Trending February 2024 # How To Repost On Tiktok: Everything You Need To Know # Suggested March 2024 # Top 7 Popular

You are reading the article How To Repost On Tiktok: Everything You Need To Know updated in February 2024 on the website Hatcungthantuong.com. We hope that the information we have shared is helpful to you. If you find the content interesting and meaningful, please share it with your friends and continue to follow and support us for the latest updates. Suggested March 2024 How To Repost On Tiktok: Everything You Need To Know

Sharing a video is the only option for users to send their favorite posts to others, and this has thus far been accomplished in three ways — 1) copying the link to the original video and sharing it to others via networking apps, or 2) saving a video to the camera gallery and reuploading it as one’s own, or 3) stitching or dueting with other’s contents to share it on one’s profile. If your purport is pure, then you can save all this trouble and directly share or repost someone else’s video on TikTok. 

Wait, so TikTok has a “repost” button now? Since when? Since very recently. Let’s see how it works and how to use the feature.

TikTok “Repost” in a glance

Let’s take a look at how it is done.

Related: How to use TikTok filters: Everything you need to know

How to repost on TikTok

Launch the TikTok app on your device.

Tap the Home icon to go to your ‘For You’ feed.

Hit the arrow icon of the share button.

Tap Repost found next to the contacts at the top row.

The first time you use this function, a popup informs you about its purpose and effects. Hit Repost to continue.

Enter your message in the text box and hit Add.

What happens after you repost a video on TikTok?

Related: How to Find Your Favorites on TikTok (Videos, Sounds and Effects)

How to undo TikTok repost

As we already discussed, the TikTok videos you find by any other means such as a link you received over the DMs, from a creator’s profile, or from the TikTok Discover do not come with a repost button. Hence, once you repost something, you ought to remove it immediately lest you lose the opportunity to do it later.

To undo a repost, all you have to do is tap the Repost button again.

A popup asks you to confirm the activity. Hit Remove to proceed. 

How does TikTok repost work: Things to know

The purpose of the TikTok reposting function is to allow users to share their favorite content with their followers and the shared content gets sort-of organically recommended to the mutual follows.

As a video-sharing platform with its root deeply embedded in content sharing and global discovery, why hasn’t TikTok introduced a similar feature to its arsenal? 

It is likely that it wasn’t not considered, but because of the lurking threat of power abuse involved that has likely kept the repost feature from making its way over until now. TikTok’s “for you” page recommendations are brought to each user after grinding through the sieve of the algorithm that only allows ‘related’ or interesting content to users. So, if you follow a few popular creators who form a pact to support each other by reposting each other’s content, imagine what kind of mess your FYP would fall into?

However, the repost feature on TikTok seems to have found a workaround for this. Because, even if you follow some influential TikToker, their reposts won’t show up on your feed unless they follow you as well. Hence, manipulation to cheat the algorithm is expected to be less with such a root formula guiding the repost function.

However, reposting is also a necessary function expected by users as we often come across videos that we wish to share with someone else. Previously, the most used method was to share the link of a video with others in DMs. There is the second category of users who often download other creators’ content and upload it as their own. However, reposting someone else’s original content is highly frowned upon on TikTok. Hence, the repost feature is anticipated to remedy the situation at hand and allow for easy video sharing and recommendations.

Related: How to Trim on TikTok: 3 Ways to Trim Videos Easily

Who can view your reposts?

Alternate ways to “repost” a video on TikTok

While it may not be “reposting” in the real sense of the word, another way to share a TikTok video is by sending the link to it to someone else. Sharing the link to videos comes in handy when you find a video from anywhere on the app; that is to say, fetching and sharing the link does away with the restriction of the repost button to the home feed on TikTok.

Method 1: Copying the link to a video for sharing

Launch the TikTok app on your device.

On any video that’s playing, tap the Share icon.

Tap the link icon to copy the link.

You can paste the link in the text box of any interactive app like WhatsApp, Instagram, etc.

Another method to repost a video is by uploading someone’s content as your own. Here is how it is done.

Method 2: Downloading a video to re-upload it 

Launch the TikTok app on your device.

On any video that’s playing, tap the Share icon.

Tap Save video to download it to your camera roll. 

Now, open the TikTok app and tap the Record button to go to the create page.

Tap Upload to access the videos in your camera roll.

Select the videos and tap Next.

In the editing page, add overlays and effects of choice, and tap Next.

Add captions and adjust the privacy setting on the processing page. Hit Post.

The videos uploaded thus would have watermarks of the original creator’s details on the video, you can use the link to download the videos from websites that remove watermarks from downloaded videos.

Method 3: Stitching or Dueting with other creators’ videos.

