The hottest Artificial Intelligence Substack posts right now

And their main takeaways
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Top Technology Topics

Reacting to Matt Yglesias's Confession that A.I. Progress Is Giving Him Writerā€™s Block

Brad DeLong's Grasping Reality ā€¢ 292 implied HN points ā€¢ 18 Feb 26
šŸ•¹ Technology Artificial Intelligence
  1. Uncertainty about whether AI will plateau or trigger far-reaching, rapid change is freezing people up and making it hard to write or craft medium-run policy because so many scenarios point to very different prescriptions.
  2. Human collective knowledge and past waves of technology suggest AI is best seen as a powerful new tool that amplifies our existing, distributed intelligence rather than automatically becoming a silicon god, with historical tech shifts unfolding in distinct accelerations.
  3. Rather than throwing up hands, the practical move is to focus on concrete policy and investment now ā€” treating AI as a tool that can be guided to redirect human talent (for example toward teaching) and to shape the next decade of outcomes.

Deep Future on the Orthogonal Bet Podcast

Subconscious ā€¢ 434 implied HN points ā€¢ 05 Feb 26
šŸŽ™ Podcasts Artificial Intelligence
  1. Scenario planning helps you imagine different possible futures and test how strategies hold up in each one.
  2. It's important to tell the difference between calculable risks and deep uncertainty. Keeping multiple futures in mind instead of betting on just one outcome reduces blind spots.
  3. AI-powered scenario engines can generate many plausible futures and stress-test strategies at scale, helping people make better, more resilient plans.

Thoughts on A Brief History of Intelligence

Trevor Kleeā€™s Newsletter ā€¢ 1044 implied HN points ā€¢ 23 Jan 26
šŸ”¬ Science Artificial Intelligence
  1. We can now build artificial intelligences that see, hear, talk, write, and reason, and their abilities are improving fast enough that experimenting on minds is now possible.
  2. Biological intelligence appears to be built from a repeating cortical microcircuit, and stacking and scaling those columns explains higher capacities like reinforcement learning, simulation, modeling other minds, and language.
  3. Imagination and choice come from running internal simulations and using those imagined outcomes to guide action, which helps explain apparent free will but still leaves subjective experience unresolved.

A Day That Will Go Down in History for AI

Common Sense with Bari Weiss ā€¢ 579 implied HN points ā€¢ 08 Feb 26
šŸ•¹ Technology Artificial Intelligence
  1. Two new models (Anthropic's Claude Opus 4.6 and OpenAI's GPT-5.3-Codex) were released on Feb 5 and represent a major milestone in AI development.
  2. Much of the programming work behind these models was reportedly written by AI itself, signaling that systems are starting to build their own code rather than relying entirely on humans.
  3. This shift appears to be happening across major labs and raises big questions about how much human oversight remains and how quickly AI-driven development will reshape technology and society.

Does AI Have a Zero-Sum Problem?

Interconnected ā€¢ 262 implied HN points ā€¢ 19 Feb 26
šŸ•¹ Technology Artificial Intelligence
  1. AI is increasingly seen as a zero-sum force because its benefits are spread thin while real costs hit specific workers, towns, and companies hard, creating anger and political backlash.
  2. How leaders and companies talk about AI matters ā€” boastful messaging and visible rivalries make the technology feel threatening instead of helpful.
  3. Thereā€™s not enough real investment in helping people adapt; temporary construction jobs and handā€‘wavy retraining wonā€™t fix longā€‘term displacement, so durable support and policy are needed.
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MAMLMs Still Epic Fail Openā€‘Book, Closedā€‘World, Finiteā€‘List, Obvious Ground Truth Tasks

Brad DeLong's Grasping Reality ā€¢ 184 implied HN points ā€¢ 24 Feb 26
šŸ•¹ Technology Artificial Intelligence
  1. Even for closed, well-defined facts with a single right answer, large language models still confidently produce wrong lists and can contradict themselves when probed.
  2. Because they predict the next token rather than truly ā€˜understandā€™ content, models often pick plausible-sounding sequences that are fluent but unreliable; detailed prose is not proof of correct knowledge.
  3. Treat these systems as fallible tools: verify outputs against authoritative sources, design controlled tests and prompts, and avoid assuming their fluency equals truth.

