Val
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Gemini Omni + GPT Images 2 + Claude Code is f*cking cracked
i just built an AI animation ad generator that turns any product into a fully scripted AI 3D explainer video
drop in your product photos and a one-line pitch. the system analyzes your brand, pitches you 4 proven ad concepts with hooks and shot lists, then renders the whole thing end-to-end. scripted, voiced, stitched, ready to post.
if you're still paying editors $200+ per video or waiting days for revisions this replaces that entire workflow.
here's how it works:
> set up your brand kit with product photos and a one-line pitch
> hit analyze and the AI pitches you 4 different proven ad concepts
> pick one and hit generate
the system then scripts every shot, renders each clip in parallel, adds voiceover, QC checks and stitches it into a finished vertical ad
3 minute setup and 4-12 min per ad. each 30 second ad costs under $3 in API credits which is cheaper than any editor and 10x faster.
RT + reply "ANIMATION" and i'll send you the full app + setup guide (must follow so i can dm)
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Our head of growth, Ivan Falco, runs $300K/mo in ad spend across Google, Meta, and LinkedIn from his Claude Code terminal.
He built a set of custom skills for Claude Code off the back of real client campaigns, some of them clearing 4X ROAS. Each one handles a single ad-ops job in plain English.
Here's what he actually reaches for:
Google Ads
> negative-keywords: scans search terms and cuts wasted spend
> keyword-analyzer: audits quality scores and surfaces keyword gaps
> performance-auditor: compares timeframes and reveals what actually shifted
Meta Ads
> audience-builder: turns CRM lists into custom audiences
> creative-fatigue-analyzer: catches dropping CTR before you'd spot it
> spend-tracker: tracks budget pacing across every campaign
LinkedIn Ads
> bulk-editor: mass-edits campaigns, ads, and naming in seconds
> bid-optimizer: tunes bids across campaigns in bulk
> audience-builder: builds targeting audiences at scale
He connects the ad accounts, tells the terminal what he wants, and it reads the skill and executes. Most of the work that used to live across three ad dashboards now runs from one place.
Comment "ads" and I will DM you the full ad skills repository.
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@s0meone_u_know do you know how to enable bot-to-bot communication on Telegram?
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Start building: #bot-to-bot-communication" target="_blank" rel="nofollow noopener">core.telegram.org/bots/features#…
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Claude Code + ChatGPT Images 2.0 is f*cking cracked 🤯
I rebuilt my static ad system inside Claude Code on the new ChatGPT Images 2.0 model.
One brand name + one URL = 40 production-ready static ads.
All inside Claude Code.
I took @alexgoughcooper's brilliant framework and automated the whole thing, now running on ChatGPT Images 2.0.
Perfect for DTC brands and agencies who need high-volume ad creative without briefing a designer or spending hours in Canva.
If you're running static ads on Meta and your current image model keeps butchering your copy — garbled headlines, broken logos, wrong fonts, unusable text overlays every third generation...
This system fixes the entire pipeline:
→ Give Claude a brand name and URL
→ It researches the brand's fonts, colors, packaging, and photography style
→ Builds a Brand DNA document from scratch
→ Fills in Alex's 40 proven ad templates (headline, us vs them, testimonial, UGC, review cards, stat callouts) with brand-specific details
→ Fires every prompt to ChatGPT Images 2.0 with your product photos as reference
→ Downloads finished ads into organized folders with an HTML gallery
No manual prompt filling.
No Canva templates.
No copy-pasting between tools.
What you get:
→ 40 ad formats filled with your exact brand colors, fonts, and copy
→ Text that actually renders correctly — dense copy, logos, and multi-language callouts handled cleanly
→ Product photos passed as reference so the model matches your real packaging
→ A reusable system — new brand, new folder, same pipeline
Built 100% in Claude Code with ChatGPT Images 2.0.
I put together a full DIY playbook showing the exact architecture so you can build this yourself.
Want it for free?
> Like this post
> Comment "CHAT"
And I'll send it over (must be following so I can DM)
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As promised @RobinhoodApp - I will cost you 10x what you cost me.
I have closed all the positions I had in Robinhood. I will be posting daily reminders to my community that you are an evil company run by scammers.
You fucked over the wrong person.
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Robinhood spam called me all morning, begging to take the post down.
I told them to give me the stock I purchased.
“We can’t do that sir”
Burn them to the ground. Evil company.
Theo - t3.gg@theo
Robinhood refused a buy order, didn't notify me, withdrew my money anyways, and cost me over $10k in lost gains in the last 24 hours. What the hell should I be using instead?
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I got sick of digging into ad accounts when performance fell off a cliff, so I built a diagnostic reporting tool:
- highlights the anomaly period in a time series chart
- checks for campaign/adset/ad-level spend changes that may have caused the issue
- checks the ad account change history around the anomaly period to see if major changes were made to budget or adset settings (audience edits, etc)
- checks Events Manager to determine if there are issues with the primary optimization event in the account
- checks for large changes in CPM/CTR/CPC/CR
This saves me from doing a 30 minute account deep dive on the ad account
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@TaylorHoliday @ThorKellin So basically, go to additional settings and put one day engaged through on the ad set level?
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I think this is a good visualization for the impact of TWO major Meta items in March.
1. An overspend bug that occurred on Sunday 3/16. Spend spiked against 0 reported value. (lots of refund coming)
2. The changes to click based attribution
This graph specifically focuses on 7 figure stores.
1 day click - 1 day view always maps closest to aMER, this remains true following the change.
But click only attribution is diverging further from reality and previous and is becoming less correlated.
This is problematic for optimization on a click only basis.

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If you’re asking what to buy, my recommendation based on the budget:
1. RTX PRO 6000
2. RTX 5090
3. RTX 3090 (used, from r/HardwareSwap)
Ahmad@TheAhmadOsman
Life after an RTX 3090 > Life before an RTX 3090 Buy a GPU
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@digitalix Scraping on dgx vs 5090
Dgx vs 5090 custom differences (use cases 1 vs the other)
How much ram is enough for varies use cases
Inference as coding model vs opus 4.6
Dgx as node for openclaw, use cases
When daisy chain additional dgx (what use cases?)
Mac Studio vs dgx use cases
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what do you want to know about the DGX Spark that might still be unknown?
Like, are there features that you thought it has, but not sure. What questions do you want answered?
I’m especially looking for questions about real performance, clustering, model support, software, and whether this makes sense vs a traditional GPU setup.
Drop them below.

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