The hottest Automation Substack posts right now

And their main takeaways
Category
Top Technology Topics

Dirty Work: The Return of Blue Collar

State of the Future • 144 implied HN points • 04 Jun 25
💼 Business Automation
  1. AI is taking over many white-collar jobs, especially those that are routine and easily automated. Many of these roles aren't as valuable as we once thought.
  2. There are plenty of blue-collar jobs available that can provide real satisfaction and meaning. These jobs often require skills that AI cannot replicate.
  3. Blue-collar jobs are likely to gain more respect and higher status in the future. We should encourage young people to consider these careers now.

It’s GPT-5 Day!

Alex's Personal Blog • 98 implied HN points • 07 Aug 25
🕹 Technology Automation
  1. Smartsheet was recently sold for $8.4 billion, but its former CEO left the company shortly after due to changes that frustrated staff. This suggests challenges that can arise with private equity ownership.
  2. AI continues to grow, especially in coding, and companies see huge revenue potential in this area. Predictions about its rapid growth can sometimes sound unbelievable but may turn out to be true.
  3. The financial model for AI companies can look strange because they often spend a lot upfront on developing new models, but eventually, they can become profitable as they ramp up revenue from these models.

If AGI Means Everything People Do... What is it That People Do?

Am I Stronger Yet? • 250 implied HN points • 27 Feb 25
🕹 Technology Automation
  1. There's a big gap between what AIs can do in tests and what they can do in real life. It shows we need to understand the full range of human tasks before predicting AI's future capabilities.
  2. AIs currently struggle with complex tasks like planning, judgment, and creativity. These areas need improvement before they can replace humans in many jobs.
  3. To really know how far AIs can go, we need to focus on the skills they lack and find better ways to measure those abilities. This will help us understand AI's potential.

AI is yet another platform shift

Generating Conversation • 280 implied HN points • 30 Jan 25
🕹 Technology Automation
  1. AI is a big change in technology, similar to how the printing press changed information sharing. It will automate some jobs but also create many new opportunities.
  2. As AI makes tasks cheaper and easier, more people will want to use these services. This means new demands and markets will open up that we didn't see before.
  3. For AI to be successful, it needs to work well with what businesses are already doing, and building trust with customers is very important.
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Weekly Top Picks #96

The Algorithmic Bridge • 276 implied HN points • 03 Feb 25
🕹 Technology Automation
  1. OpenAI has launched two new AI agents, Operator and Deep Research, which focus on web tasks and detailed reports. Deep Research is particularly useful right now.
  2. OpenAI's o3-mini model is now free and demonstrates strong reasoning capabilities. This shows that powerful AI tools can be accessible to everyone.
  3. AI technology is evolving rapidly, and companies can benefit collectively from its advancements. Telling an AI to think longer can actually improve its performance.

Evaluating The Quality Of RAG & Long-Context LLM Output

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 08 Jul 24
🕹 Technology Automation
  1. Evaluating the performance of RAG and long-context LLMs is tough because there isn't a common task to compare them on. This makes it hard to know which system works better.
  2. Salesforce created a new way to test these models called SummHay, where they summarize information from large text collections. The results show that even the best models struggle to match human performance.
  3. RAG systems generally do better at citing sources, while long-context LLMs might capture insights more thoroughly but have citation issues. Choosing between them involves trade-offs.

What happens when an AI is your customer?

Generating Conversation • 116 implied HN points • 10 Jul 25
🕹 Technology Automation
  1. AI is becoming a key player in business, not just as a tool, but as a customer. Companies need to prepare for this shift.
  2. The interaction between AIs and human support will be different, requiring new approaches in design and efficiency.
  3. Businesses that adapt to AI-driven processes will have an advantage over those that don't, especially in sales and support.

