DEV Community: Mavos.by.Kyklos

Lyrisee How-To: Fix Transcription Errors With the Lyric Editor

Mavos.by.Kyklos — Wed, 17 Jun 2026 11:44:01 +0000

If you've run a song through Lyrisee and a few words came out wrong — "veins" became "vains", an artist name got mangled, a slang term got autocorrected — the lyric editor lets you fix it without re-processing.

Opening the Editor

After processing completes, click ✎ Edit lyrics in the top right of the controls panel.

The editor opens as an overlay with one row per line. Each row shows:

Time — the line's start timestamp (editable)
Text — the transcribed words for that line (editable)
Delete — remove a line entirely

Fixing a Mis-Transcribed Word

Click the text field for any line and edit it directly.

Before: "pain is all i fill i got nothing to gain"
After:  "pain is all I feel I got nothing to gain"

The corrected words get re-aligned to the original word-level timestamps automatically using difflib.SequenceMatcher. If you replace one word with one word, it inherits the original timing exactly. If you replace two words with three (or vice versa), the time window is split evenly across the new tokens.

Nudging a Line's Start Time

Sometimes Whisper starts a line a fraction of a second early or late. Click the time field and adjust:

12.480  →  12.600

All words within that line shift by the same delta.

Adding or Removing Lines

Use the + Add line button at the bottom to insert a line with a manual timestamp and text. Use the ✕ button on any row to remove it.

This is useful when Whisper splits a line mid-phrase or merges two lines into one.

Applying Changes

Click Apply & re-render. The canvas updates immediately — no re-processing, no waiting. The typography engine replays with your corrected text and preserved timing.

Downloading the Corrected Lyrics

Click Download to save a lyric_data.json with your corrections. Next session, load the audio file and then drag in the saved JSON — it skips the whole processing pipeline and goes straight to playback.

Tips for Common Cases

Rap slang and AAVE
Whisper sometimes over-corrects. If "finna" became "going to", the AI repair should have caught it — but if not, fix it in the editor. The POS tagger re-runs on edited text, so "finna" will get the right grammatical role.

Artist names and proper nouns
Whisper often mishears names. Fix in the editor — capitalized words automatically get PROPN tagging, which affects styling (they render with slightly different weight).

Sung words vs spoken words
If a word was held for 2 seconds and Whisper assigned it a 0.1s window, you can't fix that in the text editor — but it's rare. The word will still appear at the right moment; it just exits earlier than the hold lasts.

Background vocals and ad-libs
Whisper picks up background vocals. If you don't want them, delete those lines in the editor.

Open Lyrisee → https://acecalisto3-lyrisee.hf.space

How Lyrisee Syncs Lyrics to Audio: Word-Level Timestamps Explained

Mavos.by.Kyklos — Wed, 17 Jun 2026 11:43:13 +0000

Standard lyrics-sync apps take a line's timestamp and guess when each word lands. If a line starts at 4.2s and ends at 6.8s across 8 words, each word gets ~0.3s — regardless of whether the singer held one word for a full second and rapped the next seven in a burst.

Lyrisee doesn't guess. Every word gets its own measured start and end time.

How Word-Level Timestamps Work

Lyrisee uses faster-whisper — an optimized implementation of OpenAI's Whisper model — with word_timestamps=True.

Whisper processes audio in 30-second chunks and produces attention weights over the spectrogram. faster-whisper uses those attention weights to find exactly when the model's "attention" peaks for each word token — that peak is the word's center timestamp. Start and end are derived from the attention envelope.

from faster_whisper import WhisperModel

model = WhisperModel("tiny.en", device="cpu", compute_type="int8")
segments, info = model.transcribe(audio_path, word_timestamps=True, vad_filter=True)

words = []
for seg in segments:
    for w in seg.words:
        words.append({
            "text": w.word.strip(),
            "start": round(w.start, 3),
            "end":   round(w.end,   3),
        })

The result: a list like:

[
  {"text": "dark",    "start": 12.480, "end": 12.720},
  {"text": "nights",  "start": 12.720, "end": 13.200},
  {"text": "running", "start": 13.440, "end": 13.800},
  {"text": "cold",    "start": 13.880, "end": 14.120}
]

Why This Matters for Typography

With interpolated timing, a held note ("niiiights") would show the word for exactly its share of the line duration — even though the singer held it 4× longer than a normal word.

With word-level timing, "niiiights" shows exactly as long as it sounds. The typography breathes with the vocal performance.

AI Lyric Repair

Whisper sometimes mishears words — especially rap (fast delivery, slang, AAVE, deliberate wordplay). Lyrisee sends the raw transcript to Gemini for correction:

System: You are Lyrisee's lyric-repair stage. Fix transcription errors using
the subject matter, rhyme scheme, and surrounding lines. Preserve the artist's
voice: slang, contractions, profanity, proper nouns. Do not rephrase correct words.

Input:
1. dark nights running through my vein
2. pain is all i fill i got nothing to gain
3. [...]

Output:
1. dark nights running through my veins
2. pain is all I feel I got nothing to gain

After repair, corrected tokens are re-aligned to the original word timings using difflib.SequenceMatcher. Sync stays tight even after word corrections.

Beat Tracking

In parallel, librosa analyzes the audio for beat positions:

import librosa

y, sr = librosa.load(audio_path, mono=True)
tempo, frames = librosa.beat.beat_track(y=y, sr=sr)
beats = librosa.frames_to_time(frames, sr=sr).tolist()
# [0.371, 0.742, 1.114, 1.485, ...]