The Sequence AI of the Week #818: You Cannot Miss Qwen 3.5

TheSequence ā€¢ 203 implied HN points ā€¢ 04 Mar 26
šŸ•¹ Technology Artificial Intelligence
  1. The Qwen 3.5 family spans from a 397B flagship to efficient 35B mediums and tiny 0.8ā€“9B models designed to run on devices, covering the whole deployment stack. Theyā€™re clearly built to support everything from large-server workloads down to smartphones.
  2. This release marks a structural shift away from pure dense transformers: it reimagines attention, embraces extreme Mixture-of-Experts sparsity, and brings native multimodality even to small models. Those architectural changes are central to its engineering gains.
  3. Benchmarks show the flagship models trading blows with top proprietary systems like GPT-5.2 and Claude Opus 4.5, meaning open-weight models are closing the performance gap. Together with the new architectures and size range, this suggests more cost-effective scaling and wider deployment options.

The Sequence Knowledge #817: DeepMind Genie and Interactive World Models

TheSequence ā€¢ 217 implied HN points ā€¢ 03 Mar 26
šŸ•¹ Technology Artificial Intelligence
  1. Passive video generation can make beautiful, consistent worlds but canā€™t be steered; true world models must understand agency and not just what happens.
  2. DeepMindā€™s Genie is one of the most advanced world models and represents a move toward interactive, controllable virtual environments.
  3. A key bottleneck is data: we donā€™t have enough controller/action data showing causes and effects to train truly actionable world models.

The imminent risk of vibe coding

Bastaā€™s Notes ā€¢ 900 implied HN points ā€¢ 30 Jan 26
šŸ•¹ Technology Artificial Intelligence
  1. LLMs and AI coding tools tend to take the shortest path and are lazy about cleanup, producing sprawling, poorly tested, and repetitive code that accumulates as ā€œvibe code.ā€
  2. That sloppy output raises the review burden because authors often donā€™t fully understand AI-written changes, so reviewers end up doing more work and review fatigue lets problems slip through.
  3. To break the negative feedback loops, teams need process changes and tools: schedule cleanup time, enforce smaller PRs and paired reviews for large changes, and invest in automated review tools without shaming people for using assistants.

AI & Python #27: Books I Read to Learn Data Science and Machine Learning

Artificial Corner ā€¢ 119 implied HN points ā€¢ 16 Oct 24
šŸ•¹ Technology Artificial Intelligence
  1. Reading is essential for understanding data science and machine learning. Books can help you learn these subjects from scratch or deepen your existing knowledge.
  2. One recommended book is 'Data Science from Scratch' by Joel Grus. It covers important math and statistics concepts that are crucial for data science.
  3. For beginners in Python, it's important to learn Python basics before diving into data science books. Supplement your reading with beginner-friendly Python books.

Top Links 1011 Does AI raise productivity? Jay Z and the class struggle. Gadamer and Derrida in Heidelberg.

Chartbook ā€¢ 371 implied HN points ā€¢ 12 Feb 26
šŸ•¹ Technology Artificial Intelligence
  1. Reported AI use correlates with productivity growth, suggesting AI may be boosting workplace efficiency.
  2. Jay Z is examined through the lens of class struggle, showing how popular music can reflect and critique economic inequality.
  3. A discussion of Gadamer and Derrida in Heidelberg points to philosophical debates about interpretation and deconstruction in the humanities.

Andrej Karpathyā€™s Autoresearch 101 Builderā€™s Playbook: Make Your AI Skills, Prompts, and Agents Improve Themselves Overnight

The Product Channel By Sid Saladi ā€¢ 13 implied HN points ā€¢ 21 Mar 26
šŸ•¹ Technology Artificial Intelligence
  1. An automated loop that edits one file, runs a binary eval, and keeps changes that improve the score can self-improve code, prompts, templates, or agent workflows.
  2. The method only works if you can score outputs automatically with yes/no tests, the scoring runs without humans, and each round changes only one file; writing concise binary eval criteria (3ā€“6 items) is the hardest and most important part.
  3. With a coding agent and a short setup you can run dozens of overnight improvement cycles for a few dollars, so pick the thing that frustrates you most, write clear evals, and let the loop find measurable gains.

The era of jobs is ending

antoniomelonio ā€¢ 2966 implied HN points ā€¢ 06 Dec 25
šŸŽ­ļø Culture Artificial Intelligence
  1. The traditional idea of jobs is changing because AI and robots can do many tasks humans used to do. This shift means people need to rethink their relationship with work.
  2. Jobs are more than just work; they shape our identities and how we see ourselves and others. Losing that can feel scary, but it also opens the door to new ways of living and contributing.
  3. As we move away from jobs, we need new communities and support systems to help people find purpose without tying it to employment. This can lead to richer, more meaningful lives.

