u/leadg3njay

The LLM is not the product

A local model by itself is not the same thing as having ChatGPT on your own server.

The model is the engine.

You still need the wrapper.

That is where Open WebUI comes in. It gives you the actual interface, model selector, settings, and API layer so the local model becomes something you can use from a browser or call from other tools.

This is the part beginners skip.

They download a model, get stuck in terminal land, and think local AI is too hard.

It is not too hard.

You just need the model plus the interface.

Once those are connected, now you have something you can use like a real internal tool.

Stop sending high-ticket offers from 3 mailboxes

If you are selling high-ticket B2B and trying to run cold email from one domain with three inboxes, you are probably underbuilt.

One domain. Three mailboxes. 25 emails per mailbox per day.

That is only about 75 emails per day if you are being safe.

For high-ticket clients, I like having closer to 50 mailboxes.

That does not mean you max them out.

Each mailbox can theoretically send up to 50 per day after warmup, but I prefer 10 to 20.

The point is redundancy.

If one domain gets flagged, you still have others sending while that one warms back up.

Do not build cold email infrastructure with zero margin for error.

Sharing a small piece of my content system that quietly saves me a ton of time, in case it's useful to anyone here.

Every YouTube thumbnail I make needs a photo of me in it. I used to stop and take a new picture every time I needed a different expression or outfit. Constant, and honestly kind of miserable.

So I built the photos once, as a database.

It's a full set of AI generated photos of myself. Smiling, pointing, angry, sad, different outfits, different hair. All fake. I generated them with Flux Lora and ran a feedback loop that compared every output back to real photos of me until the likeness was actually right. That comparison loop is the part that matters. Skip it and the images slowly drift until they stop looking like you.

Now each image is tagged by expression and outfit. When a thumbnail needs "surprised, hoodie," the system just grabs the matching one. I never pose for a thumbnail again.

The bigger lesson I keep relearning: the thing that makes AI useful usually isn't the prompt. It's the boring reusable asset you build once. A prompt takes a day to copy. The system around it is why two people can run the same prompt and get completely different results.

What have you built once and reused forever in your own workflow?

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Something I learned building against GoHighLevel: the public API doesn't expose smart lists or email campaigns. Not badly documented. Not there. Which explains why every integration you've tried stops at contacts and workflows.

The GHL web app talks to a different, internal API, and you can drive that one from a CLI. Auth is Firebase-token based and less scary than it sounds. I pointed Claude Code at it and now it builds smart lists with real filter logic (email valid, DND, blacklist tags) and writes broadcast campaigns with subject-line A/B testing.

Design decision worth stealing if you build anything similar: the tool has no send call. Every campaign the AI writes lands as a draft that a human has to click. I deliberately never implemented sending, because an AI that can write to 18,000 contacts and mail them in the same breath is a liability, not a feature.

Happy to answer questions about how the internal API works. If you want the full setup instructions, my latest YouTube video walks through a complete build start to finish.

Title: Cut my thumbnail cost from $50 each to $1.44 for three

Old process: 2-3 thumbnail designers, $50 per thumbnail, and honestly most of the output was mediocre.

New process: an AI workflow that hands me three finished, upscaled variants for a dollar 44 total, in roughly ten minutes.

Same end result, same standard, I still pick which one actually runs. The only thing that changed is the cost and the wait time. I didn't find a better designer, I built a better process and the process replaced the designer.

Curious how many other people are still paying per-unit for creative work that could just be systemized instead.

Title: The GHL API key alone won't give your AI full access

Most people wiring an AI agent into GoHighLevel stop after generating a private integration API key. That gets you the public API, which only exposes 36 tools. Fine for basic stuff, not enough to actually operate the account the way you would by hand.

There are two more pieces most setups miss:

Location ID: sits right in your URL, after "location/". Easy to grab, easy to forget.

Session token: this is the one nobody expects. GHL stores it in Firebase local storage. You can pull it manually through Chrome DevTools (Application tab, IndexedDB, Firebase local storage) or grab it faster with an extension built for it.

Hand your agent all three and it stops being limited to the public tool list. It can operate the account the way a human would inside the UI.

Anyone else building on GHL's internal endpoints instead of the public API?