When you stitch or duet with other videos, they appear on your profile along with the additions you have made to it. For instance, when you stitch with a video, you can take a 5-second byte from someone else’s video as a starter clip for your own video. Similarly, when you duet with some, you can “share” the screen with them by appearing on split-screen with their original content. You can read more about stitching with someone else’s video on TikTok by following the link below.

Related: How to Stitch a Video on TikTok: Everything You Need to Know

There, now we have seen all the ways to repost and share someone’s video on TikTok. The new Repost feature is definitely a bonus to recommend the videos we like to our followers and similarly view the content they like in our for you feed recommends. 

RELATED

You're reading How To Repost On Tiktok: Everything You Need To Know

Is The Tiktok Creator Fund Worth It? Everything You Need To Know

TikTok launched the Creator Fund in spring 2023 with an initial investment of $200 million. But reviews from participants have been mixed.

It’s hard to imagine what viral moment will take the world by storm this year, but we can almost guarantee it’ll trend on TikTok first. And the app’s endless popularity means there are plenty of ways to monetize.

Among them is the TikTok Creator Fund, which launched last year with a whopping initial investment of $200 million USD and a promise to reach $1 billion in the next three years.

Yes, there’s presumably a large bag of TikTok money just waiting to be claimed by the smartest, most engaging content creators. But what exactly is the TikTok Creator Fund, and is it worth your time?

We’ve answered all of your questions about this exciting (and potentially controversial) new program.

Bonus: Get a free TikTok Growth Checklist from famous TikTok creator Tiffy Chen that shows you how to gain 1.6 million followers with only 3 studio lights and iMovie.

What is the TikTok Creator Fund?

@tiktok_uk

The Creator Fund is open! We’re so excited to celebrate creativity with a European fund of €254 million. See link in bio for more info 🥳

♬ Originalton – TikTok UK

It’s right there in the name: the TikTok Creator Fund is a monetary fund for creators. It’s not an ad revenue sharing program like YouTube’s AdSense, nor is it a form of arts grant. It’s simply a way for TikTok to share income with creators who are killing it on the platform.

TikTok first launched the Creator Fund in the spring of 2023 with an initial investment of $200 million USD. In the company’s own words, the fund was launched “to encourage those who dream of using their voices and creativity to spark inspirational careers.”

The TikTok Creator Fund was an instant success (although not without its controversies, as you’ll soon read). The fund is so popular, in fact, that the company will increase it to $1 billion within the next three years.

TikTok has been decidedly secretive about their payout structure, but the general idea is that users who meet their requirements will be compensated for well-performing videos. How TikTok calculates their payouts is based on factors like views, video engagement and even region-specific performance.

It should go without saying, but the videos also need to adhere to the Community Guidelines and Terms of Service, so you’ll have to rack up your views without breaking the rules.

How much does the TikTok Creator Fund pay?

When TikTok users first learned about this enormous fund, they understandably had dollar signs in their eyes (no filter necessary). But even with multiple millions at play, high-performing TikTok users shouldn’t expect a life-changing payday just yet.

There are no hard rules about how much the TikTok Creator Fund pays its contributors. But plenty of creators have gone on record to explain their own experience with the Creator Fund.

The general consensus is that TikTok pays between 2 and 4 cents for every 1,000 views. Some quick math suggests you might expect $20 to $40 after reaching a million views.

At first glance, that might look pretty bad. But remember: the fund should inspire creators to, well, keep creating. Master your TikTok game and you could be hitting millions of views on a regular basis.

Once you’ve racked up at least $10 from the Fund, you can simply withdraw your Creator Fund payout using an online financial service like Paypal or Zelle.

Who can join the TikTok Creator Fund?

The TikTok Creator Fund is available for users based in the US, UK, France, Germany, Spain and Italy. Yes, Canadians and Australians are out of luck for now, but rumour has it the fund will launch in their respective countries later in 2023.

As long as you’re in the right location, there are a few other requirements to join the Creator Fund.

You need to have a Pro account (and it’s easy to make the switch if you don’t)

You need to have at least 10,000 followers

You need to have received at least 100,000 views in the last 30 days

You also need to be 18 or older and make sure you’re following the TikTok Community Guidelines and terms of service. And in order to make money off your work, you should be making original content.

If you meet those requirements, you’re good to sign up for the Creator Fund. But should you?

Get better at TikTok — with Hootsuite.

Access exclusive, weekly social media bootcamps hosted by TikTok experts as soon as you sign up, with insider tips on how to:

Grow your followers

Get more engagement

Get on the For You Page

And more!

Try it for free

Is it worth it to join the TikTok Creator Fund?

As with any new social media feature, there has been plenty of debate (and downright drama) over the TikTok Creator Fund. From valid concerns to surprising benefits, let’s dig into the pros and cons of the fund:

Pros Money!