The renderer uses beat timestamps to trigger visual "hit" animations — the canvas reacts to the music, not just the lyrics.

POS Tagging

spaCy tags each word with its part of speech (NOUN, VERB, ADJ, PROPN, etc.). The visual engine uses POS to set default sizing — nouns and verbs render larger, function words smaller — before AI art direction overrides specific words.

The Output Format

Everything feeds into a single lyric_data.json:

{
  "words": [
    {"text": "dark", "start": 12.48, "end": 12.72, "pos": "ADJ",
     "dir": {"emphasis": 2, "register": "heavy", "glow": true, "icon": null}},
    {"text": "nights", "start": 12.72, "end": 13.2, "pos": "NOUN",
     "rhyme": 3}
  ],
  "beats": [0.371, 0.742, 1.114],
  "metaphors": [{"start": 12.48, "metaphor": "fall"}],
  "rhyme_families": [["veins","gains","pain","brain"]],
  "rhyme_palette": {"0": "#5CE1E6", "1": "#FF2E2E", "3": "#FFD166"}
}

The frontend reads this file and the renderer handles the rest — no backend connection needed during playback.

Try It

https://acecalisto3-lyrisee.hf.space — upload any song, watch it render.

Lyrisee: Turn Any Song Into Kinetic Typography With AI (How It Works)

Mavos.by.Kyklos — Wed, 17 Jun 2026 11:43:05 +0000

What is Lyrisee?

Lyrisee takes any audio file — MP3, M4A, WAV, or video — and turns it into a real-time kinetic typography experience. Words appear, animate, and exit in sync with the music, styled based on what the lyrics mean, not just how they sound.

Under the hood it's a full AI pipeline:

Transcription — faster-whisper with word-level timestamps (every word gets an exact start/end time)
Beat tracking — librosa finds every beat drop
AI art direction — Gemini reads the full lyrics, picks a visual metaphor per line, decides which words to hit hard, assigns icon symbols where literal (🔥 on "fire", 💸 on "money")
Rhyme coloring — CMUdict finds true rhyme families; rhyming words share a color
Typography engine — a Three.js renderer plays it all back, animated to the audio

Step 1: Open Lyrisee

Go to https://acecalisto3-lyrisee.hf.space.

The interface has two panels:

Left: controls, upload, playback
Right: the canvas where the kinetic typography renders

Step 2: Load Your Audio

Click Choose File (or drag and drop) and select any audio file:

MP3, WAV, M4A, FLAC, OGG
Video files work too (MP4, WebM) — the audio track is extracted

The file stays local — it's only sent to the backend for transcription, never stored.

Step 3: Enable Cloud AI (Recommended)

Toggle the Cloud AI switch on (it's on by default).

Under it you'll see the AI provider dropdown — Gemini is selected by default and is what powers the art direction.

Cloud AI does:

Word-level transcription via faster-whisper
Lyric repair (fixes mishear errors using context and rhyme scheme)
Visual art direction (metaphor per line, word emphasis, icon assignments)
Rhyme family detection

If Cloud AI is off, Lyrisee falls back to in-browser transcription using a small on-device model — faster but lower quality and no art direction.

Step 4: Click Process

Hit the Process button. The log panel shows live progress:

[upload] your-song.mp3 (8.8 MB) received
[pipeline] provider=gemini
[asr] loading faster-whisper 'tiny.en' (int8 cpu) …
[asr] transcribing (word timestamps) …
[asr] detected language: english (99%)
[asr] 312 words
[beats] 143 beats @ ~92 BPM
[ai] repaired + art-directed -> 318 words, 28 line cues, 14 rhyme families
[done] 318 words · 143 beats

Processing time depends on song length — typically 30-90 seconds for a 3-4 minute song.

Step 5: Play

Once processing completes, hit Play (or press Space).

The canvas comes alive:

Words appear and exit in sync with audio playback
Rhyming words glow in matching colors
Lines with heavy emotional weight animate differently (scale, drift, snap)
Icon symbols appear on words like "fire", "money", "cage" where the AI decided they hit

Controls

Control	What it does
Space	Play / pause
← → Arrow keys	Seek ±5 seconds
F	Fullscreen
✎ Edit lyrics	Open the lyric editor to fix transcription errors

The Lyric Editor

If the AI mishears a word (happens with heavy slang or unusual pronunciation), click ✎ Edit lyrics.

Each row is one line. Edit the text, nudge the start time if needed, then click Apply & re-render. The canvas updates live with your corrections.

Visual Constructs

The Construct dropdown lets you switch visual styles mid-session:

Rhyme Scheme — rhyming words animate together, shared color palette
Embodiment — each line's motion matches its meaning (falling words drop, rising words rise)
Kinetic Art — pure typographic energy, no semantic logic, maximum visual noise
Chameleon — the AI picks the best construct per line based on content

FAQ

What audio formats work?
MP3, WAV, FLAC, M4A, OGG, MP4, WebM. If it has audio, Lyrisee can process it.

How accurate is the transcription?
For clear vocals, 90-95%. For heavy reverb, distortion, or layered vocals, lower — use the lyric editor to fix errors.

Does it work for non-English songs?
The current model (tiny.en) is English-optimized. Multi-language support via tiny (no .en) is available by changing the backend model size.

