The hottest Technology Substack posts right now

And their main takeaways
Category
Top Technology Topics

AT&T Wireless Service Disruption Affects Customers Due to 'Software Issue'

Vigilainte Newsletter • 0 implied HN points • 28 Aug 24
🕹 Technology Telecommunications Software Development Customer Service Cybersecurity
  1. AT&T is facing a major service disruption due to a software issue, causing many customers to lose their ability to make calls or use data.
  2. People are frustrated with the lack of communication from AT&T's support, which has been overwhelmed and unable to provide clear solutions.
  3. This outage is especially bad timing for AT&T, as they just got fined by the FCC for not notifying 911 about a previous outage.

Important Vulnerabilities

Vigilainte Newsletter • 0 implied HN points • 22 Aug 24
🕹 Technology Cybersecurity Vulnerabilities Software Updates Security risks
  1. There's a serious security flaw in the GiveWP WordPress plugin that lets hackers run harmful code. Updating to the latest version can fix this problem.
  2. FastAdmin has a vulnerability that can expose sensitive files due to bad handling of inputs. Upgrading to the new version is crucial to protect your information.
  3. Kubernetes Ingress-NGINX is at risk from a bug that could give attackers control of the system. Make sure to update to the latest version to keep your cluster safe.

We could play this game all damn day

Links I Would Gchat You If We Were Friends • 0 implied HN points • 04 Nov 14
🕹 Technology Internet of Things Apps Crowdsourcing Artificial Intelligence
  1. The future could involve constant connectivity to everything around us, not just the Internet.
  2. Tinder, a popular hook-up app, faced turmoil when its CEO was fired unexpectedly.
  3. Crowdsourcing and surge pricing intersected when a woman crowdfunded her expensive Uber ride, turning her into an internet sensation.

Cybersecurity Insights: July Bulletin

Vigilainte Newsletter • 0 implied HN points • 08 Aug 24
🕹 Technology Cybersecurity Cloud Computing Data Protection
  1. DDoS attacks are getting stronger, as shown by a major one that took down Microsoft's Azure cloud. This means companies need better protections to keep their services running.
  2. Many companies are facing vulnerabilities, like a default password issue from Acronis that attackers can exploit. It's really important for everyone to manage their passwords securely.
  3. Cybercriminals are using sophisticated methods like fake ads and Generative AI to spread malware and steal data. We all need to be careful when clicking online and keep our software updated.

Apps Are Dead; They Just Don’t Know It Yet

FREST Substack • 0 implied HN points • 10 Mar 26
🕹 Technology Software AI Data Platforms Mobile APIs UX
  1. Apps as isolated containers are becoming unmanageable because AI makes building software cheap, so organizing your digital life around thousands of separate apps won’t scale.
  2. The app model arose from economic moats like hard distribution and costly infrastructure, and those moats are eroding as infrastructure is commoditised and AI lowers development costs.
  3. The future is fluid computation over shared data, where AI lets you manipulate any data across tools and interfaces without being locked into individual apps.
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The Why of Security Red Teams

Locks and Leaks • 0 implied HN points • 29 Jun 23
🕹 Technology Security Red-Teaming Vulnerabilities Budgeting
  1. Red Teaming is essential for organizations with high-value assets, significant threats, or discovered vulnerabilities to test and strengthen their security measures proactively.
  2. Red Teams assess threat actors tactics, uncover vulnerabilities, address organizational hubris, challenge security assumptions, and protect business and assets through rigorous testing.
  3. Red Teaming is not just a tool but a philosophy that promotes critical thinking to improve security measures, ensure defense readiness, and make informed decisions to safeguard organizations and valuable resources.

Can AI works get copyright protection? (Redux)

Joshua Gans' Newsletter • 0 implied HN points • 07 Sep 23
🕹 Technology AI Copyright Digital Art AI Tools
  1. Copyright protection for AI-generated works is a complex issue that raises questions about authorship, ownership, and the role of AI in the creative process.
  2. The distinction between human creativity and AI technology is blurred in digital works like music, photography, and writing, where AI tools play a significant role.
  3. Determining authorship of AI-generated works involves considering the fine line between human input in guiding AI creations and the independent creative ability of machines.

Begun the Copyright Wars have

Joshua Gans' Newsletter • 0 implied HN points • 09 Jul 23
🕹 Technology Copyright AI Chatbots Data Privacy
  1. Comedian Sarah Silverman and others have filed a class action suit against OpenAI and Meta for alleged copyright infringement related to their works being used in training datasets for AI models like ChatGPT and LLaMA.
  2. This particular case is one of the first instances of copyright disputes emerging about written work involving AI technology.
  3. Despite attempts to prompt the AI model, ChatGPT did not directly reproduce content from the copyrighted books, leading to questions about how these AI systems were trained and what information they have access to.

