The hottest Technology Substack posts right now

And their main takeaways
Category
Top Technology Topics

Data Science Weekly - Issue 355

Data Science Weekly Newsletter • 19 implied HN points • 10 Sep 20
🕹 Technology Data science Machine Learning Artificial Intelligence Big Data Computing
  1. DeepMind and Google Maps are using advanced Graph Neural Networks to improve the accuracy of travel time predictions, making them even more reliable in cities around the world.
  2. AI is now being used to detect deepfake videos by identifying unique signals from the videos, which can help spot how they were made.
  3. There are resources available to help people get started in data science, build their portfolios, and improve their resumes to land jobs in this field.

Gradient Flow: ML in Finance, Disinformation, AI Superpowers

Gradient Flow • 19 implied HN points • 04 Jun 20
🕹 Technology Machine Learning Finance AI Remote work Data Governance
  1. Collaboration between lawyers and technologists is crucial for identifying and mitigating risks associated with AI deployment in various industries.
  2. Responsible ML tools from Microsoft focus on explainability, privacy & security, and governance & reproducibility, providing comprehensive support for ethical AI development.
  3. China and the US are considered AI superpowers, with strong research interest in Data and AI, along with vibrant startup ecosystems focused on applying these technologies.

#35

The Nibble • 7 implied HN points • 26 Nov 23
🕹 Technology AI Chips Models
  1. Facebook expressed involved in their AI chips business.
  2. OpenAI released ChatGPT with voice available for all free users.
  3. Bill Gates suggests AI advancement may lead to a 3-day work week.
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Some thoughts on the C Standard

John Breaks Stuff • 1 implied HN point • 06 Jun 25
🕹 Technology Programming Software Standards Development Computing
  1. The C programming language has some odd rules, especially about how it handles errors. For example, signed overflow is undefined behavior, meaning anything can happen if there's an error, while unsigned wraparound is defined and predictable.
  2. Different ways to represent numbers exist in C, but now most compilers only use two's complement. This can cause problems, like when dividing by negative numbers, but these issues will go away if we return to using one's complement.
  3. The C standards committee is responsible for maintaining the C language, and they're trying to modernize it. This includes creating official websites and using platforms like GitHub, which could change how the community interacts with the standard.

Smarter Agents, Self-Aware LLMs, and Knowledge from Videos

HackerPulse Dispatch • 2 implied HN points • 24 Jan 25
🕹 Technology AI Machine Learning Data science Video Processing Software Engineering
  1. New techniques can shrink the size of data storage without losing accuracy, which helps in finding information faster.
  2. Language models are getting better at learning from their own mistakes, making them smarter and more self-aware.
  3. AI can now learn complex skills just by watching videos, which shows that reading text isn't always necessary for advanced learning.

Roundup: Google Gemini, "Service-as-a-Software" and A New Era for SaaS Margins

Good Better Best • 2 implied HN points • 17 Jan 25
🕹 Technology AI SaaS Product Pricing Innovation
  1. Google has bundled its Gemini AI with Workspace plans, making it cheaper for users but risking lower profits. This strategy may help them gain more customers quickly.
  2. Human support will be essential for using AI effectively. Even with AI tools, we still need humans to refine the results and handle complex tasks.
  3. Different companies are adopting various pricing models for their AI services. Google's approach focuses on getting users to adopt their technology, while Microsoft is looking to charge more based on usage.

OpenAPI Is Not Enough

The API Changelog • 1 implied HN point • 06 Jun 25
🕹 Technology APIs Software Programming Development Standards
  1. OpenAPI is a useful tool for defining REST APIs but has limitations that can affect complex API development. It may not always help in generating high-quality code for sophisticated APIs.
  2. Alternatives to OpenAPI, like TypeSpec from Microsoft and Smithy from Amazon, are gaining attention for their ability to better define APIs and improve developer experience. They offer features like better syntax support and the ability to generate various API assets.
  3. There is a need for OpenAPI to address its limitations as more companies explore different API definition languages. This could enhance interoperability and standardization in API development.