It goes without saying that getting paid for your work is always a good thing, so payouts from TikTok are an obvious pro. Even if the amounts are small, money is a great motivator to keep uploading.

Unlimited Money!

Another great thing about the Creator Fund is that TikTok hasn’t set a limit for how much money one user can make. So if you do master the platform and break into the multi-million view zone, you could theoretically start raking in some decent cash.

Friendship!

The Creator Fund is also a great way to foster community and set apart users who have shown a dedication to the platform. From TikTok’s perspective, it’s also a great way keep their high-performing users dedicated to the app rather than switching over to YouTube or Instagram.

Cons Conspiracy…

Some users have claimed that their views have been cut (by the algorithm?) since they signed up for the Creator Fund. TikTok has denied this theory, explaining that participation in the fund has no bearing on the algorithm. Others think view counts might seem lower because there are so many more Fund recipients flooding the feed.

Confusion…

While they’re decent with general analytics, TikTok is super secretive about how they calculate payouts. The 2-4 cents rule is based on hearsay from users, as is just about everything else from the Fund. In fact, the user agreement states that reporting metrics and other private information about the fund are meant to be kept confidential.

Commitment…

Outside of hearsay, the biggest potential downside of the Creator Fund is the simple fact that you’ll need to create a ton of content, and have it perform incredibly well, in order to make cash from the app. For some, that might make TikTok feel more like a job than a fun hobby.

So is the TikTok Creator Fund worth it? It really boils down to personal choice. Knowing what we know, you’re not going to be buying a TikTok hype house with the money you make from the program, but it’s also a low-risk way to create more passive income on your content.

Assuming you meet the requirements, it doesn’t hurt to try it out. Plus, you can always quit if you’re not feeling it.

Think of it like another tool in your influencer toolbox. Pair it with other monetization options like sponsored posts via the TikTok Creator Marketplace or merch sales, brand deals, crowdfunding and other strategies.

How to join the TikTok Creator Fund

If you meet all of the requirements listed earlier in this article, it’s super easy to apply for the Creator Fund. Just follow these simple steps:

1. Make sure you have a Pro account.

If you’re already signed up for TikTok with a Pro account, you can skip this step. Otherwise, simply open the app and tap Me to go to your profile.

2. Head to Settings and Privacy. 3. Read the fine print.

It’s probably a good idea to actually read through the TikTok Creator Fund Agreement before you agree to anything. You’ll also need to confirm that you’re over the age of 18.

4. Submit and wait.

TikTok will let you know if they decide to approve your application. And don’t worry — if you get rejected, you can apply again in 30 days.

Grow your TikTok presence alongside your other social channels using Hootsuite. From a single dashboard, you can schedule and publish posts for the best times, engage your audience, and measure performance. Try it free today.

Try it free!

Want more TikTok views?

Everything You Need To Know About Scikit

Introduction

Scikit-learn is one Python library we all inevitably turn to when we’re building machine learning models. I’ve built countless models using this wonderful library and I’m sure all of you must have as well.

There’s no question – scikit-learn provides handy tools with easy-to-read syntax. Among the pantheon of popular Python libraries, scikit-learn ranks in the top echelon along with Pandas and NumPy. These three Python libraries provide a complete solution to various steps of the machine learning pipeline.

I love the clean, uniform code and functions that scikit-learn provides. It makes it really easy to use other techniques once we have mastered one. The excellent documentation is the icing on the cake as it makes a lot of beginners self-sufficient with building machine learning models.

The developers behind scikit-learn have come up with a new version (v0.22) that packs in some major updates. I’ll unpack these features for you in this article and showcase what’s under the hood through Python code.

Note: Looking to learn Python from scratch? This free course is the perfect starting point!

Table of Contents

Getting to Know Scikit-Learn

A Brief History of Scikit-Learn

Scikit-Learn v0.22 Updates (with Python implementation)

Stacking Classifier and Regressor

Permutation-Based Feature Importance

Multi-class Support for ROC-AUC

kNN-Based Imputation

Tree Pruning

Getting to Know Scikit-Learn

This library is built upon the SciPy (Scientific Python) library that you need to install before you can use scikit-learn. It is licensed under a permissive simplified BSD license and is distributed under many Linux distributions, encouraging academic and commercial use.

