The hottest Data Substack posts right now

And their main takeaways
Category
Top Literature Topics

What I've been reading (#1)

Democratizing Automation • 490 implied HN points • 21 Jun 25
🕹 Technology Data
  1. Links are important and will now have their own dedicated space. This way, they can be shared and discussed more easily.
  2. AI is being used more than many realize, and there's promising growth in its revenue. The future looks positive for those already in the industry.
  3. It's crucial to stay informed about advancements in AI, especially regarding human-AI relationships and the challenges that come with making AI more capable.

Google sees more than other AI companies

Alex's Personal Blog • 98 implied HN points • 05 Dec 25
🕹 Technology Data
  1. Google's AI has access to way more internet pages compared to other companies like OpenAI and Microsoft. This gives Google an advantage in providing better answers and improving its technology.
  2. The stock market reactions to layoffs are not always positive, as seen with companies like Meta and Amazon. Investors aren't rewarding these companies with significant stock increases after staff cuts.
  3. Micro1 is doing great by reaching $100 million in annual recurring revenue in a short time, showing that there's strong growth potential in innovative AI startups.

top 5 reading links for 2025

Five Links (and three graphs) by Auren Hoffman • 64 implied HN points • 21 Dec 25
💼 Business Data
  1. Big-picture data and history reveal where success and talent cluster, so studying patterns can show who wins prizes and where modern geniuses hide.
  2. Private tech is reshaping defense and security, and building 21st-century military or AI systems brings practical bottlenecks like energy, logistics, and policy into focus.
  3. Everyday business and social skills matter: many data businesses aren’t ideal VC targets, venture firms often ignore their own advice, and simple moves like the right intro, hosting great dinners, or focused job hunting make a big difference.

OpenAI is too cheap to beat (redux)

The AI Frontier • 59 implied HN points • 18 Jul 24
🕹 Technology Data
  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.

The Rise of Open-Sourced R&D, How Communal Resourcefulness Will Protect Us, & Wild Data Provocations

Implications, by Scott Belsky • 432 implied HN points • 23 Jan 24
🕹 Technology Data
  1. 2024 brings significant changes and implications due to societal shifts, innovation speed, and changing human desires.
  2. Customers are increasingly driving R&D by generating ideas, particularly with the help of AI tools and social validation.
  3. Communal resourcefulness, like shared threat models and blocklists, is crucial for enhancing security in the AI era.
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CROSSPOST: DAN DAVIES: Snobby About Excel

Brad DeLong's Grasping Reality • 15 implied HN points • 06 Feb 26
🕹 Technology Data
  1. Work practices matter: when spreadsheets spread beyond finance they often became undocumented, brittle files because creators didn’t expect to be held accountable.
  2. We’re replaying that mistake with AI—fast, local tinkering can produce large-scale, hard-to-check outputs, so anything public or important should be rebuilt, checked, and owned by someone.
  3. Past errors like Reinhart–Rogoff show the real harm from sloppy, unreviewed work, so adopting stricter professional standards and a sensible AI-skepticism will reduce mistakes and increase accountability.

⤴⤵ Up Wing/Down Wing #53

Faster, Please! • 365 implied HN points • 19 Jul 25
🕹 Technology Data
  1. Tech companies are investing heavily in AI, with over $90 billion going into new projects in the U.S. This includes building data centers powered by reliable energy sources to stay ahead in AI.
  2. Real estate is expanding into space as companies invest in infrastructure for lunar and orbital projects. This could change the way we think about real estate and take advantage of space resources.
  3. Google has turned Android phones into a global earthquake warning system. This tool helps people get early alerts about earthquakes, improving public safety with technology we already have.

Responsible AI in Security

Rod’s Blog • 396 implied HN points • 19 Jan 24
🕹 Technology Data
  1. AI in security offers enhanced threat detection and response capabilities by analyzing data and providing insights.
  2. Responsible AI in security involves principles like transparency, safety, human control, and privacy to ensure ethical use.
  3. Security professionals can leverage responsible AI to improve performance while safeguarding data, privacy, and safety.

The Great Sobriety for Venture Investing, Where To Start with AI, & Undeniable Data

Implications, by Scott Belsky • 707 implied HN points • 19 Sep 23
💼 Business Data
  1. The venture capital world is facing harsh realities and there are lessons to be learned about creating great products from failed ventures.
  2. Adopting AI requires a '4 P's' framework: Play, Pilot, Protect, Provoke.
  3. Financing for startups should prioritize product-led growth, focus, and discipline over raising large amounts of capital.

