The hottest Automation Substack posts right now

And their main takeaways
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In My Tribe • 167 implied HN points • 23 Dec 24
🕹 Technology AI Robotics Machine Learning Human-computer interaction Automation
  1. AI-generated podcasts can share information in new ways, like converting written essays into audio. This shows how AI can create engaging content without much input.
  2. Large Language Models (LLMs) struggle to learn new concepts as effectively as humans do because they rely on past data. Humans continue to adapt and learn from everyday experiences.
  3. The potential economic impact of robots is huge, especially for tasks like cleaning and driving. The market for humanoid robots could reach trillions, and they might also help reduce accidents.

Five Levels Of AI Agents

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 119 implied HN points • 16 May 24
🕹 Technology AI Software Machine Learning Automation Data science
  1. AI agents can make decisions and take actions based on their environment. They operate at different levels of complexity, with level one being simple rule-based systems.
  2. Currently, AI agents are improving rapidly, sitting at levels two and three, where they can automate tasks and manage sequences of actions effectively.
  3. The future of AI agents is bright, as they will be more integrated into various industries, but we need to consider issues like accountability and ethics when designing and implementing them.

How machines learn, visually explained 🧠

Year 2049 • 13 implied HN points • 17 Jan 25
🕹 Technology AI Machine Learning Data science Robotics Automation
  1. AI systems learn from data, so the quality of that data is really important. Better data means smarter machines.
  2. Machines can become biased if they are trained on biased data. It's important to watch out for this when developing AI.
  3. This is just one part of a series explaining AI. More episodes will cover different aspects of how machines learn and behave.

AI Agents With Human In The Loop

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 15 Aug 24
🕹 Technology AI Automation Chatbots NLP Frameworks
  1. AI agents can now include human input at important points, which helps make their actions safer and more reliable. This way, humans can step in when needed without taking over the whole process.
  2. LangGraph is a new tool that helps organize and manage how these AI agents work. It uses a graph approach to show steps and allows for better oversight and control.
  3. By combining automation with human checks, we can create more efficient systems that still have the safety of human involvement. This lets us enjoy the benefits of AI while also addressing concerns about its autonomy.

Data Science Weekly - Issue 540

Data Science Weekly Newsletter • 179 implied HN points • 29 Mar 24
🕹 Technology Data science AI Machine Learning Data Engineering Automation
  1. SQL is seen as an easier way to write relational algebra, but it's not ideal for building new query tools. Understanding its limits can help in learning and using SQL better.
  2. Many successful companies have developed their own AI models, showing a trend in the tech industry. Knowing about these companies can give insights into future developments in AI.
  3. Binary vector search methods can save a lot of memory compared to traditional methods. However, it's important to balance memory savings with maintaining accuracy.
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How to design effective AI Agents

A Bit Gamey • 6 implied HN points • 02 Feb 25
🕹 Technology AI Tools Development Software Automation
  1. AI apps can be categorized into two main types: workflows and agents. Workflows follow strict rules, while agents make their own decisions in changing environments.
  2. Simplicity is key when designing AI agents. It's better to start with simple solutions and add complexity only when necessary.
  3. There are established design patterns and tools to create effective AI agents. Using the right patterns can help make agents more reliable and easier to maintain.

Auto-Optimized Prompts, AI Text Detection, and Parametric RAG

HackerPulse Dispatch • 5 implied HN points • 31 Jan 25
🕹 Technology AI Machine Learning Data science Automation Innovation
  1. LLM-AutoDiff can make AI workflows more efficient by automatically optimizing prompts, leading to better performance without the need for manual work.
  2. Racing for superintelligence might cause more problems than it solves, making cooperation between nations a better option.
  3. Combining reinforcement learning with transformers can create AI that adapts and solves new problems effectively over time.

Robotics, Automation and AI Conferences

Robots & Startups • 299 implied HN points • 16 Jan 24
🕹 Technology Robotics Automation AI Conferences
  1. There are numerous robotics, automation, and AI conferences available, with a mix of academic and industry events.
  2. Consider factors such as the conference's impact factor, size, specialization, attendees, and topics to decide which events are worth attending.
  3. The post provides shortlists of academic conferences and hints at upcoming coverage of tradeshows and industry events.

