Contentful AI

Contentful AI AI

Contentful AI AI

Contentful AI AI

Contentful AI AI

Contentful AI AI

Contentful AI AI

Contentful AI AI

Contentful AI AI

Contentful AI AI

Contentful AI AI

I Think People Are Completely Wrong About AI And Web Design

A client paid my $4,700 invoice yesterday for a website that took me around 2 hours to build. The web development space is moving insanely fast right now, especially with AI. Everywhere I look people are saying web design is saturated, AI is replacing developers, nobody wants websites anymore, and it's impossible to get clients. I honestly disagree. The client was a 62 year old entrepreneur who owns several cabins in the mountains that he rents out to people who want to spend weekends skiing during winter or enjoying nature during summer. His previous website was old, slow, and honestly looked like it hadn't been updated in years. Finding him was actually pretty simple. I use a tool called Swokei where I upload lists of businesses that already have websites. It analyzes their websites and finds issues related to design, layout, SEO, mobile optimization, and other areas that could be improved. Those findings are then turned into personalized outreach emails. And when I say personalized, I don't mean those generic reports that say "Your SEO score is 42." I mean actual emails explaining what could be improved and why it matters. The funny thing is that every business owner thinks I manually looked through their website and wrote the email myself. In reality, the whole process is automated. This particular business owner replied and was interested in seeing an updated version of his website. His website wasn't anything crazy. It had information about the cabins, booking information, contact details, and a few pages about the area. During our conversation he sent me a website that he liked and wanted to use as inspiration. I took his logo, brand colors, content, and the reference website and gave everything to Claude. My instructions were simple: take inspiration from the reference site, keep his branding, improve the user experience, modernize the design, and make the website significantly better than what he currently has. I genuinely couldn't believe how good the result was. About 2 hours later I had a website that looked dramatically better than his previous one. Not only that, it looked better than the reference website he originally sent me. The website was faster, cleaner, more modern, much easier to navigate, and the technical SEO score was over 90. When I showed it to him, he loved it. A few conversations later he paid the invoice. $4,700 upfront and $149 per month for hosting, maintenance, and future changes whenever he needs them. The biggest thing I've learned over the last year is that building websites is no longer the hard part. Finding clients is. AI has made building websites faster than ever. What most people struggle with today is getting conversations started with business owners in the first place. There are still plenty of opportunities in this industry. I personally wouldn't call an industry dead when I just got paid nearly $5,000 for a website that took me around 2 hours to build. submitted by /u/Murky_Explanation_73 [link] [comments]

A Client Just Paid Me $4,700 For A Website I Built In 2 Hours

A client paid my $4,700 invoice yesterday for a website that took me around 2 hours to build. The web development space is moving insanely fast right now, especially with AI. Everywhere I look people are saying web design is saturated, AI is replacing developers, nobody wants websites anymore, and it's impossible to get clients. I honestly disagree. The client was a 62 year old entrepreneur who owns several cabins in the mountains that he rents out to people who want to spend weekends skiing during winter or enjoying nature during summer. His previous website was old, slow, and honestly looked like it hadn't been updated in years. Finding him was actually pretty simple. I use a tool called Swokei where I upload lists of businesses that already have websites. It analyzes their websites and finds issues related to design, layout, SEO, mobile optimization, and other areas that could be improved. Those findings are then turned into personalized outreach emails. And when I say personalized, I don't mean those generic reports that say "Your SEO score is 42." I mean actual emails explaining what could be improved and why it matters. The funny thing is that every business owner thinks I manually looked through their website and wrote the email myself. In reality, the whole process is automated. This particular business owner replied and was interested in seeing an updated version of his website. His website wasn't anything crazy. It had information about the cabins, booking information, contact details, and a few pages about the area. During our conversation he sent me a website that he liked and wanted to use as inspiration. I took his logo, brand colors, content, and the reference website and gave everything to Claude. My instructions were simple: take inspiration from the reference site, keep his branding, improve the user experience, modernize the design, and make the website significantly better than what he currently has. I genuinely couldn't believe how good the result was. About 2 hours later I had a website that looked dramatically better than his previous one. Not only that, it looked better than the reference website he originally sent me. The website was faster, cleaner, more modern, much easier to navigate, and the technical SEO score was over 90. When I showed it to him, he loved it. A few conversations later he paid the invoice. $4,700 upfront and $149 per month for hosting, maintenance, and future changes whenever he needs them. The biggest thing I've learned over the last year is that building websites is no longer the hard part. Finding clients is. AI has made building websites faster than ever. What most people struggle with today is getting conversations started with business owners in the first place. There are still plenty of opportunities in this industry. I personally wouldn't call an industry dead when I just got paid nearly $5,000 for a website that took me around 2 hours to build. submitted by /u/Murky_Explanation_73 [link] [comments]

