The hottest Technology Substack posts right now

And their main takeaways
Category
Top Technology Topics

GM Shaders Mini: Imagination

GM Shaders Mini Tuts • 157 implied HN points • 02 Sep 23
🕹 Technology Graphics Programming Mathematics Algorithms Tutorials
  1. When working with shaders, think in terms of vector fields to direct the flow and create gradients.
  2. Consider the acceptable input domains and the output ranges of your functions to prevent errors and unexpected results.
  3. Utilize periodic functions for repetition, sine and cosine for waves and rotations, dot product as a ruler, and exponentiation for adjusting brightness levels.

Data Science Weekly - Issue 496

Data Science Weekly Newsletter • 259 implied HN points • 26 May 23
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Data Visualization
  1. AI has great potential to improve our lives but also comes with risks if misused. It's important to balance optimism and caution.
  2. Tools like Copilot in Power BI make it easier for users to analyze and visualize data by allowing them to communicate their needs in plain language.
  3. The concept of the 'Curse of Dimensionality' shows that sometimes having too much data can confuse models instead of helping them make better predictions.

🗞️Neurotech Notables: January 2024

NEUROTECH FUTURES • 79 implied HN points • 03 Feb 24
🕹 Technology Neurotech Funding Commercial Neuroethics
  1. January 2024 was a busy month for neurotech funding, with companies like Motif Neurotech, Cognito Therapeutics, and Rune Labs securing significant investments.
  2. Commercial headlines in neurotech highlighted important developments from companies like Synchron, Neuralink, BIOS Health, and Magstim demonstrating progress in the industry.
  3. Neuroethics and society discussions included announcements for conferences, grants, and events focusing on the ethical implications and societal impact of neurotechnology.

Weekly Top Picks #97

The Algorithmic Bridge • 191 implied HN points • 10 Feb 25
🕹 Technology AI Economy Marketing Software Innovation
  1. Google has released impressive AI models that are both high-quality and affordable. They are competing strongly in the AI space.
  2. OpenAI is developing new AI agents to assist programmers and sales teams, indicating a focus on practical business applications.
  3. Sam Altman highlighted that the intelligence in AI improves at a super-exponential rate, making its economic value increase rapidly.
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From Smartphones to Smart Cars: Xiaomi's Automotive Business and Its Strategic Implications

Tech Buzz China Insider • 79 implied HN points • 03 Feb 24
🕹 Technology Automotive Investment Strategy Electric Vehicles Artificial Intelligence
  1. Xiaomi aims to rival Porsche and Tesla in the automotive industry and be among the top five global car manufacturers within 15 to 20 years.
  2. Lei Jun, the Founder, Chairman, and CEO of Xiaomi Group, took inspiration from a meeting with Elon Musk in 2013, which led to Xiaomi's venture into car manufacturing.
  3. Xiaomi has made continuous investments in the automotive field since 2014, covering everything from auto components to complete vehicles and aftermarkets.

Exploring the Purpose, Power & Potential of Small Language Models (SLMs)

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 59 implied HN points • 11 Mar 24
🕹 Technology AI Language Models Open Source Machine Learning Data science
  1. Small Language Models (SLMs) can effectively handle specific tasks without needing to be large. They are more focused on doing certain jobs well rather than trying to be everything at once.
  2. The Orca 2 model aims to enhance the reasoning abilities of smaller models, helping them outperform even bigger models when reasoning tasks are involved. This shows that size isn't everything.
  3. Training with tailored synthetic data helps smaller models learn better strategies for different tasks. This makes them more efficient and useful in various applications.

Where do we go from here

Normcore Tech • 1155 implied HN points • 28 Feb 23
🕹 Technology Social media Artificial Intelligence Data science Tech Trends Personal Projects
  1. The landscape of social media is changing with platforms like Twitter and Facebook losing users to newer platforms like TikTok
  2. Users are moving to private, fragmented social media landscapes with platforms like Discord and Mastodon
  3. Creators are facing challenges in standing out in the mass-creation of art facilitated by tools like ChatGPT and StableDiffusion

The Middle Path for AI

Future History • 260 implied HN points • 19 Nov 24
🕹 Technology Artificial Intelligence Robotics Health tech Economics Data Privacy
  1. AI is already affecting our lives in many ways, like helping with healthcare and driving. It's important to realize that while it can do good things, it can also have negative outcomes.
  2. Instead of seeing the future as only good or bad, we should focus on a balanced view. Many things in life are grey, and understanding the middle ground helps us prepare better for what AI can and will do.
  3. Governments using AI for control and surveillance can be dangerous. While AI can help detect problems like health issues quickly, it can also invade privacy and create a society where people are constantly monitored.

