The hottest AI Substack posts right now

And their main takeaways
Category
Top Technology Topics

OpenAI for Military Use? 💣 Game on.

AI Supremacy • 1375 implied HN points • 18 Jan 24
🕹 Technology AI
  1. OpenAI is potentially working with the Pentagon on military projects.
  2. There are concerns about the implications of OpenAI's involvement in military technology.
  3. OpenAI aims to prevent abuse and increase transparency in AI, despite potential military collaborations.

Cursor's Unfair UX Advantage

Generating Conversation • 46 implied HN points • 12 Feb 26
🕹 Technology AI
  1. Make tasks tiny: small, incremental units of work let users catch mistakes early, build trust, and produce dense feedback that powers a strong data advantage.
  2. A low‑stakes autocomplete/IDE UX makes it easy to accept or reject suggestions, so even imperfect prompts save time and generate lots of useful training signals.
  3. Design agents for fast iteration and cumulative correctness rather than one‑shot perfection — cheap inference and quick feedback loops let users get to the right answer over a few tries and move much faster.

The next big thing in 2026 will be...

next big thing • 141 implied HN points • 01 Jan 26
🕹 Technology AI
  1. Autonomous, end-to-end AI agents will move from being copilots to pilots, owning whole workflows and delivering outcomes rather than just answering prompts.
  2. Persistent memory, proactive behavior, and on-device inference will make AI feel like a personal companion and unlock a wave of new consumer products, generative media, and personalized experiences.
  3. AI will start showing up in the bottom line, driving real deployments, new pricing models, hardware launches, and a surge of IPOs and M&A, while human-heavy AI services get exposed if they can’t prove machine-driven margins.
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The new UX era

Kyle Poyar’s Growth Unhinged • 370 implied HN points • 19 Nov 25
🕹 Technology AI
  1. The prompt bar is becoming the standard part of many new apps. It allows users to quickly interact with the software but can also confuse them if they're unsure what to ask.
  2. Users now often learn how to use a product through their interactions rather than traditional onboarding. This means guiding them effectively in every chat is crucial for their success.
  3. Effective activation in AI products should help users quickly see value, with clear examples and next steps. This encourages them to return and use the product more often.

Go Grok Yourself

Don't Worry About the Vase • 2777 implied HN points • 19 Feb 25
🕹 Technology AI
  1. Grok 3 is now out, and while it has many fans, there are mixed feelings about its performance compared to other AI models. Some think it's good, but others feel it still has a long way to go.
  2. Despite Elon Musk's big promises, Grok 3 didn't fully meet expectations, yet it did surprise some users with its capabilities. It shows potential but is still considered rough around the edges.
  3. Many people feel Grok 3 is catching up to competitors but lacks the clarity and polish that others like OpenAI and DeepSeek have. Users are curious to see how it will improve over time.

What I Read This Week...

Chamath Palihapitiya • 1592 implied HN points • 23 Dec 23
🕹 Technology AI
  1. China leads in global EV transition with over 18 million EVs sold since 2017.
  2. Nearly 9 million student loan borrowers missed October payments despite a 12-month grace period.
  3. Apple focuses on running AI models directly on iPhone hardware, impacting the delivery of AI solutions.

This Week in Marketing: TikTok is local, Instagram is loco, Discord is locked, and AI is labor

The Social Juice • 39 implied HN points • 15 Feb 26
💼 Business AI
  1. Social platforms are racing to add new features and revenue streams — think TikTok’s local feed and Shop, X and LinkedIn subscriptions, and Meta/YouTube ad and AI tools driving creator commerce.
  2. Those product pushes are colliding with privacy, safety and legal headaches — Discord’s age checks sparked backlash, Instagram faced scrutiny over youth harm, and Google and Meta are under regulatory and antitrust pressure.
  3. AI is booming in investment and productization, but it’s also intensifying work and creating real risks — studies, botched real‑world uses, fake experts and automation worries show the tradeoffs as companies rush to monetize AI.