That Fake AI Generated Photo and the Stock Market

Joshua Gans' Newsletter • 0 implied HN points • 23 May 23
🕹 Technology AI Misinformation Social media Verification Market Reaction
  1. Fake AI-generated images caused a brief dip in the stock market, but the market quickly recovered, showing resilience to misinformation.
  2. The incident highlighted the importance of verifying information before reacting, leading to a discussion on the role of trusted sources in combating misinformation.
  3. The removal of artisanal verification on Twitter raised concerns about the impact on reliable information sources, emphasizing the need for trusted signals in a fast-paced digital world.

Agentic RAG With LlamaIndex

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 31 Jan 24
🕹 Technology AI Software Data Systems Automation
  1. Agentic RAG combines agents with retrieval-augmented generation for better search and response. This means that these agents help find and summarize information more effectively.
  2. Each document gets its own agent that works with the main agent. This setup makes it easier to manage a lot of documents and ensures relevant information is retrieved quickly.
  3. The system uses tools to answer user queries based on document content, which helps provide accurate and useful responses.

[in case you missed it] Data Science Weekly - Issue 239

Data Science Weekly Newsletter • 0 implied HN points • 23 Jun 18
🕹 Technology AI Machine Learning Data science Robotics Analytics
  1. AI can argue like a human but it doesn't really understand what it's saying. This raises questions about the limits of AI in communication.
  2. Researchers are working hard to make algorithms fair to avoid biases in machine learning. This is important as technology becomes more involved in our lives.
  3. Experts are discussing how AI and robotics can change healthcare, pointing to a future where technology plays a big role in medicine.

[in case you missed it] Data Science Weekly - Issue 244

Data Science Weekly Newsletter • 0 implied HN points • 28 Jul 18
🕹 Technology Data science Machine Learning AI Analytics Software Development
  1. Companies need to define data science roles clearly, focusing on three areas: Analytics, Inference, and Algorithms. This helps businesses meet their specific needs effectively.
  2. Google's AutoML grabs attention for simplifying machine learning tasks, but understanding concepts like transfer learning is essential to grasp its true potential.
  3. Multi-task learning allows machines to learn multiple tasks at once, making them smarter and better at complex challenges, similar to how humans learn.

How worried should we be about AI misinformation?

Joshua Gans' Newsletter • 0 implied HN points • 14 Apr 23
🕹 Technology AI Misinformation Deception Ethics
  1. AI-generated misinformation may not have a significant impact because when examined closely, the inaccuracies become apparent and unlikely to change beliefs.
  2. While AI tools could flood us with misinformation, it might not necessarily deceive people or lead to major consequences, just confusion about what to believe.
  3. There's concern that AI could be used to create more convincing misinformation, potentially leading to deception and damage, but so far, the evidence for such sophisticated manipulation is lacking.

[in case you missed it] Data Science Weekly - Issue 253

Data Science Weekly Newsletter • 0 implied HN points • 29 Sep 18
🕹 Technology AI Data science Software Development Machine Learning Statistics
  1. Uber uses machine learning and deep learning to make better forecasts for their products and services. They focus on combining traditional statistical methods with advanced techniques for accurate predictions.
  2. There's a shift in software development where deep learning is automating much of the coding process. Developers now create a basic outline, allowing the computer to generate the code from past examples.
  3. Tiny computers are increasingly replacing larger controllers in technology. This trend highlights the importance of smaller, more efficient computing solutions in the embedded world.

LLM Alignment, Hallucination & Misinformation

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 03 Nov 23
🕹 Technology AI Data science Machine Learning Natural Language Software Development
  1. It's important to have good data design and human supervision for large language models. This helps improve accuracy and creates better conversations.
  2. Large language models can produce different answers to the same question at different times. This means they are not always consistent.
  3. Misinformation and hallucinations can happen with these models, but we can reduce these issues by using better training and feedback methods.

[in case you missed it] Data Science Weekly - Issue 260

Data Science Weekly Newsletter • 0 implied HN points • 16 Nov 18
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Analytics
  1. There are many resources available for learning machine learning, so it's helpful to gather them in one place for quick access.
  2. Lyft has developed tools to handle seasonal market changes, which could help predict when driver incentives are needed.
  3. Getting a data science job can be tough, but reflecting on the journey can show how previous challenges helped lead to success.

[in case you missed it] Data Science Weekly - Issue 266

Data Science Weekly Newsletter • 0 implied HN points • 30 Dec 18
🕹 Technology Data science Artificial Intelligence Machine Learning Deep Learning Neural Networks
  1. Netflix's internal debates show the clash between creative teams and data-driven decisions. Finding a balance between creativity and data analysis is important for success.
  2. Teaching AI to write stories can be funny but also highlights the challenges of using technology for creative tasks. It takes a lot of work to make machines understand human language.
  3. Data is never completely 'raw' and always involves some human judgment. Recognizing this helps us understand how data is shaped and used in decision-making.

