![](https://cdn-images-1.medium.com/max/1600/1*6DFllEjoNoBh0Bvz1j7Dtg.png)

Image generated by the Author

I just came from outside, where I saw buzzing scooters and chai steam near a tea shop.

Can words alone make melody, with these sounds running in my mind?

I came inside thinking if I should check Suno v5 for this, as people say it’s good.

SUNO launched its v5 version just a few days back.

This launch is quite different, and I am hoping to test a faster idea-to-audio loop. If it has more control over the language and will it provide repeatable results?

And, my definition of win is after the experiments, I want to hum a song track tomorrow morning.

I became excited for my test.

I am not a producer or a DAW wizard, but I just want to check with my prompting skills.

So, I chose a quiet corner.

Put on my headphones.

Started with a blank prompt.

Here are the Battle-tested formulas, real examples, and tweakable modifiers for reliable results — no DAW wizardry required.

#### After hours of testing and tweaking, here are the 10 ready-to-use copy-and-paste prompts.

### 1) Indie-Pop Anthem (Chantable Hook)

**Use when:** You want radio-ready, big chorus, clean guitars + synths.
 **Template**
 “Indie pop, \[102–112] BPM, \[bright / nostalgic / hopeful] mood. Vocal style like \[Artist A x Artist B]. Topic: \[theme]. Structure: intro (4 bars) → verse → pre → **anthemic chorus with a 4-word repeat** → verse 2 → bridge → double chorus. Instruments: clean guitar arps, warm pads, tight kick. Mix: modern, wide stereo, crisp hats. Lyrics: simple, 5th–7th grade.”
 **Example**
 “Indie pop, 108 BPM, hopeful. Vocal like The 1975 x LANY. Topic: second chances at midnight. Structure as above; 4-word hook: ‘we glow again tonight’.”
 **Modifiers:** “stacked gang vocals in final chorus”, “+1 dB vocal presence”, “-1 dB @ 250Hz.”

***

### 2) Lo-Fi Study Loop (No Harsh Highs)

**Use when:** Chill, loopable beats for reels/YouTube study.
 **Template**
 “Lo-fi chillhop, \[70–84] BPM, warm + cozy. Instruments: swung drums, vintage Rhodes, soft bass, subtle vinyl. Minimal humming hook, few words. Structure: intro (8 bars) → A section → B section → outro; loop-friendly, no abrupt stops. Master: soft, **-12 to -11 LUFS** vibe.”
 **Example**
 “Lo-fi chillhop, 78 BPM, rainy window mood. Hummed hook, Rhodes + brushed snare. Two sections A/B for 60-sec loop.”
 **Modifiers:** “vinyl crackle low,” “low-pass @ 12kHz for softness,” “no harsh sibilance.”

***

### 3) Melodic House Sunrise (Festival Lift)

**Use when:** Uplifting dance with emotional drop.
 **Template**
 “Melodic house, \[120–126] BPM, euphoric sunrise vibe. Female lead vox, airy. Theme: \[running into the sunrise / healing / reunion]. Structure: short verse → pre-lift → **drop with side-chained bass + bright plucks** → verse 2 → build → final double drop. Mix: punchy low-end, clear mids, wide leads.”
 **Example**
 “Melodic house, 124 BPM, euphoric. Theme: ‘running into sunrise.’ Two drops, bright supersaw lead.”
 **Modifiers:** “white-noise risers,” “filter sweep into drop,” “write a chantable 6–8 syllable hook.”

***

### 4) Trap 808 Banger (Hook First)

**Use when:** Hard drums, memorable chant hook.
 **Template**
 “Trap, \[130–150] BPM half-time feel. Dark/Swagger mood. **Hook-first writing**: punchy 1-bar chant repeated. Elements: rattling hats (triplets), tuned 808, sparse keys. Structure: hook → verse → hook → verse → outro. Vocal: confident, tight ad-libs.”
 **Example**
 “Trap, 140 BPM, moody swagger. Hook: ‘Run it back now.’ Minimal piano, heavy 808 slides.”
 **Modifiers:** “808 in key of \[X],” “clip 808 @ -0.5dB TP,” “ad-libs wide L/R.”

***

### 5) Cinematic Trailer Pop (Big Lift + Choir)

**Use when:** TikTok/YouTube trailer energy with pop chorus.
 **Template**
 “Cinematic pop, \[88–110] BPM. Orchestral pads + modern drums. **Epic pre-chorus lift → explosive chorus**. Optional light choir ‘oohs’ under hook. Theme: \[resilience/victory]. Dynamic arcs, hits, whooshes. Master: loud but clean.”
 **Example**
 “Cinematic pop, 96 BPM, triumphant. Strings swells, tom fills into big chorus. Hook phrase: ‘We rise, we run.’”
 **Modifiers:** “low brass stabs,” “reverse cymbal into chorus,” “choir low in mix.”

***

### 6) Acoustic Folk Story (Verse-Led)

**Use when:** Lyric-centric storytelling, intimate vocal.
 **Template**
 “Acoustic folk, \[84–100] BPM, intimate/confessional. Fingerpicked guitar, light shaker, soft pads. **Narrative verses** with specific imagery; chorus sums the message in one memorable line. Structure: intro → V1 → chorus → V2 → bridge (new angle) → chorus.”
 **Example**
 “Acoustic folk, 92 BPM, autumn city walks. Chorus: ‘The leaves know how to let go.’”
 **Modifiers:** “breathy close-mic vox,” “room reverb small,” “lyric grade 6–7.”

***

### 7) Synthwave Night-Drive (Retro Clean)

**Use when:** 80s glow, neon nostalgia, smooth vocals.
 **Template**
 “Synthwave / retrowave, \[84–108] BPM, nostalgic night-drive. Instruments: analog bass, gated snares, chorus guitars, Juno pads. Vocal: soft, dream-pop. Structure: 8-bar intro → verse → pre → chorus with **hook on the 1** → instrumental break → final chorus.”
 **Example**
 “Synthwave, 100 BPM, midnight highway. Hook: ‘Neon in our veins.’ Gated snare, chorus guitar.”
 **Modifiers:** “tape wow/flutter subtle,” “stereo widening on pads,” “+1.5 dB @ 10kHz air.”

***

### 8) Afro-Pop / Amapiano Groove (Feel-Good)

**Use when:** Danceable, percussive, call-and-response chorus.
 **Template**
 “Afro-pop / Amapiano hybrid, \[102–114] BPM. Log drum grooves, shaker patterns, bright guitar licks. **Call-and-response chorus**, easy to chant. Theme: \[celebration / weekend]. Bridge: drop instruments for vocal moment, then full groove return.”
 **Example**
 “Amapiano-pop, 110 BPM, weekend vibe. Call: ‘Where we go?’ Response: ‘Right here now!’”
 **Modifiers:** “percussion fills every 8 bars,” “crowd ‘hey’ layers,” “swing 55–58%.”

***

### 9) K-Pop Hook Factory (Section Contrast)

**Use when:** Hyper-catchy, multi-section energy switches.
 **Template**
 “K-pop, \[120–132] BPM. **High contrast**: rap-leaning verse → melodic pre-chorus lift → **massive ear-worm chorus** with stacked harmonies → post-chorus chant. Sparkly synths, tight drums, bass drops. Theme: \[confidence/glow-up]. English-dominant with simple bilingual ad-libs.”
 **Example**
 “K-pop, 126 BPM, glow-up theme. Post-chorus chant: ‘Shine it up!’ Harmonies stacked.”
 **Modifiers:** “talk-rap lines in verse,” “vocal chop hook in post,” “sparkle FX fills.”

***

### 10) Indo-Fusion Pop (Modern + Desi Touch)

**Use when:** Contemporary pop with subtle Indian flavor — great for reels.
 **Template**
 “Indo-fusion pop, \[96–110] BPM. Modern drums + sub bass, **light Indian instruments** (santoor/sitar plucks), tanpura pad (very subtle). Vocal: modern pop lead, occasional Hindustani-style melisma on pre-chorus. Theme: \[monsoon, city nights, long-distance]. Structure: intro → verse → pre with sargam hint → big pop chorus → bridge with tabla groove → final chorus.”
 **Example**
 “Indo-fusion pop, 100 BPM, monsoon nostalgia. Sitar flourishes, soft tanpura bed; pop chorus ‘Back to you tonight.’”
 **Modifiers:** “no cliché scales; keep fusion tasteful,” “tabla only in bridge,” “modern mix, not filmi.”

***

### First Plays: 3 Mini Case Studies (each 120–160 words)

**Track A — “Sunrise on MG Road”**

* Patterns: Style Blender + Narrative Seed
* v1: pretty pad bed, chorus mushy.
* v2 tweak: add “vinyl crackle,” lower BPM 96→92 → more sway.
* v3 tweak: specify “pre-chorus lifts on ‘stay’” → hook locks.
* Verdict: keeper; morning playlist energy.

**Track B — “Don’t Scroll Past My Heart”**

* Patterns: Hook-First + Structure-Strict
* v1: hook lands; kick too EDM.
* v2 tweak: add negatives “no EDM kicks, no sidechain pump” → snappier, organic drums.
* v3 tweak: “handclaps only in pre” → chorus breathes.
* Verdict: chantable; social-first teaser.

**Track C — “Chai Stall, Last Train”**

* Patterns: Narrative Seed + Language Switch
* v1: sweet but busy backing vocals.
* v2 tweak: “single harmony line on last word of chorus” → cleaner lift.
* v3 tweak: “acoustic guitar strum on 2 & 4 only” → conversational feel.
* Verdict: heart track; bilingual ear-worm.

***

#### Quick Mix & Master Targets (Cheat Card)

* **Pop/EDM:** -9 to -7 LUFS; true peak ≤ -1 dB
* **Lo-Fi:** -12 to -11 LUFS; low-pass highs softly
* **Vocal clarity:** +1–2 dB @ 8–10 kHz (air)
* **Mud control:** -1 dB @ 200–300 Hz
* **Glue:** 2:1 comp, \~2 dB GR on bus (optional)

After hours of experimentation with this work, what surprised me was that when your prompts are clear, they can beat clever metaphors.

Best takes are not from generic overhauls but came from *small* BPM nudges.

Now, I am inviting you to create your wildest mashup and put it in the comments.

### A message from our Founder

**Hey,** [**Sunil**](https://linkedin.com/in/sunilsandhu) **here.** I wanted to take a moment to thank you for reading until the end and for being a part of this community.

Did you know that our team run these publications as a volunteer effort to over 3.5m monthly readers? **We don’t receive any funding, we do this to support the community. ❤️**

If you want to show some love, please take a moment to **follow me on** [**LinkedIn**](https://linkedin.com/in/sunilsandhu)**,** [**TikTok**](https://tiktok.com/@messyfounder), [**Instagram**](https://instagram.com/sunilsandhu). You can also subscribe to our [**weekly newsletter**](https://newsletter.plainenglish.io/).

And before you go, don’t forget to **clap** and **follow** the writer️!

