Met a guy making $1.6 million a year.
Three days ago he was at a Meta conference. Told me he saw the best AI talk of his life.
Boris Cherny was on stage. Showed how the Anthropic team actually uses Claude day to day.
Boris deleted his IDE eight months ago. Now he codes from his phone.
I watched it last night. Had to pause it twice.
Not because it was hard. Because I realized I've been using Claude like a toy.
He sent me the recording. It was never published.
Posting it below.
TBC is hiring exceptional mechanical, electrical, civil, software, and field engineers in Bastrop, Las Vegas and Nashville.
Apply for the July 1 Boring Factory video tour, and hear directly from the engineers who are designing/building Prufrock and trying to Beat The Snail!
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Jim Simons turned $100 into $130 billion using math.
He just gave the entire playbook in a free 1-hour MIT lecture.
You've been picking stocks based on Reddit and vibes.
He returned 66% per year for 30 years using equations.
This is the most valuable hour you'll spend this week.
Save the video. Watch it tonight. Build the bot this weekend. Follow
@codewithimanshu
for more high-signal content that turns lectures into income.
↓
Below what nobody is telling you about this lecture.
Everything Jim Simons taught Renaissance Technologies in the 1980s is now buildable in a weekend with Claude Code.
Pattern recognition across thousands of assets. Signal detection in noise. Automated execution.
Risk management at scale. In 1988, this required a team of 50 PhDs and millions in infrastructure.
In 2026, one person with Claude + a laptop can build a working version in 7 days.
The knowledge gap between you and a Renaissance trader is now smaller than it has ever been in history. Follow
Follow
@codewithimanshu
for daily breakdowns of the AI tools that turn this knowledge into income.
↓
What this lecture actually teaches you to build.
Simons walks through the core principles that printed $130 billion:
> Find statistical edges that are invisible to humans
> Trade only when the math says yes, never on emotion
> Run hundreds of small bets simultaneously, not one big bet
> Cut losses ruthlessly when signals weaken
> Compound relentlessly across decades These aren't trading tips.
These are the foundational principles of every AI trading bot worth running. Watch the lecture. Take notes. Then turn it into a system. Follow
@codewithimanshu
for the exact Claude prompts that turn quant theory into deployed bots.
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Your weekend playbook to turn this into profits.
Friday night: watch the Simons lecture. Take notes on every signal he mentions.
Saturday: open Claude Code. Build a backtesting framework using historical price data.
Test 3-5 of his core signal ideas.
Sunday: paper trade your best signals on Polymarket, Toobit, or Alpaca. Validate before risking real capital.
Monday: deploy a small position. $100. $500. Whatever you can lose without flinching.
Compound. Iterate. Scale.
That's how a Simons-grade trading system gets built in 2026. Not over 30 years. Over one weekend. Follow
@codewithimanshu
for the exact templates and prompts to build each piece.
↓
for more high-signal content that turns lectures into income.his is the best time in history to build wealth with AI trading.
Jim Simons needed:
> A team of 50 PhDs
> $25 million in compute
> 10 years of infrastructure
> Custom data feeds nobody else had
You need:
> Claude Code
> A laptop
> 7 days of focus
> $20/month in API costs
Same math. Same principles. Same edge.
Different barrier to entry.
People who watch this lecture and build with the knowledge will compound for the next decade.
People who save it for later, will still be picking stocks based on vibes in 2027.
Save the video. Watch it tonight. Build the bot this weekend. Follow @codewithimanshu
If you’ve tried RSI, MACD, EMAs — and still lose trades…
It’s not your fault.
Those tools were never designed to follow real market moves.
Phantom Flow is different.
It shows you where smart money is actually moving — live, on your chart.
👉 Try Phantom Flow today.
There are many “simple” features that are more complicated than they look on the surface because of a cascade of dependencies.
For complex, enterprise systems, this is true of most features.
8090’s Software Factory is built to handle this flawlessly. We first help write requirements, expand and frame dependencies and then execute with a more global knowledge of the problem.
You can learn more here: https://t.co/fkfTXgdfXK
Also, I’m completely in love with our visual system. 😍
NVIDIA CEO, Jensen Huang:
"Nobody writes prompts anymore. The new job is to write and handle loops."
He calls it the shift that defines the rest of 2026.
Interview was out just yesterday.
Watch the 23 minute talk, then save the full framework below👇
SICK. Elon Musk says the ability to prompt FSD with language is coming within ~3 months.
Basically, you’ll be able to use your voice to tell the car what to do, where to go etc.
K.I.T.T. IRL 🤖
My playbook is simple: hold these stocks through year-end, and you could be looking at enough gains to buy a car and a house.
$MU ~ Micron Technology (Buy at $1,050)
$AMD ~ Advanced Micro Devices (Buy at $517)
$AVGO ~ Broadcom (Buy at $387)
$MRVL ~ Marvell Technology (Buy at $292)
$DPRO ~ Draganfly (Buy at $5.70)
$IONQ ~ IonQ (Buy at $56)
$SATL ~ Satellogic (Buy at $6.10)
I hope everyone who subscribes to me can make $1 million in gains...
THIS GUY WIRED GMKTEC EVO-X2 BOXES, A $40 USB-C BRICK AND AN RTX 3090 INTO A $6,800/MONTH OFFLINE AI FARM
it looks like a messy rack with purple cables, mini pcs and a gpu lying on the desk. but the real point is brutal: one $1,800 gmktec evo-x2 can replace a $440/month ai stack, then the rack turns it into a local backend.
the evo-x2 runs amd’s ryzen ai max+ 395 with 128gb of unified memory shared between cpu, gpu and npu. on linux, that gives up to 110gb of usable vram for local models without renting cloud compute.
that is why the setup gets weird. qwen3-coder 30b, llama 3.3 70b, deepseek v3 and 200b-class models can run from boxes smaller than consoles. the rtx 3090 becomes a 24gb side node, not the whole system.
the $40 anker nano ii 65w brick is the funny part. cheap power, tiny machines, one old gpu and local inference. the whole stack starts looking less like a workstation and more like a private ai grid.
the subscription math is ugly. claude code max is $200/month, chatgpt pro is $200/month, cursor is $20/month and gemini adds another $20. that is $5,280/year before api usage even starts.
point claude code at localhost, pull models through ollama and run agents all day: code reviews, scraping, transcription, research reports and client automations. 3 clients at $2,200/month turns this into a $6,800/month backend.
bookmark this and read the article below