Thử Mua ĐT Gaming Black Shark chỉ 1tr7 trên LAZADA, SHOPEE. Toangggg | MUA HÀNG ONLINE

Lâm Gia

26 Jan 202408:34

Summary

TLDRThis is a video review of the Xiaomi Black Shark gaming phone purchased for only 1.7 million VND. The phone sports impressive specs like Snapdragon 845 chip and 6GB RAM, but has an underwhelming display with low brightness and a blue tint. After unboxing, the author boots up the phone and compares the display side-by-side with a Xiaomi Mi 8 SE, noting the Black Shark's inferior colors, brightness and resolution. There are also signs of glue near the fingerprint sensor and sides, likely from previous repairs. Ultimately the author decides to return the phone due to the disappointing screen.

Takeaways

😀 This is a phone review video welcoming viewers
📱 The phone being reviewed is a Xiaomi Black Shark 1 gaming phone with Snapdragon 845, 6GB RAM, 64GB storage for only 1.7 million VND
🛒 It was purchased from Shopee Vietnam
👀 Physically, the phone seems quite new and thin but has some scratches and dents
💡 The display brightness and color accuracy are quite poor compared to flagship phones of the same era
⚠️ There are signs of glue and defects around the fingerprint sensor and sides
🤔 The video creator tried to find excuses to return the phone due to the bad display
😕 This was the first phone review video of the year but it faced issues right away
❓ The creator asks viewers if he should repurchase this phone or try another one
👋 The video concludes with the creator bidding farewell to viewers

Q & A

What phone model is being reviewed in the video?
-The Xiaomi Black Shark, also called Xiaomi Leat, is being reviewed. It is a gaming phone that was released around 5 years ago.
What are the key specs of the Xiaomi Black Shark phone?
-The key specs are: Snapdragon 845 chipset, 6GB RAM, 64GB storage, 5.99 inch display.
How much did the phone cost when the reviewer purchased it?
-The reviewer purchased the 6GB/64GB variant for around 1.7 million VND or approx. $73.
What accessories did the phone come with?
-It came with an 18W charger, USB cable and warranty card.
What did the reviewer think about the display quality?
-The reviewer felt the display brightness and colors were poor. It looked washed out compared to flagship phones of the same era.
Were there any physical defects noticed on the phone?
-Yes, there was glue and scratches near the fingerprint sensor and sides. But the reviewer didn't mind due to the phone's age.
Why did the reviewer decide to return the phone?
-Due to the low quality display which was unacceptable at any price point according to the reviewer.
Will the reviewer purchase the phone again for review?
-The reviewer asks viewers to comment if they want him to repurchase the Xiaomi Black Shark and review it again, or try another phone instead.
What was the reviewer's initial impression on unboxing?
-The reviewer felt the phone was very thin and light, with a premium design and build for the price.
How did the gaming performance seem to the reviewer?
-The gaming performance was not specifically tested. But the Snapdragon 845 chip indicates it should be decent for most games.

Outlines

00:00

😊 Introducing a special gaming phone - Xiaomi Black Shark 1

The host introduces a video review of phones. He mentions that in previous videos, many viewers requested budget phone reviews. Today he will review a special gaming phone - Xiaomi Black Shark 1 - which has high specs like Snapdragon 845, 6GB RAM, 64GB storage, but costs only 1.7 million VND. He bought it on Shopee and will unbox and review it.

05:01

😕 The Black Shark 1 has a disappointing display

The host compares the Black Shark 1 display to the Xiaomi Mi 8SE. Both phones were released around the same time. However, the Mi 8SE has a much brighter and more color accurate display, despite being a mid-range phone while the Black Shark 1 was a flagship. The host concludes the Black Shark 1 uses a low-quality display, which he cannot accept. So he will return the phone.

Mindmap

Keywords

💡Review

The video is a review of a mobile phone. The host provides overview, unboxing and testing of the Xiaomi Black Shark gaming phone. A review aims to provide detailed evaluation and commentary on a product to help potential buyers.

💡Gaming Phone

The Xiaomi Black Shark is a gaming phone, specialized for gaming with high performance hardware. Gaming phones have powerful processors, graphics, cooling systems for optimal gaming experience.

💡Unboxing

The host does an unboxing, opening the box and showing the phone and accessories inside. Unboxing reveals contents and gives first impressions.

💡Display

The display quality and colors are analyzed. The host compares it to another phone display. A good display is critical for gaming and videos.

💡Benchmark

The Black Shark uses Snapdragon 845 chip which was flagship in 2018. It is compared to Xiaomi Mi 8 which used the same chip. Benchmarks measure and compare performance.

💡Design

The thin, symmetrical industrial design with glowing logo is shown. Gaming phones have aggressive styling and branding elements.

💡Build Quality

Some manufacturing defects like scratches and gaps are pointed out. Build quality affects durability and longevity of the phone.

💡Pricing

The price paid (1.7 million VND) is discussed as a factor. Pricing determines affordability and value for money.

💡Verdict

Due to the low quality display, the host concludes the phone must be returned. The review verdict recommends if the product is worth buying.

💡Content Creation

The host asks viewers for input on creating content and reviews. User comments and requests influence creation of reviews.

Highlights

Proposed a new deep learning architecture called Transformers that relies entirely on attention mechanisms.

Transformers achieved state-of-the-art results in machine translation tasks, outperforming previous models like convolutional and recurrent neural networks.

The Transformer model is composed of an encoder and decoder with multiple self-attention layers in between, allowing it to focus on relevant parts of the input sequence.

Self-attention layers allow the model to look at the entire sequence at once, capturing long-range dependencies critical for tasks like translation.

Proposed byte pair encoding to handle rare and unknown words, splitting them into subword units to improve generalization.

Demonstrated the strength of attention-based networks compared to RNNs and CNNs across multiple NLP tasks including document classification, abstractive summarization, and question answering.

Suggested that attention may lead to more interpretable models compared to RNNs, as we can visualize what parts of the input the model focuses on.

Emphasized the importance of large-scale pretraining, paving the way for later models like BERT that leveraged massive datasets.

Sparked significant interest in attention and self-attention, which became integral components of later state-of-the-art NLP architectures.

Showed attention could be applied successfully at scale to long sequences, overcoming previous limitations of attention.

Achieved new state-of-the-art results on WMT 2014 English-German and WMT 2014 English-French translation datasets.

Demonstrated the Transformer's ability to translate English sentences into French sentences with higher accuracy than previous seq2seq models.

Validated that attention-based models can surpass RNNs and CNNs for sequential modelling problems given large-scale training data.

Presented extensive ablation studies analyzing the contribution of individual components like multi-head attention and layer normalization.

Open-sourced PyTorch implementations that accelerated research and adoption of Transformers in the NLP community.