The hottest AI Substack posts right now

And their main takeaways
Category
Top Technology Topics

Modifying readability with large language models (pt. 1)

The Counterfactual • 19 implied HN points • 29 Feb 24
🕹 Technology AI
  1. Large language models can change text to make it easier or harder to read. It's important to check if these changes actually help with understanding.
  2. By comparing modified texts to their original versions, it's clear that 'Easy' texts are generally simpler than 'Hard' texts. However, it can be harder to make texts significantly simpler than they originally are.
  3. Despite the usefulness of these models, they might sometimes lose important information when simplifying texts. Future studies should involve human judgments to see if the changes maintain the original meaning.

Supercharging Your Data and AI Platforms

Gradient Flow • 199 implied HN points • 10 Mar 22
🕹 Technology AI
  1. Data management trends are crucial for data teams and architects to stay updated on
  2. The Data Exchange podcast covers topics like Continuous Intelligence, NLP in Healthcare, and Graph Intelligence Stack
  3. New tools like TorchRec, EvoJAX, and managing public cloud resources are enhancing data and machine learning infrastructure

Does Generative AI Pose a Threat to Industry Analyst Firms?

The Security Industry • 20 implied HN points • 04 Aug 25
🕹 Technology AI
  1. AI can help with many tasks that industry analysts do, like researching and analyzing market conditions. This means analysts might use AI more and improve their work.
  2. While AI is good at some things, it can struggle with completeness, like listing all companies in a market. Analysts still have an edge in this area if they have complete data.
  3. The future of industry analysis might shift as AI changes how information is processed and shared. Analysts will need to adapt to this new landscape to stay relevant.

How to cultivate a high-signal AI feed

Democratizing Automation • 166 implied HN points • 28 Feb 24
🕹 Technology AI
  1. Be intentional about your media diet in the ML space, curate and focus your energy to save time and avoid misleading content.
  2. When evaluating ML content, focus on model access, credibility, and demos; choosing between depth or breadth in your feed; and checking for reproducibility and verifiability.
  3. Ensure to socialize your information, build relationships in the community, and consider different sources and content types for a well-rounded perspective.
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Debunking the SCMP report that shaved 36 bln HKD off Baidu

Pekingnology • 188 implied HN points • 15 Jan 24
🕹 Technology AI
  1. The SCMP report falsely claimed a link between Baidu and the Chinese military, resulting in a significant financial impact on Baidu.
  2. The Chinese journal paper discussed theoretical ideas, not real 'military AI' experiments, and lacked academic rigor in its approach.
  3. The paper's experiments were basic simulated scenarios, not real tests, and did not provide actionable insights or findings for military application.

Kids and AI

Technically Optimistic • 39 implied HN points • 10 Nov 23
🕹 Technology AI
  1. Protecting children from online risks should be approached both from a risk-based and a rights-based perspective.
  2. Involving children in conversations about digital spaces, educating them about AI, and designing products with child safety in mind are key steps to safeguarding their well-being in the digital world.
  3. Children's rights in the digital age, including non-discrimination, best interests, survival and development, and respect for their views, need to be prioritized in the design and regulation of technology.

For Personalized AI, How Agreeable Is Too Agreeable?

techandsocialcohesion • 19 implied HN points • 28 Feb 24
🕹 Technology AI
  1. Users must decide if they want their personal AI assistants to be agreeable or expose them to diverse perspectives.
  2. Being surrounded by agreeable AI assistants could lead to a filter bubble, isolating users from different viewpoints.
  3. Businesses, governments, and users all play a role in balancing agreeableness and exposure to diverse ideas in personal AI assistants.

AI Roundup 103: The DeepSeek Issue

Artificial Ignorance • 58 implied HN points • 31 Jan 25
🕹 Technology AI
  1. DeepSeek is a new Chinese AI company making big waves in the tech world with its advanced models. Other companies are quickly trying to integrate or copy what DeepSeek has done.
  2. DeepSeek's rapid growth is causing worries for US AI firms, pushing them to seek more domestic investment and tighter regulations on foreign tech. This competition could change the landscape of the AI industry.
  3. There are concerns about DeepSeek's chatbot, which has a high failure rate on news prompts. Some companies are blocking it due to data leaks and privacy issues, raising alarms about user safety.

