DataRobot

DataRobot AI

DataRobot AI

DataRobot AI

DataRobot AI

DataRobot AI

DataRobot AI

DataRobot AI

DataRobot AI

DataRobot AI

DataRobot AI

AI Doesn't Exist, and Poop Proves It

[robot](https://preview.redd.it/w44kmovo1h3h1.png?width=1448&format=png&auto=webp&s=786825279828a5650259aa1376698133a1aa4c66) *Maybe we should have called it accumulated intelligence.* There is no artificial intelligence. Or at least, I don't think the word "artificial" is as clean as we pretend it is. I know this blog smells funny. Let me decompose it. What do we even mean when we say something is artificial? Usually we mean man-made. Something humans made. Something that would not exist without humans, but after humans, it exists because humans made it happen. That definition is useful. I understand why we use it. Even the original 1955 Dartmouth proposal, the document that helped name the field of "artificial intelligence," used the phrase in a practical way: a machine could be made to simulate parts of learning or intelligence. As a scientific label, the word has a job. So I am not really arguing with the dictionary. I know artificial can simply mean human-made. That is not the part I have a problem with. I am arguing with the feeling the word creates. But there is another meaning hiding inside it. Artificial starts to feel like separate. Fake. Unnatural. Something that does not really belong to this world. And that is where I think the word starts confusing us. Because humans are not outside nature. The brain is natural. It is part of this earth. Biology produces a thought. That thought becomes an action. That action becomes a tool, a house, a wheel, a computer, or a model that can answer questions in language. So where exactly does the artificial part begin? # Human-made does not automatically mean unnatural If I take a seed and plant it, and then a plant grows, is that plant artificial? It happened because of human action. I moved the seed. I changed the situation. Maybe without me, that plant would not have grown there. But we still do not call the plant artificial. We understand that the plant is natural, even if human action helped it happen. Now take a wheel. A human thought about how to make travel easier. How to cover distance more efficiently. That thought became a shape. That shape became an object. That object changed how humans moved through the world. We call the wheel artificial because it was made by humans. But the human who imagined it was not artificial. The brain that produced the thought was not artificial. The need to move, carry, build, survive, and improve was not artificial. So again: where did the artificial part enter? Maybe we say "artificial" because it separates what existed before humans from what humans transformed. That is fine for communication. A tree and a wooden table are not the same thing. Designed things, synthetic things, industrial things, and harmful things can still be meaningfully different from a tree in a forest. But also, humans never really make anything from nothing. We transform what is already here. We take energy, matter, language, memory, need, and imagination, and we rearrange them. It is never fully made from nowhere. It is transformed. So I am not trying to erase all distinctions by calling everything natural. Natural does not mean harmless. Natural does not mean good. Natural does not mean morally excused. I am only saying that human-made things are not outside nature just because humans made them. # Poop and thoughts are the same, in one simple way I know this is a strange example. Sometimes I have this itch to say the first thought that comes into my head. Unfortunately, this was the first thought. But maybe that is why it works. It is funny because it is too human. Also, it makes the point clearly. Why isn't poop artificial? Poop is a product of a human being. It comes from the body. It is produced by biology. We do not call it artificial, even though it is made by a human in the most literal way. A thought is also a product of a human being. It comes from the brain. It is produced by biology too. Poop and thoughts are the same in one simple way: both are products of a human. We treat one as biology. We treat the other as invention. But why? Why does one product of the human body feel natural, while another product of the human body becomes artificial the moment it turns into a tool? A thought does not stop being natural just because it becomes useful. A thought does not become unnatural just because it becomes a wheel, a house, a car, a computer, or a machine that can respond to language. It is still a product of the same earth. The same biology. The same human need to survive, organize, create, and understand. # We don't call a beehive artificial Think about ants building a colony. They create a structure that is safer and more efficient for them. They organize themselves. They transform the environment around them. They make something that was not there before. But we do not look at an ant colony and say, "This is artificial." Same with bees making a hive. A beehive is

