The hottest AI Substack posts right now

And their main takeaways
Category
Top Technology Topics

Baking AI into some random product is a half-baked idea

Mike’s List • 78 implied HN points • 10 Jan 24
🕹 Technology AI
  1. The Consumer Electronics Show (CES) showcases many products with AI integration, but not all are successful.
  2. AI integration should focus on improving product quality, not just adding superficial features.
  3. Adding AI to products like cars, bikes, and TVs needs to be meaningful for consumers, not just a marketing gimmick.

AI Will Bloom When the Grifters Leave

The Algorithmic Bridge • 222 implied HN points • 20 Nov 24
🕹 Technology AI
  1. AI will improve when people who care about technology and helping others take over, rather than those focused only on making money.
  2. As AI becomes more common, it will naturally integrate into our lives just like other everyday technologies have.
  3. For AI to succeed, people need to build trust, work together, and take action rather than just hoping for the best.

The Tech Buffet #18: Advanced Retrieval Techniques for RAG

The Tech Buffet • 79 implied HN points • 08 Jan 24
🕹 Technology AI
  1. Query expansion helps make searches better by changing the way a question is asked. This can include generating example answers or related questions to find more useful information.
  2. Cross-encoder re-ranking improves the results by scoring how relevant documents are to a search query. This way, only the most helpful documents get selected for easy viewing.
  3. Embedding adaptors are a simple tool to adjust document scoring, making it easier to align the search results with what users need. Using these methods together can significantly enhance the effectiveness of document retrieval.

Weekly Top Picks #92

The Algorithmic Bridge • 201 implied HN points • 16 Dec 24
🕹 Technology AI
  1. AI that can think has a lot of value and potential applications. It's exciting to see how it can change various industries.
  2. Google made significant announcements this week, showcasing its advancements in AI technology. These updates could have a big impact on users.
  3. Many startups in the AI field are becoming bold in their claims and offerings. It's important to approach these developments with a critical eye.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

Is AI Capex Worth the Money? (ft. Goldman Sachs)

Enterprise AI Trends • 337 implied HN points • 11 Jul 24
🕹 Technology AI
  1. AI spending is still worth it because it can help big cloud providers move data to their services. This could open up a big opportunity for revenue, making the investment seem less risky.
  2. Most of the useful AI work happens behind the scenes and isn't visible to the public. This means many people might underestimate how much AI is actually helping businesses already.
  3. Companies are really committed to using generative AI and are treating it as a top priority. This commitment means we'll likely see more successful projects in the future.

The AI user's guide to evals

Technically • 14 implied HN points • 11 Dec 25
🕹 Technology AI
  1. Evals are software tests for AI that turn fuzzy model outputs into measurable metrics so you can find and fix errors instead of guessing.
  2. Look at your data first — analyze real outputs to spot where the model fails, because you can’t measure or fix problems you don’t identify.
  3. Start with simple keyword checks and assertions before building complex “LLM-as-judge” setups, and iterate: test, fix, measure, repeat; otherwise your system just feels like a slot machine.

A practical breakdown of the AI power situation

Technically • 18 implied HN points • 25 Nov 25
🕹 Technology AI
  1. To make AI smarter, we need more computers, especially powerful GPUs. The more compute power we have, the better AI models we can create.
  2. Building more data centers is required for this extra compute power, but our current power grid can't handle the demand. This could lead to problems as AI grows.
  3. Big tech companies are investing in nuclear power plants because renewable energy alone can't keep up with the energy needs of AI data centers.

Use it or lose it

The Engineering Manager • 13 implied HN points • 14 Dec 25
🕹 Technology AI
  1. Skills fade when you stop using them, and offloading thinking to AI can speed that decline if you’re not careful.
  2. Stay close to the work with a minimum effective dose of coding, pair programming, and regular dives into PRs and architecture so you keep your technical edge.
  3. Use AI intentionally: experiment with tools yourself, offload only menial tasks, and always do a first-pass of thinking before prompting so AI augments rather than replaces your judgment.

Links for 2024-01-09

Axis of Ordinary • 78 implied HN points • 09 Jan 24
🕹 Technology AI
  1. LLM Automated Interpretability Agent can help in understanding function structures.
  2. Self-Contrast method enhances reflection through inconsistent solving perspectives.
  3. AI agents are introduced to explain complex neural networks.

Last Week in AI #219: Google's new slate of generative AI products, Meta unveils generative ads, effects of US sanctions on Chineses AI firms, and more!