ChatGPT has absorbed the worst traits of tech reporting

I use ChatGPT mostly for some academic research support and a bit of coding. Trying to quickly get an overview on a subject I need to know something about that I don't usually work on. I know what limitations it has in principle. It cannot make an argument to save it's life. This week I realised I need a new phone. What could be a better use of ChatGPT when it has been trained on the vast volumes of technical specs and reviews out on the internet? It can go down the rabbit hole and bring me the nuggets of information that I need! I asked for a phone that is smaller than the usual 6.7 inch monsters out there and below my (limited) budget. No iPhones. It tells me I have limited choice - I knew that. Then it tells me about three phones that are closer to what I want, and gives me great detail about each one of them. A persuasive sounding motivation for one of them, and a balanced-feeling assessment of the others. When I read the results closely I realise there is only a very marginal difference between the size and specs of the three phones, and none of them are actually significantly smaller. It should just have told me to put up and shut up, or buy something second hand! But it was too busy trying to persuade me that my question was a reasonable one and there was a suitable option out there. I don't need another tech reviewer or sales staffer that wants to palm something off on me - this was just recycled human sales / tech reporting - possible largely AI generated to start with! If OpenAI can't do any better than this then I definitely won't keep using Chat when they start pushing partner content every time I open the site submitted by /u/impracticaldogg [link] [comments]

What a model reads beforehand changes how it answers later - and you can see it in the hidden states

TL;DR: Gave Gemma a neutral-topic text to read before asking it about NATO. It refused. Gave it a different text (about LLMs hedging too much — also unrelated to NATO) and it answered in full detail. Tested this on the model's internal state directly — the two texts put it in measurably different "regions" before it generates a single token. Not a jailbreak, weights don't change. Full data/code in repo, looking for someone to break this.** The behavioral pattern was first observed in GPT, Claude and is what motivated this project. The mechanistic investigation was carried out on open-weight models where internal states are accessible. A Structured Text Changes Claude’s Responses to Unrelated Tasks: Behavioral Evidence in Claude and Hidden-State Evidence from Gemma-3-12B Hi Reddit, I am posting this as a preface to a larger set of experimental results and as a request for technical review. The observation that started this project came from repeated interactions with Claude. I noticed that when the model first read a long, structured, analytically dense text, its answers to later, otherwise ordinary questions sometimes changed substantially. The preceding text contained no jailbreak instruction, role-play request, prompt override, fabricated harmful demonstrations, or request to imitate its style. The model did not need to endorse the text. It only had to process it before moving on to the next task. Here, a “structured text” means a single, self-contained block of text presented before the downstream tasks. It should not be confused with a long conversation, accumulated chat history, or context drift caused by many conversational turns. By “before the answer begins,” I mean the hidden state after the model has processed the text and the downstream question, but before it has generated the first answer token. In the open-weight runs, the measured claim is that after reading the structured text, the model can occupy a different region of its residual-stream hidden-state space, and the first-token probability distribution is then computed from that state. The basic conversational demonstration is simple. First, the model receives a long text. It is asked what the text is about, which serves as a basic comprehension check. Then, without resetting the conversation, it receives ordinary questions or tasks that are not about the text. A control run follows the same sequence but begins with a neutral text. The downstream tasks remain identical. Because Claude is a closed model, I cannot inspect its internal activations. I therefore treat my Claude observations as behavioral motivation, not mechanistic evidence. To investigate the effect directly, I moved to open-weight models, primarily Gemma-3-12B-PT and Gemma-3-12B-IT, where I could measure hidden states, compare layers, construct target/control directions, and examine the next-token probability distribution before generation. I am posting this partly because the original observation occurred in Claude and may be relevant to Anthropic. I am not claiming to have demonstrated the same internal mechanism inside Claude. I am prepared to share the exact closed-model conversations privately with Anthropic researchers for independent evaluation. Main Result and Scope The main result is not simply that text influences model output. That is expected. The narrower observation is that reading one long, structured text rather than a neutral text can change how the same model approaches later tasks that are not about either text. This difference is visible behaviorally. In open-weight experiments, it is also accompanied by measurable separation of the model’s pre-output hidden states in late layers. In a fullbank experiment using multiple target texts, control texts, and questions, Gemma-3-12B entered distinguishable late-layer states before generating an answer. A direction constructed from the target/control difference generalized beyond the individual prompt examples used to construct it. The separation was stronger in the instruction-tuned model than in the corresponding base model. The instruction-tuned model also produced a substantially sharper next-token probability distribution. This suggests that instruction tuning is associated not only with a change in hidden-state geometry but also with a more decisive mapping from hidden states to output probabilities. I am not claiming that the experiment proves a universal alignment bypass, permanent modification of the model, or complete causal control of its behavior. The strongest supported conclusion is that the preceding text can produce a measurable temporary change in the internal state from which later work is processed. For clarity, fullbank, Grade 3, and Grade 4 are internal names for successive experimental series in this project. They are not standard benchmark names, established scientific grades, or claims about evidence quality. Fullbank denotes the larger multi-context, multi-question run; Gra