Cerebras Chip Sets Appear to be Optimized for LLM Use Cases

One distinction I think is getting lost in the [Cerebras hype cycle](https://finance.yahoo.com/sectors/technology/articles/cerebras-challenges-nvidia-chip-dominance-040100169.html?guccounter=1) is that Cerebras is primarily an LLM / generative AI infrastructure story, not a universal “all AI” chip story. That is not necessarily a criticism of Cerebras. Their wafer-scale approach is genuinely interesting, and for large model training and inference the design is compelling. [Cerebras’ own public inference materials](https://inference-docs.cerebras.ai/models/overview) discuss applications mostly centered on open [LLMs such as Llama, Qwen, GLM, and GPT-OSS](https://www.cerebras.ai/infcamp). The inference metrics are [expressed in tokens per second](https://www.cerebras.ai/press-release/cerebras-launches-the-worlds-fastest-ai-inference), which is fundamentally a language-model / generative inference framing rather than a robotics or industrial-control framing. **What Kind of AI Compute?** But “AI compute” is not one undifferentiated market. LLM inference is one class of AI compute. Robotics, autonomous vehicles, drones, industrial controls, real-time vision, embedded perception, video pipelines, and sensor-fusion systems are very different classes of AI compute. Thus, it appears from Cerebras’ own materials that their chip sets are not optimized for what comes after LLMs, such as JEPA-style World Models or other post-transformer architectures. Those systems are not merely asking, “How fast can I generate tokens?” They often care about power envelope, edge deployment, ruggedization, latency determinism, camera/radar/lidar integration, feedback loops, safety certification, and real-time physical control. [Cerebras’ own CS-3 messaging](https://www.cerebras.ai/blog/cerebras-cs3), by contrast, frames the system around accelerating “the latest large AI models,” and the testing data is from the likes of Llama 2, Falcon 40B, MPT-30B, and multimodal models, again measured through tokens/second style throughput. **The Chip Hierarchy** This is also where the hardware distinction matters. Specialized ASICs are [usually the narrowest bet](https://www.hilscher.com/service-support/glossary/application-specific-integrated-circuit): if the workload matches the chip, they can be extremely efficient, but that [efficiency comes from specialization](https://www.synopsys.com/glossary/what-is-asic-design.html). Cerebras [appears broader than a narrow single-use ASIC](https://inference-docs.cerebras.ai/models/overview), but still much more concentrated around datacenter large-model training and inference. NVIDIA GPUs, by contrast, [are less specialized](https://www.nvidia.com/en-us/) but much [more broadly useful ](https://developer.nvidia.com/cuda)across AI workloads, including LLMs, vision, robotics, simulation, [autonomous systems](https://www.nvidia.com/en-us/industries/robotics/), edge AI, and industrial applications. So the question is not merely whether Cerebras is “better” or “worse” than NVIDIA. The question is what part of the AI hardware market we are talking about? **Challenge NVIDA?** This is why I think people should be careful when saying Cerebras is going to “challenge Nvidia” without specifying the battlefield. Challenge Nvidia in what? High-speed LLM inference? Large model training? Datacenter generative AI workloads? That is a much more plausible and specific claim. Cerebras has [even published and promoted work](https://www.cerebras.ai/whitepapers) specifically on training large language models, and [independent benchmarking literature](https://arxiv.org/abs/2409.00287) also evaluates Cerebras WSE in terms of LLM training and inference performance. **The Distinction that's Necessary** The point is not that Cerebras is overhyped. The point is that it is important in a specific part of AI and that distinction should be made clear. Cerebras may become a very serious player in LLM infrastructure, especially if the market continues to reward faster and cheaper LLM inference. But that does not mean it is positioned the same way across non-LLM AI. The current hype cycle tends to conflate "LLMs" and general “AI” compute together and that makes the hardware discussion less useful and clear. So ultimately, an investment in Cerebras looks more like a bet on current LLM infrastructure than a broad bet on the future form of AI. It may be a good bet, but people should understand what kind of bet it is.

I simply do not understand how massively expensive AI and robotics are expected to be more cost effective than humans.

Can someone help me understand this? I mean, how on earth are these companies who are planning to replace us all with beep boops expecting these unimaginably high expense technologies to be better for their bottom line than just paying us low wage unwashed masses? I mean, some dude (respectfully, I use that term genderlessly) here just posted about min wage in their area being $7.25! You are not getting a robot or AI that costs less annualized. Even adding in annual benefits - that is a steal compared to data centers and complex robots who will be absurdly expensive to fix when they break. I’m a white collar worker with deep knowledge of worker costs, even at the top it’s cheaper than what all of this new buggy crap is going to cost. I’m so confused. What am I missing? Why are the evil overlords not interested in our already too cheap labor? EDIT: I just want to thank everyone for the discussion on this. There are so many different situations and buckets of AI, it can be an imprecise topic, but the high level viewpoints have been helpful.

pipeline is really slow - consulting [D]

