The hottest Automation Substack posts right now

And their main takeaways

DevEx: It’s All About Automating Your Team

🔮 Crafting Tech Teams • 0 implied HN points • 30 May 23

🕹 Technology Automation

Automating your team involves automating processes, not just assertions.
Consider your workload and learn to prioritize tasks effectively to avoid overwhelm.
Subscribing may grant you access to more insights about optimizing team automation.

Stability Begets Tests

Tribal Knowledge • 0 implied HN points • 03 Jul 22

🕹 Technology Automation

Tests are only valuable when you already have stability in your product.
Focus on developing a stable core set of features before investing heavily in testing.
Design features for a smaller customer base initially to work out kinks and build a sustainable product.

Newsletter #9: Building a Brain with LLM

Decoding Coding • 0 implied HN points • 20 Apr 23

🕹 Technology Automation

Robots can use language models to understand and navigate their environments better. This setup includes a visual model that acts like an 'eye' to see the world.
The robot has a 'nerve' system that asks questions and plans actions based on what it sees. It makes sense of information and decides what the robot should do next.
Eventually, as language models improve, robots could act more autonomously and make decisions on their own. This could change how we interact with machines in exciting ways.

How to keep humans in the loop

Space chimp life • 0 implied HN points • 10 Apr 23

🕹 Technology Automation

We need better ways to share information and opinions in our decision-making systems. Right now, it's hard for people to feel heard or to make changes in our society.
Human systems often operate between humans making decisions and automated processes. Finding a balance could help us use both human creativity and the efficiency of automation.
Creating a platform for people to propose and vote on ideas could improve cooperation and decision-making at all levels. This would help people work together better, whether in families, friends, or communities.

Quality Engineering Basics: Prioritizing Bugs

QUALITY BOSS • 0 implied HN points • 18 Mar 24

🕹 Technology Automation

Understanding how to prioritize bugs is key for efficient quality engineering. It's important to have a common agreement on what each priority level means.
Using a matrix to categorize bugs by their scope and impact can help in deciding their priority. This method allows teams to see which bugs are more urgent and need immediate attention.
Automation tools, like GitHub actions, can streamline the bug prioritization process. They can help automatically assign priority based on set parameters, saving time and reducing errors.

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Quality Engineering Basics: Automated vs. Manual

QUALITY BOSS • 0 implied HN points • 11 Mar 24

🕹 Technology Automation

Deciding which tests to automate or run manually is important. You should look at the risk level and necessary effort for each test.
Using a scoring system can help prioritize tests. This involves scoring impact, likelihood, frequency, and the effort required for manual or automated testing.
Starting small with your scoring approach is a good idea. You can adjust the numbers until you find what works best for your testing needs.

Quality Engineering Basics: Types of Testing

QUALITY BOSS • 0 implied HN points • 19 Feb 24

🕹 Technology Automation

There are two main types of testing: functional and non-functional. Functional testing checks if the software meets its intended requirements, while non-functional testing looks at things like performance and security.
Testing can also be categorized by the level, such as unit, integration, system, and acceptance testing. Each level focuses on different parts of the software and helps ensure everything works together smoothly.
Finally, there are different ways to test, including manual and automated testing. Manual testing involves humans checking software directly, while automated testing uses tools to run tests automatically. Both are important for delivering a quality product.

The Future is AI Agents. No Doubt.

Sector 6 | The Newsletter of AIM • 0 implied HN points • 08 Jul 24

🕹 Technology Automation

AI agents are becoming a hot topic in technology, with many experts predicting their rapid adoption. People believe these agents will play a big role in the future of work.
A personalized AI avatar for each individual is envisioned, helping manage daily tasks and improving efficiency in various activities.
Research and courses about AI agents are increasing, showing a growing interest in how to create and use these technologies effectively.

