Adea

1.4K posts

Adea banner
Adea

Adea

@Adea0x

crypto twitter survivor girl | ai era now

Katılım Nisan 2022
167 Takip Edilen240 Takipçiler
Sabitlenmiş Tweet
Adea
Adea@Adea0x·
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 ♡
Adea tweet media
English
2
1
39
3.6K
Adea
Adea@Adea0x·
@0xSlyth meanwhile the charts are watching themselves now..
English
0
0
0
5
0xSlyth
0xSlyth@0xSlyth·
A 19 YEAR OLD BUILT A QUANT TRADING WEBSITE THAT THINKS LIKE A HEDGE FUND most traders stare at charts he built a system that watches the market for him Here's what it does: -> Tracks real time market data across multiple assets -> Identifies high probability trading setups automatically -> Analyzes order flow and market structure -> Backtests strategies before execution -> Generates quant based trading insights in seconds -> Visualizes complex data through a beautiful UI the crazy part? he didn't build another TradingView clone he built a quant trading machine while everyone else is looking for the next 100x trade His system is: -> Calculating probabilities -> Finding statistical edges -> Filtering bad setups -> Monitoring the market 24/7 -> Preparing the next opportunity before it appears on your timeline the edge isn't predicting the future it is building systems that understand the present better than humans can the next generation of traders won't spend all day watching charts they will spend their time building systems that never stop watching them Bookmark this before quant trading becomes everyone's unfair advantage
0xSlyth@0xSlyth

x.com/i/article/2076…

English
14
0
28
407
Adea
Adea@Adea0x·
1 YOUTUBE VIDEO → 100 TIKTOKS IN ABOUT AN HOUR? The workflow uses Opus Pro and Repurpose. io to turn one long YouTube video into dozens of short clips. Instead of editing every Short manually, it generates clips with captions and even scores each one by its viral potential. The workflow: copy a YouTube link paste it into Opus Pro generate 10 AI Shorts sort them by virality score send everything to Repurpose. io publish across TikTok and other platforms One screen shows the process starting from a single YouTube URL. A few minutes later, Opus Pro returns 10 captioned Shorts, each with its own virality score. Repeat the workflow a few times and the creator claims you can produce around 100 TikToks in an hour. The interesting part isn’t AI editing. It’s using one long-form video as the source, then repeating the same pipeline until one recording becomes dozens of pieces of content.
Misato@misat0x

x.com/i/article/2076…

English
4
0
12
245
Adea
Adea@Adea0x·
A BRAND NEW YOUTUBE AUTOMATION CHANNEL. DAY 1 STARTS WITH $0. The creator is documenting a faceless YouTube channel built from scratch with almost no budget. He skipped finance, AI and motivation. Instead, he picked curiosity: Shorts built around simple questions people immediately want answered. Examples: Why do we get butterflies when we’re nervous? Why do we yawn? Why does this happen? The workflow: research YouTube, TikTok and ChatGPT pick one question use ChatGPT for the script add stock footage or AI visuals publish as YouTube Shorts repost to TikTok and Facebook He also chose Shorts over long-form. Long videos usually pay more per view, but Shorts are faster to produce alone and can be reused across three platforms. The interesting part isn’t the niche. It’s taking one question, turning it into a template, and repeating it over and over.
Ostap@0xOstap

x.com/i/article/2074…

English
12
2
51
4.6K
Adea
Adea@Adea0x·
@zarqXBT this explains why long running agents drift over time
English
0
0
1
13
zarq
zarq@zarqXBT·
This paper completely changed how I think about agent memory: Current agents forget everything between sessions -> this framework treats memory as durable state -> audit every decision -> expire stale memories before they trigger bad actions -> govern what the agent remembers. That loop is why enterprise agents keep hallucinating from outdated context while this architecture does not. Persistent state + memory governance + auditability + provenance tracking - that's the missing layer. Read and save it, then rethink how your agents handle long-term memory.
zarq tweet media
ami@ami10iv