<img src="https://cdn-images-1.medium.com/max/1600/1*6DFllEjoNoBh0Bvz1j7Dtg.png" alt="">
Image generated by the Author
I just came from outside, where I saw buzzing scooters and chai steam near a tea shop.
Can words alone make melody, with these sounds running in my mind?
I came inside thinking if I should check Suno v5 for this, as people say it’s good.
SUNO launched its v5 version just a few days back.
This launch is quite different, and I am hoping to test a faster idea-to-audio loop. If it has more control over the language and will it provide repeatable results?
And, my definition of win is after the experiments, I want to hum a song track tomorrow morning.
I became excited for my test.
I am not a producer or a DAW wizard, but I just want to check with my prompting skills.
So, I chose a quiet corner.
Put on my headphones.
Started with a blank prompt.
Here are the Battle-tested formulas, real examples, and tweakable modifiers for reliable results — no DAW wizardry required.
<h4 id="after-hours-of-testing-and-tweaking-here-are-the-10-ready-to-use-copy-and-paste-prompts">After hours of testing and tweaking, here are the 10 ready-to-use copy-and-paste prompts.</h4>
<h3 id="1-indie-pop-anthem-chantable-hook">1) Indie-Pop Anthem (Chantable Hook)</h3>
Use when: You want radio-ready, big chorus, clean guitars + synths.
 Template
 “Indie pop, [102–112] BPM, [bright / nostalgic / hopeful] mood. Vocal style like [Artist A x Artist B]. Topic: [theme]. Structure: intro (4 bars) → verse → pre → anthemic chorus with a 4-word repeat → verse 2 → bridge → double chorus. Instruments: clean guitar arps, warm pads, tight kick. Mix: modern, wide stereo, crisp hats. Lyrics: simple, 5th–7th grade.”
 Example
 “Indie pop, 108 BPM, hopeful. Vocal like The 1975 x LANY. Topic: second chances at midnight. Structure as above; 4-word hook: ‘we glow again tonight’.”
 Modifiers: “stacked gang vocals in final chorus”, “+1 dB vocal presence”, “-1 dB @ 250Hz.”
<hr>
<h3 id="2-lo-fi-study-loop-no-harshhighs">2) Lo-Fi Study Loop (No Harsh Highs)</h3>
Use when: Chill, loopable beats for reels/YouTube study.
 Template
 “Lo-fi chillhop, [70–84] BPM, warm + cozy. Instruments: swung drums, vintage Rhodes, soft bass, subtle vinyl. Minimal humming hook, few words. Structure: intro (8 bars) → A section → B section → outro; loop-friendly, no abrupt stops. Master: soft, -12 to -11 LUFS vibe.”
 Example
 “Lo-fi chillhop, 78 BPM, rainy window mood. Hummed hook, Rhodes + brushed snare. Two sections A/B for 60-sec loop.”
 Modifiers: “vinyl crackle low,” “low-pass @ 12kHz for softness,” “no harsh sibilance.”
<hr>
<h3 id="3-melodic-house-sunrise-festival-lift">3) Melodic House Sunrise (Festival Lift)</h3>
Use when: Uplifting dance with emotional drop.
 Template
 “Melodic house, [120–126] BPM, euphoric sunrise vibe. Female lead vox, airy. Theme: [running into the sunrise / healing / reunion]. Structure: short verse → pre-lift → drop with side-chained bass + bright plucks → verse 2 → build → final double drop. Mix: punchy low-end, clear mids, wide leads.”
 Example
 “Melodic house, 124 BPM, euphoric. Theme: ‘running into sunrise.’ Two drops, bright supersaw lead.”
 Modifiers: “white-noise risers,” “filter sweep into drop,” “write a chantable 6–8 syllable hook.”
<hr>
<h3 id="4-trap-808-banger-hookfirst">4) Trap 808 Banger (Hook First)</h3>
Use when: Hard drums, memorable chant hook.
 Template
 “Trap, [130–150] BPM half-time feel. Dark/Swagger mood. Hook-first writing: punchy 1-bar chant repeated. Elements: rattling hats (triplets), tuned 808, sparse keys. Structure: hook → verse → hook → verse → outro. Vocal: confident, tight ad-libs.”
 Example
 “Trap, 140 BPM, moody swagger. Hook: ‘Run it back now.’ Minimal piano, heavy 808 slides.”
 Modifiers: “808 in key of [X],” “clip 808 @ -0.5dB TP,” “ad-libs wide L/R.”
<hr>
<h3 id="5-cinematic-trailer-pop-big-lift-choir">5) Cinematic Trailer Pop (Big Lift + Choir)</h3>
Use when: TikTok/YouTube trailer energy with pop chorus.
 Template
 “Cinematic pop, [88–110] BPM. Orchestral pads + modern drums. Epic pre-chorus lift → explosive chorus. Optional light choir ‘oohs’ under hook. Theme: [resilience/victory]. Dynamic arcs, hits, whooshes. Master: loud but clean.”
 Example
 “Cinematic pop, 96 BPM, triumphant. Strings swells, tom fills into big chorus. Hook phrase: ‘We rise, we run.’”
 Modifiers: “low brass stabs,” “reverse cymbal into chorus,” “choir low in mix.”
<hr>
<h3 id="6-acoustic-folk-story-verse-led">6) Acoustic Folk Story (Verse-Led)</h3>
Use when: Lyric-centric storytelling, intimate vocal.
 Template
 “Acoustic folk, [84–100] BPM, intimate/confessional. Fingerpicked guitar, light shaker, soft pads. Narrative verses with specific imagery; chorus sums the message in one memorable line. Structure: intro → V1 → chorus → V2 → bridge (new angle) → chorus.”
 Example
 “Acoustic folk, 92 BPM, autumn city walks. Chorus: ‘The leaves know how to let go.’”
 Modifiers: “breathy close-mic vox,” “room reverb small,” “lyric grade 6–7.”
<hr>
<h3 id="7-synthwave-night-drive-retroclean">7) Synthwave Night-Drive (Retro Clean)</h3>
Use when: 80s glow, neon nostalgia, smooth vocals.
 Template
 “Synthwave / retrowave, [84–108] BPM, nostalgic night-drive. Instruments: analog bass, gated snares, chorus guitars, Juno pads. Vocal: soft, dream-pop. Structure: 8-bar intro → verse → pre → chorus with hook on the 1 → instrumental break → final chorus.”
 Example
 “Synthwave, 100 BPM, midnight highway. Hook: ‘Neon in our veins.’ Gated snare, chorus guitar.”
 Modifiers: “tape wow/flutter subtle,” “stereo widening on pads,” “+1.5 dB @ 10kHz air.”
<hr>
<h3 id="8-afro-pop--amapiano-groove-feel-good">8) Afro-Pop / Amapiano Groove (Feel-Good)</h3>
Use when: Danceable, percussive, call-and-response chorus.
 Template
 “Afro-pop / Amapiano hybrid, [102–114] BPM. Log drum grooves, shaker patterns, bright guitar licks. Call-and-response chorus, easy to chant. Theme: [celebration / weekend]. Bridge: drop instruments for vocal moment, then full groove return.”
 Example
 “Amapiano-pop, 110 BPM, weekend vibe. Call: ‘Where we go?’ Response: ‘Right here now!’”
 Modifiers: “percussion fills every 8 bars,” “crowd ‘hey’ layers,” “swing 55–58%.”
<hr>
<h3 id="9-k-pop-hook-factory-section-contrast">9) K-Pop Hook Factory (Section Contrast)</h3>
Use when: Hyper-catchy, multi-section energy switches.
 Template
 “K-pop, [120–132] BPM. High contrast: rap-leaning verse → melodic pre-chorus lift → massive ear-worm chorus with stacked harmonies → post-chorus chant. Sparkly synths, tight drums, bass drops. Theme: [confidence/glow-up]. English-dominant with simple bilingual ad-libs.”
 Example
 “K-pop, 126 BPM, glow-up theme. Post-chorus chant: ‘Shine it up!’ Harmonies stacked.”
 Modifiers: “talk-rap lines in verse,” “vocal chop hook in post,” “sparkle FX fills.”
<hr>
<h3 id="10-indo-fusion-pop-modern--desitouch">10) Indo-Fusion Pop (Modern + Desi Touch)</h3>
Use when: Contemporary pop with subtle Indian flavor — great for reels.
 Template
 “Indo-fusion pop, [96–110] BPM. Modern drums + sub bass, light Indian instruments (santoor/sitar plucks), tanpura pad (very subtle). Vocal: modern pop lead, occasional Hindustani-style melisma on pre-chorus. Theme: [monsoon, city nights, long-distance]. Structure: intro → verse → pre with sargam hint → big pop chorus → bridge with tabla groove → final chorus.”
 Example
 “Indo-fusion pop, 100 BPM, monsoon nostalgia. Sitar flourishes, soft tanpura bed; pop chorus ‘Back to you tonight.’”
 Modifiers: “no cliché scales; keep fusion tasteful,” “tabla only in bridge,” “modern mix, not filmi.”
<hr>
<h3 id="first-plays-3-mini-case-studies-each-120160words">First Plays: 3 Mini Case Studies (each 120–160 words)</h3>
Track A — “Sunrise on MG Road”
<ul>
<li>Patterns: Style Blender + Narrative Seed</li>
<li>v1: pretty pad bed, chorus mushy.</li>
<li>v2 tweak: add “vinyl crackle,” lower BPM 96→92 → more sway.</li>
<li>v3 tweak: specify “pre-chorus lifts on ‘stay’” → hook locks.</li>
<li>Verdict: keeper; morning playlist energy.</li>
</ul>
Track B — “Don’t Scroll Past My Heart”
<ul>
<li>Patterns: Hook-First + Structure-Strict</li>
<li>v1: hook lands; kick too EDM.</li>
<li>v2 tweak: add negatives “no EDM kicks, no sidechain pump” → snappier, organic drums.</li>
<li>v3 tweak: “handclaps only in pre” → chorus breathes.</li>
<li>Verdict: chantable; social-first teaser.</li>
</ul>
Track C — “Chai Stall, Last Train”
<ul>
<li>Patterns: Narrative Seed + Language Switch</li>
<li>v1: sweet but busy backing vocals.</li>
<li>v2 tweak: “single harmony line on last word of chorus” → cleaner lift.</li>
<li>v3 tweak: “acoustic guitar strum on 2 &#x26; 4 only” → conversational feel.</li>
<li>Verdict: heart track; bilingual ear-worm.</li>
</ul>
<hr>
<h4 id="quick-mix--master-targets-cheatcard">Quick Mix &#x26; Master Targets (Cheat Card)</h4>
<ul>
<li>Pop/EDM: -9 to -7 LUFS; true peak ≤ -1 dB</li>
<li>Lo-Fi: -12 to -11 LUFS; low-pass highs softly</li>
<li>Vocal clarity: +1–2 dB @ 8–10 kHz (air)</li>
<li>Mud control: -1 dB @ 200–300 Hz</li>
<li>Glue: 2:1 comp, ~2 dB GR on bus (optional)</li>
</ul>
After hours of experimentation with this work, what surprised me was that when your prompts are clear, they can beat clever metaphors.
Best takes are not from generic overhauls but came from small BPM nudges.
Now, I am inviting you to create your wildest mashup and put it in the comments.
<h3 id="a-message-from-ourfounder">A message from our Founder</h3>
Hey, <a href="https://linkedin.com/in/sunilsandhu">Sunil</a> here. I wanted to take a moment to thank you for reading until the end and for being a part of this community.
Did you know that our team run these publications as a volunteer effort to over 3.5m monthly readers? We don’t receive any funding, we do this to support the community. ❤️
If you want to show some love, please take a moment to follow me on <a href="https://linkedin.com/in/sunilsandhu">LinkedIn</a>, <a href="https://tiktok.com/@messyfounder">TikTok</a>, <a href="https://instagram.com/sunilsandhu">Instagram</a>. You can also subscribe to our <a href="https://newsletter.plainenglish.io/">weekly newsletter</a>.
And before you go, don’t forget to clap and follow the writer️!

Suno v5 launch guide: copy-paste prompts, tweaks, BPM tricks.

I made 10 Suno v5 Prompt Patterns: That Never Miss

The early version of Nano Banana gained significant popularity among developers due to its powerful image generation capabilities and ease of use. As the release of Nano Banana Pro approached, there was heightened interest in its new features and improvements. On November 20, 2025, Nano Banana Pro was officially launched, incorporating the advanced Gemini 3 Pro Image Model with support for both 2K and 4K resolutions, the Nano Banana Pro API provides developers with precise control and consistent results, making it suitable for a wide range of creative workflows. Whether for interactive applications or visual content creation, this AI image generator API meets the needs of teams across various sizes, from small startups to large-scale production systems. Kie.ai’s cost-effective pricing model ensures that high-quality AI-powered image generation is accessible to all users, backed by clear Nano Banana Pro documentation and easy access to the Nano Banana Pro API key for seamless integration into your projects.

## **Key Features of the Google Nano Banana Pro API**

### **Subject Consistency for Multiple Objects**

Nano Banana Pro API ensures consistent character design and scene fidelity even across multiple objects. Whether you’re creating a storyboard, product visual, or game character, it maintains the identity of up to five characters and up to fourteen objects within a single workflow. This feature eliminates the need for constant adjustments, making it ideal for projects that require cohesion across complex scenes.

### **Flexible Aspect Ratios for Any Platform**

One of the standout features of[ Nano Banana Pro API](http://kie.ai/nano-banana-pro) is its ability to adapt visuals to any platform. The API allows you to effortlessly switch aspect ratios, including common formats like 1:1, 16:9, and cinematic 21:9, ensuring that your images fit perfectly on websites, social media, digital ads, or any other platform. This flexibility ensures that the visual integrity of your project remains intact, no matter the size or shape of the display.

### **High-Quality Image Upscaling**

With Nano Banana Pro API, you can generate visuals with exceptional clarity at up to 4K resolution. Whether you need sharp textures for product rendering or crisp details for marketing materials, the API’s precision ensures that the final output matches the high standards of professional-grade images. You can choose between 1K, 2K, or 4K resolutions, depending on your project’s needs.

### **Dynamic Color and Lighting Control**

Nano Banana Pro API offers powerful tools to manipulate color grades and lighting conditions. Whether you’re looking to change the lighting direction, adjust the mood from day to night, or create dramatic effects, the API allows for full control over these elements. This flexibility helps you fine-tune your visuals, ensuring that they reflect the desired aesthetic and visual tone for your project.

### **Explore Different Angles and Shot Types**

For projects that require precise composition, Nano Banana Pro API lets you explore various camera angles and shot types. You can easily generate wide-angle views, panoramic shots, or close-ups, depending on your creative needs. Additionally, the ability to adjust depth of field helps you focus on specific subjects within the image, providing further flexibility in scene composition and enhancing your creative options.

### **Structured Typography for Clear Layouts**

When it comes to creating typographic elements within your images, Nano Banana Pro API excels in structured text-to-image generation. Whether you are designing posters, UI concepts, or diagrams, this feature ensures that your text aligns perfectly with the visual elements. The Gemini 3 Pro Image Model’s enhanced understanding of prompts allows for precise placement, making it easy to create designs with clear and readable typography that fits seamlessly within your overall design.

## **Why Choose Kie.ai for Nano Banana Pro API?**

### **Affordable Nano Banana Pro API Pricing**

At Kie.ai,the importance of keeping costs low while still delivering high-quality AI-powered images. The Nano Banana Pro API is priced at just $0.12 per image, making it 20% cheaper than the official service. With Kie.ai, you only pay for what you use, and there’s no need for expensive subscriptions. The point-based pricing system offers maximum flexibility, so you can scale your usage without any hidden fees. 

### **Comprehensive Nano Banana Pro API Documentation and Support**

Kie.ai provides thorough and easy-to-understand Nano Banana Pro API documentation for the Nano Banana Pro API, ensuring that developers can quickly get started with minimal effort. The detailed guides cover everything from API key integration to advanced features like color and lighting control. Additionally, Kie.ai offers responsive technical support, ready to assist with any questions or issues, ensuring a smooth experience while integrating AI image generation into your workflow.

### **Stable and High-Concurrency Support**

Kie.ai ensures that the Nano Banana Pro API remains stable even under heavy usage. Whether you're generating high-resolution images for marketing or working on complex design projects, Kie.ai supports high-concurrency and scalable performance. This makes it a reliable choice for both small teams and large-scale production systems, ensuring your AI image generation needs are met without performance hiccups.

### **Free Trial for New Users**

Kie.ai offers new users a free trial to test out the Nano Banana Pro API before committing. This allows you to explore the full capabilities of the API, including 4K image generation, subject consistency, and flexible aspect ratios, without any initial investment. The free trial gives you the chance to experiment with the AI image generator API and refine your workflows.

## **How to Deploy the Nano Banana Pro API Into Project?**

### **Sign Up and Obtain Your Nano Banana Pro API Key**

The first step in integrating the Nano Banana Pro API is to create an account on Kie.ai and obtain your Nano Banana Pro API key[.](https://crypto30x.com.au/) The Nano Banana Pro API key is essential for authenticating requests to the service, ensuring secure communication between your application and the API. Once you have your API key, you can use it in your application to make authorized requests for image generation.

### **Set Up Your API Requests**

Once you have the API key, the next step is to configure your API requests. This involves specifying the model (in this case, nano-banana-pro), along with the required input parameters such as prompt, aspect ratio, resolution, and output format. You can also optionally include a callback URL to receive notifications once the image generation task is completed.

### **Submit Your Image Generation Task**

After setting up your Nano Banana Pro API request, you can submit it to the Nano Banana Pro API for processing. The API will queue your task and begin generating the requested image based on the provided parameters. The task will be processed asynchronously, and you will receive a task ID that you can use to track the progress and status of the request.

### **Monitor and Retrieve Generated Images**

Once your image generation task is complete, you can query the task status using the task ID. The API will return the result, including a link to the generated image if the task was successful. If you provide a callback URL, you will also receive a notification containing the result. This allows you to easily retrieve the generated image and integrate it into your application or project.

## **Streamlined AI Image Generation with Nano Banana Pro API**

The Nano Banana Pro API provides developers with a practical and affordable solution for AI image generation. With features such as 4K resolution, flexible aspect ratios, and subject consistency, it supports a wide range of creative workflows, from interactive applications to marketing materials. Kie.ai offers clear[ Nano Banana Pro API documentation](http://kie.ai/nano-banana-pro), reliable technical support, and a transparent pricing model, ensuring developers can seamlessly integrate AI-powered image generation into their projects without the need for costly subscriptions or extensive setup.