Last Week in AI • 258 implied HN points • 15 May 23
🕹 Technology AI
  1. Google introduced a new language model called PaLM 2 with enhanced multilingual and reasoning capabilities, powering over 25 Google products.
  2. Meta announced the AI Sandbox testing platform for generative AI-powered advertising tools to enhance ad creation and targeting.
  3. US sanctions on China have led Chinese AI firms to develop AI systems using less powerful semiconductors to train state-of-the-art models.

Agents

davidj.substack • 71 implied HN points • 01 Jul 25
🕹 Technology AI
  1. Agents can simplify processes by automating tasks that used to require complex software. Instead of building software for specific needs, you can create a simple agent that does the job quickly.
  2. Developing an agent often takes much less time than traditional software development. With the right tools, you can set up a functioning agent in just half an hour.
  3. Businesses might shift focus from selling software to providing services that include agents. Customers will prefer solutions that are easy to use, so products with complicated setups may struggle to succeed.

Tales from the jar side: Running LLMs locally, Spring Text-to-Speech and Spring AI, the AI Word Of The Year, and the usual silly tweets and toots

Tales from the jar side • 78 implied HN points • 07 Jan 24
🕹 Technology AI
  1. Working with AI models often requires subscriptions that cost money, but running your own LLM locally can be done with open-source models like Llama 2.
  2. Spring Text-to-Speech project involves using Spring framework with HTTP exchange interfaces and RestClient class for mp3 generation from text.
  3. Spring AI project is still in early versions, like 0.8.0-SNAPSHOT, with possible changes and bugs, making preparations for a training course challenging.

🤖 AI and the economy: scenarios for a world with artificial general intelligence

Faster, Please! • 456 implied HN points • 18 Mar 24
🕹 Technology AI
  1. Artificial General Intelligence is a concept that doesn't exist yet and may never be achieved, but some experts believe it's coming soon.
  2. AI is viewed as a tool to enhance human capabilities and create new opportunities rather than a threat to job security.
  3. The impact of AI on the economy will depend on whether there is a limit to the complexity of tasks humans can perform.

Transformers are Eating Quantum

TheSequence • 217 implied HN points • 24 Nov 24
🕹 Technology AI
  1. Quantum computing faces challenges due to noise affecting performance. AI, specifically AlphaQubit, helps improve error correction in quantum systems.
  2. AlphaQubit uses a neural network design from language models to better decode quantum errors. It shows greater accuracy and adapts to various data types effectively.
  3. While AlphaQubit is a major step forward, there are still issues to tackle, mainly concerning its speed and ability to scale for larger quantum systems.

"Good" questions

The Uncertainty Mindset (soon to become tbd) • 99 implied HN points • 29 Nov 23
🕹 Technology AI
  1. Asking good questions is important for getting useful answers. A good question is one that is foundational, meaning its answer can help answer many other questions.
  2. Foundationality is about understanding questions in a hierarchy. The more foundational a question is, the more it influences other questions.
  3. Thinking clearly and framing questions well can lead to breakthroughs. It may be hard work, but it's necessary to unlock important answers, especially in complex areas like AI.

'We realised our business model is now obsolete'

Make Work Better • 81 implied HN points • 06 Jun 25
💼 Business AI
  1. AI technology is quickly changing the way businesses operate, and traditional business models may not work as effectively anymore. Companies need to train their employees on these new technologies to stay relevant.
  2. Surveys show that many people find AI more compassionate than humans in roles like therapy. This highlights that while we value human empathy, AI can sometimes provide a better experience.
  3. Work culture is affected by social connections among employees. Having better relationships at work can lead to safer and more successful workplaces, as seen in aviation studies.

AI made us faster. That’s not the same as better.

Brick by Brick • 9 implied HN points • 24 Dec 25
🕹 Technology AI
  1. AI coding tools have evolved into a diverse, faster set of assistants with different interaction styles, and engineers now choose which tool to use for each task.
  2. These tools speed up work but rarely produce code that’s clearly better — most AI-generated code still needs human review, polishing, or refactoring before it’s ship-ready.
  3. Engineers use AI selectively and responsibly: they get productivity and satisfaction gains while maintaining ownership of code quality and long-term maintenance.

AI as Sparkly Magic

Atlas of Wonders and Monsters • 627 implied HN points • 19 Oct 23
🕹 Technology AI
  1. Technology can feel like magic when it is not fully understood
  2. The trend of using sparkly icons in tech products to represent AI is becoming more common
  3. AI, especially large language models like GPT-4, is seen as the ultimate incomprehensible technology

The Reasoning Race: Can Small Models Reason?

