test.txt

Here are several fun ways to manipulate your PowerShell script to make Piper TTS sound funny, strange, or robotic. Since Piper is neural TTS, standard "robot" filters don't apply, so we have to break the neural logic or the text input to get weird results.

1. The "Chipmunk" & "The Sloth" (Speed Manipulation)

The easiest way to change the "vibe" is to change the speaking speed. Piper uses the --length_scale flag.

Values < 1.0: Makes it faster and higher pitched (Chipmunk/Caffeinated).

Values > 1.0: Makes it slower and deeper (Zombie/Demon).

PowerShell Snippet:

powershell

# 1. The Caffeinated Chipmunk (Very Fast, High Pitch)

 $model = "$env:TEMP\piper_voices\en_US-lessac-medium.onnx"

 $out = "$env:TEMP\piper_fast.wav"

 $text = "I have had way too much coffee and I cannot stop talking about code reviews."

echo $text | py -m piper --model $model --output $out --length_scale 0.6

Start-Process $out

# 2. The Zombie (Very Slow, Deep Pitch)

 $out_slow = "$env:TEMP\piper_slow.wav"

 $text = "Brains... I need to merge your branch... brains..."

echo $text | py -m piper --model $model --output $out_slow --length_scale 2.5

Start-Process $out_slow

2. The "Depressed Robot" vs. "Manic Psycho" (Noise Scale)

Piper uses a parameter called noise_scale (or sometimes noise_w). This controls the variability in the speech.

Low --noise_scale (e.g., 0.1): The voice becomes very flat, monotone, and deterministic. It sounds like a depressed AI or a cheap GPS.

High --noise_scale (e.g., 0.9): The voice becomes extremely unstable, changing pitch randomly within words. It sounds manic, emotional, or "drunk."

PowerShell Snippet:

powershell

 $model = "$env:TEMP\piper_voices\en_US-lessac-medium.onnx"

 $out_flat = "$env:TEMP\piper_flat.wav"

 $out_maniac = "$env:TEMP\piper_maniac.wav"

 $text = "I am completely stable and my emotional state is perfectly normal."

# The Depressed Robot (Flat monotone)

echo $text | py -m piper --model $model --output $out_flat --noise_scale 0.1

Start-Process $out_flat

# The Manic Psycho (Unstable pitch)

echo $text | py -m piper --model $model --output $out_maniac --noise_scale 0.9

Start-Process $out_maniac

3. Text Hacking: The "Glitch" & The "Stutter"

Neural TTS reads text literally. You can exploit this to create speech impediments or digital glitches.

PowerShell Snippet:

powershell

 $model = "$env:TEMP\piper_voices\en_US-lessac-medium.onnx"

 $out = "$env:TEMP\piper_glitch.wav"

# Fun text tricks:

# 1. Stuttering: Repeat first letters

# 2. Glitching: Use random punctuation to break the sentence flow

# 3. Elongation: Use hyphens on vowels

 $text = "S-s-s-system error. I am... f-f-f-failing to compile. B-b-b-boop. Noooooooo way."

echo $text | py -m piper --model $model --output $out

Start-Process $out

4. The "Demonic" Pitch Shift (FFmpeg required)

If you want a truly scary strange voice (like a movie monster or a demon), you need to pitch-shift the audio after Piper generates it. Piper generates natural pitch, but we can drop it using ffmpeg (assuming you have ffmpeg installed or in path).

This generates the audio, then crushes the pitch down and speeds it up slightly to keep the timing similar.

PowerShell Snippet:

powershell

 $model = "$env:TEMP\piper_voices\en_US-lessac-medium.onnx"

 $raw_out = "$env:TEMP\piter_raw.wav"

 $demon_out = "$env:TEMP\demon_final.wav"

 $text = "Your pull request has been denied. I will consume your soul."

# 1. Generate normal TTS

echo $text | py -m piper --model $model --output $raw_out

# 2. Use FFmpeg to lower pitch (asetrate=44100*0.6 makes it 60% speed/pitch)

#    then atempo=1.6 speeds it back up so it doesn't sound slow, just deep.

ffmpeg -y -i $raw_out -filter:a "asetrate=44100*0.6,atempo=1.6" $demon_out

Start-Process $demon_out

5. The "Silent Movie" Effect (Speed + Fast Pitch)

Combine the speed flag with text that usually implies seriousness for a comedic effect.

