AI for the masses

Understanding Language Models: A Non-Technical Guide to Large Language Models (LLMs)

In the world of artificial intelligence (AI), one term you might have come across is “Large Language Models” or LLMs. But what exactly are these models, and why are they important? This blog post aims to demystify LLMs in a non-technical way.

What are Large Language Models?

Imagine having a conversation with a computer, and it understands and responds to you just like a human would. This is the kind of interaction that Large Language Models make possible. In simple terms, LLMs are computer programs trained to understand and generate human-like text. They are a type of artificial intelligence that can read, write, and even converse in natural language.

How do Large Language Models Work?

LLMs learn from vast amounts of text data. For instance, they might be trained on millions of books, articles, and websites. By analyzing this data, they learn the patterns and structures of the language, such as grammar and common phrases.

When you ask an LLM a question or give it a prompt, it doesn’t search the internet for an answer. Instead, it generates a response based on the patterns it has learned from its training data. It’s like having a conversation with a very well-read friend who has an answer or a story for almost everything!

Why are Large Language Models Important?

LLMs are transforming the way we interact with technology. They power virtual assistants, chatbots, and customer service systems, making these systems more conversational and user-friendly. They can also help with tasks like drafting emails, writing articles, or even creating poetry!

Moreover, LLMs can be a powerful tool for education. They can provide explanations on a wide range of topics, making learning more accessible and engaging.


Large Language Models are an exciting development in the field of artificial intelligence. They are making our interactions with technology more natural and conversational. While the technology behind LLMs might be complex, the concept isn’t: they are computer programs that have learned to understand and generate human-like text. As LLMs continue to improve, we can look forward to even more innovative and helpful applications.

AI for the masses

Demystifying Machine Learning: A Simple Guide for the Non-Tech Savvy

Machine Learning (ML) is a buzzword that’s been making waves in the tech world and beyond. But what exactly is it? For those of us who aren’t tech experts, machine learning might seem like a complex and intimidating concept. But fear not! This blog post aims to break down machine learning into simple, understandable terms.

Understanding Machine Learning

Imagine teaching a child how to recognize different types of fruits. You show them apples, bananas, oranges, and explain their unique characteristics. Over time, the child learns to identify these fruits on their own. This is, in essence, what machine learning is all about. It’s a type of artificial intelligence (AI) that involves teaching computers how to learn from data to make decisions or predictions.

How Does Machine Learning Work?

Machine learning works by feeding a computer system a lot of data, which it uses to learn patterns and make decisions. For instance, a machine learning system could be trained to recognize spam emails by analyzing thousands of emails, learning from the patterns it sees, and then using this knowledge to identify whether a new email is spam or not.

Types of Machine Learning

There are two main types of machine learning: supervised and unsupervised learning.

  • Supervised Learning: This is like teaching a child with a guidebook. You provide the computer with input data and the correct output. The system then learns the relationship between the input and output. For example, you could train a system to recognize dogs by showing it many pictures of dogs (input) and telling it that these are dogs (output).
  • Unsupervised Learning: This is like letting a child explore and learn on their own. The system is given a lot of data and must find patterns and relationships within the data itself. For example, you could give a system a bunch of news articles, and it might categorize them into different topics based on the words used in the articles.

Why is Machine Learning Important?

Machine learning is transforming the world in many ways. It’s used in healthcare to predict diseases, in finance to detect fraudulent transactions, in retail to recommend products, and much more. It’s making our lives easier, safer, and more personalized.


Machine learning might seem complex, but at its core, it’s about teaching computers to learn from data, just like how we learn from our experiences. It’s a powerful technology that’s changing the world in incredible ways, and it’s something we can all understand and appreciate.

AI for the masses

Unveiling the Power of Coral AI: A New Era of Machine Learning

Artificial Intelligence (AI) has become an integral part of our lives, influencing everything from our daily routines to business operations. One of the most exciting developments in the field of AI is the emergence of edge computing, which brings computation and data storage closer to the location where it’s needed, improving response times and saving bandwidth. Google’s Coral AI is a prime example of this technology, offering a suite of hardware and software tools that make it possible to develop and run local AI models.