Overall, scikit-learn uses the following libraries behind the scenes:

NumPy: n-dimensional array package

SciPy: Scientific computing Library

Matplotlib:  Plotting Library

iPython: Interactive python (for Jupyter Notebook support)

SymPy: Symbolic mathematics

Pandas: Data structures, analysis, and manipulation

Lately, scikit-learn has reorganized and restructured its functions & packages into six main modules:

Classification: Identifying which category an object belongs to

Regression: Predicting a continuous-valued attribute associated with an object

Clustering: For grouping unlabeled data

Dimensionality Reduction: Reducing the number of random variables to consider

Model Selection: Comparing, validating and choosing parameters and models

Preprocessing: Feature extraction and normalization

scikit-learn provides the functionality to perform all the steps from preprocessing, model building, selecting the right model, hyperparameter tuning, to frameworks for interpreting machine learning models.

Scikit-learn Modules (Source: Scikit-learn Homepage)

A Brief History of Scikit-learn

Scikit-learn has come a long way from when it started back in 2007 as scikits.learn. Here’s a cool trivia for you – scikit-learn was a Google Summer of Code project by David Cournapeau!

This was taken over and rewritten by Fabian Pedregosa, Gael Varoquaux, Alexandre Gramfort and Vincent Michel, all from the French Institute for Research in Computer Science and Automation and its first public release took place in 2010.

Since then, it has added a lot of features and survived the test of time as the most popular open-source machine learning library across languages and frameworks. The below infographic, prepared by our team, illustrates a brief timeline of all the scikit-learn features along with their version number:

The above infographics show the release of features since its inception as a public library for implementing Machine Learning Algorithms from 2010 to 2023

Today, Scikit-learn is being used by organizations across the globe, including the likes of Spotify, JP Morgan, chúng tôi Evernote, and many more. You can find the complete list here with testimonials I believe this is just the tip of the iceberg when it comes to this library’s popularity as there will a lot of small and big companies using scikit-learn at some stage of prototyping models.

The latest version of scikit-learn, v0.22, has more than 20 active contributors today. v0.22 has added some excellent features to its arsenal that provide resolutions for some major existing pain points along with some fresh features which were available in other libraries but often caused package conflicts.

We will cover them in detail here and also dive into how to implement them in Python.

Scikit-Learn v0.22 Updates

Along with bug fixes and performance improvements, here are some new features that are included in scikit-learn’s latest version.

Stacking Classifier & Regressor

Stacking is an ensemble learning technique that uses predictions from multiple models (for example, decision tree, KNN or SVM) to build a new model.

This model is used for making predictions on the test set. Below is a step-wise explanation I’ve taken from this excellent article on ensemble learning for a simple stacked ensemble:

The base model (in this case, decision tree) is then fitted on the whole train dataset

This model is used to make final predictions on the test prediction set

The mlxtend library provides an API to implement Stacking in Python. Now, sklearn, with its familiar API can do the same and it’s pretty intuitive as you will see in the demo below. You can either import StackingRegressor & StackingClassifier depending on your use case:

from

sklearn.linear_model

import

LogisticRegression

from sklearn.ensemble import RandomForestClassifier from chúng tôi import DecisionTreeClassifier

from

sklearn.ensemble

import

StackingClassifier

from

sklearn.model_selection

import

train_test_split

X

,

y

=

load_iris

(

return_X_y

=

True

)

estimators

=

[

(

'rf'

,

RandomForestClassifier

(

n_estimators

=

10

,

random_state

=

42

)),

(

'dt'

,

DecisionTreeClassifier

(

random_state

=

42

)

)

]

clf

=

StackingClassifier

(

estimators

=

estimators

,

final_estimator

=

LogisticRegression

()

)

X_train

,

X_test

,

y_train

,

y_test

=

train_test_split

(

X

,

y

,

stratify

=

y

,

random_state

=

42

)

clf

.

fit

(

X_train

,

y_train

)

.

score

(

X_test

,

y_test

)

Permutation-Based Feature Importance

As the name suggests, this technique provides a way to assign importance to each feature by permuting each feature and capturing the drop in performance.

But what does permuting mean here? Let us understand this using an example.

Let’s say we are trying to predict house prices and have only 2 features to work with:

LotArea – (Sq Feet area of the house)

YrSold (Year when it was sold)

The test data has just 10 rows as shown below:

Next, we fit a simple decision tree model and get an R-Squared value of 0.78. We pick a feature, say LotArea, and shuffle it keeping all the other columns as they were:

Next, we calculate the R-Squared once more and it comes out to be 0.74. We take the difference or ratio between the 2 (0.78/0.74 or 0.78-0.74), repeat the above steps, and take the average to represent the importance of the LotArea feature.

We can perform similar steps for all the other features to get the relative importance of each feature. Since we are using the test set here to evaluate the importance values, only the features that help the model generalize better will fare better.

Earlier, we had to implement this from scratch or import packages such as ELI5. Now, Sklearn has an inbuilt facility to do permutation-based feature importance. Let’s get into the code to see how we can visualize this:



As you can see in the above box plot, there are 3 features that are relatively more important than the other 4. You can try this with any model, which makes it a model agnostic interpretability technique. You can read more about this machine learning interpretability concept here.

