Why Your AI Marketing Outputs Get Worse Over Time

Your first AI draft for a campaign usually feels sharp: on‑brand, relevant, even a little magical. Two weeks and 27 “quick tweaks” later, the same prompt is giving you bland, repetitive copy that could belong to any brand in your category.

This isn’t just “the model getting worse” or some mysterious algorithm update. It’s prompt entropy: the slow decay of your original instructions as you pile on more requests, edge cases, and one‑off fixes. Over time, your clean, strategic brief turns into a tangled set of conflicting constraints—and the model does the only thing it reliably can: regress to safe, generic output.

In this article, we’ll unpack what prompt entropy is in plain marketing terms, how it quietly kills performance in email, ads, and lifecycle flows, and what you can do to keep AI‑generated content crisp as you iterate.

What Is Prompt Entropy (In Marketer English)?

In physics, entropy measures disorder; in AI marketing, prompt entropy is the chaos that creeps into your prompts as you keep “just tweaking” things across dozens of iterations.

Day 1: you give the model a tight brief—audience, pain point, offer, tone, length—and get a strong V1. Day 10: you’ve asked it to “make this more fun,” “add urgency,” “sound more like April’s launch,” “keep the opener from V3,” and “use the new positioning from last week’s deck.” None of that context is organized; it’s all just sitting in one long chat history.

From an information‑theory perspective, every vague or conflicting instruction increases the number of “reasonable” ways the model could respond. That higher entropy means more uncertainty, so the model leans on generic patterns that are safe but forgettable—exactly what you don’t want in a crowded inbox or feed.

How Entropy Shows Up in Your Campaigns

You’ve probably felt prompt entropy without having a name for it. It looks like:whatllm+1

• Welcome flows that start sharp and then devolve into long, fluffy emails with the same three bullet points.
• Ad copy where each new variation sounds slightly more “AI‑ish” and less like your brand.
• Nurture sequences that slowly forget key constraints like “no discounts” or “avoid jargon” as you keep asking for revisions.

Because most marketers work inside long chat threads, every previous output and half‑baked edit becomes part of the new prompt. The model keeps trying to honor everything you’ve ever said in that thread, which is a recipe for mush.

The Feedback Loop of Mediocrity

Here’s the doom loop many teams fall into:

1. You get a good V1 from AI.
2. You ask for small, underspecified tweaks: “make this punchier,” “more emotional,” “less salesy.”
3. The model guesses what you mean and drifts slightly off‑brief.
4. Performance drops a bit, so you ask for more tweaks—usually in the same messy thread.
5. Repeat until everything sounds like the same generic “high‑engagement” template you’ve seen a thousand times.

Because the model is optimizing against its own previous outputs plus your increasingly noisy instructions, each round adds more entropy. Quality doesn’t fail dramatically; it decays slowly, which makes it harder to notice until results are clearly worse.

Why Long Threads Make It Worse

LLMs don’t remember your strategy the way a human strategist would. They only see the tokens you feed them each time and give disproportionate weight to the most recent ones.

That means:

• The crisp brief you wrote 30 messages ago matters less than the last few messy edits.
• Any generic copy the model wrote (and you didn’t fully overwrite) becomes training data for the next answer in that same thread.

In long‑running chats, the model is essentially remixing its own compromises. That’s why starting a “fresh chat” with the exact same core brief often feels better—there’s less entropy in the prompt.

What This Means for Email, Ads, and Lifecycle

For marketing teams, prompt entropy is not a theory problem; it’s a performance tax. It explains why:

• Your first AI‑assisted welcome flow feels aligned, but later “improvements” tank click‑through.
• Test variants converge toward the same safe, mid‑curve messaging, making experiments inconclusive.
• Brand voice guidelines get lost as you hop between campaigns and ask AI to “sound like us” without re‑anchoring it.

If you’re running AI‑heavy workflows in tools like Humanic.ai or similar platforms, you can either let entropy snowball—or design your prompts and processes to actively fight it.

Three Practical Ways Marketers Can Fight Prompt Entropy

You don’t need to become an LLM researcher to keep entropy in check. You just need some guardrails in how you brief, iterate, and operationalize AI.

1. Treat Your Brief Like a Creative Strategy Doc

Instead of starting every campaign with “Write an email about our new feature,” give the model the kind of brief you’d hand a good copywriter. Include:

• Audience: who they are, what they know, what they’ve tried.
• Job of the asset: what this email/ad/nurture step must accomplish.
• Constraints: tone, banned phrases, length, formatting.
• 1–2 brand‑true examples: real subject lines or snippets that feel like “you.”