PowerShell Snippet:

powershell

 $model = "$env:TEMP\piper_voices\en_US-lessac-medium.onnx"

 $out = "$env:TEMP\piper_benji.wav"

 $text = "Oh my goodness, the code is compiling! Quick, grab the source code before the manager sees us!"

# Set length_scale to 0.65 (Super fast)

echo $text | py -m piper --model $model --output $out --length_scale 0.65

Start-Process $out

Summary of Flags to Play With

If you want to experiment, append these to your py -m piper command:

Flag

Effect

"Fun" Value

--length_scale 0.5	Super Fast / High Pitch	Chipmunk / Hyperactive

--length_scale 2.5	Super Slow / Low Pitch	Zombie / Giant

--noise_scale 0.1	Monotone / Flat	Depressed Robot

--noise_scale 0.8	Highly Variable	Drunk / Manic

go deeper and check out more related repositories on github and voice research on ar5iv.org etc.

Thought Process

Great question – this is exactly where Piper + a little creativity gets fun.

Below I’ll go deeper on:

Concrete knobs inside Piper you can already use (with fun values).

GitHub repos that build on Piper and/or add audio effects.

Voice conversion / TTS research on ar5iv that you can steal ideas from (even if you don’t retrain models).

A few ready-to-use PowerShell patterns for weird/funny voices.

I’ll assume you’re still on Windows/PowerShell like in your snippet.

High‑level picture

Here’s the overall “fun weird voice pipeline” you’re aiming at:

Input text

Piper TTS with tweaked inference: length_scale, noise_scale, noise_w

Post-processing audio: pitch shift, time stretch, flanger, glitch, reverb

Optional neural voice conversion: VITS / RVC / SVC style

Funny output: chipmunk, demon, drunk robot, glitchy AI, musical TTS

You don’t have to do all steps – even just (B) or (B+C) is enough to get very different personalities.

1. Piper’s internal knobs: what they are and fun ranges

Piper exposes a small set of inference knobs that map directly to VITS-style stochastic duration and latent variability.

From Piper’s TRAINING.md and a Piper MCP wrapper, we have:

In the model config, under inference:

noise_scale – “noise added to the generator (default: 0.667)”

length_scale – “speaking speed (default: 1.0)”

noise_w – “phoneme width variation (default: 0.8)”

The MCP server describes them at call time as:

length_scale: Speech speed (lower = faster)

noise_scale: Voice variation control

noise_w_scale: Pronunciation variation

Research side: VITS uses a stochastic duration predictor and latent variables so a single text can be spoken with many different rhythms and pitch shapes

arxiv

 . Piper’s knobs are basically your user-level handles on that same underlying variability.

Fun “presets” using just these:

Chipmunk / hyper-caffeinated dev:

length_scale = 0.6

noise_scale = 0.8

noise_w = 1.0

→ Very fast, more random pitch & timing variation.

Depressed GPS / tired robot:

length_scale = 1.4

noise_scale = 0.2

noise_w = 0.4

→ Slow, flat voice, little variation, very “drained”.

Drunk / unstable announcer:

length_scale = 0.9–1.1

noise_scale = 1.0–1.2

noise_w = 1.2

→ Pronunciation durations start to wobble, pitch becomes more random.

In your current usage pattern (via py -m piper), you can’t directly pass these on the command line unless you modify the model’s *.onnx.json config or use a wrapper. Some easy paths:

Edit the model’s .onnx.json:

Open something like: en_US-lessac-medium.onnx.json

Under inference, set:

"length_scale": 0.6

"noise_scale": 1.0

"noise_w": 1.2

Then run Piper as usual. This matches how the training docs describe the inference section.

Or use an existing Piper wrapper that already exposes these:

Piper TTS MCP Server exposes length_scale, noise_scale, noise_w_scale as call parameters.

You could call that from PowerShell (HTTP requests) and get different “personas” just by changing numbers.

PowerShell-ish idea (conceptual; you’d need the MCP endpoint or your own tiny Flask wrapper):

powershell

# Pseudo-code assuming a local Piper HTTP API like the MCP server exposes

 $body = @{

    text         = "The code review has begun. Your pull request looks... interesting."

    length_scale = 0.6   # super fast

    noise_scale  = 1.0   # more variation

    noise_w_scale = 1.2  # wobbly pronunciation

} | ConvertTo-Json

Invoke-RestMethod -Method Post -Uri "http://localhost:5000/speak" -Body $body -ContentType "application/json" | Select-Object -ExpandProperty audio_url

Even without that, you can pre-bake a few “fun” .onnx.json configs:

en_US-lessac-medium-chipmunk.onnx.json

en_US-lessac-medium-zombie.onnx.json

en_US-lessac-medium-drunk.onnx.json

…and just swap the JSON file (or rename the model) when you want a different persona.

