Jeonlees
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In the future, when buying stocks, you might not even need to open your brokerage app.
German broker Scalable Capital has just launched a new feature: over 1 million of its clients can directly check their holdings, analyze portfolios, and even execute trades through ChatGPT and Claude. It manages client assets exceeding €60 billion.
Previously, when you saw Nvidia plunge, you had to open your broker, search the ticker, enter the quantity, and confirm the price. Those extra tens of seconds sometimes really helped stop an impulsive trade. Now, you just tell the AI "help me add to my position," and there’s almost no buffer between emotion and execution.
Scalable’s CEO believes that AI could help users achieve better returns in the long run, but he also admits this hasn’t been proven yet.
On the flip side, with a shorter trading entry point, it’s easy to imagine users chasing rallies, adding to positions, and repeatedly adjusting.
This is just a personal market observation, DYOR.
Junzheng's dark pool trading today is honestly a bit of a pity.
The public subscription was nearly 700 times oversubscribed, so market expectations were already high. The issue price was HKD 100, and when the dark pool opened, there were indeed buyers pushing the price up, reaching a high of 109.3, which means a maximum profit of HKD 930 per lot.
After hitting 109.3, the price dropped all the way down to a low of 97.5, a difference of 11.8%. If you didn't sell at the high or hesitated even a little, your unrealized gains could quickly turn into unrealized losses. In the end, the price returned to just over 99, basically meaning all the effort was for nothing.
This is the most noteworthy aspect of Junzheng today: it’s not that no one was trading, but that there were traders, yet the follow-up buying from above was clearly insufficient.
Also, the A-shares have been falling these past two days. Junzheng A-shares dropped more than 3 points today. Previously, many people used the "large A/H discount" as a core logic for subscribing to Junzheng's new shares, but with A-shares falling on their own, the discount advantage on the H-shares side naturally shrinks.
There is another factor many tend to overlook: the A-shares.
When I wrote about Junzheng before, one point I focused on was the A/H discount.
On August 17, Junzheng A-shares closed at RMB 155.39, which, when converted at the exchange rate, was roughly equivalent to about HKD 181. The H-shares were listed at HKD 100, so the discount was indeed very exaggerated, leading many to believe there should be a substantial safety margin below HKD 100.
But here’s the problem—the H-shares hadn’t officially listed yet, and the A-shares had already fallen.
Today, Junzheng A-shares closed at RMB 133.75, down 3.42%. From August 17’s 155.39, that’s nearly a 14% drop in one week.
At the current exchange rate, RMB 133.75 roughly corresponds to about HKD 156.
So, of course, HKD 100 for the H-shares is still cheap, with a discount of about 35%, but it’s no longer as exaggerated as the over 40% discount at the start of the subscription.
I think this is very important.
Many people’s logic for buying Junzheng isn’t really that HKD 100 is very cheap, but rather, "The A-shares next door are at HKD 170-180, so if I buy H-shares at HKD 100, how can I lose?"
Now that the A-shares are dropping, this anchor naturally moves down as well.
This is just my personal market observation, DYOR.
$TMX So far termmax has successfully countered
Pre-market 0.19
Title sponsorship lasted a year, wrote for a year
Received less than 300u
Did various tasks nonstop during this period, ended up like this
cnm termmax @TermMax
In the end, it's better to do Binance Square's creative desk tasks + do booster than to keep grinding
Jeonlees
After following Hu Chuliang to create an AI workstation, I finally don't have to struggle with stock review and selection by myself anymore.
The Hu Chuliang AI workstation that has been trending all over the internet recently really understands ordinary people.
Who would have thought? She was originally just a fashion and beauty blogger who casually posted a daily video of herself using AI to build a personal workstation, which instantly exploded with millions of likes and sparked a wave of imitations across the internet. No flashy skills, no professional coding, just the most authentic daily relief for ordinary people—that's the core reason it broke through all circles.
Many people blindly copied only the fancy interface without learning the essence. Hu Chuliang’s own approach is very straightforward: no messing with complex settings, no coding, just handing over all the trivial chores and repetitive tasks to AI. Topic review, schedule check-ins, content organization—all entrusted to AI for automatic execution, while she focuses only on core creation.