Resilient Cyber Newsletter #9

Resilient Cyber • 19 implied HN points • 13 Aug 24
🕹 Technology Cybersecurity AI Automation Compliance Vulnerability Management
  1. Microsoft is tying employee bonuses to security performance, highlighting the importance of prioritizing security in their culture. This means employees are encouraged to choose security over other goals like speed or profit.
  2. There's growing interest in using AI for cybersecurity tasks, including identifying vulnerabilities and automating processes. This technology could help improve security practices but also presents challenges.
  3. The market for security automation is expected to grow significantly. This means companies are looking for ways to streamline their security processes and keep up with new threats efficiently.

eBook: Mastering AI Agents

TheSequence • 77 implied HN points • 07 Feb 25
🕹 Technology AI agents Machine Learning Automation Software Development Data Analysis
  1. You can learn to create effective AI agents with the right guidance. There's a helpful eBook that covers how these agents work and when to use them.
  2. The book reviews three frameworks for developing AI agents, helping you choose what's best for your needs. It also shares case studies to show real-life applications.
  3. It addresses common reasons AI agents fail and provides solutions to avoid these problems. This can help ensure your AI projects succeed.

The Automation Paradox: How More Tech Can Mean More Human Challenges

UX Psychology • 297 implied HN points • 12 Jan 24
🕹 Technology Automation UX Design Human factors Autonomous Vehicles Language Models
  1. Increased automation can lead to unexpected complications for human tasks, creating a paradox where reliance on technology may actually hinder human performance.
  2. The 'Irony of Automation' highlights unintended consequences like automation not reducing human workload, requiring more complex skills for operators, and leading to decreased vigilance.
  3. Strategies like enhancing monitoring systems, maintaining manual and cognitive skills, and thoughtful interface design are crucial for addressing the challenges posed by automation and keeping human factors in focus.

🤖 AI and the economy: scenarios for a world with artificial general intelligence

Faster, Please! • 456 implied HN points • 18 Mar 24
🕹 Technology AI Economy Future Automation Labor
  1. Artificial General Intelligence is a concept that doesn't exist yet and may never be achieved, but some experts believe it's coming soon.
  2. AI is viewed as a tool to enhance human capabilities and create new opportunities rather than a threat to job security.
  3. The impact of AI on the economy will depend on whether there is a limit to the complexity of tasks humans can perform.

A metrics layer is like your New Year's resolution…

HyperArc • 39 implied HN points • 11 Jul 24
🕹 Technology Data Analytics Software Development Artificial Intelligence Business Intelligence Automation
  1. A metrics layer helps standardize how companies measure data, making it easier for everyone to understand what is important. It can automate calculations, like rolling averages, which saves time and reduces confusion.
  2. Traditional business intelligence tools often lose useful underlying information, which makes it hard to understand how certain metrics were created. More context is needed to ensure decisions are well-informed and based on complete data.
  3. HyperArc offers a solution by capturing the team's insights and reasoning during analysis. It helps keep track of not just the final metrics, but also the thought process behind them, making it easier to revisit and understand decisions in the future.

Event-Driven Agent Mesh

SUP! Hubert’s Substack • 40 implied HN points • 21 Nov 24
🕹 Technology AI Architecture Software Data processing Automation
  1. An agent mesh is a modern system where multiple AI agents work together to handle tasks more efficiently. This helps break down complex work into smaller parts that specialized agents can manage.
  2. The event-driven architecture allows agents to join or leave the mesh easily, making the system scalable and adaptable to changing needs. This means agents can respond quickly to new information or demands.
  3. Using technologies like Kafka with an agent mesh enables fast communication between agents and helps ensure that no data is lost. This makes the entire system more reliable and capable of handling a lot of information at once.

