You are reading the article Looking For Liquidity? Here’s Everything To Know About Nft Lending updated in December 2023 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 January 2024 Looking For Liquidity? Here’s Everything To Know About Nft Lending
The last 24 months have been a whirlwind for NFT enthusiasts, with unprecedented demand for digital ownership creating a new and exciting asset class right before our eyes. But eventually, all new toys lose their shine. And after a crazy period of buying, selling, and trading NFTs, investors seek new ways to leverage their assets.
Enter the rise of fractionalized ownership, staking, and NFT’s hottest new sector: lending.
You read that right. People are lending their relatively-illiquid JPEGs for instant payouts in crypto and cash. And it’s become a massive sector of the market.
It’s finally time to break down the basics of NFT lending — how it really works, and the different types of lending models.
But first, a definition.
What is NFT lending?NFT lending is the act of collateralizing your NFT as a loan in exchange for immediate crypto payment. And it solves the asset class’ most significant problem: liquidity. Relative to other asset classes, NFTs are relatively illiquid — meaning it’s not easy to quickly sell your NFT for its designated market value in cash (or cryptocurrency). In other words, it can take months for someone to buy your JPEG. Additionally, for investors with sizable investment allocations tied up in NFTs, quick access to liquid capital can sometimes be a tall order. Loans also provide NFT owners with a means to generate non-taxable income, as opposed to the tax implications of a sale.
Here’s how it works: The borrower needs a loan and puts up an asset as collateral (NFT). The lender supplies the loan in exchange for interest. But if the borrower can’t repay the loan on the agreed terms, the lender will receive the collateral. In most cases, this process is autonomously executed by smart contracts on the blockchain.
But in all cases, NFT lending is executed via one of four main models, each with its own benefits and drawbacks.
Peer-to-Peer: NFT lending platforms made simpleThe simplest form of NFT lending is peer-to-peer, since it closely resembles the relationship between a borrower and a lender you can find at your local bank.
Most transactions take place on peer-to-peer NFT lending platforms like NFTfi, and follow a similar process. But unlike borrowing against an asset with a stable price, NFTs are a bit more tricky. The market is incredibly volatile, which means the market value of an NFT today may be significantly different than its value down the line. So how do you appraise its current value?
The truth is, it depends. Most peer-to-peer lending platforms use a simple offer system to allow anyone to make loans and set terms without a centralized or third-party intermediary.
A user will list their NFT on the platform and receive loan offers based on the lender’s perceived collateral value of the NFT. If the borrower accepts the offer, they will immediately receive a wrapper ETH or DAI from the lender’s wallet. Simultaneously, the platform will automatically transfer the borrower’s NFT into a digital escrow vault (read: smart contract) until the loan is either repaid or expires. If the borrower defaults on the loan, the smart contract automatically transfers the NFT into the lender’s wallet.
Consolidating multiple NFTs with Arcade, and moreThe bottom line? Peer-to-peer lending has emerged as the most favorable option for both borrowers and lenders, mainly due to its ease of use and security. The flexibility for both parties to set terms helps to account for rare NFT traits, and the smart contract logic within the escrow process is fairly straightforward. However, it’s important to note that peer-to-peer lending may not be the quickest model, since it relies on a borrower finding a lender willing to agree to set terms mutually.
According to Richard Chen, General Partner at cryptocurrency-focused investment firm 1confirmation, peer-to-peer lending is not only the safest model, but also the most liquid and competitive on the lending side.
“If you list a CryptoPunk on NFTfi, you’ll get a dozen offers pretty quickly,” said Chen in an interview with nft now. As DeFi yields have fallen, DeFi lenders have shifted toward NFT lending, since that’s where the highest yields in crypto are right now.”
Peer-to-Pool NFT lendingAs the name suggests, peer-to-pool lending allows users to borrow directly from a liquidity pool, rather than wait to find a suitable lender match. To assign value to the collateralized NFTs, peer-to-pool platforms like BendDAO use blockchain bridges (Chainlink oracles, to be specific) to obtain floor price information from OpenSea and then allow users to instantly access a set percentage of their NFTs floor price as an NFT-backed loan. The NFT is then simultaneously locked within the protocol.
When liquidation happens, it’s not based on the time of repayment. Instead, it occurs when the health factor of the loan — which is a numeric representation of safety comprised of the collateralized market value and the outstanding loan amount — falls below a certain threshold. However, the borrower has 48 hours to repay the loan and reclaim their collateral.
Meanwhile, lenders who supplied liquidity to the liquidity pool receive interest-bearing bendETH tokens, where the price is pegged one-to-one with the initial deposit.
“Given the illiquidity of NFTs, The price oracles used in peer-to-pool can be manipulated much more easily compared to other tokens,” said Chen. To him, good NFT appraisal tools like Deep NFT Value exist, but there’s “no oracle infrastructure yet, so teams are running their own centralized oracles which are prone to infrastructure hacking risk.”
Non-fungible debt positionsA spin-off of MakerDAO’s collateralized debt position structure, where borrowers over-collateralize ETH (a risky asset) in exchange for DAI (a less-risky stablecoin), non-fungible debt positions offer a similar deal. But on NFDP platforms like JPEG’d, instead of depositing ETH in exchange for a DAI, borrowers deposit select blue-chip NFTs and receive $PUSd, a synthetic stablecoin pegged to USD, in return.
Like peer-to-pool lending, JPEG’d uses custom chainlink oracles to fetch and maintain on-chain pricing data. The goal? To combine floor prices and sales data to price collateral in real-time with high accuracy.
Non-fungible debt positions are still very new, and will need to mature more before it’s considered a reputable lending model. Collateralized debt positions on MakerDAO are over-collateralized by 150 percent (or 1.5 times), to mitigate the volatility of ETH. NFTs are even more volatile, and the lack of need for over-collateralization raises some concern about the unpredictability of the NFT market and future liquidations. Additionally. JPEG’d is currently the only platform offering this structure, and is limited solely to CryptoPunks, so the available market is tiny, and the platform risk is quite high. All things considered, non-fungible debt positions should command close scrutiny as it unfolds.
