The hottest Scaling Substack posts right now

And their main takeaways
Category
Top Technology Topics

Modern Meditations: Immad Akhund

The Generalist • 740 implied HN points • 18 Dec 25
💼 Business Scaling
  1. Constant learning is the core skill—learn new domains, talk to experts, and treat excellence as the result of daily grinding and perseverance.
  2. Constraints are valuable: more resources don’t always speed things up, and growing headcount too fast can reduce productivity, so prefer measured, sustainable scaling.
  3. Be optimistic about long-term progress while thinking big—study history to understand patterns and imagine bold projects like space habitats and new immersive tech.

How gardening teaches you this crucial lesson about scaling your business

VERY GOOD PRODUCTIZED GUIDES • 59 implied HN points • 16 Sep 24
💼 Business Scaling
  1. Create systems that allow you to enjoy what you love, even when life gets busy. This gives you the freedom to step away without worry.
  2. Think about tasks you do daily that take more than 10 minutes. Find ways to automate them or get help to save time.
  3. Building these efficient systems might take time upfront, but once they're in place, they let you scale your business and work more smoothly.

An AI rumor you won’t want to miss

Marcus on AI • 7153 implied HN points • 10 Nov 24
🕹 Technology Scaling
  1. The belief that more scaling in AI will always lead to better results might be fading. It's thought we might have reached a limit where simply adding more data and computing power is no longer effective.
  2. There are concerns that scaling laws, which have worked before, are just temporary trends, not true laws of nature. They don’t actually solve issues like AI making mistakes or hallucinations.
  3. If rumors are true about a major change in the AI landscape, it could lead to a significant loss of trust in these scaling approaches, similar to a bank run.

From Bushwackers to F1 Drivers: Evolving Employee Types as Startups Scale

Gad’s Newsletter • 47 implied HN points • 02 Feb 26
💼 Business Scaling
  1. Startups need different people as they grow: bushwackers to invent in chaos, off-road drivers to stabilize and scale, and F1 drivers to optimize and run at high efficiency.
  2. The biggest scaling mistake is hiring the right people for the wrong stage — add structure at the right time and integrate new roles carefully so you don’t smother innovation or collapse under chaos.
  3. Even mature companies must preserve some exploratory teams and have leaders translate between archetypes so experimentation and process coexist and each group is rewarded appropriately.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

Caterpillar to Butterfly

Software Design: Tidy First? • 1237 implied HN points • 14 Feb 25
🕹 Technology Scaling
  1. As organizations grow, the need for specialist skills becomes more important. It's not enough to have hobbyists; experts are needed to handle complex tasks.
  2. When specialist teams form, their priorities might clash with client teams' needs. Client teams often want quick fixes, while specialists aim for quality work.
  3. To handle increased requests, organizations should empower client teams to solve their own issues. This self-service approach helps manage workloads and creates better efficiency.

The Sequence 802: The Thinking Machine: A Deep Dive into Test-Time Compute and the New Scaling Paradigm

TheSequence • 21 implied HN points • 05 Feb 26
🕹 Technology Scaling
  1. For years AI advanced by scaling up pre-training—more data, bigger models, and huge GPU time to bake capabilities into fixed weights.
  2. Test-time compute flips that idea by letting models use extra computation during inference to reason, plan, backtrack, and self-correct—basically "letting the model think."
  3. The big implication is that model performance depends not just on training compute but also on how much compute is allowed at inference, changing tradeoffs for how we build and deploy AI.

The Freedom Tradeoff

Jay's Data Stream • 29 implied HN points • 07 Jan 26
💼 Business Scaling
  1. Bootstrapping buys you control over decisions and the freedom to choose your lifestyle. It also forces you to prioritize immediate profitability and often limits rapid scaling.
  2. Taking venture capital adds constant pressure to grow quickly — a ‘boot on your neck’ — which can drive fast scaling but reduces autonomy and can cause burnout or loss of control.
  3. There’s a clear tradeoff between outsized freedom and outsized growth, so you need to decide what you’re optimizing for. You can try to self-impose urgency to grow, but it’s not the same as the external forcing function investors provide.