5 SIEM Capabilities for Detection Engineering

Detection at Scale • 59 implied HN points • 15 Apr 24
🕹 Technology Automation
  1. Detection Engineering involves moving from simply responding to alerts to enhancing the capabilities behind those alerts, leading to reduced fatigue for security teams.
  2. Key capabilities for supporting detection engineering include a robust data pipeline, scalable analytics with a security data lake, and embracing Detection as Code framework for sustainable security insights.
  3. Modern SIEM platforms should offer an API for automated workflows, BYOC deployment options for cost-effectiveness, and Infrastructure as Code capabilities for stable long-term management.

🤖👷‍♂️ Beyond disruption: AI, expertise, and the future of middle-class workers

Faster, Please! • 822 implied HN points • 14 Feb 24
🕹 Technology Automation
  1. Tech progress involves creative destruction - some jobs are lost, but new ones are created, especially in AI-related fields.
  2. Advances in artificial intelligence are reshaping the workforce as companies invest in AI systems and technologies.
  3. The impact of AI on the job market is a big question for the future - will it lead to widespread technological unemployment or follow historical patterns of job creation and loss?

We don’t need another SQL chatbot

benn.substack • 1227 implied HN points • 14 Jul 23
🕹 Technology Automation
  1. We want chatbots to handle tedious job tasks but maybe not the fun parts.
  2. Building a good text-to-SQL bot requires more than just using large language models like GPT.
  3. Technology can help us focus on creative tasks rather than just automating mechanical work.

I'm Not Loving the AI Jobs Discourse Right Now

I Might Be Wrong • 5 implied HN points • 06 Feb 26
🕹 Technology Automation
  1. The public conversation about AI and jobs is poor quality and often full of fear-mongering and bad faith arguments.
  2. There are three distinct AI risks — alignment, misinformation, and job displacement — and they deserve different levels of concern: alignment is very worrying, misinformation is less novel, and the jobs debate is the most overheated.
  3. Treating labor as a cost is a normal business perspective, and criticizing companies for that misses that paychecks are a real benefit for workers and that firms respond to economic incentives.

These 7 Software Engineering Skills Give You an Unfair Advantage

Brain Bytes • 119 implied HN points • 17 Jan 24
🕹 Technology Automation
  1. Thinking like a hacker helps in identifying and fixing security flaws before they are exploited, crucial in today's cybersecurity landscape.
  2. Understanding different devices through cross-platform critical thinking gives a competitive edge and promotes reusability of business logic.
  3. Scripting and automation for repetitive tasks enhances productivity by ensuring consistency, accuracy, and freeing up time for more complex work.

What AGI can do for your average enterprise?

Metacritic Capital • 4 implied HN points • 10 Feb 26
🕹 Technology Automation
  1. Large companies already run as software-driven hive minds, so AGI will mostly make legacy systems work better instead of radically changing operations for firms like airlines.
  2. LLMs will automate a lot of knowledge work and reduce the need for human coordination, letting individuals oversee many more tasks, but competitors will have access to the same gains so margins won’t necessarily leap upward.
  3. The net effect is far more software and fewer people organizing production, pushing humans toward creative, adversarial, sales, and care roles, while the biggest transformative gains may come in fields like biology rather than mature industries.

Unbundling the Job

State of the Future • 91 implied HN points • 22 Jul 25
💼 Business Automation
  1. Jobs used to provide more than just income; they offered stability and social security. Now, this connection is breaking down, and we need to rethink how people can find support.
  2. With AI changing how work is done, many entry-level jobs are becoming less structured. Tasks that used to teach skills are being automated, making it harder for new workers to learn and grow.
  3. As traditional job structures weaken, we need new systems to support people. This means finding ways to provide benefits like health care and security without needing a full-time job.

Unleashing LLMs in Cybersecurity: A Playbook for All Industries

Gradient Flow • 259 implied HN points • 20 Apr 23
🕹 Technology Automation
  1. Large Language Models (LLMs) are gaining interest in various industries, especially in cybersecurity, and can be used as a playbook for implementation in other domains.
  2. Custom LLMs can be created for cybersecurity applications, leading to potential advancements like specialized chatbots and content generation for enhanced security measures.
  3. LLMs are transforming automation processes in cybersecurity, offering improved accuracy and convenience, and displaying potential for impact across multiple industries through domain-specific adaptations.

