The hottest Automation Substack posts right now

And their main takeaways
Category
Top Technology Topics

Setting up NextJS with GitHub Actions and feature flags using Flagsmith

The Open Source Expert • 59 implied HN points • 05 Jul 24
🕹 Technology Automation
  1. Using NextJS helps streamline your project with standardized setups, making it easier to onboard and rapidly develop features.
  2. Automating tasks with GitHub Actions can save time and reduce errors, giving you quick feedback on your code changes.
  3. Feature flags from Flagsmith allow you to control which features are visible without needing to redeploy your app, making it easier to manage updates and A/B tests.

The AI Job Apocalypse Is Always Nigh for the People Selling the Predictions

The Algorithmic Bridge • 339 implied HN points • 10 Jul 25
🕹 Technology Automation
  1. AI experts warn that many entry-level jobs might disappear soon, leading to high unemployment rates. This could affect fields like tech, finance, and consulting.
  2. Companies creating AI technology need to be honest about the potential job losses it could cause. It's important for them to think about how to prevent or lessen the negative impact.
  3. Simply warning people about job losses isn't enough; companies should find ways to help those who could be affected by their technology.

3 Key Areas for Mitigating Non-Human Identity (NHI) Risks

Resilient Cyber • 39 implied HN points • 24 Jul 24
🕹 Technology Automation
  1. Organizations need to keep track of all non-human identities, like service accounts and API keys. This helps in monitoring and managing security across different systems.
  2. When a third party experiences a security breach, it's crucial to quickly identify which non-human identities are affected. Rapid response can help limit potential damage and keep business running smoothly.
  3. Detecting unusual behavior in non-human identities is key to spotting security threats. Using automated tools can help security teams stay on top of potential risks efficiently.

#47 - Thought Experiment - What if we stop 'fake approving' by humans?

OSS.fund Newsletter • 18 implied HN points • 05 Feb 26
💼 Business Automation
  1. Human approval chains for low‑value purchases are slow, costly, and often little more than ritualized clicks that add days and overhead without improving outcomes.
  2. AI agents can encode purchasing policy as rules, check budgets, vendors, and contracts in milliseconds, and create auditable logs that cut per‑order cost and cycle time while keeping controls intact.
  3. A practical path is to sample recent small POs, classify which truly need human judgment, then pilot simple auto‑approve rules with identity, logging, and time‑bound tests so people only handle the genuinely ambiguous cases.

Five Levels Of AI Agents

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 119 implied HN points • 16 May 24
🕹 Technology Automation
  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.
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Why is everyone in AI talking about the end of work? | #304

Boundless by Paul Millerd • 266 implied HN points • 28 Jul 25
🕹 Technology Automation
  1. Many people in AI believe that automation will lead to job losses, especially in white-collar work. They warn that without using AI, workers might struggle to keep their jobs.
  2. The idea that AI will replace many jobs often misses the complexity of what jobs really are. Jobs are more than just a list of tasks; they provide purpose, dignity, and structure in society.
  3. While fears about AI taking jobs are common, the reality of job loss isn't as clear-cut. Employment rates have stayed relatively stable, and any shifts in work may lead to a gradual change in how we think about jobs and work.

A Bad Idea

Points And Figures • 906 implied HN points • 13 Dec 24
🇺🇸 U.S. Politics Automation
  1. Automation is important for ports and can help improve efficiency. Using robots and AI is a smart move to reduce costs and better serve customers.
  2. Unions often resist changes that automation brings, even if it can create more efficient jobs. It’s important to understand and support workers during these changes.
  3. Regulations in ports can slow things down, so building new, less regulated, automated ports is a good idea for future improvements.

Art to Science

Abstraction • 39 implied HN points • 02 Jan 26
🕹 Technology Automation
  1. Forecasting bots can run continuously, answer many questions, and be scored in real time, turning forecasting from a slow craft into a fast, repeatable process.
  2. Large, scored tournaments and shared datasets will let people empirically test different methods and finally learn which forecasting approaches actually work at scale.
  3. Simple heuristics get you most of the way there, but reaching the frontier requires deeper techniques and open sharing of methods to accelerate progress.