Multiclass Support for ROC-AUC

The ROC-AUC score for binary classification is super useful especially when it comes to imbalanced datasets. However, there was no support for Multi-Class classification till now and we had to manually code to do this. In order to use the ROC-AUC score for multi-class/multi-label classification, we would need to binarize the target first.

Currently, sklearn has support for two strategies in order to achieve this:

from sklearn.datasets import load_iris  from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import roc_auc_score X, y = load_iris(return_X_y=True) rf = RandomForestClassifier(random_state=44, max_depth=2) rf.fit(X,y) print(roc_auc_score(y, rf.predict_proba(X), multi_class='ovo'))

Also, there is a new plotting API that makes it super easy to plot and compare ROC-AUC curves from different machine learning models. Let’s see a quick demo:

from

sklearn.model_selection

import

train_test_split

from

sklearn.svm

import

SVC

from

sklearn.metrics

import

plot_roc_curve

from

sklearn.ensemble

import

RandomForestClassifier

from

sklearn.datasets

import

make_classification

import

matplotlib.pyplot

as

plt

X

,

y

=

make_classification

(

random_state

=5

)

X_train

,

X_test

,

y_train

,

y_test

=

train_test_split

(

X

,

y

,

random_state

=

42

)

svc

=

SVC

(

random_state

=

42

)

svc

.

fit

(

X_train

,

y_train

)

rfc

=

RandomForestClassifier

(

random_state

=

42

)

rfc

.

fit

(

X_train

,

y_train

)

svc_disp

=

plot_roc_curve

(

svc

,

X_test

,

y_test

)

rfc_disp

=

plot_roc_curve

(

rfc

,

X_test

,

y_test

,

ax

=

svc_disp

.

ax_

)

rfc_disp

.

figure_

.

suptitle

(

"ROC curve comparison"

)

plt

.

show

()

In the above figure, we have a comparison of two different machine learning models, namely Support Vector Classifier & Random Forest. Similarly, you can plot the AUC-ROC curve for more machine learning models and compare their performance.

kNN-Based Imputation

In kNN based imputation method, the missing values of an attribute are imputed using the attributes that are most similar to the attribute whose values are missing. The assumption behind using kNN for missing values is that a point value can be approximated by the values of the points that are closest to it, based on other variables.

The k-nearest neighbor can predict both qualitative & quantitative attributes

Creation of predictive machine learning model for each attribute with missing data is not required

Correlation structure of the data is taken into consideration

Scikit-learn supports kNN-based imputation using the Euclidean distance method. Let’s see a quick demo:

import

numpy

as

np

from

sklearn.impute

import

KNNImputer

X

=

[[4

,

6

,

np

.

nan

],

[

3

,

4

,

3

],

[

np

.

nan

,

6

,

5

],

[

8

,

8

,

9

]]

imputer

=

KNNImputer

(

n_neighbors

=

2

)

print

(

imputer

.

fit_transform

(

X

))

You can read about how kNN works in comprehensive detail here.

Tree Pruning

In basic terms, pruning is a technique we use to reduce the size of decision trees thereby avoiding overfitting. This also extends to other tree-based algorithms such as Random Forests and Gradient Boosting. These tree-based machine learning methods provide parameters such as min_samples_leaf and max_depth to prevent a tree from overfitting.

Pruning provides another option to control the size of a tree. XGBoost & LightGBM have pruning integrated into their implementation. However, a feature to manually prune trees has been long overdue in Scikit-learn (R already provides a similar facility as a part of the rpart package).

In its latest version, Scikit-learn provides this pruning functionality making it possible to control overfitting in most tree-based estimators once the trees are built. For details on how and why pruning is done, you can go through this excellent tutorial on tree-based methods by Sunil. Let’s look at a quick demo now:

from sklearn.ensemble import RandomForestClassifier from sklearn.datasets import make_classification X

,

y

=

make_classification

(

random_state

=

0

)

rf

=

RandomForestClassifier

(

random_state

=

0

,

ccp_alpha

=

0

)

.

fit

(

X

,

y

)

print

(

"Average number of nodes without pruning

{:.1f}

"

.

format

(

np

.

mean

([

e

.

tree_

.

node_count

for

e

in

rf

.

estimators_

])))

rf

=

RandomForestClassifier

(

random_state

=

0

,

ccp_alpha

=

0.1

)

.

fit

(

X

,

y

)

print

(

"Average number of nodes with pruning

{:.1f}

"

.

format

(

np

.

mean

([

e

.

tree_

.

node_count

for

e

in

rf

.

estimators_

])))

End Notes

The scikit-learn package is the ultimate go-to library for building machine learning models. It is the first machine learning-focused library all newcomers lean on to guide them through their initial learning process. And even as a veteran, I often find myself using it to quickly test out a hypothesis or solution I have in mind.

