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What is AI recruiting?

AI recruiting is applying machine learning on the recruiting-related big data to improve the recruiting process of the organization. AI in recruitment is designed to reduce the time spent on repetitive, high volume tasks through automation.

What are its benefits?

Hires that better match the role requirements: AI tools can enable this by:

Enabling HR teams to process more applications: Automation of high volume tasks such as screening resumes and scheduling interview time saves time of HR teams. As a result, they can have time to process more applicants which can lead to an increase in the quality of hires

Increased effectiveness in filtering applicants: Manual processes have an error rate of 3-4% because people get bored and lose their focus while completing repetitive work. AI tools can filter out candidates that are definitely not a good fit for the role, enabling teams to focus on candidates that are more likely to be successful in the job

A higher acceptance rate: Faster responses from the HR team lead to a higher acceptance rate because the company will be less likely to lose candidates to their competitors during the process of hiring.

Lower hiring costs: Automation can help companies focus their HR teams on higher value activities rather than repetitive recruitment related tasks.

A study by a recruitment technology company indicates that early AI recruitment adopters provide

Why is it important now?

Talent is always a scarce resource. Especially in fields like AI, companies are engaged in fierce competition to attract talent. Now with the rise of deep learning in 2010s, machines can support humans in repetitive tasks that involve decision making. Therefore, it is not surprising that AI-powered approaches are becoming more common in recruiting.

How does it work?

Ideal, a recruitment AI vendor, summarized the recruitment process and identified areas where AI can help in the below image.

In cases where companies are aiming to hire for positions that require experience such as a CTO or head of data science, companies need to proactively reach candidates. This adds two more steps to the process outlined in the image:

Recruiters should search both internal databases as well as professional networks like Linkedin and XING (in German speaking companies) for candidates that can fill the open position. Machine learning algorithms can make the search among thousands of profiles easier however we have not yet come across vendors that offer such services. Alternatives include keyword searches and filters such as experience but these rules based approaches require fine-tuning for each new candidate search.

Businesses can also leverage web scraping tools which automate the extraction of candidate data from job listings and websites (e.g. LinkedIn).


For instance, Bright Data’s data collector can be used to target job websites, extract relevant data automatically, and send it to end users in the designated form. See the following video for an in-depth view of how Bright Data works:

Personalized messages are necessary for outreaching to interesting candidates. A machine learning model can fine tune the messages based on the candidate and candidate’s online profile.

3. Intelligent Screening&Matching

Though HR departments can track data of candidates from various recruiting websites and social media, it is time consuming. AI-powered recruitment solutions enable recruiters to automate screening candidates’ resumes and matching the most suitable candidates to the job.

4. Recruiter Chatbot

Recruiter chatbots interact with candidates via personalized messages, write quick responses to their questions and ask relevant questions to collect more data about them. It is important to clarify that the candidate is interacting with a chatbot since chatbots can fail to answer questions which should not be misunderstood as a deficiency of the company’s HR team.

Check out data-driven list of conversational AI and chatbot vendors.

5. Online Interview Analysis

AI-powered solutions can analyze online interviews focusing on a candidate’s facial expressions during the interview. These solutions capture mood, and other characteristics of the employee. Unilever uses this technology and claims that it has increased ethnic and socioeconomic diversity within the company. We are relatively skeptical about this application and will be looking for more proof before we see this as an area to invest in.

See our article about virtual event platforms for more detail on how these tools work.

What are its challenges?

Modern AI systems are data hungry. HR teams need to make sure that their AI powered recruiting system need is able to access various recruiting websites (LinkedIn, Google for Jobs, etc.) along with social media platforms (Facebook, Instagram, etc.) and company’s historical data on candidate data and job performance. 

Machine learning algorithms are trained on historical, potentially biased data. Companies need to be aware of areas where human intelligence tends to be biased and overcome such biases in their machine learning models. 

Further Reading

If you want to learn about AI use cases and recruitment, feel free to read our articles on

If you believe your business can benefit from AI and ML, you can start reviewing our data-driven lists where we compare ML/AI platforms, development services and consultants.

If you still have questions about ai recruiting or any other AI solution, we would like to help:

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.





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Guide To The Essential Roles Of Agile And Agile Team

Introduction To Agile Roles

Project scheduling and management, project management software & others

The word agile means flexible or to move quickly. The Agile teams are formed as a part of project management, especially for the software development in which the whole project is divided into different modules or phases, and continuous assessments of the project after completion of every module or phase and the solution is achieved through the collaboration between the different cross-functional agile teams working on the project.