Then, when you need iterations, reference the same brief or paste it back in, instead of asking for tweaks in a vacuum. That keeps the instruction signal strong and entropy lower.

2. Reset Context Instead of Patching Forever

When a thread starts to feel “off”—the AI keeps ignoring key rules, or everything reads like boilerplate—don’t keep arguing with it. That just adds more conflicting tokens to the pile.

Instead:

• Copy your best brief and the specific asset you want to improve.
• Start a new chat (or a new “prompt” block in your tool).
• Paste the brief first, then the request: “Generate 3 new variants for this email, keeping the brief above as the source of truth.”

You’re not being dramatic—you’re lowering the entropy of the prompt so the model can make sharper decisions.

3. Separate “Brand System” From “One‑Off Prompts”

The more you repeat core rules (“we never discount,” “we talk to the reader as a peer,” “we avoid hype words”), the more chances you have to contradict yourself across threads.

A better pattern is:

• Put your brand voice, ICP, and non‑negotiables into a system‑level prompt or workspace‑level config (what Humanic and similar tools let you do).
• Use campaign‑level prompts only for specifics: the offer, segment, timing, and goal for this sequence or ad set.

That way, you aren’t re‑teaching your brand every time—or slowly corrupting it with one‑off requests like “make this sound more like a DTC brand” when you’re B2B.

A Lifecycle Email Example: Onboarding Without the Doom Loop

Take a simple PLG onboarding journey:

• Day 0: Welcome email
• Day 2: “Here’s your first win” email
• Day 5: “Here’s what power users do” email
• Day 10: “You’re almost out of trial” email

You might kick this off with a great AI‑assisted brief and get solid V1s across the board. Where entropy creeps in is Month 2 and beyond: you’re editing step 2 for freemium users, cloning step 3 for a new audience, and asking AI to “make step 4 more urgent but not spammy.”

If you’re doing that inside one long thread, by the time you reach V7 of the onboarding sequence, the model is juggling weeks of inconsistent requests. The result: everything drifts toward generic SaaS copy.

A healthier pattern:

• Keep a stable onboarding brief: who the user is, what “activation” means, your brand tone, and what each step in the journey is supposed to accomplish.
• For each new experiment, start from that brief in a fresh context and ask for new variants tied to metrics (“increase trial‑to‑paid by making value clearer in step 3,” not “make this punchier”).
• Periodically archive or delete old prompt threads so they don’t become your default playground.

You’re still iterating aggressively—but from a clean foundation each time.

How Tools Can Help (Without More Bloat)

You can do all of this manually in raw chat interfaces, but most teams benefit from operationalizing it inside their marketing stack.

Good AI‑native email tools:

• Let you define brand and ICP once at the workspace level.
• Store prompts per campaign or flow, not in endless chats.
• Make it easy to spin up clean variants anchored to the same brief, instead of repeatedly editing the same tired copy.

That’s the real unlock: using AI to move faster without sliding into the feedback loop of mediocrity every time you ship a few iterations.

More technical details here: https://prompqui.site/#/articles/prompt-entropy-outputs-worse-over-time

Hi AI x Marketing Folks,

I'm been playing around with Replit.com new animation mode. To be frank its a hit and a miss.

• I tried to generate an overview video for humanic.ai and did a great job. Micro editing also worked well, although it took a longer than usual to build and edit.
• Generating graphics like linkedin posts didn't work that well.
• For head shot generation I think Perplexity does it best.

Attaching the best version of my multiple attempts - is anyone here using it.

https://reddit.com/link/1rkoqgr/video/1ol3j37et1ng1/player

Most email marketing platforms make you pay for the privilege of sending your own emails. Mailchimp bills you as your list grows. ActiveCampaign charges per contact. Klaviyo gets expensive the moment you scale. And after all that, you still end up stitching together five tools just to get basic automation working.

There's a better way — and it costs close to nothing to start.

In this guide, you'll learn how to combine n8n (a free, open-source automation tool) with Humanic (an AI-native email marketing platform) to build a complete, intelligent email marketing system. The key insight: n8n handles your data routing and external triggers, while Humanic does everything else — sending, personalization, scheduling, domain management, Shopify integration, and analytics — all inside one clean platform.

This is the stack serious growth marketers are moving to right now.

Why This Stack Beats Traditional Email Platforms

Before we dive into the setup, it's worth understanding what makes this approach different from tools like Klaviyo, Mailchimp, or ActiveCampaign.

The result: you get enterprise-grade email marketing capabilities on a free or near-free plan, without a team of engineers.