NFT rentals and leasing via capitalBreaking rank with the other three structures, NFT renting allows NFT holders to lease out their NFTs in exchange for upfront capital. Platforms like ReNFT operate similar to peer-to-peer marketplaces, enabling renters and tenants to transact with varying rental terms and agreements without waiting for permission.
Like exchanges on NFTfi, all rental transactions are facilitated by smart contracts. But instead of a borrower sacrificing an NFT as collateral and locking it into a digital vault, the NFT is transferred to another person’s wallet for a specified period. In exchange, the “borrower” receives a lump sum of cryptocurrency. At the end of the predetermined period, the NFT is automatically returned to its owner. This is the simple form of “lending,” since there are no repayment terms, interest, or worry of liquidation.
Unlike other forms of lending where lenders are rewarded by earning interest, NFT rentals generally give lenders access and credibility. The NFT space thrives on social proof, and owning an expensive NFT can increase attention and recognition in the space. Some communities are also token-gated, where renting an NFT helps users gain exposure to people and experiences they may not otherwise acquire. Similar to renting clothing, cars, or other items of status-laden material, the emerging sector of NFT rentals is poised to become one of the most enduring ones.
Ultimately, whether NFT lending is the right decision for you specifically boils down to your time horizon and risk tolerance. Like all crypto protocols, it’s essential to do your own research and not over-leverage or invest money you’re not comfortable losing.
You're reading Looking For Liquidity? Here’s Everything To Know About Nft Lending
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-LearnThis 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-learnScikit-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 UpdatesAlong with bug fixes and performance improvements, here are some new features that are included in scikit-learn’s latest version.
Stacking Classifier & RegressorStacking 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 DecisionTreeClassifierfrom
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 ImportanceAs 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-AUCThe 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 ImputationIn 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
)
(
imputer
.
fit_transform
(
X
))
You can read about how kNN works in comprehensive detail here.
Tree PruningIn 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
)
(
"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
)
(
"Average number of nodes with pruning
{:.1f}
"
.
format
(
np
.
mean
([
e
.
tree_
.
node_count
for
e
in
rf
.
estimators_
])))
End NotesThe 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
Everything You Need To Know About Twitter Advanced Search
And while many still view Twitter as a mere social media platform for following friends and being followed, a significant number are exploiting its immense potential as a business marketing platform, specifically for lead generation.
In fact, 82% of B2B marketers preferred using Twitter in 2023, which makes it second only to Linkedin for that audience.
Who Will Marketers Find With Twitter Search?Contrary to earlier expectations – that Twitter growth would decline or flatten out – statistics show that Twitter growth is anything but stagnant.
Based on Twitter’s most recent quarterly filing, usage data shows that the platform grew by 24% relative to the preceding quarter, representing an additional 14 million users.
In the US, as of September 2023, 52% of Twitter users reported accessing (and probably using) the social media platform every, single, day.
And the Twitter Search function— specifically its Advanced Search feature— has huge business potential, through lead generation, for changing the marketing landscape.
What Exactly Is the Twitter Advanced Search Feature?There are two Search features: the general Search feature (that you’ve most likely used) and the Advanced Search feature.
Let’s first explain the general Search feature.
The general Search feature is on the right-hand side of your laptop.
If you’re using a smartphone, however, the general Search is represented by the magnifying icon at the bottom of your mobile phone screen.
You can’t glean much from these general searches because your search range is limited.
That’s where the Advanced Search feature comes in.
How to Access Twitter’s Advanced Search FeatureYou can access the Twitter Advanced Search by first using the general Search feature referenced above.
On your laptop, key in your search phrase. Let’s choose [Tesla], the company associated with Elon Musk.
After typing [Tesla] in the general search field, you’ll spot some three straight dots on the right-hand side of the screen.
If you point your cursor at the three dots, the word “more” will pop up.
By now, you’re probably saying to yourself, “That’s great for desktop users. But what about mobile?”
Advanced Search FieldsYou’re going to get a pretty long list of search fields to choose from, which can be a little overwhelming. For example, under the heading “Words,” this list appears:
All of these words.
This exact phrase.
Any of these words.
None of these words.
These hashtags.
Written in (language).
Other headings include:
People.
Places.
Dates.
Other.
What’s cool about these fields is that you aren’t limited to choosing just one. So, for example, you could perform a search that combined an exact phrase, from specific accounts, during a specific date range.
Before moving on to what happens after you hit Search, let’s explore some of the common search queries and how you can use them to refine your searches.
How to Use the “Words” Field in Advanced SearchPerhaps the most commonly used search feature, there are six fields under the “Words” Search category on Twitter’s Advanced Search.
Let’s take a look at each so you understand what they mean.
All of These WordsUse this field to find tweets that include the words or phrase you type. To ensure the best search results, use quotation marks.
If you’re searching for tweets and you aren’t sure of the exact phrase, you can query some relevant and specific words — whatever you think the original tweet or tweets must have contained.
This Exact PhraseUse this field to enter a very specific phrase.
So if you were searching for tweets about Heisman Trophy winners that attended USC, for example, you would enter [USC Heisman Trophy Winners] instead of just [Heisman Trophy Winners].
One cool thing is that quotes are automatically added in this field.
Any of These WordsUse this field to search for a group of related words.
The search field would add the word “or” between each word or phrase you typed to trigger the most specific results.
None of These WordsIf you want to search Twitter but omit certain words or phrases, this is your search option. Using this field will eliminate any tweets that contain words or phrases you don’t want to be included in a search result.
For example, you may want results for [USC] but exclude any mention of [USC football].
This is an effective way for businesses to exclude search results for the names of other companies in the same industry.
