The hottest AI Substack posts right now

And their main takeaways
Category
Top Technology Topics

Import AI 352: Asteroids and AI policy; privacy-preserving AI benchmarks; and distributed inference

Import AI • 159 implied HN points • 11 Dec 23
🕹 Technology AI
  1. Preparing for potential asteroid impacts requires coordination, strategic planning, and societal engagement.
  2. Distributed systems like LinguaLinked challenge traditional AI infrastructure assumptions, enabling local governance of AI models.
  3. Privacy-preserving benchmarks like Hashmarks allow for secure evaluation of sensitive AI capabilities without revealing specific information.

Data at Depth 11: Diversity, Adversity, Case Study With GPT-4 and External Validation

Data at Depth • 59 implied HN points • 18 Apr 24
🕹 Technology AI
  1. Documenting and analyzing your journey as a creator can help identify patterns of growth and areas for improvement, like diversification across social media platforms.
  2. Engaging in strategic thinking, research, and creation can lead to significant accomplishments, such as getting articles published and boosted, validating your skills as a writer.
  3. When using tools like GPT-4 for tasks like title generation, it's crucial to validate their output externally to ensure accuracy and effectiveness.

This Week in Marketing: TikTok in USA, Anti-links Meta, Netflix vs. YouTube, and Hashtags

The Social Juice • 19 implied HN points • 21 Dec 25
💼 Business AI
  1. TikTok will be sold to or controlled by US owners to avoid a ban, but the deal is controversial and could create new problems.
  2. Meta is tightening what creators can post — Instagram limits hashtags to five and Facebook is testing fees for sharing links — while the company faces scams, ad-fraud accusations, and regulatory pressure.
  3. Platforms are competing over video and podcasts: Netflix is signing video-podcast deals and YouTube is expanding podcast/TV features, forcing advertisers and creators to rethink where they distribute and buy podcast ads.

TITAA #47.5: IKEA Diagrams of Demons

Things I Think Are Awesome • 216 implied HN points • 15 Oct 23
🕹 Technology AI
  1. The post discusses using an IKEA-diagrams LoRa of SDXL for fun, generating impossible things like 'happiness' and 'poetry.'
  2. The diagrams in the post show steps to make a robot, angel, and golem, each with unique and interesting instructions.
  3. The post also touches on AI tools for code and reinforcement learning from an AI perspective.

Revolutionising Energy Storage: The AI and Experimental Design Fusion in Battery Tech

Intercalation Station • 139 implied HN points • 24 Jan 24
🕹 Technology AI
  1. The use of machine learning and adaptive experimental design is revolutionizing battery technology for more efficient, reliable, and sustainable energy storage solutions.
  2. Machine learning enhances consumer electronics by optimizing battery life and performance, showing practical benefits in devices like smartphones and electric vehicles.
  3. The combination of machine learning and adaptive experimental design leads to quicker research and innovation in battery technology, making advancements more tailored, responsive, and impactful across industries.
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Use the AI Transition Period to Transition Your Career

Jakob Nielsen on UX • 137 implied HN points • 19 Jun 25
🕹 Technology AI
  1. You have a short window to adapt your career before AI changes everything. It's important to start learning new skills now rather than relying on old methods.
  2. Embrace the idea of inventing your own future. Instead of waiting to see how AI will impact jobs, actively work on creating new ways to integrate AI into your work.
  3. Focus on developing key human skills like agency, judgment, and persuasion. These skills will be crucial as AI takes over routine tasks and collaboration becomes more essential.

You can’t build a moat with AI (redux)

Generating Conversation • 256 implied HN points • 20 Feb 25
🕹 Technology AI
  1. Using AI like LLMs isn't unique anymore. Just having AI in your product doesn't really set it apart from competitors.
  2. To really stand out, focus on making a great user experience and integrating your product into how users already work. This makes your tool more valuable and hard to replace.
  3. Data is crucial for AI. It's not just about having lots of data; it's about using it smartly over time to improve your product and understand your users better.