Title: Built a database of AI photos of myself so I stop posing for thumbnails

Every thumbnail needs a picture of me in it. Instead of taking a new photo every time, I built a full database once: smiling, pointing, angry face, sad face, different outfits and hairstyles. Every image is AI generated, made using Flux Lora and a feedback loop that compared each output back to real photos of me until it actually looked right.

Now when a thumbnail needs a specific expression or outfit, the system already knows what that looks like and grabs it, tagged by expression and clothing.

Built once. Never have to think about it again, and never have to take another awkward selfie for content.

Anyone else systemizing the "you" part of your content instead of reshooting constantly?

I make my AI interview me before it builds anything.

Most bad AI work I see is not the model being stupid. It's the model politely guessing at a fuzzy request.

So I built a rule into my flow: if the goal has more than about 10% unknowns, it is not allowed to start. It has to ask me questions first.

Annoying? a little.

But it saves me from the worst version of AI speed: getting the wrong thing done instantly.

The weird part is the questions are usually the work. Once the agent knows the edge cases, the output is boringly good.

Actually, scratch that, not always good. But it is at least wrong in smaller ways.

Anyone else forcing a clarifying step before execution, or are you still letting the model guess and cleaning it up after?

Title: How much of your business runs on people you've never actually met?

Heading out to a 3 day AI mastermind soon, hosted by a company I've worked with for years and never met in person.

Got me thinking about how much of running an online business happens with people you've only ever talked to over Zoom or Slack. You build real trust, real deals, sometimes years of history, without ever being in the same room.

Kind of a strange dynamic when you step back and think about it. Curious how common this actually is outside of my own bubble.

What's your setup? Anyone running a big chunk of their business with people they've never met face to face?

Getting the reply is the hard part. Then a lot of people immediately blow it, so here is how I handle that first response.

The mistake I see most: someone replies with a small question, and the sender instantly pivots to "great, here is my calendar, book a 30 minute demo." You just turned a human moment into a sales trap and most people back out right there.

What works better for me:

Answer the actual question first. If they asked something, be useful before you ask for anything. That one move earns more calls than any script.

Match their energy. If they wrote one short line, do not reply with six paragraphs. If they were warm, be warm back. Mirroring keeps it feeling like a conversation.

Make the next step tiny. "Want me to send a quick example?" or "Happy to share how that works, want the short version here or on a quick call?" beats demanding a big meeting on message two.

Keep the thread alive, do not close on it. The goal of reply two is reply three, not a signature.

The cold part ends at the first reply. After that it is a normal conversation, and normal conversations do not open with a hard pitch.

How do you handle that first reply? Curious what openers work for other people.

Title: Why leads sourced outside Apollo get 5 to 10x the reply rate

Got a theory I'm trying to prove out over the next year: campaigns perform significantly better when leads come from anywhere other than Apollo, Zoom Info, or Clay.

The logic: those emails aren't just sitting in Apollo, they're everywhere, and every spam filter and inbox rule is already dialed in against them because everyone's hammering the exact same people.

Rough rule of thumb: the harder it is to find someone's email, the better the campaign performs. Scraping Google Maps yourself beats straight Apollo data. Finding the actual business owner's email instead of a role-based info@ or contact@ inbox beats both.

We've even built out niche databases for this reason (creator economy, Instagram bios). Had a client who only wanted veteran-owned businesses, not something you can filter for in a standard B2B database.

Anyone else notice reply rates dropping the more common the lead source is?

Why I won't send a cold email for the first 3 to 4 weeks on new domains

Setting up infrastructure for a new cold email buildout this week, mailboxes and domains and sending platform, took about two days. That's the easy part.

The part that takes real time is warm-up. 3 to 4 weeks minimum before a single cold email goes out from a brand-new domain.

During that time we're sending a lot of email, none of it cold. It's all warm-up traffic, mailboxes emailing each other back and forth, getting marked as important, training the email service providers on the other end that these domains and mailboxes send legitimate mail.

Here's why it matters: once real cold emails start going out, some percentage of recipients are always going to mark them as spam, no matter how good the copy is. A brand-new domain with zero trust built up gets buried by that fast. Weeks of positive warm-up signal already in place is what counteracts it instead.