These HashtagsIf you want to search for a hashtag, whether it’s a hashtag of your brand or the hashtag of a competitor, this is the relevant search option. For example, if you’re a sneaker company and you want to check out what people are tweeting about regarding a big brand, you could type [#Nike] to get results.
Any LanguageUse this field if you want to get information about tweets written in one of the more than 50 foreign languages listed.
This may help if you are analyzing the foreign market in your industry or have a brand that operates in multiple countries.
How to Use The “Account” Field When Doing Advanced SearchFormerly named “People,” under “Accounts,” there are three different fields that can alter your search results depending on what you type.
Just type the username of a person with a Twitter account, or several usernames separated by a comma.
Then choose whether you want results exclusively sent by that account, to that account, or that mentioned that account.
Just remember to use proper Twitter accounts that begin with @.
How to Use The “Engagement” Field When Doing Advanced Twitter SearchThe depth of engagement is measured by the number of retweets, replies, and likes a tweet receives. A tweet that generates thousands of retweets is doubtless an engaging tweet.
The way to do this is to specify the minimum number of retweets, likes, or replies in the search field or any combination of those three.
By doing this, you will absolutely weed out posts that add more noise and don’t add value to your search.
How to Use The “Dates” Field When Doing Advanced Twitter SearchIf you remember a tweet but you can’t remember the exact date it was tweeted, the “Date” option has you covered, that is if you can remember the period within which the tweet was sent out.
Under the heading ‘Dates,’ you can select a specific date range and only receive search results from that time.
You’ll simply enter the date ranges, and the relevant tweet will be displayed, regardless of how long ago the phrase was tweeted.
One thing to note is that the first tweet was sent on March 21, 2006, so the system would default to that date if you entered a date earlier than that one.
Don’t Forget the Search OperatorsThese will help you weed out the useful tweets from the pics of what users had for dinner.
How to Understand Advanced Twitter Search ResultsTwitter has an algorithm that will determine how you get the search results and in which order.
While what drives the nature and order of Twitter’s Advanced Search results isn’t known with certainty, several metrics may play a role.
These may include tweets that have elicited a certain level of reaction, relevance, time-lapse of the tweet, and location, among others.
After you have typed in all the information to generate a search, the results page will include the headings:
Top: This is a list of what Twitter considers the tweets that are triggering the most reaction.
People: These are the accounts of users based on your search criteria.
Videos: These are tweets with links to videos on other websites.
After reviewing the results, you may realize that you need to refine or broaden your search. You can do that by simply going back to the Advanced Search page.
Depending on the level of analysis needed, there are also paid programs that can email alerts based on hashtags, business name, etc. An example of this is Twilert, which is basically a Google Alerts for Twitter.
There are several ways to use this Twitter search feature to drive revenue through lead generation.
One way to do this is to assess what customers complain about regarding your competitors’ products. Then you can use this information to improve your product.
The Advanced Search feature can provide your business with valuable demographic information, information about prospects in your local area, and lead generation opportunities.
When your goal is lead generation, you will want to focus on these areas:
People.
Places.
Dates.
Other.
1. Competitor mentions
2. Product or service mentions
Type in the name of a product or service you offer under Words. If you sell computer security software, you would type that phrase + a “?”.
You can use the results to create a database of people who have made inquiries about products or services you sell and reach out to them about what you can offer.
But they are effective because they identify people who are unhappy with products or services they purchased or people who have expressed interest in those products and services but may have not yet engaged with a seller.
But there are other ways.
Check out these 8 Terrific Tips to Optimize a Twitter Business or Brand Profile.
Another Tool in the Your Marketing Tool ChestTwitter Advanced Search helps you to analyze your market, judge how your competitors are doing based on positive or negative sentiment, and improve your geotargeting based on the number of tweets in specific locations.
It may take a few searches before you understand all the ways you can manipulate the results.
But once you do, you can hone in on the information you need to help boost your Twitter marketing efforts.
More Resources:
Image Credits
Everything You Never Wanted To Know About Your Genes
Everything You Never Wanted to Know About Your Genes George Annas on the promise and shortcomings of the pending genetics privacy law
Bioethicist George Annas says the current legislation is a watered-down version of a bill drafted at BU in the 1990s.
A bill that would bar insurers and employers from discriminating against people whose genetic tests show they may be prone to cancer, Alzheimer’s, or other diseases has been stalled in Congress for years. Last week, the Genetic Information Nondiscrimination Act finally made it through the House and now must win Senate approval. President Bush has said that he will sign the bill, which prohibits insurers from denying coverage or raising rates for people whose genes suggest a predisposition to a disease or medical condition and prevents employers from basing hiring decisions on that information.
To learn about the history and the promise of the act, BU Today spoke with George Annas, the Edward R. Utley Professor of Health Law, Bioethics, and Human Rights at the School of Public Health and chair of the SPH health law, bioethics, and human rights department.
The insurance companies weren’t that happy with this. This was back in the mid 1990s, when most people involved in the genome project felt that once we had the genome, we were going to be able to predict lots of stuff. And maybe someday we will. But we’re not there yet. But the insurance companies felt, oh my god, if you can predict, here’s what’s going to happen: people are going to go and get their genome done, find out they only have 10 years to live, and then go and buy a billion-dollar policy. Well, of course the way to get around that was to limit the amount of money you could buy a policy for without a genetic test, for example, or answering questions about family history. And employers were worried that we were going to find out some stuff about your genetics that was going to show that you’re susceptible to x, y, z disease, and therefore if you work for us, you have a higher probability of getting that disease.
As a result of that, they backed off of genetic privacy, and said, let’s assume that everybody’s going to find this information out anyway, and let’s just do genetic discrimination. Let’s tell people that they shouldn’t worry about being tested because we are going to prohibit their insurance companies and their employers from discriminating against them on the basis of a genetic test. And that’s what this is. So this is the ultimate legislation. The bill has been so watered down. It doesn’t really do very much.