I deeply relate to this, as I have been working on an AI stock selection workstation myself, and the pain points are exactly the same.
Previously, reviewing, selecting stocks, and organizing market data was truly exhausting. Daily massive market data, piles of research report PDFs, scattered sector news—all filtering, verifying, and summarizing done manually. Time-consuming, tedious, purely repetitive labor, yet an essential part of trading review that you simply can't avoid.
This is also why I started using Tencent WorkBuddy and fully understood the logic behind Hu Chuliang’s viral success: truly useful AI is never just a tool; it’s your exclusive new colleague who takes on the busywork for you.
Most AI on the market still stays at the "ask a question, get an answer" stage.
You ask about market conditions, indicators, or review ideas, it only gives you a bunch of theoretical templates. In the end, data organization, content summarization, and structured review still require you to stay up late doing it yourself. Frankly, it’s just a different way of working overtime, exactly the "ineffective AI usage" Hu Chuliang rejects.
But WorkBuddy is a completely different work mode: no need for fine-tuning commands, no step-by-step guidance, no finishing touches by yourself.
My current stock selection and review process is super relaxed:
I just package the day’s market data tables, industry reports, sector news, and scattered trading notes all together, tell it my needs—batch sorting of valid information, cleaning redundant data, organizing stock selection logic, outputting a structured daily review summary.
It autonomously handles all the tedious steps.
Automatically reads multiple documents in detail, filters valid market information, removes invalid noise, organizes data dimensions, and sorts out a clear stock review framework.
No intervention from me at all, and finally delivers ready-to-use review documents, stock selection data summaries, and structured analysis results.
I used to think such detailed busywork and data organization could only be done personally, and only bosses deserved exclusive assistants.
Now it’s completely clear: by 2026, everyone should have their own AI assistant.
Whether it’s office chores, data organization, or our stock review and data research, those most time-consuming, annoying, and unproductive mechanical tasks can all be handed over directly to WorkBuddy.
It specializes in curing all kinds of human unwanted busywork.
No more wasting half your time on repetitive labor; hand the trivial chores to your new AI colleague and focus only on core judgment and deep thinking.
This is the ultimate truth behind Hu Chuliang’s AI workstation craze: she has hit all the hot spots of short videos, e-commerce, and AI, understanding the fundamental needs of ordinary people best—AI is not for hype or gimmicks, it’s to help ordinary people reduce burdens and liberate themselves. You don’t have to be a big shot; ordinary people can also use AI to ditch ineffective busyness.
Meet your new teammate — WorkBuddy @TencentAI_News
Hand over the tasks you don’t want to do, keep your core energy for yourself.
#WorkBuddy #MeetYourNewTeammate #LetWorkBuddyDoIt #AIAtWork #WorkSmarter #Productivity #AIStockSelection #AIWorkstation #HuChuliangAIWorkstation
After following Hu Chuliang to create an AI workstation, I finally don't have to struggle with stock review and selection by myself anymore.
The Hu Chuliang AI workstation that has been trending all over the internet recently really understands ordinary people.
Who would have thought? She was originally just a fashion and beauty blogger who casually posted a daily video of herself using AI to build a personal workstation, which instantly exploded with millions of likes and sparked a wave of imitations across the internet. No flashy skills, no professional coding, just the most authentic daily relief for ordinary people—that's the core reason it broke through all circles.
Many people blindly copied only the fancy interface without learning the essence. Hu Chuliang’s own approach is very straightforward: no messing with complex settings, no coding, just handing over all the trivial chores and repetitive tasks to AI. Topic review, schedule check-ins, content organization—all entrusted to AI for automatic execution, while she focuses only on core creation.
I deeply relate to this, as I have been working on an AI stock selection workstation myself, and the pain points are exactly the same.
Previously, reviewing, selecting stocks, and organizing market data was truly exhausting. Daily massive market data, piles of research report PDFs, scattered sector news—all filtering, verifying, and summarizing done manually. Time-consuming, tedious, purely repetitive labor, yet an essential part of trading review that you simply can't avoid.
This is also why I started using Tencent WorkBuddy and fully understood the logic behind Hu Chuliang’s viral success: truly useful AI is never just a tool; it’s your exclusive new colleague who takes on the busywork for you.
