate
        self.min_freq = 20.0
        self.max_freq = 8000.0
        self.n_bars = 32  # total frequency bins

        # Create a 2D axis, then hide all spines/ticks
        self.ax = self.fig.add_subplot(111)
        self.ax.set_visible(False)
        self.ax.set_facecolor('white')
        self.ax.axis('off')  # remove axes, ticks, labels entirely

        # We'll set up an x-range from -n_bars//2 to +n_bars//2
        # so that 0 is the center.
        half_n = self.n_bars // 2
        self.x_positions = np.arange(-half_n, half_n)  # e.g. if n_bars=32, x in [-16..+15]

        # We'll store data in an internal array of bar heights
        self.bar_heights = np.zeros(self.n_bars, dtype=np.float32)

        # We'll create a container of patches manually
        # Each freq bin will yield 2 "bars": top and bottom (mirrored)
        # We'll do that on the left side and the right side is also mirrored
        # Actually, to keep it simpler, we can just do one patch per bin
        # for the top, and one for the bottom, for a total of 2 * n_bars patches.
        # But we want the lower freq in the center => let's reorder the bins:
        #   bin 0 (lowest freq) => x=0
        #   bin 1 => x=-1
        #   bin 2 => x=+1
        #   bin 3 => x=-2
        #   bin 4 => x=+2
        # etc...
        # We'll create an array that maps bin index => x position
        # so bin_order[i] = x position for the i-th bin in ascending freq
        # Then we can place bars accordingly.
        bin_indices = np.arange(self.n_bars)
        # We'll reorder them in the pattern center(0), left(-1), right(+1), left(-2), right(+2) ...
        # e.g. [0,1,2,3,4,5, ...] => [0,1,2,3, ...]
        # We'll build a pattern to get x offsets.
        # For 32 bins, bin 0 is center, bin 1 is x=-1, bin 2 is x=+1, bin 3 is x=-2, ...
        # We'll define a small helper:
        def center_out_positions(n):
            # returns an array of x-locations of length n, in the pattern described
            # Example: n=5 => [0, -1, +1, -2, +2]
            coords = []
            offset = 1
            sign = -1
            current = 0
            coords.append(0)
            for i in range(1, n):
                coords.append(sign * offset)
                # toggle sign
                sign *= -1
                # after sign flips to +, we increment offset
                if sign < 0:
                    offset += 1
            return np.array(coords, dtype=int)

        self.bin_x_map = center_out_positions(self.n_bars)  # length 32, from center outward

        # Create bar patches by hand
        self.bar_patches_top = []
        self.bar_patches_bottom = []

        # We'll use a simple approach: we'll create a rectangle at each bin x
        # with width=0.8, height=some fraction in [0..1], and mirror it above/below y=0.
        self.width = 0.8
        for _ in range(self.n_bars):
            # We won't actually create them as "BarContainer" but raw rectangles we can manage
            rect_top = plt.Rectangle((0,0), self.width, 0, color='blue')
            rect_bottom = plt.Rectangle((0,0), self.width, 0, color='blue')
            self.ax.add_patch(rect_top)
            self.ax.add_patch(rect_bottom)
            self.bar_patches_top.append(rect_top)
            self.bar_patches_bottom.append(rect_bottom)

        # We also want to scale the x-limits so all patches fit:
        # If half_n=16, then x in [-16..15], each bar has width=0.8 => let's go about -16.5..15.5
        self.ax.set_xlim(-half_n - 0.5, half_n - 0.5)
        # We'll also scale y-limits to -1..+1 for now (since we only show bars up to 1 in height)
        self.ax.set_ylim(-1.0, 1.0)

        # Turbo colormap for coloring by bar height
        self.cmap = plt.get_cmap('turbo')
        self.norm = Normalize(vmin=0.0, vmax=1.0)
        self.scalar_map = ScalarMappable(norm=self.norm, cmap=self.cmap)

    def update_frame(self, frame):
        if not self.active:
            return

        audio_data = self.audio_manager.read_frames(num_frames=1024)
        if audio_data.shape[0] < 1:
            return
        mono = audio_data[:, 0]
        block_len = len(mono)

