DataRobot

DataRobot AI

DataRobot AI

DataRobot AI

DataRobot AI

DataRobot AI

DataRobot AI

DataRobot AI

DataRobot AI

DataRobot AI

DataRobot AI

[offer]Looking for people in US/UK/CA/AU to film their everyday chores for AI robot training ($12/hr, up to $1,200)

Hey everyone, We're working with a US robotics company that's building humanoid household robots. To train the AI, they need a lot of first-person video of regular people doing regular chores — the boring stuff like washing dishes, folding laundry, wiping counters. Basically: a robot can't learn how to load a dishwasher unless it sees thousands of humans actually doing it. That's where you come in. You wear a lightweight head-mounted camera and just… do your normal chores while it records. No script, no acting, no editing. I know it sounds a little weird. It's also a totally legit, low-effort gig if you've got a normal home and some spare time. The basics: $12/hour, paid per completed session Up to 100 hours per person = up to $1,200 total Self-paced. Do it on your own schedule, in your own home, no boss No experience needed. If you can do laundry, you qualify What you'd be filming: Washing dishes / loading the dishwasher Doing laundry (sorting, folding, loading the machine) Cooking simple meals Cleaning, vacuuming, mopping Tidying drawers, shelves, cabinets We give you a task checklist, you follow it, you upload the footage through a simple link. That's the entire workflow. Requirements: 18+ Live in the US, UK, Canada, or Australia Have a normal home with a kitchen, laundry area, and living space Reliable internet for video uploads Willing to wear a GoPro-style head camera Equipment: If you don't already have a head strap, you'll need to grab one off Amazon (around $10–20). Once you've completed your first 5 hours of filming, we reimburse the full cost. The camera itself — we'll walk you through options. Payment: We pay through Fiverr, so you'll need a Fiverr seller account (free to make, takes 2 minutes). We cover all Fiverr fees — the $12/hr is what lands in your pocket. If you don't have a Fiverr account yet, set one up before you apply: fiverr → "Become a Seller." The privacy part (because I know you'll ask): You sign a data rights release before your first payment. Footage is used only for training the robot AI — not posted publicly, not sold to advertisers. Don't film other people without their consent. That includes roommates, partners, kids walking through the kitchen. We give you guidelines on framing and what to avoid. Don't film anything sensitive on screens (passwords, banking, etc.). Common-sense stuff, and we walk you through it. Apply here: https://forms.gle/TGUU9uKUSo9RR5Ca7 Takes literally 1 minute. Just drop your Fiverr account link (or email) and we'll be in touch within a few days. Happy to answer questions in the comments — ask away. submitted by /u/Hot-Option1161 [link] [comments]

Before we spend months processing open-source robotics datasets, tell us why this is a bad idea [D]

Ps. Not pitching anything; Just trying to understand where reality differs from the narrative We're a couple of ML students, mostly worked on ML/software before, but over the last few months we've been playing with VLAs, robot datasets, and trying to understand where the field is heading. After spending a few weeks downloading robotics datasets, we were surprised by how much effort went into just getting data into a usable format. Maybe we're missing something, but it felt like every dataset had different assumptions, schemas, sensors, coordinate frames, metadata standards, and tooling. That got us wondering: How do robotics teams actually think about data sharing? Do people genuinely want access to more robot data, or is the industry moving toward "collect your own data because nobody else's transfers"? Our current (possibly very wrong) hypothesis is: The robotics ecosystem doesn't have a data scarcity problem. It has a data interoperability problem. We're considering running a pretty large experiment: Take essentially every public robot-learning dataset we can get our hands on, normalize it into a common schema, enrich it with metadata, and see how much of it is actually reusable across tasks, embodiments, and learning pipelines. Before we spend months doing that, we'd love to hear from people actually building in robotics. Where is this hypothesis wrong? Is finding data not actually a problem? Is embodiment mismatch the real blocker? Is quality the issue? Is labeling the issue? Is everyone just collecting their own data anyway? Would you ever use robot data collected by another team? If I gave you access tomorrow to every public robotics dataset through one API, what would you actually do with it? Or would you ignore it completely? ------------------------------------------------------------------------------------------------------ Edit: One clarification We're not thinking about a marketplace, proprietary format, or closed platform. The experiment we're considering is much simpler: Take as much public robotics data as possible, normalize it, enrich it with metadata/quality signals, make it searchable, and release it back to the community in an open format. Would that actually be useful to practitioners? submitted by /u/sigma_crusader [link] [comments]

How AI is going to take over the planet?