The Power of Coral AI

Coral AI is a platform that allows developers to build intelligent devices with local AI. It’s a part of Google’s initiative to democratize AI and make it accessible to various industries. The platform includes a range of products, from system-on-modules (SOMs) and USB accelerators to development boards and cameras, all designed to facilitate the creation of local AI models.

Coral AI’s Edge TPU (Tensor Processing Unit) is a high-speed machine learning (ML) accelerator specifically designed for edge computing. It’s capable of executing state-of-the-art mobile vision models, such as MobileNet V2, at 100+ frames per second, in a power-efficient manner. This makes it ideal for use in mobile and embedded systems.

Applications of Coral AI

Coral AI devices can be used in a wide range of applications. For instance, in the retail industry, Coral AI can be used to develop smart checkout systems that can identify products without the need for barcodes. In the manufacturing sector, it can be used to monitor equipment and predict maintenance needs, thereby reducing downtime.

In the healthcare industry, Coral AI can be used to develop devices that can monitor patient health in real-time, providing critical insights and alerts when necessary. In agriculture, it can be used to develop systems that monitor crop health and optimize irrigation.

The Future of AI with Coral

Coral AI is not just a product; it’s a vision for the future of AI. By bringing AI closer to the edge, Coral is making it possible to process data locally in real-time, without the need for constant internet connectivity. This opens up a world of possibilities for developers and businesses, enabling them to create intelligent devices that can operate independently and make decisions based on local data.

Moreover, Coral AI is designed with privacy in mind. Since data is processed locally, there’s less need to send sensitive information to the cloud, reducing the risk of data breaches.


Coral AI is a powerful tool that’s pushing the boundaries of what’s possible with AI. By bringing AI to the edge, Coral is not only making AI more accessible but also more efficient, secure, and responsive. Whether you’re a developer looking to build your next AI project or a business looking to leverage the power of AI, Coral offers a versatile and powerful platform to help you achieve your goals. The future of AI is here, and it’s closer to the edge than ever before.

AI for the masses

The Consequences of Using Model-Generated Content in Training Large Language Models

In a recent study titled “The use of model-generated content in training large language models (LLMs)”, the authors delve into a critical issue that has significant implications for the field of machine learning and artificial intelligence. The paper discusses a phenomenon known as “model collapse,” which refers to the disappearance of the tails of the original content distribution in the resulting models due to the use of model-generated content in training large language models.

This issue is not isolated but is ubiquitous amongst all learned generative models. It is a matter of serious concern, especially considering the benefits derived from training with large-scale data scraped from the web.

The authors emphasize the increasing value of data collected from genuine human interactions with systems, especially in the context of the presence of content generated by large language models in data crawled from the Internet.

The paper suggests that the use of model-generated content in training large language models can lead to irreversible defects. These defects can significantly affect the performance and reliability of these models, making it a crucial area of research and development in the field of AI and machine learning.

The document provides a comprehensive analysis of the issue and offers valuable insights into the challenges and potential solutions associated with training large language models. It is a must-read for researchers, data scientists, and AI enthusiasts who are keen on understanding the intricacies of large language model training and the impact of model-generated content on these processes.

The cause of model collapse is primarily attributed to two types of errors: statistical approximation error and functional approximation error.

Statistical approximation error is the primary type of error, which arises due to the number of samples being finite, and disappears as the number of samples tends to infinity. This occurs due to a non-zero probability that information can get lost at every step of re-sampling. For instance, a single-dimensional Gaussian being approximated from a finite number of samples can still have significant errors, despite using a very large number of points.

Functional approximation error is a secondary type of error, which stems from our function approximators being insufficiently expressive (or sometimes too expressive outside of the original distribution support). For example, a neural network can introduce non-zero likelihood outside of the support of the original distribution. A simple example of this error is if we were to try fitting a mixture of two Gaussians with a single Gaussian. Even if we have perfect information about the data distribution, model errors will be inevitable.

These errors can cause model collapse to get worse or better. Better approximation power can even be a double-edged sword – better expressiveness may counteract statistical noise, resulting in a good approximation of the true distribution, but it can equally compound this noise. More often then not, we get a cascading effect where combined individual inaccuracy causes the overall error to grow. Overfitting the density model will cause the model to extrapolate incorrectly and might give high density to low-density regions not covered in the training set support; these will then be sampled with arbitrary frequency.