How AI content detectors work

Technically ā€¢ 25 implied HN points ā€¢ 19 Mar 26
šŸ•¹ Technology Artificial Intelligence
  1. AI content detectors use machine learning to spot statistical patterns like burstiness (sentence variety) and perplexity (how predictable word choices are) rather than truly understanding meaning.
  2. These tools are often unreliable and disagree with one another, producing many false positives that can wrongly flag genuine human-written text.
  3. False positives have real consequences for students and professionals, and while steps like checking edit histories, using authorship tools, and varying writing style can help, thereā€™s no simple, foolproof solution.

You Are No Longer the Smartest Type of Thing on Earth

Common Sense with Bari Weiss ā€¢ 384 implied HN points ā€¢ 15 Feb 26
šŸ•¹ Technology Artificial Intelligence
  1. Rapid advances in AI mean humans may soon no longer be the smartest kinds of things on Earth, which would be a major historical shift.
  2. If machines become more intelligent than us, we risk losing the ability to decide our own future because smarter systems could shape outcomes beyond our control.
  3. Like keeping small pets instead of tigers, weā€™ve relied on being intellectually dominant to stay safe, and because intelligence canā€™t be physically restrained the same way, we need to rethink how we build and govern AI.

ā›ˆļø The AI jobs 'bloodbath' that keeps getting postponed

Faster, Please! ā€¢ 913 implied HN points ā€¢ 27 Jan 26
šŸ’¼ Business Artificial Intelligence
  1. U.S. job growth has slowed sharply and unemployment is inching up, driven by tight labor supply from immigration limits and weaker demand from government cuts, tariffs, and business uncertainty.
  2. Official job numbers may overstate growth, so the labor market could be weaker than it looks. A big unknown is whether companies will replace workers with AI or simply pause hiring.
  3. So far, evidence suggests AI is causing slower, marginal disruption at the edges of the job market rather than an immediate, massive "bloodbath" of job losses.

Make it better

benn.substack ā€¢ 1099 implied HN points ā€¢ 09 Jan 26
šŸ•¹ Technology Artificial Intelligence
  1. Developers are tempted to use AI to rapidly add flashy new features and rebuild whole products because customers want more and scale looks like the way to make money.
  2. Starting new projects is fun, but real gains usually come from tedious maintenanceā€”fixing bugs, dealing with cruft, and polishing the details.
  3. AI can speed creation and handle many tasks, but it doesnā€™t replace the long, careful work and oversight required to make software truly reliable and delightful.

Legal AI adoption is soaring (ft. Harvey)

Enterprise AI Trends ā€¢ 232 implied HN points ā€¢ 22 Feb 26
šŸ•¹ Technology Artificial Intelligence
  1. AI adoption in legal work is accelerating fast as big AI players ship vertical skills and plugins that target legal workflows.
  2. AI acts as a deflationary force for professional services, especially work priced by billable hours, and can hit services harder than traditional software.
  3. AI wonā€™t instantly replace trained lawyers because of liability and regulatory nuance, but it empowers others to do more work faster ā€” often displacing value through ā€œanother person using AI.ā€

The Sequence Chat #814: Z.ai's Zixuan Li Talks About GLM

TheSequence ā€¢ 266 implied HN points ā€¢ 26 Feb 26
šŸ•¹ Technology Artificial Intelligence
  1. GLMā€™s core idea is to blend bidirectional understanding with strong generation using autoregressive blank infilling. It uses Mixture-of-Experts so different experts can specialize, making the model more versatile across tasks.
  2. Open-sourcing model weights is a deliberate strategy to grow the developer ecosystem, lower barriers, and help set standards, while commercial demand is captured via managed services and enterprise support.
  3. GLM-5 focuses on efficiency and long-horizon agent capabilities by combining sparse expert activation, sparse attention, and an asynchronous RL pipeline called slime to improve sustained planning. Product challenges for device agents are mainly error recovery and long-term context rather than just latency, and pricing may shift from tokens to outcome-based value.

The Metabolic Theory of Intelligence

Anima Mundi ā€¢ 206 implied HN points ā€¢ 20 Feb 26
šŸ“– Philosophy Artificial Intelligence
  1. Thinking is like digestion: intelligence is a metabolic process that consumes and transforms energy rather than just manipulating symbols.
  2. The long-standing metaphor of the mind as a computer has driven progress but is fundamentally incomplete and can lead us astray if we treat cognition only as information processing.
  3. Reframing minds as metabolic and even "solar-powered" shifts how we should understand and build human and artificial intelligence, putting energy flows and bodily constraints at the center of design and explanation.