Skipping straight to cold sends to save a few weeks is exactly the move that torches the whole campaign before it gets a fair shot.

How long are people running warm-up before their first cold send?

The real 3-phase timeline behind a new cold email buildout

Walked a new client through our buildout process this week and figured it's worth sharing since people assume mailboxes going live means the system is done.

Phase one: infrastructure. About two days to get mailboxes spun up and warming so reputation score climbs before anything gets sent.

Phase two runs alongside that warmup, not after it. List building and copywriting happen in parallel so the client already has eyes on who we're targeting and what the copy says before launch.

Phase three is where the real testing happens. Launch day isn't one email, it's a stack of variations, something like 20 running at once, and anything that flops gets cut.

After 30 days of that, the system is actually mature and proven, not just live.

Anyone else run a phased rollout like this?

Ran a real batch through my AI thumbnail system and figured I'd share the actual numbers instead of just the highlight reel.

Three variations generated. Two came back genuinely ready to publish, one looked professional and one looked natural with zero fixes needed. The third one struggled: face looked a little off, and it used the wrong name entirely, some version of "Joe Zeppelin" instead of my real name. Had to send it back for one more fix.

Whole thing took about 10 minutes total, most of that was research. Cost for the batch: roughly a dollar fifty.

For comparison, I used to run two or three human thumbnail designers and pay $50 a thumbnail, and honestly most of them weren't even that good.

I still don't just ship whichever one I like best. I run it as an actual A/B test through YouTube's test report and see which one wins with real viewers, then try to figure out why.

Anyone else tracking real before/after cost numbers on their AI workflows instead of just vibes?

I started my first marketing agency in my second year of medical school. It wasn't the plan, it was a side thing I told myself I'd keep small.

It took off instead. Med school wants all of you, and so does a growing business. I tried running both for a while and it just didn't work.

So I left medicine. Not because it was going badly, because the side thing had already outgrown the plan.

I haven't seen a patient in six years. I trained as a family medicine doctor, but that turned out to be a skill I picked up, not the actual identity. Building was always the identity.

Anyone else had a side project quietly become the main thing before you were ready to admit it?

Everyone talks about the resource delivery part. Comment triggers fire, person gets the DM, done.

But that's where most people stop, and it's leaving a lot on the table.

Here's what I added:

- After the resource DM sends, a second DM fires ~8 minutes later
- It says something like: "Thanks for the comment. Curious what brought you here. Are you running a business or starting one?"
- That kicks off a real conversation
- A setter qualifies from there and books the call

A few other things worth noting:
- The delay is random, under 30 min, so it doesn't feel bot-like
- A GHL tag tracks first-timers only. Repeat commenters don't get this
- The whole thing runs in one GHL workflow with a Supabase backend. No ManyChat.

Anyone else running a follow-up sequence after comment trigger delivery? Curious what setups people are using.

Running an automated plan-review loop (I cap it at 3 rounds so it doesn't burn credits chasing diminishing returns) on a recent ad campaign build.

Round one found 23 issues. Round two found 15. Round three still found 8. All fixed into plan version four.

I almost stopped there. Then I ran a fourth round anyway, reasoning that if round three still found 8, round four could easily catch one more.

The part I think matters more than the fix count: instead of just executing the now-clean plan, I stopped and gave it one more instruction. Based on everything found this round, all the gotchas and bugs, update the project's actual rules and documentation.

That's the real payoff of a review loop like this. Every issue it catches becomes a permanent rule that future similar builds inherit automatically, instead of getting rediscovered from scratch on the next campaign. You have to explicitly ask for that documentation step, it doesn't happen on its own.

Anyone else turning review findings into standing documentation, or is everyone just fixing and moving on?

The whole space is obsessed with cold email templates and copy tweaks. Almost nobody wants to look at the offer, because the offer is harder to fix and more personal.

Here's the uncomfortable version. A mediocre email for a genuinely great offer outperforms a beautifully written email for a weak offer. Pretty much every time.

If your copy is solid and you're still getting silence, that's not a copy problem. That's the market telling you something about what you're actually selling. Rewriting the subject line for the tenth time won't change that.

Fix the offer first. Make it something people actually want. The outreach gets dramatically easier once you do, because you're no longer trying to talk people into something weak.