It’s also worth mentioning that modern computers also have a further computational error coming from the way floating point numbers are represented. This error is not evenly spread across different floating point ranges, making it hard to estimate the precise value of a given number. Such errors are smaller in magnitude and are fixable with more precise hardware, making them less influential on model collapse.

For more detailed insights, you can access the full paper here.

AI for the masses

Guidelines that would help regulate AI

Transparency Requirement

AI systems should be designed and operated as transparently as possible. The logic behind the AI’s decision-making process should be understandable by humans. This is particularly important for AI systems used in critical areas like healthcare, finance, or criminal justice.

Data Protection and Privacy

AI systems often rely on large amounts of data, which can include sensitive personal information. Strict data protection measures should be in place to ensure the privacy of individuals. This includes obtaining informed consent before data collection and ensuring data is anonymized and securely stored.

Accountability and Liability

Clear lines of accountability should be established for AI systems. If an AI system causes harm, it should be possible to determine who is legally responsible. This could be the developer of the AI, the operator, or the owner, depending on the circumstances.

Fairness and Non-Discrimination

AI systems should not perpetuate or amplify bias and discrimination. They should be tested for bias and fairness, and measures should be in place to correct any identified bias.

Safety and Robustness

AI systems should be safe to use and robust against manipulation. This includes ensuring the AI behaves as intended, even when faced with unexpected situations or adversarial attacks.

Human Oversight

There should always be a human in the loop when it comes to critical decisions made by AI. This ensures that decisions can be reviewed and, if necessary, overridden by a human.

Public Participation

Stakeholders, including the public, should be involved in decision-making processes about AI regulation. This ensures that a wide range of perspectives are considered and that regulations align with societal values and expectations.

Continuous Monitoring

AI systems should be continuously monitored to ensure they are operating as intended and not causing harm. This includes regular audits and evaluations.

Ethical Considerations

AI systems should adhere to ethical guidelines, respecting human rights and dignity. This includes considerations like respect for autonomy, beneficence, non-maleficence, and justice.

Education and Training

There should be a focus on education and training to ensure that those working with AI understand the ethical, legal, and societal implications. This includes training in ethical AI design and use for developers, operators, and decision-makers.

AI for the masses

Regulation of AI not just a necessity, but an imperative.

Artificial Intelligence (AI) has become an integral part of our daily lives, influencing sectors ranging from healthcare to finance, and from transportation to entertainment. As AI continues to evolve and become more sophisticated, it brings about significant benefits, including increased efficiency, improved decision-making, and the potential for groundbreaking innovations. However, the rapid advancement of AI also presents a myriad of challenges and risks, making the regulation of AI not just a necessity, but an imperative.


Ethical Considerations

AI systems, particularly those employing machine learning, often make decisions based on patterns they identify in the data they have been trained on. If this data is biased, the AI’s decisions may also be biased, leading to unfair outcomes. For instance, an AI system used in hiring might discriminate against certain demographic groups if it was trained on biased hiring data. Regulations can ensure that AI systems are transparent and fair, and that they adhere to ethical standards.

Privacy and Security

AI systems often rely on large amounts of data, which can include sensitive personal information. Without proper regulation, this could lead to privacy infringements. Moreover, as AI becomes more integrated into critical systems like healthcare or transportation, they become attractive targets for cyberattacks. Regulatory standards can help ensure that AI systems have robust security measures in place and handle data in a manner that respects privacy.

Accountability and Transparency in AI Systems

Accountability in AI systems is a critical aspect that needs to be addressed by regulations. As AI systems become more complex, their decision-making processes can become less transparent, often referred to as the “black box” problem. This lack of transparency can make it difficult to determine why an AI system made a particular decision, which becomes problematic when a decision results in harmful consequences.

Regulations can mandate the development and use of explainable AI or XAI. XAI refers to AI systems designed to provide clear, understandable explanations for their decisions. This not only helps users understand and trust the AI’s decisions but also makes it easier to identify and correct errors when they occur.