2. GitHub repos that add effects or interesting voice workflows

Here are especially relevant repos for “fun/strange” builds on Piper:

2.1 Webpiper – Piper + JSON effect chains (pitch shift, flanger, etc.)

Webpiper is a FastAPI service on top of Piper that lets you define “effect chains” in JSON. Key bits:

Uses Piper as the TTS engine.

POST /api/v1/synthesize accepts:

text, voice, speed, noise_w (Piper-side)

effects: a list of audio effects applied after synthesis.

Effects include:

pitch_shift – changes pitch without changing duration (pitch_change from –100 to +100)

flanger – metallic modulation (rate, min_delay, max_delay, feedback, dry, wet)

random_semitone_sawtooth_wave – sawtooth modulation with random semitone shifts

normalize – peak normalization

speed_change – playback speed by resampling (positive = faster, negative = slower)

Example request body from the README:

json

{

  "text": "Hello, world!",

  "voice": "siwis-medium",

  "speed": 1.0,

  "noise_w": 0.8,

  "effects": [

    {

      "name": "pitch_shift",

      "params": { "pitch_change": 10 }

    },

    {

      "name": "random_semitone_sawtooth_wave",

      "params": {

        "min_freq": 170.0,

        "max_semitones": 6,

        "pitch_duration": 0.4,

        "wet": 0.3

      }

    },

    {

      "name": "normalize",

      "params": {}

    }

  ],

  "lite_file": true

}

Why this matters for you:

You can re-implement the same ideas in PowerShell with pydub/ffmpeg or just call Webpiper itself.

It gives a concrete menu of “strange” effects you can chain:

pitch_shift + flanger → sci‑fi radio / alien.

random_semitone_sawtooth_wave + speed_change → glitchy A/V malfunction or “musical” TTS.

2.2 PiperGen – Pretrain Piper with OpenAI voices

Repo: theboringhumane/openai-voices.piper (“PiperGen”).

Idea: Use OpenAI TTS audio as training data for Piper, so the resulting Piper voices get more natural prosody and tone

github

 .

For “fun” purposes, you could:

Capture an especially expressive OpenAI voice (e.g., “overly cheerful assistant”, “narrator”, etc.).

Train/fine-tune a Piper model on that style (following Piper’s TRAINING.md).

Then push the resulting model into extreme parameter regimes (chipmunk / demon) for a stylized personality.

This is more involved (requires training setup), but it’s a direct route to “Piper but styled like X.”

2.3 AgentVibes & Pantella – multi-voice TTS characters

AgentVibes (paulpreibisch/AgentVibes) adds voice to Claude Code sessions.

Interesting for: how they choose voices per persona/context, and how they string TTS into a larger interactive system.

Pantella (Pathos14489/Pantella) is a Mantella fork for speaking with NPCs in Skyrim/FO4 with multiple TTS options.

Interesting for: per-NPC voice assignments and “in‑world” voice styles (e.g., gruff, haughty, etc.).

Even if you don’t use these as-is, they show patterns you can copy:

Map “characters” → specific Piper models + specific inference configs + specific effect chains.

Add random variations in noise_scale / noise_w so each line from the same character feels slightly different.

2.4 Neural voice conversion (VITS / RVC / SVC) to turn Piper into other characters

These don’t modify Piper directly, but they can take Piper output and turn it into something totally different (anime, singer, monster, etc.):

so-vits-svc and forks (e.g., voicepaw/so-vits-svc-fork):

SoftVC VITS Singing Voice Conversion – keeps pitch/intonation, changes speaker; can run in real-time

github

+1

 .

NeuralVC (zzy1hjq/NeuralVC):

Real-time voice conversion using VITS + HuBERT-Soft + a speaker encoder

github

 .

Research behind this style of work:

Text-free, non-parallel many-to-many voice conversion using normalizing flows keeps everything except speaker identity (prosody, content, etc.)

assets.amazon

 .

MSM-VC and related work explicitly model speaking style at multiple levels (global, local, frame) to transfer style while preserving content

ieeexplore.ieee

+1

 .

Fun application:

Use Piper as your “clean speaker”.

Feed Piper’s WAV into an SVC model trained on:

An anime character,

A very deep “monster” voice,

A singer.

Optionally keep Piper’s extreme parameters (length_scale, noise_scale) to get “fast anime voice” or “drunk, singing AI”.

