NickHotnCool

[StockWhale @thestockwhale]

Nuclear is one of the ONLY sectors in the world right NOW that can 30X your money.

And it's still so EARLY. If you WANT to be rich, buy nuclear every single chance you can get.

Here are the top nuclear stocks to buy this week:

1. NuScale Power $SMR
2. Oklo $OKLO
3. ASP Isotopes $ASPI
4. Terrestrial Energy $IMSR
5. GE Vernova $GEV
6. Vistra Corp. $VST
7. Cameco Corporation $CCJ
8. Nano Nuclear Energy $NNE
9. X-Energy $XE
10. BWX Technologies $BWXT
11. Lightbridge $LTBR
12. Centrus Energy $LEU

ETFs include $NUKZ, $URA, $NLR, and $XLU

All my buy and sell signals in Discord @ https://t.co/GaBnArAAKe.

[Movez @0xMovez]

Anthropic pays $750,000+ a year for engineers who can build LLM architectures from scratch.

This 2-hour Stanford lecture gives you the exact pipeline LLM engineers get paid $750K/year for.

Data + architecture + scaling laws + post-training.

Bookmark it & watch today. Then read article below.

[regent0x @regent0x_]

karpathy said it best - most people paying for claude aren't actually using claude. they're typing prompts into a $20/mo chatbox

meanwhile claude code ships with built-in features that replace 90% of plugins people install and nobody knows they exist

i had 23 plugins. deleted all of them. my sessions got 3x longer and my outputs got sharper

watch the video then read the full breakdown below - you'll probably uninstall half your setup by tonight

[self.dll @seelffff]

karpathy's CLAUDE.md hit #1 on github trending.
220,000 stars. most devs still haven't read it.

it's 65 lines.
it took AI coding accuracy from 65% to 94%.

the 4 rules inside:

→ think before coding
state your assumptions. ask when unsure. never guess.

→ simplicity first
write the minimum code that solves the problem.
no abstractions nobody asked for.

→ surgical changes
don't touch code unrelated to the request.
every changed line must trace back to what was asked.

→ goal-driven execution
turn vague instructions into verifiable success criteria
before writing a single line.

that's it.
65 lines. 4 rules. 94% accuracy.

save this before everyone else does.

[rewind @rewind02]

Andrej Karpathy spent 4 minutes in an interview explaining a single idea

about how most people haven’t even started learning how to use AI

and everyone paying $20/month for a subscription.. that's not really using Claude at all

his point is that the real skill gap is the ability to build with AI

he identified 4 behaviors that break Claude Code and put them all into one file

a developer expanded it into 21 rules and published it - 82,000 stars and #1 on GitHub Trending

coding accuracy jumped from 65% to 94%

here's what these 21 rules actually are and why most developers using Claude every day have never configured them

the full breakdown is covered in the article below 👇

[Mike Investing @MrMikeInvesting]

The $NVDA CEO literally just told you what to invest in for 2026…

In 2025 he called out to buy Neocloud & Semiconductor names which have since rallied hard:

$NBIS at $21 & is now up 1,020%
$CRWV at $34 & is now up 230%
$TSM at $180 & is now up 138%

Now Jensen is calling for sustainable energy stocks:

Bloom Energy ~ $BE
Eos Energy ~ $EOSE
Plug Power ~ $PLUG
Fuelcell Energy ~ $FUEL
Enlight Renewable ~ $ENLT
Oklo Inc ~ $OKLO
Iren ~ $IREN

These names have the ability to 5-10x over the next few months.

Don’t miss out…

[Matt Giannino @MarketMovesMatt]

SEMICONDUCTOR stocks ranked by PEG ratio.

PEG under 1 = potentially mispriced growth.
PEG over 2 = danger zone.

Here's how they stack up right now:

Under 1x — cheapest growth:
• $MU ~0.4x
• $ON ~0.5x
• $MRVL ~0.7x
• $AMD ~0.7x
• $NVDA ~1.0x
• $CRDO ~1.0x

Approaching fair value:
• $COHR ~0.9x
• $AVGO ~0.9x
• $TSM ~1.1x
• $LITE ~1.3x

Getting expensive:
• $ARM ~1.5x
• $ASML ~1.7x
• $LRCX ~2.0x
• $KLAC ~2.0x

The market is pricing $NVDA as fairly valued.