Broad Classification of the Agile Roles Based on the Project Hierarchy

Below is some classification of agile roles, which are as follows:

1. Project Level Roles

The project-level teams include the following profiles/roles:

Business Sponsor

Business Visionary

Technical Coordinator

Project Manager

Business Analyst

Project Level roles are the project steering roles that take the entire governance decision of the project. The project managers are the ones responsible for the budget analysis of the project. The project management level professionals provide strategic control and decisions related to project execution and implementation.

2. Solution Level Roles

The Solution Development Team roles include:

Business Ambassador

Solution Developer

Solution Tester

Business Analyst

Team Leader

These are the roles related to product/software/service development. These professionals are the developers, programmers, testers, etc., who develop and test every technical aspect of the product/software/service.

3. Other Supporting Roles

Business Advisors

Technical Advisors

Workshop Facilitator

Roles of an Agile Team

The roles of an Agile Team are as described below.

1. Stakeholder/Owner

The product owner is the stakeholder or the client, and the product owner is the most crucial role as the client has the vision and shares that vision of the end product with the team. The stakeholders are constantly updated with the project’s progress so that the product can be reviewed at the end of every phase. The stakeholder and the scrum master together prioritize the work.

2. A Scrum Master/Project manager

A scrum master is the top dog of any organization. A scrum master is the project manager who outlines the entire project and guides the agile team. They supervise the daily activities of the project, mentor the team, and track and monitor the project’s progress. A scrum master is the one who decides the project deadline and eradicates any obstruction in the way of project implementation.

3. Agile Team Members 4. Development Team Members

The development team members, often called “techies,” are those involved in the project responsible for creating the product, service, or software. Development team members comprise the programmers, testers, engineers, etc., who play key roles in the product or software development. The development team members are expected to be multi-taskers and multi-skilled professionals to utilize their skills for the project implementation.

5. Agile Coach

An Agile coach or mentor is a highly experienced professional who has implemented and executed Agile projects. An Agile coach or mentor shares his experience and expert opinion with the Agile team members. The Agile mentor is the one who studies the project thoroughly and shares the feedback for the enhancement of the project. The Agile mentors apply agile principles and knowledge while executing the project while not involved in product/software development.

6. System Architect Roles

The Agile architect is the cornerstone for any successful project as the architects define and design the overall architecture. The role and involvement of the architect depend on the type of project. The system architect can be an individual or a small team that works closely with the Scrum Team and the product owner or the stakeholders for the project’s success. The Architects define the major and minor elements of the project and design the interface among the different subsystems of the project. They actively participate in stand-up meetings with the client/stakeholder, seeking to comprehend and articulate the project’s progress and any challenges encountered.

7. Product Management

The Product Management team works with the customer, and their role is to collect and understand the customer’s needs and requirements. Also, the product management team communicates with the product owner to establish peace and balance between the product owner and the customer to define and understand the system feature.

Recommended Articles

This has been a guide to Agile Roles. Here we have discussed the Essential Roles of Agile and the Agile team with the Broad Classification. You can also go through our other suggested articles to learn more –

What Is Ai For Recruiting And How Does It Work?

AI is changing the way we live and work. It is impacting how work happens across all industries. Human Resource Management is a popular field that is getting impacted by AI. AI-powered tools and technology have become game-changers for hiring in the ever-changing recruiting world. AI can boost applicant engagement, minimize prejudice, speed hiring procedures, and improve talent analytics and insights. The global market for AI for recruitment will reach $1.5 billion by 2026. Let’s explore the numerous ways in which AI is changing the recruiting sector.

What is AI in Recruiting?

AI, sometimes called machine intelligence, applies computer algorithms and machine learning techniques to tasks that call for human intelligence. Some of the tasks that AI can do in recruitment include candidate sourcing and screening, communication and engagement, skill evaluation and selection, bias reduction, and talent analytics.

Source: LinkedIn

Read More: Impact of Machine Learning on HR

Automated Candidate Sourcing from Various Platforms

To find the right applicants, AI recruiting software may scan and analyze millions of job posts, social media profiles, and other internet sources. As a result, finding prospects takes far less time and effort, freeing up recruiters to work on other important duties.