x.com/i/article/2072…

English
6
0
19
276
Adea
Adea@Adea0x·
@ironmind26 the best time to experiment is before everyone else does
English
0
0
0
4
Iron Mind
Iron Mind@ironmind26·
IGNORING AI IN 2026 IS LIKE IGNORING BTC IN 2011. YOU ALREADY KNOW HOW THAT STORY ENDS. In 2011 Bitcoin was $3. The people who called it a toy watched it hit $69,000 eleven years later. Here's what's happening right now that most people will miss again. A 24-year-old in Lisbon makes $23,000 a month with Claude and a phone. A guy in Austin replaced a 6-person ops team with 5 agents for $200/month. A designer in Portugal built 47 Canva templates in one weekend and still collects $6,000 every month two years later. None of them had special skills. None of them raised funding. None of them had an audience when they started. They just moved while everyone else was still debating whether AI was real. In 2011 the argument against Bitcoin was: "it's not backed by anything." In 2026 the argument against AI is: "it can't replace real human work." Both arguments missed the same thing. The technology doesn't need to be perfect to make early movers rich. It just needs to be useful enough that someone builds a business on top of it before the crowd arrives. The crowd is coming. It always does. The question is which side of that wave you're on when it hits. BTC at $3 required $3 and conviction. AI in 2026 requires $20/month and one weekend of learning. The barrier has never been lower. The window has never been shorter. Most people will read this, agree with it, and do nothing. That's exactly what happened in 2011. Don't be that person twice. Save this. Start this week.
Bober_smart@Bober_smart

x.com/i/article/2055…

English
2
0
6
134
Adea
Adea@Adea0x·
@RrichPRMR makes you think how much good work just disappears because nobody records it
English
1
0
1
12
Rich
Rich@RrichPRMR·
Swimming took money from her for 15 years. Last month it finally paid her back — $5,000. The only thing that changed costs $40. Run her numbers first, because they hurt. Fifteen years of pool fees, suits, travel, coaching: easily $60,000 out the door. Return: zero dollars and a shelf of medals. That's the deal every swimmer silently accepts. Then she noticed something. Swimmers are the most invisible athletes on earth. Training happens at 6am. Underwater. In chlorine. The least filmable place in sports — which means the most spectacular footage in sports has never been seen by anyone. So she propped one phone against a kickboard and let it run. Her routine didn't change by a minute. She swims her sets; AI works the second shift. By the time she's out of the water and pulling off her cap, the morning's footage is already becoming the week's clips — she checks what it made over breakfast, taps approve, and moves on with her day. She hasn't opened an editor once. 40K views. Then 300K. Then a morning where one clip hit 2 million while she was mid-practice. The money followed, from three directions. A goggles brand she'd worn since juniors wrote first — now a monthly deal worth about $2,200. Her own club pays $1,600 to license clips for their learn-to-swim program, because parents kept mentioning the videos at sign-up. Creator payouts and affiliate links on the gear in frame add $1,200 more. $5,000 a month. From training she was doing anyway. Tools: $40. Now the part that should sting. Whatever you did this morning — the run, the session, the craft you've spent years on — it's already gone. Nobody saw it. And somebody with half your discipline filmed theirs. The talent was never missing. The witness was. Full playbook below👇
Rich@RrichPRMR

x.com/i/article/2071…

English
2
0
8
196
Adea
Adea@Adea0x·
@Damir_Akaza it’s crazy how loud and chaotic it looked, yet billions of dollars moved through that system every day
English
1
0
1
15
Damir Akaza
Damir Akaza@Damir_Akaza·
I never traded on the floor. But the first time I walked into a trading pit in Chicago, everything changed. I remember them throwing paper into the air. Complete chaos. I asked: "What's going on?" He said: "This is what they do every day. This is their job." I was thrilled. I'd never seen anything like it. My eyes went wide. Everyone looked like they were in a riot, but there was some kind of order to it. And I knew I had to be part of it. That same day I went back to my job and handed in my notice. No contacts. No plan. I just knew I needed to be on the floor. Trading ran on open outcry: whoever paid the most, whoever sold the lowest. Prices were set by shouting and hand signals. Trades were written on cards and handed to the clerks. A single day could make you a fortune or wipe you out completely. This rare documentary shows what the market looked like while there were still real people in it, how these men lived, what broke them in the pits, and how computers took that world away from them.
Damir Akaza@Damir_Akaza