Service as a Software: The New SaaS

Jakob Nielsen on UX • 27 implied HN points • 19 Dec 24
🕹 Technology AI Software SaaS Automation Consulting
  1. AI is changing how we work by making professional skills available almost instantly and at a low cost. This shift will allow tasks that used to require human expertise to be done by software.
  2. The new idea of 'Service as a Software' (SaaS) could disrupt many professional jobs by automating services like consulting, legal work, and design. This could lead to a significant boost in the economy.
  3. As AI becomes smarter and cheaper, it's expected to make high-quality expertise available to more people, changing how businesses operate and creating new opportunities in various fields.

Language Agent Tree Search — LATS

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 59 implied HN points • 12 Jun 24
🕹 Technology AI NLP Automation Software Data
  1. The LATS framework helps create smarter agents that can reason and make decisions in different situations. It's designed to enhance how language models think and plan.
  2. Using external tools and feedback in the LATS framework makes agents better at solving complex problems. This means they can learn from past experiences and improve their responses over time.
  3. LATS allows agents to explore many possible actions and consider different options before making a choice. This flexibility leads to more thoughtful and helpful interactions.

what happens when we hollow out work?

State of the Future • 39 implied HN points • 12 Mar 25
🕹 Technology AI Automation Workplace Social Issues Future Trends
  1. People might react strongly to job losses caused by AI. Some may feel despair and turn inward, while others might fight back and protest.
  2. History shows that when people feel powerless due to industrial changes, they often rebel. This could happen again with the rise of automation in the workforce.
  3. To move forward, we need to find new meaning and purpose in our lives that aren't tied to work. Embracing community and personal connections may be key to thriving in a future with less traditional employment.

The Sequence Engineering #474: The Super Popular Eliza Framework for Building AI Agents

TheSequence • 77 implied HN points • 22 Jan 25
🕹 Technology AI Web3 Development Frameworks Automation
  1. The Eliza framework is becoming very popular, especially in the web3 and crypto spaces. It helps developers create AI applications by automating essential tasks.
  2. Despite not being widely known, Eliza has gained a lot of attention on platforms like GitHub, showing its growing appeal.
  3. Eliza offers a flexible design, making it a strong choice for building agentic apps. It's more than just a tool for crypto; it's useful for various types of AI projects.

Language is not enough

Erika’s Newsletter • 412 implied HN points • 11 Apr 23
🕹 Technology Automation Programming AI Metadata
  1. Writing code is a major barrier in lab automation, often leading to less sophisticated protocols created through GUI interfaces.
  2. Natural language is insufficient to accurately represent complex biological protocols, resulting in trial and error to get experiments working.
  3. Programming robots in English may improve user interfaces, but additional challenges remain in making lab automation more effective than human scientists.

Putting ChatGPT on the Stand as an Expert Witness

Autonomy • 11 implied HN points • 11 Jan 25
🕹 Technology AI Ethics Automation Data Privacy
  1. AI could start playing a role in court by acting as an expert witness, answering questions just like a human would. This could change how legal arguments are made and maybe even lead to AI gaining more credibility.
  2. Lawyers might use AI not just for expert opinions, but also to gather evidence and build arguments. This means the AI helps in the background, but it’s the lawyer who presents the case in court.
  3. In the future, we might see cases where AI itself is called to testify, which could change how we view the trustworthiness of expert opinions in law. An AI might be seen as more reliable since it has no personal stakes in the outcome.

MLOps Basics - For Data Engineers.

Data Engineering Central • 393 implied HN points • 15 May 23
🕹 Technology Machine Learning Data Engineering MLOps Training Automation
  1. Working on Machine Learning as a Data Engineer is not as hard as it seems - it falls somewhere in the middle of difficulty.
  2. Machine Learning work for Data Engineers focuses on MLOps like feature stores, model prediction, automation, and metadata storage.
  3. The key aspects of MLOps include automating tasks, using tools like Apache Airflow, and managing metadata for a stable ML environment.

CUAs Can Become APIs

The API Changelog • 3 implied HN points • 31 Jan 25
🕹 Technology AI APIs Automation Software Computer Science
  1. CUAs, or Computer-Using Agents, can perform tasks on computers like humans do. They are designed to help with tasks even when normal APIs are unavailable.
  2. As CUAs can act on your behalf after initial help, they can eventually work automatically. Their ability to do this raises questions about how much control we want to give them.
  3. Making CUAs available as APIs is technically simple. This opens up many questions about what tasks should be accessible and who gets to use them.