That’s the kind of thing researchers worry about. Now, companies worry about some of the same issues. They worry about, what if they find a correlation in these genes and make a product out of it — do they own it or can the people who provided the tissue claim some ownership in it?
I don’t know if you read about Jim Watson. This is too perfect. James Watson discovered the structure of DNA and is a giant booster of all things genetic. He wants genetic engineering; he wants to make designer babies, if we can ever do it. So a company told Watson that they’d do his genome for $1,000, and he said great, and then he called them back and said, wait a minute, just don’t do the section of the genome that codes for Alzheimer’s disease. I don’t know if it’s because he didn’t want to know or if he didn’t want anybody else to know. Genetic privacy is two things: one, things you don’t want to know yourself, and two, things you want to know but you want to make sure nobody else knows. That’s the best ad for genetic privacy. Mr. Genetics says no, there are some things better left unknown.
Chris Berdik can be reached at [email protected]. Art Jahnke can be reached at [email protected].
Explore Related Topics:
Everything We Think We Know About Iphone 5C
It’s now widely accepted Apple will, for the first time in iPhone history, launch not one but two new iPhones this coming Tuesday. One, a flagship iPhone 5S, and the other a long-rumored budget iPhone that should help the company tap emerging markets where telcos rarely subsidize devices.
Critics assume the so-called iPhone 5C will somehow flop because it’ll have a plastic shell. In reality, coupled with Apple’s marketing prowess and brand power, the iPhone 5C will widen Apple’s price umbrella and replace the $450 off-contract iPhone 4S as Apple’s most affordable iPhone yet – without offering a two-year old hardware.
Basically an iPhone 5 redesigned around the polycarbonate plastic casing offered in a variety of bright colors, the iPhone 5C is meant to improve Apple’s standing in emerging markets like China, India and Russia while appealing to those who are looking for a more affordable alternative to Apple’s flagship iPhone.
Here’s what we think we know so far about Apple’s controversial plastic iPhone, based on the widely-accepted rumors, leaks and reports by reliable bloggers and credulous publications.
The name: just don’t call it cheapAt first, journalists dubbed it the budget iPhone, with big media such as WSJ, Reuters and Bloomberg opting for the politically correct nicknames to refer to a ‘less-pricey’, ‘low-cost’ or ‘inexpensive iPhone’.
It wasn’t until chúng tôi published a set of photos showing a bunch of boxes with the label ‘iPhone 5C’ on the side that the blogosphere adopted the moniker.
Since then, a number of analysts and outlets have confirmed the name, including WSJ, Bloomberg and China Telecom. So, what does the ‘C’ in the iPhone 5C denomination stand for? A number of theories are floating around: some say the ‘C’ means Colors.
One thing is pretty certain: the ‘C’ does not stand for cheap. Apple’s never added the letter ‘C’ to an iPhone model before, leading some watchers to conjure the company might perhaps market the handset as an ‘iPhone C’. Which brings me to my question of the day.
iPhone 5C packaging render.
iPhone 5C packaging render.
Assuming Apple sticks to its mid-cycle S-upgrades, what is a 2014 plastic iPhone going to be called? An iPhone 5CS? An iPhone CS? An iPhone 6C? What about 2023? There should be an iPhone 6S in 2023 so what’ll they call its budget variant, an iPhone 6CS?
Perhaps Apple should think about rethinking its iPhone naming convention?
Design: scratch-resistant plastic shellBorrowing vague design cues from the iPhone 3G/3GS, the iPhone 5C ditches the two-tone aluminum iPhone 5 design for a rounded polycarbonate plastic casing that’s much easier to shape than aluminum. Even the lens cover appears to be plastic (the iPhone 5 uses sapphire crystal for added protection). The all-plastic casing obviously reduces parts and assembly costs, allowing for a more affordable device.
The backplate will be colorized and the front panel should be all black, though that’s inconclusive. Taking a closer look at the inside of the backpanel, the iPhone 5C uses both metal and plastic parts to support the internal components.
A leaked manual clearly depicts the Lightning connector and standard hardware buttons, including the mute switch and Home, power/sleep and volume up/down buttons.
The volume up and down buttons (below) are more of a pill shape, somewhat resembling the buttons found on the fifth-generation iPod touch. Now, even though it’s made from plastic, the handset’s casing is described as pretty thick and substantial, making the iPhone 5C slightly thicker, wider and heavier than its counterpart, as seen below.
According to a scratch-resistance video by Taiwan’s Apple Daily, the iPhone 5C has a surface hardness of 8H on the pencil hardness test, or three times stronger than the regular PET film used to protect the iPhone’s display from scratches.
KGI Securities analyst Ming-Chi Kuo pegs the plastic body between 0.4 and 0.6 millimeters thick versus the average plastic casing at between 0.7 and 1 millimeter. The mix of glass fiber and plastic allows for a stronger, thinner and lighter appearance compared to other plasticky casings.
Along the bottom (from left to right) there’s the headphone jack, the mic hole, space for a screw, the Lightning connector, another hole for a screw and the cutouts for the speaker – again, resembling more the fifth-generation iPod touch than the iPhone 5.
On the back, there’s an iSight camera, noise-canceling microphone and standard LED flash (leaving the rumored dual-LED flash exclusive to the iPhone 5S).
The expected cutout for the SIM tray on the left appears to be sized appropriately for the Nano SIM cards, like the iPhone 5. Like all prior handset models, the branding on the back simply says ‘iPhone’, with no model-specific moniker.
The display: four-inch Retina screenThere’s no question the iPhone 5C will pack in a four-inch display, even if the less-informed analysts have conjured up that the screen won’t be Retina for cost-saving reasons. That couldn’t be further from truth.
Make no mistake about it, Apple’s 2013 iPhone lineup is going to be all-Retina. The same goes for unfounded talk of a 3.5-inch display, which can be easily disputed by a myriad of leaked back shells.
