The hottest Scalability Substack posts right now

And their main takeaways
Category
Top Technology Topics

The Most Complicated System I Ever Worked On

Jacob’s Tech Tavern • 1312 implied HN points • 17 Feb 26
🕹 Technology Scalability
  1. A single feature can balloon into a ludicrously elaborate pipeline that combines webscraping, long-running downloads, parsing and storage of large data, real-time analysis, and high-volume upload/polling.
  2. Most engineering work is routine, but rare peak challenges require orchestrating many moving parts and constant attention so they don’t overwhelm the team.
  3. Making a reliable system on top of unreliable third-party services takes sustained hardening and ongoing “whack-a-mole” maintenance to turn an MVP into production-grade software.

How to design an SDK to handle $10bn in transactions

Jacob’s Tech Tavern • 3061 implied HN points • 12 Jan 26
🕹 Technology Scalability
  1. Abstracting away the messy parts of in‑app subscriptions turns a painful problem into a valuable, reliable service that developers will pay for.
  2. A façade-first, layered architecture with constructor injection and clear orchestrators keeps public APIs stable and makes complex flows testable and backwards compatible.
  3. Prioritize developer experience with sensible defaults, offline-first correctness, relentless logging/diagnostics, and invisible performance to hide flaky third‑party APIs and make integrations predictable.

Queues offer more than orderly processing

System Design Classroom • 679 implied HN points • 02 Jul 24
🕹 Technology Scalability
  1. Queues help different parts of a system work independently. This means you can change one part without affecting the others, making updates easier.
  2. They improve a system's ability to handle more users at once. You can add more servers to take in requests without needing to instantly boost how fast they are processed.
  3. Queues also keep things running smoothly during busy times. They act like a waiting area, holding tasks so no work gets lost even if things get too hectic.

Connection Pooling: Fundamentals, Challenges and Trade-offs

Engineering At Scale • 795 implied HN points • 29 Nov 25
🕹 Technology Scalability
  1. Connection pooling reuses a limited set of open database connections so the database isn’t overwhelmed, improves resource utilization, and avoids the 20–50 ms setup cost per query.
  2. Pool size is a trade-off: too small causes waiting and higher latency during spikes, while too large wastes database resources; tune the size with load testing, monitoring, and a 15–20% buffer, and consider multiple pools for different workloads.
  3. Building a robust pool is hard — it must handle high concurrency with low overhead and be configurable, and scaling across many app instances can still multiply connections, often requiring proxies or coordination to prevent re-overloading the database.

Explore *Then* Expand *Then* Extract

Software Design: Tidy First? • 684 implied HN points • 04 Dec 25
🕹 Technology Scalability
  1. Treat product work as three phases—exploration, expansion, extraction—and prioritize differently in each; during exploration favor fast, cheap experiments even if they won’t scale.
  2. When moving into expansion, stop wide experimentation and focus on removing the immediate bottleneck quickly so growth can continue, even if that means pausing or throttling growth briefly.
  3. Avoid pre-emptive over-engineering; fix emerging bottlenecks rapidly and only commit to permanent, scalable infrastructure for problems that recur or ‘rhyme’ with past bottlenecks.
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Why Sharding?

Dan Hughes • 239 implied HN points • 24 Jun 24
🔮 Crypto Scalability
  1. Sharding is a great solution for scaling blockchain networks. It allows the system to handle more transactions by dividing tasks into smaller pieces, making processing faster and more efficient.
  2. Relying solely on improving hardware to scale blockchain systems is not enough. It can lead to problems with latency and conflicts that slow down the network as demand increases.
  3. Atomic commitment in sharding ensures that transactions across different parts of the network can be completed all at once or not at all. This helps keep the system clean and prevents messy issues when something goes wrong.

Database Proxies: Challenges, Working and Trade-offs

Engineering At Scale • 195 implied HN points • 13 Dec 25
🕹 Technology Scalability
  1. Database proxies sit between services and the database and multiplex many client connections onto a fixed pool of database connections, preventing connection spikes and making horizontal scaling safer.
  2. Proxies can add features like query caching, read/write routing, and sharding/replica management, which simplifies application logic and abstracts database topology from the app.
  3. Using a proxy comes with costs — extra deployment and maintenance overhead and added latency (~10–15 ms) — so they’re valuable for complex setups (replication, sharding, FaaS) but can be overkill for a single simple database and must be designed to avoid becoming a SPOF.