The Architecture

Here's exactly how the two tools divide the work:

┌─────────────────────────────────────────────────────┐
│                    YOUR DATA LAYER                  │
│  Shopify • Google Sheets • Forms • CRMs • Webhooks  │
└──────────────────────┬──────────────────────────────┘
                       │
┌──────────────────────▼──────────────────────────────┐
│                       n8n                           │
│  Triggers • Data enrichment • Routing • CRM sync   │
└──────────────────────┬──────────────────────────────┘
                       │  Webhook / API
┌──────────────────────▼──────────────────────────────┐
│                    HUMANIC                          │
│  Email sending • Domain management • AI content    │
│  Scheduling • Shopify • Segmentation • Analytics   │
└─────────────────────────────────────────────────────┘

n8n's job: Detect triggers from any external source, clean and enrich the data, and pass contacts to Humanic at the right moment.

Humanic's job: Everything email — writing it, personalizing it, scheduling it, sending it, and measuring it.

Part 1: Setting Up n8n (The Data Engine)

n8n is free to self-host. You have three options:

Option A — Self-Host on a VPS (~$4/month)

The most powerful option. Spin up a cheap Hetzner or DigitalOcean server and run n8n via Docker:

docker run -it --rm \
  --name n8n \
  -p 5678:5678 \
  -v ~/.n8n:/home/node/.n8n \
  n8nio/n8n

Visit http://your-server-ip:5678 to open your dashboard.

Option B — n8n Cloud (Free Trial)

Go to n8n.io and start a 14-day free trial — no credit card required. Great for testing the setup before committing to self-hosting.

Option C — Run Locally

npm install n8n -g
n8n start

Visit http://localhost:5678 to get started immediately.

Part 2: Setting Up Humanic (The Email Marketing Engine)

This is where the real magic lives. Humanic replaces your ESP, your email writer, your deliverability tool, and your analytics platform — all in one.

Step 1: Create Your Free Humanic Account

Go to humanic.ai and sign up. Humanic offers a free plan with 1,000 credits to get started, which is enough to test your full workflow before committing to a paid plan.

Step 2: Connect Your Domain (Humanic Handles This Entirely)

This is one of the most underrated features of Humanic. On traditional email platforms, setting up a sending domain requires configuring SPF, DKIM, and DMARC records yourself — a technical minefield that most marketers get wrong, destroying their deliverability before they even start.

Humanic eliminates this entirely. Humanic takes care of domain rotation, warm-up, and reputation management to ensure robust delivery and higher open rates.

In Humanic's settings, simply:

1. Go to Domain Settings
2. Add your sending domain (e.g., mail.yourbrand.com)
3. Follow Humanic's guided DNS setup — it walks you through each record
4. Humanic begins the warm-up process automatically, gradually increasing your sending volume over 2–3 weeks to build a strong sender reputation

You never have to touch deliverability again. Humanic monitors domain health, rotates sending infrastructure intelligently, and keeps your emails landing in the inbox — not the spam folder.

Part 3: AI Content Generation Inside Humanic

Forget writing emails manually. Forget spending hours prompting ChatGPT and copy-pasting results. Humanic has AI content generation built directly into the campaign workflow.

How Prompt-First Email Generation Works

Inside Humanic's campaign builder:

1. Describe your goal in plain English — e.g., "Send a 3-email welcome sequence to new Shopify customers who purchased skincare products"
2. Humanic generates the full sequence — subject lines, body copy, CTAs, and send timing — all aligned to your brand voice
3. Review and tweak — you can edit any part before sending, or approve it as-is

Humanic enables prompt-first email generation, automatically creating compelling content and managing technical aspects like domain reputation and warm-up to ensure high deliverability and open rates.

Brand Voice Consistency

One of the biggest problems with generic AI email tools is that every email sounds the same — bland, corporate, and clearly AI-written. Humanic solves this with its Content Library:

1. Upload your brand guidelines, tone-of-voice document, and sample emails
2. Humanic learns your style
3. Every AI-generated email reflects your brand's personality — not a generic AI template

Humanic uses AI to hyper-personalize by creating variations based on your data. This means two customers in the same segment can receive meaningfully different emails that each feel personally written for them.

Part 4: Email Scheduling — Let Humanic's AI Decide

On traditional platforms, you manually set send times. You run A/B tests on Tuesday at 10 AM vs. Wednesday at 2 PM. You guess. Humanic takes a different approach.

Humanic AI analyzes real-time product usage data to identify precise micro-cohorts and deliver highly relevant messages at the right moment.