Will it be plastic with white or black front?
Will it be plastic with white or black front?
Bottom line: worst case, Apple could save a few bucks by outfitting the device with a non-IPS LCD technology, meaning poor man’s viewing angles like on the iPod touch.
Cameras: no surprises hereBy all accounts, the iPhone 5C features the same front-facing FaceTime camera with a rather paltry 1.2-megapixel photos and bearable 720p HD video with up to 30 frames per second. The back-facing iSight camera is thought to be of an eight-megapixel variety with full HD 1080p video capture at up to 30 frames per second – essentially the same as on the iPhone 5.
iPhone 5C protective case rendering.
Internals: similar to the iPhone 5The rumor-mill seemingly agrees that many of the internals inside the iPhone 5C will match up with the currently available iPhone 5. This totally makes sense: Apple’s been building the iPhone 5 for a year and they must have optimized manufacturing and parts costs by now.
I imagine Apple’s supply chain maestro Tim Cook opted to re-use the iPhone 5 components to keep bill of materials at a minimum as opposed to engineering brand new parts. Here is reliable analyst Ming-Chi Kuo’s view of iPhone 5C internals.
The iPhone 5 logic board (left) lines up nicely with the iPhone 5C screw holes (right).
Networking: no Gigabit Wi-Fi, NFC and LTE+The iPhone 5 logic board (left) lines up nicely with the iPhone 5C screw holes (right).
At a minimum, the iPhone 5C should support all flavors of fourth-generation Long-Term Evolution (LTE) technology the iPhone 5 does. We’re not expecting support for up to three times faster LTE Advanced. LTE Advanced (also known as LTE-A or LTE+) allows for theoretical simultaneous download and upload speeds of 300 megabits per second, or up to three times faster than current LTE theoretical speeds.
While LTE Advanced networking is conceivable on the iPhone 5S, cost considerations and the currently very low penetration rate of this latest standard make the ultra-fast 150MBit LTE Advanced a no-go on the iPhone 5C.
On the other hand, if China Mobile will sell the iPhone 5C, the device must support the telco’s nascent TD-LTE 4G network. There could also be a special China Mobile version of the iPhone 5C, even if that runs contrast to Apple’s penchant for keeping things simple.
Whether or not the iPhone 5C supports 802.11ac – the latest in Wi-Fi networking – is up for the debate. Also known as Gigabit Wi-Fi, the standard promises three times the data throughput of the conventional Wi-Fi standard.
Now, only the 2013 editions of the MacBook Air family and the AirPort Express/Time Capsule wireless appliances sport 802.11ac networking . The upcoming Mac Pro will have it and it’s fairly safe to speculate that Gigabit Wi-Fi will be part of the iPad 5 and iPad mini 2, as well as Haswell-focused iMac, Mac mini and MacBook Pro refreshes.
AirDrop, one of the headline new iOS 7 features, is hardware-dependent. Even though AirDrop does not specifically require 802.11ac chips, the feature is currently supported only on the iPhone 5, iPad 4, iPad mini and fifth-generation iPod touch.
That said, we imagine the same Wi-Fi networking capabilities from the iPhone 5 will be supported on the iPhone 5C, at a minimum. Chances of Apple giving the iPhone 5C LTE Advanced and 802.11ac capabilities are slim – it’s perfectly plausible these features are exclusive to the flagship iPhone 5S.
No, we’re not expecting NFC either.
Summing up, we think the iPhone 5C will feature Bluetooth 4.0, 802.11n Wi-Fi on 2.4 and 5GHz bands, aGPS and GLONASS, as well as DC-HSPA+ and LTE.
Colors: five bright colorwaysThe iPhone 5C is expected to come in various colorways, some of them revealed by the frequent part-spotter Sonny Dickson. In total, the handset should be offered in five different colors: blue, green, yellow, white and red – the latter appearing to be more of a pinkish hue.
Sketchy reports have also called for a completely black iPhone 5C, though the leaked shots were later debunked as fake.
The fake iPhone 5C backplate.
The fake iPhone 5C backplate.
Here is a potential green variant without the sticker.
And the following image apparently represents a bunch of iPhone 5C units being warmed up at Pegatron’s Shanghai plant for quality assurance purposes.
Pegatron is Apple’s primary assembler of the iPhone 5C as Apple seeks to diversify risk after last year Foxconn manufacturing glitches with the iPhone 5 (hint: scratches, nicks and Scuffgate), according to WSJ.
The contract manufacturer, named after the mythical flying horse Pegasus, became a minor producer of iPhones in 2011 and started assembling iPad minis last year.
Packaging: plastic box ala iPod touchApple’s never made a plastic iPhone box before so folks were taken aback when chúng tôi published photos depicting boxes with the label ‘iPhone 5C’ on the side.
Though we can’t vouch for their authenticity, a number of photos from various sources have corroborated that the device will come inside a transparent plastic box that looks a lot like the iPod touch packaging.
Here’s iPhone 5C packaging shared by chúng tôi in late-July.
And below is the current iPod touch plastic box.
And here’s apparently a blue iPhone 5C, still in its retail packaging.
Watchful readers could point out Apple since 2012 has been color-matching the external coloring of the iPod nanos with the wallpaper, as can be seen below.
Summing up, as the iPhone 5C is all about building a more affordable device for cash-strapped buyers. Therefore, it’s not entirely inconceivable that cost saving measures would extend to the product’s retail packaging. Another benefit of transparent plastic boxes: folks get to see the actual device right on store shelves.
Pricing: think mid-range, not cheapThe iPhone 5C should be sold for a significantly lower price than the flagship iPhone 5S when purchased off-contract, with a full retail price pegged at $350-$450 or $400-$500, depending on whom you ask. At any rate, That’s considerably more affordable than the $649 price of the unsubsidized iPhone 5.