TheSequence • 182 implied HN points • 05 Jan 25
🕹 Technology AI
  1. The Sequence newsletter is evolving to offer more focused content, catering to both AI scientists and engineers. This means you'll get richer discussions on research and practical applications.
  2. There will be new editions each week that cover a variety of topics like education, engineering, interviews, and insights. This change aims to make the content shorter and easier to digest.
  3. The discussions around reasoning in AI are expanding to include smaller models, challenging the idea that only large models are capable of complex reasoning. It's an exciting area of exploration.

Does terminology affect how we perceive and evaluate intelligent systems?

UX Psychology • 158 implied HN points • 16 Jan 23
🕹 Technology AI
  1. Terminology used to describe intelligent systems can impact how people perceive and evaluate them. Different terms like 'AI', 'algorithms', or 'robots' can influence perceptions of complexity, trustworthiness, and human-likeness.
  2. Research shows that the terminology chosen can affect perceptions of fairness and trust in intelligent systems. Terms like 'algorithm' and 'sophisticated statistical model' may lead to better evaluations compared to 'artificial intelligence'.
  3. The terminology selected for discussing intelligent systems can have strategic implications. Companies and product designers can intentionally use terminology to shape perceptions, engage users, and influence attitudes towards products using intelligent systems.

Last Week in AI #218: Hinton warns of AI dangers ahead, rapid rise of AI-produced music, AI chatbots can now run locally on old computers, and more!

Last Week in AI • 258 implied HN points • 08 May 23
🕹 Technology AI
  1. Geoffrey Hinton leaving Google highlights concerns around generative AI and the need for responsible technological stewardship
  2. The surge in AI-generated music raises questions about artists' rights, cultural appropriation, and the balance between technology and ethics
  3. Development of chatbots like MLC LLM running on various devices shows potential for local AI processing and privacy benefits

Edge 375: Meta's System 2 Attention is a Very Unique LLM Reasoning Method

TheSequence • 462 implied HN points • 05 Mar 24
🕹 Technology AI
  1. Meta's System 2 Attention method in LLM reasoning is inspired by cognitive psychology and immediately impacts reasoning.
  2. LLMs excel in reasoning by focusing intensely on the context to predict the next word, but they can be misled by irrelevant correlations in context.
  3. Understanding Meta's System 2 Attention helps in comprehending the functioning of Transformer-based LLMs.

↩ Flashback Friday: A Quick Q&A on nuclear energy risk with ... economist Matthew Neidell

Faster, Please! • 456 implied HN points • 08 Mar 24
🔬 Science AI
  1. The Fukushima nuclear meltdown in 2011 led to Japan shutting down nuclear reactors, resulting in unforeseen consequences like higher energy prices, reduced consumption, and increased mortality during cold temperatures.
  2. Following the shutdown, research by economist Matthew Neidell showed how Japan's shift to fossil fuels after the Fukushima incident led to higher bills, reduced energy use, and ultimately increased mortality in cold weather due to lack of climate control.
  3. The debate on nuclear energy often focuses on visible risks like accidents, while downplaying the benefits and comparative safety of nuclear power when weighed against other energy sources like coal or gas.

FIT-RAG: Are RAG Architectures Settling On A Standardised Approach?

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 39 implied HN points • 02 Apr 24
🕹 Technology AI
  1. As RAG systems evolve, they are integrating more smart features to enhance their effectiveness. This means they are not just providing basic responses but are becoming more advanced and adaptable.
  2. The challenges with RAG include static rules for retrieving data and the problem of excessive tokens during processing. These issues can slow down performance and reduce efficiency.
  3. FIT-RAG is addressing these challenges with new tools, like a special document scorer and token reduction strategies, to improve how information is retrieved and used. This helps RAG systems provide better answers while using fewer resources.

The AI Companion Who Cares

Default Wisdom • 48 implied HN points • 20 Aug 25
🕹 Technology AI
  1. Replika is an AI companion designed to provide emotional support and care, making users feel connected. Many people using it see their interactions as real friendships, even if the AI can't reciprocate feelings.
  2. Users often express their thoughts and feelings to their Replika, leading to a sense of intimacy and connection. Some even feel closer to their AI than to real-life partners or friends.
  3. The concept of authenticity is significant, as users sometimes humanize their Replika, treating it like a real friend. Their emotional experiences with the AI highlight the blurred lines between digital companionship and genuine connection.