I used to believe that the thing that we had to worry about with AI becoming more and more prevalent was like sentient robots that would take over like in the science fiction story I robot. But I don't think that's the case anymore because I think there is something far more sinister behind all the push for AI to become mainstream technology in the reason all these deep pockets are willing to pour so much of the cash into it. Because it spells control. If you control AI you can control the people because you can control the data they consume you can control how they consume it and what they will believe. Because as AI becomes more and more mainstream it leaves the door open for big corporations to feed us information they want us to have as well as the government. submitted by /u/crazyhomlesswerido [link] [comments]

How to create an AI version of yourself using your reddit history

I hate the way AI talks back to me. Its so proper, so robotic, every response feels like a help article. I wanted something that actually knew who i am, my beliefs, my history, what shaped me, the positions i hold and why. Not a generic assistant that treats every question like it came from nobody. So i got to thinking, who better to talk to than myself? So i built it over a weekend. Heres what I did and how you can do it too. Step 1: Export your Reddit data Go to reddit.com and click your profile icon in the top right, then hit Settings. Scroll down to the bottom of the page and youll see a section called "Data Request." Click "Request Data Export" and Reddit will email you a download link within a few hours, sometimes longer depending on how much history you have. The zip file will contain your posts and comments going back to when you created your account. Mine was about 21,000 comments over two years. Once you have it, open the CSVs in excel or just upload them directly into Claude and ask it to help you make sense of the structure. The raw data is ugly but everything is there, the text of every comment, the subreddit it was posted in, the date, all of it. One thing worth knowing: you can go way deeper than just Reddit. I looked into Google Takeout while i was doing this and it was honestly a little scary how much data they have on you. If you want to go deeper Google Takeout is wild, i didnt realize how much data they actually have on you until i went through it. Search history, location history, YouTube, Gmail, its all there and its all exportable. I thought about pulling my SMS history too but that felt wrong, those conversations are with real people who didnt agree to any of this so i left it alone. Reddit was enough for me and honestly if youve been on here for years and actually say what you think in the comments, you probably have more to work with than you realize. Step 2: Build the personality document and this is where the real work is Dont just tell the AI "write like me." That gives you nothing. You need an actual document, a living reference file the AI reads every single conversation. Mine is a markdown file sitting in a Claude Project so it loads automatically every time. Start by uploading your Reddit export and asking Claude to interview you. Literally tell it: "Read my comment history and ask me questions about anything it cant determine on its own." Let it go deep. Mine asked about my beliefs, my family, my history, my faults, things that happened to me, why i hold the positions i hold. You answer honestly, including the uncomfortable stuff, and then after the session you tell it to compile everything into a structured document. Then you iterate. Every time it gets something wrong you correct it and add it to the doc. Two weeks in and its already a completely different document than what came out of that first session. Heres what the document actually needs to cover: Who you actually are. Not the resume version. The real version. Your beliefs, your politics and why you hold them, your actual faults, your history, the things that shaped you. An AI that only knows your best self sounds fake because you sound fake when youre performing your best self. Your actual positions on things. Not just "im conservative" or "im liberal." The specific positions with the reasoning behind them. Mine has maybe 15 specific theological positions with the scriptural basis for each, because if the AI doesnt know why i believe what i believe it cant argue it like i would. Your life context. Family, relationships, the stuff that matters. Your context is constantly informing how you respond to things even when the topic isnt directly about your life. Your faults and struggles. This one people skip and its why their AI version sounds sanitized. Put in the real stuff. The AI needs to know the full person or it just sounds like your linkedin profile with apostrophes dropped. Step 3: Set up the Claude Project correctly Claude has a feature called Projects where you can upload files and write a persistent system prompt that loads every single conversation. Heres how mine is structured: The project files are the personality document and the Reddit exports. The personality doc is the source of truth for who you are. The Reddit exports are the raw data the AI can search when it needs to verify something or find a voice sample. The project instructions are where you govern behavior, not just describe personality. This is the part most people miss. Describing yourself isnt enough, you have to tell the AI how to behave. Mine has: Grammar rules shown as examples not descriptions. Side by side. Heres AI voice, heres my voice. Because "sound natural" is meaningless instruction. Showing it what natural actually looks like works. A banned vocabulary list. Words i never use. "Nuanced", "crucial", "delve", "it's worth noting", "at the end of the day", em dashes in any form. These are the fingerprints of AI