Uber was Built in Silicon Valley of India

Sector 6 | The Newsletter of AIM • 439 implied HN points • 03 Jan 24
🕹 Technology Data
  1. During the COVID-19 pandemic, Uber's tech team in Bangalore focused on managing both Uber Ride and Uber Eats effectively.
  2. They realized that they could save resources by combining their tech systems instead of using separate ones.
  3. The team found that some tech functions were useful for both services, which allowed them to make improvements in efficiency and performance.

Has Google gone too woke? Why even the biggest models still struggle with guardrails

Marcus on AI • 2608 implied HN points • 21 Feb 24
🕹 Technology Data
  1. Google's large models struggle with implementing proper guardrails, despite ongoing investments and cultural criticisms.
  2. Issues like presenting fictional characters as historical figures, lacking cultural and historical accuracy, persist with AI systems like Gemini.
  3. Current AI lacks the ability to understand and balance cultural sensitivity with historical accuracy, showing the need for more nuanced and intelligent systems in the future.

Further Trouble in Hinton City

Marcus on AI • 2687 implied HN points • 08 Feb 24
🕹 Technology Data
  1. Recent evidence challenges claims of Generative AI systems not storing things or understanding them deeply
  2. Trivial perturbations affect GenAI systems significantly, indicating a lack of deep understanding
  3. GenAI systems effectively store things but struggle with novel designs and understanding simple concepts

Searching for insight

benn.substack • 1099 implied HN points • 29 Nov 24
💼 Business Data
  1. Many jobs in areas like think tanks or journalism are more about creating a background or illusion rather than producing real change or value. They serve as props for the more influential figures.
  2. There's a concern that as AI becomes capable of producing content, it might not be because it’s better, but because the original jobs might not have mattered as much as once thought.
  3. In analytics, there's a question of whether the insights businesses claim to offer are real or just part of the narrative they tell to appear competent and important.

4 Strategies To Elevate Your Communication Style From Engineer To Leader

SeattleDataGuy’s Newsletter • 412 implied HN points • 10 Jun 25
💼 Business Data
  1. When communicating with non-technical stakeholders, focus on the impact of your work rather than the technical details. Using clear language helps everyone understand why your projects matter.
  2. Highlight the risks of inaction to motivate decision-makers. Making them aware of potential costs or missed opportunities can encourage them to prioritize your recommendations.
  3. Use analogies relevant to your audience to make complex ideas easier to grasp. Relating technical concepts to familiar ideas can help build understanding and trust.

Why security has become easier

Frankly Speaking • 355 implied HN points • 02 Jul 25
🕹 Technology Data
  1. Security tools have improved a lot and are easier to use now. Companies can set up basic security measures quickly without needing huge teams.
  2. AI helps security teams by automating tasks and making their work faster. When used correctly, it can save time on repetitive tasks.
  3. There is now better data on security breaches which helps teams prioritize what risks to focus on. This makes good security practices more accessible and easier to implement.

We’re Not Ready for the AI Power Surge

Common Sense with Bari Weiss • 426 implied HN points • 11 Jun 25
🕹 Technology Data
  1. The rapid growth of AI technology is increasing the demand for energy, which may strain the current power grid in America.
  2. New AI models are becoming more powerful, and their popularity is likely to lead to even higher energy consumption as usage increases.
  3. Some experts express concern about the future energy needs for AI, while others believe the impact on electricity usage per query is low.

⤴⤵ Up Wing/Down Wing #28

Faster, Please! • 1005 implied HN points • 14 Dec 24
🕹 Technology Data
  1. New chips using fiber optics can transfer data way faster, which may cut down AI training times and save energy. This could really speed up tech advancements.
  2. Businesses are finding out that human skills are still important when using AI tools. People are getting new jobs related to organizing data so AIs can work better.
  3. SpaceX is becoming super important for US defense technology. Its innovations may give the US an advantage over rivals like China in military capabilities.

Open Thread 317

Astral Codex Ten • 2340 implied HN points • 26 Feb 24
🕹 Technology Data
  1. Some users who were supposed to be unbanned were not truly unbanned, leading to a need for them to reach out to get it fixed.
  2. Substack acknowledges issues with page and comment loading speed, with plans to improve that in the future.
  3. GPT-6's training might require only 0.1% of the world's computers, according to Ben Todd's findings, a significant discrepancy from previous estimations.

LLMs are becoming commodities

The AI Frontier • 99 implied HN points • 30 May 24
🕹 Technology Data
  1. LLMs are growing similar and it's hard to tell them apart. Companies must now find new ways to stand out as features become alike.
  2. The race to create better models is very fast, and some newer models are catching up to the established ones. This means that model quality is no longer the main thing that makes a provider unique.
  3. For businesses and users, having more options is good for getting better deals. But, many people will likely stick with known brands rather than trying new, less familiar choices.