AI for Small Business Owners

The Small Business Corner • 39 implied HN points • 16 May 24
🕹 Technology Automation
  1. People tend to react to new technology in one of three ways: celebrate it, bash it, or adopt it pragmatically.
  2. AI tools can significantly benefit small business owners by saving time, cutting costs, and enhancing productivity.
  3. Adopting AI tools strategically into business processes can lead to efficiency, cost reduction, and innovation, helping small businesses stay competitive.

How to Learn Like Humans Do

Cybernetic Forests • 199 implied HN points • 06 Aug 23
🎨 Art & Illustration Automation
  1. AI is designed to learn and make art the way humans do, as AI models are replicas of the human brain.
  2. The process of creating art historically involved specific, defined steps that have been automated by AI, making art production more efficient and accessible.
  3. AI has streamlined the traditional artistic process, removing inefficiencies and making art creation more uniform and universally accessible.

Agents are Coming

Bojan’s Newsletter • 196 implied HN points • 07 Oct 23
🕹 Technology Automation
  1. AI agents have the potential to revolutionize automation in various industries.
  2. Technical work is only a portion of tasks, and non-technical work can be challenging to automate.
  3. Despite challenges, advancements in AI and automation tools continue to show promise for the future.

Introducing Fencer

microapis.io • 196 implied HN points • 21 Feb 23
🕹 Technology Automation
  1. API security testing requires a holistic approach covering all components
  2. There is a need for open source automated API security testing tools
  3. Automating API security testing can help catch vulnerabilities and reduce breach risks

Job Cuts Due to AI: What to Make of Them?

Last Week in AI • 298 implied HN points • 28 Jul 23
🕹 Technology Automation
  1. Some workers are losing jobs due to advancements in AI technology like ChatGPT.
  2. Predictions vary on the impact of AI on future jobs, with some foreseeing significant automation that could affect millions of jobs globally.
  3. We are still in a transitional phase with AI technology, and the impact on the workforce is heterogeneous, with job cuts stemming from various economic factors.

3 Years of ChatGPT

Olshansky's Newsletter • 22 implied HN points • 03 Dec 25
🕹 Technology Automation
  1. AI is already here as an amplifier of human intelligence and is being used daily across personal and professional tasks; agent-driven tools have massively increased productivity, especially for coding.
  2. High-quality, unique data and expert-labeled "golden" datasets are the most valuable assets for building useful AI systems; simple benchmarks and naive fine-tuning are limited, while reinforcement fine-tuning and dedicated context engineering will drive real gains.
  3. Practical changes are coming in the next few years: local inference stations, agentic e-commerce, consolidation of tooling, and new roles like context engineers and AI bootcamps; foundational roles like architects will remain and superintelligence isn’t expected soon.

Hybrid Evaluation: Scaling human feedback with custom evaluation models

LLMs for Engineers • 159 implied HN points • 15 Nov 23
🕹 Technology Automation
  1. Human feedback is still very important for evaluating models, especially in areas like customer support, but it can slow things down and increase costs.
  2. Combining human input with automated, model-based evaluation can help improve efficiency and accuracy, reducing errors significantly.
  3. Using fewer human-labeled examples with smart bootstrapping techniques can still yield good results, making it cheaper and faster to train evaluation models.

Intents: the end-game for computing interfaces?

Router by Dmitry Pimenov • 2 HN points • 11 Sep 24
🕹 Technology Automation
  1. Computing interfaces are evolving from specific command-based systems to more user-friendly methods that focus on overall goals. This makes it easier for developers to work on what really matters instead of getting bogged down in details.
  2. Intent-driven interfaces allow us to express our thoughts directly to machines, removing the need for complicated steps. This means we can communicate what we want in a more natural way.
  3. The rise of AI and new technologies is shifting how we interact with computers. Soon, we may even communicate our intentions directly from our minds, making technology feel more personal and easier to use.

