Roman Alexander Wellington

493 posts

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Roman Alexander Wellington

Roman Alexander Wellington

@RomanAlexanderW

Founder & CEO | #1 Best Selling Author Insta: @romanalexanderw

Scottsdale, Az Katılım Ekim 2020
647 Takip Edilen1.5K Takipçiler
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Roman Alexander Wellington
Roman Alexander Wellington@RomanAlexanderW·
Anyone who knows me, knows I'm a huge fan of @kaskade. When I heard Kaskade was doing a Christmas concert in #LA I was so excited! Got to go with my friend Scott, his son Max, and we had an absolute blast! EDM & Christmas music might sound like a weird combo, but it was awesome! Also, the venue was insane! As long as Kaskade does it, we'll be coming back each year as a new Christmas tradition! Sharing some clips from my favorite songs of the night! #Christmas2024 🎅🎄😇🧿
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Michael Guo
Michael Guo@Michaelzsguo·
It is defined in the goal. I use a goal-forge skill to turn the objective into a concrete goal package: success criteria, constraints, execution plan, attempt log, and working notes. The agent pursuing the goal runs autonomously against that package. The “sidecar” is a separate conversation where the agent/I monitor the run by inspecting logs, processes, artifacts, eval outputs, and the agent’s written updates. We act as an external reviewer/operator: catching drift, spotting bad assumptions, diagnosing stuck processes, and helping decide whether to continue, pause, or redirect. When we want to steer the agent, we usually prompt it directly with a concise directive based on sidecar findings, for example: “Pause training; the eval contract is unstable. First fix the dataset validator, rerun the audit, and update ATTEMPTS with the failure mode.” github.com/michaelpersona…
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Michael Guo
Michael Guo@Michaelzsguo·
While DeepSeek is pursuing the goal, my Codex agent and I monitor it in the sidecar and guide or correct it as needed. So I thought I would ask Codex to objectively judge DeepSeek’s capability based on multiple rounds of interaction. Keep in mind, Codex does not know it is talking to DeepSeek. It thought it was another Codex agent. Here is Codex’s evaluation of DeepSeek V4 Pro:
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John
John@johnvirality·
ONE person can run the production workflow behind $100M+ in revenue and 20B+ views from their laptop with arcads all without: - big production teams - 100+ separate subscriptions - tab switching between 6 different AI tools it puts seedance 2.0, kling 3.0, nano banana pro, and a dozen other models behind one all-in-one dashboard so i documented the ENTIRE production architecture... covering product photography, character generation, video animation, the 6-shot script formula, and the scaling math that makes $10-30k/mo realistic for a single creator like + comment "ARCADS" and i'll send you the ENTIRE system (must be following + RT for priority access)
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Roman Alexander Wellington
Roman Alexander Wellington@RomanAlexanderW·
@SpaceTimeViking Damn it ... I swore I was going to find some "normal" friends. Then here you are, all nerdy and shit. Ughhh fiinnneeee ... I'll go grab my agent harness if we're getting freaky like that. Be back in 5 😂
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ÆON FORGE ✨
ÆON FORGE ✨@SpaceTimeViking·
If this kind of content turns you on we should be friends.
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ÆON FORGE ✨
ÆON FORGE ✨@SpaceTimeViking·
Appreciate the shoutout! The Qwen3.6-27B model is the first one I hand Abliterated vs relying on other abliterated models. I converged several techniques and hit the kv drift jackpot and it’s now smarter than the OG source model. It no longer has self censorship overhead. github.com/AEON-7/Qwen3.6…
Bitcoin Comfy@BitcoinComfy

@Hikari_07_jp @rifrafgiraffe have a look at @SpaceTimeViking qwen3.6 27b ultimate uncensored (there is also a mixed approach to uncensoring documented). I tried to replicate on the rtx 6000 and i cannot get nowhere close, it's the best uncensored model out there, have a look at the techniques used