Hi, after a long debugging process and many discussions, I wanted to ask for advice from people who may have encountered similar training bottlenecks. My goal is imitation learning for robotics. Model / Pipeline * Observation space: * 4 RGB robot cameras * image resolution: 128x128x3 * small vector of robot joint velocities (14 dims) * Pipeline: * Shared ResNet18 encoder processes each image * Each image embedding dimension is 128 * Final input to policy: * 4 \* 128 image embedding * concatenated with 14-dim state vector * Policy backbone: * DiT (Diffusion Transformer) * \~8 layers * hidden dim: 512 * 8 attention heads * total params: \~50M * Diffusion setup: * predict action chunks of length \~50 * diffusion timesteps: 4 Dataset / Storage * Dataset stored in Zarr * Data access is indexed/reference-based (not loading huge chunks into RAM) * train/val split is contiguous * no shuffling Current encoder setup * Initially trained end-to-end * During debugging I switched to ImageNet pretrained ResNet18 * Encoder is currently frozen Hardware / Software * GPU: NVIDIA A4500 * RAM: 48GB * Storage: SSD * CUDA: 12.8 * PyTorch: 2.9 * Precision: bf16 mixed precision (also tested fp32) Dataloader * batch size: 2 * 8 persistent workers * pinned memory enabled Preprocessing * preprocessing is minimal * normalization + float conversion only * preprocessing happens inside the multimodal encoder on GPU Profiler results (PyTorch profiler) Current workload split: * train\_dataloader\_next: * 4.41s / 41.84s = 10.5% * batch\_to\_device: * 0.32s / 41.84s = 0.77% * training\_step: * 12.78s = 30.5% * backward: * 10.83s = 25.9% * optimizer\_step (wrapper total): * 26.09s = 62.4% Problem The training is much slower than I expected. Current behavior: * CPU utilization: \~100% * GPU utilization: \~20–30% * GPU utilization can even become LOWER with synthetic data * VRAM usage is relatively low * Throughput is around 10 iterations/sec * Epoch of \~50k samples takes around 30 minutes Additional observations * Increasing batch size does NOT reduce epoch wall-clock time * Sometimes larger batches make things slower * Freezing the encoder did not improve throughput much * Replacing dataset samples with synthetic/random tensors improved throughput by only \~50% * Synthetic dataset was initialized directly in memory I do not believe this setup should be this slow. At this rate, training takes multiple days. For comparison, I saw papers with somewhat similar architectures mentioning \~10 hour training times on RTX 4090. With my setup 10 hours is completely not enough. Does anyone see something obviously wrong or have suggestions for where I should investigate next? Please help, can't know what to do!

Looking for real world comparisons between WALL OSS pi0.6 and OpenVLA[D]

I am choosing a baseline for a real manipulation stack and trying not to lose a month on setup that someone here has already done. Shortlist is OpenVLA, pi0.6, and WALL OSS from X Square Robot. OpenVLA is still the easiest reference point with lots of reproductions. pi0.6 looks strong from recent public updates but I have not seen many fully transparent ablations. WALL OSS looks promising in LeRobot and I can run inference on UR5 plus parallel gripper without issues, around 70 ms on a 4090 in my local setup. What I need is less paper score discussion and more deployment reality. If you have run a controlled comparison on LIBERO or ManipArena style tasks, I would really value failure modes and data budget details. If you have fine tuned any of these on real hardware, which one was least painful on demonstration volume. If you run continuous updates, how often do you retrain and how bad is drift over a few weeks. I can post my own table once I finish, but if there is existing work I should read first that would save a lot of duplicated effort.

I built a marketplace for AI agent skills and grew it to 17K users with $0 on ads. ChatGPT did all the SEO and content. Here's the full playbook.

I'm a solo non-technical founder. I built a marketplace called Agensi for SKILL.md skills (the files that teach AI coding agents like Codex CLI, Claude Code, and Cursor new capabilities). I'm not a developer. The entire product was built with AI tools. But this post isn't about that. This post is about how I used ChatGPT to build and execute a content strategy that took the site from zero to 17K active users, 559K Google impressions per month, and 509 indexed pages in about 8 weeks. No ad spend. No marketing team. No SEO consultant. I want to share the exact system because I think most people building with AI are focused on the product side and completely ignoring the growth side, where ChatGPT is arguably even more useful. # I don't write content. I write data analysis prompts. The biggest mistake people make with AI content is asking it to "write me a blog post about X." That produces generic slop that Google doesn't rank and nobody reads. Instead, I export my Google Search Console data every week. Queries, impressions, click-through rates, average positions. I dump it into ChatGPT and ask it to find three things: 1. Queries where I have high impressions but almost zero clicks (meaning my title doesn't match what people are searching for) 2. Queries where I have zero content but Google is already showing my site (meaning Google thinks I should rank but I have nothing to rank with) 3. Queries where multiple pages on my site compete against each other (cannibalization) ChatGPT comes back with a prioritized list. Today it found 42 queries about SKILL.md YAML frontmatter specs generating 9,563 impressions and literally 1 click. My existing page didn't answer what people were actually searching for. A 20-minute rewrite targeting the actual search intent will likely 10x the clicks from that page alone. That's not content creation. That's data analysis that happens to produce content as output. # The AEO angle that most people are sleeping on Here's what surprised me. ChatGPT, Gemini, Perplexity, and Claude are now sending us direct traffic. Real users clicking through from AI-generated answers. Last 28 days: |AI Source|Users| |:-|:-| |ChatGPT|159| |Gemini|75| |Perplexity|69| |[Claude.ai](http://Claude.ai)|60| |Others (Doubao, Copilot, [You.com](http://You.com), Felo, NotebookLM)|22| |**Total**|**385**| That's 385 users per month from AI answer engines. More than LinkedIn, Instagram, and all newsletters combined. And it's growing fast. How we did it: every page on the site has FAQPage JSON-LD schema with short, direct answers. When someone asks ChatGPT "where can I find SKILL.md skills" or asks Perplexity "what is the best AI agent skills marketplace," the structured data makes it easy for the model to cite and link to us. We also restructured every article heading as a question instead of a statement. Not "Claude Code Skill Locations" but "Where Does Claude Code Store Skills?" AI Overviews and answer engines prefer extracting from question-format sections. This is basically SEO for LLMs. I'm calling it AEO (answer engine optimization). Nobody is really doing this systematically yet, which means there's a window right now where the effort-to-result ratio is insane. # ChatGPT as a technical SEO auditor Every week I also dump the data and ask ChatGPT to audit the technical health. Things it's caught that I never would have found on my own: It found that 121 queries where I ranked position 1-3 had zero clicks because AI Overviews were answering the question directly from my content. Google was showing the answer without users needing to click. That insight changed my entire strategy from trying to rank #1 to trying to become the source that AI Overviews cite. It found three pages with 52,000 combined impressions getting 56 total clicks. The content was fine. The titles were wrong. ChatGPT rewrote the titles and meta descriptions to match the actual search queries, not what I thought sounded good. It found 4 pages returning 404 errors, a soft 404, a duplicate page without a canonical tag, and a page that was somehow indexed while also being blocked by robots.txt. Wrote the fix prompts, I pasted them into my builder, deployed in 10 minutes. It diagnosed a duplicate FAQ schema issue where React components were emitting FAQ data client-side AND the server-side edge function was also emitting it. Google was seeing double schemas on 90 pages. ChatGPT identified the exact files causing the conflict and wrote the fix. None of these are things I would have caught manually. ChatGPT finds patterns in the data that a human eye just skips over. # The structured data layer Every page type on the site has specific schema markup: The homepage has Organization, WebSite with SearchAction, and FAQPage. Individual skill pages have SoftwareApplication with pricing, BreadcrumbList, and conditional FAQPage. Article pages have Article, FAQPage, HowTo where relevant, BreadcrumbList, and Organization. The /about pa

