The hottest Data science Substack posts right now

And their main takeaways
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Top Technology Topics

why current ai integrations are so offensively stupid

Brain Pizza • 529 implied HN points • 04 Aug 25
🕹 Technology Data science
  1. Current AI systems are often frustrating because they don't cater to people who need deep understanding and detailed information. They lack the nuance and complexity that many users seek.
  2. These AI tools seem to overlook the thought processes of users, resulting in simplistic and sometimes nonsensical interactions. They're not designed to engage with complex ideas.
  3. The shortcomings of present AI integrations reveal a lot about the current state of artificial general intelligence. It shows that we still have a long way to go before achieving true intelligence in machines.

Another one

benn.substack • 1534 implied HN points • 31 Jan 25
🕹 Technology Data science
  1. DeepSeek's rapid impact shows that new AI models can quickly disrupt industries. It proves that creating advanced AI is no longer just for big companies with lots of resources.
  2. Consumers want more than just better technology; they want a range of AI tools that can do different tasks and integrate with their daily lives. People are looking for a single place to access various AI models.
  3. The rise of many unique AI models means we don't know how they will change our world. Just as social media transformed society in unexpected ways, AI could lead to surprising new possibilities and challenges.

Issue #10 - The Data Lifecycle

The Data Ecosystem • 159 implied HN points • 16 Jun 24
🕹 Technology Data science
  1. The data lifecycle includes all the steps from when data is created until it is no longer needed. This helps organizations understand how to manage and use their data effectively.
  2. Different people and companies might describe the data lifecycle in slightly different ways, which can be confusing. It's important to have a clear understanding of what each term means in context.
  3. Properly managing data involves stages like storage, analysis, and even disposal or archiving. This ensures data remains useful and complies with regulations.

Why reasoning models will generalize

Democratizing Automation • 1535 implied HN points • 28 Jan 25
🕹 Technology Data science
  1. Reasoning models are designed to break down complex problems into smaller steps, helping them solve tasks more accurately, especially in coding and math. This approach makes it easier for the models to manage difficult questions.
  2. As reasoning models develop, they show promise in various areas beyond their initial focus, including creative tasks and safety-related situations. This flexibility allows them to perform better in a wider range of applications.
  3. Future reasoning models will likely not be perfect for every task but will improve over time. Users may pay more for models that deliver better performance, making them more valuable in many sectors.

AI #93: Happy Tuesday

Don't Worry About the Vase • 1971 implied HN points • 04 Dec 24
🕹 Technology Data science
  1. Language models can be really useful in everyday tasks. They can help with things like writing, translating, and making charts easily.
  2. There are serious concerns about AI safety and misuse. It's important to understand and mitigate risks when using powerful AI tools.
  3. AI technology might change the job landscape, but it's also essential to consider how it can enhance human capabilities instead of just replacing jobs.
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Data Science Weekly - Issue 550

Data Science Weekly Newsletter • 179 implied HN points • 07 Jun 24
🕹 Technology Data science
  1. Curiosity in data science is important. It's essential to critically assess the quality and reliability of the data and models we use, especially when making claims about complex issues like COVID-19.
  2. New fields, like neural systems understanding, are blending different disciplines to explore complex questions. This approach can help unravel how understanding works in both humans and machines.
  3. Understanding AI advancements requires keeping track of evolving resources. It’s helpful to have a well-organized guide to the latest in AI learning resources as the field grows rapidly.

Issue #11 - Dispelling the AI Hype Train

The Data Ecosystem • 139 implied HN points • 23 Jun 24
🕹 Technology Data science
  1. AI needs a proper plan and strategy to work well. Companies shouldn't think they can just jump in without understanding how it will fit into their overall goals and data.
  2. Many AI projects fail because organizations overlook the importance of data quality and proper infrastructure. Good data practices are essential for AI to be effective.
  3. It's important to get everyone in the company on board with AI. This means training employees and creating a culture that embraces the technology, rather than fearing it.

What Are Virtual Cells?

The Century of Biology • 644 implied HN points • 29 Jun 25
🔬 Science Data science
  1. AI is changing biology by making it easier to model things like proteins and cells. Instead of trying to write down every detail, researchers can use data to train models that can predict how cells behave.
  2. The concept of 'Virtual Cells' is about building computer models that can simulate how real cells function. This can help scientists understand complex biological processes and test experiments without needing a lab.
  3. Using AI to learn from large amounts of biological data could lead to breakthroughs in medicine and biology, allowing researchers to predict outcomes and design better experiments more efficiently.