Furthermore, regulations can establish clear lines of accountability for AI’s actions. This could involve assigning legal responsibility to the organizations that develop or use AI systems. For instance, if an autonomous vehicle causes an accident, the manufacturer of the vehicle could be held responsible. By establishing clear accountability, regulations can ensure that victims of harmful AI decisions have legal recourse.

Economic Impact and the Future of Work

The rise of AI has significant implications for the economy and the future of work. AI systems can automate tasks that were previously performed by humans, leading to increased efficiency and productivity. However, this automation could also lead to job displacement, as workers in certain sectors may find their skills are no longer in demand.

Regulations can play a crucial role in managing this transition. For instance, they could encourage or require companies to retrain workers whose jobs are threatened by automation. This could involve partnerships with educational institutions to provide workers with the skills they need for the jobs of the future.

Moreover, regulations could promote the development and use of AI in a way that creates new jobs. For instance, they could provide incentives for companies to use AI to augment human workers, rather than replace them. This could involve using AI to automate routine tasks, freeing up workers to focus on more complex and creative tasks.

Furthermore, as AI continues to transform the economy, it may be necessary to reconsider traditional economic measures and policies. For instance, if AI leads to significant job displacement, it could fuel calls for policies like universal basic income. Regulations could play a role in facilitating these discussions and implementing these policies.

In conclusion, the economic impact of AI is complex and multifaceted. Regulations can help manage this impact, ensuring that the transition to an AI-driven economy is fair and beneficial for all.


The Way Forward: A Comprehensive Approach to AI Regulation

Navigating the path towards effective AI regulation requires a comprehensive, multi-faceted approach. This involves not only the creation of new laws and standards but also the adaptation of existing legal and ethical frameworks to accommodate the unique challenges posed by AI.

Firstly, the development of AI regulations should be a collaborative effort involving a wide range of stakeholders. Policymakers should work closely with AI developers, researchers, ethicists, and representatives from various sectors affected by AI. This would ensure that regulations are grounded in a deep understanding of AI technologies and their potential societal impacts. Public input should also be sought to ensure that regulations align with societal values and expectations.

Secondly, international cooperation is crucial. AI technologies, much like the digital economy in which they operate, do not respect national borders. An AI developed in one country can be used and potentially cause harm in another. As such, international standards and agreements are needed to ensure consistent regulation of AI across borders. This could involve bodies like the United Nations or the International Standards Organization, as well as regional bodies like the European Union.

Thirdly, regulations need to be adaptable and future-proof. The field of AI is evolving at a rapid pace, with new technologies and applications emerging regularly. Regulations that are too specific may quickly become outdated, while those that are too vague may not provide sufficient guidance. One solution could be the use of ‘regulatory sandboxes’, which are controlled environments in which new AI technologies can be tested and monitored before being widely adopted. This allows for the real-world impacts of these technologies to be assessed and for regulations to be updated accordingly.

Lastly, education and awareness-raising are key components of the way forward. As AI becomes more prevalent, it is important for the public to understand how these systems work, how they are used, and what their rights are in relation to these systems. This could involve public education campaigns, as well as requirements for companies to provide clear, understandable information about their AI systems.

In conclusion, the necessity of AI regulation is clear. While AI presents enormous potential, it also brings significant risks and challenges that need to be managed. Through thoughtful, balanced, and adaptable regulation, we can harness the benefits of AI in a manner that is ethical, secure, accountable, and economically fair. The task is complex and challenging, but with international cooperation and a commitment to shared principles, it is within our reach.

AI for the masses

Learning Soccer Juggling Skills with Layer-wise Mixture-of-Experts


This is based on the research paper titled “Learning Soccer Juggling Skills with Layer-wise Mixture-of-Experts,” which was presented at the SIGGRAPH ’22 Conference. The paper was authored by a group of researchers dedicated to the advancement of physics-based simulation and control for generating soccer juggling animations. The full paper can be accessed and read here. The credit for this insightful and innovative research goes to the authors of the paper. Their work contributes significantly to the field of reinforcement learning and its application in sports simulation.

In the world of sports, soccer juggling is a skill that requires a high level of control and precision. This skill involves keeping a soccer ball in the air by bouncing it off various parts of the body without letting it touch the ground. Recently, a group of researchers developed a system that uses physics-based simulation and control to generate soccer juggling animations. Their work, titled “Learning Soccer Juggling Skills with Layer-wise Mixture-of-Experts,” was presented at the SIGGRAPH ’22 Conference.