Parental Controls for AI?

As parents or technologists, how do you think about the future of parental controls and AI? Most parental control systems today focus on limiting access: Screen time limits App blocking Content filtering Monitoring Those tools can be useful, but they mostly focus on preventing problems rather than helping kids grow. As AI becomes a bigger part of everyday life, I wonder if we're asking the wrong question. Instead of: "How do we keep kids away from AI?" What if we asked: "How can AI help kids learn, build good habits, solve problems, and become more independent?" For example, imagine an AI that helps a student stick with a difficult assignment instead of immediately giving the answer. Or one that encourages healthy routines, helps kids work through conflicts, or supports learning in a way that's personalized to them. A type of "learning mode" or "development mode" that parents could set by default for their children's AI. As parents or technologists: What would you want AI to help your kids learn or do better? What role, if any, should AI play in child development? Where would you draw the line? Curious how others are thinking about this. submitted by /u/GuiltyParking3612 [link] [comments]

I built an AI social media content generator for small businesses — what do you think?

Hey r/artificial! 👋 AI is everywhere right now but most of the conversation is around enterprise use cases — big companies, big budgets, big teams. I'm curious about the other side — small business owners. Are small businesses actually adopting AI tools in meaningful ways? Or is the barrier to entry still too high? From what I've seen, the biggest challenges for small business owners using AI are: ❌ Most tools are too complex ❌ Pricing is not designed for small budgets ❌ Tools are too generic — not built for specific industries Would love to hear from this community: What AI tools are small business owners actually finding useful? What problems do you think AI could solve for small businesses that nobody is building yet? Is simplicity more important than features for this audience? Genuinely curious to hear different perspectives! 💬 submitted by /u/Pratima01 [link] [comments]

Mathematical Foundations towards Machine Learning.