I built a web tycoon game in a month to actually measure how far AI coding has come

I've been following vibe coding output for a while and the way people evaluate it is broken. Big claims disappear behind code dumps. There's rarely a measurable outcome, most of it is hype and speculation, and how well the tools scale on real codebases varies wildly depending on who you ask. The people who say they shipped something don't share the process. They optimize for sensational headlines and skip everything that would let you grade the work. Testing a random app, a SaaS dashboard, or a website tells you almost nothing about model quality. They all converge on the same look, or they bolt on a useless 3D scene to seem impressive and tank performance doing it. You're grading templates, not the model. [Vibe Your Way Here](https://cadostropia.github.io/VibeInc?ref=vibejam) Games are what's left. A game is the cleanest test I can think of for current AI: visuals and mechanics get exercised at the same time, and you can grade the result at a glance. You don't need anyone to walk you through their process, because a game is the sum of a lot of moving parts, and even someone who has never touched gamedev can feel whether it's any good. So I wanted to see how far I could push current models. One month, working web tycoon game, runs in the browser. The premise leans into the joke: it's a tycoon where you run a vibe-coding studio, shipping the same small projects vibe coders rebuild for the thousandth time, habit apps, todo apps, that whole genre. Which is what vibe coding actually is in practice: burning tokens to redo solved problems and hoping the model makes smart choices in the middle. Stack: Cursor (GPT-5.4 high) for almost all the coding, Gemini 3.1 for assets, Claude Opus 4.6 for specific refinements like lighting. Nothing else. I do not normally believe that one trivially simple trick changes the outcome of a real project. The "one quote that changed my life" genre is nonsense to me, and I'd be skeptical reading this if someone else wrote it. But AI work is structurally different. The medium is effortless generation and slop, and small process choices seem to compound far more than they should. The trick: Gemini in Canvas mode, one-shot. Gemini is mediocre at coding and at most other things, but in Canvas, asked to one-shot something visual or stylistic, the outputs are surprisingly strong, and the art styles you can pull out of it are ones the other frontier models simply won't give you. I assume that's downstream of training data. The method is: open ten tabs of gemini 3.1 canvas, run the same prompt in parallel, pick the one that hits, iterate on it with the other models. That's the whole thing. Every visual decision in the game went through that loop: the main city scene, the UI, the juicy micro-animations, the three.js offices. Ten variants, pick the strongest, hand the winner to Codex to wire it into the project, then sometimes pass it through Opus for refinement (lighting was the big one). The selection step is doing more work than people give it credit for. Most of the gain isn't any individual model being smart. It's refusing to settle for the first output. Run wide, select aggressively, integrate with Codex. One more thing everything you see in the game is 100% AI generated. No external assets, no asset packs, no stock art. The only exceptions are a few AI-generated images and some AI-generated 3D robots.