        # Compute FFT magnitude and freq axis
        fft_data = np.abs(np.fft.fft(mono))
        half_len = block_len // 2
        fft_data = fft_data[:half_len]
        freqs = np.fft.fftfreq(block_len, 1 / self.samplerate)[:half_len]

        # Filter to [min_freq..max_freq]
        mask = (freqs >= self.min_freq) & (freqs <= self.max_freq)
        freqs = freqs[mask]
        fft_data = fft_data[mask]

        if fft_data.size == 0:
            # no data in that range
            return

        # Split into n_bars bins (ascending freq)
        bin_edges = np.linspace(self.min_freq, self.max_freq, self.n_bars + 1)
        bar_heights = np.zeros(self.n_bars, dtype=np.float32)
        for i in range(self.n_bars):
            low, high = bin_edges[i], bin_edges[i+1]
            bin_mask = (freqs >= low) & (freqs < high)
            if np.any(bin_mask):
                bar_heights[i] = np.mean(fft_data[bin_mask])

        # Normalize to [0..1]
        max_val = np.max(bar_heights)
        if max_val > 0:
            bar_heights /= max_val

        # Now update each patch. The i-th bin goes at x=self.bin_x_map[i]
        # Top bar: y=0..height
        # Bottom bar: y=0..-height
        # Color by the same height
        for i in range(self.n_bars):
            h = bar_heights[i]
            x_center = self.bin_x_map[i] - self.width/2.0

            color_val = self.scalar_map.to_rgba(h)
            rect_top = self.bar_patches_top[i]
            rect_bottom = self.bar_patches_bottom[i]

            # top rectangle
            rect_top.set_x(x_center)
            rect_top.set_y(0.0)
            rect_top.set_width(self.width)
            rect_top.set_height(h)
            rect_top.set_color(color_val)

            # bottom rectangle
            rect_bottom.set_x(x_center)
            rect_bottom.set_y(-h)
            rect_bottom.set_width(self.width)
            rect_bottom.set_height(h)
            rect_bottom.set_color(color_val)


# --------------------------------------------------
#  9) NEW VISUALIZATION (TIME WAVEFORM LINE)
#     *Points colored by distance from x-axis
#     *Line thickness also scaled by |y|
# --------------------------------------------------
class WaveformLineVisualizer(VisualizationBase):
    """
    Plots the time-domain waveform as a line. 
    - The color of each point corresponds to |amplitude|.
    - The line thickness increases with |amplitude|.
    Uses a LineCollection for per-segment control.
    """
    def __init__(self, fig, audio_manager):
        super().__init__(fig, audio_manager)

        self.samplerate = audio_manager.samplerate
        self.ax = self.fig.add_subplot(111)
        self.ax.set_visible(False)
        self.ax.axis('off')
        self.fig.patch.set_facecolor('white')

        # We create an (initially empty) LineCollection
        self.line_collection = LineCollection([], cmap='turbo', norm=Normalize(0,1))
        self.ax.add_collection(self.line_collection)

        # We'll fix the x-limits to show maybe 1024 samples 
        # (or you can do something more dynamic).
        self.ax.set_xlim(0, 256)
        # y-limits might be [-1..+1], or [-something.. +something]
        self.ax.set_ylim(-0.20, 0.20)

    def update_frame(self, frame):
        if not self.active:
            return

        audio_data = self.audio_manager.read_frames(num_frames=1024)
        if audio_data.shape[0] < 1:
            return

        mono = audio_data[:, 0]
        n = len(mono)
        if n < 2:
            return

        # x coords
        x = np.arange(n)
        y = mono

        # Build segments (n-1) line segments connecting consecutive points
        segments = []
        colors = []
        widths = []

        # We'll find absolute max amplitude for normalization
        # so that color = |y| / max_amp
        max_amp = np.max(np.abs(y)) if np.any(y) else 1e-6

        for i in range(n-1):
            x1, y1 = x[i],   y[i]
            x2, y2 = x[i+1], y[i+1]
            segments.append([[x1, y1],[x2, y2]])

            # We color this segment by the average amplitude of its endpoints
            avg_amp = 0.5*(abs(y1) + abs(y2))
            c = avg_amp / (max_amp + 1e-12)
            colors.append(c)