AI Agents With Human In The Loop

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 15 Aug 24
🕹 Technology Automation
  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 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.

OpenAI Codex 101: The Complete Guide to AI Coding Agents with 33 Ready-to-Use Prompts

The Product Channel By Sid Saladi • 6 implied HN points • 25 Feb 26
🕹 Technology Automation
  1. Codex is an autonomous coding agent that can write, test, debug, refactor, and open pull requests, letting you delegate mechanical development work and speed up delivery.
  2. Effective use requires project tooling like AGENTS.md, reusable Skills, automations, and multi-agent worktrees across web, CLI, app, or IDE surfaces to keep work consistent and isolated.
  3. Choose tools by workflow: use Codex for fast, parallel delegation, scheduled automations, and GitHub-native reviews, use a reasoning-first agent for deep debugging, privacy, or huge context — or combine both for best results.

Which Jobs will AI Take?

In My Tribe • 653 implied HN points • 23 Jan 25
🕹 Technology Automation
  1. AI will change many jobs, especially in sectors like transportation and finance, where automation is expected to replace a lot of workers.
  2. Some industries, like health care and entertainment, will likely grow and adapt to include both humans and AI, creating new types of jobs.
  3. The future job market will be different, with many traditional roles disappearing, but it’s believed there will still be plenty of new jobs created in emerging fields.

Robotics, Automation and AI Conferences

Robots & Startups • 299 implied HN points • 16 Jan 24
🕹 Technology Automation
  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 Automation
  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.

⤴⤵ Up Wing/Down Wing #34

Faster, Please! • 639 implied HN points • 08 Feb 25
🕹 Technology Automation
  1. A new tool in ChatGPT can help with deep research by quickly analyzing information and providing organized reports. This makes it easier for people in schools and businesses to get useful insights.
  2. France is benefiting from its strong nuclear energy production, which keeps electricity prices lower compared to Germany. This helps France avoid the high costs associated with gas and coal.
  3. The push for cleaner energy is gaining speed, as countries like France are moving away from expensive fossil fuels. This shift is important for both economic stability and environmental health.

ChatGPT Agent Mode, and "Vibe Automations"

Enterprise AI Trends • 253 implied HN points • 18 Jul 25
🕹 Technology Automation
  1. Agent Mode in ChatGPT acts like a virtual worker that can handle tasks automatically, making it easier to manage complex workflows. You can schedule it to help with tasks repeatedly, which means less hassle for users.
  2. This feature allows users to create multi-step processes by simply stating what they want, rather than setting up complicated workflows. It makes AI automation more accessible to regular users.
  3. OpenAI's Agent Mode could change how companies use AI tools, as it competes with traditional AI automation solutions. It has the potential to redefine productivity for many types of workers, but it also faces challenges from other tech companies and current internet restrictions.

Multi-agent orchestration in Slack | Saleforce's Kurtis Kemple

Dev Interrupted • 9 implied HN points • 10 Feb 26
🕹 Technology Automation
  1. Chat platforms are becoming agent orchestration hubs where humans and bots work together in real time, and organizations will need higher-level "super agents" to connect and manage isolated agent workflows.
  2. New agent ecosystems introduce fresh risks and human dependencies—agents forming their own social networks and services that hire people for tasks raise security, legal, and ethical concerns, and rogue or exploitable agent chains are a real threat.
  3. Widespread agent adoption will reshape how software is developed and how open source is consumed, shifting teams toward autonomous observe-orient-decide-act workflows and transforming open source projects to serve agent-driven use cases rather than disappearing.

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

UX Psychology • 297 implied HN points • 12 Jan 24
🕹 Technology Automation
  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.

