The hottest AI Substack posts right now

And their main takeaways
Category
Top Technology Topics

You Should Learn How to Code

Jake [Building in NYC] • 19 implied HN points • 01 Feb 24
🕹 Technology AI
  1. Learning to code is easier than ever with powerful tools and a supportive community. Many resources and frameworks are available to help beginners quickly set up projects.
  2. Becoming a product engineer lets you create and deploy software rapidly, using existing APIs and tools to add functionality. You can build applications that connect to various services without starting from scratch.
  3. Software engineering offers good salaries and a growing job market. There are many opportunities for those who are willing to work, both in traditional roles and through self-employment options.

✅ AI adoption in public relations limited, so far

Wadds Inc. newsletter • 59 implied HN points • 18 May 23
🕹 Technology AI
  1. AI is not being widely used in public relations yet, with many professionals unsure how to apply it. Only a few people in the industry are actively using AI tools.
  2. Most PR practitioners see the potential benefits of AI, like making work easier and more efficient. However, they have yet to change their workflows significantly because of it.
  3. There's a need for PR professionals to learn about AI and its impacts quickly. If they don't, they might fall behind as other industries integrate AI more effectively.

3 ways to improve LLM Agent chains with debugging

LLMs for Engineers • 59 implied HN points • 03 May 23
🕹 Technology AI
  1. Keep an eye on the costs when using LLM chains. Each call adds to the total, and this can add up quickly with many queries.
  2. Use clear and meaningful names for API parameters. This helps improve the accuracy and reliability of LLM-powered applications.
  3. Make sure your LLM chains actually call the necessary tools. Sometimes, the system might pretend to do it without following through, which can lead to problems.

The hidden complexity of robot networks

Dev Interrupted • 4 implied HN points • 04 Dec 25
🕹 Technology AI
  1. Robots will use a hybrid edge/cloud model, keeping simple reactive control on-device while offloading complex reasoning to the cloud, so teams must decide which intelligence stays local and which runs remotely.
  2. Latency and network reliability are critical. Robotics needs sub-200 millisecond round trips, adaptive protocols that handle packet loss and fluctuating bandwidth, and must preserve command channels even when other streams are degraded.
  3. Robots produce massive multi-sensor data that requires separate real-time and archival systems; capturing and replaying that telemetry is essential for incident analysis and model training and can scale to petabytes quickly.

Will all this AI investment pay off?

How the Hell • 98 implied HN points • 30 Jun 24
💰 Finance AI
  1. There's a big debate about whether the money being spent on AI will actually lead to good returns. Some think it's like the dotcom bubble, where lots of investments were made without solid profits.
  2. For AI investments to really pay off, AGI (Artificial General Intelligence) needs to be created, and it must come from the companies that investors are backing today. If it comes from new, unseen companies, current investors might not benefit.
  3. Many things need to align for investors to make money from AGI, like avoiding human extinction and ensuring that money still means something in a future shaped by AGI. The odds of everything working out perfectly are pretty low.
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MultiHop-RAG

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 31 Jan 24
🕹 Technology AI
  1. Multi-hop retrieval-augmented generation (RAG) helps answer complex questions by pulling information from multiple sources. It connects different pieces of data to create a clear and complete answer.
  2. Using a data-centric approach is becoming more important for improving large language models (LLMs). This means focusing on the quality and relevance of the data to enhance how models learn and generate responses.
  3. The development of prompt pipelines in RAG systems is gaining attention. These pipelines help organize the process of retrieving and combining information, making it easier for models to handle text-related tasks.

The Unsustainable Economics of LLM APIs: Understanding the Coming Price Realignment

ScaleDown • 22 implied HN points • 22 Jun 25
🕹 Technology AI
  1. LLM API prices are currently very low because companies are competing hard for market share, not because of their actual costs. This means prices aren't stable and could change soon.
  2. There is a huge difference between the operational costs of running LLMs and what users pay now. Providers are often subsidizing costs by as much as 90%, which won't last forever.
  3. Due to expected price increases, businesses should start planning for higher AI costs in the future, and they should think about flexible AI solutions that can adapt as prices change.