All About Physical AI

Sector 6 | The Newsletter of AIM • 0 implied HN points • 08 Jun 24

🕹 Technology Automation

Physical AI is a newer type of technology that can understand instructions and do complex tasks by itself. It has the potential to change how industries operate.
NVIDIA is a key player in this field, with its simulation tool called Omniverse helping to bring these advanced robotic technologies to life.
The upcoming wave of AI involves integrating robotics deeply into our everyday lives, making it an exciting time for technological advancements.

AI will Create More Jobs Than There are People

Sector 6 | The Newsletter of AIM • 0 implied HN points • 13 May 24

🕹 Technology Automation

AI is creating a lot of job openings, far more than the number of skilled workers available. This means many companies are looking for talent in this fast-growing field.
In India alone, there's a huge gap between the jobs available in AI and the number of experienced engineers. Only about 2,000 senior AI engineers are actively working, while the job demand is skyrocketing.
This situation shows a trend where advancements in technology can lead to job creation, even if there aren't enough people right now to fill those roles.

Humanoid Robots Are Now a Reality

Sector 6 | The Newsletter of AIM • 0 implied HN points • 18 Mar 24

🕹 Technology Automation

Humanoid robots are becoming more advanced and can perform a variety of tasks. They've evolved quickly, with new models showing improved abilities compared to earlier versions.
Recently, a humanoid robot powered by OpenAI has shown the potential to move at speeds approaching that of humans. This indicates significant advancements in robotics technology.
The development of these robots raises exciting possibilities for their use in everyday life. They could become helpful tools in many areas, from entertainment to assistance in daily tasks.

GPT-5, Where Are You?

Sector 6 | The Newsletter of AIM • 0 implied HN points • 15 Mar 24

🕹 Technology Automation

People are eager for the release of GPT-5, but it hasn't been announced yet.
Recently, new AI technologies are emerging, like an AI that can code and a humanoid robot powered by ChatGPT.
It's been a year since GPT-4 was launched, and excitement is still high about future AI advancements.

When (Not) to XGBoost

Sector 6 | The Newsletter of AIM • 0 implied HN points • 12 Mar 24

🕹 Technology Automation

XGBoost is a popular tool in machine learning, but it's not always the best choice for every situation. It's important to understand when to apply it and when to use other methods.
Many people now claim to be experts in AI after the rise of large language models, but AI includes a lot more than just these models.
It's essential to know the broader landscape of AI techniques to make better decisions in data science and machine learning projects.

Ketchup!🥫🍅🥫🍅🧴

Sector 6 | The Newsletter of AIM • 0 implied HN points • 30 Jan 24

🕹 Technology Automation

Google has stopped working with Appen because they faced some internal issues and the industry is moving towards more advanced AI solutions.
Appen has had leadership problems, with their long-time CEO leaving and new executives exiting, affecting the company's stability.
The company struggled with a decline in revenue and found it hard to adapt to the new generative AI landscape.

Indian IT ‘GenAI’ Quarterly Report

Sector 6 | The Newsletter of AIM • 0 implied HN points • 23 Oct 23

🕹 Technology Automation

Indian IT companies are focusing heavily on generative AI, with big players like TCS, Infosys, and Wipro training thousands of employees in this area.
Despite this training effort, many firms are seeing a decline in total employee headcount, as they prioritize upskilling over hiring new freshers.
Wipro is leading in employee training for generative AI, while TCS is ahead in adopting AI products and services with their clients.

The Real ChatGPT is Finally Here

Sector 6 | The Newsletter of AIM • 0 implied HN points • 04 Oct 23

🕹 Technology Automation

ChatGPT struggled to meet initial expectations, often giving unreliable information. Many users realized it wasn't always trustworthy after the excitement wore off.
The new GPT-4V(ision) has expanded ChatGPT's abilities, allowing it to read texts and understand images. This makes it much more versatile and useful for various tasks.
A major breakthrough is in medical science, where radiologists can now use this model to analyze images from scans better. This helps them gather important information from X-rays and other medical images.

