$Tradr 2X Long CRWV Daily ETF (CWVX.US)$ heading to 3? 😭

$Microsoft (MSFT.US)$
👇🏻🤭🔮🎲🧮. Chip is always the foundation, next comes the server, finally the software. Classical truth of Computer Science 101.
Put it in another way - he knows nvidia is the king, growth is limited. Software is the frontier now, it can only grow, be the pioneer & 🎲🎲🎲. First Move.

$BigBear.ai Holdings (BBAI.US)$
To those who went of saying 3 3 3! Well, Good luck to you.
To all, moral of the fucking story, stop listening to groundless speculations. Study the business instead.

$Amazon (AMZN.US)$ The situation has become extremely unpleasant.

$Apple (AAPL.US)$ The midfielder of Lao Li's team has become the goalkeeper.

$Fortinet (FTNT.US)$ Believe me or not, FTNT is now a strong buy after the earnings report.

$Meta Platforms (META.US)$ it fails to hold 637…. 605 next…

$Microsoft (MSFT.US)$  still its not going up 😔

$Tesla (TSLA.US)$ Investment Notes
Elon Musk today disclosed more details about Tesla's I5 chip, the company’s fifth-generation self-developed AI training chip.
It is 40 times faster than NVIDIA chips, with computing power increased by 8 times, memory enhanced by 9 times, and bandwidth improved by 5 times.
The code path has been shortened to approximately five steps, and the cost per inference is ten times cheaper compared to NVIDIA.
Energy efficiency per watt has tripled. The chip will be manufactured by Samsung using Taiwan Semiconductor’s lithography technology, with all production taking place in Texas and Arizona by 2026, Musk added.
Taiwan Semiconductor and Samsung will produce slightly different versions due to variations in how they translate the design into its physical form.
Samples and a small number of products will be available in 2026, but mass production can only commence in 2027. I6 will utilize the same factory, with an expected performance improvement of approximately twofold, aiming for rapid follow-up.
I5 aims to achieve mass production by mid-2028. I6 will require a different factory due to its higher risk. What do these figures reveal?
The first layer is the de-NVIDIA-ization process. Tesla has been using NVIDIA’s GPUs to train FSD and Optimus, which is costly.
The unit price of NVIDIA’s H100 ranges from $30,000 to $40,000, and a training cluster requires tens of thousands of GPUs.
Tesla’s self-developed chips reduce inference costs by tenfold compared to NVIDIA, implying that training costs could decrease by an order of magnitude.
This is not just about cost savings; it's about strategic independence. NVIDIA faces supply shortages, long delivery cycles, and significant price fluctuations.
After developing its own chips, Tesla is no longer constrained by the supply chain.
Layer 2 involves the vertical integration of Tesla's self-designed chips, ...