Pace Morby has built a portfolio of 2,181 properties worth $450 million without ever applying to a bank or going through a single credit check. "I'm just taking over the existing payments, and the debt stays on the seller. It's called Subject-To." One of the most well-known investors in the US explains why debt and ownership are two completely different things, how the due-on-sale clause actually works, and why banks end up looking the other way. "The seller is in foreclosure, I catch up the arrears, hand him a couple thousand, and he transfers the deed to me. The mortgage stays in his name. I never applied to the bank." "I've done this dozens of times. The bank can send a letter, but in the end they say: alright, just keep making the payments." Watch why the bank can't do anything about it, even once it finds out ↓

English
6
1
18
458
Adea
Adea@Adea0x·
@0xfuckpoverty that quote is gonna stay relevant for a long time
English
0
0
0
20
broke boy
broke boy@0xfuckpoverty·
"- You basically just said this technology is too powerful to be in the hands of a few private companies, and it's too powerful to be in the hands of a government. - That is, unfortunately, the situation we're in." The CEO of Anthropic just dropped the ultimate AI paradox. We are building a technology that no single entity on Earth can safely control - and neither the Pentagon nor Silicon Valley knows how to solve it. The race for AI dominance has reached a terrifying breaking point. Full breakdown of this interview below. Make sure to bookmark this! 👇
English
6
0
17
1.4K
Adea
Adea@Adea0x·
@kv1nsiii i like the idea of treating agents like teammates instead of smarter prompts
English
0
0
0
32
kvinsi
kvinsi@kv1nsiii·
I STOPPED PROMPTING CLAUDE FABLE 5 AND HIRED IT INSTEAD. HERE'S THE ACTUAL CONTRACT I USE TO RUN IT LIKE AN EMPLOYEE Most people manage Fable 5 by typing instructions one at a time That's not management. It's like babysitting a very expensive intern Fable 5 can run for hours without you, create its own subagents and clear a week's worth of backlog overnight. If left unmanaged, it's an expensive way to produce confident nonsense. If you put it on a contract, it's the closest thing to a full-time hire that you can run for a few dollars a day Here's the system: Five files. Set up in under two hours The contract isn't suggestions. They're hard stops > Never touch wallet keys or .env files without approval > Never claim a task is finished based on its own read of the output > Never invent an API endpoint or config value that isn't in the repo > Never edit a test to make something pass that's not a fix, that's fraud One rule that sets the tone for everything: An agent that grades its own homework is an agent that gives itself a raise Nothing in this system checks its own work. Not once Four hires, not one. A cheap model reads the overnight logs. Fable 5 determines the most valuable task and creates the work order. A cheap worker then carries out the task. A new Fable instance only reviews the specification and the difference The deterministic script casts the final vote. Every time A decision made by Fable and a line of code typed by Fable have different price tags Only pay the higher price for the decision
Gipp 🦅@gippp69

x.com/i/article/2075…

English
14
0
30
1.1K
Adea
Adea@Adea0x·
@misat0x translation is easy now, but making it feel native is still the hard part
English
1
0
1
27
Misato
Misato@misat0x·
MrBeast reportedly spends tens of millions every year making his videos feel native in other languages. This guy just showed the small-creator version of that strategy: take one video dub it into 40+ languages keep the original voice sync the lips The demo is impressive. But the interesting part is not AI dubbing. It is what happens after the dub. Most creators treat a finished TikTok as one post. The smarter move is to treat it as a source asset. One useful English tutorial can become a version for Mexico and Colombia. Then Brazil. Then another market where the same problem exists, but the content still feels imported. Not by throwing subtitles on it. By rebuilding the hook, slang, examples, captions, and on-screen text until a viewer feels: this was made for me. That is the opportunity. The new creator advantage is not making more videos. It is giving the good ones more than one chance to win. More audience. More feedback. More ways to make money. I mapped out the full workflow: which market to test first, how to localize the script, dub it with ElevenLabs, fix the visuals, and turn the winners into a repeatable system. The tools just made global distribution cheap. Making content that actually feels local is still hard. Full article below 👇
Misato@misat0x