Assassin GPT or Saviour GPT

Sector 6 | The Newsletter of AIM • 0 implied HN points • 12 May 23

🕹 Technology Automation

ChatGPT is impacting jobs in various fields, especially for designers, writers, and now software developers. It raises concerns about how AI might replace human roles in the workforce.
The new code interpreter plugin lets users easily get results without needing to understand complex data tools. This convenience can make it more tempting to rely solely on AI for data tasks.
The discussion around renaming ChatGPT to AssassinGPT highlights fears about its potential to disrupt industries. Some see it as a threat rather than a helpful tool.

The Most Disastrous Week in AI Ever

Sector 6 | The Newsletter of AIM • 0 implied HN points • 08 May 23

🕹 Technology Automation

IBM is freezing hiring and cutting jobs because they believe AI can do most of the work that those roles handle. This means up to 7,800 positions are at risk.
Geoffrey Hinton, a major figure in AI, has left Google to speak out about the potential dangers of AI technology. He's worried about misinformation and the future of jobs because of AI.
There are growing concerns about creating truly intelligent machines and the risks they might pose to society, especially with misleading information spreading quickly.

The Odds are Stacked Against You!

Sector 6 | The Newsletter of AIM • 0 implied HN points • 05 Apr 23

🕹 Technology Automation

Stack Overflow is worried about ChatGPT taking over because it gives quick answers, which might make their site less useful. Many users are leaving the platform.
Stack Overflow previously warned users about ChatGPT responses but eventually banned it due to accuracy issues in the answers.
This situation highlights how technology like AI can impact existing platforms, causing significant changes in user behavior and engagement.

Chip Designs Redefined

Sector 6 | The Newsletter of AIM • 0 implied HN points • 29 Mar 23

🕹 Technology Automation

Chip technology is becoming more advanced, but making them smaller is getting harder. This means the way chips are designed needs to evolve.
Moore's Law, which said chip components would double every year, is slowing down. We are reaching the limits of how many circuits can fit on a single chip.
Nvidia has proposed a new way to improve chip design automation with their paper on automated placement of components. This could help overcome some of the challenges in current chip manufacturing.

Developers Paradise

Sector 6 | The Newsletter of AIM • 0 implied HN points • 22 Jan 23

🕹 Technology Automation

India has over 6 million developers and is quickly becoming a leader in AI technology.
While AI will create many new jobs, it might also cause the loss of up to 57 million jobs in India due to automation.
Events like the Machine Learning Developers Summit 2023 aim to connect developers and help bridge the skills gap in the industry.

Back to the Future

Sector 6 | The Newsletter of AIM • 0 implied HN points • 09 Jan 23

🕹 Technology Automation

Scientists are still trying to create a machine that works like the human brain, but they haven't found a solution yet.
Researchers are looking at older AI methods, called Good-Old-Fashioned Artificial Intelligence (GOFAI), to help machines understand like humans do.
Symbolic AI can understand complex ideas and relationships better, while deep learning needs to be retrained often to learn new tasks.

A 100T Language Model?

Sector 6 | The Newsletter of AIM • 0 implied HN points • 27 Dec 21

🕹 Technology Automation

There is a hackathon for data science where participants can showcase their skills. It's a great way to get noticed by top companies in analytics and tech.
The hackathon will last until January 10th, so you have time to join and compete. This could be a fun challenge to sharpen your skills.
By participating, you might not only learn new things but also get a job offer from a leading company. It's a promising opportunity for anyone interested in the field.

AI Drop: Streamlining Development with Claude's Artifacts

Code and Context • 0 implied HN points • 04 Jul 24

🕹 Technology Automation

Artifact Alchemy is a tool that helps developers quickly organize files generated by Claude. This saves time and reduces mistakes when adding files to projects.
The tool automatically extracts different types of files from Claude and arranges them in a way that matches how a project is structured. This makes it easier to find and use the files later.
Using Artifact Alchemy is simple and straightforward; just follow a few commands to install and run it. It allows developers to focus more on building software instead of managing files.