While Apple is believed to offer the iPhone 5C both on and off-contract from day one – unlike past iPhone releases where a contract-free variant arrived a few months following the launch – carrier subsidies should bring the upfront payment down to something like $99 for customers willing to sign on the dotted line.
Assuming the off-contract iPhone 5C starts out at $450 for the basic model with sixteen gigabytes of storage (or perhaps even 8GB?), the 32/64GB tiers should translate to $550 and $650, respectively. Sorry, we’re not expecting a 128GB iPhone 5C variant – yes, another iPhone 5S exclusive.
One caveat: should Apple drop the iPhone 5/4S/4 from the lineup come September 20, the on-contract iPhone 5C could just as well become Apple’s new “free” iPhone.
I’m only speculating here, but the aforementioned price matrix would be consistent with Apple’s strategy of discounting previous-generation iPhones. Oh, we also don’t expect the iPhone 4S to be offered shortly as its smaller 3.5-inch screen would feel oddly out of place in the iPhone 5/5S/5C four-inch Retina lineup.
Last but not least, it’ll be interesting what ad tactics Apple’s marketing wizards cook up to pitch a plastic iPhone to consumers without sounding like a lesser-buy.
Availability: September 20, coming later to China Mobile and NTT DoCoMoWe’re expecting both the iPhone 5C and iPhone 5S to be available across Apple’s key markets beginning Friday, September 20, with iOS 7 preloaded. This should give Apple a period of ten days to collect pre-orders between the September 10 announcement and wider September 20 availability.
As evidenced by employee vacation blackouts – not only by Apple, but major U.S. carriers as well – the device is expected to launch simultaneously across AT&T, Verizon and T-Mobile networks. Sprint hasn’t cancelled their retail employee vacations yet so we’re guessing the telco could land the new iPhones a bit later.
The iPhone 5 launched last September in the United States, United Kingdom, Australia, Canada, France, Germany, Hong Kong, Japan and Singapore. By the end of 2012, the handset launched on 240 different carriers in a hundred countries.
WSJ all but confirmed that Apple has finally inked a landmark distribution agreement with China Mobile, the world’s largest carrier. Two days ago, Reuters confirmed that Japan’s NTT DoCoMo will start carrying iPhones this Fall. Both are huge developments for Apple as the iPhone isn’t yet available on some of the world’s largest wireless carriers.
China Mobile has 700+ million subscribers, more than AT&T and Verizon combined! NTT DoCoMo is 60 million mobile users strong. Just adding these two carriers to the fold should add up significantly to iPhone sales and Apple’s bottom line.
Sales predictionsAccording to one estimate, expanded distribution will help the company push 13 million iPhone 5S/5C units in the first ten days of sale – or more than a million units a day – per Pacific Crest analyst Andy Hargreaves. Those would be record numbers, in accordance with the ‘most successful launch in Apple’s history’ prediction.
All told, Hargreaves expects up to 13 million iPhone sales before the September quarter closes and some 31 million units during the Christmas quarter.
iPhone 5C videosIf picture is worth a thousand words, a video may be worth a million.
• iPhone 5C mystery case by Macotakara
You’re going to love these hands-on clips.
Signing off…About the flagship iPhone 5S: two-tone iPhone 5 design, colorized backplate choices – including the controversial gold/champagne and graphite variants – at least a third-faster A7 chip and improved camera with much better low light shooting, 120FPS slow motion video capture, a 12 or 13-megapixel sensor and dual-LED flash.
And of course, the central feature: a redesigned Home button with an AuthenTec fingerprint sensor for user authentication (Slide To Identify?) and more. It should be the handset’s killer feature. If it means anything, insiders liken it to the seismic shift Siri was two years ago.
Apple recently began training its AppleCare support staff and retail employees on iOS 7 and iTunes Radio so we’re expecting both devices to hit Apple Stores and carriers simultaneously. Those of you looking to dump your old device, Apple will pay up to $280 credit for used iPhones.
So what do you guys think?
Did I miss anything?
Everything You Need To Know About Nintendo And Its Consoles
Nintendo at a glance
Nintendo isn’t the only console manufacturer on the market, but it’s definitely been around the longest. First formed in 1889, the company created its first games in the 1970s before launching its first dedicated home console in 1983. Fast-forward to 2023 and the Nintendo Switch is a major sales sensation, reinforcing the company’s position in the market.
Unlike Sony and Microsoft though, Nintendo’s gaming business is its only real business, to begin with. So it doesn’t have the ability to fall back on businesses like TVs, computing, movies, and music when the going gets tough.
Nintendo consoles
Hadlee Simons / Android Authority
NESThe Nintendo Entertainment System (NES) was the company’s first proper home console, having previously launched arcade machines and the Game & Watch handheld. 1983’s NES delivered 2D visuals and support for up to 512 colors, with games coming on a cartridge.
Nintendo’s machine revived a console space that had been decimated by the videogame crash of 1983, owing to a relatively competitive price as well as quality games like Super Mario Bros, The Legend of Zelda, and Metroid.
SNES Nintendo 64 GameCubeNintendo’s successor to the N64 was the GameCube, coming in late 2001. And its design made for a breath of fresh air compared to the serious black boxes touted by Sony and Microsoft. The GameCube instead was a purple cube, featuring powerful hardware that was easy to work with, a carry handle on its back, and a disc-based format (albeit holding 1.4GB of data) for the first time in Nintendo’s home consoles.
The GameCube saw Nintendo slide further down the rankings, as the PS2 and even the original Xbox beat it at the sales tills. In any event, people who bought the Cube were treated to top-notch wares like Metroid Prime, The Legend of Zelda: Wind Waker, Mario Kart: Double Dash, Animal Crossing, and Super Smash Bros Melee.
WiiNintendo’s fortunes were revitalized in late 2006 when the company launched the Wii as a follow-up to the GameCube. The new console had a modest power boost over its predecessor, but the real game-changer was the TV remote-style controller that offered motion gestures.