The latest release definitely has some significant upgrades as we just saw. It’s definitely worth exploring on your own and experimenting using the base I have provided in this article.

Related

Xiaomi 12S: Everything You Need To Know

Just as importantly, the trio of 12S phones unveiled in China – the 12S, 12S Pro, and 12S Ultra – are the first to bear a Leica logo, marking the two companies’ new long-term camera collaboration.

Here’s everything we know so far about the three phones, including their prospects of launching in the West.

When will the Xiaomi 12S launch worldwide?

Xiaomi revealed the three 12S phones in China on 4 July, but what about the rest of the world?

The Xiaomi 12 and 12 Pro took a couple of months to get a global launch, so on that timetable we’d expect to see the 12S arrive globally some time in September.

That’s assuming they’ll launch at all. Last year’s Mi 11 Ultra never officially reached the West at all, and Xiaomi has already confirmed to Tech Advisor that “The series will not be coming to the UK.”

There’s still a chance it hits some other markets outside China of course, though right now we’d consider it a long shot. Hopefully we’ll find out more soon.

How much do the 12S phones cost?

With a launch only in China, we only have Chinese prices for the phones so far, but here’s how they convert worldwide:

Xiaomi 12S – From ¥3,999 (around $600/£490/€570)

Xiaomi 12S Pro – From ¥4,699 (around $700/£580/€670)

Xiaomi 12S Ultra – From ¥5,999 (around $900/£740/€860)

What are the Xiaomi 12S specs?

Now that Xiaomi has released the 12S series in China, we know the specs for all three phones. Here’s how they stack up:

Xiaomi 12S specs

The regular 12S will be the smallest and cheapest of the phones in the series, but don’t hold that against it. It’s available in black, purple, green, and a new white finish.

Xiaomi

It uses the same size 6.28in display as the Xiaomi 12, keeping things nice and compact. As you’d expect, it will be a 120Hz AMOLED at a Full HD+ resolution – exactly the same as its predecessor.

There are still some major performance upgrades though. The most obvious it that, like all the 12S phones, it will use the Snapdragon 8+ Gen 1 chipset. That should result in some modest performance gains, and perhaps better battery life – though the actual cell is the same 4500mAh capacity, with 67W wired and 50W wireless charging.

The camera has also been upgraded. The main rear lens is still 50Mp, but it now uses the improved Sony IMX707 sensor – the same used in the 12 Pro earlier this year – with OIS. Like all three of the new phones, it also features Leica branding on the rear to reflect the camera company’s work helping Xiaomi to optimise image quality.

As with all phones in the series, the Xiaomi 12S launches with Android 12 and MIUI 13 installed.

Here are the full Xiaomi 12S specs:

6.28in, 120Hz, FHD+ AMOLED display

Qualcomm Snapdragon 8+ Gen 1

8/12GB RAM

128/256GB storage

50Mp, f/1.9 OIS main camera

13Mp, f/2.4 ultrawide camera

5Mp telemacro camera

32Mp selfie camera

4500mAh battery

67W wired charging

50W wireless charging

Gorilla Glass Victus

152.7 x 69.9 x 8.2 mm

180g

Xiaomi 12S Pro specs

As with the regular Xiaomi 12 line, the 12S Pro sits only a little above the 12S in the hierarchy, and is differentiated by the fact that it’s larger, and has better specced cameras and display.

Xiaomi

Like the 12S you’ll get the Snapdragon 8+ Gen 1, but with slightly improved performance thanks to a larger cooling area. The chipset choice is essentially the only key upgrade from the 12 Pro.

The bigger 6.73in E5 AMOLED display will likely be a big draw to many, backed up by a dynamic 1-120Hz refresh rate and high 2K resolution – though this is essentially unchanged from the 12 Pro.

You also get the same fast charging as the 12 Pro (120W wired, 50W wireless) and the same rear camera setup.

Here are the full specs:

6.73in, 1-120Hz LTPO, 2K AMOLED display

Qualcomm Snapdragon 8+ Gen 1

8/12GB RAM

256GB storage

50Mp, f/1.9 OIS main camera

50Mp, f/2.2 ultrawide camera

50Mp, f/1.9 2x zoom camera

32Mp selfie camera

4600mAh battery

120 wired charging

50W wireless charging

Gorilla Glass Victus

163.6 x 74.6 x 8.2 mm

205g

Xiaomi 12S Ultra specs

The 12S Ultra is a more different phone. That’s obvious immediately from the design – not only is the 12S Ultra larger, it also has a totally different circular rear camera module, surrounded by a 23K gold ring.