AI companies are just SaaS companies

benn.substack • 869 implied HN points • 20 Dec 24
🕹 Technology Data
  1. AI companies have a lot in common with traditional SaaS companies. They’re selling software services, often built on complex tech, rather than just cool algorithms.
  2. The success of AI models like ChatGPT depends heavily on branding and user experience. People care more about how easy and useful the software is than just the tech behind it.
  3. OpenAI is at a crossroads, needing to adapt its business model and offerings to stay ahead, especially as competition increases and tech costs rise.

wind and rain and sun and data

Dan Davies - "Back of Mind" • 334 implied HN points • 19 Jan 24
🕹 Technology Data
  1. Supply and demand for electricity become more unpredictable with an increasing proportion of wind and solar energy
  2. The profit motive drives the application of information processing power and bandwidth to solve energy planning problems
  3. Market trading and the profit motive are ways to match the variety of the energy problem with the regulatory system

The Inference Economy: Part II

Generating Conversation • 93 implied HN points • 13 Nov 25
💼 Business Data
  1. Token demand is increasing because we're processing more data with AI and using more tokens per request. This means we need to find better ways to manage how many tokens we're using.
  2. Choosing the right model for the right task is crucial to save costs. Using smaller models for simple tasks can help a lot instead of automatically reaching for the biggest and best models.
  3. Switching between different LLM providers can be beneficial for reducing costs, but it requires careful planning to handle potential security concerns. It’s important to think about how and when we use more complex models.

Understanding Registers and Data Movement in x86-64 Assembly

Confessions of a Code Addict • 288 implied HN points • 16 Jul 25
🕹 Technology Data
  1. Registers are vital for data movement in x86-64 assembly language. They help store and manage data as the CPU processes it.
  2. Understanding how the size of registers has evolved is key. For example, early registers were 16-bit, but now they handle 64-bit data.
  3. Using hands-on exercises with assembly code can improve your grasp of how these registers work. Observing register values in a debugger is a great way to learn.

Training large language models on my loved ones' data

Reboot • 26 implied HN points • 11 Jan 26
🕹 Technology Data
  1. Expert data labelers produce the high-quality reasoning traces that power recent LLM advances, yet they work as precarious gig labor with opaque rules, unstable pay, and no real career path.
  2. AI companies capture huge value from this labor and then displace or sideline those workers as models learn to generate synthetic data, causing layoffs and downward pressure on wages.
  3. There are simple, practical protections that could help: portable credentials, transparency about how data is used, the ability for workers to communicate and appeal, and explicit credit or recognition for their contributions.

DeepSeek's Edge

ChinaTalk • 741 implied HN points • 12 Jan 25
🕹 Technology Data
  1. DeepSeek has no business model, which allows its team to experiment freely without pressure to earn money. This gives them a unique advantage over most other AI labs that need to focus on revenue.
  2. DeepSeek runs its own data centers instead of relying on external cloud services. This means they have better control over their resources and can optimize their setup for efficiency.
  3. The company's success comes from their innovative software optimization techniques. By being smart about how they use their hardware, they've achieved high performance even with limited resources.

January 2024: What if ACH had attachments?

Kunle.app • 314 implied HN points • 17 Jan 24
🕹 Technology Data
  1. Payments innovation has focused on optimizing speed and cost over the past two decades.
  2. The messaging layers in payment systems have a bandwidth constraint that limits the communication of metadata and important contextual information.
  3. Increasing the bandwidth in the messaging layer of payments could allow for self-reconciling payments and eliminate the need for parallel systems for information exchange.

While the West Hesitates China Marches Forward

The Algorithmic Bridge • 637 implied HN points • 21 Feb 25
🕹 Technology Data
  1. China is rapidly adopting AI technology, using systems like DeepSeek across government operations to improve efficiency and decision-making. This shows their proactive approach to embracing innovation.
  2. DeepSeek has emerged as a competitive AI model that rivals established Western technologies, highlighting China's growing capabilities in the tech sector. China is focused on getting results, not just discussing ideas.
  3. The cultural mindset in China emphasizes efficiency and action, contrasting with the West's tendency to debate and regulate rather than implement. This difference in attitude could impact global technological leadership.

Expanding AI Horizons: The Rise of Function Calling in LLMs

Gradient Flow • 279 implied HN points • 25 Jan 24
🕹 Technology Data
  1. Function Calling in AI enables models to interact with external functions, going beyond basic text generation to execute actions based on requests.
  2. Combining Retrieval Augmented Generation (RAG) with Function Calling enhances AI systems, allowing them to access external APIs to improve adaptability and assist in various tasks.
  3. Despite its potential, Function Calling in AI faces challenges like security risks, ethical alignment, technical limitations, and the need for advancements in contextual understanding for full potential realization.

Data: the new oil or a new byproduct?