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hunter
hunter@hxxntrr·
F*ck paying $149/month for credit repair. AI does the entire thing in 5 minutes Disputes. Letters. Legal codes. Bureau addresses. Settlement scripts. For free. Credit repair companies have been billing Americans $4 billion a year to copy-paste templates a robot does for free now.... Like + RT + comment "credit" and i'll send it to you (must be following)
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Jack J.
Jack J.@jack_9947·
Nobody is talking about this. But it's the fastest way to build a client-facing SaaS tool without a developer. Four copy-paste prompts that take any GTM idea from plain English description to production-grade code - masterplan, scaffold, ticket backlog, and implementation - in one afternoon with zero assumptions and zero rework. Usually, I charge $199 for the full breakdown. Today, I'm giving it away FREE. Like + reply "SAAS" and I'll send you: (Follow me so I can DM you.) Free for the next 48 hours only.
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Jack J.
Jack J.@jack_9947·
I just built a fully mapped /goal playbook covering every layer a GTM engineer needs to run autonomous batch jobs from a single Claude Code command. Feed it your lead lists, your outreach briefs, and your client data → it maps every condition, safety clause, and evaluator check to the right task → so you know exactly what to write, what the evaluator checks, and how to keep Claude running until the job is actually done. All inside one free reference document. Perfect for GTM engineers and RevOps leaders who are still prompting Claude turn by turn through batch jobs, writing goal conditions the evaluator can never verify, and walking away from sessions only to come back and find Claude spinning in a loop burning credits. If you're running GTM batch work on Claude Code in 2026, you already know the math - the engineers processing the most aren't prompting more, they're running /goal sessions with verifiable finish lines that confirm completion without human intervention. Most engineers write vague conditions with no safety clause. The evaluator can never say yes and the session runs indefinitely. This playbook solves it: → The two-agent loop explained - worker model does the work, evaluator model confirms completion after every turn, reason for failure becomes guidance for the next turn automatically → Complete condition-writing guide with bad vs good examples - the three-component anatomy of a finish line the evaluator can actually verify → Auto mode setup across three methods - session toggle, settings.json, and CLI flag - for fully hands-off unattended batch runs → 20 copy-paste /goal prompts across lead research, cold email, reply handling, client reports, and content ops - every one with a verifiable finish line and safety clause already included → Three safety rules covering safety clauses, usage checks before walking away, and small batch testing before full runs → Four diagnostic patterns for when a goal gets stuck - loop detection, file path mismatches, incorrect output verification, and startup failures No vague conditions the evaluator can never confirm. No sessions running indefinitely burning a full day of credits. No coming back to find Claude on iteration 18 of a task that went wrong at iteration 2. What you get: - Two-agent loop mechanics and status indicator guide - Condition-writing framework with anatomy of a strong finish line - 20 copy-paste GTM /goal prompts ready to run today - Three safety rules and four diagnostic patterns for stuck sessions - /goal vs /loop vs Stop hooks decision guide for every task type Built from direct testing across lead enrichment, outreach copy, reply classification, and client reporting batch jobs. Want it for free? > Like this post > Comment "GOAL" And I'll send it over (must be following so I can DM)
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Alfie Carter
Alfie Carter@AlfieJCarter·
Nobody is talking about this. But it's the most complete GTM system ever built on Claude. 131 skills across outbound, content, SEO, analytics, and strategy - one plugin, one install, every session starts already knowing your brand, ICP, voice, and competitors. Usually, I charge $199 for the full breakdown. Today, I'm giving it away FREE. Like + reply "PLUGIN" and I'll send you: (Follow me so I can DM you.) Free for the next 48 hours only.
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Boring Local SEO
Boring Local SEO@boringlocalseo·
Are we going to pretend HomeAdvisor/Angi selling shared emergency leads is business as usual in the AI age? I sat in on a dispatch room last week. The owner showed me two numbers on a sticky note: $212 (their average paid lead) and 19 seconds (how long it takes the customer to get 3 AI recommendations). The lead seller isn't losing to better marketing. They're losing to a different interface. What wins now (water damage / HVAC / plumbers / locksmiths): 1) publish the one page nobody wants to write: pricing + response time + what's included + what's a scam 2) get cited by 10 third-party pages that already rank for "best [service] in [city]" 3) show proof inside the content: photos, permits, estimates, real ranges 4) monitor whether AI is actually naming you (not just ranking you) using localrank.so Lead sellers thrive on comparison shopping. AI collapses comparison shopping into a shortlist. Comment "LOCALRANK" + bookmark this and I'll DM you the Rented Lead Escape Plan. (Must be following)
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Jonny Vandel
Jonny Vandel@Jonnyvandel·
Operator; infinite canvas memory. we gave your agents a photographic memory so it will reference: > every workflow > every phone > every post > every competitor > every account > every dm > every viral video so overtime... it's stops actually like Opus 4.7, and starts acting like you. like, rt + comment "BRAIN" and i'll dm you where to start (must be following for dm)
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NVIDIA GeForce FR