AI-based Resume Screening and Shortlisting

AI tools can automatically screen and shortlist resumes based on specific keywords, skills, and qualifications, improving the efficiency and accuracy of the process. This also reduces the chances of human bias and errors in AI resume screening.

Leveraging NLP for Candidate Matching

AI-powered natural language processing (NLP) examines job descriptions, resumes, and candidate profiles to find the best applicant for a specific job. This improves the quality of job-appropriate candidates. 

AI-powered Chatbots for Candidate Interactions

AI chatbots can provide 24/7 support to candidates, answering their questions and providing relevant information about job openings and the recruitment process. This improves the candidate experience and reduces the workload on recruiters.

Personalized Candidate Communication and Engagement

AI can personalize communication with candidates based on their preferences and interests, improving their engagement and likelihood of accepting job offers. This also helps recruiters to build stronger relationships with candidates.

Streamlining Interview Scheduling and Logistics

AI handles scheduling and logistics for interviews automatically, which relieves recruiters of some of their administrative duties and increases process effectiveness. The smooth and trouble-free experience this offers the applicant also enhances their experience.

AI in Skill Assessment and Selection

Online Coding Assessments and Technical Evaluations

Using online assessments and evaluations, AI-powered skill assessment tools can evaluate candidates’ technical skills, such as coding and programming. This ensures that candidates with the right skills are selected for technical roles.

Behavioral and Situational Assessments Using AI Algorithms

AI algorithms can analyze candidates’ responses to behavioral and situational questions to assess their soft skills and cultural fit. This helps to ensure that the right candidates are selected for roles that require strong interpersonal and communication skills.

Predictive Analytics for Candidate Suitability and Performance

Using candidate data analysis, AI can forecast an applicant’s fit and performance for a certain position, empowering recruiters to make better AI for recruiting decisions. Additionally, this aids in formulating talent development and retention strategies and identifying high-potential applicants.

Bias Mitigation and Diversity Enhancement

Identifying and Mitigating Unconscious Bias in the Recruitment Process

Unconscious prejudice can severely impact the hiring process’s fairness and inclusion. AI has a critical role to play in recognizing and reducing these biases. Employers may lessen the impact of bias in applicant evaluation by exposing AI algorithms to various representative datasets. To ensure that candidates are evaluated exclusively based on their credentials and skills, these algorithms may analyze resumes and profiles without considering personal information like names, gender, or ethnicity.

Promoting Diversity and Inclusivity Through AI Algorithms

It is possible to design AI algorithms to encourage inclusion and diversity in hiring. AI technologies may ensure that a diverse pool of candidates is considered for each post by integrating diversity criteria into the selection process. Furthermore, by actively seeking out suitable applicants from underrepresented groups, these algorithms may spot trends of underrepresentation and promote a more diverse workforce.

Ensuring Ethical AI Usage in Candidate Evaluation and Decision-making

Ethical considerations are vital when utilizing AI in candidate evaluation and decision-making. Transparency and explainability are key factors in ensuring ethical AI usage. Recruiters should have a clear understanding of how AI algorithms make decisions and be able to explain these processes to candidates. Regular audits of AI systems can help identify and rectify any biases or unintended consequences that may arise.

AI-powered Talent Analytics and Insights

Data-driven Insights for Workforce Planning and Talent Acquisition Strategies

AI-powered talent analytics might give useful insights for talent acquisition and workforce planning initiatives. AI technology can uncover trends and patterns in applicant behavior by researching recruiting data, such as the most successful methods of locating applicants, their preferences, and the variables that impact their decision-making. These specifics can assist recruiters in making their hiring procedures as efficient as possible to attract and retain top talent.

Predictive Analytics for Identifying High-potential Candidates

To find high-potential workers, employers may use predictive analytics to examine various data points, such as a candidate’s educational history, job experience, talents, and behavioral tendencies. Recruiters may increase the chance of a successful hire by focusing on engaging and developing the most qualified applicants.

Monitoring Recruitment KPIs and Performance Metrics Seamless Integration with Applicant Tracking Systems (ATS)

Integrating AI Tools with Existing ATS Platforms

The capabilities of existing applicant tracking systems (ATS) may be enhanced by integrating AI tools, increasing data accuracy and streamlining candidate administration. These connections may automate several processes, including applicant interactions, scheduling interviews, and reviewing resumes, giving recruiters more time to concentrate on important responsibilities.

