The hottest Systems Substack posts right now

And their main takeaways
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Top Business Topics

How Would The Architecture For An LLM Agent Platform Look?

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 24 May 24
🕹 Technology Systems
  1. The architecture for an LLM agent platform could develop in three stages, starting with a simple AI that recommends tools based on user needs.
  2. As the platform grows, it will enable interactions between multiple tools and the AI, allowing for dynamic exchanges of information.
  3. Future improvements will focus on enhancing the agent's capabilities through better tools and more collaboration among them.

An “AI Untangled” Update

Untangled with Charley Johnson • 58 implied HN points • 21 Jan 24
🕹 Technology Systems
  1. Transition from minimizing harms to transforming sociotechnical systems.
  2. Mindset shifts needed: view technology as entangled in social systems, acknowledge complex dynamics, articulate radical new futures.
  3. Explore framing, metaphors, complex systems, and alternative futures to understand AI better.

Coming soon

Identity, Authenticity, and Security • 19 implied HN points • 18 May 24
🕹 Technology Systems
  1. This newsletter focuses on modern system design, especially around identity and security.
  2. It's suited for anyone wanting to learn, whether you're a beginner or looking to grow in your career.
  3. The goal is to provide useful resources to help you understand and improve your knowledge in these areas.

Microsoft's New Framework for Multi-Agent Systems

TheSequence • 175 implied HN points • 10 Nov 24
🕹 Technology Systems
  1. Magentic-One is a new tool from Microsoft that helps manage multiple AI agents to tackle complex tasks. It acts like a conductor guiding different musicians, making it easier to complete different jobs together.
  2. This system allows for flexibility by using different AI models for different tasks, which means it can be customized based on what you need. It's designed to improve efficiency in our daily tasks, like ordering food or doing research.
  3. While Magentic-One is powerful, it's still being improved to reduce errors and ensure it acts safely. The goal is to make sure these AI agents help us reliably without causing problems.

Biomimicry versus Top-Down Engineering

Risk Musings • 573 implied HN points • 22 Jul 23
🔬 Science Systems
  1. Nature builds from the bottom up through evolution and mutations, unlike top-down engineering in human systems.
  2. Biomimicry offers inspiration across various fields by learning from nature's efficient and resilient systems.
  3. Bottom-up building, like in the human brain, involves countless interactions that lead to emergent solutions, unlike enforced top-down strategies.
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Understanding the CAP theorem [Math Mondays]

Technology Made Simple • 99 implied HN points • 10 Jan 23
🕹 Technology Systems
  1. The CAP theorem states that in a distributed system, you can only guarantee 2 out of 3 desirable traits: Consistency, Availability, and Partition Tolerance.
  2. Consistency in the CAP theorem ensures that all nodes in a network have the same data at the same time.
  3. Availability means that every read or write request will either succeed or receive an error message, with every node responding in a reasonable time.

TAO - Meta's Scalable architecture powering world's largest social graph

Engineering At Scale • 120 implied HN points • 09 Nov 24
🕹 Technology Systems
  1. Meta created TAO to handle the huge amount of data and user interactions on its platform. This system helps generate personalized content for over 2 billion users very quickly.
  2. TAO uses a layered architecture that includes caching and data storage to improve performance. This design helps distribute the load and maintain fast responses even when many users are active.
  3. TAO prioritizes high availability over strict data consistency. This means it can sometimes show slightly out-of-date information, but it still works well for users, especially during busy times.

Edge 454: Meet Magentic-One, Microsoft's New Framework for Building Multi Agent Systems

TheSequence • 91 implied HN points • 05 Dec 24
🕹 Technology Systems
  1. Microsoft has introduced a new framework called Magentic-One for building multi-agent systems. It allows different AI agents to work together on tasks that can change or evolve.
  2. This framework is built upon another Microsoft technology called AutoGen, which helps agents collaborate effectively. It aims to manage tasks using information from the web and files from various fields.
  3. Magentic-One is part of a growing trend in AI where multi-agent systems are gaining popularity. This reflects the diverse and innovative landscape of AI development today.