This simple input method meant you could swing the controller to swing a baseball bat, point the controller at a specific area on the screen to aim an in-game weapon, or conduct a bowling motion to get a strike in ten-pin bowling.
This premise meant that the Wii was the most popular console of its generation, out-selling the Xbox 360 and PlayStation 3. The Wii’s initial performance was no doubt helped by the inclusion of Wii Sports as a pack-in title, and this combo even gained popularity in some old-age homes. We also got gems like the Super Mario Galaxy games, Xenoblade Chronicles, Metroid Prime 3, and Kirby’s Epic Yarn.
Wii UOdd name aside, 2012’s Wii U delivered an interesting concept, featuring a gamepad with a tablet-sized screen as well as supporting Wii remotes. This allowed for asynchronous gameplay, such as the remote-toting players using the TV to search for the gamepad-toting user (who was using the controller’s built-in screen). You could also use the gamepad’s small screen to play full-fledged games in case the TV was being used.
Unfortunately for Nintendo, the combo of a weird, unpolished controller (it had poor battery life and a resistive touchscreen) and underpowered internals resulted in the Wii U being the firm’s least successful home console since the Virtual Boy. It nevertheless hosted some quality games and arguably remains the best place to legally play retro games owing to backward compatibility with Wii games and the expansive Virtual Console digital service.
Switch Game Boy AdvanceHow do you top the Game Boy and Game Boy Color? Nintendo’s thinking was to essentially make a handheld that was more than a match for the SNES. That meant a 32-bit CPU, support for 32,768 colors, and the addition of L and R shoulder buttons. In fact, the GBA was powerful enough to run a variety of SNES ports and even a host of 3D games like Duke Nukem 3D, Doom, and more.
One particularly smart feature was backward compatibility with Game Boy and Game Boy Color games, so consumers could still play their old library of titles after upgrading to the new machine. Toss in roughly 15 hours of juice via two AA batteries and you had a really solid machine that destroyed all comers at the time.
Nintendo DSThe follow-up to the GBA saw Nintendo rip up conventions and decide that two screens were better than one. That was the premise of 2004’s Nintendo DS, featuring a clamshell design with a traditional display up top and a resistive touch-screen at the bottom. Nintendo also added extras like a stylus (complete with stylus slot), a microphone, and a second cartridge slot for backwards compatible GBA games.
This all made for a very quirky design, and the console wasn’t a runaway hit at first. But games like Brain Training, Nintendogs, Animal Crossing: Wild World, and more resulted in the console capturing a vast casual gamer market and becoming a massive sales success. Nintendo would go on to offer a variety of variants, such as the DS Lite and DSi range.
Nintendo 3DS2011’s 3DS saw the company pick up where the DS left off, with the new console having a similar clamshell design featuring one screen up top and a touchscreen below. This also enabled backwards compatibility with legacy DS games.
But the big trick with this new handheld was glasses-free 3D visuals, giving you a cool sense of immersion and offering a slider switch so you could adjust the strength of the effect. The handheld had a respectable level of power too, even seeing ports like Metal Gear Solid 3: Snake Eater, The Legend of Zelda: Ocarina Of Time 3D, and Luigi’s Mansion.
Nintendo later offered variants like the New Nintendo 3DS range (featuring more power and an integrated right control pad), as well as the 2DS. The latter device dropped the 3D functionality and abandoned the clamshell form factor, but offered a significantly cheaper price tag.
Nintendo controllers
Unlike Sony and its PlayStations, Nintendo has generally steered clear of using the same basic controller design for most of its consoles. Instead, with a few exceptions, we’ve seen new gamepad designs for each generation.
The NES controller back in the 1980s introduced the D-pad for the first time, while also featuring Start and Select buttons and two face buttons. Nintendo would build on this with the SNES controller, bringing four face buttons in total as well as a pair of shoulder buttons. This gamepad also delivered a more rounded design as opposed to the NES controller’s sharp corners.
Nintendo hasn’t been shy about coming up with some crazy controller designs.
What would the controller for the Wii’s successor look like? Well, the Wii U would bring a huge controller that had a tablet-sized touchscreen on it (seen above). This allowed users to either get a different perspective in games or play titles on the smaller screen entirely if the TV was in use. The rest of the Wii U controller was pretty traditional, featuring two analog sticks, four shoulder triggers, four face buttons. The gamepad did however feature a selfie camera.
Nintendo’s Switch also has some radically different controller designs, as it offers two so-called Joycon controllers. These controllers enable handheld gaming when they are attached to the Switch. But slide them off and they can be used separately, such as for local multiplayer. Each controller has two shoulder triggers, an analog stick, and two more hidden shoulder buttons that are only visible when the controllers are detached from the Switch itself. These controllers still maintain motion functionality and also offer so-called HD Rumble for better vibration.
What about Nintendo accessories?The house of Mario has sold numerous accessories for its consoles over the years. The NES got a lightgun, Robotic Operating Buddy toy, a modem, and multi-tap. But perhaps the most notable add-on was the Famicom Disk System for Japan, which was an add-on that offered disk-based games. These disks were rewritable and consumers could buy games via vending machines with their old disks.
SNES owners had quite a few accessories too, depending on their region. This included a mouse, light gun, and the Satellaview satellite modem for downloading new games and content. One noteworthy accessory was the Super Game Boy, which allowed users to play their Game Boy games via the home console.
The Nintendo 64 also had its share of accessories released throughout its lifespan, including quite a few being quirky and/or technologically interesting. Prominent accessories in this regard include the Expansion Pak (giving 4MB of extra RAM for sharper visuals or better performance), the Rumble Pak to enable controller vibration, and a microphone for voice commands in Hey You Pikachu. This console also received a Japan-only add-on dubbed the Nintendo 64DD, using proprietary rewritable disks and offering online functionality.