3. Research on ar5iv that you can raid for ideas

You don’t have to implement all of these; they’re more like “conceptual menu” for what’s possible.

3.1 Controllable TTS survey – how people control emotion/prosody

Survey: “Towards Controllable Speech Synthesis in the Era of Large Language Models”.

Emphasizes fine-grained control over emotion, prosody, timbre, and duration.

Discusses control strategies: latent style tokens, explicit features, and more recent LLM-prompted control.

What you can steal:

Even though Piper only exposes a small subset of these, you can think of:

length_scale as a crude duration control (like the “duration” dimension in controllable TTS).

noise_scale/noise_w as your crude “prosody variation / expressiveness” controls, similar to style tokens or latent prosody spaces.

3.2 Apple’s controllable prosody work – mapping dimensions to effects

Apple’s “Controllable Neural Text-to-Speech Synthesis Using Prosody Control” shows a model conditioned on pitch, pitch range, phone duration, energy, and spectral tilt.

They bias each dimension from –1.0 to +1.0 to get different speaking styles.

Mapping to your “fun” pipeline:

Pitch → emulate with post-process pitch_shift (Webpiper or ffmpeg).

Pitch range → push with noise_scale in Piper, then further with audio modulation.

Duration → length_scale in Piper, then speed_change effects.

Energy → compression/normalization and reverb.

Spectral tilt → EQ / filters (e.g., low-pass for “muffled radio”).

3.3 Global prosody style transfer – without text

MIT/IBM Watson Lab’s blog on global prosody style transfer without text transcriptions emphasizes transferring pitch and rhythm between speakers while ignoring lexical content

mitibmwatsonailab.mit

 .

This is basically what VC (voice conversion) systems do.

Relevance:

If you want the same text but a totally different “vibe”, you can:

Generate neutral audio with Piper.

Use a VC system that’s good at style transfer (e.g., MSM-VC

ieeexplore.ieee

 ) to impose:

Overly dramatic narration.

Shouted sportscaster style.

Quiet “late night radio” style.

3.4 Prompt-based emotion control (ProEmo, etc.)

ProEmo: Prompt-Driven Text-to-Speech Synthesis Based on Emotion and Intensity prompts

arxiv

 .

Uses LLMs to manipulate prosody guided by textual emotion cues (e.g., “angry,” “sad,” “excited”)

arxiv

 .

Piper doesn’t do this natively, but you can approximate it with:

Emotion-specific text preprocessing:

Add parentheticals: “(slowly) I can’t believe this code compiles.”

Stutters, elongated vowels: “Nooooooo way.”

Emotion-specific audio post-processing:

Angry: more distortion, slight speed-up, slight pitch-up.

Sad: slower, lower pitch, more reverb.

4. Concrete fun recipes (PowerShell-style)

I’ll stick to relatively lightweight tooling:

Piper for TTS.

ffmpeg for audio effects (pitch shift, time-stretch, etc.).

Optional: run Webpiper instead of rolling your own effects.

4.1 Chipmunk Code Reviewer (Piper + pitch up via ffmpeg)

Concept:

length_scale 0.6 for speed.

noise_scale 0.8 for variation.

Use ffmpeg’s asetrate + atempo to raise pitch but keep duration similar (classic chipmunk trick).

PowerShell sketch:

powershell

 $model = "$env:TEMP\piper_voices\en_US-lessac-medium.onnx"

 $raw   = "$env:TEMP\piper_raw.wav"

 $out   = "$env:TEMP\piper_chipmunk.wav"

 $text = "The code review has begun. Your pull request looks interesting. I have questions."

# 1) Generate with Piper (you'd edit the .onnx.json to set length_scale/noise_scale, or use a wrapper)

echo $text | py -m piper --model $model --output $raw

# 2) Pitch up with ffmpeg (example: +4 semitones)

#    asetrate = 44100 * 2^(n/12); for n=4, factor ≈ 1.26

#    Then atempo ≈ 1/1.26 ≈ 0.79 to restore original duration

ffmpeg -y -i $raw -filter:a "asetrate=44100*1.26,atempo=0.79" $out

Adjust asetrate and atempo for:

More extreme chipmunk: factor ≈ 1.5–2.0, atempo ≈ 0.67–0.5.

Deeper “giant” voice: factor < 1.0 (e.g., 0.7) + atempo ≈ 1.4.