The Sequence Research #683: Orchestrating Intelligence: Sakana AI’s Multi-Model Tree Search Architecture

TheSequence • 119 implied HN points • 11 Jul 25
🕹 Technology AI
  1. Training large AI models can lead to diminishing returns, meaning bigger models don't always perform much better than smaller ones. It's becoming clear that just making models larger isn't the only solution.
  2. Sakana AI suggests that instead of one giant model, we could use several smaller models working together. This collaboration might lead to better problem-solving, similar to how humans think and deliberate.
  3. Their approach is called Adaptive Branching Monte Carlo Tree Search, which allows multiple models to reason together and improve over time. This could change how we think about building AI systems.

AI is yet another platform shift

Generating Conversation • 280 implied HN points • 30 Jan 25
🕹 Technology AI
  1. AI is a big change in technology, similar to how the printing press changed information sharing. It will automate some jobs but also create many new opportunities.
  2. As AI makes tasks cheaper and easier, more people will want to use these services. This means new demands and markets will open up that we didn't see before.
  3. For AI to be successful, it needs to work well with what businesses are already doing, and building trust with customers is very important.

The Sequence Knowledge #670: Evaluating AI in Software Engineering Tasks

TheSequence • 133 implied HN points • 24 Jun 25
🕹 Technology AI
  1. Software engineering benchmarks are important to assess how well AI can help with coding. These tests look at more than just generating code; they check if AI can understand bigger projects and fix actual bugs.
  2. One standout benchmark is SWE-bench, which uses real GitHub issues and pull requests. It challenges AI models to solve bugs and pass tests like human engineers would.
  3. These benchmarks are designed to figure out if AI can work alongside engineers reliably, just like a helpful teammate.

HILL: Solving for LLM Hallucination & Slop

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 39 implied HN points • 23 May 24
🕹 Technology AI
  1. HILL helps users see when large language models (LLMs) give wrong or misleading answers. It shows which parts of the response might be incorrect.
  2. The system includes different scores that rate the accuracy, credibility, and potential bias of the information. This helps users decide how much to trust the responses.
  3. Feedback from users helped shape HILL's features, making it easier for people to question LLM replies without feeling confused.

Do Large Language Models have a "theory of mind"?

The Counterfactual • 219 implied HN points • 14 Sep 23
🕹 Technology AI
  1. Large language models (LLMs) show some ability to understand the beliefs of other characters in scenarios, indicating a form of Theory of Mind. This means they can predict behaviors based on what a character knows or believes.
  2. However, LLMs don't perform as well as humans on these tasks, suggesting their understanding is not as deep or reliable. They score above chance but below the typical human accuracy.
  3. Research on LLMs and Theory of Mind is ongoing, raising questions about how these models process mental states compared to humans and if traditional tests for mentalizing are sufficient.

Data Laundering: How Stability AI managed to get millions of copyrighted art works without paying artists [Finance Fridays]

Technology Made Simple • 219 implied HN points • 12 Aug 23
🕹 Technology AI
  1. Data laundering involves converting stolen data to be used illegally or sold as legitimate data.
  2. Tech companies, like Stability AI, can get around artist copyright by using creative methods with AI art.
  3. It's essential to ensure fair compensation for artists and creators whose work is used, and to establish better regulations for copyright protection in data usage.

Evaluating The Quality Of RAG & Long-Context LLM Output

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 08 Jul 24
🕹 Technology AI
  1. Evaluating the performance of RAG and long-context LLMs is tough because there isn't a common task to compare them on. This makes it hard to know which system works better.
  2. Salesforce created a new way to test these models called SummHay, where they summarize information from large text collections. The results show that even the best models struggle to match human performance.
  3. RAG systems generally do better at citing sources, while long-context LLMs might capture insights more thoroughly but have citation issues. Choosing between them involves trade-offs.

What happens when an AI is your customer?

Generating Conversation • 116 implied HN points • 10 Jul 25
🕹 Technology AI
  1. AI is becoming a key player in business, not just as a tool, but as a customer. Companies need to prepare for this shift.
  2. The interaction between AIs and human support will be different, requiring new approaches in design and efficiency.
  3. Businesses that adapt to AI-driven processes will have an advantage over those that don't, especially in sales and support.