Data Science Weekly - Issue 354

Data Science Weekly Newsletter • 19 implied HN points • 03 Sep 20
🕹 Technology Artificial Intelligence Machine Learning Data science Programming Software Development
  1. A machine learning algorithm recently helped discover 50 new planets from old NASA data, showing how AI can unlock new discoveries.
  2. There has been a noticeable drop in deep learning job postings in the past six months, revealing that many companies are reassessing the importance of this technology.
  3. Apple has introduced a residency program for AI and machine learning, offering training and hands-on experience for those with relevant backgrounds.

Episode 9: Burkhard on Qt Embedded Systems

burkhardstubert • 19 implied HN points • 31 Aug 20
🕹 Technology Software Development Embedded Systems Programming Intellectual Property
  1. CppDepend did not meet expectations for finding dependency cycles in code, as it only detected a small number and struggled with parsing, suggesting the need for better tools in C++ dependency analysis.
  2. Understanding and leveraging usage rights in software development is crucial. Keeping some usage rights can allow developers to create reusable parts and explores pricing options for clients.
  3. There are valuable strategies to prevent bugs in software, focusing on clear requirements, effective architecture, and implementing unit tests through Test-Driven Development (TDD) for improving code quality.

Data Science Weekly - Issue 353

Data Science Weekly Newsletter • 19 implied HN points • 27 Aug 20
🕹 Technology Machine Learning Data science AI Development Software Engineering User Experience
  1. Effective testing is crucial for machine learning systems. It's important to understand that these systems require different testing strategies compared to traditional software.
  2. There are hidden challenges in becoming a machine learning engineer. Many of these insights come from the experiences of those already in the field, beyond what you learn in books.
  3. New resources and courses are constantly being developed in data science. For example, fast.ai just released a new deep learning course and libraries, which can help beginners get started.

Data Science Weekly - Issue 352

Data Science Weekly Newsletter • 19 implied HN points • 20 Aug 20
🕹 Technology Data science Machine Learning Artificial Intelligence Algorithms Computing
  1. minGPT is a smaller version of the GPT model that aims to be simple and easy to understand. It’s only about 300 lines of code, which makes it a good resource for learning.
  2. Biased training data, like the CoNLL-2003 dataset, can lead AI models to perform poorly on diverse names and future data. This can cause ongoing issues with how these models recognize different groups.
  3. Reinforcement learning has challenges in real-world applications due to assumptions that often don't hold up. Researchers need to address these challenges to make RL more practical and effective.

Coforge + Nylas, Blues $25M, IPinfo Core

The API Changelog • 1 implied HN point • 03 Jun 25
🕹 Technology AI APIs Software Integration Funding
  1. Mistral AI has launched an Agents API that helps automate complex tasks for businesses. This can make workflows smoother by letting different AI agents work together.
  2. Coforge and Nylas have teamed up to improve communication in Salesforce with new APIs. This will help companies manage their messaging and scheduling more effectively.
  3. Blues, a company specializing in IoT solutions, raised $25 million to grow and innovate. They aim to turn regular products into smart devices with better connectivity.

Data Science Weekly - Issue 351

Data Science Weekly Newsletter • 19 implied HN points • 13 Aug 20
🕹 Technology Data science Machine Learning AI Computer Vision Analytics
  1. Machine learning models need regular maintenance after deployment. It's important to monitor data and model behavior to avoid problems and improve performance.
  2. Collaboration and good understanding of problems are key in AI development. This helps teams create better applications and make profits.
  3. New tools and resources are becoming available for data science, like access to research papers on Kaggle. These can help improve machine learning techniques and open up new possibilities.

Deep Learning Platform, TinyML, Privacy ↔ Contact Tracing

Gradient Flow • 19 implied HN points • 07 May 20
🕹 Technology Deep Learning Machine Learning COVID-19 Virtual Conferences Work
  1. Deep learning models are being implemented in tiny devices with tools like TinyML for ultra-low-power systems.
  2. Distributed training for deep learning models is made simpler and cheaper with libraries like RaySGD.
  3. Technology like facial recognition for contact tracing can also raise concerns about privacy and mass surveillance.

Special #1 - SCM

Infra Weekly Newsletter • 9 implied HN points • 08 Jul 23
🕹 Technology Software Development Version Control Community support Performance Features
  1. Source Code Management (SCM) has evolved over the years, from centralized to distributed systems like Git and Mercurial.
  2. Mercurial is known for its simplicity, ease of use, and better management of mono repositories compared to Git.
  3. Git offers benefits like widespread adoption, community support, flexibility in workflows, and better performance in certain areas.

