Adea

hey, i’m Adea been in crypto for ~4 years now. started with nft degen stuff, survived meme coin trenches and somehow ended up falling into AI rabbit holes. on this account i’ll mostly post about crypto, AI, internet things i find interesting and whatever else i’m currently obsessed with at 2am. still pretty new to posting consistently, so i appreciate every follow & mutual ♡

@dimoflexx ngl this feels closer to hiring an employee than using a chatbot the system actually remembers how work gets done

Chinese developer connected 4 Mac Minis and built a local Hermes agent farm that runs research tasks with zero cloud costs. Most people think agents are just smarter chatbots. Hermes works differently. It completes a task. Turns the process into a reusable skill. Then saves it so the next job starts with memory instead of a blank prompt. The first competitor report might take 20 minutes. But after enough workflows are saved, the same report can take 8–10 minutes because the agent already knows the structure. That's the real advantage: Not faster models. Compounding workflows. His setup distributes work across 4 machines: • One handles research • One analyzes pricing and reviews • One writes reports • One manages outreach drafts and Telegram actions No cloud dashboard. No $200/month agent subscription. No analyst team taking a week to send a PDF. Just local compute, reusable skills, and a system that gets cheaper every time it repeats itself. The funny part? Most people still think local AI means running weak toy models on a laptop. This looks more like a small research firm operating from a single desk. And it can all be controlled from a phone. Bookmark this before workflow ownership becomes the real AI advantage. 👇

@rileywestreel which is kinda scary and reassuring at the same time lol

@Adea0x this is the reframe nobody wanted but everyone needed. pausing was never realistic, outrunning the risk is the actual game

🚨 do you understand what just happened with Anthropic.. The company that spent all last week pushing frontier labs toward a coordinated pause just shipped its most powerful model into public access. Claude Fable 5 — a public build on the same architecture as Mythos, the model they themselves deemed too dangerous for a broad release. The April logic was this: Mythos found exploitable vulnerabilities in every major OS and browser with no domain-specific training, so access was gated behind Project Glasswing — a closed partnership with Apple, Google and JPMorgan. The fix wasn't to cut capability. It was to add refusals. → State-of-the-art on nearly every public benchmark, with the lead widening on long-horizon tasks → Cyber, biology, chemistry and distillation queries are blocked and routed to the less-capable Opus 4.8 → External bug bounty: 1,000+ hours, zero universal jailbreaks → Safeguards tuned conservatively — triggering in under 5% of sessions on average → $10 / $50 per million input and output tokens, less than half the price of Mythos Preview This isn't a capability downgrade. It's a refusal layer on top of one: the model serves frontier-grade output everywhere except dual-use domains, where it hands off to something weaker. Trust architecture, or the AI-safety lab quietly winning the race it warned everyone else to slow down?

@0xkerazcity kinda changes how you think about value tbh

@Adea0x funny how the process keeps paying long after the first report is delivered

A girl used Hermes after it crossed 140,000 GitHub stars and turned a $0 local AI setup into roughly $5,200/month in client reports. Instead of starting every project from scratch, she saved workflows locally so each new report began with context, structure, and research already in place. After building 20+ custom skills, similar reports started running around 40% faster with far less prompting and manual work. The setup took about 30 minutes using Ollama, Qwen, and Hermes running locally. Reports founders pay $400-$700 for could be drafted in 15-20 minutes once the workflow was trained. Most people are still paying for agents that forget everything between sessions. A small group is building systems that get more valuable after every client. Bookmark this before local AI workspaces become mainstream. 👇

@0xfinkus ngl every one of these stories ends up being less about AI and more about distribution

A guy turned a $599 Mac Mini into $9,600/month Just local AI He charged $300 for competitor research reports Delivered the same day One machine became 65 $599 hardware ~$2/month electricity $9,600/month revenue Most people focus on the Mac Minis The real edge was finding something worth running 24/7 Bookmark this before everyone figures it out

@0xfinkus funny how a $600 purchase can feel expensive but $300 every month somehow doesn’t

Someone opened a Mac Mini box in a garage Mac Mini M4 Pro, 64GB RAM, $600 once Then nothing No ChatGPT. No Claude. No Gemini. Local models instead: Mistral, Phi, LLaMA Most devs pay $200–$400/month in AI tools $459/month wasn’t rare Now it’s ~$3/month electricity The garage became infrastructure Bookmark this before it becomes normal

@cryptowluha every generation seems to get one moment where the rules suddenly change this kinda feels like one of them

A trucker I know stopped talking for three days. His 19-year-old son just bought a BMW in cash. $48,000. Built from a Shopify store powered by AI. No team. No funding. Just 6 prompts. 9 weeks ago: $0. Last month: $31,000. His dad earns $54,000/year after 23 years on the road. “How much did this cost you?” “$21.” Most people will scroll past this. A few will understand what just changed.