Topsoil • 511 implied HN points • 30 Jun 23
🕹 Technology Data
  1. Data in agriculture is essential for advancements like Generative AI, automation, and precision agriculture.
  2. Challenges in farm digitization include issues like connectivity, interoperability, data quality, trust, and incentives.
  3. Farmers derive value from data through decision-making, enabling technologies, sharing with advisors, compliance, and future income opportunities.

Import AI 346: Human-like meta-learning; a 3 trillion token dataset; spies VS AI

Import AI • 459 implied HN points • 30 Oct 23
🕹 Technology Data
  1. UK's intelligence services are slightly worried about the safety implications of generative AI technologies, particularly in amplifying existing risks like cyber-attacks and digital vulnerabilities
  2. Research shows that a basic Transformer neural net architecture can meta-learn and match human performance in inferring complex rules from small data, hinting at AI systems increasingly displaying human-like qualities
  3. Facebook's Habitat 3.0 software enables training and testing agents to collaborate with humans by simulating realistic 3D environments with humanoid avatars, human-in-the-loop interactions, and benchmark tasks for human-robot interaction

It's A New Year, But Businesses Are Still Baffled By The Same Old Problems With Data And AI

High ROI Data Science • 317 implied HN points • 15 Jan 24
🕹 Technology Data
  1. CEOs face challenges with limited skills and expertise in implementing AI initiatives.
  2. Businesses struggle with data complexity and ethical concerns when it comes to utilizing AI.
  3. Companies need to align AI opportunities with business goals, estimate costs upfront, and prioritize continuous reskilling for successful AI implementation.

Google rules the AI roost

Alex's Personal Blog • 65 implied HN points • 24 Nov 25
🕹 Technology Data
  1. GLP-1s are showing promise in helping with addiction treatment. They might change how we approach addiction care, offering a new tool beyond traditional methods.
  2. Microsoft is creating a marketplace where publishers can sell content for AI use. This could lead to better AI development while allowing content creators to earn from their work.
  3. Google's Gemini 3 Pro is currently leading the AI model race, surpassing competitors like OpenAI and generating excitement in the AI community. This signals a shift in the AI landscape with Google gaining a strong position.

Import AI 335: Synth data is a bad AI drug; Facebook changes the internet with LLaMa release; and Chinese researchers use AI to figure out chip design

Import AI • 459 implied HN points • 31 Jul 23
🕹 Technology Data
  1. Synthetic data during AI training can be harmful if not used in moderation, as shown by researchers from Rice University and Stanford University
  2. Chinese researchers have successfully used AI to design semiconductors based only on input and output data, demonstrating the potential for economic and national security implications
  3. Facebook has released Llama 2, a powerful language model with freely available weights, potentially changing the landscape of AI deployment on the internet

Import AI 343: Humanlike AI; LLaMa 2 protests; the NSA's new AI center

Import AI • 439 implied HN points • 09 Oct 23
🕹 Technology Data
  1. Google DeepMind and 33 labs created a large dataset for training robots, showing that using heterogeneous data and high-capacity models improves robot performance.
  2. Protests have begun against Facebook for releasing AI models that can be easily modified, raising concerns about AI safety becoming a political issue.
  3. Generative image models are displaying human-like qualities in tasks, like shape bias and understanding perceptual illusions, suggesting a convergence between AI systems and humans.

Did OpenAI Just Kill Glean?

Enterprise AI Trends • 253 implied HN points • 26 Jun 25
🕹 Technology Data
  1. ChatGPT can now perform 'Deep Research' using private documents from sources like Google Drive and Dropbox. This makes creating reports much easier for users.
  2. The ability to generate reports is significant because a lot of middle managers spend a lot of time on this task. It's a huge time-saver.
  3. This new feature could impact other apps that provide similar research functions, like Glean, making it a competitive landscape for AI applications.

Language Agent Tree Search — LATS

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

What Are Companies Doing With Generative AI? A Tale Of 3 Strategies.

High ROI Data Science • 297 implied HN points • 12 Jan 24
🕹 Technology Data
  1. Companies are using Generative AI tools to decrease training times and improve customer service in retail.
  2. Some companies are implementing Generative AI without a clear business problem statement, leading to undefined outcomes.
  3. Retailers like Walmart are strategically using Generative AI to change customer workflows, improve online shopping experiences, and increase revenue.

(Mostly) Closing The Book On Murder in 2023

Jeff-alytics • 216 implied HN points • 29 Jan 24
📰 News Data
  1. Murder rates likely fell by about 12% in over 200 cities in 2023.
  2. Some cities saw an increase in murder, like Topeka, Greensboro, and Shreveport.
  3. The murder trend appeared positive in 2024 with fewer cities showing an increase.