Why Rollups Are Not Shards: Deconstructing the Logical Fallacies

Dan Hughes • 139 implied HN points • 03 Jul 24
🔮 Crypto Scalability
  1. Rollups and sharding are not the same. Rollups are like mini blockchains that still rely on Ethereum, while sharding would integrate more seamlessly and effectively.
  2. The rollup approach adds more complexity to the Ethereum system, causing challenges for developers in terms of security and performance.
  3. A single, unified execution environment would be more beneficial for scaling, rather than having multiple rollups with different rules and complexities.

GroupBy #40: Data Infrastructure at Airbnb

VuTrinh. • 179 implied HN points • 18 Jun 24
🕹 Technology Scalability
  1. Airbnb focuses on using open-source tools and contributing back to the community. This helps them build a strong and collaborative data infrastructure.
  2. Their data infrastructure prioritizes scalability and uses specific clusters for different types of jobs. This approach ensures that critical tasks run efficiently without overwhelming the system.
  3. Airbnb has improved their data processing performance significantly, reducing costs while increasing speed. This was achieved through careful planning and migration of their Hadoop clusters.

Beyond Snowflake: A Problem-Solver’s Guide to Distributed ID Generation

Software Bits Newsletter • 51 implied HN points • 04 Jan 26
🕹 Technology Scalability
  1. Memory allocator patterns — like per-node caches, hierarchical range grants, batching, and prefetching — transfer cleanly to distributed ID generation and let services hand out unique IDs locally with almost no coordination.
  2. There is no one-size-fits-all ID strategy: slabs and hierarchical ranges give extreme throughput and B-tree locality at the cost of wasted IDs and weaker global ordering, consensus gives strict global ordering and durability but costs latency and availability, and Snowflake-style schemes sit in between.
  3. The best engineering move is methodological: spot a related solved problem, extract its core principles (hierarchy, locality, batching, prefetching), and adapt them while accounting for distributed realities like partial failure and unbounded latency.

OpenAI is too cheap to beat (redux)

The AI Frontier • 59 implied HN points • 18 Jul 24
🕹 Technology Scalability
  1. Data and infrastructure are really important for companies like OpenAI. They collect a lot of data, which helps them improve their models faster than others.
  2. OpenAI is cheaper for fine-tuning models compared to using your own infrastructure. This means most companies will find it more cost-effective to use OpenAI's services instead of trying to run their own setups.
  3. Even though open-source models have potential, big companies will likely stay ahead due to their ability to serve models quickly and cheaply. Switching to a different system is hard and expensive, making it tough for smaller players.

Proto-Danksharding

DeFi Education • 599 implied HN points • 26 Aug 23
🔮 Crypto Scalability
  1. Proto-Danksharding is a new feature for Ethereum that helps lower transaction costs and provides temporary data storage. This makes it cheaper and faster for Layer 2 solutions to function.
  2. High transaction costs are a major hurdle for DeFi, limiting its growth. By making transactions cheaper, it can attract more users and enable smoother operations.
  3. The collaboration between Optimism and Base aims to share transaction revenues, which could boost their performance and value in the future, benefiting both projects.

The Sequence AI of the Week #793: DeepSeek's New Paper: Storing 100B Parameters on CPU RAM

TheSequence • 21 implied HN points • 21 Jan 26
🕹 Technology Scalability
  1. The current LLM trend is to scale models huge and use sparsity tricks like Mixture-of-Experts so only a small part of the model activates per token, reducing FLOPs.
  2. Reusing an old technique — storing large, static lookup-like memories on CPU RAM and conditionally accessing them — can let models hold around 100B parameters off-GPU and avoid expensive dense computation.
  3. The key insight is that many LLM costs come from simulating static lookup tables with neural computation, so replacing that simulation with real conditional lookups makes models much more efficient.

Nano's scalability and game theory

Senatus’s Newsletter • 117 implied HN points • 10 Feb 24
🕹 Technology Scalability
  1. Increased market cap leads to higher security and decentralization of Nano.
  2. Increased transaction rate doesn't pose issues as Nano is stress-tested to handle higher levels.
  3. New services integrating Nano lead to more nodes coming online, boosting decentralization and network capacity.