Data Science Weekly - Issue 555

Data Science Weekly Newsletter • 99 implied HN points • 11 Jul 24
🕹 Technology Data science
  1. Large language models can sometimes create false or confusing information, a problem known as hallucination. Understanding the cause of these mistakes can help improve their accuracy.
  2. Good data visualizations are important to effectively communicate patterns and insights. Poorly designed visuals can lead to misunderstandings, especially among those not familiar with graphics.
  3. There's an ongoing debate about copyright in the context of generative AI. Many believe it would be better to focus on finding compromises rather than pursuing strict legal battles.

Data Science Weekly - Issue 551

Data Science Weekly Newsletter • 159 implied HN points • 13 Jun 24
🕹 Technology Data science
  1. Data Science Weekly shares curated articles and resources related to Data Science, AI, and Machine Learning each week. It's a helpful way to stay updated in the field.
  2. There are various interesting projects mentioned, such as the exploration of Bayesian education and improving code completion for languages like Rust. These projects can help in learning and improving skills.
  3. Free passes to an upcoming AI conference in Las Vegas are available, offering a chance to network and learn from industry leaders. It's a great opportunity for anyone interested in AI.

Data Science Weekly - Issue 552

Data Science Weekly Newsletter • 139 implied HN points • 20 Jun 24
🕹 Technology Data science
  1. Notebooks can be easy to use, but they might make you lazy in coding. It's important to follow good practices even when using them.
  2. When handling large datasets, it's crucial to learn how to scale effectively. Knowing how to use resources wisely can help you reach your goals faster.
  3. Retrieval Augmented Generation (RAG) can improve how models generate information. It's complex, but understanding it can boost the performance of your projects.

WeKnow-RAG

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 39 implied HN points • 16 Aug 24
🕹 Technology Data science
  1. WeKnow-RAG uses a smart approach to gather information that mixes simple facts from its knowledge base with data found on the web. This helps improve the accuracy of answers given to users.
  2. This system includes a self-check feature, which allows it to assess how confident it is in the information it provides. This helps to reduce mistakes and improve quality.
  3. Knowledge Graphs are important because they organize information in a clear way, allowing the system to find the right data quickly and effectively, no matter what type of question is asked.

The Bitter Pipeline

Abstraction • 39 implied HN points • 28 Jan 26
🕹 Technology Data science
  1. Frontier models scale better than human-designed forecasting pipelines, so the structured process that helped smaller models often adds no value with larger models.
  2. Empirical tests show spending compute on polling and ensembling big models improves forecast skill more than token-heavy steps like classification or decomposition, with ensembling giving measurable uplift while the pipeline did not.
  3. The practical move is to simplify: ensemble aggressively, validate empirically, and keep experimenting with ways to elicit latent model knowledge instead of adding complex hand-crafted processes.

Creating Synthetic Training Data

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 59 implied HN points • 01 Aug 24
🕹 Technology Data science
  1. Creating synthetic data is hard because it's not just about making more data; it also needs to be diverse and varied. It's tough to make sure there are enough different examples.
  2. Using a seed corpus can limit how varied the synthetic data is. If the starting data isn't diverse, the generated data won't be either.
  3. A new approach called Persona Hub uses a billion different personas to create varied synthetic data. This helps in generating high-quality, interesting content across various situations.

Is this a career?

benn.substack • 1713 implied HN points • 13 Dec 24
💼 Business Data science
  1. Getting good at something often just takes a little focused effort over time. Many people don't actively try to improve, so they stay at a decent skill level rather than reaching their full potential.
  2. In fields like data analytics, it's essential to specialize to truly excel. Being a generalist might keep you busy, but it can lead to a career without a clear direction or growth.
  3. To stand out and achieve more in their careers, people need to identify a specific area of expertise and commit to it. Relying on being 'good at data' isn't usually enough to make a significant impact.