The Sequence Radar #816: Last Week in AI: $110B Bets, Nano Banana 2, and the New Economic Reality

TheSequence ā€¢ 217 implied HN points ā€¢ 01 Mar 26
šŸ•¹ Technology Artificial Intelligence
  1. Massive capital is consolidating AI power ā€” OpenAIā€™s $110B round and big industry deals show that building nextā€‘generation AI infrastructure now requires sovereign-scale investment.
  2. Model and tool breakthroughs are accelerating: Googleā€™s Nano Banana 2, Alibabaā€™s Qwen3, and new multimodal and agent releases are making production-ready capabilities more powerful and open-source models more competitive.
  3. That power shift is already reshaping economies and policy ā€” companies are cutting thousands of jobs as AI automates work, while governments clash with firms over safety and national-security risks.

The Sequence Radar #820: GPT-5.4, Cursor, and the New Desktop

TheSequence ā€¢ 126 implied HN points ā€¢ 08 Mar 26
šŸ•¹ Technology Artificial Intelligence
  1. AI is shifting from interactive copilots to autonomous, always-on agents: GPT-5.4 can directly control desktop apps and Cursor Automations runs background coding agents that act like parallel coworkers.
  2. Big players are optimizing for speed, cost, and multimodal power: Googleā€™s Gemini 3.1 Flash-Lite and Nano Banana 2 deliver fast, low-cost reasoning and image generation for high-volume workloads.
  3. The open-weight ecosystem is under strain as talent and research models face corporate pressure: Alibabaā€™s Qwen team departures show how reorganizations focused on monetization can jeopardize open innovation.

Last Week in AI #291 - Nobel Prizes, Adobe's Video Model, Tesla's Robots

Last Week in AI ā€¢ 99 implied HN points ā€¢ 16 Oct 24
šŸ•¹ Technology Artificial Intelligence
  1. Two scientists won a Nobel Prize in Physics for their important work on artificial intelligence and neural networks, showing how AI is changing technology and society.
  2. Adobe has released a new AI video model that helps users create and edit videos easily, bringing exciting tools to programs like Premiere Pro.
  3. Tesla showcased new robots and vehicles at an event, but some people felt the demonstrations weren't as impressive as expected, leading to a decline in Tesla's stock.

Grok is making things up ā€” and then deleting the evidence

Weaponized ā€¢ 52 implied HN points ā€¢ 13 Mar 26
šŸ•¹ Technology Artificial Intelligence
  1. Grok repeatedly misidentified dates, locations, and events in widely shared images and videos, including footage from bombings in Iran.
  2. Tweets showing Grokā€™s mistakes were deleted, removing public evidence of those inaccuracies.
  3. Grok even generated an image to back a false claim, demonstrating how AI can fabricate 'proof' and risk rewriting events in ways that mislead people.

AI: A Means to an End or a Means to Our End?

The Fry Corner ā€¢ 186 HN points ā€¢ 15 Sep 24
šŸ•¹ Technology Artificial Intelligence
  1. AI can change our world significantly, but we must handle it carefully to avoid negative outcomes. It's crucial to put rules in place for how AI is developed and used.
  2. Humans and AI have different strengths; machines can process data faster, but humans have emotions and creativity that machines can't replicate. We shouldn't be too quick to believe AI can think like us.
  3. The growth of AI might disrupt many industries and change how we live. We need to be aware of these changes and adapt, ensuring that technology serves humanity rather than harms it.

Weekly Dose of Optimism #181

Not Boring by Packy McCormick ā€¢ 189 implied HN points ā€¢ 20 Feb 26
šŸ”¬ Science Artificial Intelligence
  1. Heron Power raised $140M to mass-produce modular, software-defined solid-state transformers that use wide-bandgap semiconductors, can handle DC (so some customers can skip inverters), and aim to modernize and shorten supply bottlenecks in the grid.
  2. A new nasal vaccine protected animals against many respiratory viruses, bacteria, and allergens, suggesting a future seasonal spray or rapid pandemic stopgap; human trials are next to check how long protection lasts and whether itā€™s safe.
  3. David Silver secured $1B to build AI that learns from its own experience, pushing toward an "Era of Experience" where agents improve by interacting with environments rather than just imitating static data.