4.2 Depressed Robot (Piper + flatness)

Use inference alone (no ffmpeg required if you edit config):

In the .onnx.json:

"length_scale": 1.4

"noise_scale": 0.2

"noise_w": 0.4

Text style:

powershell

 $text = "I have reviewed your pull request. It is... fine. I suppose. Merge it if you must."

echo $text | py -m piper --model $model --output $out

You get a very monotone, slow, tired-sounding robot.

4.3 Glitchy AI (Piper + random pitch modulation / speed jitter)

Two ways:

A) Use Webpiper’s random_semitone_sawtooth_wave effect:

POST to Webpiper with an effects list containing:

random_semitone_sawtooth_wave with min_freq ≈ 170, max_semitones ≈ 6, pitch_duration ≈ 0.3–0.4, wet ≈ 0.4–0.6.

This adds a “wobbly pitch” that sounds like an unstable voice synthesizer.

B) DIY with ffmpeg + multiple segments:

Split Piper’s output into sentence-sized chunks (using silence detection).

Apply slightly different pitch_shift/speed_change to each chunk.

Concatenate back.

Even a simple “speed wobble” can feel like a glitchy AI:

powershell

# Very simple single-file wobble using vibrato in ffmpeg

ffmpeg -y -i $raw -filter:a "vibrato=f=6.0:d=0.5" $out

4.4 Demonic Announcer (deep pitch + reverb)

Use Piper with normal or slightly slower speed: length_scale ≈ 1.2.

Drop pitch with asetrate < 1.0 and compensate speed with atempo.

Add reverb.

Example ffmpeg filter:

powershell

ffmpeg -y -i $raw `

  -filter:a "asetrate=44100*0.7,atempo=1.43,aformat=sample_fmts=s16:sample_rates=44100,areverb" `

  $out

(You may need to tweak reverb parameters; this is just the idea.)

4.5 Multi-character system inspired by Pantella / AgentVibes

Borrowing the idea from Pantella and AgentVibes, define a few “characters” as small PowerShell objects:

powershell

 $characters = @{

    "narrator"   = @{ model = "en_US-lessac-medium.onnx";  length_scale = 1.0; noise_scale = 0.667; noise_w = 0.8 }

    "chipmunk"   = @{ model = "en_US-lessac-medium.onnx";  length_scale = 0.6; noise_scale = 0.8;  noise_w = 1.0 }

    "zombie"     = @{ model = "en_US-lessac-medium.onnx";  length_scale = 1.4; noise_scale = 0.2;  noise_w = 0.4 }

    "drunk"      = @{ model = "en_US-lessac-medium.onnx";  length_scale = 1.0; noise_scale = 1.1;  noise_w = 1.3 }

}

Then:

Have your script select a character per “role” in a dialog.

For each line:

Temporarily edit the .onnx.json inference block to match the character.

Call Piper.

Optionally run through an effect chain (Webpiper or ffmpeg) tuned per character.

This is very close to how projects like Pantella assign voices per NPC, and how AgentVibes makes AI sessions feel alive with consistent voice personas.

5. If you want to go really deep (training / research-level)

These are more advanced, but here’s the path:

Fine-tune Piper on a stylized dataset:

TRAINING.md shows how to fine-tune from an existing Lessac checkpoint.

Record a dataset with a specific “persona” (e.g., over-the-top game show host, shy whispering scientist).

Fine-tune and then push the resulting model to extreme inference settings.

Train a VITS-style model with explicit style tokens:

Global Style Tokens (GST) and Local Style Tokens (LST) papers show you can learn discrete or local style embeddings that control prosodic variation

proceedings.mlr

+1

 .

You can inspect learned tokens and pick ones that correspond to “energetic”, “calm”, “whisper”, etc., and then switch them per character.

Prompt-driven emotion (ProEmo-like) with LLM + Piper:

Use an LLM to insert emotion markers: “(excited) This actually works!”

arxiv

 .

Map those markers to:

Different Piper models or configs.

Different effect chains (pitch shift, reverb, etc.).

This mirrors the prompt-based emotion control in recent work like ProEmo

arxiv

 .

6. Putting it all together (short version)

Within Piper:

length_scale → speed (time dilation).

noise_scale → voice variation (pitch/prosody randomness).

noise_w → pronunciation timing weirdness.

Around Piper:

Use Webpiper’s JSON effects (pitch_shift, flanger, random_semitone_sawtooth_wave, speed_change) for instant strange voices.

Or re-implement similar effects in ffmpeg/pydub.