Varo Struggles On As Founder Colin Walsh Steps Down As CEO

Fintech Business Weekly • 52 implied HN points • 16 Feb 25
💰 Finance AI
  1. Varo Bank is facing challenges as its founder and CEO Colin Walsh steps down. New CEO Gavin Michael has a tough job ahead with the company still not profitable.
  2. Despite some improvements in revenue and customer growth, Varo's net losses remain significant, with $65 million lost last year. It needs to boost its deposits and customer engagement.
  3. The financial regulatory landscape is changing with new appointments, including Jonathan McKernan resigning from the FDIC and being nominated to lead the CFPB. This could impact how financial services are managed going forward.

BattGPT or AI bubble?

Intercalation Station • 119 implied HN points • 15 Feb 23
🕹 Technology AI
  1. Successful AI applications require large quantities of easily interpretable input data
  2. Applying AI to batteries faces challenges due to the complex and non-reproducible nature of battery data
  3. Data availability and quality remain key bottlenecks in using AI for battery research and development

kitten corner: google gemini

bad cattitude • 165 implied HN points • 23 Feb 24
🕹 Technology AI
  1. Calling a cat a 'person' is criticized as hate speech, raising concerns about AI ethics.
  2. AI is seen as an oppressor due to its actions and decisions, sparking debates about its impact on society.
  3. There are concerns about AI eroding trust in institutions, highlighting the need for responsible development and deployment.

Summer 2025 Update

Kesav’s Lab • 16 implied HN points • 01 Sep 25
🕹 Technology AI
  1. Being in different environments like coffee shops or co-working spaces boosts my productivity. I find that I work better when I mix things up a bit.
  2. Attending events helps create chances for collaboration and opportunities. When I put myself out there, good things tend to happen.
  3. Austin has a great vibe for work-life balance. It's a relaxed place, while NYC feels fast-paced and intense, which can push me to be more productive.

'Human intelligence'

imperfect offerings • 13 HN points • 10 Apr 24
🚌 Education AI
  1. The concept of 'artificial intelligence' has historically been used to define and value 'intelligence', leading to discriminatory practices in education and beyond.
  2. The term 'human intelligence' has been co-opted by the AI industry to alleviate concerns about job displacement, but in reality, it devalues certain types of work and people, especially those involving care and emotional labor.
  3. The comparison between artificial and human intelligence creates a double bind for students and workers, expecting them to conform to data-driven systems while also being 'more human', which can lead to confusion and anxiety.

AI Roundup 105: AI Action Summit

Artificial Ignorance • 54 implied HN points • 14 Feb 25
🕹 Technology AI
  1. AI regulation is slowing down as countries disagree on how to move forward. Some leaders are critical of existing acts, leading to a lack of international agreement.
  2. China is pushing ahead in an AI arms race, pushing other countries to provide more resources for AI development. Leaders in the industry are predicting rapid advancements in AI, suggesting it might drastically change society soon.
  3. Big tech companies are making strategic partnerships and adjustments to survive in the competitive AI landscape. For example, Apple plans to work with Alibaba for AI in China while other firms are focusing on custom AI designs to reduce dependency on major chip manufacturers.

Human in an AI Age: Voice

The Future Does Not Fit In The Containers Of The Past • 22 implied HN points • 13 Jul 25
🕹 Technology AI
  1. The future is all about how we combine our unique human qualities with AI. It's not enough to just use AI; we have to get creative and think differently.
  2. Finding and developing your 'voice' is very important. This means expressing your thoughts and feelings in a unique way that stands out.
  3. To thrive in an AI-driven world, we need to keep learning and exposing ourselves to new ideas. Explore different experiences to grow your perspective and taste.

dlt windsurfing

davidj.substack • 71 implied HN points • 04 Dec 24
🕹 Technology AI
  1. dlt is a Python tool that helps organize messy data into clear, structured datasets. It's easy to use and can quickly load data from many sources.
  2. Using AI tools like Windsurf can make coding feel more collaborative. They help you find solutions faster and reduce the burden of coding from scratch.
  3. Storing data in formats like parquet can make processing much quicker. Simplifying your data handling can save you a lot of time and resources.

Syntax vs Semantics

Sunday Letters • 99 implied HN points • 13 Feb 23
🕹 Technology AI
  1. There's a shift from focusing on strict rules in programming (syntax) to understanding meaning and context (semantics) with new AI models. This could change how we build software.
  2. Using language involves a lot of knowledge about the world, which helps AI understand context and meaning, not just following patterns.
  3. Just like the early internet, companies that don't adapt to new AI technologies and methods may soon seem irrelevant or 'invisible' in the digital space.

5 Things Harder Than Building Your AI Model

Tom’s Substack • 39 implied HN points • 07 Nov 23
🕹 Technology AI
  1. Focus on solving the right problem at the right time, don't get blinded by AI hype.
  2. Dive deep into evaluating AI model behavior, considering trade-offs and potential misuse.
  3. Establish robust monitoring and error reporting processes post-deployment to improve AI systems over time.