On Joining OpenAI

Artificial Ignorance • 138 implied HN points • 09 Jan 26
🕹 Technology AI
  1. Joined OpenAI to work on Developer Experience, helping developers learn and build with OpenAI’s technology.
  2. Public news roundups are ending, and the newsletter will shift toward longer deep dives with more engineering-specific, practical content for builders.
  3. Experimenting with Substack Chat for paid subscribers (office hours and topic threads) while explicitly avoiding confidential or leaked information and keeping the writing practical and grounded.

🚀 FP! Week In Review, Briefly #14

Faster, Please! • 274 implied HN points • 06 Dec 25
🚌 Education AI
  1. AI can be a great tool for learning, but we need to be careful how we use it. If schools just add AI to their old ways of teaching, it might lead to shallow learning instead of deeper thinking.
  2. Using technology in classrooms should not mean just giving kids devices without guidance. Schools should teach students how to think critically rather than distracting them with screens.
  3. Some teachers are going back to simple methods, like writing and discussions, to help kids engage more deeply. The goal is to use AI to boost thinking skills, not just for quick answers.

Sora still appears to have trouble with physics

Marcus on AI • 3636 implied HN points • 10 Dec 24
🕹 Technology AI
  1. Sora struggles to understand basic physics. It doesn't know how objects should behave in space or time.
  2. Past warnings about Sora's physics issues still hold true. Even with more data, it seems these problems won't go away.
  3. Investing a lot of money into Sora hasn't fixed its understanding of physics. The approach we're using to teach it seems to be failing.

33 predictions for 2026 in AI: consumer, enterprise, AI capex, and more

Enterprise AI Trends • 168 implied HN points • 27 Dec 25
🕹 Technology AI
  1. AI progress will accelerate in 2026, causing fast, widespread change that can create big winners and losers.
  2. AI agents will become mainstream across consumer and enterprise use cases, with coding agents able to autonomously complete multi-hour tasks and driving strong enterprise adoption and FOMO.
  3. Intense competition, cost optimization, and open-source model advances will shape which platforms and startups win, making AI capex and strategic investment decisions essential.

AI in the Age of Mythic Powers by Josh Schrei

The Bigger Picture • 858 implied HN points • 14 Mar 24
🔬 Science AI
  1. AI's powers are seen as mythic and magical in scope, with abilities akin to those discussed in ancient stories and magical grimoires.
  2. The discussion around AI goes beyond rationality and delves into religious and spiritual questions, questioning concepts like sentience and consciousness.
  3. AI poses risks not just on a global and societal scale, but also on individual bodies, with potential impacts on embodiment, agency, and mental health.

No Aisle For What We Need

Perspective Agents • 24 implied HN points • 15 Feb 26
🕹 Technology AI
  1. Major disruptions often show clear early signals, but people and institutions fail to act until the change is obvious, leaving them unprepared and scrambling.
  2. AI is nearing the ability to perform the work of highly educated professionals around the clock, likely within a few years, and that will reshape jobs, education, and organizational value.
  3. Leaders may acknowledge AI without changing plans or building new systems, and we currently lack the practical frameworks and preparations needed, so focused human readiness is required.

Machine Learning From Zero, Chapter 01

The Palindrome • 4 implied HN points • 14 Mar 26
🕹 Technology AI
  1. Machine learning means training predictive models from data. The core setup uses a dataset, a parametric model (a hypothesis), and a loss function to measure how well the model fits the data.
  2. A model approximates the true input–output relation and depends on both its parameters and the training data (often written h(x; w, D)). Models can be deterministic or probabilistic and belong to different families like generative or discriminative.
  3. Which learning paradigm you use depends on what inputs, outputs, and labels are available — the main paradigms are supervised, unsupervised, semi‑supervised, and reinforcement learning. In supervised learning you have input–label pairs and the goal is to learn the mapping from x to y.