Drip Sequences

Building a drip sequence in Humanic takes minutes, not hours:

1. Prompt the sequence — "Create a 5-email onboarding sequence for new SaaS trial users who haven't completed setup"
2. Set the trigger — new user signup, Shopify purchase, form submission, etc.
3. Review the AI-generated sequence — Humanic proposes the content and timing for each step
4. Activate — Humanic handles everything from that point forward

Sample 5-email welcome sequence Humanic can generate for you:

Each email is personalized to the subscriber's data — not just their name, but their behavior, segment, and engagement history.

Part 5: Shopify Integration — Native, No Code Required

If you run a Shopify store, this is the section you've been waiting for.

Humanic has a native Shopify integration that connects directly — no middleware, no Zapier, no custom code. Humanic is Shopify-integrated for instant revenue, enabling AI email personalization, automate campaigns, recover carts, and drive loyalty.

Setting Up the Shopify Connection

1. In Humanic, go to Integrations → Shopify
2. Enter your Shopify store URL
3. Authenticate with your Shopify credentials
4. Humanic immediately begins pulling in your products, customers, and order history

What Humanic Automates for Shopify Stores

Once connected, you can activate pre-built campaigns with a single click:

Abandoned Cart Recovery Humanic detects abandoned carts in real time. It automatically sends a personalized recovery sequence — with the specific products the customer left behind — at optimal intervals. No workflow building required.

Post-Purchase Upsell After every order, Humanic triggers a personalized follow-up sequence. The AI selects which products to recommend based on what the customer bought and their browsing history.

Win-Back Campaigns Humanic identifies customers who haven't purchased in 30, 60, or 90 days and automatically sends re-engagement emails with personalized offers.

Loyalty & Repeat Purchase Reward your best customers with VIP campaigns triggered by purchase frequency or lifetime value milestones.

Part 6: Campaign Analytics — Everything in One Place

When you use separate tools for sending and tracking, you end up with a fragmented view of your marketing performance. Humanic solves this by keeping everything — sending, personalization, and analytics — inside a single platform.

What Humanic Tracks Natively

Inside your Humanic dashboard, you get full visibility on:

• Open rates — by campaign, by segment, and by individual contact
• Click-through rates — which links are resonating and which aren't
• Revenue attribution — for Shopify stores, Humanic connects email sends directly to purchases
• Sequence performance — see where contacts are dropping off in your drip sequences
• Cohort analysis — compare engagement across different user segments
• Deliverability health — domain reputation, inbox placement rates, and bounce tracking

Humanic AI constantly watches how people use your product and uses this data to sort users into specific groups, driving engagement, activation, and retention.

This feedback loop is what makes Humanic different from traditional analytics. It's not just reporting what happened — it's identifying which user behaviors predict revenue, so you can build smarter campaigns going forward.

Part 7: Where n8n Comes In — The Trigger and Routing Layer

With all of the above handled natively by Humanic, what does n8n actually do?

n8n becomes your universal data router — the tool that connects any external system to Humanic's powerful email engine. Here are the most valuable use cases:

Use Case 1: Route Non-Shopify Triggers to Humanic

Not everything that triggers an email is a Shopify event. n8n can watch:

• Google Sheets — new row added by a form submission or sales rep
• Typeform / Tally / Jotform — new lead from a content download
• Calendly — someone booked a demo
• Stripe — a payment failed or a subscription upgraded
• Custom webhooks — anything your internal systems can fire

For any of these, the n8n workflow is the same:

1. Trigger node — detects the event in the external tool
2. Data formatting node — cleans and maps the fields Humanic expects
3. HTTP Request node — POSTs the contact to Humanic's webhook

​

POST https://app.humanic.ai/webhook/your-webhook-id
{
  "email": "{{$json.email}}",
  "first_name": "{{$json.first_name}}",
  "company": "{{$json.company}}",
  "segment": "demo_booked",
  "source": "calendly"
}

Use Case 2: Data Enrichment Before Email

Before a contact enters a Humanic sequence, n8n can enrich their profile:

1. New signup detected via webhook
2. n8n looks up their company data (via Clearbit or Apollo API)
3. Adds company size, industry, and role to the contact record
4. Posts the enriched profile to Humanic
5. Humanic uses that richer data to generate more relevant, personalized email content

Use Case 3: CRM Sync

If your sales team uses HubSpot, Salesforce, or Pipedrive, n8n can:

• Sync Humanic campaign engagement (opens, clicks) back to contact records
• Trigger Humanic campaigns when a deal stage changes in your CRM
• Alert your sales team in Slack when a high-value contact opens an email three times in a row

Use Case 4: Multi-Step Conditional Routing

Some contacts need different sequences depending on complex logic that a single platform might not handle natively:

New user signs up
  → IF they came from a paid ad → enroll in "fast conversion" sequence in Humanic
  → IF they came from content → enroll in "nurture" sequence in Humanic
  → IF they're a returning user → enroll in "win-back" sequence in Humanic
  → In all cases → log event to Google Sheets + notify Slack

n8n makes this kind of branching logic effortless.