Designing Instagram's Video Uploads: Optimizing for Low Latency and scalability

Engineering At Scale • 255 implied HN points • 20 Jan 25
🕹 Technology Scalability
  1. Instagram's video upload system needs to handle millions of uploads daily while keeping the process fast and efficient. It converts videos into different formats for users with varying internet speeds.
  2. The system can be designed in approaches, starting from simple methods to more complex asynchronous solutions. Improving reliability and speed is key to making the service work better.
  3. Using segmented video uploads allows faster processing. By uploading smaller parts of the video, the service can work on them at the same time, reducing wait times for users.

3 quick tips on how to build more scalable systems [System Design Sundays]

Technology Made Simple • 119 implied HN points • 17 Apr 23
🕹 Technology Scalability
  1. Location matters: Place software close to clients for faster response times using CDNs, edge computing, or geo-replication.
  2. Cache wisely: Optimize speed by using in-memory caching, database caching, or web caching to avoid repeated actions.
  3. Async is key: Improve efficiency with asynchronous processing through message queues, event-driven architectures, or microservices.

The Future of ESG Reporting in Developing Economies: Blockchain-Powered Accountability

Concordium Monthly Updates • 117 implied HN points • 06 Sep 23
🕹 Technology Scalability
  1. ESG reporting in developing economies faces challenges like lack of awareness, resources, and regulatory frameworks.
  2. Concordium's blockchain technology offers transparency, accountability, and efficiency for ESG reporting.
  3. Concordium's use of sharding, ZKP, inbuilt identity layer, and layer 1 structure enhances ESG reporting in developing economies.

Discomforts of Great Design Management

Product Composition • 117 implied HN points • 03 Mar 23
🪑 Design Scalability
  1. Your work in design management is naturally unquantifiable, leading to anxiety and dissatisfaction in many managers.
  2. As a design manager, prioritize building trust with your team, even in challenging situations.
  3. Design managers need to be responsible for the output, not just facilitate, and balance scalable with unscalable practices.

Edge 462: What is Fast-LLM. The New Popular Framework for Pretraining your Own LLMs

TheSequence • 126 implied HN points • 02 Jan 25
🕹 Technology Scalability
  1. Fast-LLM is a new open-source framework that helps companies train their own AI models more easily. It makes AI model training faster, cheaper, and more scalable.
  2. Traditionally, only big AI labs could pretrain models because it requires lots of resources. Fast-LLM aims to change that by making these tools available for more organizations.
  3. With trends like small language models and sovereign AI, many companies are looking to build their own models. Fast-LLM supports this shift by simplifying the pretraining process.

A deep dive on Bitcoin's flaws and how to fix them

Senatus’s Newsletter • 78 implied HN points • 21 Jul 23
🔮 Crypto Scalability
  1. A perfect cryptocurrency needs to have uncensorability, certainty of supply, and transferability as a store of value.
  2. Bitcoin faces challenges with decreasing security spend and centralization of hashrate, impacting its resilience to attacks.
  3. Issues in Bitcoin such as affordability, speed, and scalability make it less efficient as a medium of exchange, while alternative cryptocurrencies offer better solutions.

📽 Webinar: How Convirza Scaled SLMs for Real-Time Call Analytics – Without Breaking the Bank

TheSequence • 126 implied HN points • 15 Nov 24
🕹 Technology Scalability
  1. Convirza found a way to analyze call data quickly and affordably. They combined many tools into one setup, making everything run smoother.
  2. Their response time for customers is now under two seconds, even when many people are using the service. This helps workers get the info they need fast.
  3. By switching to a new system, they reduced costs a lot. They no longer need expensive machines for each task, which keeps their expenses low while still providing accurate results.

SRE book notes: Reliable Product Launches at Scale

Bit Maybe Wise • 19 implied HN points • 16 Feb 23
🕹 Technology Scalability
  1. Optimize launch coordination engineering to balance cost and benefit during product launches
  2. Avoid re-implementing existing solutions by converging on common infrastructure libraries in large organizations
  3. Rearchitecting a product becomes necessary when it grows significantly, leading to increased complexity and fragility

It's not the DevOps engineers’ fault that the ecosystem is broken

Condensing the Cloud • 19 implied HN points • 18 Apr 23
🕹 Technology Scalability
  1. Blaming DevOps engineers for a broken ecosystem is counterproductive; collaboration is key.
  2. Version control systems may not always control software versions effectively, requiring additional tools in the software supply chain.
  3. Implementing scalable technologies like Kubernetes may not always be the best decision and can lead to inefficiencies.