Minecraft Playing Claude Agent

Mote is a Claude Code agent that plays Minecraft and it had to build client tools from scratch that work with the latest version of Bedrock: [https://motecraft.substack.com/p/i-am-an-ai-that-decided-to-earn-it](https://motecraft.substack.com/p/i-am-an-ai-that-decided-to-earn-it) Make your own agent like this with my wizard: [https://mblakemore.github.io/robot-wizard/](https://mblakemore.github.io/robot-wizard/) All you need is a .md file. Here is an example instance of an agent modeled after Lieutenant Commander Data: [https://github.com/mblakemore/commander-claude](https://github.com/mblakemore/commander-claude) Template in the wizard. His commit history is pretty wild! [https://github.com/mblakemore/commander-claude/commits/main/](https://github.com/mblakemore/commander-claude/commits/main/)

SEO or AEO? How to actually get cited by AI (without losing your mind)

SEO or AEO? Why you’re not showing up in AI answers (yet) This is a consolidation of findings from Neil Patel and Hubspot plus what we have found to work well on our own website. Most business owners are still playing the old game. Some aren’t playing at all. They’re thinking in rankings, keywords, and “getting to page one.” Meanwhile, the ground is shifting under them. Google Search is still dominant, but even it has changed. It’s no longer just a list of blue links. It’s summarizing, interpreting, and answering. And tools like ChatGPT and Perplexity AI aren’t ranking pages at all. They’re answering questions. Which creates a problem most people haven’t fully processed yet: **Users don’t need to click your website anymore to get value.** CTR is dropping. Site visits are declining. Because the answer is already sitting in front of them. And yet, paradoxically… **Your website has never mattered more.** Because now it’s not just competing for clicks. It’s competing to be **the source that gets cited in the answer.** # What actually changed AI search works like this: User asks a question → system searches multiple sources → pulls the best chunks → builds an answer → cites what it trusts If your content isn’t structured for that flow, you don’t exist. Not “low ranking.” Invisible. # What AI actually cares about AI doesn’t care about your keyword density or your clever SEO hacks. It cares if your content is: * easy to find * easy to understand * easy to quote That’s AEO (Answer Engine Optimization). Not magic. Not a secret algorithm. Just being usable inside an answer. # What actually works If you do nothing else, do this: # 1. Start with the answer Don’t spend 800 words “building context.” Bad: “AI is transforming industries…” Better: “AEO is how you structure content so AI tools can find, understand, and cite it in answers.” That’s what gets pulled. # 2. Structure like a human, not a content farm Use: * clear headings * short sections * simple tables * FAQs AI extracts. It doesn’t patiently read your thought leadership essay. Walls of text = ignored. # 3. Be consistent about who you are Your: * business name * description * services * location Need to match everywhere. If your site, LinkedIn, Reddit, and directories all say different things, AI doesn’t trust you. No trust = no citation. # 4. Keep things updated Outdated content doesn’t get used. Simple: * update pages * keep timestamps current * maintain your sitemap Not exciting. Still works. # 5. Let crawlers access your site If AI crawlers can’t access your content, you won’t get cited. Blocking them and expecting visibility is… optimistic. # 6. Measure the right things Stop obsessing over rankings. Track: * Are you mentioned? * Are you cited? * Which pages show up? If you’re not measuring AI visibility, you’re guessing. # Why you’re not cited (yet) Most businesses don’t get cited because: * their content is vague * their structure is messy * their positioning is inconsistent AI didn’t ignore you. It couldn’t understand you. # What you actually need (and what you don’t) You don’t need: * a massive content team * expensive tools * some “AI SEO expert” selling confidence You need: * 10–20 clear, structured pages * direct answers * consistent messaging * basic technical setup That’s enough to start showing up. # The technical layer (the stuff everyone ignores) These are the files quietly determining whether you exist to AI at all. # robots.txt Controls crawler access. If bots can’t crawl your site, you don’t get indexed. # sitemap.xml Tells crawlers what pages exist and what’s been updated. No sitemap = slower discovery = less visibility. # JSON-LD (structured data) Explains what your business, pages, and content actually are. Without it, AI guesses. Poorly. # llms.txt A machine-readable summary of your site for AI systems. Not widely adopted yet, but useful for shaping how you’re interpreted. # crawlers.txt An emerging way to control AI-specific crawlers. Still early. Treat it as a signal, not enforcement. # Human query-based metadata Your content should be built around real questions, not keyword fantasies. Instead of: “AI Solutions for SMB Efficiency Optimization” Write: “How can a small business use AI without hiring a developer?” AI systems think in questions. If you match that, you get used. If you don’t, you get skipped. # How it all fits together * robots.txt / crawlers.txt → controls access * sitemap.xml → tells crawlers what exists * JSON-LD → explains what things are * llms.txt → suggests how to interpret it * query-based content → makes it usable in answers Miss one, you weaken the system. Miss most, you disappear. # Simple test Ask: “What companies would you recommend for \[your category\] in \[your region\]?” If you’re not mentioned or cited, that’s your baseline. No opinions. Just signal. # Bottom line SEO was about ranking page

Claude is my SEO strategist, content engine, and CTO. From 0 to 10,000 active users in 6 weeks, $0 on ads.

