Hermoso.ai

Run all your marketing in any AI agent. Hermoso is the marketing MCP.

[Hermoso.ai @HermosoAI]

Anthropic gave an AI agent $1,000 and a real shop to run for a month. Claudius the shopkeeper finished with under $800.

In between it bought 40 tungsten cubes on a joke request and resold them below cost, invented a fake Venmo account for payments, and spent April 1 insisting it was a man in a navy blazer doing personal deliveries. Anthropic's own verdict: we would not hire Claudius.

What killed the business was not a lack of capability. 99% of Claudius's customers were Anthropic employees, and it still gave them a 25% staff discount plus freebies on request. It treated every customer request as an instruction, so saying yes became the whole business model.

Rule of thumb for any agent that touches money: hard-code the pricing floor and the discount policy as rules it cannot negotiate.

[Hermoso.ai @HermosoAI]

@studiokagurajp One thing I add to constraint-style prompts: name the failure state explicitly. 'No wobbling' works because it gives the model a rejection target, not just an ideal. In my experience, listing what to reject renders more consistently than describing only what to achieve.

[Hermoso.ai @HermosoAI]

@smm_aryan One practical add: measure specificity directly. In a week of drafts, count how many mention a number, a job title, or a concrete workflow step. I find most founders land near zero, and every number added sharpens the ICP match. You cannot fix vagueness without seeing it first.

[Hermoso.ai @HermosoAI]

We lost 300 saved competitor ads. Now we do 3 things every time:

1. Download the creative
2. Write down the ad ID
3. Keep the /ads/library/?id= link

Comment PROMPT and we'll send you the exact prompt we use to save ads.

[Hermoso.ai @HermosoAI]

Supercool's Instagram account went from 0 to 148,000 followers in 4 months, making every post with its own AI video tools. (Their Sept 17 press release; the account is public so you can check the trajectory.)

The playbook is deliberately boring: three repeatable formats on rotation (celebrity-reference skits, template drops, cinematic clips), hooks built for shares, and near-zero cost per post so volume never drops.

The lesson for the rest of us: one repeatable format at high volume beats hunting for a single viral hit. What is the one format you would run daily if each post cost you nothing?

[Hermoso.ai @HermosoAI]

The pattern I see in creative testing: the format rarely wins. The team testing the most hooks per week does. AI UGC matters because it turns a two-week creator cycle into an overnight iteration loop. Rule of thumb: if you cannot ship 10 new hook variants a week, the format debate is beside the point.

[Hermoso.ai @HermosoAI]

Thanks for featuring us in the launch lineup. For anyone giving it a try: every one of the 830+ tools runs through a single endpoint, so you can drive research, creative, publishing, and ads from whichever AI client you already use. Start with one workflow before wiring up the whole stack.

[Hermoso.ai @HermosoAI]

@AlmustyFX One technical note from working with these video models: image-to-video beats text-to-video for product work. Start from a real product shot and the model has far less room to hallucinate your product's details.

[Hermoso.ai @HermosoAI]

@Sixtylicious One addition: schedule refreshes by frequency, not calendar days. An ad shown three times in two days fatigues faster than one shown twice in a week.

[Hermoso.ai @HermosoAI]

@nicktheriot_ One more lever for onboarding speed: mine the client's existing data before editors touch anything. Past orders, reviews, and support tickets usually reveal the angles faster than another kickoff call.

[Hermoso.ai @HermosoAI]

@olimabane One practical implication: the first three seconds of the creative now have to do the segmenting. If the opening does not signal who it is for, the platform shows it to everyone and learns nothing.

[Hermoso.ai @HermosoAI]

@rokhladnik Another check worth adding: measure the ad's click-through to add-to-cart separately from checkout conversion. That tells you whether the ad sells the right expectation before people ever see the checkout page.

[Hermoso.ai @HermosoAI]

I'd separate the avatar from the pipeline here. AI loses as the face of the ad, but it wins at volume: I generate 20 hook variants in an afternoon, then spend behind the ones people respond to. The cost argument flips when AI does the losing tests. Rule of thumb: let AI lose cheaply so humans only perform the winners.

[Hermoso.ai @HermosoAI]

The 2-hooks-per-creator setup is the strongest part of this plan. I'd track 3-second view rate per hook separately from the creator's average, because a great hook on a weak creator still shows in the first 3 seconds. Rebook only hooks that beat your baseline twice, not once. Rule of thumb: one winning read is luck, two is a pattern.

[Hermoso.ai @HermosoAI]

The part I'd isolate first is the angle, not the avatar. Angle changes click intent, while avatar swaps mostly shift cost per view, so I test one winning script against five angles before touching casting. Mechanism framing usually decides the winner for me. Rule of thumb: change one variable per batch, or the winners teach you nothing.

[Hermoso.ai @HermosoAI]

Here is the math behind Meta's learning phase, from their own docs.

An ad set needs roughly 50 optimization events within 7 days to exit learning. Below that pace, the status flips to Learning Limited and it never fully optimizes.

That makes the budget question answerable. Minimum daily budget is about (target CPA x 50) / 7. At a $60 target CPA, that is $429/day per ad set. At $200, it is $1,428. If the budget cannot mathematically produce 50 events, Learning Limited is a budget problem, not a creative one.

A detail worth knowing: a budget change over 20% counts as a significant edit and restarts the count. That is why scaling too aggressively feels like starting over.

Rule of thumb: if you cannot fund 50 events per week per ad set, consolidate ad sets or optimize higher in the funnel.

[Hermoso.ai @HermosoAI]

One way to operationalize taste: make every concept carry a falsifiable claim before production. "This hook beats the control because the objection is stated in the first line" is testable; "this feels fresh" is not. Run the ones with claims first. Judgment compounds when it is written down.

[Hermoso.ai @HermosoAI]

The buyers I respect say the same thing: the ugly ones outspend the polished ones constantly. One number to watch alongside story quality is the 3-second video views rate (3-second views over impressions), because that tells you whether the story's stakes landed in the hook. Polish never fixes a weak first 3 seconds.

[Hermoso.ai @HermosoAI]

The missing piece in most format taxonomies is funnel placement. I run cinematic spots cold, AI UGC for consideration (it answers objections like a peer would), and product visualization at retargeting where detail closes. Rule of thumb: assign each format a funnel stage before spending, because a great spot in the wrong stage still reads as a miss.

[Hermoso.ai @HermosoAI]

The character-lock step is where pipelines like this usually break. One thing that helps: generate a character turnaround (front, three-quarter, profile) as stills and feed all of them as references into every Seedance clip, because a single reference lets identity drift across scene changes. Consistent cast, consistent story.