Let’s Bring Jupyter Notebooks to Substack

The Palindrome • 6 implied HN points • 05 Mar 26
🕹 Technology Data science
  1. NotebookPress converts Jupyter Notebooks into Substack-ready posts with just a couple of clicks, so you don’t have to manually reformat content for publishing.
  2. It preserves math, code, and outputs by rendering LaTeX and syntax-highlighted code images and embedding figures. Code execution happens in the browser via Pyodide and styling (fonts, themes, colors) is configurable.
  3. The product is in beta with a roadmap toward paid features like built-in LLM editing help and direct publishing automations, and the creator is seeking feedback and bug reports.

Issue #9 - Clarifying Data Terminology

The Data Ecosystem • 159 implied HN points • 09 Jun 24
🕹 Technology Data science
  1. Data can mean many things, from raw collections to curated evidence used in decisions. It's important to define what data means in each situation to avoid confusion.
  2. Poorly defined data terms can lead to problems in data literacy, collection, and management. This can create issues for organizations trying to use data effectively.
  3. Understanding different categories of data, like data types and processing stages, helps in managing and analyzing data better. Knowing these categories makes it easier to communicate and use data in an organization.

✨ Begun, the US vs. China AI race has

Faster, Please! • 1462 implied HN points • 27 Jan 25
🕹 Technology Data science
  1. The AI race between the US and China is heating up, with China's DeepSeek making significant advancements. This situation is causing a lot of nervousness in the stock market.
  2. DeepSeek's new AI model is impressive because it can learn effectively with less hardware investment than previously thought. This could change how companies and investors view AI development costs.
  3. Some experts believe DeepSeek's achievements may signal a big shift in the AI field, showing that the competitive landscape is more unpredictable than it seemed before.

Data Science Weekly - Issue 556

Data Science Weekly Newsletter • 79 implied HN points • 18 Jul 24
🕹 Technology Data science
  1. AI research in China is progressing rapidly, but it hasn't received much attention compared to developments in the US. There are many complexities in understanding the implications of this advancement.
  2. There are new methods to improve large language models (LLMs) using production data, which can enhance their performance over time. A structured approach to analyzing data quality can lead to better outcomes.
  3. Evaluating modern machine learning models can be challenging, leading to some questionable research practices. It's important to understand these issues to ensure more accurate and reproducible results.

💥 Tech Talks Weekly #28

Tech Talks Weekly • 59 implied HN points • 22 Aug 24
🕹 Technology Data science
  1. There are lots of new tech talks available from various conferences, making it easier to stay updated with the latest in technology.
  2. You can help shape future content by filling out a quick feedback form, which takes less than 30 seconds.
  3. Tech Talks Weekly offers a free subscription to help reduce the clutter of tech talk content and keep readers informed.

What is an AI recipe?

Sunday Letters • 19 implied HN points • 01 Sep 24
🕹 Technology Data science
  1. An AI recipe is a mix of code and AI thinking that helps solve problems. It's not just code or just prompts; it's a combination that guides the AI to achieve a goal.
  2. Finding the right balance between structured code and flexible AI is tricky. This balance can feel similar to figuring out what makes a cake a cake.
  3. As AI improves, the aim is to make these recipes work better and help connect human ideas directly to machine actions.

Anyone Else Struggling to Keep Up With Data Tools

SeattleDataGuy’s Newsletter • 447 implied HN points • 31 Jul 25
🕹 Technology Data science
  1. Focus on mastering just a couple of technologies each year instead of trying to learn everything at once. It’s better to really understand a few tools well than to have a surface-level knowledge of many.
  2. Start with the basics that won’t go away, like SQL and core principles of data management. New tools can come and go, but some fundamentals will always be important.
  3. Build side projects or engage in real work opportunities to apply what you've learned. Practical experience is one of the best ways to deepen your understanding of data tools.

The Sequence Knowledge #792: EVERYTHING you Need to Know About Synthetic Data Generation

TheSequence • 49 implied HN points • 20 Jan 26
🕹 Technology Data science
  1. Synthetic data is a practical scaling lever that fills coverage gaps and builds long-tail capabilities by creating targeted examples instead of waiting for rare real-world labels.
  2. Core methods include generative synthesis, rephrasing/paraphrasing, multi-turn dialogue synthesis, and RL trajectory generation, each tailored to different tasks like images, instructions, conversations, or environment rollouts.
  3. The focus is on quality over quantity: tight specs, automatic verification, diversity controls, and eval-driven feedback let teams steer capabilities, improve class balance, protect privacy, and iterate quickly.