$MU at 0.4x is the most mispriced 
growth story in semis right now.

Screenshot this.

[Lin @Speculator_io]

SpaceX $SPCX is planning to go public on June 12.

It's the biggest IPO in history and will instantly reprice the entire space sector.

These are the key space sectors to watch:

Launch Service Providers
$RKLB Rocket Lab
$FLY Firefly Aerospace

Space Imaging
$PL Planet Labs
$SATL Satellogic
$GSAT Globalstar
$BKSY BlackSky Technology
$SPIR Spire Global
$HAWK HawkEye 360

Satellite Communications
$ASTS AST SpaceMobile
$GSAT Globalstar
$SIDU Sidus Space
$SATS EchoStar
$IRDM Iridium Communications
$ETL Eutelsat
$TSAT Telesat
$GILT Gilat Satellite Networks
$VSAT Viasat

Space Infrastructure
$RDW Redwire Space
$LUNR Intuitive Machines
$MDA MDA Space
$VOYG Voyager Space
$YSS York Space Systems

Speciality Materials
$CRS Carpenter Technology
$MTRN Materion
$HXL Hexcel
$ATI ATI
$GLW Corning
$PKE Park Aerospace

Aerospace & Defense
$RTX RTX Corporation
$LMT Lockheed Martin
$KTOS Kratos Defense & Security
$VOYG Voyager Space
$LHX L3Harris Technologies
$NOC Northrop Grumman
$BA Boeing
$AIR Airbus
$HO Thales

Space Components
$TDY Teledyne Technologies
$APH Amphenol
$KRMN Karman Space
$RBC RBC Bearings
$PH Parker Hannifin
$AME AMETEK
$VELO Velo3D
$GHM Graham
$HEI Heico
$DCO Ducommun
$ATRO Astronics

[Jesse Cohen @JesseCohenInv]

Save and bookmark for later

[Bilaal- BD investing @bdinvestingg]

The white house is telling you exactly where to invest…save this post 👇

[RonnieV @TheRonnieVShow]

Rotation is coming...

BOOKMARK IT!

[StockWhale @thestockwhale]

J.P. Morgan JUST said that quantum computing is, "a core focus" for them and they are, "investing BILLIONS."

If you want to become a multi-billionaire this year, you need to go all-in on quantum right now - there's no waiting.

Here's the best quantum stocks to buy:

1. IonQ $IONQ
2. D-Wave Quantum $QBTS
3. Rigetti Computing $RGTI
4. Quantum Computing $QUBT
5. Infleqtion $INFQ
6. Xanadu $XNDU
7. Arqit Quantum $ARQQ
8. Horizon Quantum $HQ
9. X-Energy (honorable mention) $XE
10. ETF's include: $QTUM, $QNTM, and $CHPX

All my buy and sell signals in Discord @ https://t.co/GaBnArAAKe.

[Justin Banks @RealJGBanks]

THE SUPER CYCLE PHASES

Phase 1 was Semis:
$NVDA $ARM $AMD $AVGO $INTC

Next money rotates into the next layers:

Memory:
$MU $WDC $STX $SNDK

Photonics / Optical:
$NOK $LITE $COHR $AAOI $GLW $CIEN $MRVL

Now it’s hitting

Compute / AI Data Centers:
$IREN $CIFR $WULF $CORZ $NBIS $CRWV $P

Next

Materials / Rare Earths:
$MP $USAR $UUUU $FCX $AA

Networking:
$ANET $AVGO $MRVL $CSCO

Power / Grid / Cooling:
$VRT $ETN $GEV $CEG $SMR $OKLO

Space:
$ASTS $RKLB $LUNR $PL

Defense / Drones:
$KTOS $AVAV $ONDS $LMT

Robotics / Autonomy:
$TSLA $PATH $SYM $SERV

[StockWhale @thestockwhale]

We're up 800%+ and we're only in the 3rd inning. There will be red days ahead, which means BUY.

Here's the top 10 stocks to buy on every SINGLE dip until I tell you when to sell:

1. Micron $MU
2. Western Digital Corp. $WDC
3. Seagate Technology $STX
4. GE Vernova $GEV
5. Lam Research Corp. $LRCX
6. Sandisk $SNDK
7. Advanced Micro Devices $AMD
8. Corning $GLW
9. Kopin $KOPN
10. Arista Networks $ANET

Never miss a bull-run again - all my buy and sell signals in Discord @ https://t.co/GaBnArAAKe.