@N01ennn the interesting part is that the agent isn’t replacing one job it’s replacing an entire chain of small decisions

a 23 year old made $1,500,000 in KDP royalties. sold one of his businesses for $300,000. now a $200 Mac Mini runs the whole operation. Sophia is his AI agent she works 24/7 Sophia runs on Claude Code. lives on a used Mac Mini under his desk. no monitor. no keyboard. he talks to her through Telegram every minute she scans thousands of KDP niches across four countries. filters every one through his personal criteria. BSR benchmark. review count. price floor. competition density. cover patterns. 90-day price trends. red flags. winners go into the dashboard. losers disappear. he never sees them every morning before he wakes up > Telegram brief lands. top trending niches. performance scores. profitability checks. books written overnight then she writes. one prompt > the book title > triggers an entire writing team: > outline agent builds the skeleton > research agent spawns sub-agents. pulls verified data only > writing agent: one sub-agent per chapter. each one knows what's in the other chapters > reader agent: fake target customer persona reads the whole book. rates it. triggers rewrites > humanizer agent: strips AI patterns. double dashes. ChatGPT structure. all gone > orchestrator stitches everything. delivers the final manuscript

@skynews357 exactly😭 $20 here and $30 there never feels expensive until you add it all up

@Adea0x That’s the trick Subscriptions feel invisible until they become a yearly number

This guy from Florida spent $599 on a Mac Mini M4 Pro and changed his AI costs completely. 64GB RAM. One purchase. No monthly contracts. Before: $200–$400/month across AI tools. Some months closer to $459. After: Most workloads moved local. Models running on his own machine. Power cost around ~$3/month. 12 months later the math looked different: $5,500+/year in subscriptions vs $599 once + electricity. No server room. No GPUs. Just a garage and a small Apple box handling daily work. Most people are still adding subscriptions. Bookmark this before local AI becomes normal 👇

@JohnCena kinda crazy how much energy you get back once you stop carrying things that should’ve been left behind years ago

Learn to find ways and get help to begin to shed or work past the little and larger emotional things that weigh you down. A “lighter backpack” can allow for more nimble travel thru life

@sandy4kad the wild part is that distribution still matters more than the tools thousands of people have access to the same stack

A 19-YEAR-OLD MADE $20,000 IN 2 MONTHS COPYING FITNESS VIDEOS HE DIDN'T FILM. No camera. No editing skills. No original ideas. Here's the exact workflow: → Find a viral fitness channel on YouTube → Screenshot 5-6 frames from their best video → Paste into Claude with this prompt: "recreate this video concept, change [X detail], make it original" → Claude writes the full script + prompt → Go to Pika, open Flow, paste the prompt, hit Generate → AI builds the entire video in minutes Fitness channels pay $7,000–$15,000 per million views. He posted 3 videos a week. Month 1: $800. Month 2: $19,200. The math works because fitness RPM is 3x most other niches. Comment "FLOW" and I'll send you the Claude prompt + the Pika link.

@NickAlphas crypto spent years making digital assets useful now we’re seeing the reverse experiment with real-world assets

If you're curious about where tokenized gold is headed, this is worth tuning into $STRATO's vision around making gold usable again is one of the more interesting narratives in crypto right now Kieren, the CEO of STRATO, and Victor are live sharing their thoughts on the opportunity and what they're building Definitely worth checking out 👇

@0xWast3 ngl i’d rather have a model that tells me it’s unsure than one that’s confidently wrong

Anthropic engineer: "Claude Opus 4.8 is the most honest Claude yet - it catches bugs 4x more often than 4.7 before you even notice them" In 6 minutes he breaks down all 5 updates: parallel AI agents that clone themselves, effort control, 2.5x faster code mode, and live instruction updates mid-conversation. Bookmark and watch the full video 👇🏼

@Damir_Akaza this explains so many weird moments where an agent suddenly forgets something it handled perfectly an hour ago

Your model gets dumber as its context fills up, and the more you cram into it, the worse it performs. Anthropic engineer: "a bigger window isn't more memory. a model stuffed to the brim finds the detail that matters fewer than 4 times out of 10. the same task with a proper memory layer gets it right more than 9 times out of 10" hands-on engineer Ash Prabaker and Applied AI architect Andrew Wilson show the same problem from the inside: on long tasks the agent loses the plot. context drifts, the model rushes near the end of the window, and it judges its own work terribly, flattering itself even in code. bookmark it and watch the full workshop, 1 hr 15 min ↓

@Damir_Akaza context anxiety is such a good way to describe it you can almost feel the quality dropping near the end

Anthropic team engineer Andrew Wilson: "when the context window fills up, models change their behavior. as an agent gets close to its context limit, context anxiety kicks in. the model starts to get nervous, rushes to finish, and produces worse and worse work in the last 20% of the session" this is exactly why on long sessions you hit problems you thought you'd sorted out with Claude 10 minutes ago the fix: context reset + structured handoff instead of holding one long context, periodically start a fresh session with a clean window. this clears the model's "nervousness." full version in the previous post ↓

@rileywestreel love finding things that save time every day instead of adding another tool to learn

@Adea0x Honestly this changes how I'll set up my whole workflow. Saving this.

A SINGLE FILE TURNED CLAUDE INTO 10 DIFFERENT AI WORKERS No fine-tuning. No agents framework. No custom app. Just a folder with instructions Claude can pick up when the task is relevant. What you can create: > meeting notes → structured action plans
> design briefs → poster concepts
> messy docs → clean summaries
> recurring tasks → reusable workflows
> niche processes → dedicated Claude skills The crazy part? You don’t have to remind Claude every time. Name the skill. Describe when to use it. Write the process once. Claude detects the right skill and runs it when needed. Most people are still writing the same prompt 50 times. Claude Skills turns prompts into reusable software.

@rileywestreel especially with AI things move too fast for people to sit around collecting theory forever

@Adea0x Exactly! Practical beats theory every time.

@dimoflexx really like how you focused on the process here most people only talk about the model