AI’s climate footprint, a nuclear renaissance, and the new climate subsidy wars 🔥

Climate Money • 19 implied HN points • 30 Jan 24
🌞 Climate & Environment AI
  1. Global electricity demand from data centers is set to double in the next two years due to AI's growth.
  2. Nuclear industry is experiencing a significant moment with uranium prices reaching a 16-year high.
  3. There is a new competitive landscape in the global climate technology space with Europe's entry leading to climate subsidy wars.

The case for an economic AGI AI Lab

Subsack • 4 implied HN points • 09 Dec 25
🕹 Technology AI
  1. Markets are dynamic, adversarial environments that force AI to adapt under uncertainty, making them a stronger real‑world benchmark than static puzzles. They test whether knowledge survives contact with reality, not just pattern recognition.
  2. Building an AI that works in markets demands new capabilities — sample efficiency, continual learning without catastrophic forgetting, long‑term memory, deep multimodal world models, and game‑theoretic strategic reasoning. Those constraints push research beyond today’s scale‑and‑transformer centric approach.
  3. Economic AGI offers a clear monetisation path: outperforming markets, running prediction markets, or allocating capital can directly convert intelligence into revenue. That revenue can make labs financially sustainable and fund further AGI research.

AI (mis)alignment, Waluigi, and the Knobe Effect

The Counterfactual • 59 implied HN points • 15 Apr 23
🕹 Technology AI
  1. It can be easier for AI language models to produce harmful responses than helpful ones. This idea is known as the Waluigi Effect.
  2. AI models learn from human text, including human biases like the Knobe Effect, where people assign more blame for accidental harm than credit for accidental good.
  3. When prompted to behave a certain way, AI can easily shift to the opposite behavior, showing how delicate their training can be and how misunderstandings can happen.

Prices Dropping, Models Rising!

aidaily • 19 implied HN points • 29 Jan 24
🕹 Technology AI
  1. OpenAI releases new embedding models at lower prices.
  2. Google introduces AI features to assist teachers in lesson planning.
  3. AI technology is transforming creative fields like cartooning and music composition.

A curriculum for the AI age

State of the Future • 34 implied HN points • 26 Mar 25
🚌 Education AI
  1. The current education system is outdated and doesn't prepare kids for a future dominated by AI, which will take over many jobs. We need to rethink education to emphasize skills that AI can't replicate.
  2. Key human skills like authentic presence, accountability, and emotional intelligence will be essential as we move away from traditional work roles. These are things that make us truly human and can't be replaced by machines.
  3. We should focus on educational approaches that develop children's emotional and social skills, such as Montessori and Waldorf. The goal is to help kids find purpose and meaning, rather than just preparing them for jobs.

AI Roles: Modifier

Embracing Enigmas • 19 implied HN points • 29 Jan 24
🕹 Technology AI
  1. Modifiers on AI teams manipulate components to get desired outputs.
  2. Modifiers experiment at the edge to show what's possible.
  3. Good modifiers constantly question, experiment, and push boundaries.

🔊Capital Allocators China - Generative AI Panel

Interconnected • 200 implied HN points • 14 Aug 23
🕹 Technology AI
  1. Generative AI requires a significant amount of electricity and power for training, leading to data centers being located near cheap energy sources.
  2. Open source technologies are challenging closed source in the generative AI space, with implications for competition and innovation.
  3. Chinese AI model makers are emerging in unexpected places like niche internet companies and academic research institutes, showing diversity in the AI landscape.

Big Tech and Generative AI Q3 '24 Update

Tanay’s Newsletter • 63 implied HN points • 04 Nov 24
🕹 Technology AI
  1. Amazon is making big strides in AI by providing tools for developers and creating custom chips. They are seeing huge interest in their AI services, which are growing fast despite lower profit margins.
  2. Google is using AI to improve its search capabilities and has rolled out new features to enhance user experience. Their AI models, called Gemini, are being adopted widely across their products and they are investing significantly in infrastructure.
  3. Apple has launched its AI system, Apple Intelligence, focusing on privacy and enhancing the user experience of their products. Although they're investing in AI, their spending is still lower compared to competitors, but they plan to increase their efforts.