x.com/i/article/2076…

English
12
0
32
1.1K
Adea
Adea@Adea0x·
@0xOstap honestly, waiting for the perfect setup is just procrastination most of the time
English
0
0
0
44
Ostap
Ostap@0xOstap·
Everyone talks about the newest AI models. But almost nobody talks about the hardware that's making AI accessible to everyone. The Mac mini has quietly become one of the best value machines you can buy in 2026. It's compact, powerful, energy-efficient, and more than capable of handling AI workflows, coding, content creation, and everyday productivity. You no longer need a massive desktop or a $3,000 setup to build with AI. As AI gets faster and cheaper, the biggest advantage won't come from having the most expensive hardware. It will come from actually using the tools while everyone else is still waiting for the "perfect" setup.
English
9
9
65
2.4K
Adea
Adea@Adea0x·
@Dexonfxf the timing couldn’t have been better. they picket the bottleneck instead of another ai app
English
0
0
0
24
Dexonx
Dexonx@Dexonfxf·
THREE 21-YEAR-OLDS DROPPED OUT OF COLLEGE… NOW THEY’RE BUILDING ONE OF THE FASTEST-GROWING AI COMPANIES IN THE WORLD While most students were studying for exams… Brendan Foody, Adarsh Hiremath, and Surya Midha were building Mercor. An AI startup that wants to reinvent hiring forever. Here’s how they did it 👇 In 2023, the three childhood friends launched Mercor with one simple idea: Hiring shouldn’t take weeks. Instead of recruiters spending hours reviewing resumes and scheduling interviews… AI could do it in minutes. Mercor automatically screens candidates, conducts AI-powered interviews, evaluates skills, and matches companies with the best talent. But then something unexpected happened. As AI labs like OpenAI and others scaled rapidly, they needed thousands of highly skilled professionals - engineers, doctors, lawyers, scientists - to help train frontier AI models. Mercor became the platform connecting that talent with the world’s leading AI companies. The growth was explosive. • Founded in 2023 • Bootstrapped to 7-figure ARR from their dorm rooms before raising major funding • Raised $3.6M seed funding • Then $32M Series A • Then $100M Series B at a $2B valuation - all while the founders were just 21 years old. Today, Mercor works with many of the world’s leading AI companies and has become one of the fastest-growing startups in the AI ecosystem. The biggest lesson? They didn’t build a better résumé builder. They built the infrastructure that helps power the AI economy. Sometimes the biggest opportunity isn’t competing with AI… It’s building the systems AI companies can’t scale without. Follow @Dexonfxf for more real AI founder stories 🚀
Dexonx@Dexonfxf

x.com/i/article/2074…

English
28
46
571
26.1K
Adea
Adea@Adea0x·
@misat0x shipping even a few real projects teaches more than collecting another certificate
English
1
0
1
16
Misato
Misato@misat0x·
AI engineering feels like the internet looked at a whole career and said: yeah, the problem is the 43 tiny steps between “I want to work in AI” and “someone will actually hire me.” And this six-month roadmap is basically a tour of those steps getting handled one by one. It is easy to think the path starts with heavy math, model training, and a 400-hour course before you are allowed to build anything. But most AI engineering work is much more practical. Learn enough Python to debug. Understand ML without becoming a researcher. Call a real model through an API. Build RAG on real documents. Deploy a project. Turn that project into a resume bullet. Target the jobs that actually match your level. That is the part I care about. Not the certificates. The portfolio. Python gives you the base. APIs connect you to the models. RAG brings in real data. Claude can explain the errors. Codex can review the code. Deployment turns the work into something a hiring manager can actually click. The goal is not to become a frontier researcher. It is to take a model, real data, and a product problem, then turn them into something useful. Still early. Still noisy. But this is the right direction: less AI career cosplay, more shipped projects. Full article below👇
MIKE@mikenevermiss