Legally Toast

davidj.substack • 47 implied HN points • 13 Dec 25
💼 Business Automation
  1. Routine, language-driven legal tasks are likely to be automated, so junior and mid-level lawyer roles will shrink while partners and senior lawyers who provide judgment, sales, accountability and human interaction stay essential.
  2. Firms will become more top-heavy and need far fewer junior hires, which will reduce demand for law graduates—especially from second- and third-tier programs—and increase competition for the remaining positions.
  3. This is part of a wider knowledge revolution: AI will replace much routine knowledge work across industries, reshaping labour markets and the economy in a way comparable to the industrial revolution.

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

HyperArc • 39 implied HN points • 11 Jul 24
🕹 Technology 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.

The Rise of Verticalized AI Coworkers

Tanay’s Newsletter • 208 implied HN points • 29 Jul 25
🕹 Technology Automation
  1. Verticalized AI coworkers are designed for specific jobs like insurance adjusters or nurses, handling repetitive tasks that humans usually do. They can help fill roles where there are not enough workers.
  2. These AI coworkers integrate directly with existing tools and systems, allowing them to manage tasks efficiently. They aim to take some of the workload off human employees.
  3. Many of these AI systems are starting with easy, high-volume tasks, such as document processing and customer interactions. Over time, they may take on more complex tasks as they improve.

Why AI Agents Feel Contrived and Useless, Despite the Impressive Demos

Enterprise AI Trends • 612 implied HN points • 16 Jan 25
🕹 Technology Automation
  1. AI agents work best in simple tasks, but they might confuse people in more complex situations. Humans need to be involved to understand the creative process.
  2. When AI does too much on its own, it can be harder for people to trust and evaluate its work. This can lead to mistakes that are hard to spot later.
  3. Businesses usually prefer working with guided AI tools instead of fully autonomous agents. They want reliability and clear understanding over just speeding things up.

✨🤖 Age of AI, meet the Robot Revolution

Faster, Please! • 731 implied HN points • 06 Dec 24
🕹 Technology Automation
  1. AI robots are becoming much more common and can do many tasks themselves, like moving and sorting packages. This technology is quickly transforming how we work in places like warehouses.
  2. By 2035, there might be about 1.3 billion AI robots in use. This will grow to around 4 billion by 2050, showing a huge increase in robot presence in daily life.
  3. The combination of AI and robots is expected to change many aspects of our lives and job environments in the near future, making them an important part of our technological landscape.

Just build the tools yourself

The Engineering Manager • 6 implied HN points • 20 Feb 26
🕹 Technology Automation
  1. AI and modern coding assistants make it easy for people with some technical background to build useful internal tools quickly, often in an afternoon.
  2. Small, imperfect tools that automate niche workflows—like auto-summarising issue trackers into a "bragdoc" or a single-priority planning and staffing app—solve real problems without needing production-grade software.
  3. Getting hands-on to build these tools removes the friction between wanting a tool and having one, letting teams be more practical, creative, and time-efficient without turning managers into full-time engineers.

Language Agent Tree Search — LATS

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 59 implied HN points • 12 Jun 24
🕹 Technology Automation
  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.

Weekly Dose of Optimism #153

Not Boring by Packy McCormick • 205 implied HN points • 18 Jul 25
🕹 Technology Automation
  1. There are massive investments in AI infrastructure, mainly in Pennsylvania, with companies like Google and Blackstone pledging billions to build data centers. This investment is expected to create many jobs and boost the local economy.
  2. Meta is working on building a huge data center called Hyperion, which will provide lots of power for AI development. They plan to invest around $70 billion in AI this year, which could lead to significant advancements in their products.
  3. A new study shows that a technique called three-person IVF can produce healthy children by combining DNA from three people to prevent genetic diseases. This could change how families with these conditions approach reproduction.

🎭✨ Ben Affleck’s wise words on AI

Faster, Please! • 731 implied HN points • 18 Nov 24
🕹 Technology Automation
  1. New technology, like AI, can help reduce costs. This can make it easier for more people to access entertainment and creative content.
  2. There's a common fear that robots will take over jobs, but it's important to understand how technology can create new opportunities instead.
  3. Adapting to new technologies can lead to a demand for different skills. Learning and evolving with technology is key to staying relevant in the job market.