Can I download the result?
Yes — the Download button in the lyric editor saves a corrected lyric_data.json you can reload without re-processing.

Is it free?
The HF Space is free to use. It runs on shared CPU, so processing can take 1-2 minutes for longer tracks. Enterprise/private deployments with GPU available — see the landing page.

Try Lyrisee → https://acecalisto3-lyrisee.hf.space

Stop using ChatGPT wrong: the developer prompt guide that ships code

Mavos.by.Kyklos — Sun, 14 Jun 2026 08:32:30 +0000

Stop using ChatGPT wrong: the developer prompt guide that ships code

As developers, we've all been there - stuck in a sea of code, searching for a lifeline to get the job done. And then, we discover AI tools like ChatGPT. But, just like using Google without proper search queries, most devs use ChatGPT (and similar AI tools) without getting the most out of them. There's a gap between being a casual user and a power user - and that's what we're here to bridge.

The core mistake

The biggest mistake developers make when using ChatGPT is providing zero context, assigning no role, and specifying no format. Let's illustrate this with an example:

# Bad prompt
Write a function to sort a list of integers.

This prompt is doomed to fail. Why?

No context: What programming language are we using? What are the performance requirements?
No role: Are we looking for a simple implementation or an optimized one?
No format: Do we want a code snippet, a full function, or an explanation?

Compare this to a well-crafted prompt:

# Good prompt
I'm a Python developer working on a data processing pipeline. 
I need a function to sort a large list of integers efficiently. 
Please provide a high-quality, readable code snippet with type hints and a brief explanation.

The difference is stark. By providing context, assigning a role, and specifying a format, we've set ChatGPT up for success.

The SPAR framework for dev prompts

To take your ChatGPT game to the next level, we introduce the SPAR framework:

S - Spec

Clearly define the task: What do you want to achieve?
Provide context: What's the project about, what language are you using, etc.?
State the requirements: Are there any specific constraints or performance needs?

P - Pattern

Reference existing solutions: Are there similar problems or patterns you've used before?
Provide examples: Include code snippets, APIs, or data samples

A - Anti-pattern

Highlight potential pitfalls: What mistakes should ChatGPT avoid?
Specify error handling: How should errors be handled or reported?

R - Refinement

Define the output format: What do you want ChatGPT to return (code, explanation, etc.)?
Request refinement: Are there specific aspects you'd like ChatGPT to focus on?

Let's see the SPAR framework in action:

# SPAR prompt example
## Spec
I'm working on a Node.js project and need to implement authentication using JSON Web Tokens (JWT). 
The task is to create a function that generates a JWT token given a user's details.

## Pattern
I've used similar libraries like `jsonwebtoken` before. 
The function should take in a user's ID, name, and email.

## Anti-pattern
Please avoid using any deprecated libraries or functions. 
Error handling should be minimal, just throwing a generic error for now.

## Refinement
The output should be a code snippet in JavaScript, with a brief explanation of the approach. 
Please focus on security best practices.

5 copy-paste prompts for real tasks

Here are five actionable prompts you can use right away:

1. Debugging

I'm encountering a bug in my Python code where a list comprehension is throwing a `TypeError`. 
The code is:

python
data = [{'id': 1, 'name': 'John'}, {'id': 2, 'name': 'Jane'}]
result = [item['id'] for item in data if item['active']]

The error message is: `TypeError: 'dict' object is not subscriptable`. 
Please help me identify the issue and provide a corrected code snippet.

diff

2. PR review

I've got a pull request with the following changes:

diff

const fetchData = async () => {
const response = await fetch('https://api.example.com/data');
return response.json();
};

The reviewer mentioned that this might introduce a potential security risk. 
Can you review the code, highlight potential issues, and suggest improvements?

javascript

3. Test writing

I need to write unit tests for a JavaScript function that calculates the area of a rectangle:

javascript
function calculateArea(width, height) {
return width * height;
}

Please provide a set of test cases using Jest, covering different scenarios (e.g., positive numbers, negative numbers, edge cases).

java

4. Refactoring

I've got a legacy Java method that's a bit hard to read:

java
public String processData(List data) {
StringBuilder result = new StringBuilder();
for (String item : data) {
result.append(item).append(",");
}
return result.toString().trim().substring(0, result.length() - 1);
}

Can you refactor this code to be more readable and efficient, while maintaining the same functionality?

plaintext

5. Architecture

I'm designing a new e-commerce platform and need to decide on a database schema. 
The platform will have users, products, orders, and reviews. 
Please suggest a high-level database architecture, including tables, relationships, and indexes.

Chaining prompts for complex features

For more complex tasks, you might need to chain multiple prompts together.

For example, let's say you want to implement a new feature for a web application:

Initial prompt: Describe the feature and its requirements.
Follow-up prompt: Provide more context and clarify specific aspects.
Refinement prompt: Request changes or improvements.

Here's an example:

# Initial prompt
I'm building a web app and want to implement a search bar with autocomplete functionality. 
The search bar should query a backend API for suggestions.

# Follow-up prompt
The API returns a list of suggestions in JSON format: `[{ label: 'Suggestion 1', value: 'suggestion-1' }]`. 
Can you provide a basic implementation using React and the `react-autosuggest` library?

# Refinement prompt
The implementation should include a caching mechanism to reduce API calls. 
Can you modify the code to use a simple cache with a 5-minute TTL?

