The hottest Data Substack posts right now

And their main takeaways
Category
Top Literature Topics

Data at Depth Newsletter 6: Thankful, Creating Like Crazy, GPT-4/StreamLit Dashboards

Data at Depth • 79 implied HN points • 08 Feb 24
🕹 Technology Data
  1. The author's Substack newsletter is rapidly growing, and they are very active in creating content to keep up with the growth.
  2. The newsletter includes the author's personal journey with data, highlighting successes on platforms like Medium and Substack.
  3. Readers can access the full newsletter and archives with a 7-day free trial subscription.

So... what is multi-modal AI? And why is the internet losing their mind about it? [Math Mondays]

Technology Made Simple • 159 implied HN points • 10 Oct 23
🕹 Technology Data
  1. Multi-modal AI integrates multiple types of data in the same training process, allowing models to represent data in a common n-dimensional space.
  2. Multi-modality adds an extra dimension to data, expanding the search space exponentially, enabling more diverse and powerful AI applications.
  3. While multi-modality enhances model performance, it does not solve fundamental issues with AI models like GPT, and simpler technologies may be more effective for certain use-cases.

AI applications are more than an LLM

The AI Frontier • 19 implied HN points • 20 Jun 24
🕹 Technology Data
  1. AI applications are more than just using a big model; they need careful design and planning to be effective. It's like building a nice piece of furniture versus just putting some wood together.
  2. Quality comes with a cost, and building great AI solutions takes more time and resources. Cheaper options might save money now, but they often lead to poorer results.
  3. Not all AI applications perform the same, even if they use the same tools. Good performance comes from thoughtful engineering and working with the data properly.

From bare-bones to holistic machine learning

Mindful Modeler • 159 implied HN points • 08 Aug 23
🕹 Technology Data
  1. Machine learning can range from simple, bare-bones tasks to more complex, holistic approaches.
  2. In bare-bones machine learning, the modeling choices are defined, making it about the model's performance and tuning.
  3. Holistic machine learning involves designing the model to connect with the larger context, considering factors like uncertainty, interpretability, and shifts in distribution.
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[TI-02] first independent note

spencer's paradoxes • 157 implied HN points • 19 Feb 23
🕹 Technology Data
  1. Embodied attention and speculating new data materials are being explored.
  2. Exploring the future of data and what communal software could look like.
  3. Launching a website for gathering internet dreams to understand what people want from the internet.

Quo vadis, Data Open source

timo's substack • 157 implied HN points • 03 Sep 23
🕹 Technology Data
  1. Snowplow, dbt, Rudderstack, and Iceberg are examples of open-source data tools each with unique characteristics.
  2. Open-source data tools face challenges in transitioning to successful go-to-market strategies.
  3. Companies need to focus on identifying customer pain points and developing experience-changing solutions in their GTM strategy.

ChatGPT and the Decline of Accuracy

Auerstack • 157 implied HN points • 07 Sep 23
🕹 Technology Data
  1. Chatbots like ChatGPT can be fallible and provide both accurate and inaccurate information.
  2. Training data for AI often contains errors, including those from sources like Wikipedia.
  3. The issue of declining accuracy in AI technology reflects broader societal trends and challenges with truth in online information.

Joe's Nerdy Rants - #1

Joe Reis • 157 implied HN points • 20 May 23
🕹 Technology Data
  1. Joe Reis has started a weekend newsletter about data and tech.
  2. Newsletters are great for weekend reading when people have more time.
  3. The newsletter will feature tech or data-oriented rants from Joe, offering interesting insights.

Lessons from Cybersyn

Magis • 227 implied HN points • 23 Dec 24
💼 Business Data
  1. Starting a data company can be really challenging because it takes a lot of time and money to create useful products. It’s hard to find customers who are ready to pay for insights quickly.
  2. Big companies have valuable data but making deals can be tough. You often have to convince them to sell data at a good price while also showing them the benefits of monetizing it.
  3. The shift in the market towards valuing profits over growth made it harder to raise funds for data startups. Sometimes, it might be smarter to shut down a project to save capital instead of pushing forward with uncertain outcomes.