Enhancing Data Accuracy and Efficiency in Candidate Management Streamlining the End-to-end Recruitment Process

AI solutions may speed up the full AI for recruiting process from sourcing to onboarding by automating various functions and connecting with current ATS platforms. By reducing time to hire, enhancing applicant experience, and boosting recruiter productivity, these solutions free up recruiters to concentrate on cultivating connections with prospects and selecting the best candidates.

Overcoming Challenges and Ethical Considerations

Addressing Privacy and Data Protection Concerns

When employing AI for recruiting, privacy and data protection are crucial factors to consider. According to recruiters, candidates’ data must be processed and maintained per all applicable data protection rules and regulations. Before employing AI technologies to analyze applicants’ applications, recruiters should also be upfront with prospects about how their data will be used and get their permission.

Ensuring Transparency and Explainability of AI Algorithms

To ensure ethical AI usage, transparency and explainability are essential components. The ability to explain the decision-making processes of AI algorithms to prospects is a skill that recruiters should possess. Moreover, frequent audits of AI systems can aid in locating and eliminating any potential biases or unforeseen outcomes.

Balancing Automation with a Human Touch in Candidate Engagement

While automation can streamline various recruitment tasks, recruiters must ensure that candidates receive a personalized and human experience. By leveraging AI-powered chatbots and other communication tools, recruit


Ideal is an AI-powered recruitment automation software that streamlines the recruitment process by automating tedious and time-consuming tasks such as resume screening, candidate matching, and interview scheduling. Its intelligent algorithms learn from hiring decisions and help recruiters identify the best candidates faster.

Zoho Recruit

Using AI for candidate sourcing, matching, and screening, Zoho Recruit is an end-to-end applicant tracking solution. Some of its AI-powered features are intelligent resume processing, applicant interaction tracking, and sophisticated reporting capabilities.

Phenom AI

Phenom AI is a talent experience platform driven by AI that enables recruiters to interact with applicants at every level of the recruiting procedure. Chatbots for candidate interaction, customized job suggestions, and sentiment analysis for applicant feedback are some of its AI features.


HireVue is a platform for video interviews and assessments that employs AI to examine applicant behavior and facial expressions to give insights on fit and potential. Some of its AI characteristics are predictive analytics for applicant performance and machine learning algorithms to enhance recruiting outcomes.


Entelo is an AI-driven recruiting tool that aids in the identification and interaction of various prospects for employers. Some of its AI features are diversity analytics, talent rediscovery, and predictive analytics for spotting high-potential applicants.

Also Read: AI and ML Tolls for Human Resource Management

Future Trends in AI for Recruiting

The future of AI in recruitment looks promising as AI technology develops. In the upcoming years, we might anticipate the following trends:

Advancements in NLP and Sentiment Analysis

Using sentiment analysis and natural language processing, AI technologies may examine resumes, job descriptions, and applicant interactions to determine a candidate’s suitability for a position. Recruiters will be able to make better recruiting judgments in the future because of increasingly accurate and sophisticated systems that recognize subtleties in language and emotions.

Integration of AI with Video Interviewing and Assessment Tools

Video interviewing and assessment tools are becoming increasingly popular in recruitment. By integrating AI with these tools, recruiters can assess a candidate’s body language, facial expressions, and tone of voice to gain deeper insights into their personality, communication skills, and cultural fit. This can lead to better hiring decisions and improved candidate experience.

Continuous Learning Algorithms for Adaptive Recruitment

AI-powered recruiting systems may continually learn from data and prior hiring choices to increase their precision and efficacy. We anticipate increasingly sophisticated algorithms in the future, which will allow recruiters to remain ahead of the competition and entice the greatest talent. These algorithms will be able to respond to shifting applicant preferences and recruiting trends.

Final Word Frequently Asked Questions

Q1. How AI is used in recruitment?

A. AI is used in recruitment to automate processes such as resume screening, candidate sourcing, and interview scheduling. It can analyze resumes, assess candidate skills, and even conduct video interviews, saving time and improving efficiency in the hiring process.

Q2. What is the benefit of using AI for recruitment?

A. The benefit of using AI for recruitment includes faster candidate screening, reduced bias, improved candidate matching, and enhanced overall recruitment accuracy. AI can handle large volumes of data, identify top candidates, and make data-driven decisions, leading to better hiring outcomes.