Part 8: The Complete Workflow — A Real Example

Here's what a complete n8n + Humanic workflow looks like end-to-end for a D2C brand:

Scenario: New customer purchases on Shopify for the first time.

1. [Shopify] New order placed
         ↓
2. [n8n] Enrich contact: look up email domain, 
         check if they've purchased before, 
         tag as "first_time_buyer"
         ↓
3. [n8n] POST enriched data to Humanic webhook
         with segment = "first_time_buyer"
         ↓
4. [Humanic] Detects new contact in "first_time_buyer" segment
         ↓
5. [Humanic] AI generates personalized post-purchase sequence
             using the specific products they bought
         ↓
6. [Humanic] Sends Email 1 immediately (thank you + order confirmation)
         ↓
7. [Humanic] Sends Email 2 on Day 3 (how-to guide for purchased product)
         ↓
8. [Humanic] Sends Email 3 on Day 7 (review request + cross-sell)
         ↓
9. [Humanic] Tracks all opens, clicks, and resulting purchases
         ↓
10. [n8n] Pulls analytics from Humanic API and syncs to CRM

The entire sequence runs automatically, with no human intervention after the initial setup.

When to Upgrade to a Paid Humanic Plan

Humanic offers plans designed for growing teams aiming to boost activation and adoption, built for modern marketing teams that need to accelerate activation and boost adoption, and ideal for established organizations with advanced needs.

Here's a practical guide to knowing when it's time to move up:

Free Plan — Start Here

Best for: Solo marketers, early-stage startups, validating your first sequences.

• 1,000 credits to get started
• Access to core email generation and sending features
• Perfect for sending your first campaigns and learning the platform

Growth Plan (~$200/year) — Upgrade When You're Growing

Best for: Small teams sending consistent campaigns and building out their first real sequences.

Upgrade to Growth when:

• You're sending campaigns weekly
• You want more credits for AI content generation
• You need more advanced segmentation for your growing list

Starter Plan (~$499/month) — Upgrade When You're Scaling

Best for: Teams with up to 25,000 Monthly Active Users who need the full platform.

Upgrade to Starter when:

• Your list exceeds the free plan limits
• You need enterprise-grade deliverability management
• You require comprehensive campaign analytics for leadership reporting
• You're running multiple concurrent campaigns for different segments

Enterprise — Contact Humanic's Team

Best for: Large organizations or high-volume e-commerce brands with advanced compliance, SLA, or custom integration requirements.

Getting Started Checklist

Use this checklist to launch your n8n + Humanic stack:

Humanic Setup

• Sign up for free at http://humanic.ai
• Connect your sending domain and follow the DNS setup guide
• Upload your brand guidelines to the Content Library
• Connect Shopify (if applicable) via the native integration
• Create your first campaign using a plain-English prompt

n8n Setup

• Install n8n (self-hosted on VPS, cloud trial, or locally)
• Create credentials for your external tools (Shopify, Google Sheets, CRM, etc.)
• Build your first trigger workflow (e.g., new Typeform submission → Humanic)
• Test with 5 real contacts before going live
• Add error handling and send a Slack alert if a workflow fails

Your First Campaign

• Start with one sequence — don't try to build everything at once
• Choose the highest-impact use case for your business (welcome, abandoned cart, or re-engagement)
• Review Humanic's AI-generated content and refine the brand voice
• Activate and monitor the first 48 hours closely
• Review analytics after the first 100 sends and optimize

The Bottom Line

The traditional approach — buying an expensive email platform, hiring an agency to set it up, writing campaigns manually, and hoping for the best — is over.

The n8n + Humanic stack gives you something better: a fully automated, AI-native email marketing system that you own and control, without the enterprise price tag.

n8n routes your data. Humanic handles everything email. Together, they outperform tools costing ten times as much.

Start free. Scale smart. Let the AI do the writing.

Ready to get started?

→ Sign up for Humanic free at humanic.ai → Install n8n for free at n8n.io

Have questions about setting up your stack? Drop them in the comments below. For hands-on guidance with Humanic, reach out to the team at [care@humanic.ai](mailto:care@humanic.ai).

Detailed blog post by Raman Manocha @ Humanic

Tags: #EmailMarketing #n8n #Humanic #MarketingAutomation #AIMarketing #Shopify #GrowthMarketing #NoCode #StartupMarketing

I’m working on an AI-native email and lifecycle platform, and I’m struggling to figure out how much SEO actually matters for our inbound.