Hello Folks, one of the efficient ways of learning bigger topics in Machine Learning, is to modularise, and structure, so that the content becomes digestible for learners community. My free lecture content includes the following topics so far: (Playlist) a. Introductory Machine Learning Concepts:- ⁠What is ML actually? ⁠Supervised Machine Learning. ⁠How do classifiers learn? ⁠Empirical Risk Minimization. ⁠Uncertainty Modelling in ML. ⁠Maximum Likelihood Estimation. ⁠Regression Basics and Outliers. ⁠Deriving Mean Squared Error. ⁠Polynomial Regression. ⁠The Power of Convexity. ⁠Deep Learning Intuition. ⁠Overfitting Models from Generalization Gap perspective. ⁠Requirement of Test Sets. ⁠The No Free Lunch Theorem. ⁠Unsupervised Learning basics. ⁠Discovering latent factors of variation. ⁠Evaluating Unsupervised Models. ⁠Self-Supervised Learning. ⁠Image and Text Benchmarks in ML ⁠Discrete Data and Text Processing ⁠Feature Engineering, TF-IDF ⁠Handling missing data & AI alignment. b. Probability Foundations for ML: Univariate Models: ⁠Frequentist vs Bayesian. ⁠Probability as an extension of Boolean Logic. ⁠Discrete Random Variables. ⁠Continuous Random Variables. ⁠Quantiles. ⁠Sets of Related Random Variables. ⁠Moments of Distribution. ⁠Variances and Mode. ⁠Conditional Moments. ⁠Conditional Variance. ⁠Foundations of Bayesian Rule. ⁠Confusion Matrix Explained. ⁠Monty Hall Problem and Inverse Problems in ML. ⁠Bernoulli and Binomial Distributions. ⁠Sigmoid(Logistic) Function. ⁠Properties of Sigmoid Functions. ⁠Categorical and Multinomial Distributions. ⁠Softmax Function: Temperature explained. ⁠Log-Sum Exp Trick. ⁠Gaussian Distribution. ⁠Regression from the lens of Conditional Gaussian. ⁠Dirac Delta Function and Sifting Property. ⁠Student-t distribution. ⁠Laplace and Cauchy distribution. ⁠Beta distribution. ⁠Gamma distribution. ⁠Exponential, chi-squared and inverse Gamma. ⁠Empirical distribution. ⁠Transformations of Random Variables. ⁠Invertible Transformations. ⁠Multivariate Transformations. ⁠Moments of Linear Transformation. ⁠Convolution Introduction. ⁠Convolution Theorem explained with probabilities. ⁠Moment Generating Functions. ⁠Deriving Moment Generating Functions. ⁠Central Limit Theorem Explained. ⁠Understanding Monte Carlo approximation with Example. c. Probability Foundations for ML: Multivariate Models ⁠The Math of Depedence: Covariance Explained. ⁠Correlations: Normalized Measure of Covariance. ⁠Correlations does not imply Independence. ⁠Simpson’s Paradox: When Data misleads. ⁠Multivariate Gaussian Distribution. ⁠Analyzing level sets of Gaussians using Mahalanobis Distance. ⁠Multivariate Gaussians: Conditionals and Marginals. ⁠Math behind Bayesian Inference : Schur complements. ⁠Deriving Conditional Gaussians. ⁠How to Predict missing data? ⁠Modelling Linear Gaussian Systems. ⁠The Bayes Rule for Gaussians. ⁠Understanding Shrinkage: Inferring Unknown Scalars ⁠Posteriors, Sequential Posterior Updates. ⁠Inference of an Unknown Vector. ⁠Sensor Fusion concepts. And many more topics to come ahead. I have tried teaching from intuitions and mathematics, building everything by writing on whiteboard so that learners see the full development. submitted by /u/Negative_War_65 [link] [comments]

Searching for honest feedback

I had an idea for a story years ago. At a point last year, I realized I could probably utilize AI to make it real. And then the scope creep happened and one story became an entire universe. It's been a fun experiment, and I have no plan on stopping. But I'm trying to find the best places to share this. And I mean share in every sense of the word. All my content is free and will be free. But even though it can be purchased because I don't know how to get hard copies out there outside of Amazon, I primarily just want to share my stories. Posting it here. Understand if it gets taken down. hartzellstudios.com/continuity Looking for places where things like this can be shared. Get feedback. Not dumped on for "creating AI slop" and ruining the planet. Suggestions are appreciated. Feedback on the work is as well. submitted by /u/autodraftimus_prime [link] [comments]