How to metacognate

Sunday Letters • 99 implied HN points • 29 Jan 24
🕹 Technology Automation
  1. Working with complex models can be hard when they get confused by incorrect or incomplete information. This can lead to mistakes and conflicts in what they remember.
  2. Creating a stable pattern for how tasks are done can help models work better by giving them a solid structure to follow. This is like giving the model a framework to lean on for more complicated tasks.
  3. As models improve, the need for extra coding to guide their thinking may lessen. Better memory strategies will likely help them function more effectively over time.

Idle Thoughts On Programming and AI

Artificial Ignorance • 105 implied HN points • 03 Jul 25
🕹 Technology Automation
  1. AI is changing coding really fast, and many people don't realize just how quickly new tools and technologies are emerging. We're now seeing AI that can take on bigger coding tasks, even working in the cloud.
  2. The role of programmers is shifting. Instead of just writing code, developers must focus more on their intentions and project planning, as AI tools take care of many coding details.
  3. There are new challenges with security and hiring due to AI's impact on the industry. Companies need to rethink how they assess candidates and ensure safety as coding becomes easier with AI.

The rise of AI work

The AI Frontier • 5 HN points • 22 Aug 24
🕹 Technology Automation
  1. AI products should focus on automating work that humans often find tedious. This helps measure their true value to consumers and businesses.
  2. Companies can choose to specialize deeply in one area or offer a broad service across multiple tasks. Each approach has its own strengths and weaknesses.
  3. Finding a middle ground might be beneficial, as it allows companies to manage a workflow that spans several tasks, though they should focus on making sure their quality remains high.

AGI Emergence: Tracing the Path from Artificial Narrow Intelligence to the Birth of Superintelligence

The Future of Life • 39 implied HN points • 08 May 24
🕹 Technology Automation
  1. AI is evolving through different levels, starting from basic text generation to more advanced reasoning and problem-solving abilities.
  2. As AI develops, it will be able to perform tasks across various domains, becoming competitive with humans in many jobs.
  3. Eventually, AI may reach a point of superintelligence, where it surpasses human understanding and decision-making abilities, posing potential risks if not aligned with human values.

Arguing with Software

David Friedman’s Substack • 251 implied HN points • 13 Jan 25
🕹 Technology Automation
  1. Dealing with automated systems can be frustrating. Sometimes, your complaints are answered by software that just sends form letters instead of real help.
  2. Getting issues resolved often requires persistence. If you keep pushing for a solution, a real person may eventually step in to help.
  3. It's important to remember that companies aren't people. They may prioritize efficiency over empathy, which can impact how they handle problems.

The Sequence Engineering #661: Create Your Own Deep Research Agent with DeerFlow

TheSequence • 119 implied HN points • 11 Jun 25
🕹 Technology Automation
  1. DeerFlow is an open-source tool that helps automate research tasks. It uses multiple agents to make research faster and easier.
  2. The framework can do many tasks, like searching the web and creating reports, with little help from people. This makes it very efficient.
  3. It's designed for developers and engineers who want to build research systems that can grow and adapt easily.

The robotics company working with OpenAI to eat every warehouse worker's lunch

All-Source Intelligence Fusion • 651 implied HN points • 05 Mar 24
🕹 Technology Automation
  1. Brett Adcock's humanoid robot company aims to replace human workers in warehouses with subscription-based robots that can work 20 hours a day, 7 days a week.
  2. Figure AI collaborates with OpenAI to combine robotics and AI, aiming to create 'embodied AI' by leveraging OpenAI's strengths in language models and Figure's expertise in robotics.
  3. Adcock positions Figure AI to compete with Elon Musk's humanoid robotics effort 'Optimus' and dismisses other competitors due to limitations in hardware or software capabilities.