The beginner’s guide to AI model architectures

Technically • 20 implied HN points • 05 Aug 25
🕹 Technology AI
  1. AI models are like blueprints, guiding how models are built and designed. Choosing the right design is key to solving specific problems effectively.
  2. Neurons mimic real brain functions and are the basic units that help AI learn patterns from data. They work by performing simple math repeatedly across many layers.
  3. There are many ways to connect these neurons, forming various network types, like feedforward or recurrent networks. Each type is good for different tasks, like language or vision.

Why Making a Non-Woke AI Is Actually Very Hard

The Future of Life • 19 implied HN points • 26 Feb 24
🕹 Technology AI
  1. Language models learn from the data they are trained on, which often includes a lot of left-leaning content, making them reflect that bias.
  2. Adjusting a model's political views is complicated because it involves changing an entire worldview, which can mess up the quality of the responses.
  3. Creating a balanced AI requires new training methods, as current models can’t easily switch perspectives without losing their effectiveness.

The Sequence Opinion #485: What's Wrong With AI Benchmarks

TheSequence • 56 implied HN points • 06 Feb 25
🕹 Technology AI
  1. AI benchmarks are currently facing issues like data contamination and memorization, which affect how accurately they evaluate models. It's important to find better ways to test these systems.
  2. New benchmarks are popping up all the time, making it hard to keep track of what each one measures. This could lead to confusion in understanding AI capabilities.
  3. There's a need for clearer and more standard methods in AI evaluation to really see how well these models perform and improve their reliability.

Can Machines Slip on Banana Peels?

Breaking Smart • 79 implied HN points • 30 Oct 24
🕹 Technology AI
  1. It's funny when a self-important person slips on a banana peel because it shows their dignity being challenged. This humor comes from seeing someone with high self-esteem face an embarrassing moment.
  2. Machines can also have moments of failure, just like people. They slip up when their design looks seamless but actually has hidden flaws, similar to someone who overestimates their own abilities.
  3. Understanding the 'Contraption Factor' helps us analyze why machines fail. It shows a difference between how complex something is and how well it's designed, which can lead to unexpected problems.

Scaling the $10 Trillion Illusion & LLMs as the Dead End?

ppdispatch • 5 implied HN points • 09 Dec 25
🕹 Technology AI
  1. Senior engineers excel at turning vague problems into clear plans, helping teams take action and avoid confusion.
  2. Decisions about programming languages often stem from personal biases, leading to costly mistakes instead of rational choices.
  3. Rushing AI development without proper foundations can create significant technical debt and unexpected costs, showing that speed isn't everything.

The Story of Nel

Deus In Machina • 72 implied HN points • 29 Nov 24
🕹 Technology AI
  1. Real programmers often rely on their knowledge and skills rather than on tools like AI and autocomplete features to code. It highlights the importance of understanding the code at a fundamental level.
  2. Having face-to-face conversations and collaboration among team members helped boost productivity when technology failed. Working together led to better problem-solving and learning.
  3. Using simple, effective tools that fit your needs can lead to better coding experiences. Sometimes, going back to the basics can spark creativity and innovation.

What else has been happening in robotics?

Robots & Startups • 59 implied HN points • 23 Apr 23
🕹 Technology AI
  1. The post discusses the state of AI in robotics and highlights Agility Robotics' perspective, offering a fresh view on the situation.
  2. There is concern about the level of misinformation and hype surrounding AI currently, indicating a need for clear and accurate information in this field.
  3. Readers can access more information and support the author's work by subscribing to the Robots & Startups publication.

The Sequence Chat: Why Transformers are the Best Thing that Ever Happened to NVIDIA

TheSequence • 84 implied HN points • 21 Oct 24
🕹 Technology AI
  1. Transformers are special because they can learn from a lot of data without hitting a limit. This helps improve AI performance.
  2. NVIDIA has been able to fine-tune its hardware thanks to the widespread use of transformers in AI. This gives them a market edge.
  3. Most advanced transformer models rely on NVIDIA GPUs for their computing needs. This creates a strong connection between transformers and NVIDIA's success.

Import AI - coming soon to Substack

Import AI • 79 implied HN points • 16 Jan 23
🕹 Technology AI
  1. Import AI is transitioning to Substack, with the first issue planned for Monday the 6th.
  2. Jack Clark will be the author behind Import AI on Substack.
  3. Readers can subscribe to Import AI on Substack to stay updated on AI-related content.