1-Up Studio (Mother 3, Sword of Mana)
Entertainment Planning and Development (The Legend of Zelda: Breath of the Wild, Animal Crossing: New Horizons, Splatoon 2)
Nintendo Software Technology (Mario vs Donkey Kong, Wave Race: Blue Storm)
Monolith Soft (Xenoblade Chronicles series, Project X Zone)
NDCube (Clubhouse Games, Super Mario Party)
Next Level Games (Luigi’s Mansion 3, Luigi’s Mansion: Dark Moon, Super Mario Strikers)
Retro Studios (Metroid Prime series, Donkey Kong Country: Tropical Freeze)
Notable competitorsThe Kyoto company has had several major rivals over the years, spanning both home and handheld console arenas. Some of these rivals are no longer in business, but there are still a couple of active contenders worth knowing.
Sony
Oliver Cragg / Android Authority
You could definitely argue that Sony is Nintendo’s arch-rival in the last three decades. The rivalry was actually born out of a scuppered partnership between the two in the early 1990s. Nintendo and Sony were working on a CD-based add-on for the SNES, but contractual disputes between the two companies meant that Nintendo halted the tie-up at the last minute.
Rather than let all its development work go to waste, Sony kept working on a CD-based console. This became the PlayStation, launching in 1994 in Japan and 1995 in the US. The original console would beat the Nintendo 64 in terms of global sales, while 2000’s PlayStation 2 would absolutely obliterate the competition (Nintendo’s GameCube included).
Microsoft
Oliver Cragg / Android Authority
The house of Windows was a late entrant to the console wars, joining with the original Xbox back in 2001. The company’s first effort pioneered several features that are now commonplace in the console gaming space, such as an integrated broadband adapter and hard drive.
Unbelievably, Microsoft’s first home console actually out-sold the GameCube, although it was a distant second to the all-conquering PlayStation 2. Nevertheless, this showed that the Xbox name was here to stay and that Nintendo couldn’t rest on its laurels.
Other rivals over the yearsBest moments in Nintendo history
Oliver Cragg / Android Authority
The NES revives the industryHow many companies can say they’re responsible for revitalizing an entire industry? Nintendo is one of them, as the NES was launched just after the great video game crash of 1983. The crash was caused by a flood of low-quality games and a ton of consoles, resulting in the industry almost destroying itself.
But the release of the NES in the early to mid-1980s rejuvenated the industry in a massive way, owing to competitive pricing and a slew of high-quality games. It’s tough to argue that the years that followed would be as fruitful for the industry if the NES weren’t released.
Pokemon runs rampant Nintendo DS beats Sony PSPNintendo’s home console business was a disappointment in the early 2000s due to the flagging performance of the GameCube relative to the PS2. But one bright spot was its long-running handheld division, with the Game Boy Advance line proving to be extremely popular.
Then Sony announced and launched its first handheld, the PlayStation Portable, in 2004. It’s easy to forget right now, but there was a real feeling from many observers that Sony would beat Nintendo if it ever got into the handheld space.
The PSP indeed sold very well, but there’s no denying that the DS was more popular. According to VGChartz, the DS sold over 150 million units compared to the PSP’s 81 million. Nintendo would maintain this momentum with the 3DS, which absolutely obliterated the Vita and resulted in Sony leaving the handheld business.
Nintendo Wii destroys everythingIt’s not exactly one moment, but the Wii’s massive success was a huge story from 2007 onwards. The console was hard to get at its November 2006 launch and this continued to be the case for months down the line. In fact, the machine wound up selling just over 100 million units, ahead of the PS3 and Xbox 360.
The Wii’s success was all the more satisfying due to the fact that the previous console (GameCube) had sold so poorly while the N64 also played second-fiddle to the PS1. So it represented Nintendo returning to the top of the industry.
Nintendo’s Switch is a sales sensationIt’s not necessarily one moment, but Nintendo obliterating all comers with the Switch certainly has to be up there. The company’s previous console, the Wii U, had been a disastrous commercial failure, so the pressure was on for the company to deliver on the Switch.
That’s indeed exactly what happened from 2023 onwards, as the new hybrid console quickly flew off the shelves and became tough to get. It was a very welcome change from the Wii U era, showing that Nintendo still had what it took to blow the industry away.
Worst moments in Nintendo history
Oliver Cragg / Android Authority
The Virtual Boy is a horrible failureNintendo would probably like to forget that the Virtual Boy ever happened. The 1995 console offered stereoscopic 3D visuals years before the 3DS would do the same. But the console proved to be a massive failure and was discontinued after less than a year.
A big part of its failure was the limited color palette, only displaying red and black colors that resulted in headaches being reported by consumers and reviewers. Then there was the weird head-mounted display, which was mounted on a stand and required you to put it on a table. No wonder it failed.
The Wii U stumbles and falls (hard)Nintendo was fresh off the massive success of the Wii when it opted to release the radical Wii U console. Featuring a decent graphical bump over the Wii and a controller with an integrated screen, it felt like Nintendo was onto something really cool at first. And quite a few studios hopped aboard to support the machine.
But the Wii U stumbled out of the gate, and third-party developers gradually dialed back support for the console as a result. Toss in the Xbox One and PS4 out-shining Nintendo’s console, and it was a repeat of the GameCube all over again. Except the Wii U somehow sold fewer units than the purple cube.
JoyCon driftWhen the Nintendo Switch was launched in March 2023, some owners quickly discovered an issue that became known as JoyCon Drift. To put it simply, the JoyCon analog stick would drift uncontrollably, meaning that an in-game character would move without you wanting to do so.
It was a disappointing flaw but what made it much worse was the fact that Nintendo didn’t make any real attempt to address the issue for a long time. It’s since revised the design of the Switch OLED variant’s JoyCon to combat this issue, but says the issue won’t ever go away as it’s related to wear-and-tear.
Update the detailed information about Looking For Liquidity? Here’s Everything To Know About Nft Lending 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!