[Mike Investing @MrMikeInvesting]

The best way to get rich in 2026 is by simply owning the entire AI ecosystem…

Cloud Infrastructure ~ $GOOGL, $AMZN, $MSFT

NeoCloud ~ $CRWV, $NBIS, $CIFR, $IREN

Security ~ $CRWD

Compute ~ $NVDA, $AMD, $MU, $ASML, $AVGO, $TSM

Power & Cooling ~ $CEG, $BE, $VRT

Data ~ $MDB, $ORCL

Memory ~ $SNDK, $MU, $STX

You’ll look back on this post later this year, & will be thankful you own these names.

Save this for later…

[NickHotnCool @NickHotnCool]

@msangellynnn When are you coming to Madison, WI my queen 👑?

[Marcos Milla @MarcosMillaYT]

We’re going to experience the greatest wealth transfer over the next decade…10 stocks to own are:

1) AMD $AMD
2) NVIDIA $NVDA
3) Micron $MU
4) Taiwan Semi $TSM
5) Sandisk $SNDK
6) Google $GOOG
7) Robinhood $HOOD
8) Western Digital $WDC
9) Broadcom $AVGO
10) Microsoft $MSFT

[Om Patel @om_patel5]

SOMEONE BUILT A SINGLE CLAUDE.MD FILE THAT FIXES EVERY BAD HABIT CLAUDE CODE HAS AND IT HIT 78.5K STARS

it's based on andrej karpathy's public observations about how LLMs write code

the problem he pointed out is that claude makes silent assumptions, overcomplicates everything, writes 1000 lines when 100 would do, and sometimes deletes code it doesn't fully understand as a side effect

so forrestchang turned karpathy's critique into 4 behavioral principles and dropped them in one claude.md file:

1\ surface your assumptions

don't pick an interpretation silently. if there are multiple ways to read the task, say so. if uncertain, ask. push back when something doesn't make sense instead of just running with a bad plan

2\ minimum viable code

no speculative features, no abstractions for single use code, no "flexibility" you weren't asked for.

if you wrote 200 lines and 50 would work, REWRITE IT. ask yourself if a senior engineer would call this overcomplicated

3\ surgical changes only

don't touch code you don't fully understand, don't refactor unrelated stuff as a side effect, don't delete comments because they look unnecessary. only change what the task actually requires

4\ goal driven execution

give claude success criteria instead of step by step instructions.

karpathy's exact quote: "LLMs are exceptionally good at looping until they meet specific goals. don't tell it what to do, give it success criteria and watch it go"

one file has 78.5k STARS AND 7.4k FORKS on a single github repo.

install is one curl command that drops it straight into your ~/.claude folder

[Nainsi Dwivedi @NainsiDwiv50980]

MIT has done the unthinkable.

They built an AI that doesn't need RAG, and it has perfect memory of everything it's ever read.

It's called Recursive Language Models (RLMs).

Right now, if you want an AI to analyze a massive dataset or document, you have two bad options.

You either stuff it all into a giant context window, where the AI gets confused and suffers from "context rot."

Or you use RAG to chop it up into summaries, permanently deleting the nuance.

This paper replaces both.

Instead of forcing the AI to read a giant prompt in one pass, RLMs treat long documents as an external environment.

The AI is placed in a sandbox. The data is stored as a Python variable.

When you ask it a question, the AI doesn't just blindly try to remember the answer.

It writes code to actively search, slice, and filter the document itself.

Then, it recursively spawns smaller "sub-AIs" to read specific snippets in parallel.

It never summarizes. It never deletes data.

It preserves every single piece of original context.

The results rewrite the limits of AI memory.

It successfully handles inputs up to two orders of magnitude beyond normal context windows, scaling easily to 10 million+ tokens.

On the hardest long-context reasoning benchmarks, a standard model scored a dismal 0.04. The RLM architecture hit 58.00.

All while costing less than running a standard massive prompt.

We’ve spent the last two years burning millions in compute trying to build bigger and bigger context windows.

But the future of AI isn’t about forcing a model to swallow a giant wall of text.

It’s about teaching it how to read.

[NickHotnCool @NickHotnCool]

@iHarnoorSingh claude