Q3. How is AI being used in HR and recruitment?

A. AI is being used in HR and recruitment to streamline various tasks. It assists in candidate sourcing by leveraging algorithms to identify potential matches from diverse sources. AI-powered chatbots provide automated support to candidates, answering FAQs and improving the candidate experience.

Q4. What is the future of AI in recruitment?

A. The future of AI in recruitment looks promising. Advancements in natural language processing, sentiment analysis, and predictive analytics will enable more accurate candidate assessments. AI may also play a role in skills mapping, talent pipelining, and personalized candidate experiences, revolutionizing the recruitment landscape.


Guide To Rpa’s Benefits In Analytics In 2023

We had explained RPA extensively in layman’s terms and outlined RPA benefits. As an excellent data aggregator, one of the frequently cited benefits of RPA is improved analytics and big data analytics which has been a priority for executives for the past decade. However, the benefits of RPA to analytics are limited to data federation as it enables multiple databases to function as one.

How does RPA contribute to analytics?

We should consider the analytics funnel above to see where RPA can contribute. Bots have essentially 2 critical functions from a data standpoint:

Create meta data: As they complete tasks, they record their progress and the issues they face for diagnostic purposes. This data can be used for both the client or the RPA provider to identify RPA bugs and improve bot performance.

Enable access to data in legacy systems: Since they overtake tasks that require interfacing with legacy systems, they make previously difficult to access data accessible. This can transform data collection capabilities of enterprises, especially those that depend on legacy systems.

Therefore, bots do not essentially improve analytics capabilities but aid in data collection. Even RPA vendors agree with this, underlining that core benefit of RPA is in data federation: the capability to collect data from many different sources and aggregate it in an easy-to-analyze format.

How does data federation contribute to company performance?

Firstly, data federation should not be a major concern for an SME or startup. However for large companies, it is a major concern as legacy systems historically held large companies back in terms of easy access to data. Now with access to granular data about processes, large companies can reap 2 important benefits:

Process optimization thanks to process mining

Granular data about processes can help identify bottlenecks and inefficiencies, enabling corporations to increase both speed and efficiency of the process. Furthermore, it makes dissemination of best practices easier. Since process flows can easily be visualized with the help of data, process flows in different regions can be compared to find the best processes for the whole company. For example, in one of process mining case studies, Piraeus Bank optimized their loan application processes from 35 minutes to 5 minutes, thanks to process mining technology.

For complex inter-related processes, machine learning techniques could be used to find optimizations that analysts could easily miss. Here are some examples from PwC:

Machine learning might come up with the suggestion that ordering material X from supplier A in the week of Christmas instead of the first week in January will result in a 50% improvement in order fulfilment in January. You could change the RPA robot setting in line with this suggestion to make sure orders to the relevant suppliers are placed during Christmas week, while your staff are on vacation. Apart from its ability to generate simple correlations, machine learning combined with today’s computing power is increasingly capable of identifying unknown relationships within multiple business processes. For instance, it can potentially correlate procumbent processes with sales processes to analyze directly what supply chain management actions need to be taken to improve sales.

Process simulation

Some major decisions like outsourcing, workforce reductions or expansions are made haphazardly, based on urgencies without considering future implications. Such decisions tend to have long-lasting impact because once a process is outsourced or its headcount increased, it is difficult to roll-back such decisions due to risk averseness inherent in humans. Managers, especially those in well performing companies, would like to see how rolling back such changes will not impact operations. Process simulation provides an answer. By simulating how a process flow will be impacted by changes, analysts can show the impact of major changes on the process. RPA systems can provide the necessary data for such simulations.   RPA can have other fundamental benefits for cost management and elimination of manual errors as we outlined in our comprehensive list of RPA benefits.  

For more on RPA

To explore RPA and its use cases in detail, feel free to read our in-depth articles:

If you still have any questions about RPA, feel free to download our in-depth whitepaper on the topic:

And if you believe your business will benefit from an RPA solution, feel free to scroll down our data-driven list of RPA vendors.

And let us guide you through the process:

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.





A Step By Step Guide To Creating The Best Instagram Post In 2023

An Instagram post’s success is due to more than just the image you use, you need to consider a range of other factors to get your posts performing well

As a social media manager or digital marketing specialist, you want to be able to create the best Instagram post possible in order to entice your audience to buy from you and choose your brand over the competition.

