The hottest Technology Substack posts right now

And their main takeaways
Category
Top Technology Topics

Driving changes: how to deal with pushbacks

Teamwork in Tech • 0 implied HN points • 25 May 23
🕹 Technology Engineering Innovation Management Documentation Systems
  1. Pushbacks on initiatives can happen due to various reasons like disbelief in promised goals, dissatisfaction with implemented solutions, or disagreement on the importance of the problem.
  2. To prevent pushback, ensure clear communication, involve stakeholders in defining and solving problems, and provide supporting evidence and documentation.
  3. When facing pushback, focus on understanding the perspectives of those involved, address concerns proactively, and work collaboratively to find solutions that benefit everyone.

Newsletter #20: PDFTraige

Decoding Coding • 0 implied HN points • 08 Nov 23
🕹 Technology AI NLP Software Data science Machine Learning
  1. PDFTriage helps AI understand the structure of documents, like research papers. By using this structure, it can give better answers to specific questions about the document.
  2. It has three stages: first, it creates a detailed structure of the document; next, it queries data based on this structure; and finally, it answers user questions using the gathered information.
  3. This approach shows how thinking about how humans write and organize information can improve how AI systems work. It allows the AI to pull relevant details effectively.
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Redis-driven Dataflow for Clickstream Aggregation

Bytewax • 0 implied HN points • 19 Oct 23
🕹 Technology Data processing Python Containers
  1. Bytewax framework strikes a balance between being user-friendly without hiding underlying mechanisms.
  2. When writing custom connectors with Bytewax, focus on transforming messages in the `next_batch` method and delegate other processing to the dataflow.
  3. Consider the partitioned nature of inputs and utilize `list_parts` and `build_part` methods for handling multiple data streams in Bytewax.

Data Science Weekly - Issue 210

Data Science Weekly Newsletter • 0 implied HN points • 30 Nov 17
🕹 Technology Data science Machine Learning Artificial Intelligence Automation Computer Vision
  1. Computer vision is making big strides, and it's important to keep track of these changes as they can impact society in various ways.
  2. The idea of an 'intelligence explosion' is challenged, suggesting that it's a misunderstanding of how intelligent systems and self-improving technologies function.
  3. Recent studies indicate that many comments about net neutrality may have been faked, highlighting issues with data integrity and trust in public opinions.

Newsletter #15: ViperGPT

Decoding Coding • 0 implied HN points • 15 Jun 23
🕹 Technology Artificial Intelligence Machine Learning Software Development Computer Vision Programming
  1. ViperGPT is a new AI model that can answer questions about images and videos. It combines powerful text and vision models to understand visual inputs better.
  2. The model generates Python code based on user questions, allowing it to be flexible and efficient. It uses all available online Python code for improvement.
  3. ViperGPT's execution engine runs the generated code and provides results based on the visual content. This helps users make sense of raw data in a more meaningful way.

Data at Depth Newsletter 10: Winning Some, Losing Some, GPT 4 Chart Image Analysis

Data at Depth • 0 implied HN points • 04 Apr 24
🕹 Technology Data Analysis Content creation
  1. The author discusses their own creator journey, including a slowdown in Substack subscribers and some boosts on Medium.
  2. The author shares insights on their recent creative activities, such as creating 8 new articles and receiving 3 boosts on Medium.
  3. There is mention of a Python/AI tutorial that the author has been working on.

Newsletter #4: Probabilities with Python

Decoding Coding • 0 implied HN points • 02 Mar 23
🕹 Technology Programming Data science Machine Learning Statistics Software Tools
  1. NumPy is a powerful tool for working with probability distributions in Python. You can easily generate data and calculate probabilities using its features.
  2. Common probability distributions like Normal, Binomial, and Poisson can be modeled using NumPy. Each distribution has its own formula to calculate probabilities.
  3. De Morgan's Laws help in calculating probabilities of complements in events. They show how to relate the union and intersection of events, which can be useful in probability theory.

Newsletter #5: Backprop from scratch

Decoding Coding • 0 implied HN points • 09 Mar 23
🕹 Technology Artificial Intelligence Machine Learning Programming Data science Neural Networks
  1. Derivatives show how small changes in inputs affect the output of a function. This is important for understanding how neural networks adjust to improve their predictions.
  2. In neural networks, understanding how changes in weights and inputs influence the output helps us optimize performance. By adjusting weights based on calculated gradients, we can make the network learn better.
  3. The chain rule is key when calculating how different layers of a neural network affect the final output. It allows us to connect changes in inputs through to the overall output, helping us to fine-tune the model.

Hidden Benefits of SLOs

Certo Modo • 0 implied HN points • 27 Feb 23
🕹 Technology Tech
  1. Service Level Objectives (SLOs) reveal important customer experience metrics beyond just uptime, such as latency and error rate.
  2. Developing SLOs fosters cross-functional collaboration within an organization, breaking down silos and promoting a unified approach to reliability.
  3. Implementing SLOs can lead to investments in improved observability, enhancing infrastructure management and monitoring for long-term operational benefits.

December Newsletter

RSS DS+AI Section • 0 implied HN points • 01 Dec 25
🕹 Technology AI Data science Research Ethics Applications
  1. Data science and AI are constantly evolving, with new technologies and tools emerging regularly. Keeping up with these changes is important for anyone interested in the field.
  2. Ethics in AI is a major topic right now. It's essential to discuss bias, regulation, and the moral implications of using AI in our lives.
  3. There are many opportunities to get involved in data science communities, whether through volunteering or participating in discussions. Joining these groups can help shape the future of data science.