The researchers’ system is designed to easily specify different soccer juggling skills using either crude hand-designed pose sequences or motion capture data. Transitions between skills are introduced as directed edges in a control graph, and reinforcement learning (RL) is used to train control policies based on this graph. To support efficient and effective learning, the system employs a layer-wise mixture-of-experts architecture.


The researchers designed a control graph that specifies various juggling skills and their transitions. These skills are learned via a random walk on the graph. The policy generates the action based on the upcoming control nodes and the simulation state. The policy is trained based on the reward feedback via RL. A simulation episode terminates if the constraints in the control node are violated, and the edge weight of the specific node will be updated to adjust the probability of traversing an edge during the random walk.

The researchers also introduced a layer-wise mixture-of-experts (MOE) architecture. A linear MOE layer consists of multiple linear layers (experts) that are used independently to construct different outputs. These outputs are blended together via the expert weights. A layer-wise MOE consists of multiple layers of linear MOE, and a common gating network is used to generate the expert weights for all linear MOE layers.


The researchers found that different skills exhibit different gating patterns. They also observed that layer-wise MOE induces better specialization, which can reduce the interference effect between tasks. The slightly worse utilization is expected since the control graph is unbalanced.

The adaptive random walk supports the learning of challenging transitions. In all cases, training with adaptive random walk converges faster. Even without the adaptive random walk, the layer-wise MOE is better than alternatives with the adaptive random walk, further demonstrating the benefit of using the layer-wise MOE.


The researchers concluded that their system can perform a variety of full-body soccer juggling skills and the related transitions, including foot, knee, head, and chest juggling, as well as the around the world foot juggle. They also found that their learned policy can withstand perturbations equivalent to a moderate breeze. Surprisingly, they discovered that their policy is able to juggle novel shapes such as box, cylinder, and ellipsoid, with sizes similar to that of the soccer ball.

This research contributes to the field by proposing an overall method for learning difficult soccer juggling skills. It shows that a layer-wise mixture-of-experts architecture provides significant benefits for this multi-skill RL problem. The researchers also introduced an adaptive random walk training strategy in support of efficient learning.


AI for the masses

Implementing Artificial Intelligence for Non-Player Characters in Video Games

Artificial Intelligence (AI) has become an integral part of modern video game development, enhancing the gaming experience by making non-player characters (NPCs) more realistic and interactive. NPCs, controlled by the game’s AI, can exhibit complex behaviors, make decisions, and adapt to the player’s actions, thereby creating a dynamic and immersive gaming environment. This essay will explore the process of implementing AI for NPCs in video games.

Understanding AI in Video Games

AI in video games is fundamentally different from traditional AI. While traditional AI aims to create a system that can perform tasks that would require human intelligence, AI in video games is designed to create an enjoyable and engaging experience for the player. This often involves creating NPCs that behave in a believable and predictable manner, rather than exhibiting true intelligence.

AI Techniques for NPCs

  1. Finite State Machines (FSM): FSM is a simple AI technique where an NPC can be in one of a finite number of states, such as patrolling, chasing, or attacking. The NPC transitions between these states based on certain conditions, such as the player’s proximity.
  2. Behavior Trees: A more advanced technique, behavior trees, allow for more complex NPC behavior by structuring AI as a tree of tasks. These tasks can be simple actions, like moving to a location, or more complex behaviors composed of other tasks.
  3. Utility AI: This technique involves assigning a utility score to different actions based on the current state of the game. The NPC then performs the action with the highest utility score. This allows for more dynamic and adaptable NPC behavior.
  4. Machine Learning: Some games use machine learning techniques to train NPCs. This involves using large amounts of data to train an NPC to respond to different situations. This can result in more unpredictable and realistic NPC behavior.

Implementing AI for NPCs

The first step in implementing AI for NPCs is to define the desired behavior. This could be as simple as an NPC that patrols a certain area, or as complex as an NPC that can engage in combat, navigate complex environments, and interact with the player.

Once the desired behavior is defined, the appropriate AI technique can be selected. For simple behaviors, a FSM may be sufficient. For more complex behaviors, a behavior tree or utility AI may be more appropriate. If the goal is to create an NPC that can learn and adapt, machine learning techniques may be used.