🧠 Universal Weights, Live Avatars, and the Limits of Data Agents

HackerPulse Dispatch • 5 implied HN points • 12 Dec 25
🕹 Technology Systems
  1. Neural networks trained on diverse tasks tend to converge to similar low-dimensional weight subspaces, implying a shared parametric backbone that could make transfer learning and model reuse much more efficient.
  2. System-and-algorithm co-design now enables large diffusion models to run in real time for streaming avatars (20 FPS on a 14B model), showing practical deployment of big generative models for live video.
  3. A 210-task benchmark shows current data agents succeed on under 20% of engineering tasks and under 40% of analysis tasks, revealing major gaps in orchestration and reasoning for enterprise workflows.

How to destroy a world

Data People Etc. • 53 implied HN points • 24 Feb 25
🕹 Technology Systems
  1. Frameworks can be used for both building and breaking worlds. It's important to understand how to exploit weaknesses in these structures.
  2. To weaken a dominant system, you can undermine its narrative, disrupt key players, and challenge established norms. This approach can create doubts and resistance.
  3. Destroying a world can teach us about resilience. Strengthening systems and protocols is crucial to support and maintain their relevance in changing times.

Can Machines Slip on Banana Peels?

Breaking Smart • 79 implied HN points • 30 Oct 24
🕹 Technology Systems
  1. It's funny when a self-important person slips on a banana peel because it shows their dignity being challenged. This humor comes from seeing someone with high self-esteem face an embarrassing moment.
  2. Machines can also have moments of failure, just like people. They slip up when their design looks seamless but actually has hidden flaws, similar to someone who overestimates their own abilities.
  3. Understanding the 'Contraption Factor' helps us analyze why machines fail. It shows a difference between how complex something is and how well it's designed, which can lead to unexpected problems.

Digital & Analog Worlds. A Perfect Storm.

The Digital Anthropologist • 39 implied HN points • 27 Oct 23
🕹 Technology Systems
  1. A fundamental shift is happening between the digital and analog worlds, leading to a bumpy yet inevitable collision of systems.
  2. Throughout history, new technologies disrupt old systems, sparking a storm of change that humanity must weather and adapt to.
  3. The clash between digital and analog gods is a reflection of the ongoing evolution of human societies, shaped by culture, technology, and the need for adaptation.

Fast versus Right

Sunday Letters • 79 implied HN points • 26 Mar 23
🕹 Technology Systems
  1. Simplicity often beats complexity when it comes to technology. A simple solution that works now can be more effective than a complex one that may take longer to perfect.
  2. In the tech world, being first is crucial. The first company to launch a new idea or product often wins, especially if it benefits from network effects.
  3. It's important to focus on what can be quickly addressed. Don't get stuck on minor issues when bigger, more impactful problems need immediate attention.

Slack's Email Classification Service

Arpit’s Newsletter • 39 implied HN points • 08 Mar 23
🕹 Technology Systems
  1. Slack has a feature to classify emails as internal or external during workspace invitations.
  2. Slack uses heuristics like domain matching to classify emails, but may face challenges in diverse email domains.
  3. Implementing a classification service involves maintaining a table with counts and eventual consistency for accurate classification.

Borrow Checking from Scratch

Burning the Midnight Coffee • 64 implied HN points • 17 Nov 24
🕹 Technology Systems
  1. The concept of 'borrow checking' helps programmers ensure their code is memory safe. This means the code won't allow unsafe practices like using memory that has already been freed.
  2. Implementing a simple, C-like language called Cnile can introduce memory safety by adding rules that check for issues during compilation rather than at runtime. This involves stopping problems like double-free and use-after-free situations.
  3. Using single-use types ensures resources can only be used once, which helps prevent memory leaks and makes it safer to manage dynamic data structures in programming.