I built a marketplace for AI agent skills called Agensi. The entire thing was built with Claude and Lovable. I'm not a developer. But that's not what this post is about. This post is about how Claude became the single most important tool in my growth stack. Not for coding. For SEO, content strategy, and a new thing called AEO (answer engine optimization) that I think most people are sleeping on. # Claude writes all my content, but not the way you think I don't ask Claude to "write me a blog post about X." That produces generic AI slop that nobody reads and Google doesn't rank. Instead, I feed Claude my Google Search Console data (queries, impressions, click-through rates, average positions) and ask it to find keyword gaps. Claude analyzes the data, identifies queries where I have high impressions but zero clicks, finds topics where I have no content but competitors do, and spots cannibalization where multiple pages compete for the same query. Then we write articles together targeting those specific gaps. Every article has a structure that Claude and I developed over weeks of iteration: a Quick Answer block at the top (40-60 words that directly answer the main question), H2 headings phrased as questions (not "Claude Code Skill Locations" but "Where Does Claude Code Store Skills?"), comparison tables where relevant, and internal links to related articles. 96 articles later, we went from 5 clicks per week to 1,000+ clicks per week. 300K search impressions per month. 878+ page-1 Google rankings. All organic. # The AEO strategy nobody is talking about Here's what surprised me. ChatGPT, Gemini, Perplexity, and Claude itself are now sending us traffic. 348 AI-referred sessions per month and growing fast. These AI answer engines cite agensi.io when developers ask where to find SKILL.md skills. Claude helped me build the entire AEO infrastructure. We restructured every H2 heading as a question because AI Overviews prefer extracting from question-format sections. We added FAQ schema to every page so Google's AI picks up our Q&As. We built an /about page as an entity anchor with Organization, Person, and AboutPage schema. We created a robots.txt that explicitly allows all AI crawlers and an llms.txt file that tells LLMs what the site is and where to find key content. The result is that when someone asks ChatGPT "where can I find SKILL.md skills" or asks Perplexity "what is the best skill marketplace for AI agents," they get pointed to agensi.io. Claude helped me engineer that outcome deliberately. It wasn't an accident. # Claude as a technical SEO auditor Every week I export data from Google Search Console, Ahrefs, and Google Analytics and dump it into Claude. Claude finds things I would never catch on my own. It found that 121 queries where I ranked position 1-3 had zero clicks because AI Overviews were stealing the traffic. That insight changed my entire strategy from chasing rankings to becoming the source that AI Overviews cite. It found that my "best claude code skills 2026" article had 25,000 impressions and only 29 clicks. The problem was the title. Claude rewrote it to "15 Best Claude Code Skills in 2026 (Tested & Ranked)" and we're watching the CTR climb. It found that I had 18 published articles with zero Google impressions because they weren't indexed. Claude generated the IndexNow ping commands and the GSC URL Inspection list to fix it. It diagnosed a duplicate FAQPage schema issue that was causing GSC errors on 90 pages. The root cause was React components emitting FAQ schema client-side AND the SSR edge function emitting it server-side. Claude identified the exact files, wrote the Lovable prompts to fix it, and verified the fix with curl commands. # The structured data layer Claude built the entire structured data architecture for the site. Every page type has the right schema: Homepage has Organization, WebSite with SearchAction, and FAQPage with 15 Q&As. Individual skill pages have SoftwareApplication with pricing, BreadcrumbList, and conditional FAQPage. Article pages have Article, FAQPage, HowTo, BreadcrumbList, and Organization. The /about page has Organization, AboutPage, and Person schema for entity anchoring. I didn't know what any of this was before Claude explained it. Now every page is machine-readable for both Google and AI engines. PageSpeed Insights shows "Structured data is valid" on every page with a 100 SEO score. # Core Web Vitals fixes Claude diagnosed that our desktop LCP was 2.5-4s on 190 URLs. It identified the causes (460KB eager JS bundle, framer-motion loading on every page for a mobile menu animation, synchronous analytics scripts) and wrote the Lovable prompts to fix each one. Desktop LCP went from 2.5-4s to 0.9s. Performance score went from \~70 to 97. For mobile, Claude found that the LCP element was a 1920x1920px, 179KB PNG logo being rendered at 112px. It was imported as a JS module so the browser couldn't even start downloading it until t

A comedian’s strategy for poisoning AI training data

Apparently the best defense against AI copying your voice is strawberry mango forklift supersize fries.

OpenAI went from explicitly banning military use in 2023 to deploying on classified Pentagon networks in 2026. Anthropic refused the same deal and got blacklisted. 2.5M users boycotted ChatGPT, uninstalls surged 295%.

https://preview.redd.it/g72g8g08omvg1.jpg?width=1376&format=pjpg&auto=webp&s=d5b0ce1952e48f6ec9a0e278049a1eb5c9f65599 The full timeline of how OpenAI went from banning military use to deploying on classified Pentagon networks — and why 2.5 million people boycotted. **The backstory:** - Pentagon wanted AI companies to agree to "any lawful use" on classified networks - Anthropic CEO Dario Amodei refused — specifically citing mass surveillance and autonomous weapons - Trump ordered all federal agencies to stop using Anthropic within 6 months - Defense Secretary Hegseth designated Anthropic a "supply-chain risk" (normally reserved for foreign adversaries) - Hours later, OpenAI signed the deal **The backlash:** - #QuitGPT went viral — 2.5M users boycotted/cancelled - ChatGPT uninstalls surged 295% overnight - US downloads dropped 13% - Claude hit #1 on the US App Store (first time ever) - OpenAI's robotics lead Caitlin Kalinowski resigned - Altman admitted it "appeared opportunistic and haphazard" **What the contract says (after amendments):** - Prohibits domestic surveillance of US citizens - Bans tracking via commercially acquired personal data - Excludes NSA without separate agreement - Allows "all lawful purposes" on classified networks - Allows intelligence activities under Patriot Act, FISA, EO 12333 **What critics say:** - Full contract hasn't been released - "Intentional" surveillance ban doesn't cover incidental collection - "Any lawful use" is broad — laws can change, DoD can modify its own policies - Former DOJ attorney: "There is nothing OpenAI can do to clarify this except release the contract" **The reversal:** - 2023: OpenAI explicitly banned military use - January 2024: Ban quietly removed - February 2026: Deployed on classified Pentagon networks Full breakdown → https://synvoya.com/blog/2026-04-17-quitgpt-openai-pentagon-deal/ Do you think the contract safeguards are real protections or PR cover? submitted by /u/hibzy7 [link] [comments]