After selecting the AI technique, the next step is to implement it. This involves programming the NPC to perform the desired actions and react to the game environment. This can be a complex process, requiring a deep understanding of both programming and game design.

Testing and refining the AI is a crucial part of the process. This involves playing the game and observing the NPC’s behavior to ensure it behaves as expected. If the NPC’s behavior is not as desired, the AI may need to be adjusted or a different AI technique may need to be used.


Libraries for Implementing AI in Video Games

The implementation of AI in video games has been made significantly easier with the advent of various libraries and frameworks. These tools abstract away many of the complexities associated with AI, allowing developers to focus on creating engaging and dynamic NPCs. This chapter will explore some of the most popular libraries available for implementing AI in video games.

  1. TensorFlow and PyTorch

For developers interested in implementing machine learning-based AI, TensorFlow and PyTorch are two of the most popular libraries. Both libraries provide a comprehensive ecosystem of tools, libraries, and community resources that help researchers and developers build and deploy machine learning models. They support a wide range of neural network architectures and provide tools for training models, preparing data, and evaluating performance.

  1. Scikit-learn

Scikit-learn is a Python library that provides simple and efficient tools for predictive data analysis. It is built on NumPy, SciPy, and matplotlib, and it is open source and commercially usable. While not specifically designed for video games, it can be used to implement machine learning-based AI for NPCs.

  1. Unity ML-Agents

The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents. Agents can be trained using reinforcement learning, imitation learning, neuroevolution, or other machine learning methods through a simple-to-use Python API. This can be used to create NPCs that can learn and adapt to the player’s actions.

  1. BehaviorTree.CPP

BehaviorTree.CPP is a C++ library for creating behavior trees. It is designed to be used in real-time applications like video games. It provides a way to create complex, hierarchical, and reusable behaviors for NPCs.

  1. Unreal Engine’s AI Tools

Unreal Engine, one of the most popular game development engines, provides a suite of AI tools. This includes a behavior tree implementation, a navigation system for pathfinding, and a perception system for sensing the game environment. These tools can be used to create complex and dynamic NPC behaviors.

  1. OpenAI Gym

OpenAI Gym is a Python library for developing and comparing reinforcement learning algorithms. It provides a wide variety of environments for training agents, including classic control tasks, Atari 2600 games, and simulated robotics tasks. While not specifically designed for video game development, it can be used to train machine learning-based AI for NPCs.

There are numerous libraries available for implementing AI in video games, each with its own strengths and weaknesses. The choice of library depends on the specific requirements of the game and the desired behavior of the NPCs. Regardless of the library chosen, the goal is the same: to create engaging and dynamic NPCs that enhance the player’s gaming experience. As AI technology continues to advance, we can expect to see even more powerful and easy-to-use libraries for game development in the future.

Implementing AI for NPCs in video games is a complex process that involves defining the desired behavior, selecting the appropriate AI technique, implementing the AI, and testing and refining the behavior. Despite the complexity, the use of AI in video games can greatly enhance the player’s experience by creating dynamic and interactive NPCs. As AI technology continues to advance, we can expect to see even more realistic and engaging NPCs in future video games.

AI for the masses

Copilot for CLI: Your Personal Shell Wizard


Have you ever found yourself struggling to remember a specific shell command or an obscure flag? Or perhaps you’ve wished you could just tell the shell what you want it to do in plain English? If so, you’re in luck. GitHub is currently developing a tool that aims to bring the power of GitHub Copilot right into your terminal: Copilot for CLI.

What is Copilot for CLI?

Copilot for CLI is a tool designed to translate natural language into terminal commands. It’s like having a shell wizard by your side, ready to assist you with comprehensive knowledge of flags and the entire AWK language. When you need something more complicated than a simple `cd myrepo`, you can turn to this guru and just ask – in regular, human language – what you want to get done.

Three Modes of Interaction

Copilot for CLI provides three shell commands: `??`, `git?`, and `gh?`.

– `??` is a general-purpose command for arbitrary shell commands. It can compose commands and loops, and even throw around obscure find flags to satisfy your query. For example, you could use `?? list js files` or `?? make get request with curl`.