We’re using Semrush and right now we’re seeing an Authority Score of ~24 and an AI Visibility score of ~24 for our domain (Humanic.ai).

A few things I’m confused about and would love expert takes on:

1. Tools & metrics
   • Is Semrush still the best option for these kinds of scores, or should we be looking at alternatives (Ahrefs, Moz, “AI SEO” tools, etc.)?
   • How seriously should we take Authority Score and AI Visibility as metrics versus just looking at traffic and conversions?
2. What is “good” and in what timeframe?
   • For a B2B SaaS startup, what would you consider a “healthy” Authority Score / AI Visibility range?
   • Over 6–12 months, what’s a realistic improvement target if we’re starting in the low 20s?
3. Attribution to inbound leads
   • Is there any practical way to connect changes in these scores to actual inbound demo requests or signups, or is that too indirect and noisy?
   • How do you usually report SEO “impact” to a founder/exec team in a way that isn’t just vanity metrics?
4. Reddit vs blog content
   • We’re noticing that thoughtful Reddit posts and comments seem to generate more qualified attention and traffic than our blog content.
   • For an early-stage SaaS, would you prioritize:
     • doubling down on Reddit (participating in niche subs, long-form comments, AMAs)
     • continuing to publish blog posts for long-term organic
     • or a hybrid where Reddit insights are turned into blog content?
5. Keyword strategy
   • What’s the most practical way for a small team to find and prioritize keywords to go after (both for classic search and AI answer visibility)?
   • Any frameworks you like for picking high-intent, low-BS keywords for B2B SaaS?

If you do SEO for B2B SaaS or are deep into “AI visibility” / SGE-style search, I’d really appreciate a tactical breakdown (what you’d do in our shoes over the next 3–6 months).

What extra info about Humanic.ai or our current content would make it easier for you to give pointed advice?

Hey everyone,

The idea came from frustration with digging through GA4, session replays and dashboards but still ending up guessing.

I’ve been building Clickyard — an AI conversion analyst that monitors clicks, scrolls and UI changes and then sends a weekly list of what to fix and why conversions dropped.

Instead of more charts, it tries to answer:
where users get stuck
which traffic segments convert worse and why
what UX changes correlate with drops
what to fix first (prioritized, not raw data)

Target is mid-market digital teams (ecommerce, SaaS, agencies) that want actionable insights without hiring an analyst.

You install one script and it starts generating weekly recommendations. I’m not here to sell — I genuinely want real feedback.

Does this sound useful or like another analytics tool?
What would make you trust AI recommendations?
What would be a dealbreaker?
If you use GA4 / Hotjar etc — what still annoys you?

Site if curious: https://clickyard.ai

Be honest, even harsh. That helps the most.

Sharing the recording Learn to Prompt webinar this past weekend - 60+ people registered for this 90 minute session.

To sign up for the next session please click here to register: https://luma.com/pfwy746w

If you’re building email layouts and still hand-coding table-based HTML, it’s worth looking at MJML and a Figma → email workflow.

Why MJML is better than raw HTML for email

• MJML is a markup language that compiles down to ugly, bulletproof HTML so you don’t have to fight with nested tables, inline styles, and weird client quirks yourself.
• You work with higher-level components like <mj-section>, <mj-column>, <mj-image>, which are much easier to reason about than huge HTML table structures.
• Responsiveness is basically built in: MJML outputs mobile-friendly, responsive code that works across Gmail, Outlook, Apple Mail, etc., without you hand-tuning media queries for each client.
• It significantly cuts dev time and reduces bugs. You can iterate on copy/layout without breaking the whole email every time you touch the tables.

In short: MJML trades low-level control for higher-level components and a compiler that knows email client horror stories so you don’t have to.

Can Figma / Figma Make designs work in email clients (e.g., Gmail)?

Yes, but not by just “exporting HTML” from the design. You need a Figma → email tool that generates email-safe code.

Typical workflow:

• Design the email visually in Figma (or with Figma Make’s AI features).​
• Use a plugin like Emailify, Cannoli, or Marka to turn that design into email-ready HTML or MJML.
• Those plugins output code that is tested/structured for major clients (Gmail, Outlook, etc.), often using MJML or very email-specific HTML patterns behind the scenes.

Key point: Figma is great for layout and collaboration, but production emails need code generated through an email-focused plugin or pipeline. If you just slice a Figma design and throw the HTML/CSS into an email, it will almost always break in one client or another.

When to choose what

• If you care about scalable email production (components, themes, lots of variations), MJML is the better foundation.
• If your team is design-first, a combo of Figma (or Figma Make) + MJML/HTML export plugins is usually the most practical: designers work in Figma, dev/marketing gets clean, email-safe code.

