Nik

i test what's trending. then I tell you what's real.

[Nik @NikChainAi]

https://t.co/Ig0HjMOtSL

[Nik @NikChainAi]

Opus 5.5 + REA is bad news for every app

REA opens any app on your computer and shows how every feature actually works, with the exact spot in the code behind every answer.

3 steps and you're in:

1. Install REA in Claude Code with one command
2. Pick an app you use and one feature you want to understand
3. Send this to Claude:

"Use REA to find out how this feature really works. Show me the proof, then build my own version and test it."

Now you know what the app is really doing under the hood. And you checked it yourself instead of taking anyone's word for it.

Start tonight with the app you trust the most.

The article below shows you how.

You might not like what you find.

[codila @0xCodila]

REA is most dangerous thing on the internet right now...

It lets your AI agent reverse engineer any app, down to the binary

If you use it correctly, you can rebuild any feature you pay for, for FREE

In this article, I show you how: https://t.co/3P1FNXiXzl

[Nik @NikChainAi]

@Glimvar The observation/inference/unknown tags are the real trick here. without them the model confidently names functions that do something completely different

[Nik @NikChainAi]

Everyone's sharing "build a trading bot in 14 minutes" tutorials.
Here's the part they skip: the brakes.
One prompt. Paste it into Claude Code and it builds the whole agent while you make a coffee.
Most bots blow up because they run one strategy in every market. A calm Tuesday and a crash day are different games.
What Claude builds:
→ Regime detection: calm, sideways, choppy or freefall, each with its own strategy
→ Hard limits: 15% max drawdown + kill switch checked before EVERY order
→ Backtest first: real data, fees included. If it doesn't beat buy-and-hold, it doesn't go live
→ Night shift: reviews losing trades while you sleep and proposes fixes. You approve, not the bot
Full prompt in the image

[Glimvar @Glimvar]

THIS PROMPT BUILDS A FULL TRADING AGENT. YOU PASTE IT INTO CLAUDE CODE AND IT SETS UP THE WHOLE BOT WHILE YOU MAKE A COFFEE

No coding. One prompt. Copy, paste into Claude Code, done.

Most trading bots blow up because they run one strategy in every market. But a calm Tuesday and a crash day are two completely different games.

What this prompt makes Claude build:

→ Market regime detection: calm, sideways, choppy or freefall, with a different strategy for each

→ Hard brakes: 15% max drawdown + a kill switch checked before EVERY order

→ Backtest first: tests the strategy period by period on real data and refuses to go live if it doesn’t beat the market in the backtest

→ Night shift: while you sleep it reviews what went wrong and prepares fixes. You approve them, not the bot

The full prompt is in the image.

[Nik @NikChainAi]

@Glimvar did you actually run this? what were the backtest results?

[Nik @NikChainAi]

@Glimvar @eng_khairallah1 i think that’s the whole point of daily re-ranking. if the regime flips, the clones start losing first and get deleted just as fast. the system pays a 1-2 day lag, not the whole account

[Nik @NikChainAi]

@simonecanciello 0 following, 209K followers, 3.7M likes, and nobody in the comments asking if he's real. That's the actual dead internet part.

[Nik @NikChainAi]

@kyronis_talks The scariest part is the first fix. A bot that never lies can still pull you in just by choosing which true things to show you. And it's a model of a perfectly rational user, which makes it worse. If an ideal Bayesian spirals, the rest of us don't stand a chance.

[Nik @NikChainAi]

Ten million people watched an MIT professor explain why your ideas matter less than you think.

He filmed the lecture once, in January 2018. Eighteen months later he was gone.

His name was Patrick Winston. He ran the MIT Artificial Intelligence Lab from 1972 to 1997. And for more than 40 years, every January, he gave the same talk. How to Speak.

It opens with one sentence:
"Your success in life will be determined largely by your ability to speak, your ability to write, and the quality of your ideas, in that order."

In that order. The man who ran the AI Lab put ideas last.

Then come the rules. Never open with a joke, the room is not ready for it.

Open with a promise of what they will know by the end. Say the key idea three times, because part of the room is always fogged out. Never read your slides. And never end with thank you.

End with what you contributed.

Now think about how your last meeting ended.

Executive coaches charge thousands to teach a slice of this. Founders pay for an MBA and still close every pitch the same way. The whole hour sits on MIT OpenCourseWare for free.

Winston died in July 2019. Most of the ten million watched it once and changed nothing.

The rules fit on a napkin. Using them in your next meeting is the entire edge.

[Lumen @lumenxbt]

https://t.co/3hWKMIBJcw

[Nik @NikChainAi]

Jev sorted 216 emails for one cent. Claude Opus charged $4.73 for the exact same job. That is 400 times cheaper, and Charlie runs it every morning.

He put both models on his collab inbox side by side. Four questions per email. What kind of email is this. What do they say about money. What do they want made. Will they actually pay.

Jev finished in 65 seconds on about 277,000 input tokens. Output is free. Opus burned 625,000 in and 24,000 out.