The Sequence Opinion #682: The Boundary of Autonomy: When AI Can Go Solo

TheSequence • 98 implied HN points • 10 Jul 25
🕹 Technology Automation
  1. Autonomous AI can make decisions without humans, but it still has big challenges to overcome. Balancing smart algorithms with real-world chaos is tough.
  2. There are certain areas where using autonomous AI might be more effective than others. These domains might be really suited for AI to take charge.
  3. The development of effective autonomy in AI is something researchers are actively exploring. It's an exciting topic that can change how we use technology.

It is starting to get strange.

One Useful Thing • 1209 implied HN points • 02 May 23
🕹 Technology Automation
  1. AI like GPT-4 is becoming more powerful and capable in real-world tasks
  2. Code Interpreter feature in GPT-4 allows AI to read, generate, and understand code and data autonomously
  3. Microsoft Copilot and GPT-4 plugins are revolutionizing work tasks like data analysis and document creation

The Rise of the Agentic Workforce

Tanay’s Newsletter • 220 implied HN points • 29 Jan 25
💼 Business Automation
  1. AI is becoming more common in workplaces, taking on roles similar to human coworkers. This means more companies are using AI to help with tasks that were once done by people.
  2. These AI workers are designed to do specific jobs, promising to make work easier and faster. They are often created to handle certain tasks well, rather than do everything a human does.
  3. AI workers can change how businesses spend money, as they focus on labor budgets instead of software budgets. This could lead to new pricing models based on actual work done, rather than just user licenses.

If You Want Good, Prepare for Evil

Earthly Fortunes • 176 implied HN points • 08 Apr 23
🕹 Technology Automation
  1. Threat modeling is essential in cyber-security to build defense against evil.
  2. Avoid extreme mindsets and focus on practical, realistic approaches in threat modeling.
  3. Hyperboles, speculations, and strong emotions detract from effective threat modeling in cyber-security.

Should You Build a Custom Data Orchestration System? Here’s What to Consider

SeattleDataGuy’s Newsletter • 317 implied HN points • 23 Oct 24
🕹 Technology Automation
  1. Building your own data orchestration system can lead to many challenges, like handling dependencies and scheduling tasks correctly. It's important to think if it's really necessary or if existing tools will work better.
  2. A custom orchestrator needs to manage various functions like logging, alerting, and integrating with other tools. Without proper features, it can become complex and hard to maintain.
  3. Before you decide to create your own solution, consider what makes it different and better than what's already available. Make sure to also think about how you’ll get people to use your new system.

The End of Typing?

Workforce Futurist by Andy Spence • 293 implied HN points • 20 Nov 24
🕹 Technology Automation
  1. Voice AI is changing how we work by making it easier to interact with technology using natural speech. This means less typing and more talking, similar to how we chat in real life.
  2. There are great uses for voice AI at work, like in training for customer service and leadership. It helps people practice important conversations in safe environments, leading to better performance.
  3. Implementing voice AI takes effort and thought. Companies need to find ways to use it effectively while also considering privacy and ethical issues. It’s about fitting the right tool to the right job.

10 simple internal tools any EM can build in under 2 hours

Leading Developers • 65 implied HN points • 19 Aug 25
🕹 Technology Automation
  1. Engineering managers can build simple internal tools in just a couple of hours. This helps solve problems for their teams and boosts productivity.
  2. There are various tool ideas like a demo-data preparator or a kudos board that can enhance team engagement and streamline processes.
  3. Using platforms like Base44 or Cursor can make developing these tools easier and more efficient, even for non-technical managers.

FlowMind Is An Automatic Workflow Generator

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 25 Jun 24
🕹 Technology Automation
  1. FlowMind is a new tool that helps create automatic workflows using advanced AI. It takes user requests and generates code to complete tasks quickly.
  2. The system uses APIs to gather information and provides real-time feedback, allowing users to adjust the workflows as needed. This makes the process more interactive.
  3. FlowMind aims to improve the reliability of AI by reducing errors and making sure there is no direct connection to sensitive data. It focuses on keeping user data safe while handling requests.