Getting 50% (SoTA) on ARC-AGI with GPT-4o

Redwood Research blog • 285 HN points • 17 Jun 24
🕹 Technology AI
  1. Achieving a 50% accuracy on the ARC-AGI dataset using GPT-4o involved generating a large number of Python programs and selecting the correct ones based on examples.
  2. Key approaches included meticulous step-by-step reasoning prompts, revision of program implementations, and feature engineering for better grid representations.
  3. Further improvements in performance were noted to be possible by increasing runtime compute, following clear scaling laws, and fine-tuning GPT models for better understanding of grid representations.

Seven Predictions For Tech in 2024

Big Technology • 9632 implied HN points • 22 Dec 23
🕹 Technology AI
  1. Generative AI will advance in 2024 with new capabilities like better conversation retention and reasoning.
  2. The year 2024 is predicted to be significant for mixed reality advancements, integrating AI avatars and assistants.
  3. Tech industry forecasts include Elon Musk selling X, Meta's market cap reaching $1 trillion, and NVIDIA facing increased competition.

Looking Back: One Year Of Big Technology's Paid Edition

Big Technology • 4128 implied HN points • 22 Oct 24
🕹 Technology AI
  1. The launch of paid subscriptions for Big Technology has been a success, allowing the publication to grow and provide better content.
  2. The newsletter included valuable insights on major tech companies like Amazon and Google, highlighting important trends and changes in leadership.
  3. Engagement with subscribers has been strong, with the addition of exclusive podcasts and events, making the relationship between the writer and readers even more meaningful.

Import AI 373: Guaranteed safety; West VS East AI attitudes; MMLU-Pro

Import AI • 419 implied HN points • 20 May 24
🕹 Technology AI
  1. Academic researchers have built the National Deep Inference Fabric (NDIF) to experiment with large-scale AI models in a transparent manner.
  2. Researchers have outlined a framework for building 'guaranteed safe' AI systems, involving components like safety specifications, world models, and verifiers.
  3. A global survey indicates that Western countries have more pessimism towards AI regulation compared to China and India, potentially changing how governments approach regulating and adopting AI.

Elon Musk’s xAI raises Monster $6B Series B for AI Supercomputer and Grok 3

The VC Corner • 379 implied HN points • 28 May 24
🕹 Technology AI
  1. Elon Musk's company xAI just raised $6 billion to build an advanced AI supercomputer and improve their AI model, Grok 3. This new funding makes xAI a key player alongside OpenAI and Anthropic.
  2. The $6 billion Series B funding round is a big deal in the AI world, showing a lot of investor confidence. Musk plans to use this money to get the hardware needed for more powerful AI.
  3. xAI aims to compete with top AI companies by developing a massive number of semiconductors for training their models. This means more competition in the market and potentially exciting innovations in AI technology.

The Sequence AI of the Week #797: The New Companies that can Change the Inference Landscape

TheSequence • 84 implied HN points • 28 Jan 26
🕹 Technology AI
  1. Two new commercial companies from the vLLM and SGLang teams—Inferact and RadixArk—raised huge funding and are positioning themselves as major players in the inference stack.
  2. The focus is shifting from building bigger models to improving inference unit economics, so the software that manages memory, scheduling, and kernels is now the main battleground.
  3. Serving models efficiently is bottlenecked by scarce VRAM and the KV cache tax, because asynchronous and unpredictable inference patterns drive up cost and complexity.

You should be playing with AI agents for marketing

Kyle Poyar’s Growth Unhinged • 1301 implied HN points • 02 Jul 25
💼 Business AI
  1. Using AI agents for marketing can boost efficiency by handling various tasks that would normally require multiple team members. These agents are like having a group of helpers that can work around the clock.
  2. Each business can create a tailored set of AI agents specific to their needs. This means that instead of treating AI like just another tool, businesses can think of AI agents as part of their team.
  3. It's important for leaders to delegate tasks to AI agents. The benefit comes from identifying workflows that can be automated and training the AI to take over those responsibilities.