Full prompt library

While these prompts should give you a great starting point, having a comprehensive library of tested and optimized prompts can save you even more time. If you're interested in learning more about our prompt library, check out our resources page.

By following the SPAR framework, using actionable prompts, and chaining prompts for complex features, you'll be able to unlock the full potential of ChatGPT and ship code faster than ever before. Happy coding!

Looking for a complete resource? I packaged up 200 prompts / the full playbook: *AI Prompt Engineering Mastery Pack — $12***

How to Build a Prompt Library That Your Team Will Actually Use

Mavos.by.Kyklos — Sat, 13 Jun 2026 17:37:42 +0000

Every week I see posts like "what are your favorite prompts?" followed by 50 generic suggestions, and then the same thread appears again two weeks later.

The problem isn't that people don't share prompts. It's that most prompt libraries aren't built for reuse — they're just dumps of things that worked once.

Here's how I built one that my whole workflow actually depends on.

The structure that makes a library usable

A prompt is only reusable if you document three things alongside it:

The exact prompt (with placeholder variables in brackets)
The use case (what specific output you need it for)
The quality bar (what "good output" looks like so you know when to stop iterating)

Without #3, you don't have a library — you have a list of experiments.

Categories that cover 90% of knowledge work

The prompts that anchor each category

Email / edit_shorter.md:

Decisions / devils_advocate.md:

Copywriting / landing_page.md:

Operations / sop_builder.md:

Maintenance

Once per month:

Review every prompt you used that week
If you edited the output heavily, the prompt needs work
If you used it 5+ times without editing, it's a keeper

Remove anything you haven't used in 2 months. A smaller, higher-quality library beats a comprehensive one you don't trust.

The full set I've built

I've been doing this for a year and have 47 prompts that have survived the monthly review.

Get the AI Prompt Pack () — organized exactly this way, by category and use case, with notes on when each one is appropriate.

The most useful part might be what's NOT in it — the 150 prompts I tested and cut because they didn't hold up.

How I Found ChatGPT Prompts That Actually Work (Testing Framework + 5 Best)

Mavos.by.Kyklos — Sat, 13 Jun 2026 17:29:28 +0000

Most ChatGPT prompt guides give you the same 20 prompts recycled endlessly. Here's the framework I used to find ones that actually produce consistent ROI.

Testing framework

I treated prompt testing like A/B testing: define a task, run 5+ variants, compare output quality.

Three criteria for any prompt to make the cut:

Output quality — Can I use it directly, or does it need heavy editing?
Time saved — Does this measurably reduce time vs. doing it manually?
Consistency — Does it work every time, not just sometimes?

Most viral prompts fail criterion 3.

The 5 highest-ROI prompts I found

Email editing:

"Rewrite this to be 40% shorter without losing meaning. Cut filler, redundancy, and hedging. Show only the final version."

Decision support:

"List every way this plan could fail, every assumption I'm making, and every risk I'm ignoring. Be brutal."

Client communication:

"I need to decline this request professionally while preserving the relationship. The situation is: [situation]. Draft a response that is direct, warm, and leaves the door open."

Copywriting:

"Write a landing page headline and 3 sub-bullets for this product. Headline = the outcome the buyer wants. Bullets = the 3 most common objections a skeptical buyer would have."

Operations:

"Turn these rough notes into a step-by-step SOP. Each step should start with an action verb and be specific enough that someone new could follow it without asking questions."

The full library

I packaged 47 prompts that passed all three criteria into a download organized by use case:

Get the AI Prompt Pack ($12)

Grouped by: email, copywriting, operations, decision-making, client communication, research, content, sales.

Drop your highest-ROI prompt in the comments — I'm still refining the library.

From $0 to First Sale: The 6-Week AI-Assisted GTM Roadmap I Actually Used

Mavos.by.Kyklos — Sat, 13 Jun 2026 17:22:25 +0000

I launched my first digital product six weeks ago. No startup funding, no email list, no prior audience. Here's the week-by-week breakdown.

Week 1: Audience Research (the right way)

I spent week 1 only reading. No building.

r/entrepreneur, r/SideProject, r/Freelance: searched "I wish someone had told me" and "finally figured out" — people documenting real pain points
Product Hunt: 1-3 star reviews of competitors — these reveal exactly what's missing
Twitter: "[competitor] doesn't" — real objections from real users

AI move: I dumped 50+ Reddit comments into Claude and asked: "What is the single most common frustration? Give me the exact language people use."

Insight: people didn't want another tool — they wanted a proven process.

Week 2: Product Definition

The product needed to be: process-oriented, under , explainable in one sentence.

Result: A 6-week GTM roadmap for solo founders launching their first digital product.

Prompt that worked:

"Here's my product idea. Here are the 5 most common complaints about competing products. Rewrite my description to directly address complaints 1, 3, and 4."

Week 3: Building

I wrote the playbook in Notion — 14 hours total.

AI shortcut: I wrote bullet points first, then used:

"Expand these bullets into a practical how-to guide section. Second person, specific tools, real examples, no filler."

Week 4: Storefront

Vercel + simple JSON-based store + Stripe payment links.

The lesson: your landing page matters more than your product quality for the first 50 sales. Buyers evaluate your ability to explain the problem, not the product itself.

Landing page prompt:

"Write a headline and 3 sub-bullets for this product. Headline = outcome, not process. Bullets = the 3 most common objections a skeptical buyer has."