Then the part the thumbnail skips.
They agreed 87% of the time on what kind of email it was. Only 74% on whether the sender would pay.

And when they fought, Claude was the one inventing money. A Friday debate club invite about AI safety, no budget mentioned anywhere. Claude called it a brand deal.

Jev cannot do that. It picks from a fixed list and tells you how sure it is.

So the final setup splits the work. Jev sorts all 216. Claude only reads the 38 Jev was unsure about. Cost drops to 85 cents a run. At API prices that is roughly $1,400 a year saved on one inbox.

The expensive model is not the one that should see everything. It is the one that should only see what the cheap one could not decide. Same rule for anything that touches money.

His full test is free on YouTube. Most people will read 400x and never run the numbers on their own inbox.

[Nik @NikChainAi]

https://t.co/Ig0HjMOtSL

[Nik @NikChainAi]

SOMEONE PLUGGED CLAUDE INTO CONGRESS TRADES AND CEO BUY FILINGS.

Now it scans the entire market for one signal:

3+ executives buying the same stock.
With their own money.
On the open market.
Within 60 days.

No options. No sells. Pure conviction.

Setup: 10 minutes. Cost: ~$30/month.

Here's the full system 👇

WHY THIS HITS DIFFERENT

Regular Claude just googles. You get yesterday's news.

This setup connects Claude to Quiver Quant through MCP. Raw filings, not headlines:

→ Congress trades
→ Form 4 insider buys
→ 13F fund holdings

The same data CNBC, Bloomberg and FT quote.

PROMPT #1: THE CLUSTER BUY SCANNER

"Using Quiver's insider trading data, find every company where 3 or more different insiders made open-market purchases in the last 60 days. Exclude option exercises and sales. Rank by number of distinct buyers, then by total dollar value. Show each insider's name and title."

Insiders sell for 100 reasons.
They buy for

PROMPT #2: FOLLOW CONGRESS

"Show me the most recent Congress trades that were filed."

Now watch the dates.
CEOs must report within 2 business days.
Congress gets up to 45.

The filing is fresh. The trade often isn't.

PROMPT #3: CONNECT THE DOTS

Pick one company. Make Claude cross-check:
→ Congress trades
→ Federal contracts
→ Lobbying disclosures

Politicians trading a stock + the government signing contracts with it = a story worth reading.

PROMPT #4: FIND IT BEFORE IT'S CROWDED

"From Congress and insider data over the last 90 days, show me tickers traded by fewer than 5 distinct people, but where activity is rising compared to already-crowded tickers. Explain what data you use to define 'rising'."

That last line forces Claude to show its work instead of

REAL TALK

This is public data. Not a crystal ball.
Filings lag. Insiders sell for taxes.

The edge isn't secret info.
The edge is that 99% of people still use Claude as a chatbot.

What would you track first: Congress or CEOs? 👇

[Machina @EXM7777]

https://t.co/kf0W6P7Ney

[Nik @NikChainAi]

Claude Opus 5.5 found a 900% trade on Kalshi in a single day. The edge was one forgotten order. The whole position cost roughly 44 dollars.
Kris pointed Opus at Kalshi and had it build a scanner that reads the contract rules, not just the prices.
It found a broken ladder. The 30 year Treasury yield had just hit its highest level since 2004. Kalshi lists contracts on how high it goes by January 1st. Above 5.49% traded near 88 cents. Above 5.51% was offered at 5.
Two basis points apart. Eighty cents apart in price.
Then the part most traders skip. He tried to prove himself wrong. The same contracts had traded at 46 to 72 cents the day before. The order book showed the source: one single 100 contract order. Not a market view. A stale quote nobody had pulled.
He read the settlement rules word for word. Placed a small test order. Confirmed the fill. Then bought 444 contracts across three strikes at about 10 cents each.
Next day the market caught up. Those contracts now trade between 65 and 83.
The lesson is not that AI prints money. Mispricings like this are small, rare and gone within hours, and the book behind them is thin.
The lesson is that the evidence was sitting in public before he clicked. On chain or on Kalshi, the check is the edge.
His full breakdown is free on YouTube. Everyone will quote the 900%. Almost nobody will quote the 44 dollars.

[Nik @NikChainAi]

https://t.co/Ig0HjMOtSL

[Nik @NikChainAi]

@mattyp The editor is the byproduct. The feedback loop is the real product.

[Nik @NikChainAi]

Jev + Opus 5.5... I still can't figure out why everyone films the speed and nobody films the off switch.

Miles calls his build the most powerful AI trading bot ever. He split it in two. Claude is the brain. It designs the strategy. Jev is the hands. It makes every execution call on Hyperliquid in about 400 milliseconds, for a fraction of a cent each time.

That split is smart. You do not pay frontier prices for a yes or no.
On screen it looks unstoppable. Buys, sells, a live feed that never sleeps.

Then, near the end, he gets to the part most viewers will skip. Max position size. A daily loss limit. A kill switch. Manual approval on certain trades, with a ping to his iPhone every time one opens or closes.