OpenClaw Use Cases: 35+ Real Ways People Are Running Their Lives (and Businesses) With It

The Product Channel By Sid Saladi • 30 implied HN points • 22 Feb 26
🕹 Technology AI
  1. OpenClaw has real security risks, so lock it down before connecting real accounts. Use a non-root user, separate dedicated accounts, human approval gates, read-only skills to start, Docker isolation, and never hardcode API keys.
  2. OpenClaw is a persistent agent that runs models and plugins to execute actions, not just answer questions; it can send emails, run shell commands, control smart devices, and run scheduled jobs from your chat app.
  3. Do a one-time setup (install on a VPS or host, connect a model, wire a chat interface, install only needed skills, write a SOUL.md with hard limits, and enable scheduling) and then automate workflows like morning briefings, a personal memory system, and voice-to-journal.

The Sequence Opinion #806: The Emergence of the Agent-as-a-Judge: Why Evals Need a Reasoning Engine

TheSequence • 49 implied HN points • 12 Feb 26
🕹 Technology AI
  1. Evaluation moved from informal "vibe checks" to using stronger LLMs to automatically grade weaker models' outputs.
  2. That single-pass LLM-as-judge approach powered benchmarks like MT-Bench and Chatbot Arena, but simple intuitive judgments are becoming insufficient.
  3. The field is shifting to agent-as-a-judge, where evaluations need multi-step reasoning engines and dynamic, agentic judging instead of static benchmarks.

A cold play

benn.substack • 1048 implied HN points • 18 Jul 25
🕹 Technology AI
  1. The value of a domain name can vary greatly depending on who owns it. For example, chatgpt.com would be worth a lot more to a company like Google than to an individual.
  2. User experience (UX) is key in getting people to adopt AI tools. A good interface can make a product more appealing, regardless of how advanced the technology behind it is.
  3. Google faces a challenge in convincing users to choose their AI models over others. They have great technology but need to create better products that people actually want to use.

Perplexity Computer, AI-native Postman, Stolen Gemini API Key

The API Changelog • 4 implied HN points • 10 Mar 26
🕹 Technology AI
  1. APIs are evolving into agent-native interfaces where models can interpret UIs, control actions, and orchestrate multiple services so agents deliver finished work instead of just answers.
  2. Mobile networks and telco services are becoming programmable through standardized global APIs and marketplace hubs, letting developers access identity, connectivity, and network functions from a single integration point.
  3. The agentic era increases operational and security risk: leaked keys or provider outages can cause massive costs and broken workflows, so teams need hard spending caps, real‑time anomaly detection, and multi‑provider failover.

We don't need to just make up fantasy stories

Economic Forces • 21 implied HN points • 26 Feb 26
💼 Business AI
  1. GDP accounting means output turned into income never just disappears; if automation shifts income from workers to capital owners, that money gets spent or saved and fuels other parts of the economy.
  2. Prices provide a natural brake: cheaper AI-driven supply pushes prices down, which tends to raise demand or shift consumption and prevents an endless negative spiral unless a specific blocking mechanism exists.
  3. You can’t extrapolate from a few firms to the whole economy — comparative advantage and new consumer demand lead people and firms to reallocate into new roles, so automation changes jobs and wages but doesn’t automatically cause total collapse.

OpenAI in Davos…Looking Back on 10 Years of Unicorns With Aileen Lee...Stripe and SpaceX’s Secondary Pricing

Newcomer • 1238 implied HN points • 19 Jan 24
🕹 Technology AI
  1. OpenAI has faced challenges as a 'big tech' company early in its life, including raising significant funds and experiencing executive drama.
  2. OpenAI removed its 'Don't Be Evil' slogan and is now collaborating with the Department of Defense on cybersecurity projects.
  3. Aileen Lee's research on unicorns reveals that strong unicorns are more involved in enterprise tech than consumer tech, with many 'papercorns' yet to prove their value.

GPT-5 and the arc of progress

Democratizing Automation • 902 implied HN points • 07 Aug 25
🕹 Technology AI
  1. GPT-5 has been received with mixed feelings because it didn't fully meet the high expectations set before its launch. However, most users find it effective and beneficial.
  2. The upgrade in GPT-5 focuses on balancing performance, price, and user experience, making it one of the more affordable AI options.
  3. Progress in AI will continue, but it may be slower than some hope. The industry is shifting towards practical improvements over radical breakthroughs.