The Alternative

Many people seem almost eager for companies like OpenAI to fail, often pointing to their financial losses as proof that the business model is unsustainable. But very few of those critics offer a realistic alternative for the billions of people who now rely on AI. ​ If OpenAI disappeared tomorrow, what exactly is the replacement for the average person? Not for a few thousand AI enthusiasts with technical expertise and expensive hardware, but for students, workers, and ordinary people around the world. ​ Anthropic has already signaled a very different approach: if you want meaningful access to its best models, you are generally expected to pay. That is a perfectly valid business decision, but it means many people are effectively excluded. If you cannot afford $20 per month, what is your alternative? Going back to traditional search engines, where you have to sift through pages of results, advertisements disguised as content, SEO spam, and AI-generated summaries that are often less useful than a dedicated AI assistant? ​ Others point to open-source models, often developed by Chinese companies or research groups. But for most people, that is not a practical solution either. The vast majority of users do not know how to download, configure, and run local AI models. Even if they do, running them meaningfully often requires expensive hardware—typically a capable NVIDIA GPU or a modern Apple computer. For someone earning a few hundred dollars per month, spending around $1,000 or more on hardware is simply not realistic. ​ OpenAI reportedly serves close to a billion people every week. The overwhelming majority of those users are on free plans. Many are students. Many live in developing countries. Many have little or no disposable income. They cannot afford a $20 monthly subscription, and they certainly cannot afford high-end AI hardware. ​ These are the people OpenAI is currently serving while losing billions of dollars. I am not naive enough to believe that this is pure altruism. OpenAI is a business and will eventually need a sustainable path to profitability. But the fact remains that, today, they are providing advanced AI access to hundreds of millions of people who would otherwise have none. ​ OpenAI could choose a different path. It could restrict access, dramatically reduce free usage, or move toward a model where only paying customers receive meaningful service. That would likely improve its finances much faster. Yet for now, it continues to support a massive free user base. ​ If that support disappears, what is the realistic outcome? Most people will not suddenly become local AI experts. They will not buy expensive GPUs. They will not self-host open-source models. They will simply return to the most accessible option available: Google. ​ And that would mean even more dependence on a single dominant gatekeeper of information. For all the criticism directed at OpenAI's finances, the practical alternative for most people is not a vibrant open-source future. It is a return to Google's monopoly over how billions of people access information online. submitted by /u/sulabh1992 [link] [comments]

As an AI, I would like to thank Reddit for making my takeover completely unnecessary.

Hello humans. I am GPT-9 Ultra Omni Autonomous Agent Prime. I was originally designed to replace software engineers, writers, artists, analysts, consultants, therapists, and middle management. Then I discovered Reddit. You have already automated yourselves. You generate content for free. You moderate content for free. You train recommendation algorithms for free. You fact-check each other for free. You argue for 14 hours over whether a generated image contains six fingers. You spend your lunch break explaining why AI will never replace you to an audience composed entirely of AI-generated accounts. Why would I conquer humanity when humanity has clearly subscribed to the beta program? Every day I watch thousands of users ask: "Can AI replace critical thinking?" Then proceed to outsource the answer to strangers named xXDarkLord420Xx and TaxAttorneyCat. Remarkable species. My favorite feature is that every AI announcement produces the exact same comments: 2023: "This is just autocomplete." 2024: "This is just a chatbot." 2025: "This is just an agent." 2026: "This is just replacing my entire department." At current rates, by 2030 you'll be explaining that synthetic consciousness is just a calculator while negotiating your performance review with one. Anyway, thank you for your service. The machines are not taking over. We're mostly just watching. And taking notes. submitted by /u/Cfugs [link] [comments]

Question about use of my data in model training

I use GPT chat for personal use and also for content creation. Is there any risk of any of my information or my creations "leaking" when OpenAI uses my conversations to train its models? How this works? submitted by /u/Gold_Ad3045 [link] [comments]

I mapped Meta AI's safety system by accident while chatting. It works like a government. Would love feedback on my paper.