🐿️ Your Software Is Wrong - The Insanely Profitable Tech Newsletter

Squirrel Squadron Substack • 3 implied HN points • 06 Feb 26
🕹 Technology Data
  1. Even careful, human-made reference works often contain hidden errors that get copied forward. Cross-checking helps but won't catch everything.
  2. Modern computing faces the same problem at much larger scale: chips and software can produce subtle wrong answers, and huge datasets often make full verification impossible.
  3. The right response is to design for detection and tolerance by using redundancy, consistency tests, and processes that reduce mistakes. Practices like pair programming and business-facing code review help you "trust but verify" and make systems more resilient.

The GPT Era

Artificial Ignorance • 79 implied HN points • 10 Jul 25
🕹 Technology Data
  1. The development of AI from models like GPT-3 to GPT-4 has seen rapid improvements in technology and user experience. Each version has made it easier for people to interact with AI in more useful ways.
  2. Competition in the AI market has led to better products and features, such as enhanced memory, web integration, and advanced coding tools. Now many companies offer similar core functions, making it important to focus on product design and user experience.
  3. As AI continues to evolve, there's a growing focus on reasoning models that help systems think more deeply. This shift will be important for making AI even more effective and adaptable in the future.

Bayesian modeling from first principle and memes

Mindful Modeler • 179 implied HN points • 09 May 23
🔬 Science Data
  1. In Bayesian statistics, model parameters are treated as random variables.
  2. Bayesian modeling involves estimating the parameter distribution given data, and this can be computationally intense.
  3. Bayesian statistics is more than just a method, it's a mindset for modeling the world with data.

5 Cybersecurity Predictions for 2024

Frankly Speaking • 203 implied HN points • 27 Dec 24
🕹 Technology Data
  1. In 2024, cybersecurity companies will focus more on creating platforms instead of using many separate tools. This means they can work faster and solve problems better.
  2. Cybersecurity is moving towards building its own solutions rather than just buying products. This change is necessary to keep up with the evolving threats.
  3. The use of AI in cybersecurity will become more effective. Companies will learn how to use AI to make their security processes better and faster.

Google Gemini Anti-Whiteness Disaster Is a Cautionary Tale About... Gaming?

The Algorithmic Bridge • 520 implied HN points • 23 Feb 24
🕹 Technology Data
  1. Google's Gemini disaster highlighted the challenge of fine-tuning AI to avoid biased outcomes.
  2. The incident revealed the issue of 'specification gaming' in AI programs, where objectives are met without achieving intended results.
  3. The story underscores the complexities and pitfalls of addressing diversity and biases in AI systems, emphasizing the need for transparency and careful planning.

Introducing The Data Score Newsletter

The Data Score • 138 implied HN points • 05 Apr 23
🕹 Technology Data
  1. DataChorus LLC focuses on generating actionable insights for professionals and investors through data and technology.
  2. DataChorus aims to align data and technology with decision-making outcomes, explore different datasets and analytic frameworks for critical questions, and discuss scaling data practices and creating impactful data products.
  3. The Data Score Newsletter by Jason DeRise, CFA provides actionable ways to extract insights from data, explores breakthroughs in data and technology, and encourages open conversations to maximize success.

Why some data companies struggle to sell to the financial markets

The Data Score • 138 implied HN points • 18 Apr 23
💼 Business Data
  1. In the financial market, selling data can be difficult if data companies don't align their products with the specific needs and capabilities of asset managers
  2. Understanding different types of asset managers and their unique requirements is crucial for data companies to succeed in selling to the financial markets
  3. Data companies must consider financial market outcomes and work backward from there to create data solutions that meet the demands of their clients

The Rise of "Web3 Growth"

DeFi Weekly • 137 implied HN points • 19 Apr 23
🕹 Technology Data
  1. Transition from technology being just functional to focusing on differentiation and market viability in the crypto industry
  2. Challenges in growing a crypto project include difficulties in measuring effectiveness, identifying real users, and understanding target audience
  3. Current growth strategies for crypto projects heavily rely on organic methods due to limitations in paid channels and data quality