Week 5: Distribution

Two dev.to articles targeting search keywords my buyers used. Each ended with a natural product mention. Genuine Reddit participation — useful answers to real questions, not promotions.

Week 6: First Sale

Came from a dev.to article. Someone read it, clicked, bought. No email list, no DMs, no ads.

I packaged the full process — including all the AI prompts I used at each stage — into the product.

→ Get the GTM Playbook ()

If you've launched a digital product, what surprised you most about the first sale? Drop it in the comments.

5 ChatGPT Prompts That Actually Make Money (I've Tested 200+)

Mavos.by.Kyklos — Sat, 13 Jun 2026 17:05:26 +0000

I run a solo business and I've been methodical about finding prompts that actually produce ROI vs. prompts that sound impressive in a tweet.

After testing 200+, here are the 5 I'd keep if I could only keep 5.

1. The Ruthless Editor

Rewrite this to be 40% shorter without losing any meaning. 
Cut filler, redundancy, and hedging language. 
Show me only the final version, not your commentary.

Use it on: Emails, proposals, blog posts, landing pages. This prompt alone has saved me hours of editing per week. The key is the "40%" — vague instructions like "make it shorter" return garbage. Specific constraints produce clean output.

2. The Devil's Advocate

You are a highly skeptical critic of the following plan. 
List every way this could fail, every assumption I'm making, 
and every risk I'm ignoring. Be brutally honest. 
Do not soften your critique to be polite.

Use it before: Pitching clients, launching anything, making a hire, signing a contract. I run every major decision through this. It's caught at least three expensive mistakes this year.

3. Plain English Explainer

Explain [complex topic] like I'm a smart professional 
who has never worked in this field. Use one concrete analogy. 
Keep the total response under 150 words.

Use it when: Writing for non-expert clients, creating onboarding docs, building landing page copy. Forces clarity. If Claude can't explain it simply, your copy can't either.

4. Ideal Customer Persona (the version that works)

Based on this product/service description, tell me:
1. The single biggest fear my ideal customer has that they'd never admit publicly
2. The exact language they use when describing this problem to themselves (not to others)
3. What they've already tried that hasn't worked, and why they believe it failed

Use it when: Writing sales copy, planning content, building a product feature. This is the prompt that changed how I write every sales page.

5. Cold Email That Gets Replies

Rewrite this cold email so that:
- It opens with their specific problem, not our solution
- The ask is one concrete question, under 10 words
- The whole email is under 100 words
- It contains zero corporate jargon

Use it on: Every cold outreach before you send it. Open rates don't matter — reply rates do. This prompt is ruthless about it.

These five are from a library of 47 prompts I've built and refined over the last year running a solo business. If this kind of thing is useful to you, I packaged the full set into a $12 download.

→ Get the full AI Prompt Pack ($12)

It includes prompts for client acquisition, content creation, copywriting, operations, and decision-making — organized by use case so you don't have to hunt.

If you have a prompt that's genuinely changed your workflow, drop it in the comments. I'm always looking to add to the library.

The SaaS launch playbook that got me to $1k MRR in 6 weeks

Mavos.by.Kyklos — Fri, 12 Jun 2026 23:09:39 +0000

The SaaS Launch Playbook that Got Me to $1k MRR in 6 Weeks

I'll never forget the feeling of launching my SaaS product and watching it go from zero to $1,000 in monthly recurring revenue (MRR) in just six weeks. It was a whirlwind of activity, but also a carefully planned and executed strategy. In this article, I'll share my SaaS launch playbook, breaking down the key steps I took each week to achieve this rapid growth.

Week 1: Defining my Ideal Customer Profile (ICP)

The first week of my launch was all about defining my Ideal Customer Profile (ICP). I knew I had to be specific, or I'd end up trying to sell to everyone and appealing to no one. I used a method called "Customer Avatar Canvas" to help me get clear on who my ideal customer was.

To create my ICP, I asked myself the following questions:

What are their pain points?
What are their goals and aspirations?
What are their biggest challenges?
What motivates them?
What are their preferred communication channels?

After conducting research and interviews with potential customers, I defined my ICP as:

Job title: Marketing Manager or Founder
Industry: E-commerce or Digital Marketing
Company size: 10-50 employees
Pain point: Struggling to manage and analyze customer feedback

Having a clear ICP helped me focus my marketing efforts and create a product that truly solved a problem for my target audience.

Week 2: Pricing Experiment

The second week was all about pricing. I knew I had to get it right, or I'd be leaving money on the table. I decided to run a pricing experiment to see what would work best for my product.

Here's what I tried:

I created three different pricing tiers: $29, $49, and $99 per month
I set up a landing page with a pricing comparison table
I drove traffic to the page using Facebook ads and LinkedIn ads

The results were surprising:

The $29 tier got 20% of the sign-ups
The $49 tier got 60% of the sign-ups
The $99 tier got 20% of the sign-ups

What worked was offering a "middle" tier that provided the most value for the price. I settled on $49 as my primary pricing tier, with the other two tiers as options for customers who wanted more or less.

