After opening a $HYPE long last night.
I figured I'd let @CoInvestAI from @LiquidTrading help me think through potential TP levels.
The key resistance zone we both identified was around $68–70.
⚠️ Some useful advice from Co-Invest caught my attention:
▫️ Open Interest was high.
▫️ Funding Rate was slightly positive.
▫️ $65.5–66.0 was identified as the first major resistance zone where selling pressure could appear.
▫️ If price repeatedly failed to reclaim $66, the odds of a pullback toward $61–62 would increase.
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👇🏼 What happened next was pretty interesting.
Late at night, HYPE rallied almost exactly to $65.8, got rejected, and shortly after broke below the short term 1H trendline.
At first, I was planning to start taking profit around $68, but after seeing that reaction, I had to go back and rethink my plan.
Good reminder that a plan is important, but reacting to new information is just as important.
「 Not all robotics data is created equal 」
The robotics industry is going through a data gold rush. But the key question isn't who can collect the most data.
It's who can collect the right data.
Unlike traditional AI, robots operate under real world physical constraints, which means not all data carries the same value.
This becomes even more important for "Foundation Models". 🦾
These models don't just need large amounts of data. They need data that directly connects what a robot sees with what it can actually do.
That's why TELEOPERATION remains one of the highest quality sources of robotics data today. It creates direct observation to action pairs while helping bridge the gap between human behavior and robotic capabilities.
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「 My Thoughts on This 」
The winners in the Physical AI era may not be the companies with the most robots or the largest datasets.
They may be the ones that can most effectively collect, curate, and distribute high quality data.
That's why I find @PrismaXai's approach compelling.
Rather than focusing solely on collecting more data, they emphasize collecting the right data needed to build robots that can reliably operate in the real world.
gPrisma everyone.
Everyone is selling robotics data. Most of it isn't what you actually need.
The right kind of data depends entirely on what you're training. And that's the question almost no one asks before the check gets cut.
Everyone is selling robotics data. Most of it isn't what you actually need.
The right kind of data depends entirely on what you're training. And that's the question almost no one asks before the check gets cut.
The first episode of Doesn't Grasp by @PrismaXai is definitely worth a listen.
The first guest is Dane Kouttron, a robotics engineer who has spent more than a decade building and teaching robots to paint.
He's spent years working directly with real hardware and tackling the messy problems that come with it.
🎙️ In this Episode:
What starts as a conversation about robot art quickly turns into a deeper discussion about what's missing in today's AI and robotics landscape, why real-world experience matters, and why building capable robots is much harder than most people think.
If you're interested in robotics, AI, or how machines actually learn from the physical world, give it a listen.
A full hour, but surprisingly easy to get through. 🦾
"There is a robot art competition. The one rule: don't do pointillism."
Our first guest on Doesn't Grasp is Dane Kouttron, the engineer who's spent nearly a decade teaching robots to paint.
He's the rare guy who actually builds the machines most people only theorize about.
"There is a robot art competition. The one rule: don't do pointillism."
Our first guest on Doesn't Grasp is Dane Kouttron, the engineer who's spent nearly a decade teaching robots to paint.
He's the rare guy who actually builds the machines most people only theorize about.
In a world full of agents, knowing who has access to what is critical.
@Subzero_Labs we use our own product Latch (powered by @RialoHQ ) to track access across employees and agents alike.
Soon you can use it too.
Sign up for early access 👉 https://t.co/BtYLOQoLvH
The internet's access model was built for humans.
Passwords. Sessions. API keys. OAuth tokens. Wallet keys.
Different shapes, same assumption: if you hold the secret, you can act.
That assumption breaks when the actor is software.
Early access is open: 👉 https://t.co/BtYLOQoLvH
Introducing Liquid Social.
Your trades, P&L, streak, and bias, live on your profile.
Connect your X, build your profile, and follow the people you actually trade alongside.
Bad access control has already cost companies billions. Now everyone's deploying agents as their newest employees and handing them all-access master keys on day one.
We're building a better more secure way, powered by @RialoHQ 🧵
👉 https://t.co/cbCIjBK9tH
This is huge!
What started as an internal tool for managing agents, now it will enable you & everyone unleash agents securely!
Sign up 👉 https://t.co/P12bb0WAq7
Alright, let's try something new 👀
Recently, @LiquidTrading just launched an AI trading agent called “Co-Invest” (@CoInvestAI).
The cool part is that you can connect it directly to ChatGPT or Claude, so it fits right into the tools you're already using every day.
It's kinda like extending another model, except this one needs a quick manual setup first. Nothing complicated though. Takes about 2–3 minutes and you're good to go.
(They've got a pretty detailed guide here ➡️ https://t.co/8mfjKyhQF0)
In the example below, I connected it to ChatGPT and asked it to pick a few assets and build a $100 trading portfolio for me.
My only job?
Ask and Buy
That's pretty much it.
After playing around with it for a bit, I can already see a bunch of ways to take this further.
If you're curious how it works (it's honestly way easier than you'd expect).
I put everything in the video below.👇🏼
Alright, let's try something new 👀
Recently, @LiquidTrading just launched an AI trading agent called “Co-Invest” (@CoInvestAI).
The cool part is that you can connect it directly to ChatGPT or Claude, so it fits right into the tools you're already using every day.
It's kinda like extending another model, except this one needs a quick manual setup first. Nothing complicated though. Takes about 2–3 minutes and you're good to go.
(They've got a pretty detailed guide here ➡️ https://t.co/8mfjKyhQF0)
In the example below, I connected it to ChatGPT and asked it to pick a few assets and build a $100 trading portfolio for me.
My only job?
Ask and Buy
That's pretty much it.
After playing around with it for a bit, I can already see a bunch of ways to take this further.
If you're curious how it works (it's honestly way easier than you'd expect).
I put everything in the video below.👇🏼