Beyond Piper:

Use VITS-based voice conversion (so-vits-svc, NeuralVC, etc.) to completely re-skin Piper’s output into other characters or styles

github

+1

 .

Research inspiration:

Controllable TTS survey for taxonomy of controls (emotion, prosody, duration).

Apple prosody control for which knobs matter (pitch, range, duration, energy, spectral tilt).

Prompt-based emotion TTS (ProEmo) for how text prompts can drive style

arxiv

 .

If you tell me which direction you want to push first (pure parameter tweaks vs. post-processing vs. full training + VC), I can sketch a more concrete mini-project tailored to that.

------------------

Yes, absolutely — CLIs can (and should) learn a ton from Grok's interface philosophy, even though they live in a completely different medium.

Grok's core UX traits that translate surprisingly well to a modern CLI are:

- **low ceremony** — almost no boilerplate to get useful output

- **progressive disclosure** — show partial results early instead of making the user wait for everything

- **personality in text, not in chrome** — helpful sarcasm, brevity, confidence without being patronizing

- **context awareness** — remember recent commands / conversation state without forcing the user to repeat themselves

- **fail gracefully & suggest fixes** — errors should teach rather than punish

- **speed-first feel** — perceived latency < 300 ms for simple queries, visual feedback during longer ones

Here’s how a CLI could steal those ideas in practice (2026 taste):

### 1. Progressive / streaming output (like Grok typing)

Most CLIs dump everything at once after 2–10 seconds of silence.  

Instead: stream tokens as they arrive.

```bash

$ master ask "explain how htmx sse works in 3 sentences"

Thinking… █▌

htmx sse lets the server push HTML fragments to the client over a persistent connection.

You attach sse-connect="/events" and sse-swap="my-event" to an element → htmx auto-updates it when the server sends matching events.

Best part: zero client JS, works with any SSE endpoint.

Done ✓ (1.8 s)

```

Implementation tip: use ANSI escape codes for cursor control + carriage return (`\r`) to overwrite the current line while streaming.

### 2. Extremely terse happy path

Grok never makes you type `--model claude --max-tokens 400 --temperature 0.7` just to ask something.

```bash

$ master " Oslo weather next 3 days"

Oslo forecast:

Today: -3 °C, snow showers, wind 25 km/h

Tomorrow: -5 °C, partly cloudy

Day after: -2 °C, light rain turning to snow

Want hourly? → master weather --hourly

```

Default to sensible presets → add flags only when deviating.

### 3. Subtle personality & helpful snark (Grok-style)

```bash

$ master refactor this ugly controller

[diff shown]

Before: 42 lines of sadness

After: 9 elegant lines

You had 7 nested conditionals. I removed them. You're welcome.

Rule: "If your method looks like a nesting doll, it needs therapy."

```

Or on failure:

```bash

$ master run --script broken.rb

SyntaxError on line 23: unexpected keyword_end

Looks like you closed something you never opened.

Want me to guess the fix? (y/n)

```

### 4. Stateful context without --session flags

Remember recent topics/commands automatically (like Grok remembers conversation):

```bash

$ master ask "best norwegian winter hikes"

[long answer about Rondane, Jotunheimen…]

$ master more details on Rondane

Rondan has 10+ peaks over 2000 m. Classic route: Rondvasshøgde – Rondane summit (easy–medium, 6–8 h round trip). Best in Feb–Mar for snowshoeing.

$ master show map

[ascii art or link to static map]

```

Store in `~/.master/context.json` or use a tiny sqlite db.

### 5. Visual feedback during wait (minimal & classy)

For > 1.5 s operations:

```text

master thinking… ▁▂▃▄▅▆▇█  (spinning bar or braille dots)

```

Or just typewriter dots:

```text

master thinking…

```

No emoji spinners, no rainbow progress bars — keep it calm & terminal-native.

### 6. Zero-config install + instant usefulness

Like Grok's “just open the app and start typing”:

```bash

curl -sSL https://get.master.sh | sh

master "hello"

→ Hi Ragnhild, what's on your mind tonight? ❄️

```

### Quick wish-list summary for a Grok-inspired CLI

- Stream answers character-by-character or word-by-word

- Default to short, confident, slightly cheeky tone

- Zero mandatory flags for 90% of use-cases

- Remember context across invocations

- Helpful error messages that suggest next action

- Subtle ANSI progress during thinking (no noise)

- One-command install that just works

Would you like a tiny proof-of-concept Ruby CLI script that demonstrates streaming output + personality + context memory (under 100 lines), or would you rather focus on specific commands / integrations first? 😄