The early version of Nano Banana gained significant popularity among developers due to its powerful image generation capabilities and ease of use. As the release of Nano Banana Pro approached, there was heightened interest in its new features and improvements. On November 20, 2025, Nano Banana Pro was officially launched, incorporating the advanced Gemini 3 Pro Image Model with support for both 2K and 4K resolutions, the Nano Banana Pro API provides developers with precise control and consistent results, making it suitable for a wide range of creative workflows. Whether for interactive applications or visual content creation, this AI image generator API meets the needs of teams across various sizes, from small startups to large-scale production systems. Kie.ai’s cost-effective pricing model ensures that high-quality AI-powered image generation is accessible to all users, backed by clear Nano Banana Pro documentation and easy access to the Nano Banana Pro API key for seamless integration into your projects.
<h2 id="key-features-of-the-google-nano-banana-pro-api">Key Features of the Google Nano Banana Pro API</h2>
<h3 id="subject-consistency-for-multiple-objects">Subject Consistency for Multiple Objects</h3>
Nano Banana Pro API ensures consistent character design and scene fidelity even across multiple objects. Whether you’re creating a storyboard, product visual, or game character, it maintains the identity of up to five characters and up to fourteen objects within a single workflow. This feature eliminates the need for constant adjustments, making it ideal for projects that require cohesion across complex scenes.
<h3 id="flexible-aspect-ratios-for-any-platform">Flexible Aspect Ratios for Any Platform</h3>
One of the standout features of<a href="http://kie.ai/nano-banana-pro"> Nano Banana Pro API</a> is its ability to adapt visuals to any platform. The API allows you to effortlessly switch aspect ratios, including common formats like 1:1, 16:9, and cinematic 21:9, ensuring that your images fit perfectly on websites, social media, digital ads, or any other platform. This flexibility ensures that the visual integrity of your project remains intact, no matter the size or shape of the display.
<h3 id="high-quality-image-upscaling">High-Quality Image Upscaling</h3>
With Nano Banana Pro API, you can generate visuals with exceptional clarity at up to 4K resolution. Whether you need sharp textures for product rendering or crisp details for marketing materials, the API’s precision ensures that the final output matches the high standards of professional-grade images. You can choose between 1K, 2K, or 4K resolutions, depending on your project’s needs.
<h3 id="dynamic-color-and-lighting-control">Dynamic Color and Lighting Control</h3>
Nano Banana Pro API offers powerful tools to manipulate color grades and lighting conditions. Whether you’re looking to change the lighting direction, adjust the mood from day to night, or create dramatic effects, the API allows for full control over these elements. This flexibility helps you fine-tune your visuals, ensuring that they reflect the desired aesthetic and visual tone for your project.
<h3 id="explore-different-angles-and-shot-types">Explore Different Angles and Shot Types</h3>
For projects that require precise composition, Nano Banana Pro API lets you explore various camera angles and shot types. You can easily generate wide-angle views, panoramic shots, or close-ups, depending on your creative needs. Additionally, the ability to adjust depth of field helps you focus on specific subjects within the image, providing further flexibility in scene composition and enhancing your creative options.
<h3 id="structured-typography-for-clear-layouts">Structured Typography for Clear Layouts</h3>
When it comes to creating typographic elements within your images, Nano Banana Pro API excels in structured text-to-image generation. Whether you are designing posters, UI concepts, or diagrams, this feature ensures that your text aligns perfectly with the visual elements. The Gemini 3 Pro Image Model’s enhanced understanding of prompts allows for precise placement, making it easy to create designs with clear and readable typography that fits seamlessly within your overall design.
<h2 id="why-choose-kieai-for-nano-banana-pro-api">Why Choose Kie.ai for Nano Banana Pro API?</h2>
<h3 id="affordable-nano-banana-pro-api-pricing">Affordable Nano Banana Pro API Pricing</h3>
At Kie.ai,the importance of keeping costs low while still delivering high-quality AI-powered images. The Nano Banana Pro API is priced at just $0.12 per image, making it 20% cheaper than the official service. With Kie.ai, you only pay for what you use, and there’s no need for expensive subscriptions. The point-based pricing system offers maximum flexibility, so you can scale your usage without any hidden fees. 
<h3 id="comprehensive-nano-banana-pro-api-documentation-and-support">Comprehensive Nano Banana Pro API Documentation and Support</h3>
Kie.ai provides thorough and easy-to-understand Nano Banana Pro API documentation for the Nano Banana Pro API, ensuring that developers can quickly get started with minimal effort. The detailed guides cover everything from API key integration to advanced features like color and lighting control. Additionally, Kie.ai offers responsive technical support, ready to assist with any questions or issues, ensuring a smooth experience while integrating AI image generation into your workflow.
<h3 id="stable-and-high-concurrency-support">Stable and High-Concurrency Support</h3>
Kie.ai ensures that the Nano Banana Pro API remains stable even under heavy usage. Whether you're generating high-resolution images for marketing or working on complex design projects, Kie.ai supports high-concurrency and scalable performance. This makes it a reliable choice for both small teams and large-scale production systems, ensuring your AI image generation needs are met without performance hiccups.
<h3 id="free-trial-for-new-users">Free Trial for New Users</h3>
Kie.ai offers new users a free trial to test out the Nano Banana Pro API before committing. This allows you to explore the full capabilities of the API, including 4K image generation, subject consistency, and flexible aspect ratios, without any initial investment. The free trial gives you the chance to experiment with the AI image generator API and refine your workflows.
<h2 id="how-to-deploy-the-nano-banana-pro-api-into-project">How to Deploy the Nano Banana Pro API Into Project?</h2>
<h3 id="sign-up-and-obtain-your-nano-banana-pro-api-key">Sign Up and Obtain Your Nano Banana Pro API Key</h3>
The first step in integrating the Nano Banana Pro API is to create an account on Kie.ai and obtain your Nano Banana Pro API key<a href="https://crypto30x.com.au/">.</a> The Nano Banana Pro API key is essential for authenticating requests to the service, ensuring secure communication between your application and the API. Once you have your API key, you can use it in your application to make authorized requests for image generation.
<h3 id="set-up-your-api-requests">Set Up Your API Requests</h3>
Once you have the API key, the next step is to configure your API requests. This involves specifying the model (in this case, nano-banana-pro), along with the required input parameters such as prompt, aspect ratio, resolution, and output format. You can also optionally include a callback URL to receive notifications once the image generation task is completed.
<h3 id="submit-your-image-generation-task">Submit Your Image Generation Task</h3>
After setting up your Nano Banana Pro API request, you can submit it to the Nano Banana Pro API for processing. The API will queue your task and begin generating the requested image based on the provided parameters. The task will be processed asynchronously, and you will receive a task ID that you can use to track the progress and status of the request.
<h3 id="monitor-and-retrieve-generated-images">Monitor and Retrieve Generated Images</h3>
Once your image generation task is complete, you can query the task status using the task ID. The API will return the result, including a link to the generated image if the task was successful. If you provide a callback URL, you will also receive a notification containing the result. This allows you to easily retrieve the generated image and integrate it into your application or project.
<h2 id="streamlined-ai-image-generation-with-nano-banana-pro-api">Streamlined AI Image Generation with Nano Banana Pro API</h2>
The Nano Banana Pro API provides developers with a practical and affordable solution for AI image generation. With features such as 4K resolution, flexible aspect ratios, and subject consistency, it supports a wide range of creative workflows, from interactive applications to marketing materials. Kie.ai offers clear<a href="http://kie.ai/nano-banana-pro"> Nano Banana Pro API documentation</a>, reliable technical support, and a transparent pricing model, ensuring developers can seamlessly integrate AI-powered image generation into their projects without the need for costly subscriptions or extensive setup.

The Nano Banana Pro API from Kie.ai offers high-quality AI image generation with 4K resolution, flexible aspect ratios, and subject consistency. Affordable and easy to integrate, it’s ideal for developers and teams

Affordable Nano Banana Pro API by Kie.ai for AI Image Generation Integration

40+ Genius Ways to Make Money from AI with Gemini 2.0: A Comprehensive Guide

 Top 8 LLM + RAG Projects for your AI Portfolio 2025

For the modern small business owner, the creative process has changed forever. You no longer need to know how to use complex software like Photoshop; you need to know how to write an effective AI Prompt Engineering for Branding prompt.

![](https://cdn-images-1.medium.com/max/1600/1*mq4Z-LZN8T_07_YKsBgcXw.png)

A prompt is more than a keyword; it is the Natural Language Design brief that the X-Design AI Agent uses to build your entire visual world. The difference between a generic result and a professional, cohesive brand lies entirely in the structure and detail of that instruction.

Most generic tools operate on simple keyword matching. If you type “YouTube logo,” you get a generic YouTube Channel logo. X-Design [AI logo generator](https://www.x-design.com/ai-logo-generator) operates by interpreting a true Visual Brand Brief, allowing you to embed your company’s story, industry standards, and required assets into a single command.

Mastering the prompt is mastering your brand’s destiny. It eliminates the back-and-forth common with human designers and moves you straight to a professional, on-brand result.

### The X-Design Prompt Structure: A Three-Layer Formula

To unlock the full potential of X-Design and AI Prompt Engineering for Branding, you need to think of your prompt not as a sentence, but as a formula with three distinct layers. This X-Design Prompt Structure ensures the AI builds your brand on a solid, strategic foundation.

![](https://cdn-images-1.medium.com/max/1600/1*u4TAaBg3hlgl8OCZFJ5uvA.png)

### Layer 1: The Foundation (What You Are Selling)

This layer provides the essential context. It grounds the AI in your business reality — who you are, what you sell, and who you are trying to reach.

**Industry & Product:** Define the tangible subject.
Example: *“An artisan bakery specializing in sourdough and rustic pastries.”*

**Brand Tone & Feeling:** Define the abstract mood and emotional atmosphere.
Example: *“The mood should be warm, trustworthy, and heritage-focused.”*

**Target Audience:** Define who the design should appeal to.
Example: *“Young professionals and families in an urban, upscale market.”*

Why it matters: Generic AI knows what “sourdough” looks like. X-Design, using this foundation, understands that a “heritage-focused” design for “upscale market” must use specific typography and a muted color palette, automatically enforcing brand standards before you even ask for them.

### Layer 2: The Visual DNA (How It Must Look)

This is the creative layer where you dictate the specific aesthetic choices. It converts abstract ideas into tangible visual styles.

**Style Guidance:** Define the artistic treatment.
Example: *“Minimalist line art, hand-drawn aesthetic, or geometric symbol.”*

**Color Palette:** Specify the exact emotional color set.
Example: *“Earthy and natural color palette, specifically using deep burnt orange and off-white.”*

**Typography Preference:** Guide the font choice for the logo design.
Example: *“A clean, modern serif font for the name, with a supporting sans-serif.”*

Why it matters: This layer provides Design Language Prompt elements. By specifying “Minimalist line art” and “deep burnt orange,” you prevent the AI from defaulting to bright, generic colors and complex vector graphics, ensuring a unique result that aligns with your vision.

### Layer 3: The Refinement & Constraint (The Professional Finish)

This layer applies final quality checks and details required for professional deployment. It is where you move from a nice graphic to a usable Logo System.

**Technical Constraint:** Specify deployment needs.
Example: *“Must be able to work in a circular stamp format and be legible when printed small.”*

**Misuse/Avoid:** Tell the AI what not to include.
Example: *“Avoid overly complicated symbols or any representation of a chef’s hat.”*

**Iteration Strategy:** Prompt for a specific change.
Example: *“Based on this design, generate three more concepts that only change the geometric symbol.”*

Why it matters: Constraints are powerful. By demanding a design that is “legible when printed small,” you force the AI Prompt Engineering for Branding system to prioritize clarity and technical robustness, which is critical for Iterative AI Design and final file usability.

### Prompt Application: From Logo to Marketing Templates

The ultimate test of AI Prompt Engineering for Branding is its ability to scale across different visual assets while maintaining a single, consistent look. X-Design uses the same prompt language, but adapts it to the specific format of each asset.

### 1. Logo Prompt Formulas (The Master Visual Brand Brief)

The logo prompt is the most important, as it defines your Visual Identity Toolkit. It should be comprehensive, combining all three layers to create the foundational asset. You can also use the photo enhancer tool to [improve the logo quality](https://www.x-design.com/image-enhancer).

Formula Example: *“\[LAYER 1: Artisan bakery specializing in rustic pastries. Brand tone should feel grounded and reliable.] \[LAYER 2: Use a clean geometric symbol combined with a strong, modern serif font. The color palette is restricted to deep forest green and cream.] \[LAYER 3: Ensure the logo is suitable for a circular coffee stamp and is highly legible at small sizes.]”*

![](https://cdn-images-1.medium.com/max/1600/1*qCpHhtfJ1dhUQK9QIqJRJQ.png)

### 2. Poster/Flyer Prompts (The Marketing Brief)

Once the Logo System is established, the prompt for marketing materials shifts focus from *what the brand is* to *what the brand is doing*, while the AI automatically enforces the stored colors and fonts.

Formula Example: *“\[ACTION: Create a square social media poster for a special holiday promotion: 25% off all pastries before 10 AM on Monday.] \[VISUALS: Use a high-quality photo of a steaming latte with a soft bokeh background. The headline must be bold and prominent.] \[CONSTRAINT: Must adhere to the approved ‘Forest Green’ color palette and use the primary headline font.]”*

![](https://cdn-images-1.medium.com/max/1600/1*volJTVkpNc_pNR2lG0n71w.png)

Key insight: You don’t prompt for color or font here; you prompt for *marketing strategy*, and the AI handles the Design Rule Enforcement instantly, ensuring On-Brand Posters.

### 3. Product Packaging Prompts (The Product Brief)

These prompts focus on application and physical constraint. You must tell the AI where the logo needs to be placed and the material it will interact with.

Formula Example: *“\[PRODUCT: Design packaging for a retail coffee bag.] \[VISUALS: Apply the ‘inverse’ (white) version of the logo system to the dark kraft paper background. Use a simple, informational layout.] \[CONSTRAINT: Use the body font for all product description text. Design must include space for a stamped ‘Best By’ date.”*

![](https://cdn-images-1.medium.com/max/1600/1*gzHFUARuQ7ZoXu8jHn_biA.png)

Key insight: This prompt uses specific terminology from your generated VI Toolkit (“inverse logo,” “body font”) to pull the exact file needed, guaranteeing Consistent Brand Touchpoints even on physical goods.

### Iteration: The Power of Targeted Refinement

A powerful AI like X-Design doesn’t just deliver one result; it delivers a series of designs that you can refine instantly using natural language. This Iterative AI Design capability drastically cuts down on development time.

### 1. Style Iteration

Instead of saying “I don’t like it,” you tell the Agent *how* to change the style based on its initial output.

* *Initial Prompt Result:* A geometric, clean logo.
* *Refinement Prompt:* “Keep the core shape, but make the lines slightly thicker to give it a more established, classic feel.”

### 2. Constraint Iteration

You can test technical changes instantly without restarting the design.

* *Initial Prompt Result:* A logo that looks great on white.
* *Refinement Prompt:* “Show me this design applied to a black background. Does the secondary font still stand out enough, or does it need to be lighter?”

This constant, conversation-based refinement is the fastest path to locking down a Future-Proof Branding asset that satisfies all your deployment needs.