5 Challenges of Scaling Paid Ads for Early Stage Startups

Nail It and Scale It • 59 implied HN points • 25 Jun 24
💼 Business Scaling
  1. It's hard to find out why ads aren't working. There can be many reasons, like targeting the wrong audience or having a bad website design.
  2. Early stage startups often struggle to scale quickly due to internal issues. When they get more leads, they might need to pause ads to catch up, which can hurt their momentum.
  3. Finding product-market fit takes time and constant testing. Just because something works now doesn't mean it will work later, so keep experimenting with different strategies.

What You Need to Know About GPT-4 and PaLM 2

Gradient Flow • 319 implied HN points • 01 Jun 23
🕹 Technology Scaling
  1. Leading-edge AI models like GPT-4 and PaLM 2 are becoming less open due to growing costs, IP protection, and misuse concerns.
  2. Insights from technical reports of these models help in understanding capabilities, risks, and benefits, aiding in developing strategies to manage potential harm.
  3. GPT-4 and PaLM 2 underwent rigorous testing for responsible AI behavior, outperforming predecessors in various tasks and showing advancements in performance, scalability, and efficiency.

What are your job's lighthouse questions?

Lessons • 235 implied HN points • 17 Apr 23
💼 Business Scaling
  1. Job descriptions in rapidly changing organizations expire quickly.
  2. Use guiding questions instead of bulleted lists to navigate through chaos.
  3. Lighthouse questions act as anchors in a storm of scaling by keeping focused on essential tasks.

Amazon Prime Video reduced costs by 90% by ditching Microservices [System Design Sundays]

Technology Made Simple • 159 implied HN points • 07 May 23
🕹 Technology Scaling
  1. Amazon Prime Video saw a 90% cost reduction by moving away from Microservices to a monolith architecture. This change improved scalability and reduced infrastructure costs significantly.
  2. The challenges Amazon faced with their initial microservices implementation included hitting scaling limits and high overall costs of the system. Moving to a monolith architecture helped address these issues and allowed for better scaling.
  3. While the debate between Microservices and Monoliths continues, the decision should depend on factors like team size, emphasis on scale, and complexity. Microservices offer scalability but require careful planning, while monoliths are easier to design and manage.

Beware of Scaled Agile

Ageling on Agile • 99 implied HN points • 24 Jan 24
💼 Business Scaling
  1. You don't need all the complexities of Agile scaling approaches to solve your issues.
  2. Start small and gradually add complexity based on your real needs.
  3. Focus on nailing the solution before trying to scale it.

GM Shaders Mini: The Matrix

GM Shaders Mini Tuts • 117 implied HN points • 18 Nov 23
🕹 Technology Scaling
  1. Matrices can rotate, scale, and skew both vectors and vector spaces.
  2. Matrices are multiplied with vectors or other matrices to transform them.
  3. Matrices are powerful tools in shaders for operations like color remapping and noise functions.

Bioeconomy Startups: A Dive into De-risking Strategies

The New Bioeconomy • 78 implied HN points • 12 Jan 24
💼 Business Scaling
  1. Scaling up bioeconomy startups involves understanding the process and collaborating for de-risking.
  2. Finding a consistent feedstock supply is crucial for bioeconomy startups, often requiring partnerships with established companies.
  3. De-risking product-to-market strategy involves market assessment, alliances, and communicating sustainability.

System Design - Scale 0 to Millions of Users

Better Engineers • 7 HN points • 31 Jul 24
🕹 Technology Scaling
  1. Scaling systems to handle millions of users involves understanding how to make systems work better under pressure. This can be done by adding more resources or managing them effectively.
  2. Vertical scaling means adding more power (like RAM or CPU) to existing servers, while horizontal scaling means adding more servers to share the load. Horizontal scaling is often better for high traffic situations.
  3. Using a master-slave database setup helps balance loads and keeps data safe. If one database fails, another can take over, ensuring the system runs smoothly and reliably.

GM Shaders Mini: Scaling

GM Shaders Mini Tuts • 117 implied HN points • 02 Jun 23
🕹 Technology Scaling
  1. Learn how to handle scaling, aspect ratios, and centering in shaders.
  2. Understand the importance of converting between texture and pixel coordinates.
  3. Center pixel coordinates to properly scale and center elements on the screen.