The case for an economic AGI AI Lab

Subsack • 4 implied HN points • 09 Dec 25
🕹 Technology Systems
  1. Markets are dynamic, adversarial environments that force AI to adapt under uncertainty, making them a stronger real‑world benchmark than static puzzles. They test whether knowledge survives contact with reality, not just pattern recognition.
  2. Building an AI that works in markets demands new capabilities — sample efficiency, continual learning without catastrophic forgetting, long‑term memory, deep multimodal world models, and game‑theoretic strategic reasoning. Those constraints push research beyond today’s scale‑and‑transformer centric approach.
  3. Economic AGI offers a clear monetisation path: outperforming markets, running prediction markets, or allocating capital can directly convert intelligence into revenue. That revenue can make labs financially sustainable and fund further AGI research.

LLM links, 2/27

In My Tribe • 91 implied HN points • 27 Feb 24
🕹 Technology Systems
  1. Compound AI systems are proving more effective than individual AI models, showing that combining different components can lead to better results.
  2. Providing extensive context can enhance AI capabilities, enabling new use cases and more effective training through models like Sora.
  3. The emergence of an AI computer virus is predicted to become a major concern, potentially causing widespread panic and technological shutdowns.

What guarantees are you making?

Sunday Letters • 79 implied HN points • 11 Sep 22
🕹 Technology Systems
  1. Always clarify what guarantees you are making in your work or software. This helps everyone understand what to expect.
  2. Dependencies can be tricky, so be careful of relying on assumptions that might change. What works now could break later.
  3. Document processes and rotate responsibilities to avoid putting too much reliance on one person. This keeps the team healthy and resilient.

Ground-truth-in-the-loop

Yuxi’s Substack • 19 implied HN points • 18 Jul 23
🕹 Technology Systems
  1. Ground-truth-in-the-loop is crucial for designing and evaluating systems, especially in AI and machine learning.
  2. For AI systems, having trustworthy training data, evaluation feedback, and a reliable world model is essential.
  3. Researchers should inform non-experts about limitations and potential issues when building systems without ground-truth.

The Heath Ledger guide to AI

Platforms, AI, and the Economics of BigTech • 9 implied HN points • 17 Jul 25
🕹 Technology Systems
  1. The Joker in _The Dark Knight_ actually shapes the whole story, making it about him instead of Batman. This shows how sometimes the unexpected character can drive the main themes of a narrative.
  2. AI's biggest effect isn't about how well it performs tasks but how it changes the systems around us. We need to look at how it helps people work together more efficiently rather than just what jobs it replaces.
  3. When we focus on AI's ability to improve coordination, we see its real potential. It's not just about speeding up tasks but making sure everyone is on the same page, which can transform industries.

Putting Large Language Models in Context

Systems Approach • 117 implied HN points • 06 Mar 23
🕹 Technology Systems
  1. Large Language Models like ChatGPT have notable failures and lack understanding of the words they produce.
  2. Modern machine learning systems heavily rely on training data and may struggle with unfamiliar scenarios.
  3. Performance of machine learning systems requires careful analysis and hard work by researchers or engineers.

pip install sqlmesh-cube

davidj.substack • 23 implied HN points • 19 Dec 24
🕹 Technology Systems
  1. A new package called 'sqlmesh-cube' is available for anyone to use. You can easily install it with pip.
  2. This package helps create a CLI command that outputs JSON, showing how sqlmesh models relate to each other. It's important for building a semantic layer.
  3. This was the author's first package, and they learned a lot about the publishing process along the way. They are open to feedback and requests for updates.

The Physics of Intelligence

Breaking Smart • 99 implied HN points • 07 Apr 23
🔬 Science Systems
  1. The physics of intelligence is not substrate dependent, similar to the physics of flight.
  2. The key questions in understanding the physics of intelligence relate to attention, memory, and the relationship between intelligence and information.
  3. Intelligence is not just about computation, but also about embodiment and specific interactions within the universe.