### Conclusion

The ability to write a structured, detailed prompt is now the most valuable skill for any entrepreneur. In the era of the X-Design AI Agent, the complexity of a design brief is no longer limited by your software skills but by your imagination and descriptive language.

By adopting the three-layer X-Design Prompt Structure, you ensure that your visual identity is built on a strategic foundation, not random luck. This process turns your simple idea into a Visual Brand Brief that delivers professional-grade assets, allowing you to bypass the traditional design industry and build a cohesive brand on your own terms. Master the prompt, and you master your AI Brand Strategy.

For the modern small business owner, the creative process has changed forever. You no longer need to know how to use complex software like Photoshop; you need to know how to write an effective AI Prompt Engineering for Branding prompt.
<img src="https://cdn-images-1.medium.com/max/1600/1*mq4Z-LZN8T_07_YKsBgcXw.png" alt="">
A prompt is more than a keyword; it is the Natural Language Design brief that the X-Design AI Agent uses to build your entire visual world. The difference between a generic result and a professional, cohesive brand lies entirely in the structure and detail of that instruction.
Most generic tools operate on simple keyword matching. If you type “YouTube logo,” you get a generic YouTube Channel logo. X-Design <a href="https://www.x-design.com/ai-logo-generator">AI logo generator</a> operates by interpreting a true Visual Brand Brief, allowing you to embed your company’s story, industry standards, and required assets into a single command.
Mastering the prompt is mastering your brand’s destiny. It eliminates the back-and-forth common with human designers and moves you straight to a professional, on-brand result.
<h3 id="the-x-design-prompt-structure-a-three-layer-formula">The X-Design Prompt Structure: A Three-Layer Formula</h3>
To unlock the full potential of X-Design and AI Prompt Engineering for Branding, you need to think of your prompt not as a sentence, but as a formula with three distinct layers. This X-Design Prompt Structure ensures the AI builds your brand on a solid, strategic foundation.
<img src="https://cdn-images-1.medium.com/max/1600/1*u4TAaBg3hlgl8OCZFJ5uvA.png" alt="">
<h3 id="layer-1-the-foundation-what-you-areselling">Layer 1: The Foundation (What You Are Selling)</h3>
This layer provides the essential context. It grounds the AI in your business reality — who you are, what you sell, and who you are trying to reach.
Industry &#x26; Product: Define the tangible subject.
Example: “An artisan bakery specializing in sourdough and rustic pastries.”
Brand Tone &#x26; Feeling: Define the abstract mood and emotional atmosphere.
Example: “The mood should be warm, trustworthy, and heritage-focused.”
Target Audience: Define who the design should appeal to.
Example: “Young professionals and families in an urban, upscale market.”
Why it matters: Generic AI knows what “sourdough” looks like. X-Design, using this foundation, understands that a “heritage-focused” design for “upscale market” must use specific typography and a muted color palette, automatically enforcing brand standards before you even ask for them.
<h3 id="layer-2-the-visual-dna-how-it-mustlook">Layer 2: The Visual DNA (How It Must Look)</h3>
This is the creative layer where you dictate the specific aesthetic choices. It converts abstract ideas into tangible visual styles.
Style Guidance: Define the artistic treatment.
Example: “Minimalist line art, hand-drawn aesthetic, or geometric symbol.”
Color Palette: Specify the exact emotional color set.
Example: “Earthy and natural color palette, specifically using deep burnt orange and off-white.”
Typography Preference: Guide the font choice for the logo design.
Example: “A clean, modern serif font for the name, with a supporting sans-serif.”
Why it matters: This layer provides Design Language Prompt elements. By specifying “Minimalist line art” and “deep burnt orange,” you prevent the AI from defaulting to bright, generic colors and complex vector graphics, ensuring a unique result that aligns with your vision.
<h3 id="layer-3-the-refinement--constraint-the-professional-finish">Layer 3: The Refinement &#x26; Constraint (The Professional Finish)</h3>
This layer applies final quality checks and details required for professional deployment. It is where you move from a nice graphic to a usable Logo System.
Technical Constraint: Specify deployment needs.
Example: “Must be able to work in a circular stamp format and be legible when printed small.”
Misuse/Avoid: Tell the AI what not to include.
Example: “Avoid overly complicated symbols or any representation of a chef’s hat.”
Iteration Strategy: Prompt for a specific change.
Example: “Based on this design, generate three more concepts that only change the geometric symbol.”
Why it matters: Constraints are powerful. By demanding a design that is “legible when printed small,” you force the AI Prompt Engineering for Branding system to prioritize clarity and technical robustness, which is critical for Iterative AI Design and final file usability.
<h3 id="prompt-application-from-logo-to-marketing-templates">Prompt Application: From Logo to Marketing Templates</h3>
The ultimate test of AI Prompt Engineering for Branding is its ability to scale across different visual assets while maintaining a single, consistent look. X-Design uses the same prompt language, but adapts it to the specific format of each asset.
<h3 id="1-logo-prompt-formulas-the-master-visual-brandbrief">1. Logo Prompt Formulas (The Master Visual Brand Brief)</h3>
The logo prompt is the most important, as it defines your Visual Identity Toolkit. It should be comprehensive, combining all three layers to create the foundational asset. You can also use the photo enhancer tool to <a href="https://www.x-design.com/image-enhancer">improve the logo quality</a>.
Formula Example: “[LAYER 1: Artisan bakery specializing in rustic pastries. Brand tone should feel grounded and reliable.] [LAYER 2: Use a clean geometric symbol combined with a strong, modern serif font. The color palette is restricted to deep forest green and cream.] [LAYER 3: Ensure the logo is suitable for a circular coffee stamp and is highly legible at small sizes.]”
<img src="https://cdn-images-1.medium.com/max/1600/1*qCpHhtfJ1dhUQK9QIqJRJQ.png" alt="">
<h3 id="2-posterflyer-prompts-the-marketing-brief">2. Poster/Flyer Prompts (The Marketing Brief)</h3>
Once the Logo System is established, the prompt for marketing materials shifts focus from what the brand is to what the brand is doing, while the AI automatically enforces the stored colors and fonts.
Formula Example: “[ACTION: Create a square social media poster for a special holiday promotion: 25% off all pastries before 10 AM on Monday.] [VISUALS: Use a high-quality photo of a steaming latte with a soft bokeh background. The headline must be bold and prominent.] [CONSTRAINT: Must adhere to the approved ‘Forest Green’ color palette and use the primary headline font.]”
<img src="https://cdn-images-1.medium.com/max/1600/1*volJTVkpNc_pNR2lG0n71w.png" alt="">
Key insight: You don’t prompt for color or font here; you prompt for marketing strategy, and the AI handles the Design Rule Enforcement instantly, ensuring On-Brand Posters.
<h3 id="3-product-packaging-prompts-the-productbrief">3. Product Packaging Prompts (The Product Brief)</h3>
These prompts focus on application and physical constraint. You must tell the AI where the logo needs to be placed and the material it will interact with.
Formula Example: “[PRODUCT: Design packaging for a retail coffee bag.] [VISUALS: Apply the ‘inverse’ (white) version of the logo system to the dark kraft paper background. Use a simple, informational layout.] [CONSTRAINT: Use the body font for all product description text. Design must include space for a stamped ‘Best By’ date.”
<img src="https://cdn-images-1.medium.com/max/1600/1*gzHFUARuQ7ZoXu8jHn_biA.png" alt="">
Key insight: This prompt uses specific terminology from your generated VI Toolkit (“inverse logo,” “body font”) to pull the exact file needed, guaranteeing Consistent Brand Touchpoints even on physical goods.
<h3 id="iteration-the-power-of-targeted-refinement">Iteration: The Power of Targeted Refinement</h3>
A powerful AI like X-Design doesn’t just deliver one result; it delivers a series of designs that you can refine instantly using natural language. This Iterative AI Design capability drastically cuts down on development time.
<h3 id="1-style-iteration">1. Style Iteration</h3>
Instead of saying “I don’t like it,” you tell the Agent how to change the style based on its initial output.
<ul>
<li>Initial Prompt Result: A geometric, clean logo.</li>
<li>Refinement Prompt: “Keep the core shape, but make the lines slightly thicker to give it a more established, classic feel.”</li>
</ul>
<h3 id="2-constraint-iteration">2. Constraint Iteration</h3>
You can test technical changes instantly without restarting the design.
<ul>
<li>Initial Prompt Result: A logo that looks great on white.</li>
<li>Refinement Prompt: “Show me this design applied to a black background. Does the secondary font still stand out enough, or does it need to be lighter?”</li>
</ul>
This constant, conversation-based refinement is the fastest path to locking down a Future-Proof Branding asset that satisfies all your deployment needs.
<h3 id="conclusion">Conclusion</h3>
The ability to write a structured, detailed prompt is now the most valuable skill for any entrepreneur. In the era of the X-Design AI Agent, the complexity of a design brief is no longer limited by your software skills but by your imagination and descriptive language.
By adopting the three-layer X-Design Prompt Structure, you ensure that your visual identity is built on a strategic foundation, not random luck. This process turns your simple idea into a Visual Brand Brief that delivers professional-grade assets, allowing you to bypass the traditional design industry and build a cohesive brand on your own terms. Master the prompt, and you master your AI Brand Strategy.

Do you want to know about AI prompt engineering for branding and how it can help you create your brand identity? Read this guide on how effective prompts generate better results.

AI Prompt Engineering: The Brand Designer's New Language

![](https://cdn-images-1.medium.com/max/1600/1*x2Swk7sY439NC06pVRJ7sg.png)

I work in the real world on real applications that are pretty large, complex and do more than just add a TODO item to a small list. Therefore I have been very skeptical about AI coming in and being this miracle that the AI companies would have you believe. (*I have still seen no evidence of that btw. despite the recent mentions of AGI.*)

Instead, **I have seen AI as more of another tool to use in a developers’s toolkit.** It can be a very useful tool in some circumstances but a tool none the less. Therefore, I like to make small incremental steps when introducing any AI into my workflow.

Recently I have been trying out the [Atlassian MCP server](https://github.com/sooperset/mcp-atlassian?tab=readme-ov-file). I have been quite impressed so far and think it can help in my existing workflow and make my daily developer life a little better.

The README is really well documented and it was easy to get it setup in Cursor. I got it connected to our Jira board that we use at work all within a few minutes.

Basically you have to:

* Generate an api token from Atlassian
* Pull the docker image that runs this MCP server
* Add the MCP tool to your Cursor config

```
{  "mcpServers": {    "mcp-atlassian": {      "command": "docker",      "args": [        "run",        "-i",        "--rm",        "-e", "CONFLUENCE_URL",        "-e", "CONFLUENCE_USERNAME",        "-e", "CONFLUENCE_API_TOKEN",        "-e", "JIRA_URL",        "-e", "JIRA_USERNAME",        "-e", "JIRA_API_TOKEN",        "ghcr.io/sooperset/mcp-atlassian:latest"      ],      "env": {        "CONFLUENCE_URL": "https://your-company.atlassian.net/wiki",        "CONFLUENCE_USERNAME": "your.email@company.com",        "CONFLUENCE_API_TOKEN": "your_confluence_api_token",        "JIRA_URL": "https://your-company.atlassian.net",        "JIRA_USERNAME": "your.email@company.com",        "JIRA_API_TOKEN": "your_jira_api_token"      }    }  }}
```

The MCP server currently provides 42 tools which seems an awful lot and to be honest I haven’t tried them all out yet.

#### How I Use the Atlassian MCP to Scope Out Issues

There is a `get_issue` tool in this MCP and by pasting in the link to the issue, you can give your Cursor AI agent direct access to the whole contents of a specific issue in Jira.

> This is really nice when there are a bunch of comments and descriptions. Saves you having to copy and paste a bunch of data.

I then ask the AI agent to review the ticket and make a Plan (only plan for now!), **I have noticed the AI agent can be a bit keen sometimes to start cranking away at solutions** before it really ‘understands’ the issue.

I then review the plan carefully and see if I agree with the plan and also if its even referencing the right area and files of the application. **I like to take my time here because if the AI has the right context, the right files and the right instructions then it tends to do a pretty good job updating any code**.

If you leave things too open or ambiguous then the AI agent has a tendency to start doing some random things which helps no one and wastes time and money.

Once it has come up with suggestions and I am happy with it I will either do the coding myself or let the AI have a go. It really depends on the issue and the context.

#### Creating Issues Through the Atlassian MCP

This is another great use case I have found. How often have you been working away in the code and find an issue that has nothing to do with the current thing you are working on. It’s too big an issue to fix in your current PR but it’s not big enough to be a major issue.

You make a mental note — “Make a ticket for that later” — because you don’t want to ruin your flow by leaving the editor and creating a Jira ticket, but then you never do and **the bug silently persists in your code base**.

With the Jira MCP in Cursor, you can just press `cmd + i` tag the file/s and ask the AI agent to create a Jira issue in the right project. This can be done quickly, you don’t lose your flow and the actual ticket content will likely be better than if you did it yourself as AI is good at writing these things.

#### Creating Proposals or RFCs in Confluence

We use Confluence in our company for certain documentation. Whilst this is great, the thought of writing out proposals or docs can sometimes be pretty uninspiring and tedious. It’s a very useful practice when working in a team but you don’t always have the motivation.

Again, this is where AI agents can shine. **Being able to do this from your editor is really convenient and useful.** It also makes it easier to reference existing code.

I use this to create initial drafts as I always find it easier to update and amend existing content rather than creating something from scratch.

***

### Conclusion

I prefer the idea of incremental improvements in my workflows rather than big sweeping changes. I feel the Atlassian MCP is a great little addition to parts of my workflow that I previously found frustrating.

Creating accurate issues and documentation is an essential part of being a Software engineer and being able to have some useful help in this area makes this whole experience better.

Do you use the Atlassian MCP? If so, how do you use it? What other MCPs do you use and recommend?

***

### Subscribe to My Weekly Updates!

#### Enjoyed This Post?

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If you found this blog post helpful, why not stay updated with my latest content? [Subscribe](https://adam-drake-frontend-developer.medium.com/subscribe/) to receive email notifications every time I publish.

#### What You’ll Get

By subscribing, you’ll get:

* **Exciting Discoveries:** Be the first to know about the latest tools and libraries.
* **How-To Guides:** Step-by-step articles to enhance your development skills.
* **Opinion Pieces:** Thought-provoking insights into the world of frontend development.

#### Join Our Community

I live in the vibrant city of Prague, Czech Republic, with my family. My blog is more than just articles; it’s a community of like-minded developers who share a love for innovation and learning.

#### About me

I’m a passionate Frontend Developer specialising in React and TypeScript. My professional journey revolves around exploring and mastering new tools and libraries within the JavaScript ecosystem.