#15: Maybe You Should Invest in Translation

Loeber on Substack • 6 HN points • 19 Jan 24
🕹 Technology AI Translation Internet Labor Market Investing
  1. Translation is the next big thing in AI with significant global impact
  2. Advancements in speech-to-text, text-to-speech, and style transfer technologies are converging to revolutionize language translation
  3. Ubiquitous translation will greatly increase global connectivity, impact labor markets, and present investment opportunities in software, hardware, and geographic levels

Data Science Weekly - Issue 350

Data Science Weekly Newsletter • 19 implied HN points • 06 Aug 20
🕹 Technology AI Data science Machine Learning NLP
  1. Language models like GPT-3 can do amazing things, such as creating human-like text and writing code, but there's still curiosity about their ability to make analogies.
  2. Data science is increasingly being applied to many fields, like health through biomedical NLP or analyzing complex problems with graph technologies.
  3. As companies build their data tools, there’s a trend toward developing unique solutions tailored to their specific needs, highlighting the importance of data discovery.

Making Peace with LLM Non-determinism

The Finest Tuners • 5 HN points • 07 Apr 24
🕹 Technology Machine Learning Artificial Intelligence Natural Language Software Engineering Hardware design
  1. Non-determinism in language models can be frustrating because you can't always expect the same output each time you input the same prompt. This unpredictability often stems from the way language itself works.
  2. You can reduce some of this unpredictability by using techniques like seeding and selecting better models. These methods help control how outputs are generated and make them more consistent.
  3. Understanding that language is inherently complex can help you see the random outputs as part of the model's nature, not just flaws. Embracing this chaos can lead to surprising and interesting results.

Người Việt Nam chọn mật khẩu như thế nào?

Thái | Hacker | Kỹ sư tin tặc • 39 implied HN points • 01 May 18
🕹 Technology Data security Data Analysis
  1. Many Vietnamese people use easily crackable encryption algorithms for their passwords, making them vulnerable to security breaches.
  2. Analyzing common passwords can help individuals understand which types of passwords are weak and encourage them to choose stronger ones.
  3. Interesting statistics show unique password choices of Vietnamese users, revealing preferences related to food and self-perception.

Episode 8: Burkhard on Qt Embedded Systems

burkhardstubert • 19 implied HN points • 31 Jul 20
🕹 Technology Embedded Systems Software Design Programming Linux
  1. Updating software can cause unexpected issues, as minor upgrades may change how the system works. It's important to check compatibility to avoid big problems.
  2. Good software design means creating deep modules with simple interfaces, making the software easier to understand and extend in the future.
  3. In testing and coding, having clear boundaries and small, focused modules help reduce complexity and make the code more manageable.

Databricks’ AI Strategy in a Nutshell

Enterprise AI Trends • 4 HN points • 25 Jun 24
🕹 Technology AI Data Governance Enterprise Cloud
  1. Databricks is growing in enterprise AI by focusing on data and AI governance with its Unity Catalog. This tool helps businesses manage how they use and share data and AI apps.
  2. Data governance is a big challenge for companies using AI. Without proper management, there can be serious security issues, especially with sensitive customer data.
  3. Unity Catalog makes it easier for Databricks to sell other services. Once companies start using it, they find it helps with many areas, leading to more business opportunities for Databricks.

Inside Meta’s Chaos, StackOverflow on Its Last Legs, & the Makefile Effect

HackerPulse Dispatch • 2 implied HN points • 14 Jan 25
🕹 Technology Software Development Tech Trends Corporate culture AI Impact Programming Languages
  1. StackOverflow is facing a big decline, with questions down over 70% since 2023. Many users are frustrated with the moderation and are turning to AI tools for support instead.
  2. Electron has been popular for building desktop apps, but it has some issues like heavy memory use. New frameworks like Tauri are coming up as better alternatives.
  3. The 'Makefile effect' shows that engineers often copy and adapt existing setups instead of creating new ones due to tools being too complex. This highlights the need for better tool design to make things easier.