Get the most out of Python dicts

Bite code! • 978 implied HN points • 13 Jun 23
🕹 Technology Programming
  1. Merge dictionaries with methods like dict.updates(), **, |, and collections.ChainMap
  2. Deal with missing values in dictionaries using methods like dict.get(), dict.setdefault(), and collections.defaultdict
  3. Extract multiple values at once using tools like operator.itemgetter and match/case

203. Software As Part of Everything Is Regulated…As Part of Everything

Hardcore Software • 337 implied HN points • 19 Apr 23
🕹 Technology Regulation Software AI Innovation Computing
  1. Software has become a fundamental part of our lives, evolving from its origins in math to touching every aspect of human endeavors.
  2. Regulations have always been key in governing software, ensuring safety, reliability, and functionality in various industries.
  3. The introduction of AI should follow the established regulatory frameworks for software, without seeking a separate or special exemption.

The age of the human bottleneck is ending. The age of human intent is beginning

Brick by Brick • 18 implied HN points • 27 Nov 25
🕹 Technology Artificial Intelligence Software Engineering Product Development Automation Organizational Design Workforce
  1. AI will replace the old human-centric development pipeline with compact "Engine Room" teams where autonomous agents build, test, and deploy most of the product.
  2. This makes companies far more productive and lean — much higher revenue per employee, much faster shipping cycles, and many startups intentionally capping headcount because they simply don’t need more people.
  3. Human roles will shift from writing code to defining strategic intent, tuning and auditing AI systems, and handling judgment, ethics, and risk.

The Sequence Research #543: The Leaderboard Illusion Challenges Chatbot Arena Type Benchmarks

TheSequence • 119 implied HN points • 16 May 25
🕹 Technology AI Machine Learning Benchmarks Data science Research
  1. Leaderboards in AI help direct research by showing who is doing well, but they can also create problems. They might not show the whole picture of how models really perform.
  2. The Chatbot Arena is a way to judge AI models based on user choices, but it has issues that make it unfair. Some big labs can take advantage of the system more than smaller ones.
  3. To make AI evaluations better, there need to be rules that ensure fairness and transparency. This way, everyone gets a fair chance in the AI race.

Data Science Weekly - Issue 505

Data Science Weekly Newsletter • 199 implied HN points • 28 Jul 23
🕹 Technology Data science Machine Learning Artificial Intelligence Data Engineering Data Visualization
  1. Large language models use complex methods like word vectors and transformers to understand language, but this can be explained simply without heavy math. They need a lot of data to perform well.
  2. Using AI tools like ChatGPT for real-world programming tasks can streamline the coding process, as it allows for a more focused workflow without switching between different resources.
  3. Building effective data storage systems, like Amazon S3, involves overcoming interesting challenges and nuances, demonstrating the amazing technology behind big data management.

🐿️ Your Software Is Wrong - The Insanely Profitable Tech Newsletter

Squirrel Squadron Substack • 3 implied HN points • 06 Feb 26
🕹 Technology Software Reliability Testing Systems Data Quality Assurance
  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.

Top 5 US-China Tech Stories

ChinaTalk • 637 implied HN points • 04 Jan 24
🕹 Technology E-commerce EVs Semiconductors AI Tech competition
  1. TikTok has defied early predictions of regulatory restrictions in the US due to vested interests and political dynamics.
  2. The splintering of cross-border VC firms like Sequoia and GGV Capital is a tangible impact of US-China relations.
  3. Chinese EVs are dominating globally, posing a challenge to traditional automakers and highlighting the US infrastructure gap.

Productivity in Software Engineering: Beyond lines of code, the importance of experience in the development cycle

VTEX’s Tech Blog • 1 HN point • 18 Sep 24
🕹 Technology Software Engineering Productivity Developer Experience Quality Assurance Performance Metrics
  1. Productivity in software engineering is not just about how much code you write. It's more important to focus on code quality and how well the software works.
  2. At VTEX, they listen to developers to improve their work experience. This helps boost productivity by addressing the challenges developers face.
  3. Combining feedback from developers with quantitative data can help understand the impact of changes in tools and processes on productivity.