            # The thickness also scales with c (e.g. base=0.5.. thickness=0.5+something)
            lw = 1.0 + 16.0*c
            widths.append(lw)

        segments = np.array(segments)

        # Update the line collection
        self.line_collection.set_segments(segments)
        # Per-segment color
        self.line_collection.set_array(np.array(colors))
        # Per-segment linewidth
        self.line_collection.set_linewidths(widths)

        # Optionally, we can re-scale y-limits if you want auto scaling:
        # self.ax.set_ylim(y.min() * 1.1, y.max() * 1.1)


# --------------------------------------------------
#  9) NEW VISUALIZATION: LETTERS
# --------------------------------------------------
class LettersVisualizer(VisualizationBase):
    """
    Displays a word in large letters across the screen.
    Each letter is colored according to a different FFT frequency bin.
    """

    def __init__(self, fig, audio_manager):
        super().__init__(fig, audio_manager)

        self.samplerate = audio_manager.samplerate
        self.ax = self.fig.add_subplot(111)
        self.ax.set_visible(False)
        self.ax.axis('off')
        self.fig.patch.set_facecolor('white')

        # We'll place them horizontally across the center of the figure.
        # We'll define some x-coordinates for each letter, spaced out.
        self.letters_str = "*DAMIEN* "
        n_letters = len(self.letters_str)

        # x positions from -4..+4, y=0
        x_positions = np.linspace(-4, 4, n_letters)
        self.letter_texts = []

        for i, letter in enumerate(self.letters_str):
            # Create a Text object for each letter
            txt = self.ax.text(
                x_positions[i], 0,
                letter,
                fontsize=80,      # big letters
                fontweight='bold',
                ha='center',
                va='center',
                color='white'     # default
            )
            self.letter_texts.append(txt)

        # We'll define bin edges for 9 segments in [min_freq..max_freq]
        self.min_freq = 20.0
        self.max_freq = 8000.0
        self.n_bins = n_letters
        self.bin_edges = np.linspace(self.min_freq, self.max_freq, self.n_bins+1)

        # We want no axis / ticks
        self.ax.set_xlim(-5, 5)
        self.ax.set_ylim(-1, 1)

        # For coloring
        self.cmap = plt.get_cmap('turbo')
        self.norm = Normalize(vmin=0, vmax=1)
        self.scalar_map = ScalarMappable(norm=self.norm, cmap=self.cmap)

    def update_frame(self, frame):
        if not self.active:
            return

        # 1) read frames
        audio_data = self.audio_manager.read_frames(num_frames=1024)
        if audio_data.shape[0] < 1:
            return

        mono = audio_data[:, 0]
        block_len = len(mono)

        # 2) compute FFT
        fft_data = np.abs(np.fft.fft(mono))
        half_len = block_len // 2
        fft_data = fft_data[:half_len]
        freqs = np.fft.fftfreq(block_len, 1 / self.samplerate)[:half_len]

        # Only keep [min_freq..max_freq]
        mask = (freqs >= self.min_freq) & (freqs <= self.max_freq)
        freqs = freqs[mask]
        fft_data = fft_data[mask]
        if freqs.size < 1:
            # nothing to show
            return

        # 3) Build amplitude bins for the 9 letters
        bar_vals = np.zeros(self.n_bins, dtype=np.float32)
        for i in range(self.n_bins):
            low = self.bin_edges[i]
            high = self.bin_edges[i+1]
            bin_mask = (freqs >= low) & (freqs < high)
            if np.any(bin_mask):
                bar_vals[i] = np.mean(fft_data[bin_mask])

        # 4) Normalize
        max_val = np.max(bar_vals)
        if max_val > 0:
            bar_vals /= max_val

        # 5) Assign each letter a color based on bar_vals[i]
        for i, letter_txt in enumerate(self.letter_texts):
            amplitude_fraction = bar_vals[i]
            color = self.scalar_map.to_rgba(amplitude_fraction)
            letter_txt.set_color(color)