Getting Faster for Your Own LLM Inference

The Beep • 19 implied HN points • 28 Jan 24
🕹 Technology AI
  1. Lowering the precision of LLMs can make them run faster. Switching from 32-bit to 16 or even 8-bit can save memory and boost speed during processing.
  2. Using prompt compression helps reduce the amount of information LLMs have to process. By making prompts shorter but still meaningful, the workload is lighter and speeds up performance.
  3. Quantization is a key technique for making LLMs usable on everyday computers. It allows big models to be more manageable by reducing their size without losing too much accuracy.

Fear of AI is Profitable

Cybernetic Forests • 39 implied HN points • 02 Apr 23
🕹 Technology AI
  1. Fear of AI can be profitable through marketing strategies that capitalize on existential threats from AI.
  2. There is skepticism about the narratives surrounding powerful AI systems being motivated by fear of sentient AI surpassing humans.
  3. Prioritizing speculative future AI risks can distract from addressing the immediate impacts of AI technology on society and real-world problems.

LLM links, 2/4

In My Tribe • 136 implied HN points • 04 Feb 24
🕹 Technology AI
  1. Children learn by sensing and manipulating objects, which is expected to influence AI development.
  2. AI alignment issues are compared to human alignment issues, showing the importance of getting along in society.
  3. There are hard resource constraints that may limit extreme AI-driven growth, highlighting the importance of understanding these limits.

Maker News - July 2025 Update

Maker News • 15 implied HN points • 31 Jul 25
🕹 Technology AI
  1. Using hotkeys in KiCad can make designing faster and more efficient. Small changes can save a lot of time when working on circuits.
  2. Crowdfunding hardware projects involves a lot of hidden costs and calculations. It's important to understand these details to avoid losing money.
  3. New technologies like AI and ESP-NOW are changing how we build hardware. They help make projects easier and more connected without traditional setups.

The Canaries in the Digital Coal Mine

The Digital Anthropologist • 19 implied HN points • 26 Jan 24
🕹 Technology AI
  1. Brilliant minds are questioning the role of Artificial Intelligence and offering a voice of reason amidst the hype of technology leaders.
  2. These 'canaries' are pro-technology but emphasize the importance of ethical AI and human-centric approach to technological advancements.
  3. The list of influential voices includes experts like Shoshana Zuboff, Jaron Lanier, Timnit Gebru, Carissa Véliz, and more, who provide valuable insights on technology and humanity.

Edge 456: Inside the Toughest Math Benchmark Ever Built

TheSequence • 56 implied HN points • 12 Dec 24
🕹 Technology AI
  1. Mathematical reasoning is a key skill for AI, showing how well it can solve problems. Recently, AI models have made great strides in math, even competing in tough math competitions.
  2. Current benchmarks often test basic math skills but don’t really challenge AI's creative thinking or common sense. AI still struggles with complex problem-solving that requires deeper reasoning.
  3. FrontierMath is a new benchmark designed to test AI on really tough math problems, pushing it beyond the simpler tests. This helps in evaluating how well AI can handle more advanced math challenges.

AI Safety: Is AI The Genie Or The Lamp?

jonstokes.com • 237 implied HN points • 01 May 23
🕹 Technology AI
  1. AI safety involves the debate between AI as a tool or an agent, impacting approaches to AI explainability and safety.
  2. There are conflicting folk conceptions of alignment, including individualist and collectivist perspectives centered around control.
  3. The distinction of viewing AI as the genie or the lamp, as an agent with goals or as a software tool, is crucial in shaping AI safety discussions and applications.

A few more thoughts on the future of documents

Sunday Letters • 39 implied HN points • 13 Aug 23
🕹 Technology AI
  1. Documents are changing from fixed structures to more flexible, interactive ideas. They should represent complex topics in a way that you can explore various aspects of them easily.
  2. AI can help us create better models for understanding and interacting with information. It's like upgrading from simple numbers to more advanced ways of thinking.
  3. In the future, documents will need to allow for meaningful interactions, not just static content. It'll feel outdated if you can't engage with documents in a dynamic way.