You need to look at all the elements and Instagram features that an audience responds best to.

That’s exactly what a new study about Instagram content engagement set up to do. During this extensive study, over 7.4 million posts on Instagram were stripped apart and analyzed element by element, with the sole goal being to find the recipe for creating successful and engaging posts on this platform.


Access the

Let’s discover what makes the audience tick and how to create engaging Instagram content that truly converts.

1. Leverage the power of carousels

In the case of large Instagram accounts, the median number of likes per post exceeded 5,670 for carousels and only 5,022 for videos and a low 4,172 for images. Thus, we recommend you leverage the power of carousels and use more of them in your posts.

2. Choose the right Instagram filters

In order to create a cohesive aspect of your Instagram page, it is imperative to use the right filters or themes. While you should use carousels in your posts, it is a must to ensure that your page uses the correct theme, so that people who land on your page will be enticed to find out more about you and connect with your company.

An Instagram theme is like a portfolio of images, videos and carousels, where your prospects can discover more about you. Having a correct theme in place and choosing the right filters can allow you to promote a unitary brand image to your prospects and increase the chances of conversion.

3. Choose the type of message most suited for Instagram

When creating Instagram posts, it is imperative to ensure that you are using the right type of message for your campaign. For example, if you want to organize an event, you need to create a message that spikes people’s interest. You might also want to drive people to your Facebook event page, which is much better than what Instagram has to offer in terms of event creation.

If you want to break new ground in your niche, you’ll want to capitalize on the benefits offered by giveaways. As a matter of fact, the above-mentioned study shows that events, giveaways, contests, and DIYs work much better than regular messages. Out of all of these, customers appreciate giveaways the most, because they represent signs of appreciation from the brand.

The best engagement rates are for giveaways since these work extremely well for most people. However, we recommend that you to combine the types of messages you can use in posts in order to ensure that you stay relevant to what your target audience wants.

For example, if you have a lot of techies’ clients, offering them weekly DIY posts or tutorials is a great way to make them fall in love with your brand. On the other side, if you have a lot of avid shoppers, offering them giveaways, promotions or contests with real prizes can help you stay ahead of the competition and become the prime choice of your clients.

4. Set limits to your caption length

Again, the study revealed that the perfect captions are those of under ten words. More than that and you’re ruining your engagement rate, despite the temptation of writing something more elaborate. Of course, these ten words do not include hashtags.

Moreover, even if most brands include questions in the caption, that does not automatically translate into a higher engagement rate, as this doesn’t seem to entice your audience into interacting more with your posts.

In order to maximize your engagement rate, which can rise up to 3.13% if you use less than ten words, be extremely careful at the words you choose. It’s not imperative to use questions in the captions, but it’s important to be smart and entice your viewers into reading the post and acting on it.

5. Include emojis in your posts

Another excellent way to boost your Instagram engagement rate is to make sure you include emojis in your Instagram posts. The data clearly shows that emojis by themselves increase the engagement rate by up to 1.31%.

For example, if you add emojis in the caption of an image post, you get to enjoy an increase in engagement from 2.15% to 2.72%. In the case of videos, it goes up wildly from 1.88% to 3.20%. For carousels, the engagement grows by a slim margin of 0.54%, from 2.52% to 3.06%.

So how many emojis should you include in your post? The more the merrier! The best performing posts have proven to be the ones with between 20 and 25 emojis in total. You should use around 20 emojis in total, including a few in the caption. Using them allows you to showcase your personality and make your posts more human.

The heart-eyes emojis is by far the most popular on Instagram, followed by the heart, sparkles, and camera with flash. In case you need some inspiration in choosing the right type of emoji for your post, here are the top 10 best emojis to use in 2023:

6. Capitalize on the power of hashtags

As opposed to emojis, when it comes to hashtags, it’s best to keep it simple and use a few relevant hashtags for your brand and your audience.

7. Asking questions is not relevant anymore

You may be inclined to think that asking questions in your posts encourages the audience to answer, but that is not the case.

Another interesting aspect of Instagram content is that asking questions does not necessarily increase the engagement rate. On the contrary, the engagement rates are somewhat lower in the posts that include questions than the rest: 0.19% lower for carousel posts that include questions in the caption and 0.25% lower for video posts.