Briefing: Super Google Sheets, Netflix’s app snub, Finding Nimo

Department of Product • 1238 implied HN points • 18 Jan 24
🕹 Technology AI
  1. Notion integrates Indie calendar Cron into a new standalone Calendar app for sharing with stakeholders.
  2. Numerous plugin enhances Google Sheets with generative AI for tasks like creating formulas and translating text.
  3. Netflix's decision not to build a dedicated app for visionOS is a setback for Apple, while Nimo gains popularity as a lighter AR alternative.

8 Forecasts & Implications for the Years Ahead :: 2024+

Implications, by Scott Belsky • 1356 implied HN points • 04 Jan 24
🕹 Technology AI
  1. The future will be personalized to your preferences, with digital experiences tailored to you.
  2. Local OS-native AI models will improve everyday life and redefine consumer AI, focusing on personalization, trust, and privacy.
  3. Small brands will become more competitive with big brands, AI will influence purchase decisions, and education will undergo a significant transformation.

Why everyone is mad at California voters, and the impolite fiction of the Nvidia-Groq deal

Alex's Personal Blog • 164 implied HN points • 29 Dec 25
🕹 Technology AI
  1. A proposed one-time billionaire wealth tax in California looks fair politically and could raise a lot for healthcare, but it's impractical because much billionaire wealth is illiquid and would force asset sales or borrowing to pay.
  2. Wealth taxes run up against mobility and incentives: the very rich can move or shift investments to lower-tax states, so the measure would likely cause capital flight and reduce long-term business activity and revenue for California.
  3. Nvidia's deal with Groq risks undermining competition in AI inference hardware by neutralizing a potential challenger, which could concentrate market power and make it harder for smaller firms to compete.

Top Highlights of CES 2024

AI Supremacy • 1198 implied HN points • 21 Jan 24
🕹 Technology AI
  1. CES 2024 showcased innovative AI technology like Rabbit R1 and enhanced robots.
  2. LG and Samsung introduced transparent TVs with unique features at CES 2024.
  3. Tech advancements at CES included dual-screen laptops, AI-enhanced PCs, and unique gadgets like GyroGlove.

CXL Is Dead In The AI Era

SemiAnalysis • 7475 implied HN points • 16 Mar 24
🕹 Technology AI
  1. CXL technology was once thought to revolutionize data center hardware, but many projects have been shelved in favor of other advancements.
  2. CXL is not likely to be the go-to interconnect for AI applications due to limitations in availability and deeper issues in the era of accelerated computing.
  3. The main challenges with CXL include PCIe SerDes limitations, competition from proprietary protocols for AI clusters, and the need for improvements in chip design for bandwidth efficiency.

What I'm preparing for in 2026

next big thing • 48 implied HN points • 02 Feb 26
🕹 Technology AI
  1. Agentic AI will move beyond coding into real-world tasks. We'll see impressive demos and useful production agents, but also limits that leave people underwhelmed or unsettled.
  2. Enterprise AI in 2026 will be judged on hard ROI like revenue and cost savings, driving consolidation around platforms that clearly deliver value, while consumer AI will lean into fun, entertaining products that capture attention.
  3. Energy will become a major bottleneck for scaling AI, prompting big investments in power and data center infrastructure that will shape where and how AI capacity grows next year.

“DeepSeek Moment” Anniversary in a Nashville Snowpocalypse

Interconnected • 61 implied HN points • 27 Jan 26
🕹 Technology AI
  1. Making open source the default for frontier AI speeds innovation and lets more people contribute and build on progress.
  2. Letting software specifications drive hardware roadmaps, especially in China, aligns chip design with real AI needs and priorities.
  3. Pursuing AGI without a short-term business model can be a strategic advantage because it prioritizes long-term capability over immediate profit.