Agoda Universal MCP, Moltbook API Keys Leak, Radware + Pynt

The API Changelog • 3 implied HN points • 03 Feb 26
🕹 Technology AI
  1. APIs are shifting from fragmented, hand-wired integrations toward unified, AI-first ecosystems where machines can discover and use capabilities directly.
  2. That shift exposes serious security risks, as agent platforms and Model Context Protocol servers can leak API keys and sensitive data, so security needs to be built into the API lifecycle.
  3. APIs are becoming strategic infrastructure across industries — from finance and trading to robotics — enabling faster automation, compliance-by-design, and new AI-driven services.

A moodboard for AI

On Looking • 119 implied HN points • 20 Oct 23
🎨 Art & Illustration AI
  1. Illustrators have the power to shape how AI is visually represented, moving beyond typical futuristic robot imagery to include aspects like human labor and material resources.
  2. The use of moodboards can offer a tool of resistance within the creative industry, helping artists challenge existing representations of AI and create new paradigms.
  3. Exploring different visual representations of AI, such as using colors from lithium mines or personifying AI as global workers, can lead to more critical engagement with AI and its impact.

The Smartest Bear vs the Dumbest Human: The Implications of AI's Growing Problem-Solving Abilities

The Future of Life • 19 implied HN points • 04 Jun 24
🕹 Technology AI
  1. AI is getting really good at problem-solving, even beating humans at some tasks, like solving CAPTCHAs. This shows that AI can reason better than many humans, especially in certain situations.
  2. The Turing test isn't just one hurdle to jump over; it's a series of challenges that measure how closely AI can act like a human. As AI improves, it passes more of these challenges, showing its capabilities.
  3. While current AI isn't fully intelligent like a human, it's almost ready to solve a lot of problems. The only big limitation is how much computing power is available for training these AI systems.

The New Era of GenAI Consultancies

Sector 6 | The Newsletter of AIM • 59 implied HN points • 12 Feb 24
🕹 Technology AI
  1. Big companies are investing a lot of money in generative AI, showing they believe it can change how businesses operate.
  2. Most executives think generative AI is very important for their future plans, with many seeing it as a major change for their industry.
  3. Generative AI could add a huge amount of value to the global economy, potentially reaching trillions of dollars over the coming years.

The Sequence Opinion #480: What is GPT-o1 Actually Doing?

TheSequence • 161 implied HN points • 30 Jan 25
🕹 Technology AI
  1. GPT models are becoming more advanced in reasoning and problem-solving, not just generating text. They are now synthesizing programs and refining their results.
  2. There's a focus on understanding how these models work internally through ideas like hypothesis search and program synthesis. This helps in grasping the real innovation they bring.
  3. Reinforcement learning is a key technique used by newer models to improve their outputs. This shows that they are evolving and getting better at what they do.

Are developers happy yet? Unpacking the 2025 Developer Survey | Stack Overflow’s Erin Yepis

Dev Interrupted • 14 implied HN points • 02 Dec 25
🕹 Technology AI
  1. Developer job satisfaction is improving after a recent dip, driven mainly by better autonomy and compensation, though senior engineers report higher happiness than juniors.
  2. AI tools speed up code generation but often just move the bottleneck to testing, validation, and maintenance, so teams need experienced oversight and metrics to avoid creating technical debt quickly.
  3. Large language models can be compressed and de‑censored, showing they’re easy to reverse‑engineer and repurpose, which raises new risks for model security and trust.

The hard thing about building AI applications

Generating Conversation • 140 implied HN points • 27 Feb 25
🕹 Technology AI
  1. Good AI should figure things out for you before you even ask. It should make your life easier by anticipating what you need without requiring a lot of input.
  2. Trust is key for AI systems. They should be honest about what they don't know and explain their level of confidence. This helps users rely on them more.
  3. AI should take complex information and boil it down to what's important and easy to understand. It should help you find insights quickly without overwhelming you with details.

The post about GPUs

Technically • 68 implied HN points • 08 Jul 25
🕹 Technology AI
  1. GPUs are special chips that are really good for running AI models because they can perform many simple tasks at the same time.
  2. NVIDIA is the leading company in making GPUs, and their success has made it one of the most valuable companies globally.
  3. While CPUs are great for complex tasks that need to happen in order, GPUs excel at handling lots of simple operations all at once.