– `git?` is used specifically for git invocations. Compared to `??`, it is more powerful at generating Git commands, and your queries can be more succinct when you don’t need to explain that you’re in the context of Git. For instance, you could use `git? list all commits` or `git? delete a local branch`.

– `gh?` combines the power of the GitHub CLI command and query interface with the convenience of having AI generate the complicated flags and jq expressions for you. You could use `gh? all closed PRs` or `gh? create a private repo`.

How to Get Copilot for CLI?

Currently, Copilot for CLI is in the usable prototype stage. GitHub is allowing users to try out this tool as a prototype. To get it, you can sign up for the waitlist, and GitHub will notify you when you’re admitted. Note that you will also need GitHub Copilot access to use it.


The terminal is a powerful tool, but it can take many years of regular use to become a shell wizard. With Copilot for CLI, you can have a shell wizard by your side, ready to assist you with any command or flag you might need. So why not sign up for the waitlist and give it a try?

[GitHub Next Project page]

You can check this video from IanWootten


AI for the masses

Hugging Face: Revolutionizing Natural Language Processing and AI Development

In the fast-paced world of artificial intelligence and natural language processing, Hugging Face has emerged as a groundbreaking platform, empowering developers and researchers with state-of-the-art models and tools. With its extensive library of pre-trained models, user-friendly interfaces, and collaborative ecosystem, Hugging Face has become an indispensable resource for anyone working in the field. In this article, we delve into the world of Hugging Face and explore how it is revolutionizing AI development.

The Power of Hugging Face

Hugging Face provides an open-source library that serves as a one-stop shop for natural language processing (NLP) solutions. The platform offers a vast array of pre-trained models, ranging from language translation and text classification to sentiment analysis and question-answering systems. These models are built on top of the Transformers library, which has gained immense popularity in the NLP community.

Pre-trained Models

One of Hugging Face’s main strengths lies in its extensive collection of pre-trained models. These models have been fine-tuned on large datasets and are capable of performing a wide range of NLP tasks. Leveraging transfer learning, developers can quickly adapt these models to their specific needs by fine-tuning them on smaller, domain-specific datasets. This saves valuable time and computational resources, making it easier for researchers and developers to explore and experiment with cutting-edge NLP techniques.

Model Hub and Community

Hugging Face’s Model Hub serves as a central repository for pre-trained models contributed by researchers and developers from around the world. This collaborative ecosystem encourages knowledge sharing and enables the community to collectively build on each other’s work. The Model Hub allows users to access and download pre-trained models, making it easy to incorporate the latest advancements in NLP into their own projects.

In addition to the Model Hub, Hugging Face provides a forum for users to engage with each other, ask questions, and share insights. This vibrant community fosters collaboration, promotes best practices, and accelerates the pace of innovation in the NLP domain.

Transformers Library

The Transformers library, developed by Hugging Face, is the backbone of the platform. It offers a high-level API that simplifies the process of building, training, and deploying NLP models. With just a few lines of code, developers can fine-tune pre-trained models or create new ones from scratch. The library supports multiple frameworks, including PyTorch and TensorFlow, making it accessible to a wide range of users.

User-Friendly Interfaces

Hugging Face provides user-friendly interfaces to interact with its models, making it easy for developers to incorporate NLP capabilities into their applications. The Transformers library supports various programming languages, including Python and JavaScript, enabling seamless integration into different software environments.

Through its user-friendly interfaces, Hugging Face democratizes access to advanced NLP models, allowing developers with varying levels of expertise to leverage state-of-the-art techniques without extensive knowledge of the underlying algorithms.

Hugging Face has revolutionized the landscape of NLP and AI development by providing a comprehensive platform for pre-trained models, a collaborative community, and user-friendly interfaces. Its approach of leveraging transfer learning and fine-tuning has significantly accelerated the adoption of cutting-edge NLP techniques, enabling developers and researchers to build sophisticated language models with ease.

As Hugging Face continues to evolve and grow, it will undoubtedly play a crucial role in shaping the future of AI. By democratizing access to powerful NLP models and fostering a collaborative ecosystem, Hugging Face empowers individuals and organizations to push the boundaries of what is possible in natural language processing.