AI and the future of weather forecasting

The PhilaVerse • 123 implied HN points • 02 Jul 25
🕹 Technology AI
  1. AI is changing how we predict the weather by offering quicker and more efficient methods compared to traditional forecasting. This helps provide better updates, especially for things like storms and heatwaves.
  2. While AI forecasting models are fast, they currently work at a lower resolution than traditional systems. They still depend on traditional methods for some accurate initial data.
  3. There is growing interest worldwide in using AI for weather forecasting. This technology could improve disaster preparedness, agriculture, and energy management, making it valuable for many industries.

Links for 2024-01-25

Axis of Ordinary • 117 implied HN points • 25 Jan 24
🕹 Technology AI
  1. AI advancements are being made in realistic video generation, orchestration of robotic agents, and text-to-image generation.
  2. CAR T cells could be engineered to combat aging, revealing potential in using them for more than just cancer treatments.
  3. Historical narratives are often shaped by the perspectives of those in power, rather than the marginalized or oppressed.

The Sequence Engineering #681: Building Agents with Amazon Strands

TheSequence • 119 implied HN points • 09 Jul 25
🕹 Technology AI
  1. Amazon Strands is an open-source framework that lets AI models work independently to plan and complete tasks. This means developers don’t have to write specific instructions for every single action.
  2. The framework uses three key components: a model, tools, and prompts to build intelligent agents easily. This helps in creating smarter systems with less coding effort.
  3. The essay goes into detail about how Amazon Strands works, including its structure and how it can handle multiple agents, making it a powerful tool for developers.

Unpredictable Black Boxes are Terrible Interfaces

Maneesh’s Substack • 217 HN points • 30 Mar 23
🕹 Technology AI
  1. Generative AI models can produce high-quality content but are terrible interfaces due to unpredictable output based on input controls.
  2. Well-designed interfaces allow users to predict how input controls affect outputs, reducing the need for trial-and-error.
  3. Humans, despite being imperfect interfaces, are still better collaborators than AI due to shared semantics and repair mechanisms in conversations.

Prompting is Managing

Breaking Smart • 125 implied HN points • 19 Jun 25
🕹 Technology AI
  1. Using AI tools like chatbots is similar to managing interns. It's not about doing the work yourself but overseeing the process.
  2. Focusing on sameness in writing can help maintain quality, but it may also limit creativity. Good management knows when to stick to the rules and when to encourage originality.
  3. We need to change how we teach writing and management skills for the AI era. It’s important to build skills for overseeing new technologies rather than just avoiding them.

Deep Learning Weekly: Issue 337

Deep Learning Weekly • 117 implied HN points • 24 Jan 24
🕹 Technology AI
  1. DeepMind introduces AlphaGeometry, an AI system solving complex geometry problems at Olympiad level.
  2. ElevenLabs, an AI voice startup, raises $80 million in funding, reaching a valuation of $1.1 billion.
  3. Theory suggests that large language models like LLMs are more than just 'stochastic parrots.'

Joe's Nerdy Rants #7

Joe Reis • 216 implied HN points • 01 Jul 23
🕹 Technology AI
  1. The data community deserves better events free of vendor influence.
  2. The major data platforms are in an intense competition and push to capture attention.
  3. Attending big-vendor conferences often involves dealing with aggressive selling tactics.

Understanding Large Language Models

Startup Pirate by Alex Alexakis • 216 implied HN points • 12 May 23
🕹 Technology AI
  1. Large Language Models (LLMs) revolutionized AI by enabling computers to learn language characteristics and generate text.
  2. Neural networks, especially transformers, played a significant role in the development and success of LLMs.
  3. The rapid growth of LLMs has led to innovative applications like autonomous agents, but also raises concerns about the race towards Artificial General Intelligence (AGI).

How to talk to AI

Prompt Engineering • 216 implied HN points • 29 Apr 23
🕹 Technology AI
  1. Effective communication with AI models depends on providing quality prompts.
  2. When interacting with AI, avoid asking it to rephrase or rewrite text directly; instead, focus on asking for correctness and improvements.
  3. Maintaining your unique writing style when engaging with AI is important to preserve your voice in the text.