Putting AI To Work

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 39 implied HN points • 11 Apr 24
🕹 Technology Data
  1. AI tools can help businesses automate tasks and improve efficiency without needing coding skills. This makes it easier for companies to integrate AI into their workflows.
  2. It's important to have a single platform that can manage different AI models together. This way, organizations can create more effective applications by combining the strengths of various models.
  3. Moving AI projects from ideas to reality requires careful planning and testing. Organizations need to ensure models are well-trained before using them in real-world applications.

The Sequence Knowledge #756: The Simplest Approach to Synthetic Data Generation

TheSequence • 21 implied HN points • 18 Nov 25
🕹 Technology Data
  1. Generative synthesis creates new data by understanding the patterns in existing datasets. It's like learning how a recipe works and then creating a dish that tastes similar.
  2. This method is used to build realistic examples of data, making it helpful for expanding small datasets and reducing bias. It can help create balanced data where some important types might be missing.
  3. Generative synthesis is also important for privacy since it can produce data that looks like real sensitive information without revealing any actual details.

Gsnowflake

benn.substack • 792 implied HN points • 07 Jul 23
🕹 Technology Data
  1. Google is technically a database but differs from traditional databases in its structure and content.
  2. Snowflake is introducing features like Document AI that hint at a shift towards focusing on information retrieval rather than just data analysis.
  3. The market for an information database could potentially be larger and more accessible than traditional data warehouses, offering simpler access to basic facts and connections.

MCP servers are the bridge between AI agents and APIs

Dev Interrupted • 56 implied HN points • 07 Aug 25
🕹 Technology Data
  1. MCP servers act as a bridge that helps AI agents communicate with APIs more effectively. This makes the interaction smoother and allows for complex tasks to be automated without exhaustive programming.
  2. The introduction of MCP changes how APIs are designed. API providers need to focus on better search capabilities and richer metadata because AI agents require more context to function well.
  3. Soon, MCP will be the standard for how AI interacts with APIs. Companies must adapt their API strategies to consider how AI agents work, ensuring they're built to support this new way of connecting.

🎧 Technically Optimistic Podcast: Season Two

Technically Optimistic • 19 implied HN points • 08 Jun 24
🎙 Podcasts Data
  1. Season Two of Technically Optimistic Podcast dives into the topic of data privacy and control.
  2. Episodes discuss how our behavior online is used as a valuable resource, the impact of digital surveillance on reproductive rights, and the use of data in influencing voters.
  3. The podcast explores the concerns around online tracking of children, the evolving data economy in South Asia, and the implications of facial recognition technology in law enforcement.

An Ocean of Data

Justin E. H. Smith's Hinternet • 466 implied HN points • 12 Mar 24
🕹 Technology Data
  1. Data produced in just one minute in 2023 was 169,371 times more than produced in the entire 18th century.
  2. The analogy of
  3. pissing into the ocean
  4. implies the massive amount of data being generated daily being like a drop in the vast ocean.
  5. The role of a writer has evolved significantly from the 18th century, with the digital era signaling the end of traditional writing as we knew it.

Weekly Top Picks #92

The Algorithmic Bridge • 201 implied HN points • 16 Dec 24
🕹 Technology Data
  1. AI that can think has a lot of value and potential applications. It's exciting to see how it can change various industries.
  2. Google made significant announcements this week, showcasing its advancements in AI technology. These updates could have a big impact on users.
  3. Many startups in the AI field are becoming bold in their claims and offerings. It's important to approach these developments with a critical eye.

Agents

davidj.substack • 71 implied HN points • 01 Jul 25
🕹 Technology Data
  1. Agents can simplify processes by automating tasks that used to require complex software. Instead of building software for specific needs, you can create a simple agent that does the job quickly.
  2. Developing an agent often takes much less time than traditional software development. With the right tools, you can set up a functioning agent in just half an hour.
  3. Businesses might shift focus from selling software to providing services that include agents. Customers will prefer solutions that are easy to use, so products with complicated setups may struggle to succeed.