Most AI on the market still stays at the "ask a question, get an answer" stage.
You ask about market conditions, indicators, or review ideas, it only gives you a bunch of theoretical templates. In the end, data organization, content summarization, and structured review still require you to stay up late doing it yourself. Frankly, it’s just a different way of working overtime, exactly the "ineffective AI usage" Hu Chuliang rejects.
But WorkBuddy is a completely different work mode: no need for fine-tuning commands, no step-by-step guidance, no finishing touches by yourself.
My current stock selection and review process is super relaxed:
I just package the day’s market data tables, industry reports, sector news, and scattered trading notes all together, tell it my needs—batch sorting of valid information, cleaning redundant data, organizing stock selection logic, outputting a structured daily review summary.
It autonomously handles all the tedious steps.
Automatically reads multiple documents in detail, filters valid market information, removes invalid noise, organizes data dimensions, and sorts out a clear stock review framework.
No intervention from me at all, and finally delivers ready-to-use review documents, stock selection data summaries, and structured analysis results.
I used to think such detailed busywork and data organization could only be done personally, and only bosses deserved exclusive assistants.
Now it’s completely clear: by 2026, everyone should have their own AI assistant.
Whether it’s office chores, data organization, or our stock review and data research, those most time-consuming, annoying, and unproductive mechanical tasks can all be handed over directly to WorkBuddy.
It specializes in curing all kinds of human unwanted busywork.
No more wasting half your time on repetitive labor; hand the trivial chores to your new AI colleague and focus only on core judgment and deep thinking.
This is the ultimate truth behind Hu Chuliang’s AI workstation craze: she has hit all the hot spots of short videos, e-commerce, and AI, understanding the fundamental needs of ordinary people best—AI is not for hype or gimmicks, it’s to help ordinary people reduce burdens and liberate themselves. You don’t have to be a big shot; ordinary people can also use AI to ditch ineffective busyness.
Meet your new teammate — WorkBuddy @TencentAI_News
Hand over the tasks you don’t want to do, keep your core energy for yourself.
#WorkBuddy #MeetYourNewTeammate #LetWorkBuddyDoIt #AIAtWork #WorkSmarter #Productivity #AIStockSelection #AIWorkstation #HuChuliangAIWorkstation
The most common mistake with $DOGE is taking the previous cycle's peak as the target for this cycle.
Many people think:
$DOGE has reached that point before, so it's normal to return there this time.
But the market never owes any asset a previous high.
Past prices only prove that someone was willing to transact at that valuation at that time; it doesn't mean future funds will be willing to pay the same valuation.
This is especially true for Meme assets.
Each cycle brings new competitors, and market attention is constantly divided. This cycle, DOGE faces not only its previous trapped positions but also PEPE and a large number of new Memes competing for funds.
So previous highs are not magnets.
They might instead be a huge psychological zone.
The more people plan to "sell once they break even," the more potential selling pressure near that price level is worth watching.
A truly strong asset is not strong because it reached that level before and should go there again.
It is strong because new capital structures, new demand, and new consensus are enough for the market to assign that price again.
Historical highs are records.
Not promises.
#DOGE #Dogecoin #Meme #PEPE #Crypto #欧易星球
The biggest opportunity for $SOL might not be the next round of Meme, but the real entry of stablecoins into payments.
If USDT and USDC are only used for trading, then public chain competition mainly depends on transaction volume.
But if stablecoins start entering cross-border payments, corporate settlements, and consumer scenarios, the underlying network requirements will be completely different.
It needs to be fast.
It needs to be cheap.
It needs to be stable.
Ideally, users shouldn't even feel the blockchain's presence.
This happens to be where Solana has the greatest opportunity to excel.
But there is a very practical issue here: scaling payment volume does not automatically mean $SOL captures the same amount of value.
If a $100,000 USDC transfer and a $100 transfer both generate very low fees, then even if the network is busy, token value capture may not grow linearly.
So what SOL truly needs to prove in the future is:
It can not only handle large money flows but also enable these flows to continuously increase SOL's security, staking, and network value.
Payments can make Solana bigger.
Value capture determines whether SOL can grow accordingly.
#SOL #Solana #USDC #USDT #Payments #Crypto #OKXPlanet
The real difference between $DOGE and $PEPE is not who rises faster, but who assumes different market roles.