The Messy Page

Register Spill • 216 implied HN points • 07 May 23
🕹 Technology AI
  1. The author prefers messy projects over greenfield projects because they provide more certainty and direction.
  2. Having clear product-market fit and defined requirements make a project enjoyable to work on.
  3. The author finds debugging appealing due to its clear requirements and the assurance that efforts won't be wasted.

Is AI a Silver Bullet for our Climate Problems?

This Week in MCJ (My Climate Journey) • 216 implied HN points • 07 Mar 23
🔬 Science AI
  1. AI solutions in climate problems can be biased towards easily accessible data, encouraging broader solution development is crucial.
  2. AI must quantify its confidence in recommendations for climate problem-solving due to the high cost of mistakes.
  3. Encouraging new datasets and AI methods with confidence measurement can lead to more successful projects in addressing climate challenges.

This Is The Most Important Thing We Can Build With AI

America 2.0 (by Gary Sheng) • 216 implied HN points • 05 Apr 23
🕹 Technology AI
  1. A human-powered, AI-supercharged network is crucial to make collective decisions and bring about positive change.
  2. The bottleneck to effective coordination lies in the quality of input data in attempts to coordinate.
  3. An AI-powered civic information network can revolutionize our ability to understand collective desires and serve the community better.

AI is Eating the (Research) World

Bojan’s Newsletter • 216 implied HN points • 03 Oct 23
🕹 Technology AI
  1. AI is revolutionizing research fields like computer science, starting in 2013.
  2. AI is a versatile tech applicable in diverse fields yet still underutilized in non-CS disciplines.
  3. Scarcity of good datasets limits AI's wider adoption in research, but foundational models could change that.

Deep Learning Weekly : Issue #309

Deep Learning Weekly • 216 implied HN points • 12 Jul 23
🕹 Technology AI
  1. Deep Learning Weekly Issue #309 covers topics like Code Interpreter on ChatGPT Plus and ML system design with 200 case studies.
  2. Industry innovations include AI-generated chart captions and Nvidia's AI approach to carbon capture.
  3. Learning section highlights topics like Tiny Audio Diffusion and Swin Transformer for object recognition.

Clowns to the Left, Jokers to the Right

theconnector • 216 implied HN points • 25 Apr 23
🇺🇸 U.S. Politics AI
  1. AI development poses a threat to human agency in elections and society.
  2. The politicization of AI governance is biased towards private industry interests.
  3. There is a need for comprehensive legislation to prioritize civil and human rights in the development and use of AI.

Models

Sriram Krishnan’s Newsletter • 216 implied HN points • 20 Jun 23
🕹 Technology AI
  1. Large-language models are open-sourced and ranked based on benchmarks like ChatGPT and Google Bard.
  2. Model performance improves with each iteration, leading to better models rising and lesser ones fading out.
  3. Different types of data sources contribute to the creation of unique models, with more gated data leading to more variety.

The Rise of Closed AI Systems: A Shift in the AI Paradigm

Rod’s Blog • 99 implied HN points • 15 Feb 24
🕹 Technology AI
  1. Open AI systems have been widely used in the past, promoting collaboration and sharing of AI technologies, but the trend is shifting towards closed AI systems that offer advantages like protecting intellectual property and user privacy.
  2. Closed AI systems, developed by private companies, are not accessible to the public or other researchers, leading to questions about transparency, accountability, and competition in the AI market.
  3. The emergence of closed AI systems presents a mix of benefits and challenges, such as fostering innovation and efficiency while potentially hindering collaboration and knowledge sharing in the AI community.

🤖👷‍♂️ Beyond disruption: AI, expertise, and the future of middle-class workers

Faster, Please! • 822 implied HN points • 14 Feb 24
🕹 Technology AI
  1. Tech progress involves creative destruction - some jobs are lost, but new ones are created, especially in AI-related fields.
  2. Advances in artificial intelligence are reshaping the workforce as companies invest in AI systems and technologies.
  3. The impact of AI on the job market is a big question for the future - will it lead to widespread technological unemployment or follow historical patterns of job creation and loss?