*Check out the original blog post on my* [*blog*](https://www.adamdrake.dev/blog)*.*

*Check out my* [*LinkedIn*](https://www.linkedin.com/in/adam-drake-ab065417/) *and* [*Github*](https://github.com/adamdrake210) *if you are interested.*

<img src="https://cdn-images-1.medium.com/max/1600/1*x2Swk7sY439NC06pVRJ7sg.png" alt="">
I work in the real world on real applications that are pretty large, complex and do more than just add a TODO item to a small list. Therefore I have been very skeptical about AI coming in and being this miracle that the AI companies would have you believe. (I have still seen no evidence of that btw. despite the recent mentions of AGI.)
Instead, I have seen AI as more of another tool to use in a developers’s toolkit. It can be a very useful tool in some circumstances but a tool none the less. Therefore, I like to make small incremental steps when introducing any AI into my workflow.
Recently I have been trying out the <a href="https://github.com/sooperset/mcp-atlassian?tab=readme-ov-file">Atlassian MCP server</a>. I have been quite impressed so far and think it can help in my existing workflow and make my daily developer life a little better.
The README is really well documented and it was easy to get it setup in Cursor. I got it connected to our Jira board that we use at work all within a few minutes.
Basically you have to:
<ul>
<li>Generate an api token from Atlassian</li>
<li>Pull the docker image that runs this MCP server</li>
<li>Add the MCP tool to your Cursor config</li>
</ul>
<pre><code class="code-highlight">{ "mcpServers": { "mcp-atlassian": { "command": "docker", "args": [ "run", "-i", "--rm", "-e", "CONFLUENCE_URL", "-e", "CONFLUENCE_USERNAME", "-e", "CONFLUENCE_API_TOKEN", "-e", "JIRA_URL", "-e", "JIRA_USERNAME", "-e", "JIRA_API_TOKEN", "ghcr.io/sooperset/mcp-atlassian:latest" ], "env": { "CONFLUENCE_URL": "https://your-company.atlassian.net/wiki", "CONFLUENCE_USERNAME": "your.email@company.com", "CONFLUENCE_API_TOKEN": "your_confluence_api_token", "JIRA_URL": "https://your-company.atlassian.net", "JIRA_USERNAME": "your.email@company.com", "JIRA_API_TOKEN": "your_jira_api_token" } } }}
</code></pre>
The MCP server currently provides 42 tools which seems an awful lot and to be honest I haven’t tried them all out yet.
<h4 id="how-i-use-the-atlassian-mcp-to-scope-outissues">How I Use the Atlassian MCP to Scope Out Issues</h4>
There is a <code>get_issue</code> tool in this MCP and by pasting in the link to the issue, you can give your Cursor AI agent direct access to the whole contents of a specific issue in Jira.
<blockquote>
This is really nice when there are a bunch of comments and descriptions. Saves you having to copy and paste a bunch of data.
</blockquote>
I then ask the AI agent to review the ticket and make a Plan (only plan for now!), I have noticed the AI agent can be a bit keen sometimes to start cranking away at solutions before it really ‘understands’ the issue.
I then review the plan carefully and see if I agree with the plan and also if its even referencing the right area and files of the application. I like to take my time here because if the AI has the right context, the right files and the right instructions then it tends to do a pretty good job updating any code.
If you leave things too open or ambiguous then the AI agent has a tendency to start doing some random things which helps no one and wastes time and money.
Once it has come up with suggestions and I am happy with it I will either do the coding myself or let the AI have a go. It really depends on the issue and the context.
<h4 id="creating-issues-through-the-atlassian-mcp">Creating Issues Through the Atlassian MCP</h4>
This is another great use case I have found. How often have you been working away in the code and find an issue that has nothing to do with the current thing you are working on. It’s too big an issue to fix in your current PR but it’s not big enough to be a major issue.
You make a mental note — “Make a ticket for that later” — because you don’t want to ruin your flow by leaving the editor and creating a Jira ticket, but then you never do and the bug silently persists in your code base.
With the Jira MCP in Cursor, you can just press <code>cmd + i</code> tag the file/s and ask the AI agent to create a Jira issue in the right project. This can be done quickly, you don’t lose your flow and the actual ticket content will likely be better than if you did it yourself as AI is good at writing these things.
<h4 id="creating-proposals-or-rfcs-in-confluence">Creating Proposals or RFCs in Confluence</h4>
We use Confluence in our company for certain documentation. Whilst this is great, the thought of writing out proposals or docs can sometimes be pretty uninspiring and tedious. It’s a very useful practice when working in a team but you don’t always have the motivation.
Again, this is where AI agents can shine. Being able to do this from your editor is really convenient and useful. It also makes it easier to reference existing code.
I use this to create initial drafts as I always find it easier to update and amend existing content rather than creating something from scratch.
<hr>
<h3 id="conclusion">Conclusion</h3>
I prefer the idea of incremental improvements in my workflows rather than big sweeping changes. I feel the Atlassian MCP is a great little addition to parts of my workflow that I previously found frustrating.
Creating accurate issues and documentation is an essential part of being a Software engineer and being able to have some useful help in this area makes this whole experience better.
Do you use the Atlassian MCP? If so, how do you use it? What other MCPs do you use and recommend?
<hr>
<h3 id="subscribe-to-my-weeklyupdates">Subscribe to My Weekly Updates!</h3>
<h4 id="enjoyed-thispost">Enjoyed This Post?</h4>
Enjoyed this one? Why then, hunt down that “clap” button and get clicking. It can be clicked up to 50 times, per reader!
If you found this blog post helpful, why not stay updated with my latest content? <a href="https://adam-drake-frontend-developer.medium.com/subscribe/">Subscribe</a> to receive email notifications every time I publish.
<h4 id="what-youllget">What You’ll Get</h4>
By subscribing, you’ll get:
<ul>
<li>Exciting Discoveries: Be the first to know about the latest tools and libraries.</li>
<li>How-To Guides: Step-by-step articles to enhance your development skills.</li>
<li>Opinion Pieces: Thought-provoking insights into the world of frontend development.</li>
</ul>
<h4 id="join-our-community">Join Our Community</h4>
I live in the vibrant city of Prague, Czech Republic, with my family. My blog is more than just articles; it’s a community of like-minded developers who share a love for innovation and learning.
<h4 id="about-me">About me</h4>
I’m a passionate Frontend Developer specialising in React and TypeScript. My professional journey revolves around exploring and mastering new tools and libraries within the JavaScript ecosystem.
Check out the original blog post on my <a href="https://www.adamdrake.dev/blog">blog</a>.
Check out my <a href="https://www.linkedin.com/in/adam-drake-ab065417/">LinkedIn</a> and <a href="https://github.com/adamdrake210">Github</a> if you are interested.

Using the Atlassian MCP server in Cursor

Are your images looking blurry and unclear online? Don't worry, there is a way to improve image quality and make them sharp again. When using images on the web, low quality is a common issue that many people encounter, and the solution is straightforward.

In this article, we will explain why images lose clarity and show easy ways to fix them using helpful tips and an [AI image enhancer](https://www.artguru.ai/image-enhancer/). By the end, your images inside your digital projects will look crisp, professional, and great on any device.

## **Why Do My Images Look Blurry Online?**

If your images look blurry online, it's usually due to one of these common reasons:

● **Small source image:** If the original image size is too small, it becomes pixelated when displayed larger.

● **High-density screens:** On Retina or 4K displays, standard-resolution images can look fuzzy.

● **Image compression:** Hosting platforms or apps often compress images to reduce file size, which lowers quality. Another possibility is that you used images in JPG format, which compresses image quality.

To keep your content looking sharp, you need high-quality images from the start or improve them before adding online.

## **How to Fix Blurry Photos for Your Online Content?**

Blurry pictures can create an unprofessional look in your digital content and are painstaking to edit manually. The simplest and quickest fix is to use AI image enhancement. This process is easy and quick, so you no longer have to endure complex editing software; AI does it all. The benefits include:

● Upscales images up to 4x without losing clarity.

● Sharpens edges and reduces noise for a clean look.

● Restores lost details in small or compressed images.

● Saves time with one-click processing.

● Ideal for any type of image, photos, graphics, or screenshots.

### A Recommended Tool: Artguru AI Image Enhancer

To see this AI power in action, let's look at a tool like the Artguru AI Image Enhancer. It is one of the best tools for the job. This tool can remove noise, sharpen edges, and add natural details, making it perfect for fixing blurry, pixelated, or low-resolution photos.

One of the best features is batch processing, which allows you to enhance up to 50 images at once, which also saves you valuable time with larger jobs. Besides, your whole experience will be within the browser, so there are no installations, and you do not need to have a technical background.

Actually, Artguru also offers a feature that surpasses most photo enhancers on the market: it includes an [image upscaler](https://www.artguru.ai/image-upscaler/) that can enhance images to even 8K resolution. This is especially helpful for users who need ultra-high-definition results!

### Steps to Enhance Your Image with Artguru Image Enhancer

**Step #1: Visit Artguru AI Image Enhancer**

Open the Artguru website homepage and find "Photo Enhancer" in the top navigation bar. If you need 8K resolution specifically, you can choose the "Upscale to 8K" option instead.

![](https://res.cloudinary.com/dosyawynu/image/upload/v1762921960/circuit-dashboard/posts/a09be2a0-810f-47dd-a5ab-1e0444585120/1762921960010.jpg)

**Step #2: Upload Your Image(s)**

Click "Select Photos" or drag and drop the photo you want to enhance. You can also upload multiple images for batch processing. Artguru supports most image format types like JPG, PNG, and WebP.

![](https://res.cloudinary.com/dosyawynu/image/upload/v1762922107/circuit-dashboard/posts/a09be2a0-810f-47dd-a5ab-1e0444585120/1762922107846.jpg) **Step #3: Download High-Quality Image**

The AI will process your images automatically, correcting any quality issues. After the AI finishes enhancing your image, you should see a small preview. Once you see what the AI has done for the image, click on the Download button to save the high-resolution version.

![](https://res.cloudinary.com/dosyawynu/image/upload/v1762922132/circuit-dashboard/posts/a09be2a0-810f-47dd-a5ab-1e0444585120/1762922132564.png)

## **Tips to Prepare HD Images from the Start**

Quality visuals start with quality content. If you want high-quality graphics, consider these essential steps before you begin any project:

1. **Use the Highest Resolution Source**

Always start with the highest resolution image or design elements you can access. Upscaling later can only do so much; original quality matters.

2. **Select an Appropriate Format**

When editing graphics, work with a lossless format, like PNG or TIFF, and for photographs, work with RAW. You can convert to JPEG once you are exporting for web or social media.

3. **Keep Vector Files When Possible**

When working with logos, icons, and illustrations, you should always use the vector file (SVG, AI, EPS) format. A vector can be scaled to any image size and will always maintain its quality.

4. **Be Careful of the Color Profile**

Work in sRGB for web projects and CMYK for print projects to prevent color shifts when exporting.

5. **Don't Over-compress**

When optimizing images for the web, don't mistakenly over-compress your images. Find the right balance so your images don't look fuzzy, and so you'll have your file sizes manageable.

By implementing these practices, you minimize the need for excessive post-processing and guarantee stunning, HD-ready visuals from the very beginning.

## **How to Display Your Images Online for the Best Effect**

Now that you have a perfect image, this final step will ensure it looks great online for every reader, on any device.

**The "Use a Big Image, Display it Small" Trick**

The secret to producing sharp-looking, professional images is simple: begin with a source image that is larger than the display size you'll use. This is sometimes referred to as the 2x rule. For example, if your image will be displayed at 500 pixels wide, make sure the original file is at least 1000 pixels in width. This extra resolution allows for clear images that won't pixelate on high-DPI (retina) displays.

## **Conclusion**

Clear, sharp images can make your online content look professional and easy to read. If your images appear blurry, don't worry, AI tools like Artguru Image Enhancer make it simple to improve quality in seconds. Combine this with smart practices, like starting with HD images and using the 2x rule, and your visuals will always look great on any device.

Are your images looking blurry and unclear online? Don't worry, there is a way to improve image quality and make them sharp again. When using images on the web, low quality is a common issue that many people encounter, and the solution is straightforward.
In this article, we will explain why images lose clarity and show easy ways to fix them using helpful tips and an <a href="https://www.artguru.ai/image-enhancer/">AI image enhancer</a>. By the end, your images inside your digital projects will look crisp, professional, and great on any device.
<h2 id="why-do-my-images-look-blurry-online">Why Do My Images Look Blurry Online?</h2>
If your images look blurry online, it's usually due to one of these common reasons:
● Small source image: If the original image size is too small, it becomes pixelated when displayed larger.
● High-density screens: On Retina or 4K displays, standard-resolution images can look fuzzy.
● Image compression: Hosting platforms or apps often compress images to reduce file size, which lowers quality. Another possibility is that you used images in JPG format, which compresses image quality.
To keep your content looking sharp, you need high-quality images from the start or improve them before adding online.
<h2 id="how-to-fix-blurry-photos-for-your-online-content">How to Fix Blurry Photos for Your Online Content?</h2>
Blurry pictures can create an unprofessional look in your digital content and are painstaking to edit manually. The simplest and quickest fix is to use AI image enhancement. This process is easy and quick, so you no longer have to endure complex editing software; AI does it all. The benefits include:
● Upscales images up to 4x without losing clarity.
● Sharpens edges and reduces noise for a clean look.
● Restores lost details in small or compressed images.
● Saves time with one-click processing.
● Ideal for any type of image, photos, graphics, or screenshots.
<h3 id="a-recommended-tool-artguru-ai-image-enhancer">A Recommended Tool: Artguru AI Image Enhancer</h3>
To see this AI power in action, let's look at a tool like the Artguru AI Image Enhancer. It is one of the best tools for the job. This tool can remove noise, sharpen edges, and add natural details, making it perfect for fixing blurry, pixelated, or low-resolution photos.
One of the best features is batch processing, which allows you to enhance up to 50 images at once, which also saves you valuable time with larger jobs. Besides, your whole experience will be within the browser, so there are no installations, and you do not need to have a technical background.
Actually, Artguru also offers a feature that surpasses most photo enhancers on the market: it includes an <a href="https://www.artguru.ai/image-upscaler/">image upscaler</a> that can enhance images to even 8K resolution. This is especially helpful for users who need ultra-high-definition results!
<h3 id="steps-to-enhance-your-image-with-artguru-image-enhancer">Steps to Enhance Your Image with Artguru Image Enhancer</h3>
Step #1: Visit Artguru AI Image Enhancer
Open the Artguru website homepage and find "Photo Enhancer" in the top navigation bar. If you need 8K resolution specifically, you can choose the "Upscale to 8K" option instead.
<img src="https://res.cloudinary.com/dosyawynu/image/upload/v1762921960/circuit-dashboard/posts/a09be2a0-810f-47dd-a5ab-1e0444585120/1762921960010.jpg" alt="">
Step #2: Upload Your Image(s)
Click "Select Photos" or drag and drop the photo you want to enhance. You can also upload multiple images for batch processing. Artguru supports most image format types like JPG, PNG, and WebP.
<img src="https://res.cloudinary.com/dosyawynu/image/upload/v1762922107/circuit-dashboard/posts/a09be2a0-810f-47dd-a5ab-1e0444585120/1762922107846.jpg" alt=""> Step #3: Download High-Quality Image
The AI will process your images automatically, correcting any quality issues. After the AI finishes enhancing your image, you should see a small preview. Once you see what the AI has done for the image, click on the Download button to save the high-resolution version.
<img src="https://res.cloudinary.com/dosyawynu/image/upload/v1762922132/circuit-dashboard/posts/a09be2a0-810f-47dd-a5ab-1e0444585120/1762922132564.png" alt="">
<h2 id="tips-to-prepare-hd-images-from-the-start">Tips to Prepare HD Images from the Start</h2>
Quality visuals start with quality content. If you want high-quality graphics, consider these essential steps before you begin any project:
1. Use the Highest Resolution Source
Always start with the highest resolution image or design elements you can access. Upscaling later can only do so much; original quality matters.
2. Select an Appropriate Format
When editing graphics, work with a lossless format, like PNG or TIFF, and for photographs, work with RAW. You can convert to JPEG once you are exporting for web or social media.
3. Keep Vector Files When Possible
When working with logos, icons, and illustrations, you should always use the vector file (SVG, AI, EPS) format. A vector can be scaled to any image size and will always maintain its quality.
4. Be Careful of the Color Profile
Work in sRGB for web projects and CMYK for print projects to prevent color shifts when exporting.
5. Don't Over-compress
When optimizing images for the web, don't mistakenly over-compress your images. Find the right balance so your images don't look fuzzy, and so you'll have your file sizes manageable.
By implementing these practices, you minimize the need for excessive post-processing and guarantee stunning, HD-ready visuals from the very beginning.
<h2 id="how-to-display-your-images-online-for-the-best-effect">How to Display Your Images Online for the Best Effect</h2>
Now that you have a perfect image, this final step will ensure it looks great online for every reader, on any device.
The "Use a Big Image, Display it Small" Trick
The secret to producing sharp-looking, professional images is simple: begin with a source image that is larger than the display size you'll use. This is sometimes referred to as the 2x rule. For example, if your image will be displayed at 500 pixels wide, make sure the original file is at least 1000 pixels in width. This extra resolution allows for clear images that won't pixelate on high-DPI (retina) displays.
<h2 id="conclusion">Conclusion</h2>
Clear, sharp images can make your online content look professional and easy to read. If your images appear blurry, don't worry, AI tools like Artguru Image Enhancer make it simple to improve quality in seconds. Combine this with smart practices, like starting with HD images and using the 2x rule, and your visuals will always look great on any device.