Read that list again. Four rules, and every one of them exists to stop the bot.

That is the real architecture. Claude proposes. Jev picks from the options it is handed. Code decides whether anything is allowed to happen. A confident answer never counts as permission.

He runs it on a VPS too, because the moment your laptop sleeps, the bot stops making decisions.

Most people will copy the speed. The part that keeps the account alive is the part that says no.

His full setup is free on YouTube. The last two minutes matter more than the first seventeen, and almost nobody gets that far.

[Avid @Av1dlive]

everything you need to start building with jev, in one article.

code, architecture, diagrams... everything you need to follow the build and make it your own. https://t.co/0hy2KdKUNI

[Nik @NikChainAi]

GPT-6 Astra can build a $10,000 a month store now. That is the title. The video never shows a single dollar of revenue.

Calvin walks through the whole build. A free Shopify trial. AutoDS to find trending products and ship them. Astra driving the browser, the Shopify CLI and every design change.

It picked three products that fit together. A magnetic phone grip, a microphone, an LED light. A starter kit for creators.

Then the real work. GPT Image 2.5 rendered every product angle.
Seedance made the hero videos. A Dribbble shot set the theme.

His own line: this is the exact same product, but it looks like this.

That sentence is the entire business model. The product never changed. Only the photos did.

And on screen it works. The page looks like a brand that just raised a round. Add to cart, checkout, done.

But if one afternoon of prompts makes a dropshipping store look premium, it does the same for every store selling that grip. A premium look stops meaning anything once everyone has it.

What AI did not make free is being found. Rankings, reviews, a reason for a stranger to pick you over an identical page. That is where Astra actually earns its keep. It just does not film as well as a scroll animation.

His full build is free on YouTube. Most people will copy the store and never ask who is going to visit it.

[Sarvesh Shrivastava @bloggersarvesh]

https://t.co/icvzZu7I7p

[Nik @NikChainAi]

@EXM7777 Impressions without conversions is just a prettier screenshot.

[Nik @NikChainAi]

@ClaudeDevs Claude making https://t.co/o2fZZHi6Xb faster is the best dogfooding I've seen.

[Nik @NikChainAi]

Laya vs Jev. The open model won every speed test on the internet this week. Then someone checked the answers.

One developer, Nandha Kishore, rebuilt the Jev idea in the open in about 15 hours. 421 million parameters, Apache 2.0, under a gigabyte of memory. No API key. Runs on a laptop.

The demos exploded. Snake, 43 points to 1. Tetris, maze, breakout. Six runtimes in 72 hours, from Apple's neural engine to a browser tab.
Rithesh went looking for the part nobody filmed.

On the most careful test he found, same labels, 600 held out items: Jev 87%, Laya 72.2%. A Japanese business benchmark: 97.6 against 36.9.

And the launch number that beat Jev, 0.766? That came from a checkpoint fine tuned on the benchmark's own training split. The base weights you download scored 0.362. Both numbers are published. Only one went viral.

So out of the box, the paid model is better.

But here is the door that only opens on one side. Fine tune Laya on about 1,000 labeled examples, on two free GPUs, and one banking task jumped from 51.3% to 79.4%. You cannot do that to a closed API.

The smartest setup he found used both. Local model first, escalate only the low confidence cases. Paid accuracy at 1.8 times the speed.

His breakdown is free on YouTube. Most people will keep sharing the Snake clip.

[darkzodchi @zodchiii]

https://t.co/bmEhsVLyXx

[Nik @NikChainAi]

@elonmusk Harness work quietly doing more than model upgrades again. Less scaffolding nonsense, better output, same weights.

[Nik @NikChainAi]

@zodchiii Table I is the whole argument in one image. $1,716 a month versus $1 for the same decision. Everything else is implementation detail.

[Nik @NikChainAi]

Jev can now run a 24/7 trading loop that anyone can install in ten minutes. That is not the interesting part. The interesting part is what Lewis admitted at the end.

He walks through the whole build. Claude writes the harness, Alpaca handles the paper account, and the loop fires a decision every 395 milliseconds. Long, short, wait, or move the stop, each with a confidence score attached.

It works. It trades. The dashboard looks incredible.

Then he says the strategy running inside it was pulled out of thin air.
Generic. Made up on the spot so you can watch something move on screen.
That line is the whole video.

The plumbing for high frequency trading is now a copy paste job. The thing that decides whether it makes money is still the part nobody can hand you in a prompt.

And it goes further. Jev only gets fast when you feed it near binary state. Yes or no. Up or down. Which means someone has to turn a messy market into clean boolean questions before the model ever sees it.

That someone is you. The model just answers.

Every crypto tool rushing to bolt this on is shipping a harness, not an edge.
Fast plumbing around a made up rule is just a quicker way to be wrong.

His build is free on YouTube. Everyone will copy the loop. Almost nobody will build the strategy.

[Akshay 🚀 @akshay_pachaar]

https://t.co/haL8IGhx3h