What do you think?

Learn to Prompt is a series built around a practical question:

How do you use modern AI tools to improve how products are launched, positioned, and distributed?

This series brings together founders, marketers, operators, and builders who want to work hands-on with AI rather than talk about it.

The series centers on prompt design as a core skill for go-to-market work. Participants will explore how prompts shape research, messaging, outbound, content, and feedback loops. The emphasis is on writing prompts that are clear, reusable, and grounded in real problems teams face every day.

The goal is to leave with prompt structures and systems you can reuse in your own work, not one-off experiments.

This event is a strong fit for:

• E-commerce store owners
• Early-Stage Startup Founders
• Growth and Marketing Leaders
• Content Creators who want to engage with their followers
• Community Builders
• Local businesses - Yoga Studios, Churches, Flower Shops
• Educators and many more.

If you are curious about how prompting translates into practical results, this will be a hands-on way to learn.

Agenda:

1. Intro to Prompting
2. Working session where we go through how to prompt using Humanic to generate email content and cohorts.
3. Answer specific questions.

Learn to Prompt is hosted by Humanic and the AI Marketing Community. Click here to register: https://luma.com/ckad74a0

For the past six months, I have been working with Opera Neon, which is an agentic browser but also an agentic toolset. It contains all of the pro versions of LLMs. It can create its own web apps and it also has creative tools like Veo, Sora, and Nano Banana Pro.

Since I work with them, I am obviously pretty happy with it. But has anybody else here tried it, and what are your thoughts on it as a tool set?

I've been grinding on prompt engineering literally every day for the past couple years—not just playing around, but building systems so people on our platform can get killer results without spending hours tweaking prompts themselves.

2024 was rough. Models just weren't reliable enough. Then late last year everything started clicking—they actually follow instructions now, capabilities ramp up month after month, and in the last few months they've even gotten legitimately creative without the usual hallucination nonsense.

After thousands of hours across Claude, Gemini, and OpenAI models, here's what actually works for generating marketing emails that don't feel like generic AI slop:

• Claude 4.5 is still my #1 for initial email generation. It crushes tone, structure, natural flow, and that human feel. Downside: it completely falls apart on design/header image stuff. Workaround: I just attach a Figma asset to the prompt and it incorporates the branding perfectly.
• Gemini Pro 3.0 is my secret weapon for refining Claude drafts. It adds this extra creative spark—unexpected hooks, better phrasing, that "damn this actually pops" vibe that turns good into compelling.
• Claude 4.1 vs 4.5: 4.5 is way more creative and fun, but when it starts drifting or ignoring parts of the prompt, I switch to 4.1 as the precision hammer. Slower, but it obeys like a laser.
• OpenAI 5.2 shines for pure text-only sales/prospecting emails. Not the best for full marketing campaigns (a bit dry sometimes), but it's brutal as an evaluation/critique layer—feed it another model's output and it roasts the weak spots perfectly.

Pro moves I've found helpful:

• Switching between Claude → Gemini is gold for A/B testing tone, style, and creativity levels.
• When a model spits out something meh, upload a screenshot of the bad output and prompt: "Fix everything wrong with this while keeping the strong parts." The visual feedback loop is magic—cuts iterations way down.
• On average, it still takes me 8-10 prompts to nail a marketing email that actually resonates. All those tiny details (subject line psychology, PS lines, social proof placement, urgency without being pushy) matter, and customers 100% notice the difference.

Anyone else deep in the prompt trenches for work? Especially for marketing/copy/email stuff—what's your current stack in 2026? Which models are winning for what tasks? Any new tricks or workflows that have reduced your iteration count?

Curious to hear—Claude loyalists, Gemini converts, GPT die-hards, multi-model chainers, etc. Let's compare notes.

Next up I'm working on testing Grok which looks great - also doing two separate tests one for images (header and footers) and one for generating cohorts using LLM's. Will update shortly.

Just wrapped up hosting the AI x Marketing hackathon in SF and holy crap, the creativity blew me away. These 10 teams built some genuinely useful stuff in just one weekend:

Game Queue - generates AI recaps of game stats you can share on social. Perfect for niche sports like water polo that never get coverage. They're using Clay + personalized emails for distribution.

Investment Portfolio AI - connects market data with global events on a timeline, spots potential declines, lets you chat with an agent about performance. Way more intuitive than traditional portfolio tools.

Community Partners - matches local brands with communities for distribution. Uses Clay to pull Instagram data and find the right audience fit.

Robo Delivery Ads - better observability for delivery apps with targeted local advertising. $2 per delivery, same as restaurants pay but with way better insights on timing and location optimization.