8. Consulting analytics to get inspired from old posts

Ultimately, in order to write the best and most engaging Instagram post, you need to look in the past. Study your analytics and try to determine which of your posts outperformed the others. This way, you’ll be able to better understand what your audience is expected from you.

Instagram analytics is key to your success. Inside the old analytics on Instagram, you can gain access to information like follower count, profile views, and website CTR. You can also determine the results of your stories by looking at reach, impressions, replies, next story, and live viewers.

Let’s sum up

Writing the best Instagram post is not as complicated as some marketers try to make us believe. All you need to do is to ensure that you follow the guidelines in the Socialinsider study and create high-quality, enticing content that captures the interest of your prospects and makes them stay engaged with you.

Start using more carousels, have a consistent theme for your page, use the right hashtags, leverage the power of emojis, choose the right type of message you want to share with your audience, do a lot of split testing and capitalize on the benefits offered by Instagram Analytics.

How To Use Kobold Ai Colab? A Comprehensive Guide

Do you know you can use Kobold AI on Google Colab for free without installation? 

Kobold AI Colab is a special version of Kobold AI that runs on Google Colab and lets you use any of the supported models and features.

You can use Kobold AI Colab by finding the link on the official website or the GitHub repository. All you need is a Google account and an internet connection.

In this article, we will show you the steps of using Kobold AI Colab and show you what features it offers.

Additionally, you will learn how to select a model and start a session.

What Is Kobold AI Colab?

Kobold AI Colab is a version of Kobold AI that runs on Google Colab.

It is a cloud service that provides access to GPU(Graphics Processing Unit) and TPU(Tensor Processing Unit).

You can use it for free with a Google Account, but there are some limitations, such as slowdowns, disconnections, memory errors etc.

Users may also lose their progress if they close the notebook of their session expires.

Moreover, you can choose from different models and settings to customize your experience.

Models Available In Kobold AI Colab

If you want to use it on Google Colab, you can choose from several models available in the TPU or GPU edition.

Here are some of the available models in the TPU.

Nerys by Mr. Seeker: It is a hybrid model based on Pike, a newer Janeway. It is trained on the largest dataset of novels and is heavily inclined toward SFW novel writing.

Shinen: It is a model that focuses on Japanese Light Novels and Anime with a large vocabulary. It can generate colorful descriptions and dialogues and is good at creating fantasy and sci-fi scenarios.

Pike: It is a newer version of the Janeway-trained, more extensive and diverse datasets of novels.

How To Use Kobold AI Colab?

To use it, you need to open one of the notebooks provided by the developers.

Here are the steps to use it.

Go to ColabKobold TPU to open the the notebook for the TPU edition. Alternatively, go to ColabKobold GPU to open it for the GPU edition.

If you don’t have a Google account, you can create it to use Colab. Additionally, you can save a copy of the notebook to your Google Drive for future use.

Then, select your preferred Model from the dropdown menu. You can find the description of the model below the menu.

This will take a few minutes and depends on the model size and availability of the resources.

Here, you need to acknowledge the warning, and then you will be taken to the interface of Kobold AI.

Despite this, if you choose Cloudflare and receive a 1033 error, you must refresh the error page and wait for some time.

Can You Train Your Model With Kobold AI Colab?

Different models have different styles and capabilities. Therefore, you must choose the one that suits your needs.

You can train a model with Kobold AI Colab. However, it depends on what kind of model you want to train.

Additionally, you can easily interact with the AI with KoboldAI API URL.

If you want to train a soft prompt, you can use a Colab notebook provided by the Kobold AI community.

In this case, you will need to run the development version of Kobold AI on your PC to use them.

However, if you want to train the model itself, you will need something better than the Colab service.

It is a more complex and resource-intensive process that requires a larger dataset than a 3 MB text file to get a good result.

Keep the Colab notebook page open while using it, AI and regularly check if you got a Captcha.

Failure to complete the Captcha’s in time can result in the termination of your session or a lower priority towards the TPUs and GPUs.

Alternatively, you can use Kobold AI  without Google Colab. You can run it on your PC, use Kobold Horde or download it from GitHub.

Note: Remember that Colab sessions are limited and may disconnect anytime; hence permanently save your work.

The Bottom Line

 However, Colab may not support all the features and options that Kobold AI offers.

Therefore, if you want a better and more stable experience with it, you can try running it on your PC or a dedicated cloud service.

Keep reading and find out how to use KoboldAI Pygmalion.

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