# --------------------------------------------------
#  10) VISUALIZATION MANAGER
# --------------------------------------------------
class VisualizationManager:
    def __init__(self):
        self.fig = plt.figure(figsize=(8, 6))

        self.available_devices = []
        all_devices = sd.query_devices()
        for i, d in enumerate(all_devices):
            if d["max_input_channels"] > 0:
                self.available_devices.append((i, d["name"]))

        letters = string.ascii_uppercase
        print("Available Audio Input Devices:")
        for i, (dev_index, dev_name) in enumerate(self.available_devices):
            if i >= 26:
                break
            print(f"  {letters[i]} -> index={dev_index}, name='{dev_name}'")

        self.audio_manager = AudioManager(
            device=None,
            channels=1,
            samplerate=44100,
            blocksize=256
        )
        self.audio_manager.start()

        # Title screen
        title_url = "https://soundvisualizations.blob.core.windows.net/media/demo_splashscreen_art.png"
        self.title_screen = TitleScreen(self.fig, self.audio_manager, title_url)

        # Visualizations
        self.viz1 = DancingPolarVisualizer(self.fig, self.audio_manager)
        self.viz2 = WireframeFFTVisualizer(self.fig, self.audio_manager)
        self.viz3 = PropellerArmsVisualizer(self.fig, self.audio_manager)
        self.viz4 = FrequencyBarChartVisualizer(self.fig, self.audio_manager)
        self.viz5 = WaveformLineVisualizer(self.fig, self.audio_manager)
        
        # The new Letters visualizer
        self.viz6 = LettersVisualizer(self.fig, self.audio_manager)

        self.visualizations = [self.viz1, self.viz2, self.viz3, self.viz4, self.viz5, self.viz6]

        self.active_screen = self.title_screen
        self.active_screen.activate()

        self.anim = animation.FuncAnimation(
            self.fig,
            self.update,
            interval=10,
            blit=False
        )

        self.cid_keypress = self.fig.canvas.mpl_connect("key_press_event", self.on_key_press)
        self.fig.canvas.manager.set_window_title("Audio Visualizer")

    def on_key_press(self, event):
        if event.key in ['q', 'escape']:
            self.cleanup_and_close()

        if self.active_screen == self.title_screen:
            if event.key is not None:
                letter = event.key.upper()
                if letter in string.ascii_uppercase:
                    index = ord(letter) - ord('A')
                    if 0 <= index < len(self.available_devices):
                        dev_index, dev_name = self.available_devices[index]
                        print(f"Switching audio device to {dev_name} (index={dev_index})")
                        self.audio_manager.stop()
                        self.audio_manager.device = dev_index
                        self.audio_manager.start()

            if event.key == '1':
                self.switch_to(self.viz1)
            elif event.key == '2':
                self.switch_to(self.viz2)
            elif event.key == '3':
                self.switch_to(self.viz3)
            elif event.key == '4':
                self.switch_to(self.viz4)
            elif event.key == '5':
                self.switch_to(self.viz5)
            elif event.key == '6':
                self.switch_to(self.viz6)
        else:
            if event.key == '1':
                self.switch_to(self.viz1)
            elif event.key == '2':
                self.switch_to(self.viz2)
            elif event.key == '3':
                self.switch_to(self.viz3)
            elif event.key == '4':
                self.switch_to(self.viz4)
            elif event.key == '5':
                self.switch_to(self.viz5)
            elif event.key == '6':
                self.switch_to(self.viz6)
            elif event.key == '0':
                self.switch_to(self.title_screen)

    def switch_to(self, screen):
        if self.active_screen == screen:
            return
        self.active_screen.deactivate()
        self.active_screen = screen
        self.active_screen.activate()

    def update(self, frame):
        if self.active_screen:
            self.active_screen.update_frame(frame)

    def cleanup_and_close(self):
        if self.active_screen:
            self.active_screen.deactivate()
        self.audio_manager.stop()
        plt.close(self.fig)
        sys.exit(0)

    def show(self):
        plt.tight_layout()
        plt.show()

# --------------------------------------------------
#  11) MAIN
# --------------------------------------------------
if __name__ == "__main__":
    manager = VisualizationManager()
    print("Press '1'..'6' to switch from the splash screen to a visualization.")
    print("Press a letter key (A..Z) on the splash screen to select an input device.")
    print("Press '0' to return to the splash screen from a visualization.")
    print("Press 'q' or 'Esc' to quit.")
    manager.show()