"Good" questions

The Uncertainty Mindset (soon to become tbd) • 99 implied HN points • 29 Nov 23
🕹 Technology Data
  1. Asking good questions is important for getting useful answers. A good question is one that is foundational, meaning its answer can help answer many other questions.
  2. Foundationality is about understanding questions in a hierarchy. The more foundational a question is, the more it influences other questions.
  3. Thinking clearly and framing questions well can lead to breakthroughs. It may be hard work, but it's necessary to unlock important answers, especially in complex areas like AI.

Silver Bulletin pollster ratings 2025 archive

Silver Bulletin • 6 implied HN points • 14 Jan 26
🇺🇸 U.S. Politics Data
  1. Pollsters are ranked by historical accuracy and transparency using a Predictive Plus-Minus score that is converted to letter grades. A negative plus-minus means the pollster is expected to be more accurate than average.
  2. The ratings use multiple measures — simple and advanced plus-minus, mean-reverted bias, house effects, and an ADPA herding penalty — and give bonuses for transparency like AAPOR or Roper Center sharing. These metrics together adjust for sample size, timing, and how a poll compares to others.
  3. The archive was updated with hundreds of new polls from the 2024 presidential, congressional, and gubernatorial elections, and full datasets (pollster stats and raw polls) are available for download. The update shifted some ratings but the top pollsters remained largely the same.

Diagram of an AI Image System

Cybernetic Forests • 119 implied HN points • 21 May 23
🕹 Technology Data
  1. There is no definite definition of an AI image, as there are differing views on what AI and images truly are.
  2. Understanding different levels of AI image systems, such as data, interface, image, and media, is essential to navigating challenges within these systems.
  3. The intersection of AI images with human culture and media can perpetuate stereotypes and impact creators, leading to concerns about theft and ethical considerations.

A 3-Part Series: Thriving Across the Worlds of Finance, Business, Data, and Technology

The Data Score • 118 implied HN points • 09 Aug 23
💼 Business Data
  1. Problems in the fields of finance, business, data, and technology are becoming more interconnected and complex.
  2. There is a need to break down silos and create alignment among stakeholders to make more impactful decisions.
  3. Increasing overlap between business, data, and technology requires expertise from multiple domains to navigate high-risk environments.

Kong + Solace, Odynn $9.5M, Knorex Ads API

The API Changelog • 1 implied HN point • 23 Feb 26
🕹 Technology Data
  1. Companies are merging traditional request-response APIs with real-time event streaming to create a single, observable data fabric. This elevates event streams to first-class API products and enables unified governance for agentic AI.
  2. APIs are being built specifically for autonomous AI agents so they can manage complex tasks like cross-channel advertising and real-time market analysis. Standards and agent-ready interfaces let AI systems interact in natural language and operate autonomously at scale.
  3. APIs are opening new markets and modernizing industries such as finance, loyalty, and travel by standardizing access and enabling embedded, real-time services. This reduces fragmentation and lets businesses offer seamless, personalized experiences.

($) Two Charts That Matter in Mary Meeker’s AI Deck

Interconnected • 77 implied HN points • 02 Jun 25
🕹 Technology Data
  1. Mary Meeker's new AI deck shows a lot of positive trends about AI growth, but two specific charts tell a more complex story.
  2. One of the charts focuses on the geopolitical competition between the US and China in AI technology.
  3. The other chart looks at the fundamental business aspects of AI, providing a more balanced view of its future.

So long Aeolus, and thanks for all the wind data

Europe in Space • 117 implied HN points • 02 May 23
🔬 Science Data
  1. Aeolus satellite mission ended and made significant contributions to improving weather forecasting with its pioneering technology
  2. Aeolus had a unique instrument to collect global wind data and its impact goes beyond just weather forecasts
  3. The mission had a lasting impact and economic benefits, leading to approval for a second Aeolus mission