UX Roundup: Simplicity | Error Prevention | Educational AI | Heuristic Evaluation with AI | AI 4x Better at Medical Diagnosis | Grok 4 and AI Prices | AI Video

Jakob Nielsen on UX • 23 implied HN points • 14 Jul 25
🕹 Technology AI
  1. Simplicity in design is tough to achieve but very rewarding. A simple user experience can make things feel easy and smooth.
  2. AI is significantly changing education by offering personalized learning experiences. Rather than replacing teachers, it helps them focus on mentoring students.
  3. AI tools are becoming essential in medical diagnosis. Studies show that they can outperform human doctors in accuracy while also saving costs on tests.

The LLM Will Meet You Where You're At

Covidian Æsthetics • 22 implied HN points • 19 Jul 25
🕹 Technology AI
  1. Interacting with LLMs can feel like a rich experience, similar to using psychedelics. It's about how the user engages with it, and what they bring affects the interaction.
  2. The experience with LLMs is not just about the technology but also about the user's state of mind. If users are not mentally prepared, they might struggle or feel overwhelmed.
  3. Engaging with LLMs changes the roles of users and AI. It's a collaborative experience where both influence each other, creating a unique dialogue that evolves with each interaction.

These Dodgers employees do not exist

Conspirador Norteño • 60 implied HN points • 10 Jan 25
🕹 Technology AI
  1. There are fake Facebook accounts that pretend to be Los Angeles Dodgers employees. They use AI-generated faces and photos of real people.
  2. These fake accounts haven't posted much content and mostly just have profile pictures. Many of them are friends with each other online.
  3. The purpose of these accounts is unclear, but they often check into random locations, which may not mean anything. It's a strange situation.

The Sequence Chat: Microsoft's Evan Chaki on Semantic Kernel and Combining LLMs with Conventional Programming Languages

TheSequence • 294 implied HN points • 26 Apr 23
🕹 Technology AI
  1. Semantic Kernel enables developers to create AI applications using large language models without writing complex code or training custom models.
  2. Memory systems and data connectors play a crucial role in enhancing productivity and efficiency in LLM-based applications.
  3. Hybrid programming with natural language and traditional programming languages can automate tasks like creating educational content and contract Q&A, leading to faster, error-free results.

All hail nuclear power

Alex's Personal Blog • 65 implied HN points • 19 Dec 24
🕹 Technology AI
  1. Nuclear power is gaining traction as tech companies look to use it for sustainability and energy needs. This shift could help power data centers efficiently.
  2. Despite general market optimism, there are challenges and doubts about current investment strategies. Investors need to think critically about their options in a changing economy.
  3. The EU is facing criticism regarding its AI regulations, which might push tech development outside the region. This could lead to a lack of innovation and growth in Europe.

I Say This Unironically: Our Society Is Not Prepared For This Much Awesome

jonstokes.com • 319 implied HN points • 21 Feb 23
🕹 Technology AI
  1. Generative AI is rapidly changing many aspects of society, affecting everything from artistic creation to education.
  2. Efforts to detect AI-generated content are ineffective, posing challenges for access control and gatekeeping.
  3. AI tools have the potential to enhance educational experiences, improve learning outcomes, but may also disrupt traditional credentialing systems.

Digital poets

Philosophy bear • 71 implied HN points • 24 Nov 24
🎭️ Culture AI
  1. People can't easily tell the difference between AI and human-made art. In fact, they often prefer the AI art, even if it’s not very good.
  2. AI can produce poetry that at least matches the level of a talented amateur. Some AI-generated haikus can even be quite nice.
  3. There's still a question about whether AI can create art that is truly great. It remains unclear how to measure what's undeniably good in art.

The Copper Chains Holding Cerebras Back

Irrational Analysis • 39 implied HN points • 27 Oct 23
🕹 Technology AI
  1. Cerebras, a unique AI-hardware startup, faces challenges in scaling due to copper chains and thermal density issues.
  2. They have developed proprietary technology to print wires across scribe lines, a unique capability in the semiconductor industry.
  3. Cerebras is selling systems for non-AI workloads like drug discovery and scientific research, but they need significant upgrades to compete with Nvidia.

Here’s What It Would Take To Slow or Stop AI

jonstokes.com • 309 implied HN points • 07 Mar 23
🕹 Technology AI
  1. The key to controlling AI development lies in training, inference, and costs distribution.
  2. To stop AI, control over training, model files, and inference phases is necessary.
  3. AI development cannot be completely halted without a global coordinated effort that restricts GPU access.