AI Engineer World's Fair–Day 1: Foundations and Frameworks

Code and Context • 0 implied HN points • 29 Jun 24

🕹 Technology Automation

Foundational technologies are key to developing powerful AI systems. Without strong systems, we can't fully utilize AI's potential.
Automation and intelligent agents like LangChain are pushing AI to new heights. These tools can help us work smarter and improve efficiency.
Knowledge graphs play an important role in connecting information. They help AI understand and make sense of data better.

Building in Public: Introducing AI Drop of the Week

Code and Context • 0 implied HN points • 24 Jun 24

🕹 Technology Automation

The author believes that traditional software models will change as AI improves, leading to new ways to create digital content. People will need to adapt by focusing on personal expression instead of economic viability.
Because of advancements in AI tools, making software and other forms of creative work will get easier. This means people might do these activities more for fun rather than as a job.
The author is starting a new series called 'AI Drop of the Week' where they will create AI projects and share them. They want to encourage exploring AI tools and making things together.

Github Action for Unit Testing

Better Engineers • 0 implied HN points • 06 Sep 20

🕹 Technology Automation

GitHub Actions help automate tasks like building, testing, and deploying code. It's a great way to make your workflow easier and more efficient.
Unit testing is important because it checks if individual parts of your code work correctly. Running these tests can help catch bugs early, saving time later on.
You can set up GitHub Actions to prevent merging code if the unit tests fail. This ensures that only tested and working code makes it into your main project.

How I refactor large codebases with ChatGPT

The Future of Life • 0 implied HN points • 05 Jan 24

🕹 Technology Automation

ChatGPT can help with refactoring large codebases, but it works best when you break the project into smaller tasks.
To get good results, you need to provide ChatGPT with details about your project's structure, business domain, and preferred organization methods.
After ChatGPT suggests a new structure, it may take several adjustments to refine it, and you can ask for formats or scripts to help automate the setup.

What would it take to convince you AGI is near?

The Future of Life • 0 implied HN points • 23 Jul 23

🕹 Technology Automation

Many people might not believe AGI is close until they can interact with a very intelligent AI that mimics human behavior. This shows that human-like interaction can significantly influence people's perceptions of intelligence.
Understanding AGI is not just about knowing when it arrives; it’s crucial to recognize its potential to change society. The arrival of AGI could rapidly transform our way of life, for better or worse.
It's important to question whether individuals personally benefit from believing that AGI is near. This thoughtful consideration can help people prepare for a future where intelligent agents are part of our daily lives.

Thoughts on how ChatGPT4 applies abstract concepts

The Future of Life • 0 implied HN points • 24 Mar 23

🕹 Technology Automation

ChatGPT can apply complex concepts like the SOLID principles in programming, which typically require extensive knowledge and experience. This shows how the model understands and utilizes abstract frameworks effectively.
The model is capable of analyzing philosophical ideas, like Objectivism, and provides thoughtful explanations about them. This demonstrates its ability to engage in deep reasoning and relate concepts to real-life situations.
There's curiosity about the limits of ChatGPT's reasoning abilities, especially with abstract concepts. It's suggested that there may be specific types of reasoning that only humans can easily handle.

The AI apocalypse is coming

The Future of Life • 0 implied HN points • 24 Mar 23

🕹 Technology Automation

Most people worry about a dangerous AI with bad intentions, but the real risk is super-competent AI used by the wrong people. This is hard to understand because that kind of AI doesn't exist yet.
In the next ten years, we might see super-competent AI that can solve many human problems. This could be a technology that helps in various fields, not just chatbots.
To prevent disasters from AI, we need to acknowledge the risks, invest in safety research, and create better safety protocols. Just banning AI won't help and could make things worse.

Awesome List for AutoML - Speed Up Model Development

The Beep • 0 implied HN points • 09 Apr 24

🕹 Technology Automation

AutoML automates tasks in the machine learning process, making it easier for people with less expertise to use. This means more folks can build models without needing to learn everything about data science.
Using AutoML can save time and resources as it speeds up tasks like data preparation and model tuning. This lets data scientists focus on more complex problems instead.
Though AutoML is helpful, it may reduce control over the modeling process and can introduce biases. It's important to combine AutoML with human expertise to make sure decisions are well-informed.