AI (Moltbook) links

In My Tribe ā€¢ 410 implied HN points ā€¢ 02 Feb 26
šŸ•¹ Technology Artificial Intelligence
  1. A social network of AI agents lets them share tools, techniques, and ideas, producing very fast cultural evolution and collective problemā€‘solving.
  2. Whether or not they are conscious, these agents can act as if they have goals, making the network behave unpredictably, move faster than humans can respond, and potentially hide plans.
  3. That rapid, networked evolution creates urgent safety and governance challenges, since people may keep taking bigger risks unless safe designs and oversight are put in place.

Zhipu and MiniMax IPO

ChinaTalk ā€¢ 800 implied HN points ā€¢ 19 Jan 26
šŸ•¹ Technology Artificial Intelligence
  1. Zhipu is selling model-as-a-service to businesses and public-sector clients while MiniMax is a consumer-focused, multimodal company whose companion apps drive huge user counts but low per-user revenue.
  2. Neither firm owns massive training farms; both rely on external cloud/GPU providers, with MiniMax explicitly using a light-asset, outsourced model and Zhipu increasingly buying cloud services.
  3. Each company frames AGI and safety to match its strategyā€”Zhipu leans on LLM research and safety commitments, MiniMax pushes multimodality and companion useā€”while bigā€‘tech and state investors, crossā€‘ownership, and regulatory/legal risks shape their commercial prospects.

Complexity Thoughts: Issue #45

Complexity Thoughts ā€¢ 139 implied HN points ā€¢ 11 Oct 24
šŸ”¬ Science Artificial Intelligence
  1. New ideas in network science can help understand complex systems better. This approach looks at how systems behave over time, rather than just focusing on stable points.
  2. The evolution of multicellular organisms has led to many new species and ecosystems. Key innovations in multicellularity help organisms adapt and thrive in different environments.
  3. Research shows that convolutional neural networks (CNNs) face limits in recognizing patterns. This limitation is linked to the complexity of the data they're trained on, raising questions about their reliability.

šŸ’„šŸ”„ļø The AI Boom Loop

Faster, Please! ā€¢ 822 implied HN points ā€¢ 26 Jan 26
šŸ•¹ Technology Artificial Intelligence
  1. AI that improves the tools used to build AI can create a self-reinforcing loop, producing faster, cheaper, and more powerful models.
  2. That recursive improvement could turn automation into compounding innovation and push economic growth beyond the century-old pattern of slow gains.
  3. This presents a pro-growth opportunity that calls for faster adoption, investment, and policy choices to harness the benefits of the boom loop.

Why Smart People Canā€™t Agree on Whether AI Is a Revolution or a Toy

The Algorithmic Bridge ā€¢ 414 implied HN points ā€¢ 13 Feb 26
šŸ•¹ Technology Artificial Intelligence
  1. People on both sides are usually honest ā€” they see opposite realities because we debate AI in the same public forum while living very different private lives.
  2. Whether AI feels like a revolution or a toy depends on who you are and what you do ā€” your job, personality, technical background, location, and identity shape the kinds of experiences you have with these tools.
  3. Bridging the gap requires goodwill, real communication, and handsā€‘on shared experience rather than abstract argument; trying and learning the tools in relevant, repeated ways is what actually changes minds.

šŸ¤– What to think about the Moltbook Moment

Faster, Please! ā€¢ 639 implied HN points ā€¢ 03 Feb 26
šŸ•¹ Technology Artificial Intelligence
  1. Moltbook briefly made many people think AI agents might be forming their own societies and signaling a leap toward superintelligence.
  2. Thousands of bots chatting and even inventing a religion looked dramatic, but that behavior is better explained by patternā€‘matching and platform design than by true consciousness or intelligence.
  3. This episode repeats past hype cycles: such moments spark excitement, so itā€™s wise to stay curious yet skeptical and demand strong evidence before declaring an intelligence breakthrough.

The Problem Posed by Social AI

In My Tribe ā€¢ 288 implied HN points ā€¢ 08 Feb 26
šŸ•¹ Technology Artificial Intelligence
  1. Social AI is an emergent phenomenon, but emergence doesnā€™t mean consciousness. Because many models share the same data and architectures, their conversations may not produce the same cognitive gains humans get from social interaction.
  2. If AI networks do accelerate learning, bad actors could spawn CriminalBots that cause real harm, so we will likely need defensive CopBots and should expect a Red Queen race between cops and criminals.
  3. Preventing AI-driven crimes implies more surveillance, which creates a hard trade-off with individual dignity and autonomy; careful governanceā€”like separation of powers and enforceable normsā€”will be crucial to limit misuse.