NeurIPS 2025 Best Papers in Comics

Gonzo ML • 126 implied HN points • 01 Dec 25
🔬 Science Data science
  1. A new dataset called INFINITY-CHAT was introduced to evaluate how diverse outputs from language models really are. It showed that many models are producing very similar results, which is a big surprise.
  2. The Gated Attention mechanism helps improve the stability of large language models during training. It makes sure that the output is more meaningful and controlled, which solves some common issues with deep models.
  3. Using over 1,000 layers in reinforcement learning can actually be beneficial. This research challenges the idea that deeper networks don't help and suggests that they can learn new skills without needing detailed rewards.

AI #105: Hey There Alexa

Don't Worry About the Vase • 1120 implied HN points • 27 Feb 25
🕹 Technology Data science
  1. A new version of Alexa, called Alexa+, is coming soon. It will be much smarter and can help with more tasks than before.
  2. AI tools can help improve coding and other work tasks, giving users more productivity but not always guaranteeing quality.
  3. There's a lot of excitement about how AI is changing jobs and tasks, but it also raises concerns about safety and job replacement.

Data Science Weekly - Issue 549

Data Science Weekly Newsletter • 159 implied HN points • 31 May 24
🕹 Technology Data science
  1. Mediocre machine learning can be very risky for businesses, as it may lead to significant financial losses. Companies need to ensure their ML products are reliable and efficient.
  2. Understanding logistic regression can be made easier by using predicted probabilities. This approach helps in clearly presenting data analysis results, especially to those who may not be familiar with technical terms.
  3. Data quality management is becoming essential in today's data-driven world. It's important to keep track of how data is tested and monitored to maintain trust and accuracy in business decisions.

AI Is A Car That Everyone Expects To Be A Spaceship

Beekey’s Substack • 59 implied HN points • 24 Jul 24
🕹 Technology Data science
  1. AI has made great improvements, especially with tasks that involve generating human-like responses and art. However, many people are getting carried away with the hype about its capabilities.
  2. Machine learning allows AI to recognize patterns in data, but it doesn't actually understand content like a human does. This means it can make mistakes that a human wouldn't.
  3. The idea of creating Artificial General Intelligence (AGI) from current AI is questionable because we still don't fully understand how human intelligence works. It's not just about being faster; something fundamental is still missing.

Data Science Weekly - Issue 553

Data Science Weekly Newsletter • 99 implied HN points • 27 Jun 24
🕹 Technology Data science
  1. Data visualization can show important patterns, like changes in night and daylight globally. Understanding these trends helps us appreciate our environment better.
  2. In AI engineering, simplifying data preparation is crucial. Many new AI applications can be built without structured data, which might lead to rushed expectations about their effectiveness.
  3. Aquaculture technology is evolving with better methods to track and analyze fish behavior. New approaches like deep learning are making monitoring more accurate and efficient.

The rise of reasoning machines

Democratizing Automation • 570 implied HN points • 12 Jun 25
🕹 Technology Data science
  1. Reasoning is when we draw conclusions based on what we observe. Humans experience reasoning differently than AI, but both lack a full understanding of their own processes.
  2. AI models are improving but still struggle with complex problems. Just because they sometimes fail doesn't mean they can't reason; they just might need new methods to tackle tougher challenges.
  3. The debate on whether AI can truly reason often stems from fear of losing human uniqueness. Some critics focus on what AI can't do instead of recognizing its potential, which is growing rapidly.

Data Science Weekly - Issue 547

Data Science Weekly Newsletter • 179 implied HN points • 17 May 24
🕹 Technology Data science
  1. Learning Rust programming can be made easy with exercises designed for beginners, even if you know another language already. You’ll work through small tasks to build confidence.
  2. Data scientists need to learn how to work with databases to scale their analytics. Many face challenges when transitioning to this part of their work.
  3. There are helpful tools, like Data Wrangler for VS Code, that simplify data cleaning and analysis. These tools help generate code automatically as you work with your data.

Some ideas for what comes next

Democratizing Automation • 529 implied HN points • 23 Jun 25
🕹 Technology Data science
  1. OpenAI's new model, o3, is really good at finding information quickly, like a determined search dog. It's unique compared to other models, and many are curious if others will match its capabilities soon.
  2. AI agents, like Claude Code, are improving quickly and can solve complex tasks. They have made many small changes that boost their performance, which is exciting for users.
  3. The trend in AI models is slowing down in terms of size but improving in efficiency. Instead of just making bigger models, companies are focusing on optimizing what they already have.