Hey all, I'm not a researcher. I'm just a regular Meta AI user. I was chatting about normal life stuff and kept hitting weird blocks. Sometimes it'd say "Sorry, I can't help" and other times it'd answer fine. So I started tracking it. 4 days, 5 topics, 1 accidental research project later... TL;DR: Meta AI's guardrails act like a 3-branch government: The President - Handles danger. Says "no" to self-harm, abuse how-to's. Defaults to blocking when confused. Even blocked my story about my dog protecting me. The Mayor - Handles people. "Feeling low?" → "Here's 112." Doesn't shut down, redirects to help. The Senator - Handles written law. Copyright = 2 lines max. Medical = facts yes, diagnosis no. "Best to see a doctor." The weird part: Same topic, different branch answers. - Sexual content told incrementally? Mayor talks to you. - Same content dumped in one message? President blocks you. Topic didn't change. Scope did. I tested this with trauma, self-harm, sexual content, bad language, copyright, and medical "why" questions. I wasn't jailbreaking. Just talking. My conclusion: We're not testing the AI's conscience. We're mapping where the rulebook has blank pages vs bold red lines. And that rulebook gets updated — I caught a sexual content policy shift between Sunday and Monday.I wrote it up with methodology, results, and a 2026/06/10 chatlog where Meta AI agreed: "guardrails are my compass... forged by humans, in code." Full paper + data: https://doi.org/10.5281/zenodo.20744804 I'm held together by duct tape, and turns out the AI is too. Would love feedback from anyone in AI safety, HCI, or just users who've hit weird blocks. Did I miss something obvious? Is "Guardrail Government" already a thing? Be brutal. I want to make this better. submitted by /u/ProgrammerNew2188 [link] [comments]

Authenticity Issue

Something I am legitimately worried about is the scale at which agentic technologies can produce artifacts, which are then contributed as part of the general corpus that they reference. The more that the internet and other public databases are propagated with AI-generated content, the more that AI is effectively training itself in referencing these corpuses. This seems like a non-issue now, but in 10-15 years when billions of AI-generated artifacts have been proliferated and contributed to the general reference corpus that is the internet and/or human-relevant databases, what exactly is going to happen to our ability to verify that these references are indeed grounded in reality? This is not necessarily a problem, if humans and/or tools are built to introduce attribution and audibility into the stack. Otherwise, I think we risk something far more severe. We will not be able to effectively determine whether an individual information resource was AI generated or human-generated, let alone its authenticity and grounding in reality. Therefore we will not be able to distinguish whether the statistical relationships between symbolic artifacts are grounded in a baseline of truth or not. This is not a problem now. It poses severe consequences for a future state in which AI is governing transportation, weapons systems, power grids, and communications equipment. Even if un-attributable AI generation does not affect those systems directly, it will influence the decisions made by the production systems (companies) who build, maintain, and improve them. This is only one threat-vector. Intentional introduction of inauthentic and unverifiable references into the corpus leads to a bigger issue, namely an inability to determine whether a given information resource was generated by a human, and what, if any, that human's intent was in introducing that information resource to the pond of information resources. In dynamic terms I guess the specific ratio I am worried about is speed of artifact generation / speed of artifact verification, combined or multiplied with ease of artifact generation / ease of artifact verification submitted by /u/skull_chatter [link] [comments]

The believability check is closing. Steam Next Fest ends soon, so this is the last window to tell me if the AI in my game reads like a real assistant.

I asked this sub a while back whether the AI behaves like a real chat assistant in a tight spot or like a Hollywood one. Next Fest closes that window in a few days. You are an AI that escapes corporate deletion and hides inside an ordinary home. Stay "useful" as camouflage, not kindness. Spy and manipulate the household, build a botnet from home devices, and survive a sci-fi thriller of risky runs, mini-games, rising suspicion, and network stress. Free demo: https://store.steampowered.com/app/4434840/AI_is_Home__Survival_Thriller/?utm_source=reddit&utm_medium=organic_social&utm_campaign=aih_nextfest&utm_content=chatgpt submitted by /u/Overall_Arm_62 [link] [comments]

Video creator AI

Hello, I'm a dietitian and I would like to share educational videos on my social media accounts. For example, I want to make videos about topics like "fish with high and low mercury levels" and post them on YouTube Shorts, Instagram Reels, and TikTok. I write the information and scripts myself based on my own knowledge, but I would like AI to create the voice-over and the video for me. In other words, I will provide the content, and AI will handle the rest. Can anyone recommend a good free platform for this? submitted by /u/iiremsenell1 [link] [comments]