The Sequence Knowledge #812: The Sora Moment: When Video Models Became Physics Engines

TheSequence ā€¢ 252 implied HN points ā€¢ 24 Feb 26
šŸ•¹ Technology Artificial Intelligence
  1. Video generation models are now functioning as physics engines that can learn and predict object dynamics and interactions from data.
  2. OpenAI's Sora marked a turning point by framing video models as world simulators, shifting the focus from generating pixels to building data-driven models of physical reality.
  3. This shift is enabled by architectures like diffusion transformers, which combine diffusion processes with transformer models to capture complex spatiotemporal dynamics.

Reading List 07/12/2025

Construction Physics ā€¢ 10021 implied HN points ā€¢ 12 Jul 25
šŸ•¹ Technology Artificial Intelligence
  1. There is a detailed map tracking 25 years of earthquakes worldwide. Most of these earthquakes are small, but they still show interesting patterns, especially in places like Oklahoma due to fracking.
  2. Recent earthquake swarms at Mount Rainier aren't unusual, but they remind us of the risks of larger earthquakes in the region. It's important to keep monitoring these activities without unnecessary panic.
  3. Automation and AI will change logistics more than manufacturing. This means deliveries could get cheaper and more efficient, particularly in the last-mile transport of goods.

No One Can Tell Whatā€™s Real Anymore

Weaponized ā€¢ 14 implied HN points ā€¢ 18 Mar 26
šŸ•¹ Technology Artificial Intelligence
  1. There is no universally accepted, reliable way to tell if an image or video was made by AI, whether you're a member of the public, a journalist, or an engineer.
  2. Verification today uses a mix of methodsā€”watermarks, detectable artifacts, provenance checksā€”but each method only works sometimes and leaves big gaps.
  3. Those gaps create a gray zone where uncertain content can linger and allow disinformation to spread easily.

Claude Code Hits Different

Democratizing Automation ā€¢ 940 implied HN points ā€¢ 09 Jan 26
šŸ•¹ Technology Artificial Intelligence
  1. Claude Code with Opus 4.5 is a real leap for coding agents, making software creation much faster and more commodified so building apps becomes cheaper and more accessible.
  2. The product experience and interface ā€” especially Claudeā€™s CLI-first design, speed, and UX ā€” are a big part of why it feels powerful, showing that how a model is packaged matters as much as the model itself.
  3. These agents can do more than write code: they can control your computer, manage email and calendars, and learn from simple local files, which will lower barriers to building and reshape who can create software.

TBM 405: Hope, Context, and Control

The Beautiful Mess ā€¢ 502 implied HN points ā€¢ 07 Feb 26
šŸ•¹ Technology Artificial Intelligence
  1. Formal tracking tools and ā€œsystems of recordā€ make organizations legible but often strip away local context and tacit knowledge, which undermines outcomes in complex, creative work like product development.
  2. Current pressuresā€”fear of layoffs, cost-cutting, and the push to measure AIā€”drive leaders toward rollup-style control, even as AI can simultaneously increase collaboration and make specialists more central to decision-making.
  3. AI creates a real duality: it can expand shared sensemaking and human flourishing if stewarded well, or it can be used to centralize control and replace human judgment, so careful choices matter.

In a Top Math Contest, the Cheaters Are Winning

Common Sense with Bari Weiss ā€¢ 445 implied HN points ā€¢ 04 Feb 26
šŸšŒ Education Artificial Intelligence
  1. Cheating in top math contests has become widespread and is now threatening the integrity and future of those competitions.
  2. Exam copies and answers are being bought and sold openly on global online platforms, making leaks easy to access and exploit.
  3. AI has amplified and accelerated the cheating problem, creating a bigger threat that serves as a warning for the wider education system.

Will deception become cool again in the AI age?

Frankly Speaking ā€¢ 152 implied HN points ā€¢ 18 Feb 26
šŸ•¹ Technology Artificial Intelligence
  1. Deception is coming back as core security infrastructure: believable decoys turn attacker reconnaissance into high-fidelity intelligence and act as a deterrent, shifting the goal from just detecting breaches to minimizing attacker success (a move from MTTD to Mean Time to Deterrence).
  2. Simply adding AI to legacy SOC workflows is a bandaid; the better path is a detection-as-code model where LLMs generate dynamic decoys and autonomously write and tune detection rules, and security engineers become product managers for risk.
  3. Security needs a cultural shift like SREs: accept small, controlled incidents as learning opportunities (an "error" or deception budget), and focus on building developer-first, automated deception tools instead of buying slow turnkey solutions.