[in case you missed it] Data Science Weekly - Issue 433

Data Science Weekly Newsletter • 0 implied HN points • 13 Mar 22
🕹 Technology Artificial Intelligence Machine Learning Data science Programming Robotics
  1. Deep learning is facing challenges and needs more progress to improve its effectiveness. Experts are looking at what can be done to advance AI technology.
  2. MLOps, or machine learning operations, is currently chaotic but it’s an important area of growth. The ecosystem is rapidly evolving with new tools and practices appearing every week.
  3. There are new techniques and tools emerging to help in areas like data visualization and machine learning. These developments can make it easier for both beginners and experts in the field.

How They Built The Top Health AI: Flo Health

The Healthtech Initiative • 0 implied HN points • 02 Mar 26
🕹 Technology Artificial Intelligence Machine Learning Health tech Startups Mobile Apps Data science
  1. Small, autonomous teams that own their entire stack unlocked velocity and scale, while splitting functions (like mobile and backend) slowed delivery.
  2. Only use AI when it truly outperforms simple rules—reserve models for cycle prediction, symptom analysis, personalization, and fine-tune on women’s health data to reduce bias and improve safety.
  3. Build the core competitive advantage (the health AI and data flywheel) and buy everything else, using wearable time-series models to proactively predict conditions and power growth.

Coming soon

Good Computer • 0 implied HN points • 17 Mar 24
🕹 Technology News
  1. There is a post coming soon on goodcomputer.substack.com
  2. The post was shared by Dennis Wilson on March 17, 2024
  3. Readers are encouraged to subscribe to Good Computer

Testing a mobile app using a device you don’t have

Cybervelia • 0 implied HN points • 17 May 23
🕹 Technology Mobile Apps Reverse Engineering
  1. Testing an IoT app without checking its BLE communication can be a missed opportunity.
  2. Reverse engineering an app involves dissecting core components like device name, service discovery, and behavioral analysis.
  3. Understanding how to create a fake device, connect it to the app, and analyze exchanged data can reveal valuable insights in app testing.

[Help Page] Joining the subscriber chat

Decoding Coding • 0 implied HN points • 21 Mar 23
🕹 Technology Apps Social media Communication Digital Tools User Experience
  1. There's a special chat space just for subscribers, kind of like a group chat. You can share thoughts and updates with others.
  2. To join the chat, you need to download the Substack app which works on both iOS and Android. Don't forget to turn on notifications so you can stay updated.
  3. Once you have the app, just click on the chat icon to get started. Say hi and join the conversation!

Learning eBPF: Setting Local Box

DevCube • 0 implied HN points • 09 May 23
🕹 Technology Programming
  1. eBPF is an old technology gaining traction for building distributed systems.
  2. It promises to be a good tool with huge potential for challenges in system building.
  3. Consider subscribing to learn more and support the author's work.

Newsletter #6: Prompt Engineering

Decoding Coding • 0 implied HN points • 23 Mar 23
🕹 Technology Artificial Intelligence Natural Language Software Development Programming Data processing
  1. When using language models, the way you ask or prompt them affects the answers you get. More context often leads to better responses.
  2. You can use specific prompts to generate summaries, create text in different styles, or even test your ideas by simulating expert responses.
  3. Language models can greatly assist in coding tasks by generating templates and examples quickly, but it's important to double-check the versions of any libraries they suggest.

Python Data Visualization Mastery: Selecting the Right Tool for Powerful Storytelling

Data at Depth • 0 implied HN points • 02 May 23
🕹 Technology Data Visualization
  1. In the era of big data, it's crucial to present complex information clearly and engagingly.
  2. Choosing the right data visualization techniques is key to telling a compelling story that captivates your audience.
  3. Consider using stacked area charts to visualize data, such as the top-5 CO2 emitting countries globally, to create impactful visual narratives.

Boosting Data Visualization Productivity With ChatGPT, Python & Streamlit

Data at Depth • 0 implied HN points • 03 May 23
🕹 Technology Data Visualization Python Productivity
  1. Using ChatGPT and Python with Streamlit can help beginners create data visualization applications easily.
  2. Even without experience, individuals can leverage ChatGPT to generate Python code for creating maps and charts.
  3. Consider trying the 7-day free trial to access more content on boosting data visualization productivity with ChatGPT, Python, and Streamlit.

Rapid Python Data Visualizations with ChatGPT: A Step-By-Step Guide

Data at Depth • 0 implied HN points • 30 May 23
🕹 Technology Data Visualization Python
  1. ChatGPT can simplify and speed up the process of creating Python data visualizations through prompting.
  2. The author, a Computer Science professor with over 20 years of experience, highlights the benefits of leveraging ChatGPT for data visualization.
  3. Readers can access the full post archives and a 7-day free trial by subscribing to Data at Depth.

Newsletter #11: System Design for Machine Learning - Part I

Decoding Coding • 0 implied HN points • 04 May 23
🕹 Technology Machine Learning System Design Data science Software Engineering Artificial Intelligence
  1. Before starting on a machine learning project, it's important to define clear goals and understand how ML can help achieve them.
  2. Setting up a data pipeline is crucial; it involves collecting, preparing, and analyzing data to see what features are useful for your model.
  3. When deploying machine learning models, you need to consider both hardware and software needs, including how to handle real-time data for ongoing training.

Newsletter #14: Adding Memory to LLMs

Decoding Coding • 0 implied HN points • 01 Jun 23
🕹 Technology AI Machine Learning Data science Neural Networks Innovation
  1. LLMs can forget information when they get too big, which makes their performance worse. Adding an internal memory can help them remember better and adapt to new tasks.
  2. The new framework, Decision Transformers with Memory (DT-Mem), uses a special memory module to identify and store important information effectively. This helps the model improve its decision-making.
  3. By using techniques like content-based addressing, DT-Mem can selectively add or erase information in its memory, making it smarter and more efficient in handling tasks.