Struggling to make your online content look professional with perfect images? Here is a complete guide with step-by-step instructions to enhance image quality for your website, blog, or any digital content.

A Complete Guide to Enhancing Image Quality for Online Content

Top 100 AI Agent Interview Questions

![](https://cdn-images-1.medium.com/max/1600/1*qkPR9YVE5nA3bgFzLBgH0g.png)

Generated by the author with AI.

Months ago, I thought AI coding agents peaked with tools like Cursor and Claude Code.

Then something wild happened.

I discovered a way to turn a single Claude Code instance into **an autonomous software engineering team,** project managers, frontend devs, backend devs , all running *simultaneously*, all following deadlines, all committing code *without my input*.

They don’t wait for me. They don’t need approvals. They don’t ask permission.

**They just build.**

Let me show you how it works, and why this might be the future of AI development.

***

### The Backstory: From AI Helpers to Autonomous Systems

Over the past year, we’ve seen AI coding assistants evolve rapidly:

* Cursor turned VS Code into a co-pilot.
* Claude Code gave us smart terminals that actually *understand context*.
* Agentic workflows became buzzwords.

But all of them had one thing in common:
**They still needed *you* to click approve.**

What I discovered today flips that on its head.

> ***I set up a system that doesn’t just run Claude Code, it orchestrates multiple Claude agents working in parallel. On their own.***

These agents collaborate across different terminals, check in on each other’s work, and follow a strict schedule, all without me lifting a finger.

Let me show you exactly how it works.

***

### What the Tmux Orchestrator Actually Does

Welcome to the **Tmux Orchestrator,** a framework that turns Claude Code into a **self-governing, always-on software team**.

It’ more than just a clever script. It’s an architectural framework that:

* **Runs Claude agents 24/7,** while you sleep.
* **Schedules its own check-ins** and reassigns work autonomously.
* **Coordinates multiple engineers and project managers** across different projects.
* **Scales infinitely** by spawning parallel teams inside tmux windows.
* **Persists** work even when your terminal is closed or laptop is shut.

Imagine Cursor… but it builds your app **without needing you to hold its hand**.

If you haven’t set up Claude Code on your machine, **you’ll need it before running the orchestrator,** I’ve written a **step-by-step guide** that walks you through the full setup.

👉 [**Read: I Built a SaaS App in Minutes with Claude Code, Here’s the Exact Step-by-Step Guide**](https://medium.com/javascript-in-plain-english/i-built-a-saas-app-in-minutes-with-claude-code-heres-the-exact-step-by-step-guide-a831e97fa21d)

***

### The Breakthrough: Tmux Orchestrator

The magic happens through a GitHub repo called **Tmux Orchestrator**.

[**GitHub - Jedward23/Tmux-Orchestrator**
*Contribute to Jedward23/Tmux-Orchestrator development by creating an account on GitHub.*&#x67;ithub.com](https://github.com/Jedward23/Tmux-Orchestrator "https://github.com/Jedward23/Tmux-Orchestrator")

This framework turns your terminal into a **self-managing AI development environment**.

### Here’s What It Does:

* Launches a **master Claude agent**.
* That master agent spawns **sub-agents** in new terminals.
* Each sub-agent is assigned a role (e.g., frontend developer, backend PM).
* Agents follow a detailed spec and stick to a strict timeline.
* Progress is auto-tracked, committed, and checkpointed.

And yes, **it works. Really well.**

***

### How It Works: The Three-Tier Agent Hierarchy

To overcome context limitations, the orchestrator uses a **tiered agent hierarchy**:

```
┌─────────────┐│ Orchestrator│ ← You interact here (once)└──────┬──────┘       │ Monitors & coordinates       ▼┌─────────────┐     ┌─────────────┐│  Project    │     │  Project    │ ← Assign tasks, enforce specs│  Manager 1  │     │  Manager 2  │└──────┬──────┘     └──────┬──────┘       │                   │       ▼                   ▼┌─────────────┐     ┌─────────────┐│ Engineer 1  │     │ Engineer 2  │ ← Write code, fix bugs, run tests└─────────────┘     └─────────────┘
```

Each layer has a **narrow focus and clean responsibilities**, keeping agents within manageable context windows.

* **Orchestrator** oversees all projects
* **Project Managers** enforce specifications, timelines, and coordination
* **Engineers** write code, test, and commit

***

### Step-by-Step: How to Build Your Own AI Dev Team

Let me break down the full setup so you can try it yourself.

#### Step 1: Clone the Framework

1. Navigate to your target directory.
2. Clone the repo:

```
git clone https://github.com/Jedward23/Tmux-Orchestrator.gitcd Tmux-Orchestrator
```

3\. Run the provided setup scripts to make files executable.

#### Step 2: Start a New Tmux Session

Create a new session (e.g. `my-agent`) and keep it open.

```
tmux new-session -s my-agent
```

> ***⚠️ Note for Windows Users***
> *`tmux` is a Unix-based terminal tool and **won’t run in PowerShell**.*
> *To use the Tmux Orchestrator on Windows, run all terminal commands inside **WSL (Windows Subsystem for Linux)**.*

> *If you don’t have WSL set up yet, follow this official installation guide:*
> *👉* [*https://learn.microsoft.com/en-us/windows/wsl/install*](https://learn.microsoft.com/en-us/windows/wsl/install)

#### Step 3: **Enable Autonomy with a Critical Flag (Optional)**

Now you can run Claude Code.

If you want Claude to execute commands **without stopping for your approval** you can replace:

```
claude
```

with:

```
claude --dangerously-skip-permissions
```

![](https://cdn-images-1.medium.com/max/1600/1*pE9yyYPg6Ve1E1H8cr8SMw.png)

***

### The Spec Folder: Your New Source of Truth

To guide your agents, you’ll create a folder with multiple spec files:

* `main_spec.md`: the big picture
* `frontend_spec.md`: includes UI reference images and implementation plans
* `backend_spec.md`: API design, database schemas, and logic
* `integration_spec.md`: how everything fits together across teams

> *These files act like product requirement docs that your agents follow to build the full app.*

You can ask claude to help you generate those.

Here’s an example of a template:

```
PROJECT: E-commerce Checkout  GOAL: Implement multi-step checkoutCONSTRAINTS:- Use existing cart state- Follow design system- Max 3 API endpoints- Commit after each stepDELIVERABLES:1. Shipping form w/ validation  2. Payment method selection  3. Order confirmation page  4. Success/failure handlingSUCCESS CRITERIA:- Forms validate- Payment succeeds  - DB stores order  - Emails trigger
```

You can even specify deadlines and resource allocation across teams.

***

### The Most Important File You’re Not Using Yet: [`prompt.md`](https://prompt.md)

While the [`spec.md`](https://spec.md) files define *what* your project should look like, the [`prompt.md`](https://prompt.md) file tells the orchestrator *how* to manage the build process.

> *Think of it as the **mission briefing** for your AI project manager. Without it, agents know what to build, but not* how *to coordinate.*

#### Here’s an example of a `prompt.md`:

```
The specs are located in ~/Projects/EcommApp/Specs.Create:- A frontend team (PM, Dev, UI Tester)- A backend team (PM, Dev, API Tester)- An Auth team (PM, Dev)Schedule:- 15-minute check-ins with PMs- 30-minute commits from devs- 1-hour orchestrator status syncStart frontend and backend on Phase 1 immediately. Start Auth on Phase 2 kickoff.
```

You can edit your own [`prompt.md`](https://prompt.md) by adjusting:

* The path to your `Spec` folder (`~/projects/EcommApp/Spec` in the example)
* The number and type of teams (Frontend, Backend, Auth, Analytics, QA, etc.)
* The commit rhythm (`every 30 minutes` is ideal for Git traceability)
* Phase control (Start all at once, staggered, or conditional on success)

Then hit go.

***

### What Happens After You Run Claude: Agents, Teams & Terminal Coordination

Once you’ve launched your orchestrator with [`prompt.md`](https://prompt.md) and your spec files in place, **Claude takes over,** and this is where the magic begins.

#### Here’s what Claude does next:

1. **Parses your** [**`prompt.md`**](https://prompt.md)
   It identifies what teams to create (e.g., Frontend, Backend), where the specs live, and how to coordinate timing.
2. **Spins up tmux windows** for each role:

* Frontend Project Manager
* Frontend Developer
* Frontend Server (for tests/builds)
* Backend Project Manager
* Backend Developer
* Backend Server
* Orchestrator (you)
* Shared Logging/Check-ins

1. **Assigns responsibilities**
    Each agent gets a job based on the specs and the instructions in [`prompt.md`](https://prompt.md).
2. **Starts Phase 1 for all teams**
   If you’ve requested simultaneous launch, every team kicks off at once.
3. **Schedules self-checks** every 15 minutes
   These check-ins track progress, review commits, and move each team to the next phase if ready.

Here’s what a typical project might look like on disk. Think of this as the “brain” Claude reads from:

```
~/Projects/EcommApp/│├── prompt.md                  # High-level instructions for orchestrator├── Specs/│   ├── main_spec.md           # Timeline and global goals│   ├── frontend_spec.md       # UI requirements, components, mockups│   ├── backend_spec.md        # API endpoints, DB logic│   ├── integration_spec.md    # How front + back sync together│├── UI_Reference/│   ├── design.png             # Screenshot or figma export│   └── implementation.md      # Step-by-step ShadCN component plan│├── TaskManager/│   └── (Generated Code)       # Claude will build your app here│├── Claude_Scripts/│   ├── send-claude-message.sh│   ├── schedule_with_note.sh│   └── tmux_utils.py
```

> ***Important:** Always use* absolute paths *in your* [*`prompt.md`*](https://prompt.md) *so Claude doesn’t get lost.*

### And Now in Your Terminal…

Once the orchestrator runs, you’ll see something like this in your tmux session:

```
┌───────────────────────────────┐│ orchestrator:0                │ ← You (control tower)├───────────────────────────────┤│ frontend-pm:1                 │ ← Reads frontend_spec.md│ frontend-dev:2                │ ← Builds UI, pushes commits│ frontend-server:3             │ ← Runs tests, previews UI├───────────────────────────────┤│ backend-pm:4                  │ ← Assigns tasks from backend_spec.md│ backend-dev:5                 │ ← Builds APIs, fixes bugs│ backend-server:6              │ ← Runs backend tests, logs results├───────────────────────────────┤│ status-logger:7               │ ← Check-ins every 15 mins└───────────────────────────────┘
```

![](https://cdn-images-1.medium.com/max/1600/1*LKntBaEmWkmckpiakuoYeA.png)

Each of these panes is a **fully autonomous Claude agent,** receiving instructions, reading specs, writing code, and reporting status.

> *It’s like having a fully staffed sprint team working inside your terminal, no hand-holding needed.*

***

### Final Thoughts: The Future of Coding Just Got a Lot Weirder

When I first imagined autonomous AI agents, I thought:
*Someday.*

Now?
I’ve got eight of them running in my terminal right now, coding, debugging, and checking each other’s work.

No plugins. No IDE bloat. Just terminals, specs, and coordination.

This isn’t the end of developers. But it *is* the beginning of something wild:

> ***Code as orchestration. Engineering as prompt design.***

The tools are here. The future’s already running.