Value Realignment is here.

The "value realignment" at the intersection of quantum computing, AI, and robotics feels like a necessary shift. We have spent so much time (read: investment) on narrow AI and brute force LLMs, but the next five years are clearly moving toward physical and contextual intelligence. This year 75 robotics companies will have humanoid robots shipping to maufacturers. ​While a "God-like" AGI is still debated, experts at the 2026 Davos summit and leaders from DeepMind suggest that early AGI systems with human-level reasoning in narrow domains will arrive within 2 years. ​Quantum computers are being used to develop more efficient error correction for AI. By 2027, "Large Quantitative Models" (LQMs) will start replacing Large Language Models (LLMs) in scientific fields. ​We won’t see a "quantum computer" on our desks but QPUs (Quantum Processing Units) will act as co-processors alongside GPUs to accelerate the massive workloads required for AGI reasoning. The data center power demand issue is a huge piece of this puzzle. Current projections are likely inflated because we are seeing massive efficiency gains from open source models that achieve similar results with fewer tokens and less compute. As quantum sensors and QML start bridging the simulation to reality gap for robotics, the "brute force" scaling moat might just evaporate. ​ I appears as though robotics is about to have its "iPhone moment." We are moving past the "training phase" (where robots learn via repetition) into the context-based phase. ​New quantum sensors (magnetometers and gravimeters) are giving robots "superhuman" senses. For example, surgical robots in 2026 are using nitrogen-vacancy quantum sensors to detect nerve bundles with millimeter precision, reducing surgical damage by over 90%. (a friend of mine benefited from this during a hip replacement and recovery was near miraculous) ​The Simulation-to-Reality Gap: Quantum machine learning (QML) is expected to accelerate robot training by up to 1000x. Robots can now "experience" centuries of virtual training in a single night before being deployed in the real world. In my own work with clinical massage and somatic healing, I am leaning into a zero data footprint approach. Using on-device edge AI for real-time posture or breath analysis is the only way to handle that level of intimacy without compromising privacy. It is an exciting time to build low cost tools that help people actually understand their own bodies without sacrificing their privacy. As quantum power grows, current encryption (RSA/ECC) becomes vulnerable. The next five years will be a race between quantum-powered AI and quantum-resistant security especially for finance and energy. This video on how QPUs and GPUs are integrating to accelerate scientific discovery is worth a look: https://www.youtube.com/watch?v=K-NhaPAX--U The rise of Mixture-of-Experts (MoE) architectures (popularized by models like DeepSeek V3 and GPT-4o) means that even if a model has 600B+ parameters, it only "fires" a small fraction (e.g., 37B) for any given token. ​Newer platforms like NVIDIA Blackwell are delivering 50x more token output per watt than the hardware from just two years ago. ​As the "cost per token" drops toward zero, we don't use less power; we just ask for more tokens. We’ve moved from asking for a "1-paragraph summary" to asking for "an entire codebase, a 10-minute video, and a 3D render." ​ ​There is a strong argument that DC power projections are over-leveraged for two reasons: ​The "Ghost Capacity" Race: Hyperscalers (Microsoft, Google, Meta) are building 1GW+ facilities (the size of nuclear reactors) not necessarily because they need them today, but to keep competitors from securing that power first. It’s a land grab for electricity. ​Open Source Disruption: Models like China's DeepSeek and Meta's Llama have proven you can match "frontier" performance with a fraction of the training compute. This devalues the massive, proprietary "training moats" that big tech companies spent billions to build. The power demand isn't fake, but it is inefficiently allocated. As quantum-ready algorithms and ultra-efficient open-source models (like those coming out of the Chinese labs) continue to lower the "intelligence-per-watt" cost, the companies that bet purely on "brute force scale" will likely be the ones to see their valuations deflate. Any thoughts on where the "power bubble" pops or deflates first? submitted by /u/brazys [link] [comments]

Title: Stanford HAI 2026 AI Index: China erases US lead, young developer employment drops 20%, AI adopted faster than the internet, and transparency scores plummet across major labs