Week 3: Content Strategy

In the third week, I focused on creating content that would attract and engage my ICP. I wrote three articles and shared them on specific subreddits:

Article 1: "5 Ways to Improve Customer Feedback Analysis" (published on r/marketing and r/ecommerce)
Article 2: "The Ultimate Guide to Customer Feedback Tools" (published on r/saas and r/growthhacking)
Article 3: "How to Use Customer Feedback to Drive Growth" (published on r/productmanagement and r/entrepreneur)

The articles provided value to my target audience and helped establish me as an expert in my niche. I also engaged with commenters on Reddit, answering questions and providing additional insights.

Week 4: Cold Outreach

In the fourth week, I decided to try cold outreach to see if I could generate more leads. I sent out 200 cold emails to marketing managers and founders in my ICP, using the following template:

Subject: Improve Your Customer Feedback Analysis

Hi [First Name],

I came across [Company Name] and noticed you're using [Current Tool]. I'm reaching out because I think I can help you improve your customer feedback analysis.

Our tool helps you [briefly describe the benefits]. I'd love to schedule a call to discuss further.

Best,
[Your Name]

The response rate was 15%, with 30 people responding to my email. I scheduled calls with 10 of them and closed 5 deals.

Week 5: Product Hunt Launch

The fifth week was all about launching on Product Hunt. I prepared by:

Creating a compelling Product Hunt listing with a clear description and screenshots
Building a waitlist of 500 people who were interested in trying my product
Reaching out to influencers and bloggers in my niche to promote my launch

On launch day, I:

Posted my product on Product Hunt at 9 am EST
Shared it on social media and with my waitlist
Engaged with commenters and responders on Product Hunt

The results were amazing:

I got 200 upvotes on Product Hunt
I generated 50 new leads
I closed 10 deals

Week 6: What Drove Actual Conversions

In the final week, I analyzed what drove actual conversions. I found that:

My content strategy generated 20% of my leads
Cold outreach generated 30% of my leads
Product Hunt launch generated 50% of my leads

However, what drove actual conversions was the combination of:

Providing value through content and outreach
Building trust through social proof and testimonials
Offering a clear and compelling pricing tier

Conclusion

Reaching $1,000 in MRR in just six weeks was an incredible feeling. By following this SaaS launch playbook, you can replicate my success. Remember to:

Define your ICP and create a product that solves a real problem
Experiment with pricing to find the sweet spot
Create valuable content that attracts and engages your ICP
Use cold outreach to generate leads
Launch on Product Hunt to gain traction

If you want to get your hands on my full template pack, including my ICP template, pricing experiment template, and cold outreach template, send me an email at [Your Email]. I'd be happy to share it with you and help you launch your own successful SaaS product.

Looking for a complete resource? I packaged up 200 prompts / the full playbook: *SaaS Go-to-Market AI Playbook — $19***

I tested 200 AI prompts so you don't have to — here's what actually works

Mavos.by.Kyklos — Fri, 12 Jun 2026 20:10:06 +0000

I tested 200 AI prompts so you don't have to — here's what actually works

I recently embarked on an experiment to test over 200 AI prompts, and what I found was both surprising and eye-opening. Most viral prompt lists circulating online are, frankly, useless. It turns out that the prompts that generate the most buzz on social media often fail to deliver when put to the test. In this article, I'll share my findings on why most AI prompts fall short and provide actionable insights on what actually works.

Why most AI prompts fail

After testing numerous prompts, I identified three primary reasons why most AI prompts fail to deliver:

Too generic: Many prompts are overly broad or vague, making it difficult for the AI model to provide a relevant or accurate response. For example, prompts like "Write a story about a character who learns a lesson" or "Describe a beautiful sunset" are too generic and often result in bland or unengaging output.
No context: AI models require context to generate high-quality responses. Without sufficient background information or specific details, the output is often shallow or irrelevant. For instance, a prompt like "Write a product description for a new smartphone" without any additional context or specifications will likely yield a generic and unconvincing description.
Wrong format: AI models are often sensitive to the format of the prompt. Using the wrong format or structure can lead to subpar output or even cause the model to misinterpret the prompt. For example, using a question format when a statement is more suitable can confuse the model and result in poor output.

The 5 categories that actually matter

Through my testing, I identified five categories that significantly impact the performance of AI prompts:

Specificity: The more specific the prompt, the better the output. Providing concrete details, examples, or anecdotes helps the AI model generate more accurate and engaging responses.
Context: Providing sufficient context, such as background information, definitions, or relevant data, enables the AI model to produce more informed and relevant output.
Tone and style: Specifying the desired tone and style can greatly influence the output. For example, a prompt that requests a response in a humorous tone will likely yield a more engaging and lighthearted output.
Format: Using the right format or structure can significantly improve the output. For instance, using a template or providing a clear outline can help the AI model generate more organized and coherent responses.
Constraints: Introducing constraints, such as word limits or specific requirements, can actually improve the output by forcing the AI model to be more creative and focused.

6 specific high-performing prompts with example input/output

Here are six specific prompts that consistently delivered high-quality output, along with example input and output:

Prompt 1: Specific storytelling

Input: "Write a 2-paragraph story about a character who discovers a hidden world within their reflection, using a descriptive and imaginative tone."
Output: "As she stared into the mirror, Emily noticed something strange. Her reflection began to ripple and distort, like the surface of a pond on a summer's day. Suddenly, a tiny door materialized in the glass, and a diminutive figure beckoned her to enter. Emily's curiosity got the better of her, and she stepped through the doorway, finding herself in a world that was both familiar and yet utterly alien."