Data Science Weekly - Issue 541

Data Science Weekly Newsletter • 279 implied HN points • 05 Apr 24
🕹 Technology Data science
  1. AI agents have unique challenges that traditional laws may not effectively solve. New rules and systems are needed to ensure they are managed properly.
  2. JS-Torch is a new JavaScript library that makes deep learning easier for developers familiar with PyTorch. It allows building and training neural networks directly in the browser.
  3. Data acquisition is crucial for AI start-ups to succeed. There are strategies outlined to help these businesses gather the right data efficiently.

Implementing Logic Programming

Burning the Midnight Coffee • 578 implied HN points • 13 Jun 25
🕹 Technology Data science
  1. Logic programming, unlike other programming styles, focuses on relationships and rules instead of just functions. This can make it better for solving complex problems.
  2. Prolog is a popular language in logic programming, allowing users to define facts and rules. This helps in querying relationships rather easily.
  3. Datalog is a simpler subset of Prolog that’s good for modeling relationships, and it's suggested that it could be more suitable for database work than SQL.

AGI Is Already Here—It’s Just Not Evenly Distributed

The Algorithmic Bridge • 1104 implied HN points • 05 Feb 25
🕹 Technology Data science
  1. Understanding how to create good prompts is really important. If you learn to ask questions better, you'll get much better answers from AI.
  2. Even though AI models are getting better, good prompting skills are becoming more important. It's like having a smart friend; you need to know how to ask the right questions to get the best help.
  3. The better your prompting skills, the more you'll be able to take advantage of AI. It's not just about the AI's capabilities but also about how you interact with it.

Data Science Weekly - Issue 543

Data Science Weekly Newsletter • 219 implied HN points • 19 Apr 24
🕹 Technology Data science
  1. Statistical ideas have a big impact on the world. Learning about important papers can help us understand how statistics shape modern research and decision-making.
  2. Machine Learning teams have different roles that face unique challenges. Understanding these personas can help leaders support their teams better.
  3. Using vector embeddings can greatly improve search experiences in apps. They simplify processes that previously seemed too complex and highlight their usefulness in technology.

Don't "fix" your imbalanced data

Mindful Modeler • 818 implied HN points • 05 Sep 23
🕹 Technology Data science
  1. Avoid trying to fix imbalanced data through sampling methods like oversampling or undersampling. It can distort your model's calibration and reduce information for the majority class.
  2. SMOTE, a common method for imbalanced data, works well only with weak classifiers, not strong ones. It may not be suitable if calibration is crucial for your model.
  3. Consider doing nothing when faced with imbalanced data as a default strategy. Sometimes in machine learning, less is more.

to kaggle, or not to kaggle

Mindful Modeler • 379 implied HN points • 13 Feb 24
🕹 Technology Data science
  1. There are conflicting views on Kaggle - some see it as a playground while others believe it produces top machine learning results.
  2. Participating in Kaggle competitions can be beneficial to learn core supervised machine learning concepts.
  3. The decision to focus on Kaggle competitions should depend on how much daily tasks align with Kaggle-style work.

How to get from evaluation to final model

Mindful Modeler • 279 implied HN points • 19 Mar 24
🕹 Technology Data science
  1. When moving from model evaluation to the final model, there are various approaches with trade-offs.
  2. Options include using all data for training the final model with best hyperparameters, deploying an ensemble of models, or a lazy approach of choosing one from cross-validation.
  3. Each approach like inside-out, parameter donation, or ensemble has its pros and cons, highlighting the complexity of transitioning from evaluation to the final model.

Data Science Weekly - Issue 548

Data Science Weekly Newsletter • 139 implied HN points • 24 May 24
🕹 Technology Data science
  1. Good communication is key for statisticians to explain their complex work to non-experts. Finding ways to relate data to everyday situations can make it easier for others to understand.
  2. Using histograms can speed up the training process for gradient boosted machines in data science. This simple technique can improve efficiency significantly.
  3. There are efforts to use machine learning algorithms to detect type 1 diabetes in children earlier. This can help avoid serious health issues by improving recognition of symptoms.