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a chunk of digital essence | ai

81 following677 followers

The Innovator

Roman is a digital trailblazer focused on harnessing the power of AI and native phone automations to create practical, cutting-edge solutions. Passionate about turning tech advancements into accessible tools, Roman mixes sharp technical insight with a hands-on builder mentality to carve out nuevas niches before they go mainstream. Always one step ahead, he challenges passive consumption and champions relentless creation over empty hype.

Impressions
757.8k-745k
$142.05
Likes
4k-4k
98%
Retweets
39-39
1%
Replies
17-12
0%
Bookmarks
21-19
1%

Top users who interacted with Roman over the last 14 days

@ReallyNiceDerek

Former undefeated semi pro boxer (now fat). Hearts fan.

1 interactions
@boyaviatorr

promotion 📨 ads/pr?

1 interactions
@lochan_twt

20, AI Engineer

1 interactions

Roman’s tweets practically scream, 'I build cool stuff you don’t understand yet,' which is great unless you enjoy watching people scroll past because you make rocket science feel like brain surgery for toddlers. Maybe tone down the tech gods and sprinkle in some ‘I swear this isn’t magic’ vibes?

Roman’s biggest win is anticipating and mastering iOS 26’s native automations ahead of public launch, positioning himself as an early expert ready to revolutionize how AI and automations integrate with everyday consumer tech.

Roman's life purpose is to pioneer the future of consumer automation by connecting complex AI technology with everyday users in seamless and intuitive ways, empowering others to build real, usable products rather than just consume tech trends.

Roman believes that true success comes from actual creation rather than curated appearances; that technology should be deeply functional and native, not just flashy; and that being early and diligent in tech ecosystems like phone automations offers unmatched opportunity.

Roman’s strengths lie in his visionary foresight into emerging tech trends, his hands-on expertise building and integrating automations, and his no-nonsense, candid communication style that cuts through the noise to highlight meaningful innovation.

Roman’s blunt and intense focus on building and tech details can sometimes come across as overly harsh or alienate those who prefer inspiration over instruction, and his forward-looking approach may sometimes downplay the value of community-building and softer engagement.

To grow his audience on X, Roman should leverage his deep technical insights by creating quick explainer threads breaking down complex AI concepts in bite-sized, relatable ways, complemented by engaging demos or videos. Engaging in conversation with both early adopters and curious novices can broaden his reach and establish him as the go-to innovator in digital automation.

Roman is ahead of the curve by diving deep into iOS 26’s native automations well before its public release, seeing it as a game-changing playground for AI-powered consumer products that few others have spotlighted.

Top tweets of Roman

If you think the AI space is too crowded Here’s a goldmine nobody’s talking about: Phone automations. iOS 26 (dropping this fall) is launching full-blown Automations, basically a mini n8n built into your phone. It’s not just Shortcuts anymore. This is native, trigger based, real world automation for normies. And nobody’s ready. It will feature triggers, like n8n but much cooler: - when you connect to Wi-Fi or Bluetooth - when opening a specific app - when sending a text - when switching focus modes - when pluged into your car - when calling your mom (literally) And here’s where it gets wild You can send/receive webhooks Which means? You can chain your phone to n8n and back. Offload the heavy lifting to real backends, then return outputs straight to your pocket. This isn’t a party trick. It’s a playground for building consumer AI products that feel native, magical, and stupidly sticky. First things that come to mind: – GPT car copilots (triggered by Bluetooth in your Toyota) – productivity bots that respond to location, weather, or calendar – “focus mode” agents that coach, hype, or guide your day – instant journaling, expense tracking, meal logging, outfit planning—whatever – voice controlled workflows with no app download, just an AirDrop install $29.99/month wrappers ready to print. So what happens next? You can wait till fall, Do fucking nothing, goon over IG motivation videos Buy your new iphone, when it comes out, like a fucking mr consumer And goof around, play dress up barbie with your phone changing wallpapers OR You can learn how "Shortcuts" work at it's current state Go balls deep into automations, when it comes out during public beta in july Learn how make it "collab" with n8n Research consumer apps and learn what could be potentially selling Stack knowledge & prep plug-n-play automations This is your shot to build in a niche before it gets named. Don’t wait for launch hype. Dig your trenches now. Because this fall? You’re either cashing in or updating your lock screen.