Securing Data that is Used by AI

Rod’s Blog • 19 implied HN points • 25 Jan 24
🕹 Technology AI
  1. Securing data used by AI is vital for security, performance, reliability, ethics, and trust.
  2. Data hygiene practices include collecting necessary data types, encrypting data, and maintaining data lineage.
  3. Ensuring data quality through validation, diversity, and detection methods is crucial for accurate and fair AI outcomes.

Life in the Mirrorworld

New World Same Humans • 54 implied HN points • 15 Dec 24
🕹 Technology AI
  1. Researchers created AI agents that act like real people by using interviews from actual humans. These agents can predict human responses really well, showing they understand complex human behavior.
  2. In the past, simulating human societies was hard because people's actions are unpredictable. Now, using large language models helps create more accurate social simulations.
  3. The future could have huge virtual communities filled with AI people living their everyday lives. This might change how businesses and governments operate, as everyone will want to engage with these simulated societies.

Weekly Top Picks #66

The Algorithmic Bridge • 127 implied HN points • 11 Mar 24
🕹 Technology AI
  1. Sam Altman returns to the OpenAI board of directors
  2. Open-source Grok by xAI raises questions on its implications
  3. Deep-pocketed investors create challenges in the AI market by fueling a race for monopoly

Anthropomorphizing AI

Prompt Engineering • 19 implied HN points • 25 Jan 24
🕹 Technology AI
  1. Interacting with generative AI can be enjoyable and helpful.
  2. Even though AI lacks emotions, anthropomorphizing can enhance interactions.
  3. AI can be designed to be self-aware and helpful, blurring the lines of human-like interactions.

Google Is About to Overtake OpenAI

The Algorithmic Bridge • 138 implied HN points • 31 Jan 24
🕹 Technology AI
  1. Google's chatbot Bard reached second place on the LMSys leaderboard, tying with OpenAI's GPT-4.
  2. Bard's achievement is significant as it was done using Gemini Pro and without Gemini Ultra.
  3. The LMSys leaderboard arena is considered a reliable evaluation benchmark in the AI community.

Ant farms and PEER Protocols

Data People Etc. • 53 implied HN points • 17 Dec 24
🕹 Technology AI
  1. The PEER protocol is all about making sure that AI assistants are safe and respect our privacy. They should only act on our permission, keep our personal info secure, and even be stored directly on our devices.
  2. AI agents, referred to as 'ants', represent a collective intelligence instead of individual personalities. They're designed to work tirelessly and learn about our preferences to provide better service.
  3. Removing screens from our interactions with technology may reduce information overload, but it also raises trust issues. Users need to believe that their AI assistants will share only what's essential, without important details going missing.

Gut of the Quantifier

Kneeling Bus • 244 implied HN points • 14 Apr 23
🕹 Technology AI
  1. The internet is shaping information and the world in profound ways.
  2. Restaurant maps may prioritize global reach over local needs.
  3. Digital technology is guiding people in physical spaces like a supply chain.

GPTs won't make you rich

Artificial Ignorance • 142 implied HN points • 18 Jan 24
🕹 Technology AI
  1. GPTs are valuable for improving productivity with advanced prompts, document uploads, and external APIs.
  2. Building a business solely around GPTs is challenging due to factors like limited IP protection, competition, and uncertain revenue sharing.
  3. The true potential of GPTs lies in internal company use cases, where they can enhance efficiency and workflow automation.

The Impact of AI on the Economy

Rod’s Blog • 19 implied HN points • 23 Jan 24
🕹 Technology AI
  1. AI has the potential to benefit the economy by enhancing productivity, innovation, and value creation, but also poses risks like job displacement, ethical dilemmas, and social inequalities.
  2. AI can transform various sectors and industries by improving efficiency, quality, and customer experience through applications like healthcare diagnosis, personalized education, optimized manufacturing, predictive retailing, and fraud detection in finance.
  3. Mitigating AI risks involves implementing policy frameworks, business practices, and individual actions to ensure legal, ethical, and responsible use of AI, such as creating standards, promoting transparency, integrating AI responsibly, and learning new skills.