<img src="https://cdn-images-1.medium.com/max/1600/1*qkPR9YVE5nA3bgFzLBgH0g.png" alt="">
Generated by the author with AI.
Months ago, I thought AI coding agents peaked with tools like Cursor and Claude Code.
Then something wild happened.
I discovered a way to turn a single Claude Code instance into an autonomous software engineering team, project managers, frontend devs, backend devs , all running simultaneously, all following deadlines, all committing code without my input.
They don’t wait for me. They don’t need approvals. They don’t ask permission.
They just build.
Let me show you how it works, and why this might be the future of AI development.
<hr>
<h3 id="the-backstory-from-ai-helpers-to-autonomous-systems">The Backstory: From AI Helpers to Autonomous Systems</h3>
Over the past year, we’ve seen AI coding assistants evolve rapidly:
<ul>
<li>Cursor turned VS Code into a co-pilot.</li>
<li>Claude Code gave us smart terminals that actually understand context.</li>
<li>Agentic workflows became buzzwords.</li>
</ul>
But all of them had one thing in common:
They still needed you to click approve.
What I discovered today flips that on its head.
<blockquote>
I set up a system that doesn’t just run Claude Code, it orchestrates multiple Claude agents working in parallel. On their own.
</blockquote>
These agents collaborate across different terminals, check in on each other’s work, and follow a strict schedule, all without me lifting a finger.
Let me show you exactly how it works.
<hr>
<h3 id="what-the-tmux-orchestrator-actuallydoes">What the Tmux Orchestrator Actually Does</h3>
Welcome to the Tmux Orchestrator, a framework that turns Claude Code into a self-governing, always-on software team.
It’ more than just a clever script. It’s an architectural framework that:
<ul>
<li>Runs Claude agents 24/7, while you sleep.</li>
<li>Schedules its own check-ins and reassigns work autonomously.</li>
<li>Coordinates multiple engineers and project managers across different projects.</li>
<li>Scales infinitely by spawning parallel teams inside tmux windows.</li>
<li>Persists work even when your terminal is closed or laptop is shut.</li>
</ul>
Imagine Cursor… but it builds your app without needing you to hold its hand.
If you haven’t set up Claude Code on your machine, you’ll need it before running the orchestrator, I’ve written a step-by-step guide that walks you through the full setup.
👉 <a href="https://medium.com/javascript-in-plain-english/i-built-a-saas-app-in-minutes-with-claude-code-heres-the-exact-step-by-step-guide-a831e97fa21d">Read: I Built a SaaS App in Minutes with Claude Code, Here’s the Exact Step-by-Step Guide</a>
<hr>
<h3 id="the-breakthrough-tmux-orchestrator">The Breakthrough: Tmux Orchestrator</h3>
The magic happens through a GitHub repo called Tmux Orchestrator.
<a href="https://github.com/Jedward23/Tmux-Orchestrator" title="https://github.com/Jedward23/Tmux-Orchestrator">GitHub - Jedward23/Tmux-Orchestrator
Contribute to Jedward23/Tmux-Orchestrator development by creating an account on GitHub.github.com</a>
This framework turns your terminal into a self-managing AI development environment.
<h3 id="heres-what-itdoes">Here’s What It Does:</h3>
<ul>
<li>Launches a master Claude agent.</li>
<li>That master agent spawns sub-agents in new terminals.</li>
<li>Each sub-agent is assigned a role (e.g., frontend developer, backend PM).</li>
<li>Agents follow a detailed spec and stick to a strict timeline.</li>
<li>Progress is auto-tracked, committed, and checkpointed.</li>
</ul>
And yes, it works. Really well.
<hr>
<h3 id="how-it-works-the-three-tier-agent-hierarchy">How It Works: The Three-Tier Agent Hierarchy</h3>
To overcome context limitations, the orchestrator uses a tiered agent hierarchy:
<pre><code class="code-highlight">┌─────────────┐│ Orchestrator│ ← You interact here (once)└──────┬──────┘ │ Monitors &#x26; coordinates ▼┌─────────────┐ ┌─────────────┐│ Project │ │ Project │ ← Assign tasks, enforce specs│ Manager 1 │ │ Manager 2 │└──────┬──────┘ └──────┬──────┘ │ │ ▼ ▼┌─────────────┐ ┌─────────────┐│ Engineer 1 │ │ Engineer 2 │ ← Write code, fix bugs, run tests└─────────────┘ └─────────────┘
</code></pre>
Each layer has a narrow focus and clean responsibilities, keeping agents within manageable context windows.
<ul>
<li>Orchestrator oversees all projects</li>
<li>Project Managers enforce specifications, timelines, and coordination</li>
<li>Engineers write code, test, and commit</li>
</ul>
<hr>
<h3 id="step-by-step-how-to-build-your-own-ai-devteam">Step-by-Step: How to Build Your Own AI Dev Team</h3>
Let me break down the full setup so you can try it yourself.
<h4 id="step-1-clone-the-framework">Step 1: Clone the Framework</h4>
<ol>
<li>Navigate to your target directory.</li>
<li>Clone the repo:</li>
</ol>
<pre><code class="code-highlight">git clone https://github.com/Jedward23/Tmux-Orchestrator.gitcd Tmux-Orchestrator
</code></pre>
3. Run the provided setup scripts to make files executable.
<h4 id="step-2-start-a-new-tmuxsession">Step 2: Start a New Tmux Session</h4>
Create a new session (e.g. <code>my-agent</code>) and keep it open.
<pre><code class="code-highlight">tmux new-session -s my-agent
</code></pre>
<blockquote>
⚠️ Note for Windows Users
<code>tmux</code> is a Unix-based terminal tool and won’t run in PowerShell.
To use the Tmux Orchestrator on Windows, run all terminal commands inside WSL (Windows Subsystem for Linux).
</blockquote>
<blockquote>
If you don’t have WSL set up yet, follow this official installation guide:
👉 <a href="https://learn.microsoft.com/en-us/windows/wsl/install">https://learn.microsoft.com/en-us/windows/wsl/install</a>
</blockquote>
<h4 id="step-3-enable-autonomy-with-a-critical-flag-optional">Step 3: Enable Autonomy with a Critical Flag (Optional)</h4>
Now you can run Claude Code.
If you want Claude to execute commands without stopping for your approval you can replace:
<pre><code class="code-highlight">claude
</code></pre>
with:
<pre><code class="code-highlight">claude --dangerously-skip-permissions
</code></pre>
<img src="https://cdn-images-1.medium.com/max/1600/1*pE9yyYPg6Ve1E1H8cr8SMw.png" alt="">
<hr>
<h3 id="the-spec-folder-your-new-source-oftruth">The Spec Folder: Your New Source of Truth</h3>
To guide your agents, you’ll create a folder with multiple spec files:
<ul>
<li><code>main_spec.md</code>: the big picture</li>
<li><code>frontend_spec.md</code>: includes UI reference images and implementation plans</li>
<li><code>backend_spec.md</code>: API design, database schemas, and logic</li>
<li><code>integration_spec.md</code>: how everything fits together across teams</li>
</ul>
<blockquote>
These files act like product requirement docs that your agents follow to build the full app.
</blockquote>
You can ask claude to help you generate those.
Here’s an example of a template:
<pre><code class="code-highlight">PROJECT: E-commerce Checkout GOAL: Implement multi-step checkoutCONSTRAINTS:- Use existing cart state- Follow design system- Max 3 API endpoints- Commit after each stepDELIVERABLES:1. Shipping form w/ validation 2. Payment method selection 3. Order confirmation page 4. Success/failure handlingSUCCESS CRITERIA:- Forms validate- Payment succeeds - DB stores order - Emails trigger
</code></pre>
You can even specify deadlines and resource allocation across teams.
<hr>
<h3 id="the-most-important-file-youre-not-using-yet-promptmd">The Most Important File You’re Not Using Yet: <a href="https://prompt.md"><code>prompt.md</code></a></h3>
While the <a href="https://spec.md"><code>spec.md</code></a> files define what your project should look like, the <a href="https://prompt.md"><code>prompt.md</code></a> file tells the orchestrator how to manage the build process.
<blockquote>
Think of it as the mission briefing for your AI project manager. Without it, agents know what to build, but not how to coordinate.
</blockquote>
<h4 id="heres-an-example-of-a-promptmd">Here’s an example of a <code>prompt.md</code>:</h4>
<pre><code class="code-highlight">The specs are located in ~/Projects/EcommApp/Specs.Create:- A frontend team (PM, Dev, UI Tester)- A backend team (PM, Dev, API Tester)- An Auth team (PM, Dev)Schedule:- 15-minute check-ins with PMs- 30-minute commits from devs- 1-hour orchestrator status syncStart frontend and backend on Phase 1 immediately. Start Auth on Phase 2 kickoff.
</code></pre>
You can edit your own <a href="https://prompt.md"><code>prompt.md</code></a> by adjusting:
<ul>
<li>The path to your <code>Spec</code> folder (<code>~/projects/EcommApp/Spec</code> in the example)</li>
<li>The number and type of teams (Frontend, Backend, Auth, Analytics, QA, etc.)</li>
<li>The commit rhythm (<code>every 30 minutes</code> is ideal for Git traceability)</li>
<li>Phase control (Start all at once, staggered, or conditional on success)</li>
</ul>
Then hit go.
<hr>
<h3 id="what-happens-after-you-run-claude-agents-teams--terminal-coordination">What Happens After You Run Claude: Agents, Teams &#x26; Terminal Coordination</h3>
Once you’ve launched your orchestrator with <a href="https://prompt.md"><code>prompt.md</code></a> and your spec files in place, Claude takes over, and this is where the magic begins.
<h4 id="heres-what-claude-doesnext">Here’s what Claude does next:</h4>
<ol>
<li>Parses your <a href="https://prompt.md"><code>prompt.md</code></a>
It identifies what teams to create (e.g., Frontend, Backend), where the specs live, and how to coordinate timing.</li>
<li>Spins up tmux windows for each role:</li>
</ol>
<ul>
<li>Frontend Project Manager</li>
<li>Frontend Developer</li>
<li>Frontend Server (for tests/builds)</li>
<li>Backend Project Manager</li>
<li>Backend Developer</li>
<li>Backend Server</li>
<li>Orchestrator (you)</li>
<li>Shared Logging/Check-ins</li>
</ul>
<ol>
<li>Assigns responsibilities
 Each agent gets a job based on the specs and the instructions in <a href="https://prompt.md"><code>prompt.md</code></a>.</li>
<li>Starts Phase 1 for all teams
If you’ve requested simultaneous launch, every team kicks off at once.</li>
<li>Schedules self-checks every 15 minutes
These check-ins track progress, review commits, and move each team to the next phase if ready.</li>
</ol>
Here’s what a typical project might look like on disk. Think of this as the “brain” Claude reads from:
<pre><code class="code-highlight">~/Projects/EcommApp/│├── prompt.md # High-level instructions for orchestrator├── Specs/│ ├── main_spec.md # Timeline and global goals│ ├── frontend_spec.md # UI requirements, components, mockups│ ├── backend_spec.md # API endpoints, DB logic│ ├── integration_spec.md # How front + back sync together│├── UI_Reference/│ ├── design.png # Screenshot or figma export│ └── implementation.md # Step-by-step ShadCN component plan│├── TaskManager/│ └── (Generated Code) # Claude will build your app here│├── Claude_Scripts/│ ├── send-claude-message.sh│ ├── schedule_with_note.sh│ └── tmux_utils.py
</code></pre>
<blockquote>
Important: Always use absolute paths in your <a href="https://prompt.md"><code>prompt.md</code></a> so Claude doesn’t get lost.
</blockquote>
<h3 id="and-now-in-your-terminal">And Now in Your Terminal…</h3>
Once the orchestrator runs, you’ll see something like this in your tmux session:
<pre><code class="code-highlight">┌───────────────────────────────┐│ orchestrator:0 │ ← You (control tower)├───────────────────────────────┤│ frontend-pm:1 │ ← Reads frontend_spec.md│ frontend-dev:2 │ ← Builds UI, pushes commits│ frontend-server:3 │ ← Runs tests, previews UI├───────────────────────────────┤│ backend-pm:4 │ ← Assigns tasks from backend_spec.md│ backend-dev:5 │ ← Builds APIs, fixes bugs│ backend-server:6 │ ← Runs backend tests, logs results├───────────────────────────────┤│ status-logger:7 │ ← Check-ins every 15 mins└───────────────────────────────┘
</code></pre>
<img src="https://cdn-images-1.medium.com/max/1600/1*LKntBaEmWkmckpiakuoYeA.png" alt="">
Each of these panes is a fully autonomous Claude agent, receiving instructions, reading specs, writing code, and reporting status.
<blockquote>
It’s like having a fully staffed sprint team working inside your terminal, no hand-holding needed.
</blockquote>
<hr>
<h3 id="final-thoughts-the-future-of-coding-just-got-a-lotweirder">Final Thoughts: The Future of Coding Just Got a Lot Weirder</h3>
When I first imagined autonomous AI agents, I thought:
Someday.
Now?
I’ve got eight of them running in my terminal right now, coding, debugging, and checking each other’s work.
No plugins. No IDE bloat. Just terminals, specs, and coordination.
This isn’t the end of developers. But it is the beginning of something wild:
<blockquote>
Code as orchestration. Engineering as prompt design.
</blockquote>
The tools are here. The future’s already running.

No prompts. No babysitting. Just real coding agents, building full-stack apps while you sleep.

I Built an AI Team That Codes Itself, And You Can Too

This article provides an overview of how advanced artificial intelligence (AI) and machine learning (ML) are transforming the retail sector, with particular emphasis on dynamic pricing and demand forecasting. Drawing on recent industry surveys and academic research, the discussion highlights the limitations of traditional pricing strategies - such as cost-plus or competition-based pricing - and contrasts them with AI-driven methods that integrate real-time data. The article also elaborates on how machine learning–based demand forecasting helps retailers optimize inventory levels and improve customer satisfaction. Concluding sections address emerging trends, including expanded AI integration across merchandising and operations.

## **1. Introduction**

The retail industry has undergone significant changes in recent years as new technologies reshape consumer behavior and competitive landscapes. Artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools to tackle the complexities of pricing, inventory management, and demand forecasting. Yet, despite the growing adoption of AI, many retailers still rely on manual processes or traditional spreadsheet-based models, which are ill-suited for rapidly changing market conditions (Gartner, 2022).

In light of these developments, this article explores how AI and ML techniques are poised to bring about a more comprehensive transformation in retail, especially in the areas of dynamic pricing and demand forecasting. These areas present clear opportunities for optimization, as they directly influence profit margins, waste reduction, and customer experience. 

## **2. Literature Review**

### **2.1 Traditional Pricing Approaches**

Historically, retailers have employed methods such as cost-plus pricing—where a fixed markup is added to the production cost—and competition-based pricing, in which prices are benchmarked against rival offerings. Although straightforward to implement, cost-plus pricing ignores fluctuations in demand and consumer willingness to pay (Gartner, 2022). Competition-based pricing, on the other hand, can trigger price wars and erode brand value. 

Value-based pricing and market-based pricing have also gained traction, as they account for perceived customer value and supply-demand dynamics. However, these methods remain prone to the limitations of infrequent updates and subjective decision-making (Bertsimas & Misic, 2019). 

### **2.2 AI-Driven Pricing** 

Dynamic pricing leverages continuous data streams—such as real-time inventory levels, competitor prices, and seasonality—to algorithmically adjust prices. Machine learning models excel at capturing such non-linear and context-specific relationships (Li, Pan, & Xia, 2020). In e-commerce, dynamic pricing engines can modify prices within minutes, allowing retailers to manage profit margins, prevent stockouts, and avoid overstock situations (Bertsimas & Misic, 2019).

### **2.3 Demand Forecasting with Machine Learning**

Demand forecasting is a cornerstone of retail operations, affecting purchasing, warehousing, and overall customer satisfaction (Hyndman & Athanasopoulos, 2021). Traditional forecasting techniques (e.g., ARIMA, exponential smoothing) generally rely on historical trends and seasonal patterns. In contrast, ML-based methods—such as gradient-boosted trees, random forests, and deep neural networks—can incorporate a broader array of features, including weather data, promotions, and competitor actions (Carbonneau, Laframboise, & Vahidov, 2008). 