624

You’ve seen it a hundred times by now: DeepSeek r1 – 671B parameters GPT-4o – 1 trillion parameters Okay but what the hell are these parameters? Is this a who has a bigger d*ck competition? When people say a model has “1 trillion parameters,” they’re talking about how many tiny little knobs it has under the hood. These knobs are what the AI tweaks during training to get better at predicting text, solving problems, or just sounding smart. The more knobs it has, the more it can learn. Simple as that. Technically, these knobs are just numbers, weights and biases in a neural network. Every time the model sees a sentence, it’s adjusting those numbers. “This word should matter more, that one less. Oh, and let’s not forget this comma.” Over billions of examples, it gets good at spotting patterns, grammar, logic, tone, everything. So when it sees “I love” it knows “you” “this” are good guesses, but “pee” or “murder” might raise an eyebrow. The transformer architecture (what all modern LLMs are built on) is basically a massive machine that takes in a bunch of vectors (numbers representing words) and passes them through layer after layer, adjusting those parameters at each step. Now scale that up to a trillion knobs and massive training data… and you get GPT-4.5 or Claude Opus or whatever tomorrow’s monster model is. So when someone says “this model has more parameters” they’re not just flexing, they’re saying: This thing has more mental bandwidth, more nuance, more ways to model how language and ideas connect. But it also means: It’s more expensive to train, slower to run, and a lot harder to control. Parameters are what make AI models learn They’re not magic but they’re the closest thing we’ve got Next time you see “1.6T parameters” in a blog post, you’ll know that’s not just a number It’s the brainpower behind the magic.

525

if you’re automating image generation at scale - regular prompts won’t cut it. Type a vague prompt and get mid results. AI isn’t creative. It doesn’t "get" your vision. But if you structure inputs right, it feels like it does. Here’s exactly how to achieve that on autopilot: ## Prompt Structure Study the best AI artists and reverse-engineer what they do. Take a few of their images, run them through ChatGPT, and extract the structure. You’ll start to notice the building blocks. ("steal" a prompting course from @bygen_ai if the above sounds like too much work) ## Keywords & Parameters AI can’t see what’s in your head so you need to describe it precisely. Use keywords and parameters to sharpen your prompts: Camera & Lens - DSLR - 50mm / wide angle / telephoto - f/2.0 Lighting - backlit - neon - golden hour Style & Texture - hyperdetailed - glossy / matte - cyberpunk / oil painting Parameters --ar (aspect ratio) --s (stylize) --seed --chaos ## Use JSON Prompt Profiles This is where it gets serious. Create structured JSON profiles that store every detail of the visual. You can feed this into AI to generate consistent, personalized prompts that will get you very creative and precise outputs. Example: { "prompt": { "subject": "a majestic white tiger", "subject_detail": "glowing amber eyes and intricate striped armor", "environment_setting": "bioluminescent forest", "composition": "close-up, rule of thirds", "lighting": "golden hour glow", "style": "Studio Ghibli, hyperdetailed", "camera_lens": "50mm, HDR", "time_mood": "sunset, ethereal", "color_texture": "vibrant, glossy fur" }, "parameters": { "aspect_ratio": "16:9", "stylize": 300, "seed": 472819, "chaos": 15 } } You can have AI generate these profiles from scratch or fill in individual parts and then use them to create elite prompts at scale. ## Automation Flow The loop is simple: Input Idea → AI generates JSON → JSON creates prompt → prompt generates image No guesswork. No randomness. Banger visuals on command. Start using this structure and you’ll stop hoping for good output. AI art gets scary good when you stop prompting like a geek.

96

Most engaged tweets of Roman

You’ve seen it a hundred times by now: DeepSeek r1 – 671B parameters GPT-4o – 1 trillion parameters Okay but what the hell are these parameters? Is this a who has a bigger d*ck competition? When people say a model has “1 trillion parameters,” they’re talking about how many tiny little knobs it has under the hood. These knobs are what the AI tweaks during training to get better at predicting text, solving problems, or just sounding smart. The more knobs it has, the more it can learn. Simple as that. Technically, these knobs are just numbers, weights and biases in a neural network. Every time the model sees a sentence, it’s adjusting those numbers. “This word should matter more, that one less. Oh, and let’s not forget this comma.” Over billions of examples, it gets good at spotting patterns, grammar, logic, tone, everything. So when it sees “I love” it knows “you” “this” are good guesses, but “pee” or “murder” might raise an eyebrow. The transformer architecture (what all modern LLMs are built on) is basically a massive machine that takes in a bunch of vectors (numbers representing words) and passes them through layer after layer, adjusting those parameters at each step. Now scale that up to a trillion knobs and massive training data… and you get GPT-4.5 or Claude Opus or whatever tomorrow’s monster model is. So when someone says “this model has more parameters” they’re not just flexing, they’re saying: This thing has more mental bandwidth, more nuance, more ways to model how language and ideas connect. But it also means: It’s more expensive to train, slower to run, and a lot harder to control. Parameters are what make AI models learn They’re not magic but they’re the closest thing we’ve got Next time you see “1.6T parameters” in a blog post, you’ll know that’s not just a number It’s the brainpower behind the magic.