The Future of Farcaster with Dan Romero

The Generalist • 380 implied HN points • 14 Mar 24
🕹 Technology Scaling
  1. Farcaster, a disruptive social network, is built on a permissionless protocol, attracting attention by charging every user a fee to prevent spam.
  2. Farcaster competes head-to-head with Elon Musk in the social arena, aiming to offer a fundamentally different social experience rather than just a Twitter clone.
  3. Introducing innovative features like 'Frames' mini-applications within the feed has been a game-changer for Farcaster, sparking interest among developers and users.

Unscalable *and* effective ⛰

The SaaS Baton • 98 implied HN points • 12 Apr 23
💼 Business Scaling
  1. Not everything needs to be scalable, doing unscalable things can still be effective in startups
  2. Hiring a finance executive early on can bring valuable insights and help with key financial tasks
  3. Monetizing an open-source product through SaaS can present challenges but also lead to product maturity for different use cases

Scaling Decision Making

Suzan's Fieldnotes • 98 implied HN points • 18 Jul 23
💼 Business Scaling
  1. Scaling companies need to pay attention to decision-making processes as they grow
  2. Organizations should delegate decision-making power throughout the company
  3. Centralizing decisions and leaving key voices out can hinder scaling and lead to organizational dysfunction

Unpacking Okta's Tech Architecture | Monica Bajaj - VP Developer Experience & Mark T. Voelker - VP Architecture at Okta

platocommunity • 39 implied HN points • 01 Feb 24
🕹 Technology Scaling
  1. Okta believes in leveling up both the tech stack and the people stack for successful architecture.
  2. The Architecture Charter at Okta involves setting clear guardrails and handholds to empower engineers to make informed decisions.
  3. Writing things down, utilizing frameworks like RFCs and Requests for Discussion, is crucial for communication and knowledge sharing in the organization.

When to Scale or Keep Iterating

The Data Score • 59 implied HN points • 10 May 23
💼 Business Scaling
  1. Achieving product/market fit is crucial for the success of a startup or new product as it means the product meets the needs and preferences of the target market, leading to customer satisfaction and retention.
  2. Iterating on a handcrafted approach at the start can help find product/market fit before scaling to avoid unwanted tech debt and ensure the product evolves to meet client outcomes.
  3. To determine product/market fit, look for signs like user retention, surveys showing strong customer preference, and organic growth, then iterate quickly based on critical feedback to ensure the product is indispensable to users.

August 2023 safety news: Universal attacks, Influence functions, Problems with RLHF

AI safety takes • 58 implied HN points • 27 Aug 23
🕹 Technology Scaling
  1. Understanding the origin of dangerous behavior in AI models can lead to training safer AI through the use of influence functions.
  2. Gradient-based attacks have become effective in breaking into language models and can even transfer between different models.
  3. Evaluating moral beliefs encoded in large language models can reveal inconsistencies and uncertainties, with safety-tuned models showing stronger preferences.

Navigating Culture in a Scaling Startup

Suzan's Fieldnotes • 58 implied HN points • 17 Apr 23
💼 Business Scaling
  1. Startups thrive in chaos and rapid change, which can be exciting for those who enjoy a fast pace and quick growth.
  2. Communicating effectively in a rapidly scaling startup requires balancing speed and quality, ensuring team-wide understanding and coordination.
  3. Guiding culture during rapid growth involves hiring for cultural fit, seeking feedback from peers, and finding leadership support that empowers and believes in you.

Fixing the research-to-policy pipeline

Hypertext • 19 implied HN points • 27 Mar 24
🚌 Education Scaling
  1. Challenges in evidence-based policy include interpreting research results, dealing with luck, p-hacking, and external validity.
  2. Pre-registration of RCTs and requiring data/code sharing help combat issues like luck and p-hacking in research.
  3. Scaling effective programs poses challenges of logistics, resources, and ensuring successful reproduction in multiple settings.

Lessons For Startup Founders From A TPM

Building Rome(s) • 5 implied HN points • 09 Dec 25
💼 Business Scaling
  1. Keep clarity even when the future is uncertain: set a simple vision, tactical goals, timeframes, and clear owners so the team can scale without heavyweight process.
  2. Prioritize ruthlessly and learn to say no; using a “no log” helps the team see what you intentionally set aside and keeps focus on what matters.
  3. Build minimal, evolving systems that prevent chaos and surface hidden work—use a single roadmap, release-based planning, regular demos, decision logs, and launch checklists to make dependencies and debt visible.