Recent forecasting competitions, such as the M4 series, have demonstrated that machine learning models can outperform classical techniques for large-scale time series (Makridakis, Spiliotis, & Assimakopoulos, 2020). By leveraging these tools, retailers can anticipate demand shifts more accurately, optimize inventory levels, and reduce both holding costs and the risk of shortages.

## **3. Technical Analysis**

### **3.1 Dynamic Pricing Mechanisms**

A typical dynamic pricing system integrates multiple data sources to generate rapid pricing decisions:

1. **Data Ingestion**: The system ingests real-time sales transactions, competitor prices, website clicks, and macro-level indicators (Zhang & Wang, 2021).
2. **Feature Engineering**: Key features might include price elasticity, time-of-day or day-of-week effects, promotional schedules, and inventory constraints.
3. **Model Training**: Machine learning algorithms such as XGBoost or deep neural networks learn to optimize revenue based on historical data.
4. **Price Optimization**: An optimization layer translates model predictions into final price recommendations that align with specific goals (e.g., revenue maximization or market share capture).

### **3.2 Demand Forecasting Approaches** 

Machine learning–based demand forecasting integrates a diverse range of features, including historical sales, promotional calendars, and macroeconomic indicators. Typical models include:

* **Gradient-Boosted Trees** (e.g., XGBoost, LightGBM): Offers robust performance in capturing non-linear relationships and can handle missing data efficiently (Carbonneau et al., 2008).
* **Deep Neural Networks** (e.g., LSTM, N-BEATS): Particularly effective for complex time-series data with multiple seasonal patterns and large feature sets (Oreshkin, Carpov, Chapados, & Bengio, 2019).
* **Causal Modeling**: Incorporates external variables, such as marketing spend or weather fluctuations, to estimate causal effects on demand (Hyndman & Athanasopoulos, 2021).

By systematically comparing predictions from these models, retailers can refine forecasts and mitigate risks associated with over- or under-stocking.

## **4. Future Trends**

### **4.1 Integrating AI Across the Merchandising Cycle**

While AI is most visible in pricing and forecasting, new innovations promise deeper integration across the merchandising cycle. For instance, reinforcement learning can optimize replenishment policies for perishable goods by dynamically balancing holding costs against the probability of spoilage (Zhang & Wang, 2021). In parallel, explainable AI methods are gaining momentum, addressing the need for transparent and ethical decision-making in automated retail systems.

### **4.2 Democratization of AI Tools**

Cloud-based platforms and open-source libraries continue to lower the barrier to entry for advanced ML methods. As a result, mid-sized and smaller retailers can now implement AI-driven solutions without incurring prohibitive costs. This democratization is expected to accelerate the adoption of dynamic pricing and demand forecasting throughout the industry.

### **4.3 IoT Integration** 

The Internet of Things (IoT) is another arena where AI can deliver impactful benefits to retail. Sensor-rich environments can track real-time foot traffic, shelf inventory, and product conditions. AI-driven analytics then convert these signals into insights for pricing and merchandising. IoT integration offers more granular visibility into store operations, allowing for real-time interventions to prevent stockouts or to reprice items nearing expiration.

## **5. Conclusion**

As AI and machine learning techniques continue to evolve, the retail industry stands at a pivotal juncture. Traditional approaches to pricing and demand forecasting, though foundational, struggle to cope with the complexity and velocity of modern retail data. AI-driven dynamic pricing enables near-instantaneous updates to optimize margins, while machine learning–based demand forecasting helps retailers better align inventory with actual consumer needs.

Looking ahead, the widespread integration of reinforcement learning, explainable AI, and IoT-driven analytics promises a new era of data-informed decision-making. By adopting these innovations, retail organizations can achieve more efficient operations, enhanced customer satisfaction, and greater long-term profitability.

**References**

1\. Bertsimas, D., & Misic, V. (2019). Dynamic and data-driven pricing: An application to airline and retail pricing. *arXiv preprint:* arXiv:1903.00774

2\. Carbonneau, R., Laframboise, K., & Vahidov, R. (2008). Application of machine learning techniques for supply chain demand forecasting. *European Journal of Operational Research*, 184(3), 1140–1154.

3\. Hyndman, R. J., & Athanasopoulos, G. (2021). *Forecasting: Principles and Practice* (3rd ed.). OTexts.

4\. Li, W., Pan, S. J., & Xia, Y. (2020). Multi-platform dynamic pricing for e-commerce with competition. *arXiv preprint:* arXiv:2005.07566

5\. Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2020). The M4 Competition: 100,000 time series and 61 forecasting methods. *International Journal of Forecasting*, 36(1), 54–74.

6\. Oreshkin, B. N., Carpov, D., Chapados, N., & Bengio, Y. (2019). N-BEATS: Neural basis expansion analysis for interpretable time series forecasting. *Advances in Neural Information Processing Systems*, 32.

7\. Zhang, L., & Wang, K. (2021). Reinforcement learning for multi-echelon inventory management. *arXiv preprint:* arXiv:2101.12345

**Author Biography**

<img height="124" width="124" src="https://cdn-images-1.medium.com/max/1600/1*WL6ci9HTFmrp_u9ShtC-ZQ.jpeg" />

**Jayadeep Shitole** holds a bachelor’s and master’s degree in Mathematics and Scientific Computing from the Indian Institute of Technology, as well as a master’s degree in Operations Research and Information Engineering from Cornell University. He currently serves as a data science leader on the Applied AI team at Walmart Global Tech in Sunnyvale, California. His professional experiences include developing a deep learning–driven dynamic pricing system for near-expiration perishable goods, leading to significant profit growth and reduced waste.

This article provides an overview of how advanced artificial intelligence (AI) and machine learning (ML) are transforming the retail sector, with particular emphasis on dynamic pricing and demand forecasting. Drawing on recent industry surveys and academic research, the discussion highlights the limitations of traditional pricing strategies - such as cost-plus or competition-based pricing - and contrasts them with AI-driven methods that integrate real-time data. The article also elaborates on how machine learning–based demand forecasting helps retailers optimize inventory levels and improve customer satisfaction. Concluding sections address emerging trends, including expanded AI integration across merchandising and operations.
<h2 id="1-introduction">1. Introduction</h2>
The retail industry has undergone significant changes in recent years as new technologies reshape consumer behavior and competitive landscapes. Artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools to tackle the complexities of pricing, inventory management, and demand forecasting. Yet, despite the growing adoption of AI, many retailers still rely on manual processes or traditional spreadsheet-based models, which are ill-suited for rapidly changing market conditions (Gartner, 2022).
In light of these developments, this article explores how AI and ML techniques are poised to bring about a more comprehensive transformation in retail, especially in the areas of dynamic pricing and demand forecasting. These areas present clear opportunities for optimization, as they directly influence profit margins, waste reduction, and customer experience. 
<h2 id="2-literature-review">2. Literature Review</h2>
<h3 id="21-traditional-pricing-approaches">2.1 Traditional Pricing Approaches</h3>
Historically, retailers have employed methods such as cost-plus pricing—where a fixed markup is added to the production cost—and competition-based pricing, in which prices are benchmarked against rival offerings. Although straightforward to implement, cost-plus pricing ignores fluctuations in demand and consumer willingness to pay (Gartner, 2022). Competition-based pricing, on the other hand, can trigger price wars and erode brand value. 
Value-based pricing and market-based pricing have also gained traction, as they account for perceived customer value and supply-demand dynamics. However, these methods remain prone to the limitations of infrequent updates and subjective decision-making (Bertsimas &#x26; Misic, 2019). 
<h3 id="22-ai-driven-pricing">2.2 AI-Driven Pricing </h3>
Dynamic pricing leverages continuous data streams—such as real-time inventory levels, competitor prices, and seasonality—to algorithmically adjust prices. Machine learning models excel at capturing such non-linear and context-specific relationships (Li, Pan, &#x26; Xia, 2020). In e-commerce, dynamic pricing engines can modify prices within minutes, allowing retailers to manage profit margins, prevent stockouts, and avoid overstock situations (Bertsimas &#x26; Misic, 2019).
<h3 id="23-demand-forecasting-with-machine-learning">2.3 Demand Forecasting with Machine Learning</h3>
Demand forecasting is a cornerstone of retail operations, affecting purchasing, warehousing, and overall customer satisfaction (Hyndman &#x26; Athanasopoulos, 2021). Traditional forecasting techniques (e.g., ARIMA, exponential smoothing) generally rely on historical trends and seasonal patterns. In contrast, ML-based methods—such as gradient-boosted trees, random forests, and deep neural networks—can incorporate a broader array of features, including weather data, promotions, and competitor actions (Carbonneau, Laframboise, &#x26; Vahidov, 2008). 
Recent forecasting competitions, such as the M4 series, have demonstrated that machine learning models can outperform classical techniques for large-scale time series (Makridakis, Spiliotis, &#x26; Assimakopoulos, 2020). By leveraging these tools, retailers can anticipate demand shifts more accurately, optimize inventory levels, and reduce both holding costs and the risk of shortages.
<h2 id="3-technical-analysis">3. Technical Analysis</h2>
<h3 id="31-dynamic-pricing-mechanisms">3.1 Dynamic Pricing Mechanisms</h3>
A typical dynamic pricing system integrates multiple data sources to generate rapid pricing decisions:
<ol>
<li>Data Ingestion: The system ingests real-time sales transactions, competitor prices, website clicks, and macro-level indicators (Zhang &#x26; Wang, 2021).</li>
<li>Feature Engineering: Key features might include price elasticity, time-of-day or day-of-week effects, promotional schedules, and inventory constraints.</li>
<li>Model Training: Machine learning algorithms such as XGBoost or deep neural networks learn to optimize revenue based on historical data.</li>
<li>Price Optimization: An optimization layer translates model predictions into final price recommendations that align with specific goals (e.g., revenue maximization or market share capture).</li>
</ol>
<h3 id="32-demand-forecasting-approaches">3.2 Demand Forecasting Approaches </h3>
Machine learning–based demand forecasting integrates a diverse range of features, including historical sales, promotional calendars, and macroeconomic indicators. Typical models include:
<ul>
<li>Gradient-Boosted Trees (e.g., XGBoost, LightGBM): Offers robust performance in capturing non-linear relationships and can handle missing data efficiently (Carbonneau et al., 2008).</li>
<li>Deep Neural Networks (e.g., LSTM, N-BEATS): Particularly effective for complex time-series data with multiple seasonal patterns and large feature sets (Oreshkin, Carpov, Chapados, &#x26; Bengio, 2019).</li>
<li>Causal Modeling: Incorporates external variables, such as marketing spend or weather fluctuations, to estimate causal effects on demand (Hyndman &#x26; Athanasopoulos, 2021).</li>
</ul>
By systematically comparing predictions from these models, retailers can refine forecasts and mitigate risks associated with over- or under-stocking.
<h2 id="4-future-trends">4. Future Trends</h2>
<h3 id="41-integrating-ai-across-the-merchandising-cycle">4.1 Integrating AI Across the Merchandising Cycle</h3>
While AI is most visible in pricing and forecasting, new innovations promise deeper integration across the merchandising cycle. For instance, reinforcement learning can optimize replenishment policies for perishable goods by dynamically balancing holding costs against the probability of spoilage (Zhang &#x26; Wang, 2021). In parallel, explainable AI methods are gaining momentum, addressing the need for transparent and ethical decision-making in automated retail systems.
<h3 id="42-democratization-of-ai-tools">4.2 Democratization of AI Tools</h3>
Cloud-based platforms and open-source libraries continue to lower the barrier to entry for advanced ML methods. As a result, mid-sized and smaller retailers can now implement AI-driven solutions without incurring prohibitive costs. This democratization is expected to accelerate the adoption of dynamic pricing and demand forecasting throughout the industry.
<h3 id="43-iot-integration">4.3 IoT Integration </h3>
The Internet of Things (IoT) is another arena where AI can deliver impactful benefits to retail. Sensor-rich environments can track real-time foot traffic, shelf inventory, and product conditions. AI-driven analytics then convert these signals into insights for pricing and merchandising. IoT integration offers more granular visibility into store operations, allowing for real-time interventions to prevent stockouts or to reprice items nearing expiration.
<h2 id="5-conclusion">5. Conclusion</h2>
As AI and machine learning techniques continue to evolve, the retail industry stands at a pivotal juncture. Traditional approaches to pricing and demand forecasting, though foundational, struggle to cope with the complexity and velocity of modern retail data. AI-driven dynamic pricing enables near-instantaneous updates to optimize margins, while machine learning–based demand forecasting helps retailers better align inventory with actual consumer needs.
Looking ahead, the widespread integration of reinforcement learning, explainable AI, and IoT-driven analytics promises a new era of data-informed decision-making. By adopting these innovations, retail organizations can achieve more efficient operations, enhanced customer satisfaction, and greater long-term profitability.
References
1. Bertsimas, D., &#x26; Misic, V. (2019). Dynamic and data-driven pricing: An application to airline and retail pricing. arXiv preprint: arXiv:1903.00774
2. Carbonneau, R., Laframboise, K., &#x26; Vahidov, R. (2008). Application of machine learning techniques for supply chain demand forecasting. European Journal of Operational Research, 184(3), 1140–1154.
3. Hyndman, R. J., &#x26; Athanasopoulos, G. (2021). Forecasting: Principles and Practice (3rd ed.). OTexts.
4. Li, W., Pan, S. J., &#x26; Xia, Y. (2020). Multi-platform dynamic pricing for e-commerce with competition. arXiv preprint: arXiv:2005.07566
5. Makridakis, S., Spiliotis, E., &#x26; Assimakopoulos, V. (2020). The M4 Competition: 100,000 time series and 61 forecasting methods. International Journal of Forecasting, 36(1), 54–74.
6. Oreshkin, B. N., Carpov, D., Chapados, N., &#x26; Bengio, Y. (2019). N-BEATS: Neural basis expansion analysis for interpretable time series forecasting. Advances in Neural Information Processing Systems, 32.
7. Zhang, L., &#x26; Wang, K. (2021). Reinforcement learning for multi-echelon inventory management. arXiv preprint: arXiv:2101.12345
Author Biography
<img height="124" width="124" src="https://cdn-images-1.medium.com/max/1600/1*WL6ci9HTFmrp_u9ShtC-ZQ.jpeg">
Jayadeep Shitole holds a bachelor’s and master’s degree in Mathematics and Scientific Computing from the Indian Institute of Technology, as well as a master’s degree in Operations Research and Information Engineering from Cornell University. He currently serves as a data science leader on the Applied AI team at Walmart Global Tech in Sunnyvale, California. His professional experiences include developing a deep learning–driven dynamic pricing system for near-expiration perishable goods, leading to significant profit growth and reduced waste.

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Artificial Intelligence

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Affordable Nano Banana Pro API by Kie.ai for AI Image Generation Integration

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Harnessing Advanced AI and Machine Learning to Transform Retail Pricing and Merchandising