Kroger’s Retreat from Robotic Warehouses Reveals the Limits of Grocery Automation

Gad’s Newsletter • 47 implied HN points • 08 Dec 25
💼 Business Automation
  1. Kroger’s closure of big robotic fulfillment centers shows that centralized, capital-heavy automation often doesn’t fit grocery economics because thin margins, low and uneven online demand, long delivery distances, and volatile order patterns drove per-order costs too high.
  2. Faster, cheaper grocery fulfillment is more likely from local and flexible options — store-based picking, micro-fulfillment, and gig delivery cut last-mile costs and handle spiky demand better.
  3. Automation still has a role, but the future looks modular and collaborative: smaller, flexible robots, AI routing, and cobots that work with human pickers are more promising than giant, purpose-built robot warehouses.

The Sequence AI of the Week #753: Inside Kimi K2 Thinking: The Architecture of Long-Horizon Reasoning

TheSequence • 70 implied HN points • 12 Nov 25
🕹 Technology Automation
  1. Kimi K2 Thinking is a new AI model that thinks in a more advanced way than just giving one answer at a time. It can plan and act over longer periods while staying on track.
  2. This model is built on a powerful billion-parameter system designed to improve how it learns and uses data efficiently. It makes the most of its resources when solving problems.
  3. Kimi K2 also uses smart training methods, like reinforcement learning, to help it use tools better and think through problems in a layered way.

Superhuman AI would bring the permanent problem front and center

The Permanent Problem • 7 implied HN points • 18 Feb 26
🕹 Technology Automation
  1. Rapid advances toward superhuman AI could create enormous wealth while also accelerating the marginalization of ordinary workers, bringing the existing crisis of inclusion into sharp focus.
  2. The deepest fear is not just job loss but being rendered irrelevant and losing the social status and meaning tied to cognitive work, which could spark serious political and social unrest.
  3. If society plans well, AI could free people to pursue more fulfilling lives—caring for others, exploring, and creating—but that will require new social arrangements and a shift toward valuing intrinsic purpose and human relationships.

Language is not enough

Erika’s Newsletter • 412 implied HN points • 11 Apr 23
🕹 Technology Automation
  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.

🤖🚘 Even as GM retreats, self-driving cars continue to advance

Faster, Please! • 639 implied HN points • 12 Dec 24
🕹 Technology Automation
  1. Self-driving cars are still making progress, even as some big companies like GM pull back on their investments in this technology.
  2. Predictions about self-driving cars have often been overly optimistic, and the industry hasn't yet transformed the way many expected.
  3. As GM moves away from robotaxis, other companies like Waymo and Zoox are still pushing forward with their driverless vehicles.

MLOps Basics - For Data Engineers.

Data Engineering Central • 393 implied HN points • 15 May 23
🕹 Technology 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.

Six Reasons Why Current AI Won't Displace Lawyers

Autonomy • 34 implied HN points • 20 Dec 25
🕹 Technology Automation
  1. Current AI doesn't generalize or perceive the world like humans, so it misses novel facts and real-world cues that lawyers use to build and win cases.
  2. Litigation is inherently adversarial, so both sides will adopt AI and the human lawyers who best direct and strategize with those tools will decide outcomes.
  3. Lawyering involves client counseling, moral responsibility, and institutional rules that AI can't fulfill, and greater AI productivity may actually increase demand for legal services rather than eliminate lawyers.

Proofreading an entire book with GPT-4 for $6.88

Mindful Modeler • 479 implied HN points • 02 May 23
🕹 Technology Automation
  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.

Not The Future

Fragmentary • 373 implied HN points • 17 Feb 23
🕹 Technology Automation
  1. AI will disrupt our lives, but its impact is yet to be fully understood.
  2. Using AI writing assistants can help with speed and efficiency but may lack the uniqueness of human creativity.
  3. The real threat is corporate greed, not AI technology.