NVIDIA GeForce FR@NVIDIAGeForceFR·
[🍸#GIVEAWAY🎯] Vous êtes plutôt du genre à compter sur les gadgets de Q pour prendre l’ennemi de court… ou à gérer la mission vous-même sur le terrain ? 👀 Tentez de remporter l’un de nos cadeaux 007 First Light exclusifs ! Pour PARTICIPER : 💬 Partagez votre choix d'approche en commentaire avec #007FirstLightRTX
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Lorenzo | Meta Ads & Performance Creatives 📈
Andromeda is punishing brands that run 10 hooks on one concept. Meta now reads structurally similar ads as ONE entity ID. Your hook variations are competing with themselves and capping the audience pool you reach. To fix that, here's the Claude system we use to turn 1 concept into 4 entity IDs that reach 4 different audience pools. Free for the next 48 hours: - 10 prompts run in order in Claude - Generates 4 STRUCTURAL FORMAT WRAPS per concept (podcast, street, walk-and-talk, POV) - Each wrap produces a different entity ID and different audience pool - Production-ready BRIEFS at the end Want it? Like + Comment "HOOKS" (Must be following)
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Lua
Lua@luagaroz·
Operators going from ~$10k to 7-8 figures in 2026 are combining AI deployment with distribution and growth. Growth Engineers doing this in cash/EBITDA-rich niches are already compounding 150%+ MoM. Like & comment "EBITDA" for 68 growth engineer segment/offers you can start campaigns for tomorrow.
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Brilliance | Ads & Funnels
I hate poor people. Most businesses spend $300-500+/day on ads, and yet their prospects are broke and unqualified… Leads don't pick up their setter's calls They constantly no-show And it's EASY why this is happening Your pixel is trained on the wrong set of people. I spent the past weekend packaging EVERYTHING I KNOW about conditioning your pixel to eliminate unqualified leads and train Meta to find your best buyers… Into a 15-page master pixel conditioning masterclass. NO FLUFF or theoretical BS This is pulled from our ad accounts and is quite literally a step-by-step blueprint People pay us 5-figures $ to implement this stuff. Inside, I reveal: - The core problem most businesses ignore when running paid ads - How to write ad messaging that filters bad leads BEFORE they ever click - The landing page congruence rules that qualify prospects before they book - How to structure your Typeform to route + reject unqualified leads automatically - The exact pixel event setup that trains Meta to find “buyers.” - CRM-triggered CAPI fires and warm audience exclusions for advanced signal quality - The full pre-launch pixel conditioning checklist - Plus TONNES more stuff I've used this EXACT framework to fix lead quality for businesses spending $5k-$100k+/mo on ads. (and now I'm giving it away FREE for the next 48 hours) To receive → Like + Comment "Pixel" [Must be following or I can't DM]
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Cody Schneider
Cody Schneider@codyschneider·
last Friday I deployed a Facebook ads agent for a startup and over the weekend it optimized itself from $17 phone number leads into $3 leads this is the real GTM engineering agents in the wild and I made a Notion document and a .md skill file so you can do this exact thing too it includes: 1. How to make on brand ads with nano banana 2 2. How to upload these to facebook ads via the API 3. Have an agent manage the ad account based on live data from the account via data pipeline + data warehouse Outcomes we deployed an AI agent for a startup last Friday to manage their facebook ads account day 1: the cost per phone number lead was $17 over the weekend this agent made and published 30 new pieces of ad creative, optimized the ad account, and reduced the cost per action Day 4: the cost per phone number lead was $3 this is a virtual employee that's working full time. like this post and comment "FBmanager" and I'll send you the Notion file and MD file
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NVIDIA GeForce
NVIDIA GeForce@NVIDIAGeForce·
Recruits, your first prize is here... A custom GeForce RTX 5080 Founders Edition + PC copy of the game. Comment #007FirstLightRTX to win 👇
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Julian Goldie SEO
Julian Goldie SEO@JulianGoldieSEO·
Just pulled an all-nighter learning n8n from scratch… and i have to say this tool is insane. One app. So much power. I built a full automation with logic, AI, HTTP requests, Google Sheets, YouTube uploads… all visually. 🤯 Can’t believe I slept on this for so long. Like + reply “YES” & I’ll send you the FULL workflow + setup FREE!
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Fivos Aresti
Fivos Aresti@fivosaresti·
Signal data is the FOUNDATION of a modern go-to-market system. I just wrote a complete playbook on it. Inside: 1. 5 core uses of intent signals (lead scoring, automated outreach, CRM/Slack alerts, expansion, ABM list building) 2. 3 signal categories explained in detail (1st-party, 2nd-party, 3rd-party) 3. Layer 1 Ingest stack (Tray(.)io, Zapier, Hightouch, Fivetran into your CRM/CDP/warehouse) 4. Layer 2 Normalize and Score with the weighted model (blog visit +2, pricing +8, demo +15, product spike +20) 5. Layer 3 Enrich plus Layer 4 Trigger (if/then logic for deals, alerts, nurture, retargeting, outbound) 6. Layer 5 Close the feedback loop so signals that produce meetings get weight, unproductive ones get cut This is a complete guide to capturing, scoring, and activating intent signals across first-party, second-party, and third-party data to build a real-time, signal-driven GTM system. Reply SIGNAL and I'll send it. (MUST BE FOLLOWING)
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Tommi Pedruzzi
Tommi Pedruzzi@TommiPedruzzi·
I just finished creating my most valuable PDF yet: "18 Claude Cowork Workflows for the Entire eBook Business" (44 pages). I might charge for this in the future, but for now... Reply "Claude" and I’ll DM it to you for free (must follow)
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