Xiaomi

Unlike last year’s Mi 11 Ultra there’s no space carved out for a mini rear display, and instead this bank of space is all about the actual camera – though we’ll get to that in a minute.

The phone comes in a vegan leather finish, available in two colours: Classic Black and Verdant Green. It’s also IP68-rated for water-resistance.

As for the display, you get a 6.73in 120Hz LTPO 2.0 AMOLED in a 2K resolution. As with the other two phones, the 12S Ultra uses the Snapdragon 8+ Gen 1 chipset.

The Ultra is also set to impress on battery. It features a 4860mAh battery with 67W wired charging and 50W wireless – so it actually has slower wired charging than the 12 Pro, but with a slightly larger battery. It also includes a proprietary Surge G1 battery management chipset.

Xiaomi

Now let’s get to the camera. While early reports had suggested the Ultra might be the debut of one of Samsung’s 200Mp camera sensors, in fact the phone uses a brand-new Sony sensor for its main camera, at aperture f/1.9. The new IMX989 is notable for being a 1in sensor – similar to the one equipped by Sony’s own Xperia Pro-I.

It’s paired with 48Mp ultrawide and 5x periscope lenses in essentially the same camera setup as the Mi 11 Ultra, outside of the upgraded main lens. The fourth rear lens is simply a ToF sensor for depth detection, while the selfie camera is be a 20Mp shooter.

6.73in, 120Hz LTPO 2.0, 2K AMOLED display

Qualcomm Snapdragon 8+ Gen 1

8/12GB RAM

256/512GB storage

50Mp, f/1.9 OIS main camera

48Mp, f/2.2 ultrawide camera

48Mp, f/4.1 OIS 5x periscope camera

32Mp selfie camera

4860mAh battery

67W wired charging

50W wireless charging

IP68 rating

Gorilla Glass Victus

Android Adaptive Battery: Everything You Need To Know

Limiting background apps

The most common way Adaptive Battery saves minutes to hours of battery life is by restricting how apps run in the background. As mentioned earlier, some apps can consume a lot of power without you knowing it. When Adaptive Battery is turned on and an app is running too heavily, you will get a notification with the option to put it to sleep.

Over time, Adaptive Battery will learn which apps take up the most background usage and limit some of their functions. This doesn’t really affect your phone performance, but it means the battery won’t drain drastically when your phone is idle.

Learning your habits

Another way Adaptive Battery lives up to its name is by learning the patterns of how you use your phone. After having Adaptive Battery enabled for some time, your phone will keep track of what apps you use the most, how long you use them, and how quickly your battery drains when not optimized.

Eventually, your Android phone will utilize this data to fine-tune how it expends a full battery so that it can last throughout your daily usage. A crucial piece of information in this equation is learning your charging habits. Once the time is regular enough, Android will start stretching your battery life to when it anticipates you will plug in your phone to charge.

Reducing performance

One of the subtler ways Adaptive Battery improves battery life is by slightly reducing performance. Chips take a lot of power, and your battery can last much longer if that power is reduced to match your current needs.

On Samsung Galaxy phones, a complimentary feature to Adaptive Battery in the settings lets you change your device’s processing speed. This tool can save you more battery life than Android’s Adaptive Battery does. The Exynos processor in the flagship S-series devices barely lags in the “optimized” state, either. However, the difference in performance can be more noticeable in other Android devices, but if you are out and about without a charger, that might not be a concern.

How to turn on Android Adaptive Battery

Here’s how to turn on Adaptive Battery on a Google Pixel or Samsung Galaxy phone.

Google Pixel

Navigate to the settings by swiping down from the top screen and tapping the Settings cog. Then tap Battery. Select Adaptive Preferences, and lastly, hit the toggle on Adaptive Battery.

Samsung Galaxy

Navigate to the settings by swiping down on the screen and tapping the Settings cog. Select Battery and device care. You can optimize your battery usage here. Tap the Battery readout near the top, then scroll down to select More battery settings. There, you’ll find the Adaptive battery toggle.

Adaptive Battery will treat all apps the same, but you can manually give certain apps exceptions or stricter limitations. Navigate to your Settings and select Apps. From there, select the app you want to manage, scroll down to choose Battery, and select the desired battery usage for that app.

Overall, Adaptive Battery is a great feature to extend the life of your Android device. If you find your battery isn’t lasting long enough, try turning it on or limiting the usage of energy-hungry apps. Remember that Adaptive Battery needs time to learn your usage habits and may not work immediately, but you should notice results soon enough.

FAQs

If you find your battery life isn’t lasting long enough, then you should consider turning on Adaptive Battery to meet your usage habits better.