Prompt 2: Contextual product description

Input: "Write a product description for a new eco-friendly water bottle, highlighting its sustainable materials and features, in a friendly and persuasive tone."
Output: "Introducing the EcoFlow water bottle, designed to help you stay hydrated while reducing your environmental footprint. Made from BPA-free, shatter-resistant Tritan material, this bottle is both durable and sustainable. With its leak-proof lid and insulation technology, you can enjoy your favorite beverages on-the-go while doing your part for the planet."

Prompt 3: Conversational dialogue

Input: "Write a conversation between a customer service representative and a frustrated customer, using a empathetic and solution-focused tone."
Output: "Customer: 'I'm so frustrated with my recent order. It still hasn't arrived, and I've been waiting for weeks.' Representative: 'I apologize for the delay, and I'm happy to help you track down your order. Can you please provide me with your order number so I can look into this further?'"

Prompt 4: Informative explainer

Input: "Write a 3-paragraph explainer on the benefits of meditation for stress relief, using a clear and concise tone."
Output: "Meditation has been shown to have a profound impact on both physical and mental health, particularly when it comes to stress relief. By focusing on the present moment and letting go of distracting thoughts, individuals can reduce their stress levels and improve their overall well-being. Regular meditation practice has even been linked to reduced symptoms of anxiety and depression."

Prompt 5: Creative brainstorming

Input: "Generate 5 innovative ideas for a new sustainable transportation system, using a creative and speculative tone."
Output: "Here are five potential ideas for a new sustainable transportation system: (1) Hyperloop networks for rapid, low-carbon travel; (2) Self-driving electric vehicles that can be shared or rented on-demand; (3) Advanced public transit systems that integrate AI-powered routing and scheduling; (4) Personal aerial vehicles that use electric propulsion and vertical takeoff; (5) Smart bike-sharing systems that use AI-powered routing and incentives to encourage sustainable transportation."

Prompt 6: Persuasive argument

Input: "Write a persuasive argument for why companies should prioritize diversity and inclusion initiatives, using a logical and empathetic tone."
Output: "In today's global market, companies that prioritize diversity and inclusion are more likely to attract top talent, drive innovation, and achieve long-term success. By fostering a culture of inclusivity and respect, organizations can tap into the unique perspectives and experiences of their employees, leading to more informed decision-making and better business outcomes."

The meta-prompt technique that improves any prompt by 3x

One technique that significantly improved the performance of my prompts was using a meta-prompt. A meta-prompt is a prompt that provides additional context or guidance on how to approach the task. For example:

Meta-prompt: "Act as if you're an expert in [field], and provide a [type of response] that is [tone/style]."
Example: "Act as if you're an expert in environmental sustainability, and provide a persuasive argument for why companies should prioritize eco-friendly practices, using a logical and empathetic tone."

By incorporating a meta-prompt, I found that the output improved by at least 3x, with more focused, relevant, and engaging responses.

If you're looking to supercharge your AI prompts, I recommend checking out our 200-prompt pack resource, which includes a curated selection of high-performing prompts across various categories and use cases. With this resource, you'll be able to save time, improve your output, and unlock the full potential of AI-powered content generation.

Looking for a complete resource? I packaged up 200 prompts / the full playbook: *AI Prompt Engineering Mastery Pack — $12***

[AI Income Engine 9/12] How to use LM Studio to run AI agents locally for free

Mavos.by.Kyklos — Fri, 12 Jun 2026 19:30:40 +0000

Ever dreamed of building intelligent AI agents without the crushing weight of cloud API costs or the nagging concern of data privacy? What if you could unleash the full power of large language models right on your own machine, completely free and offline?

This isn't just a fantasy; it's a practical reality thanks to tools like LM Studio. Developers often hit a wall when experimenting with AI agents: the cost of repeated API calls, the latency of remote servers, or the security implications of sending sensitive data off-premise. LM Studio shatters these barriers, transforming your local machine into a powerful AI playground. It allows you to download, run, and expose a wide array of open-source large language models (LLMs) as an OpenAI-compatible API server, making it incredibly easy to integrate with existing agent frameworks and build sophisticated, privacy-preserving applications. This article will walk you through the entire process, from setting up LM Studio to building your first local AI agent, providing actionable steps and code examples that cut through the hype and deliver real, tangible results.

Setting Up LM Studio and Your First Local LLM

The journey begins with LM Studio itself. Think of it as your all-in-one desktop application for local LLMs, abstracting away the complexities of model formats, runtimes, and GPU acceleration.

Step-by-step Setup:

Download and Install LM Studio: Head over to the LM Studio website and download the appropriate installer for your operating system (Windows, macOS, Linux). The installation process is straightforward, typically involving a few clicks.
Navigate the UI: Upon launching LM Studio, you'll be greeted by a clean interface with several key tabs:
- Home: A model browsing and discovery section.
- Chat: Where you can interact directly with a loaded model.
- My Models: Manages your downloaded models.
- Server: The crucial tab for exposing your local LLM as an API.
Find and Download a Model:
- Go to the "Home" tab. You'll see a search bar and a list of popular models. LM Studio primarily works with GGUF-formatted models, which are quantized versions optimized for local inference on CPUs and GPUs.
- For your first agent, we recommend starting with a smaller, capable model like Mistral 7B Instruct v0.2 or Llama-2-7B-chat. These models offer a good balance of performance and resource requirements.
- In the search bar, type "Mistral 7B Instruct v0.2". You'll see various versions from different uploaders and with different quantizations (e.g., Q4_K_M, Q5_K_S). Lower quantization numbers (like Q4_K_M) mean smaller file sizes and less RAM/VRAM usage, often with a slight trade-off in quality – perfect for local experimentation.
- Click the "Download" button next to your chosen GGUF file. The download size can range from 4GB to 8GB, so ensure you have sufficient disk space and a stable internet connection.
Test Your Model in Chat:
- Once downloaded, navigate to the "Chat" tab.
- In the "Select a model to load" dropdown at the top, choose the Mistral model you just downloaded. LM Studio will load it into memory. This might take a few moments.
- In the chat input area, type a simple prompt like "Hello, tell me a fun fact about AI." and press Enter. If you get a coherent response, congratulations! Your local LLM is up and running.