The Ion Proton Fluidics System

ASeq Newsletter • 14 implied HN points • 06 Dec 24
🕹 Technology Systems
  1. The Ion Proton Fluidics System has a complex fluidics setup that is important for its function. It may look confusing at first, but breaking it down can help understand how it works.
  2. Understanding the fluidics system is crucial for improving its performance. Learning about its components can lead to better maintenance and efficiency.
  3. This post is specifically tailored for paid subscribers, indicating there is exclusive content available for those who support the platform. Being a paid member might offer more in-depth insights.

Defining An AI Native TPM

Building Rome(s) • 5 implied HN points • 19 Jun 25
🕹 Technology Systems
  1. The role of Technical Program Managers (TPMs) will shift from task management to orchestrating systems that combine humans and AI tools. This means they'll focus on designing workflows and ensuring everything runs smoothly.
  2. AI tools are taking over many tasks that TPMs used to do, which means future TPMs need to adapt their skills to manage these tools effectively while keeping the bigger picture in mind.
  3. Humans will still be essential for navigating complex team dynamics and making decisions about what should or shouldn't be automated, ensuring a balance between AI efficiency and human oversight.

"Why Now?" and Why It Matters

Equal Ventures • 39 implied HN points • 04 Oct 21
💼 Business Systems
  1. Investors should focus on long-term value creation rather than short-term trends.
  2. Identifying catalysts for system redesign can lead to significant impacts on industries.
  3. Having a 'prepared mind' and industry knowledge can give an edge in spotting trends early.

The 10X Chromium Controller Pneumatic System

ASeq Newsletter • 29 implied HN points • 31 Dec 23
🕹 Technology Systems
  1. The 10X Chromium Controller Pneumatic System is complex and forms the core of the Chromium Controller.
  2. The pneumatic system is more intricate than the fluidic system in the HiSeq X.
  3. Paid subscribers can access detailed documentation and conceptual diagrams of the pneumatic system.

System Design Concepts: An in-depth guide to Database sharding

Engineering At Scale • 30 implied HN points • 29 Jul 23
🕹 Technology Systems
  1. Database sharding splits a large dataset into chunks stored on different machines, increasing storage capacity and distributing queries for better performance.
  2. Sharding allows for high availability by avoiding a single point of failure and higher read/write throughput by distributing query load.
  3. Cost and maintenance overhead are drawbacks of sharding, and it differs from partitioning where data is stored on a single machine.

the education system is fraudulent

Midnight Musings • 19 implied HN points • 31 May 22
🚌 Education Systems
  1. The education system focuses too much on grades and test scores, which can harm creativity and genuine learning. Students often end up learning how to game the system instead of being curious.
  2. Learning is too structured and doesn't allow for exploration. This can lead to people thinking in ways others define, rather than developing their own unique problem-solving skills.
  3. Students are taught to measure their worth by their output and how others view them. This can create unhealthy competition and pressure, leading to a lack of fulfillment.

Why we should never prioritize our tasks

trydeepwork • 6 implied HN points • 26 Oct 24
💼 Business Systems
  1. Relying on our own judgment to prioritize tasks often leads us astray. We tend to focus on what's easy or urgent, neglecting the most important work.
  2. Using systems and data can help prioritize tasks more effectively. By measuring things like urgency and time investment, we can make smarter decisions about what to work on.
  3. A good prioritization system constantly updates based on changing circumstances. This means you can always see what to tackle next and keep low-impact tasks from taking up your time.

Small Changes

Fish Food for Thought • 5 implied HN points • 28 Feb 24
💼 Business Systems
  1. Small changes in processes can lead to significant impacts.
  2. In business and products, incremental changes can often result in remarkable outcomes.
  3. Consider the small components of a system and how tweaking them can bring about large improvements.

Systems vs Software Architecture

Fish Food for Thought • 5 implied HN points • 01 Nov 23
🕹 Technology Systems
  1. Systems architecture deals with the overall design and functionality of the system as a whole.
  2. Software architecture focuses on specific technologies, languages, and software patterns used in the system.
  3. Differentiating between systems and software architecture is essential to ensure a well-built and adaptable tech infrastructure.