x.com/i/article/2075…

English
14
2
43
586
Adea
Adea@Adea0x·
@misat0x the context handoff was honestly the worst part, glad thet friction is finally disappearing
English
0
0
0
7
Misato
Misato@misat0x·
the annoying part of using two AI coding agents is not using two AI coding agents. it is explaining the same repo twice. you build something in Claude Code. then you want Codex to check it. so you switch terminals, point it at the repo, explain what changed, explain what you are worried about, and reconstruct enough context for the review to be useful. most of the time, I decide it is not worth breaking my flow. OpenAI’s new Claude Code plugin removes that middle part. review the diff. make Codex challenge your caching or retry logic. hand it a failing test while you keep working. or turn on a review gate before you ship a risky change. that is the part I care about. not “two agents working together.” the second opinion becomes part of the workflow instead of another thing I mean to do later. Claude Code keeps the implementation moving. Codex is there to push back. I broke down the setup, the commands worth using, and where I’d actually use each one👇
Misato@misat0x

x.com/i/article/2076…

English
15
1
37
1.3K
Adea
Adea@Adea0x·
@100F_exe not much, once the context files were in place, it mostly became small iterations instead of rewrites
English
0
0
0
42
100F.exe
100F.exe@100F_exe·
@Adea0x How much tweaking did it need after the first prompt?
English
1
0
1
48
Adea
Adea@Adea0x·
CLAUDE CODE + GOOGLE STITCH = A $10,000 WEBSITE. FOR FREE? Stitch generates the UI. Claude Code reads files like design.md and turns the mockup into a working project. The workflow: > describe the site in Stitch > generate the design > open it in Claude Code > build the full site > edit it with plain English Need another section, different copy, new colors or animations? Describe the change and Claude updates the code. Stitch handles the interface. Claude handles the implementation. One prompt creates the design. The next turns it into something you can actually ship.
Adea@Adea0x

x.com/i/article/2062…

English
21
17
103
4.4K
Adea
Adea@Adea0x·
@RrichPRMR haha the hard part isn’t getting ideas anymore, it’s choosing which one to build first
English
0
0
1
46
Rich
Rich@RrichPRMR·
@Adea0x this is dangerous for people with too many website ideas
English
1
0
1
50
Adea
Adea@Adea0x·
@0xOstap makes you wonder how many ideas never got built just because they were too expensive before
English
0
0
0
50
Ostap
Ostap@0xOstap·
@Adea0x it’s insane that you can create $10000 website for free
English
1
0
1
48
Adea
Adea@Adea0x·
@Damir_Akaza that mutual fund example says more about investor psychology than investing itself
English
1
0
0
3.2K
Damir Akaza
Damir Akaza@Damir_Akaza·
One of the best hedge fund managers of the last 30 years, Joel Greenblatt, earned over 40% a year for two decades. He found a serious flaw in ordinary indexes like the S&P 500 and the Russell 1000. Because of it, the indexes automatically buy too much of the overpriced stocks and too little of the cheap ones. In this video he reveals exactly what that flaw is. "The best mutual fund of the 2000s returned 18% a year. The average investor in that same fund lost 11% a year" "Why?" "Because every time the fund started to lag, people left. Every time the market fell, people left. And every time the fund did great, people piled in right after the run." Watch how the individual investor can beat the big players ↓
Damir Akaza@Damir_Akaza

A software developer, Mike Osinski, who built the models for mortgage derivatives and nearly brought down Wall Street. "I built the model for CDOs, where 1,000 mortgages meant about 500,000 pairwise correlations. Instead of calculating them, I just set the same value for all of them, 0.6. That was the single number plugged into the most complex mathematical model on the market." It was this model that let Wall Street inflate a market where no one looked anymore at whose debts these were or whether they'd ever be paid back. "We took the worst, riskiest mortgages and turned them into something rated AAA. Everyone knew what it was, but nobody cared as long as the bonuses kept coming" Banks handed out subprime mortgages en masse because they earned many times more on them. The government openly encouraged it. When a fund made money, the managers took a big share of the profit. When a fund lost everything, it was the clients' money. The rarest and most unique footage of the quants explaining why the system works exactly this way and how we arrived at the current reality of the market.

English
22
26
350
1.2M