Fast charging won’t damage your battery. However, if you leave your phone plugged in for an extended time, you might consider turning on Protect battery in More battery settings. This will limit the maximum charge to 85% to help extend the lifespan of your battery.

Adaptive charging can help extend the lifespan of your battery and keep your device from running hot while charging. Learn more in our guide.

Quite the opposite. Adaptive Battery reduces the amount that apps and background processes drain your battery, extending your device’s battery life.

You should typically keep the adaptive battery feature on. It learns your usage patterns and optimizes apps’ battery usage based on that, which can help extend battery life. However, if you find that important apps are being prematurely shut down or are not working correctly, you may want to consider turning it off.

No, the adaptive battery feature does not slow charging. Its purpose is to manage how apps use the battery, not how the battery charges. However, some phones have an adaptive (or smart) charging feature, separate from the adaptive battery, that can slow charging to prevent battery aging.

Yes, adaptive charging can improve battery lifespan over time. This feature is designed to control the charging speed and avoid keeping the battery at 100% for extended periods, which can reduce the battery’s overall lifespan. It usually works by learning your daily charging patterns and holding the charge at 80%, only to fill to 100% just before you typically unplug it.

Mafia: Definitive Edition – Everything You Need To Know

Mafia: Definitive Edition – Everything you need to know

443

Share

X

It is a remake of the game that shares the same name and premise, and it pays tribute to the games of the early 2000s.

It is part of the Planned Mafia: Trilogy and 2 more games are to follow in the near future.

An excellent remake of a classic.

Experience a breathtaking cityscape.

Realistic recreation of Prohibition-era USA.

Plenty of Trophies and easter eggs to collect.

You need a beefy PC to run it

Check price

What’s new in Mafia: Definitive Edition?

The short answer would be that pretty much everything and nothing is new at the same time.

Following the trend of many gaming hits of the past that got remakes, Mafia: Definitive Edition is not just Mafia with new textures and better lighting, but rather a new game built from the ground up using the latest technologies.

The overall purpose of the game was to bring back what made the original so iconic and expand upon it. This includes recreating the 1930s cityscape, improving the musical score, and more.

You can see this for yourself from the announcement trailer below:

However, there are a few things that you can try in the game that weren’t in the 2002 original, and that is called Free Ride Mode.

This interesting new feature lets gamers explore Lost Heaven in a free-form sandbox mode where they can test drive the vehicles in their garage.

More so, easter eggs and collectibles have been hinted as well.

What consoles can I play Mafia: Definitive Edition on?

Mafia: Definitive Edition can run on Xbox, PS4, and Windows PC via Steam and the Epic Games Store, but thanks to the backward compatibility it will also be playable on the Xbox Series X and Xbox Series S, as well as the PlayStation 5.

Do I need a powerful PC to run Mafia: Definitive Edition?

Since for all intents and purposes Mafia: Definitive Edition can be considered a brand-new game, don’t expect a PC that ran the 2002 version smoothly to be anywhere near powerful enough to run this game.

That being said, here are the minimum specs for Mafia: Definitive Edition:

CPU: Intel Core-i5 2550K 3.4GHz / AMD FX 8120 3.1 GHz

RAM: 6 GB

OS: Windows 10 64-bit

Video card: NVIDIA GeForce GTX 660 / AMD Radeon HD 7870

Pixel Shader: 5.0

Vertex Shader: 5.0

Sound Card: DirectX Compatible

Free disk space: 50 GB

Dedicated Video RAM: 2048 MB

And here are the recommended system requirements, as per the System Requirements Lab:

CPU: Intel Core-i7 3770 3.4GHz / AMD FX-8350 4.2GHz

RAM: 16 GB

OS: Windows 10 64-bit

Video card: NVIDIA GeForce GTX 1080 / AMD Radeon RX 5700

Pixel Shader: 5.1

Vertex Shader: 5.1

Free disk space: 50 MB

Dedicated Video RAM: 8192 MB

Of course, there’s always room for improvement when it comes to gaming, and a PC booster will help you play Mafia: Definitive Edition even better.

That is why we recommend you give Game Fire a try, as it will make your PC give it’s all during intense gaming sessions.

Game Fire

Play Mafia at maximum performance with the help of this dedicated game booster. Get it now at a special price for a limited time!

Get it free Visit website

When will the game become available?

The game is set to be released tonight at around 4 PM PDT for US users, and players can then start downloading and playing the game immediately.

Was this page helpful?

x

Start a conversation

Update the detailed information about How To Repost On Tiktok: Everything You Need To Know on the Hatcungthantuong.com website. We hope the article's content will meet your needs, and we will regularly update the information to provide you with the fastest and most accurate information. Have a great day!