Illiquid
If you only have around $20 to invest, I think fractional SPY shares on IBKR are a thing...
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If you only have around $20 to invest, I think fractional SPY shares on IBKR are a thing...
What’s your highest conviction stock under $20 per share? tweet
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Bourbon Capital (@BourbonCap) on X
What’s your highest conviction stock under $20 per share?
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Illiquid
Largest oil find in SE Asia in the last 20 years.
U.S. producer Murphy Oil's latest estimate of recoverable oil reserves in Vietnam's Hai Su Vang potentially positions it as the largest oil find in Southeast Asia over the last two decades, according to an analysis by energy consultancy Wood Mackenzie released on Wednesday.
https://t.co/A1B8BrUihr
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Largest oil find in SE Asia in the last 20 years.
U.S. producer Murphy Oil's latest estimate of recoverable oil reserves in Vietnam's Hai Su Vang potentially positions it as the largest oil find in Southeast Asia over the last two decades, according to an analysis by energy consultancy Wood Mackenzie released on Wednesday.
https://t.co/A1B8BrUihr
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App Economy Insights
Google just overtook Apple as the world's 2nd-largest company. https://t.co/sb0fM2P28Y
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Google just overtook Apple as the world's 2nd-largest company. https://t.co/sb0fM2P28Y
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Illiquid
$ACMR
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$ACMR
> China Semiconductor Industry Association’s Statement on NVIDIA H200: “The conviction and determination to adhere to the localization path must never waver”
• Wei Shaojun, Vice Chairman of the China Semiconductor Industry Association, emphasized that the United States is displaying a capricious attitude by repeatedly alternating between “easing” and “pressure” regarding advanced semiconductors, and that China’s semiconductor industry must maintain a high degree of vigilance in response. He pointed out that it remains unclear whether the recent so-called “regulatory easing” represents a genuine signal of cooperative intent or a new strategy aimed at disrupting China’s development pace and lowering its guard. Accordingly, he stated that the industry must not be deceived by superficial changes, and that the conviction and resolve to steadfastly pursue localization in core areas such as advanced processes must never be shaken. tweet
X (formerly Twitter)
Illiquid (@illyquid) on X
$ACMR
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Brady Long
If you want to grow on X you'll learn from posts, not scroll + forget.
I showed this guy how I built an audience in 60 days for the 3RD TIME. But he always "forgets."
Growing on X is easier than it used to be, but you need the right tools.
Here's how to NOT be this guy👇 https://t.co/1DRzhGnz8q
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If you want to grow on X you'll learn from posts, not scroll + forget.
I showed this guy how I built an audience in 60 days for the 3RD TIME. But he always "forgets."
Growing on X is easier than it used to be, but you need the right tools.
Here's how to NOT be this guy👇 https://t.co/1DRzhGnz8q
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God of Prompt
Everyone’s hyping Ralph but overcomplicating it. Let me break it down simply.
Ralph is an automated assistant that codes for you while you sleep.
You give it a to-do list, it works through each task one by one, and stops when everything’s done.
Here’s the concept:
Normally when you use AI to code, it’s a back-and-forth conversation.
You ask, it builds, you check, you ask again.
Ralph removes you from that loop.
You define what “finished” looks like upfront, then the AI works through tasks automatically until they all pass.
Why it actually works:
Each time Ralph runs, it starts with a clean slate (so it doesn’t get confused by old context). But before working, it reads notes from previous rounds:
- What was already built
- What was learned along the way
- What’s still left to do
It’s like hiring someone new every morning who reads yesterday’s handoff notes before starting work.
If you want to try it:
1. You need an AI coding tool (like Amp, Claude Code, or Cursor)
1. You write out your feature as small tasks. Not “build a login system” but broken down like:
- Add email and password fields
- Check if email is valid
- Show error message when login fails
1. You run the script and let it loop through each task
1. Ralph marks each task complete when it passes your tests, then moves to the next one
Real tips that actually matter:
Each task should take under 5 minutes for the AI. If you’re writing a task description longer than 3 sentences, it’s too big. Split it.
Your acceptance criteria needs to be stupidly specific. “User can log in” will fail. “Email field exists, password field exists, submit button triggers auth function, error displays on wrong password” will work.
Ralph gets smarter as it goes. By task 10, it’s learned patterns from tasks 1-9. The progress.txt file compounds knowledge. Don’t delete it mid-session.
Watch the first 3 iterations manually. You’ll catch bad patterns before they multiply across 20 commits.
Don’t use Ralph for: exploring ideas, major rewrites, or anything touching payments/security. It’s for well-defined feature work where you already know what “done” means.
Ryan’s team shipped 13 tasks in about an hour of compute time. Each iteration ran 2-5 minutes. That’s the realistic expectation.
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Everyone’s hyping Ralph but overcomplicating it. Let me break it down simply.
Ralph is an automated assistant that codes for you while you sleep.
You give it a to-do list, it works through each task one by one, and stops when everything’s done.
Here’s the concept:
Normally when you use AI to code, it’s a back-and-forth conversation.
You ask, it builds, you check, you ask again.
Ralph removes you from that loop.
You define what “finished” looks like upfront, then the AI works through tasks automatically until they all pass.
Why it actually works:
Each time Ralph runs, it starts with a clean slate (so it doesn’t get confused by old context). But before working, it reads notes from previous rounds:
- What was already built
- What was learned along the way
- What’s still left to do
It’s like hiring someone new every morning who reads yesterday’s handoff notes before starting work.
If you want to try it:
1. You need an AI coding tool (like Amp, Claude Code, or Cursor)
1. You write out your feature as small tasks. Not “build a login system” but broken down like:
- Add email and password fields
- Check if email is valid
- Show error message when login fails
1. You run the script and let it loop through each task
1. Ralph marks each task complete when it passes your tests, then moves to the next one
Real tips that actually matter:
Each task should take under 5 minutes for the AI. If you’re writing a task description longer than 3 sentences, it’s too big. Split it.
Your acceptance criteria needs to be stupidly specific. “User can log in” will fail. “Email field exists, password field exists, submit button triggers auth function, error displays on wrong password” will work.
Ralph gets smarter as it goes. By task 10, it’s learned patterns from tasks 1-9. The progress.txt file compounds knowledge. Don’t delete it mid-session.
Watch the first 3 iterations manually. You’ll catch bad patterns before they multiply across 20 commits.
Don’t use Ralph for: exploring ideas, major rewrites, or anything touching payments/security. It’s for well-defined feature work where you already know what “done” means.
Ryan’s team shipped 13 tasks in about an hour of compute time. Each iteration ran 2-5 minutes. That’s the realistic expectation.
Here's a visual of how the Ralph system works.
GitHub repo is at https://t.co/q8KLz71EVa https://t.co/xyZGQXMM8K tweet