Exposing Your Local LLM as an OpenAI-Compatible API

This is where LM Studio truly shines for agent development. Most existing AI agent frameworks (like LangChain, LlamaIndex, or even custom Python scripts) are built to interact with OpenAI's API. LM Studio provides a drop-in replacement, allowing you to leverage these tools with your local models.

**Step-

[Trend-Jacking: AI SaaS Ideas from Daily Search Trends 1/7] Predictive Analytics in Sports: Building an AI Golf SaaS for the US Open

Mavos.by.Kyklos — Thu, 11 Jun 2026 11:50:52 +0000

Trend-Jacking: AI SaaS Ideas from Daily Search Trends — Part 1 of 7
How to spot daily search trends and turn them into profitable AI products instantly.

The notion that AI can only be applied to mundane, repetitive tasks is a gross misconception that has held back innovators from truly harnessing its potential, and nowhere is this more evident than in the realm of sports, where the upcoming US Open has everyone wondering if predictive analytics can give golfers a competitive edge.
Developers and founders have long been fascinated by the prospect of leveraging AI to predict outcomes in sports, but their efforts have often been misguided, focusing on simplistic approaches that fail to account for the intricacies of human performance.
For instance, attempting to predict the trajectory of a golf ball using basic physics equations may yield some insight, but it neglects the countless variables that can affect the outcome, from wind resistance to the golfer's mental state.
The problem lies not in the technology itself, but in the approach: rather than trying to boil down complex phenomena to simplistic models, we should be using AI to identify patterns and relationships that may not be immediately apparent.
By doing so, we can create predictive models that are not only more accurate but also more nuanced, taking into account the myriad factors that influence athletic performance.
To build an AI-powered golf SaaS for the US Open, for example, one might start by analyzing data from past tournaments, using techniques such as data mining and machine learning to identify correlations between factors like weather conditions, course layout, and player performance.
Here's what actually works, and I'm going to show you exactly how over the next 6 articles in this "Trend-Jacking: AI SaaS Ideas from Daily Search Trends" series, starting with this Part 1.

Can AI Really Predict Sports Outcomes?

The idea of using AI to predict sports outcomes is not new, but it has gained significant traction in recent years, with many companies and individuals attempting to develop predictive models that can accurately forecast the outcome of games and tournaments.
However, as alluded to earlier, these efforts have often been hindered by a lack of understanding of the underlying complexities of sports, leading to models that are overly simplistic and prone to error.
To create a truly effective predictive model, one must delve deeper into the data, using techniques such as natural language processing and computer vision to extract insights from sources like player interviews, game footage, and social media posts.
For example, by analyzing the language used by golfers in pre-tournament interviews, one might be able to identify subtle patterns and cues that indicate their mental state and confidence level, which can in turn be used to inform predictive models.
To get started with this type of analysis, developers can use tools like the "AI Prompt Engineering Mastery Pack" (https://buy.stripe.com/3cI14o3Bi8Ecfmmb2Q5sC2B?utm_source=devto&utm_medium=content&utm_campaign=gophers), which provides a comprehensive set of prompt templates and examples for extracting insights from text data.

Building a Golf SaaS for the US Open

So, how can we apply these principles to build an AI-powered golf SaaS for the US Open?
The first step is to identify the key factors that influence golf performance, such as course layout, weather conditions, and player skill level.
Next, we need to collect and analyze data from a variety of sources, including historical tournament data, player statistics, and real-time sensor data from the course.
By using machine learning algorithms to identify patterns and relationships in this data, we can develop predictive models that can accurately forecast player performance and tournament outcomes.
However, launching and growing a successful SaaS product requires more than just a good idea and some clever coding – it requires a solid go-to-market strategy and a deep understanding of the target market.
To develop a comprehensive launch plan, founders can use resources like the "SaaS Go-to-Market AI Playbook" (https://buy.stripe.com/8x27sMc7O7A88XY8UI5sC2C?utm_source=devto&utm_medium=content&utm_campaign=gophers), which provides a step-by-step framework for launching and growing a successful AI-powered SaaS product.

The Future of Predictive Analytics in Sports

As we continue to explore the potential of predictive analytics in sports, it's clear that the possibilities are vast and exciting.
From developing AI-powered coaching tools to creating immersive, data-driven fan experiences, the applications of predictive analytics are limited only by our imagination and ingenuity.
In the next article in this series, Part 2, we'll be exploring the fascinating topic of whether AI can predict the lottery, analyzing the $238M Ohio Powerball with data to see if there's any truth to the notion that AI can crack the code of chance.