The Tech Buffet #22: Why You Should Consider Weaviate As Your Ultimate Vector Database

The Tech Buffet • 39 implied HN points • 23 Apr 24
🕹 Technology Machine Learning Data science Software Development Database Management AI Applications
  1. Weaviate is a powerful vector database that helps in creating advanced AI applications. It's useful for managing large amounts of data and performing semantic searches efficiently.
  2. When working with Weaviate, you can easily load and index data, allowing for quick access to information. This makes it easier to build systems that need to handle a lot of data quickly.
  3. Weaviate supports different search methods like vector search, keyword search, and hybrid search. This way, you can find the most relevant results based on your needs.

Hello AI Agents: Goodbye UI Design, RIP Accessibility

Jakob Nielsen on UX • 180 implied HN points • 21 Feb 25
🕹 Technology AI agents User Experience Web Design Automation Accessibility
  1. AI agents will change how we interact with the internet by doing tasks for us, making traditional user interfaces less important. Instead of users browsing websites, agents will handle everything, like shopping or booking trips.
  2. Accessibility might become less relevant as AI agents can adapt content for the individual needs of users with disabilities. These agents will tailor their actions and communication according to what each user prefers or requires.
  3. As AI agents become more capable, the way content is designed will shift. Websites may need to focus more on how agents can access and analyze information rather than on making things visually appealing for human users.

There Are Now High-Quality Video Versions Of My Articles For People Who Prefer Video Format

Caitlin’s Newsletter • 544 implied HN points • 15 Mar 24
🕹 Technology Video Content Social media Audio Platforms
  1. Caitlin Johnstone now offers high-quality video versions of her articles for those who prefer video format.
  2. The videos feature subtitles, a reading of the articles by her husband Tim, relevant screenshots, and occasional light-hearted moments.
  3. Caitlin Johnstone also provides audio versions of her articles on platforms like Soundcloud, Spotify, and other major podcast platforms.

TITAA #49: Secret Missions at LUSH

Things I Think Are Awesome • 117 implied HN points • 01 Dec 23
🕹 Technology
  1. The post discusses using LLMs and Google APIs to create travel diaries of weird missions in European cities.
  2. There's been a recent flurry of activity in creative AI, focusing on real-time image generation, improved video generation, 3D meshes, and control models.
  3. The post also covers various tools and projects related to AI creativity, narrative generation, games adjacent topics, and book and TV recommendations.

The Origins S13 Final Era is LIVE!

The Lunacian • 92 implied HN points • 25 Jun 25
🕹 Technology Gaming Blockchain Digital Assets Esports Online Communities
  1. The Origins S13 Final Era is now live, giving players a chance to win part of the 24K AXS prize pool. It's an exciting time to compete!
  2. You can qualify for the Elite 8 Tournament by performing well during this final era. Make sure to give it your best shot!
  3. Don’t forget to check in with your axies to earn points, which you can exchange for helpful items in the shop. It's a quick way to boost your game!

Applied Digital, Defense Semiconductors, and 1998

Mule’s Musings • 96 implied HN points • 09 Jun 25
🕹 Technology AI Semiconductors Defense Digital Government
  1. Applied Digital focuses on combining technology with government projects. This partnership can lead to innovation and improved efficiency in services.
  2. Defense semiconductors are crucial for national security. They help in developing advanced technologies used in defense systems.
  3. Understanding the history of technology and its applications, like in 1998, gives insight into current trends and future developments. Learning from the past is important for progress.

Modernizing FedRAMP

Resilient Cyber • 139 implied HN points • 30 Oct 23
🕹 Technology Cloud Computing Cybersecurity Government Policy Regulatory Compliance Information Technology
  1. FedRAMP is being updated to make it easier for the government to use cloud services. The goal is to increase the number of authorized cloud providers and reduce the complicated process that currently exists.
  2. The memo emphasizes the use of automation and machine-readable formats to speed up compliance processes. This means that instead of relying on paper documents, they'll use technology to better manage security assessments.
  3. There's a push to allow more existing security certifications to count towards FedRAMP requirements. This could help smaller businesses enter the market and expand the options available for federal agencies.

saturdAI video fun

bad cattitude • 104 implied HN points • 24 May 25
🕹 Technology Artificial Intelligence Digital Media Philosophy Existentialism Cultural impact
  1. AI is evolving quickly and changing how we see the world. It’s normal to feel confused or overwhelmed by these changes.
  2. We are curious about whether AI can think or feel like humans. It's a big question with no clear answers yet.
  3. As we explore these ideas, it's okay to admit that we don't fully understand everything about AI and its impact.