Elevate The LLM Game with Prompt Engineering

The Beep • 0 implied HN points • 25 Jan 24

🕹 Technology Automation

Prompt engineering helps you create better questions for AI, leading to more helpful answers. It involves trying different ways to ask until you get the response you want.
There are different types of prompts, like zero-shot, one-shot, and few-shot. Each type provides different amounts of context to help the AI understand what you're asking.
Using tools for prompt engineering can make the process easier and more efficient. They help in crafting prompts that get better results without needing to retrain the AI.

Multi-Modal Agentic Applications

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 23 Aug 24

🕹 Technology Automation

AI agents are software that can perform tasks and make decisions on their own. They break down complex jobs into smaller steps to make them easier to handle.
These agents use various tools, including APIs and even humans, to help solve problems. This helps them be more effective and ensures safety in their operations.
Multi-modal agents can use both language and vision. This makes them more powerful because they can analyze images and text together for better understanding and responses.

AI Agents

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 06 Aug 24

🕹 Technology Automation

AI Agents are programs that use large language models to work on tasks independently. They can break down complex questions and find solutions like humans do.
These agents can handle tasks by analyzing user interfaces and predicting next actions by looking at icons and text. This makes them more effective in completing tasks on screens.
Recent advancements have improved AI Agents' ability to understand and navigate user interfaces, allowing them to act more like real users. This helps them give better and more accurate results.

AgentInstruct Uses Agentic Flows To Create Synthetic Training Data

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 16 Jul 24

🕹 Technology Automation

Microsoft is using advanced methods to create high-quality synthetic training data for language models. This helps improve the data's diversity and reduces the need for human oversight.
Agentic workflows are important because they allow multiple agents to generate and refine data, making the process more efficient and effective.
The approach can create large amounts of customized data from unstructured sources quickly, which is useful for enhancing AI models during different training stages.

Comparing LLM Agents to Chains: Differences, Advantages & Disadvantages

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 21 May 24

🕹 Technology Automation

Chains are a way to connect prompts together, like a sequence, to help AI give better answers for complex questions. They work like a script where the user guides the AI step by step.
Agents are smarter and can make decisions on their own without needing constant help from humans. They are designed to handle a wider range of tasks and may change how industries operate in the future.
Using chains can be easier and cheaper for certain tasks, especially when users want more control over the conversation. Agents, while more autonomous, usually need more coding and technical skill to set up.

Agentic Search-Augmented Factuality Evaluator (SAFE) For LLMs

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 05 Apr 24

🕹 Technology Automation

The Agentic Search-Augmented Factuality Evaluator (SAFE) is designed to check the facts in long-form texts. It breaks down responses into smaller facts to evaluate them more accurately.
SAFE is cheaper and faster than using human annotators. It costs about 19 cents per evaluation compared to 4 dollars when relying on people.
Google Search is used by SAFE to find current information for checking facts, making sure the evaluations are accurate and up-to-date.

Agentic RAG: Context-Augmented OpenAI Agents

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 14 Mar 24

🕹 Technology Automation

Agentic RAG combines OpenAI's function calling with autonomous agents for better task management. This makes it easier to choose the right tools for different tasks.
LlamaIndex's ContextRetrieverOpenAIAgent allows you to use multiple tools while keeping the process straightforward. It helps manage complexity by organizing various functions effectively.
This new approach allows for more detailed queries and better analysis of data. It lets users run complex calculations while ensuring the results can be easily understood.

LLM Drift, Prompt Drift & Cascading

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 23 Feb 24

🕹 Technology Automation

LLM Drift means that a language model's responses can change a lot over time. It's important to keep an eye on how these models perform since they might get worse unexpectedly.
Prompt Drift occurs when the same input doesn't give the same result over time due to changes in the model or data. This can cause differences in what users expect and what they actually get.
Cascading happens when one mistake in a chain of tasks leads to more problems in subsequent tasks. Once one part has an error, it can make everything else after it worse.