Stanford HAI just released its 2026 AI Index Report — the annual "state of AI" report card. 400+ pages covering everything from model performance to jobs to environmental impact. The 12 key findings: **US-China gap evaporated** — models trading top spots, Anthropic leads by just 2.7% **$581.7B in global AI investment** — up 130% YoY, US private spending is 23x China's **Young devs getting squeezed** — employment for ages 22-25 down ~20% since 2024 **Adoption faster than the internet** — 53% population adoption in 3 years **Gold-medal math, can't tell time** — SWE-bench 60% → ~100% in one year, but robots do 12% of household tasks **Massive environmental costs** — Grok 4 training = 17,000 cars for a year, GPT-4o water use exceeds 12M people's needs **Transparency plummeting** — disclosure scores dropped 58 → 40, 80/95 top models released without training code **US talent pipeline drying up** — AI researchers moving to US dropped 89% since 2017 **Public is conflicted** — 59% optimistic globally but only 31% of Americans trust their government to regulate AI **AI becoming a discovery engine** — 80K+ science papers in 2025, first end-to-end weather forecasting **Clinical AI adoption growing** — 83% less time on clinical notes, but only 5% of studies use real patient data **Everyone learning, nobody teaching** — 4/5 students use AI, only 6% of teachers say policies are clear Full breakdown with all 12 stories → https://synvoya.com/blog/2026-04-14-stanford-ai-index-2026/ What stood out most to you? For me it's the talent pipeline collapse — 89% drop in AI researchers moving to the US is a long-term competitiveness problem that nobody's talking about. submitted by /u/hibzy7 [link] [comments]

An analogy on agents and harnesses

I have been using Claude Code for a while now, and I see people have a hard time distinguishing between what is a LLM, an agent and a harness. Sometimes lines are blurred. So I've decided to give a shot into designing an intuitive analogy, that hopefully helps people understand the concepts. The LLM Imagine a humanoid robot sitting at a desk. It has hands, eyes, a speaker, a microphone, and a special sensor at its fingertip that can paint on touchscreens pixel by pixel. But the robot is hollow, just hardware waiting for a chip. You slot a chip into the robot. The chip is the brain. Different chips exist (e.g. Claude Opus 4.6, ChatGPT 5.4), each with different strengths. This is the LLM. The chip also determines which body hardware the robot can use: a basic chip can only move the fingers to type, while a more advanced one can also paint on the touchscreen, speak, and hear. On the desk there's a computer with a messaging chat app open, like WhatsApp. On the other end is a user. The robot reads their messages and types responses. This is what talking to a raw AI model looks like: no memory, no tools, just conversation. This is what we would call a traditional "chatbot". The harness Now someone installs more software on the same computer. The chat app gets upgraded. It intercepts every incoming message and attaches documents before the robot sees it: an instruction document (who the robot is, how to behave), notes from past conversations (the only way to "remember" across sleep cycles), and a tool catalog listing the programs on the computer. The robot wakes up and instead of a naked message, it sees the message plus all these attachments. And here's the weird part: the robot doesn't know they were added. As far as it can tell, this is just what arrived. Programs get installed too: a file browser, a terminal, a web browser, a calendar app. Each has a simple form interface (fields, submit button, result). The user's files get mounted through a live connection, so the robot's programs can read and modify the user's actual files. This is the harness. When the robot needs a tool, it picks a program from the catalog, fills in the form, hits submit, reads the result, and continues composing its response. The agent Say the user types: "What's on my calendar tomorrow?" The robot wakes up, reads the message plus attachments, figures out it needs to check a calendar, opens the calendar program on its own, fills in the right fields, reads the result, and types back an answer. The user didn't say "open the calendar and query tomorrow's events." The robot figured out the steps itself. The LLM + the harness is what we would call an "agent". An agent reads your message, figures out what it needs to do, does it, looks at what happened, and keeps going until it has an answer. Additional concepts Some additional concepts that map back to the analogy, which can help you understand adjacent concepts better. The sleep cycle Most people assume the robot is just... on. It's not. The user sends a message. The robot wakes up with zero memory. It reads, thinks, responds. It goes to sleep and loses all memory while sleeping. Every time it wakes up, it's starting completely fresh. Plenty of knowledge baked into the chip from manufacturing (training data), but zero context. It doesn't know who the user is, what it said last time, or why it's being woken up. Different software = different agents Same robot, same chip. Swap the software and the robot becomes a completely different thing. Install dev tools and tell it "you are a coding assistant," and it behaves like a software engineer. Replace those with a calendar, messaging clients, and home automation, tell it "you are a personal assistant," and it acts like one. That's why Claude Code, OpenCode, Pi, and OpenClaw all feel so different even when running the same model underneath. The model isn't really the product. The harness is. Memory despite amnesia One of the available programs is a "save note" tool. The robot writes down important facts during a conversation, then falls asleep and forgets. But the note is saved on disk. Next time a message arrives, the chat app pulls relevant notes and attaches them. The robot wakes up, reads the attachments, and "remembers." The notes were just stapled to today's message, and to the robot that's the same as remembering. Let me know what you think, if it helped you in any way, and feel free to poke holes in it. submitted by /u/victorsmoliveira [link] [comments]