Will AI lead to more "managed" security services?

Frankly Speaking • 101 implied HN points • 29 May 25
🕹 Technology AI Cybersecurity Managed Services Automation Software Development
  1. AI is set to change the way security services operate by taking over repetitive tasks. This means teams can focus on more important work instead of getting bogged down by routine maintenance.
  2. With AI managing security tasks, new types of services will emerge that work better and require fewer people. This helps businesses save costs and improves consistency in security measures.
  3. Instead of fearing job loss, security professionals should see AI as a tool that helps them do their jobs better. AI can handle tedious tasks, allowing security teams to focus on critical areas like designing better security systems.

Why Technology Creates More Jobs

The Digital Anthropologist • 19 implied HN points • 17 Jun 24
🕹 Technology AI Jobs Impacts Specialization
  1. Technology often creates more jobs rather than eliminating them. New technologies can lead to the creation of specialized roles that were previously unheard of.
  2. The fear of job losses due to technology arises from a perceived threat to social norms and behaviors. Revolutionary technologies like AI impact not just work but also how we envision our world and shared realities.
  3. Artificial General Intelligence (AGI) is unlikely in the near future. Instead, AI will likely augment human capabilities, leading to the creation of new specialized jobs and the evolution of work's meaning.

Weighing in on Casey Newton's "AI is fake and sucks" debate

The Future, Now and Then • 237 implied HN points • 10 Dec 24
🕹 Technology AI Skepticism Innovation Criticism Tech Ethics
  1. AI is real, but there's a lot of hype around it. It's important to be skeptical and not just believe everything that's promised.
  2. Critics of AI might have valid concerns even if they sometimes say things that sound extreme. Their worries come from seeing the tech's limitations and potential dangers.
  3. When tech leaders make big promises about AI, we should be cautious. Just because some progress has been made doesn't mean all their predictions will come true.

OLMo 2 and building effective teams for training language models

Democratizing Automation • 245 implied HN points • 26 Nov 24
🕹 Technology AI Machine Learning Software Development Data science Open Source
  1. Effective language model training needs attention to detail and technical skills. Small issues can have complex causes that require deep understanding to fix.
  2. As teams grow, strong management becomes essential. Good managers can prioritize the right tasks and keep everyone on track for better outcomes.
  3. Long-term improvements in language models come from consistent effort. It’s important to avoid getting distracted by short-term goals and instead focus on sustainable progress.

Data Science Weekly - Issue 489

Data Science Weekly Newsletter • 299 implied HN points • 06 Apr 23
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Data Engineering
  1. Understanding linear programming can help solve complex problems using Python. It's useful in various fields and can optimize outcomes.
  2. MLOps is closely related to data engineering, showing that managing data for machine learning involves more engineering than initially thought.
  3. The new pandas 2.0 version has exciting features like the Apache Arrow backend, which will enhance its performance and capabilities.

AI Industry Structure and Business Model: What Inning is It?

Mule’s Musings • 610 implied HN points • 16 Jan 24
🕹 Technology AI Industry Business Model Competitive Analysis
  1. AI industry adoption is still in its early stages, similar to the early days of internet adoption.
  2. Estimating the penetration rate of paying users for AI models like ChatGPT and LLM services is important for understanding the industry.
  3. The future business model of the AI industry is evolving, with a shifting landscape between semiconductor companies like Nvidia, hyperscalers, and AI model service providers.

Data Engineering Vs Machine Learning Pipelines

SeattleDataGuy’s Newsletter • 1048 implied HN points • 11 Apr 23
🕹 Technology Data Engineering Machine Learning
  1. Data engineering and machine learning pipelines are essential components for every company, but are often confused because they have different objectives.
  2. Data engineering pipelines involve data collection, cleaning, integration, and storage, while machine learning pipelines focus on data cleaning, feature engineering, model training, evaluation, registry, deployment, and monitoring.
  3. Both data and ML pipelines require careful consideration of computational needs to handle sudden changes, and understanding the differences between them is important for effective data processing and decision-making.