[in case you missed it] Data Science Weekly - Issue 269

Data Science Weekly Newsletter • 0 implied HN points • 20 Jan 19
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Education
  1. Neural networks can be hard to understand. Researchers are trying to figure out what these models actually learn during training.
  2. Reinforcement learning is helping robots, like a robot dog, learn to move more like real animals without specific instructions.
  3. Using tools like Flask, you can quickly set up an API for machine learning models. This makes it easier to send data and get predictions.

There are two systems. Which one are you working on?

Bad Software Advice • 0 implied HN points • 25 Mar 26
🕹 Technology Software Engineering Product Management Systems Design AI Tools DevOps Engineering Culture
  1. You work on more than just the technical code — the system includes users, support, competitors, and the market, and missing that context can make your work irrelevant, wrongly specified, or badly prioritized.
  2. AI is lowering the cost of development, so developers are shifting from hand-coding everything to managing tools and judging agent outputs, which requires higher-level skills beyond writing code.
  3. Spend time learning the greater system and move up the stack; understanding users, support, and market forces helps you build the right thing and make better tradeoffs.

A First Look At OpenAI GPT-4

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 20 Mar 23
🕹 Technology Artificial Intelligence Machine Learning Natural Language Software Development Innovation
  1. GPT-4 is a step up from GPT-3.5, but the difference is mostly noticeable with complex tasks. For simple chat, you might not see much change.
  2. Currently, GPT-4 can't process images, but there's hope for that feature in the future. It'll be announced if it becomes available.
  3. One cool feature of GPT-4 is its ability to handle longer texts, over 25,000 words. This is great for detailed conversations or long content creation.

Count me OUT of all that

Links I Would Gchat You If We Were Friends • 0 implied HN points • 16 Mar 15
🕹 Technology Travel Social media Apps Internet Smartphones
  1. TripAdvisor has a significant impact on travel decisions; it can make or break hotels, restaurants, and tourist stops.
  2. Twitter bots are considered as artificial creatures, prompting discussions about human intelligence in social contexts.
  3. There is concern about a future lived solely through on-demand mobile apps, causing some to resist this trend.

NLP/NLU, Chatbots, Conversational UI/UX, Developer, Ubiquitous User Interfaces

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 01 Jun 21
🕹 Technology AI NLP Chatbots UI/UX Development
  1. NLP and NLU help machines understand human language better. This makes chatbots and voicebots more effective in conversations.
  2. Conversational UI/UX focuses on making user interactions with technology feel natural and engaging. Good design improves user satisfaction.
  3. Developers play a key role in building these technologies. Their skills help create seamless and intuitive interfaces for users.

[in case you missed it] Data Science Weekly - Issue 295

Data Science Weekly Newsletter • 0 implied HN points • 20 Jul 19
🕹 Technology AI Machine Learning Data science NLP Education
  1. Netflix is moving away from collaborative filtering for recommendations, focusing on more effective strategies that drive revenue.
  2. Machine learning can play a big role in tackling climate change, helping us find solutions to one of our biggest challenges.
  3. There is a growing demand for data scientists to know a variety of tools like Python, R, and SQL, so it's important to keep learning and improving your skills.

[in case you missed it] Data Science Weekly - Issue 297

Data Science Weekly Newsletter • 0 implied HN points • 03 Aug 19
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Webinars
  1. Data science teams need clear models to integrate within organizations for better collaboration and results.
  2. Using AI and machine learning can help us understand ancient games, revealing how they have changed over time.
  3. Exploring technical approaches to explainable AI is crucial as model interpretability becomes more important in the field.

[in case you missed it] Data Science Weekly - Issue 301

Data Science Weekly Newsletter • 0 implied HN points • 01 Sep 19
🕹 Technology Data science Artificial Intelligence Machine Learning Software Development Research
  1. Effective management of data science teams requires specific skills and knowledge. Leaders should know how to build and sustain their teams well.
  2. Research takes time and effort, as shown by the history of neural networks. It's important to have patience and persistence in this field.
  3. Estimating the time for software projects can be difficult. This is often due to the unpredictable nature of problem-solving involved in the work.

[in case you missed it] Data Science Weekly - Issue 302

Data Science Weekly Newsletter • 0 implied HN points • 07 Sep 19
🕹 Technology AI Data science Machine Learning Deep Learning Automation
  1. Yann LeCun is a key figure in deep learning, known for his work on convolutional neural networks, which help machines learn from data.
  2. Data scientists are in high demand, and understanding their salaries is important for those interested in entering the field.
  3. Deep learning techniques can swiftly perform tasks like face recognition, outperforming human experts in speed and accuracy.