Proofreading an entire book with GPT-4 for $6.88

Mindful Modeler • 479 implied HN points • 02 May 23
🕹 Technology AI Automation Tools Costs Workflow
  1. Proofreading an entire book with GPT-4 can help automate tasks like improving grammar, language, and cutting clutter in a draft.
  2. Using prompts to guide LLMs like GPT-4 is important for specific and successful outcomes in automated editing.
  3. The economic benefit of using GPT-4 for proofreading can be significant compared to hiring a professional proofreader, offering a balance between capabilities and cost.

OpenAI EU, Stripe + Bridge, Qualys TotalAppSec

The API Changelog • 1 implied HN point • 11 Feb 25
🕹 Technology AI APIs Fintech Cybersecurity Automation
  1. OpenAI launched the O3 Mini AI to compete with DeepSeek, aiming to offer top-notch reasoning and coding skills while being free on the ChatGPT platform.
  2. Stripe acquired the stablecoin platform Bridge for $1.1 billion, marking a significant move into the cryptocurrency sector.
  3. Qualys introduced TotalAppSec, an AI-driven tool for managing application risks that helps enhance API safety and web app security.

SIEM 4.0: The Essentialist Evolution

Detection at Scale • 59 implied HN points • 28 May 24
🕹 Technology Security AI Data Software Engineering Automation
  1. Security teams are moving towards prioritizing impactful MITRE tactics over complete ATT&CK coverage to reduce distracting alerts and focus on critical threats.
  2. Transitioning from individual behaviors to risk-based alerts allows for a more context-based approach, reducing alert volumes and enhancing significance.
  3. The evolution to SIEM 4.0 includes opening up data lakes, adopting 'as code' principles, and utilizing AI to automate routine tasks so human analysts can focus on high-value work.

The worst test suites read like word salad after a C++ compiler had a stroke. The best test suites adopt business language.

🔮 Crafting Tech Teams • 99 implied HN points • 10 Apr 24
🕹 Technology Testing Programming Automation
  1. Write tests in plain language aligned with business objectives for better understanding and communication.
  2. Ensure test names are clear and easily interpreted by humans to provide confidence and insight.
  3. Utilize BDD and Jasmine frameworks for more ergonomic testing and improved behavior analysis.

RAG Implementations Are Becoming More Agent-Like

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 99 implied HN points • 08 Apr 24
🕹 Technology AI Development Software Engineering Data science Machine Learning Automation
  1. RAG implementations are changing to become more like agents, which means they can make better decisions and adapt to different situations.
  2. The structure of prompts is really important now; it’s not just about adding data, but about crafting the prompts to improve how they perform.
  3. Agentic RAG allows for complex tasks by using multiple tools together, making it capable of handling detailed questions that standard RAG cannot.

The Five Layers of Incident Response (Part 1)

Detection at Scale • 59 implied HN points • 21 May 24
🕹 Technology Security Automation Data Detection Incident Response
  1. Detection Engineering involves automating SecOps using software engineering and data principles to enhance defense capabilities without eliminating human roles.
  2. For effective Incident Response, utilize the 'Five Layers of IR': Playbook Management, Data Layer, and Presentation Layer.
  3. The Playbook sets the strategy, Data Layer defines necessary logs for playbooks, and Presentation Layer visualizes alerts and actions for human analysis.

Jobs Apocalypse

Diane Francis • 519 implied HN points • 17 Apr 23
🕹 Technology AI Impact Job Market Automation Future Trends
  1. Many experts believe that AI development should be paused due to safety concerns. A significant number of people think AI could harm society and want it to be regulated.
  2. A Cornell study suggests 80% of American jobs could be affected by AI, especially higher-paying roles. Many workers may find their tasks taken over by AI tools, which could lead to job loss.
  3. As AI technology advances, it will likely transform many jobs, especially in knowledge work. There's a call for governments to step in and set rules to manage this change effectively.