The biggest advantage of new Memes is the odds.
With smaller market caps, more concentrated holdings, and sudden bursts of attention, price elasticity is often more exaggerated than $DOGE.
DOGE's advantage is exactly the opposite.
It is older, larger, has deeper liquidity, and stronger global recognition.
So even though both are called Memes, the reasons for investors to buy them can be completely different.
Some buy new Memes to bet on the next hundredfold story.
Some buy DOGE more like trading the entire Meme sector's Beta.
This is also why the bigger DOGE's market cap gets, the harder it is to replicate those early exaggerated multiples.
But on the other hand, a large market cap also means it can more easily absorb large funds.
So don't just compare DOGE with a newly born Meme by their gains.
One is competing for explosive power.
The other is competing for consensus longevity.
When the Meme market truly matures, it may also stratify like stocks.
Not all high Beta assets need to accomplish the same task.
#DOGE #PEPE #Meme #Dogecoin #Crypto #OKXPlanet
What’s most worth watching for $SOL’s future might not be TPS, but how many stablecoins are willing to stay here long-term.
Because trading volume can be faked.
Addresses can be created through airdrops.
Meme can generate millions of transactions in a day.
But stablecoin balances are hard to fully explain by sentiment alone.
Money willing to stay long-term on a chain indicates there really are transactions, payments, yields, or application demands here.
So now when I look at Solana, I pay more and more attention to the accumulation of assets like USDC and USDT.
If the stablecoin scale keeps rising, and DeFi and payment usage also increase, it means the funds aren’t just here for a quick trade.
But we also can’t be too optimistic.
Stablecoins on Solana don’t mean these dollars “belong to SOL.”
The real question remains:
How much do these assets contribute to staking, security, and network revenue?
Asset scale tells you whether this chain is important.
Value flowing back tells you whether $SOL is worth more.
Both things must be considered together.
#SOL #Solana #USDC #USDT #Stablecoin #Crypto #OKXPlanet
$DOGE has a very peculiar advantage: the simpler it is, the easier it is to step outside the Crypto circle.
To get ordinary people to understand ETH, you have to explain smart contracts, Gas, staking, Layer2.
To explain SOL, you also have to talk about performance and on-chain ecology.
What about $DOGE?
A Shiba Inu.
This sounds like a joke, but in the world of communication, "whether it can be understood in one second" is extremely valuable.
When ordinary users enter Crypto for the first time, they usually don’t read the whitepaper first. They first recognize the symbol, then the asset, and only then might they understand the technology behind it.
DOGE’s biggest advantage is its extremely low cognitive cost.
But simplicity also has a price.
If an asset only has a symbol and no more use cases, the market’s final valuation of it completely depends on the speed of consensus expansion.
So DOGE’s real opportunity is not to become complex.
On the contrary, it should continue to be simple while making simple things usable in more places.
The biggest mistake of Meme is thinking that once culture exists, you have to force a complex technical story.
DOGE’s most valuable aspect might precisely be that it never needs to pretend to be ETH.
#DOGE #Dogecoin #ETH #Meme #Crypto #欧易星球
If $DOGE really enters more payment scenarios, my first reaction might not be positive, but to ask: who bears the volatility?
Assuming a cup of coffee is worth $5 today.
You pay with $DOGE.
What the merchant really wants is $5 purchasing power, not DOGE that might be worth $4.5 or $5.5 tomorrow.
So the real difficulty in the payment story has never been "can it be transferred."
It's how to handle price volatility.
Does the merchant instantly convert to stablecoins?
Does the payment platform bear the exchange risk?
Does the user pay directly with DOGE, and the backend automatically settles in dollars?
If this infrastructure matures in the future, DOGE could indeed gain more use cases.
But an unconventional problem will also arise:
If the merchant sells DOGE immediately after receiving it, does it bring long-term demand, or does it increase both buying and selling simultaneously?
So seeing "support DOGE payment" cannot directly equal new holding demand.
What really matters is whether more DOGE stays in the system after payment.
Payment volume and holding demand are two completely different report cards.
#DOGE #Dogecoin #Payment #Stablecoin #Crypto #OKXPlanet