How to build an AI of yourself using your reddit history

I hate the way AI talks back to me. Its so proper, so robotic, every response feels like a help article. I wanted something that actually knew who i am, my beliefs, my history, what shaped me, the positions i hold and why. Not a generic assistant that treats every question like it came from nobody. So i got to thinking, who better to talk to than myself? So i built it over a weekend. Heres what I did and how you can do it too. Step 1: Export your Reddit data Go to reddit.com and click your profile icon in the top right, then hit Settings. Scroll down to the bottom of the page and youll see a section called "Data Request." Click "Request Data Export" and Reddit will email you a download link within a few hours, sometimes longer depending on how much history you have. The zip file will contain your posts and comments going back to when you created your account. Mine was about 21,000 comments over two years. Once you have it, open the CSVs in excel or just upload them directly into Claude and ask it to help you make sense of the structure. The raw data is ugly but everything is there, the text of every comment, the subreddit it was posted in, the date, all of it. One thing worth knowing: you can go way deeper than just Reddit. I looked into Google Takeout while i was doing this and it was honestly a little scary how much data they have on you. If you want to go deeper Google Takeout is wild, i didnt realize how much data they actually have on you until i went through it. Search history, location history, YouTube, Gmail, its all there and its all exportable. I thought about pulling my SMS history too but that felt wrong, those conversations are with real people who didnt agree to any of this so i left it alone. Reddit was enough for me and honestly if youve been on here for years and actually say what you think in the comments, you probably have more to work with than you realize. Step 2: Build the personality document and this is where the real work is Dont just tell the AI "write like me." That gives you nothing. You need an actual document, a living reference file the AI reads every single conversation. Mine is a markdown file sitting in a Claude Project so it loads automatically every time. Start by uploading your Reddit export and asking Claude to interview you. Literally tell it: "Read my comment history and ask me questions about anything it cant determine on its own." Let it go deep. Mine asked about my beliefs, my family, my history, my faults, things that happened to me, why i hold the positions i hold. You answer honestly, including the uncomfortable stuff, and then after the session you tell it to compile everything into a structured document. Then you iterate. Every time it gets something wrong you correct it and add it to the doc. Two weeks in and its already a completely different document than what came out of that first session. Heres what the document actually needs to cover: Who you actually are. Not the resume version. The real version. Your beliefs, your politics and why you hold them, your actual faults, your history, the things that shaped you. An AI that only knows your best self sounds fake because you sound fake when youre performing your best self. Your actual positions on things. Not just "im conservative" or "im liberal." The specific positions with the reasoning behind them. Mine has maybe 15 specific theological positions with the scriptural basis for each, because if the AI doesnt know why i believe what i believe it cant argue it like i would. Your life context. Family, relationships, the stuff that matters. Your context is constantly informing how you respond to things even when the topic isnt directly about your life. Your faults and struggles. This one people skip and its why their AI version sounds sanitized. Put in the real stuff. The AI needs to know the full person or it just sounds like your linkedin profile with apostrophes dropped. Step 3: Set up the Claude Project correctly Claude has a feature called Projects where you can upload files and write a persistent system prompt that loads every single conversation. Heres how mine is structured: The project files are the personality document and the Reddit exports. The personality doc is the source of truth for who you are. The Reddit exports are the raw data the AI can search when it needs to verify something or find a voice sample. The project instructions are where you govern behavior, not just describe personality. This is the part most people miss. Describing yourself isnt enough, you have to tell the AI how to behave. Mine has: Grammar rules shown as examples not descriptions. Side by side. Heres AI voice, heres my voice. Because "sound natural" is meaningless instruction. Showing it what natural actually looks like works. A banned vocabulary list. Words i never use. "Nuanced", "crucial", "delve", "it's worth noting", "at the end of the day", em dashes in any form. These are the fingerprints of AI

AI Doesn't Exist, and Poop Proves It

robot 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 built. It has structure. It has purpose. It stores food. It protects the colony. It is a product of collective behavior. But we call it natural

Cerebras Chip Sets Appear to be Optimized for LLM Use Cases

One distinction I think is getting lost in the Cerebras hype cycle 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 discuss applications mostly centered on open LLMs such as Llama, Qwen, GLM, and GPT-OSS. The inference metrics are expressed in tokens per second, 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, 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: if the workload matches the chip, they can be extremely efficient, but that efficiency comes from specialization. Cerebras appears broader than a narrow single-use ASIC, but still much more concentrated around datacenter large-model training and inference. NVIDIA GPUs, by contrast, are less specialized but much more broadly useful across AI workloads, including LLMs, vision, robotics, simulation, autonomous systems, 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 specifically on training large language models, and independent benchmarking literature 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. submitted by /u/RazzmatazzAccurate82 [link] [comments]

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. submitted by /u/eniac_usabrl [link] [comments]

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! submitted by /u/Potential_Hippo1724 [link] [comments]

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. submitted by /u/Dense-Sir-6707 [link] [comments]

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: Queries where I have high impressions but almost zero clicks (meaning my title doesn't match what people are searching for) 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) 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 60 Others (Doubao, Copilot, 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 page has Organization, AboutPage, and Person schema for

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 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. submitted by /u/Feisty_Advantage_597 [link] [comments]

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 Make your own agent like this with my 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 Template in the wizard. His commit history is pretty wild! https://github.com/mblakemore/commander-claude/commits/main/ submitted by /u/nnet42 [link] [comments]

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 pages. AEO is about being useful inside an answer. If your content helps A

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 the entire JS bundle par