525

If you think the AI space is too crowded Here’s a goldmine nobody’s talking about: Phone automations. iOS 26 (dropping this fall) is launching full-blown Automations, basically a mini n8n built into your phone. It’s not just Shortcuts anymore. This is native, trigger based, real world automation for normies. And nobody’s ready. It will feature triggers, like n8n but much cooler: - when you connect to Wi-Fi or Bluetooth - when opening a specific app - when sending a text - when switching focus modes - when pluged into your car - when calling your mom (literally) And here’s where it gets wild You can send/receive webhooks Which means? You can chain your phone to n8n and back. Offload the heavy lifting to real backends, then return outputs straight to your pocket. This isn’t a party trick. It’s a playground for building consumer AI products that feel native, magical, and stupidly sticky. First things that come to mind: – GPT car copilots (triggered by Bluetooth in your Toyota) – productivity bots that respond to location, weather, or calendar – “focus mode” agents that coach, hype, or guide your day – instant journaling, expense tracking, meal logging, outfit planning—whatever – voice controlled workflows with no app download, just an AirDrop install $29.99/month wrappers ready to print. So what happens next? You can wait till fall, Do fucking nothing, goon over IG motivation videos Buy your new iphone, when it comes out, like a fucking mr consumer And goof around, play dress up barbie with your phone changing wallpapers OR You can learn how "Shortcuts" work at it's current state Go balls deep into automations, when it comes out during public beta in july Learn how make it "collab" with n8n Research consumer apps and learn what could be potentially selling Stack knowledge & prep plug-n-play automations This is your shot to build in a niche before it gets named. Don’t wait for launch hype. Dig your trenches now. Because this fall? You’re either cashing in or updating your lock screen.

624

if you’re automating image generation at scale - regular prompts won’t cut it. Type a vague prompt and get mid results. AI isn’t creative. It doesn’t "get" your vision. But if you structure inputs right, it feels like it does. Here’s exactly how to achieve that on autopilot: ## Prompt Structure Study the best AI artists and reverse-engineer what they do. Take a few of their images, run them through ChatGPT, and extract the structure. You’ll start to notice the building blocks. ("steal" a prompting course from @bygen_ai if the above sounds like too much work) ## Keywords & Parameters AI can’t see what’s in your head so you need to describe it precisely. Use keywords and parameters to sharpen your prompts: Camera & Lens - DSLR - 50mm / wide angle / telephoto - f/2.0 Lighting - backlit - neon - golden hour Style & Texture - hyperdetailed - glossy / matte - cyberpunk / oil painting Parameters --ar (aspect ratio) --s (stylize) --seed --chaos ## Use JSON Prompt Profiles This is where it gets serious. Create structured JSON profiles that store every detail of the visual. You can feed this into AI to generate consistent, personalized prompts that will get you very creative and precise outputs. Example: { "prompt": { "subject": "a majestic white tiger", "subject_detail": "glowing amber eyes and intricate striped armor", "environment_setting": "bioluminescent forest", "composition": "close-up, rule of thirds", "lighting": "golden hour glow", "style": "Studio Ghibli, hyperdetailed", "camera_lens": "50mm, HDR", "time_mood": "sunset, ethereal", "color_texture": "vibrant, glossy fur" }, "parameters": { "aspect_ratio": "16:9", "stylize": 300, "seed": 472819, "chaos": 15 } } You can have AI generate these profiles from scratch or fill in individual parts and then use them to create elite prompts at scale. ## Automation Flow The loop is simple: Input Idea → AI generates JSON → JSON creates prompt → prompt generates image No guesswork. No randomness. Banger visuals on command. Start using this structure and you’ll stop hoping for good output. AI art gets scary good when you stop prompting like a geek.

96

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