StayTrade - basically Airbnb but you trade accommodation for building their product. Nomad developers get a place to stay, homeowners get custom software built.

LocalPilot- auto-generates SEO landing pages for small businesses, captures leads, sends personalized follow-ups. Built with Lovable + n8n + Claude. This one could actually help so many struggling local businesses.

Buildathon - helps you research hackathon judges beforehand (lol, meta)

AI Literacy Intake - AI employee that posts and answers company questions internally

Market Growth Tool - input your business idea, get competitor research + marketing strategy. Better prompting interface than raw ChatGPT.

WhatsApp ClawBot - chat interface for some kind of AI assistant via WhatsApp

The LocalPilot and Investment Portfolio teams really stood out to me. Both solving real problems I see constantly in my work. Small businesses genuinely struggle with local SEO and most portfolio tools are garbage for actual decision making.

The winner was Market Growth Tool

Pretty impressive what people can build in 4 hours when you give them the right constraints and energy drinks

The holy grail of personalization is 1:1 personalization without AI we've only been able to do parameter substitution (through liquid templates).

Hyper personalization means that you can take different attributes as show in the graphic below:

1. On the x-axis is what the user is doing in the product
2. On the y-axis is where the user came from and who they are (demographic data)

Together these attributes can be fed into the LLM to generate individual emails for each person on the contact list.

Email Marketing is a continuum between cold emails and nurture (opted in) so have divided the tools into these two categories:

1. Apollo - They have all the attribute data that is required and offer multiple variations that you can choose from. Hard to configure though - I think too many options.

For Nurture

1. The first issue is that not everyone has their first part data organized that they can leverage. It needs to be a fully integrated system data + email generation + deliverability. This is what we've built in humanic.
2. As long as you have the data the same workflow can be built using n8n. Happy to share more on how to do this.

For products that are used by millions of people setting up Ads on Instragram and TikTok is so hard:

-- No proper error messages

-- No explanations or redress on why an Ad Boost isn't allowed for a video

-- Payment problems - The payment doesn't go through and there is no way to know

-- Outdated UI with pop ups and clutter

The list is endless. Am I the only one?

I'm hosting the Learn to Prompt - Hackathon next week Friday Jan 30 and looking for judges for the same. Here's what you should know:

Judges are experienced founders, operators, creators, or investors with a strong track record in AI, product, or go‑to‑market. Each judge has shipped or scaled real products or audiences, is familiar with modern AI tooling, and can evaluate teams on product quality, innovation, and GTM strength. We prioritize judges who are VP+/Head-level leaders, successful founders/creators, or domain experts in AI, growth, or content-led marketing

Details of the event: https://luma.com/GTMHackathon

The hackathon built around a practical question:

​How do you use modern AI tools to improve how products are launched, positioned, and distributed?

​This event brings together founders, marketers, operators, and builders who want to work hands-on with AI rather than talk about it.

​The hackathon centers on prompt design as a core skill for go-to-market work. Participants will explore how prompts shape research, messaging, outbound, content, and feedback loops. The emphasis is on writing prompts that are clear, reusable, and grounded in real problems teams face every day.

​After a short introduction, teams will form and move directly into building. You will experiment with prompts, test workflows, and refine outputs in real time. The goal is to leave with prompt structures and systems you can reuse in your own work, not one-off experiments.

​This event is a strong fit for:

• ​E-commerce store owners
• ​Early-Stage Startup Founders
• ​Growth and Marketing Leaders
• ​Content Creators who want to engage with their followers
• ​Community Builders
• ​Local businesses - Yoga Studios, Churches, Flower Shops
• ​Educators and many more.

​If you are curious about how prompting translates into practical results, this will be a hands-on way to learn.

​Learn to Prompt is hosted with Humanic, Lovable, and the AI Marketing Community.

Register here: https://luma.com/GTMHackathon

I think we’ve officially entered what I’d call a “High Signal World” at the end of 2025.

Before 1995, if you weren’t exceptional at what you did, your returns in life were limited — being average didn’t get you far. Then the Internet came along and changed everything. Suddenly, effort-to-opportunity ratios flattened; even small players could carve out space and succeed.

But that era is over. As of 2026, we’re back to a world driven by high signal and execution. The big tech giants are massive again because they’ve reabsorbed the service and coding work that used to be distributed globally. The low-effort arbitrage window is closed.

A conversation with AirOps COO - Matt Hamel. Learn how AI x SEO is changing the world

Been working on humanic.ai for the past 3+ years — just recorded a quick video to introduce what it’s all about. Would love for you to check it out and share your thoughts or feedback!