How RLHF actually works

Democratizing Automation • 306 implied HN points • 21 Jun 23
🕹 Technology Scaling
  1. RLHF works when there is a signal that vanilla supervised learning alone doesn't work, like pairwise preference data.
  2. Having a capable base model is crucial for successful RLHF implementation, as imitating models or using imperfect datasets can greatly affect performance.
  3. Preferences play a key role in the RLHF process, and collecting preference data for harmful prompts is essential for model optimization.

Navigating Manager Training for Startups: A Strategic Approach

PeopleStorming • 39 implied HN points • 14 Nov 23
💼 Business Scaling
  1. Evaluate organizational complexity to determine the necessity of leadership training, focusing on skills like change management and decision-making.
  2. Identify leadership challenges related to team motivation and communication to assess the need for training in conflict management and psychological safety.
  3. Align manager training with strategic goals for growth and market penetration, emphasizing skills like vision setting and strategic planning.

Problem: Break Down A Monolith Application Into Microservices

SkylineCodes • 19 implied HN points • 10 Feb 24
🕹 Technology Scaling
  1. Decomposing a monolithic application into microservices pattern helps scale and deploy services independently which is crucial for agility and quick feature updates in a competitive market.
  2. Understanding the Scale Cube model and its dimensions (X-axis scaling, Y-axis scaling, Z-axis scaling) is essential for designing scalable and resilient software architectures.
  3. Decomposing by business capability and subdomain are effective strategies for breaking down microservices, ensuring cohesive and loosely coupled services aligned with business needs.

Massed Muddler Intelligence

Breaking Smart • 72 implied HN points • 11 Feb 24
🕹 Technology Scaling
  1. The concept of Massed Muddler Intelligence (MMI) entails a new approach to scaling AI, emphasizing the importance of agents, local trial-and-error, and muddling through over monolithic, deterministic training models.
  2. MMIs aim to leverage the principles of embodiment, boundary intelligence, temporality, and personhood to design scalable AI systems that resemble Service-Oriented Architecture in computing.
  3. Building MMIs involves compositing different elements deliberately to create a language of differentiated forms, akin to how reinforced concrete combines materials in defined geometries to achieve specific properties.

Onboarding A Million Users

Athena Scale • 19 implied HN points • 18 Apr 23
🕹 Technology Scaling
  1. Achieving a million users is a significant milestone for companies.
  2. Not all startups need to aim for 1 million active users to be successful.
  3. Handling 1 million users requires efficient infrastructure and preparation.

[System design] Sharding : In and Out Explained

The ZenMode • 42 implied HN points • 16 Mar 24
🕹 Technology Scaling
  1. Sharding is a technique to horizontally partition a data store into smaller fragments across multiple servers, aiding in scalability and reliability.
  2. Before sharding a database, consider options like vertical partitioning, database optimization, replication, and caching to improve performance without the added complexity of sharding.
  3. Different sharding strategies like Hash Sharding, Range Sharding, and Directory-Based Sharding have unique considerations and advantages based on factors like data distribution, queries, and maintenance.

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

Engineering At Scale • 30 implied HN points • 29 Jul 23
🕹 Technology Scaling
  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 Hagakure #60: Growth Without Scale

The Hagakure • 30 implied HN points • 25 May 23
💼 Business Scaling
  1. Getting people in the door is easy, but building a team is hard.
  2. Growth does not necessarily mean scalability.
  3. Key factors for scaling successfully include continuous feedback, focusing on systems over individuals, and empowering middle managers.

Scaling ain't cheap or easy

Joshua Gans' Newsletter • 39 implied HN points • 27 Nov 20
🏥 Health & Wellness Scaling
  1. Scaling up Covid-19 testing is crucial for returning to normalcy before widespread vaccine distribution.
  2. Implementing large-scale testing efforts requires a coordinated push similar to military operations and campus-wide testing strategies.
  3. Preparing for economy-wide frequent testing demands meticulous planning, infrastructure development, and preparedness on a national level.