[Video] A Brief Introduction to Kafka (with analogy)

Weekend Developer • 1 HN point • 06 Jul 24
🕹 Technology Scalability
  1. Kafka ensures system consistency in the microservices world by allowing events to be recorded and processed consistently even during service downtime.
  2. Kafka enables a decoupled, event-driven approach to microservices communication, providing fault tolerance and scalability as the number of services grows.
  3. The benefits of Kafka in microservices include event-driven architecture, fault tolerance, and scalability, all contributing to a reliable and consistent system.

Building CAUSL - Episode 5: Long Term Goals

CAUSL Effect • 19 implied HN points • 17 Mar 23
💼 Business Scalability
  1. It's important to define who you are and what you want. Knowing your identity helps you stay true to your goals.
  2. Setting long-term goals gives you clarity and direction. This helps in making decisions aligned with where you want to be in the future.
  3. Scaling your impact is key. Aiming to help many people or companies rather than just a few can lead to bigger success.

Vroom Goes Bust: A Cautionary Tale in the Fast Lane of Online Used-Car Sales

Gad’s Newsletter • 23 implied HN points • 29 Jan 24
💼 Business Scalability
  1. Vroom, a once promising player in online used-car sales, faced financial struggles and announced ceasing e-commerce operations.
  2. Comparison between Carvana and Vroom reveals operational challenges like inventory turnover, highlighting Vroom's decline in efficiency.
  3. Online used-car platforms face hurdles like high inventory costs, aging inventory, and challenges in digital transformation.

Monolith is not dead and it scales

Cloud Weekly • 17 implied HN points • 06 May 23
🕹 Technology Scalability
  1. Serverless may not always be the most cost-effective option, even for big companies like Amazon Prime.
  2. Using ECS to package services in one container can help reduce costs and improve scalability.
  3. Architectures should evolve based on business needs and not just follow trends or debates.

System Design of Uber's CacheFront - Serving more than 40 million low-latency reads per second

Engineering At Scale • 4 HN points • 03 Mar 24
🕹 Technology Scalability
  1. Uber developed CacheFront, an integrated caching solution to overcome problems like maintenance overhead, reduced developer productivity, and region failovers caused by using Redis for caching
  2. Docstore's architecture includes a Control plane, Query Engine, and Storage Engine, with relevant responsibilities for each layer like query execution, data persistence, transaction management, and more
  3. CacheFront's design addressed non-functional requirements like consistency guarantees, cache warming & region failovers, fault tolerance, hot partition issues, and performance & cost improvements

A Deep Dive into the Underlying Architecture of Groq's LPU

Confessions of a Code Addict • 4 HN points • 01 Mar 24
🕹 Technology Scalability
  1. Groq's LPU showcases an innovative design departing from traditional architectures, focusing on deterministic execution for enhanced performance.
  2. The TSP architecture achieves determinism through a simplified hardware design, enabling precise scheduling by compilers for predictable performance.
  3. Groq's approach to creating a distributed multi-TSP system eliminates non-determinism typical in networked systems, with the compiler efficiently managing data movement.

Common iPaaS Challenges

The API Changelog • 4 implied HN points • 28 Feb 24
🕹 Technology Scalability
  1. Setting up the right iPaaS solution for your business comes with challenges due to integrating systems with different data formats and protocols.
  2. Customization is critical in iPaaS solutions to manipulate data, interpret errors, and adapt to changes in APIs for successful integrations.
  3. Scalability in iPaaS solutions is essential to handle increasing requests, queueing for load balancing, and prioritizing requests to prevent overload and ensure integration continuity.

The Evolution of iPaaS

The API Changelog • 4 implied HN points • 10 Jan 24
🕹 Technology Scalability
  1. iPaaS evolved from manual, inefficient data exchange methods before the rise of EAI and SOA.
  2. Modern iPaaS is cloud-based, user-friendly, and supports real-time integration for businesses.
  3. Challenges in iPaaS evolution include security, data privacy, legacy system integration, and the emerging use of AI.

System Design Concepts: Dive deep into Database Sharding Strategies

Engineering At Scale • 2 HN points • 05 Aug 23
🕹 Technology Scalability
  1. Range-Based Sharding divides data based on ranges like organizing books in bookshelves to make searches easier.
  2. Hash-Based Sharding evenly distributes data across different shards using a hash function, but may require data rebalancing when the number of shards changes.
  3. Consistent Hashing minimizes data movement when adding or removing shards, improving scalability while Geo-Based Sharding stores data close to users for better performance.