Hello everyone. Last week, we developed an AI model that predicts greyhound race results. This model analyzes and learns from the race results of the past year to predict the likely winner of the next race and calculates the break-even point based on the odds. Developed using AutoGen, this model predicted the leading contenders with 87% accuracy, which is a notable achievement in the betting industry. Are there any of you currently working on similar projects or have any ideas? Modec is currently researching ways to increase the probability of recommending leading contenders to over 93%.

Hi everyone - huge update today! ChatGPT Ads just hit a major milestone. Here's what you need to know: The numbers: - $100M+ in annualized ad revenue, just 6 weeks after launching the ad pilot - Less than 20% of eligible US free and Go tier users are seeing ads daily - Around 85% of Free and Go users are eligible to see ads, meaning current revenue is a fraction of what's coming - 600+ advertisers are now on the platform What's coming next: - Self-serve advertiser access is on track to launch in April - Geographic expansion into Canada, Australia, and New Zealand is being explored - OpenAI hired Dave Dugan (former Meta ad exec) to lead ad sales - The ads manager dashboard is rolling out for campaign management and real-time optimization What this means for us: - The moment self-serve opens, every Google and Meta advertiser starts testing. CPMs will rise, and competition will increase fast - Right now, fewer than 7% of ads are rated "low relevance" by users, which means the ad experience is performing well, and OpenAI will keep scaling it - Reporting is still basic (weekly CSV of clicks and impressions - I expect this to change), so the advertisers who figure out performance tracking independently will have a major advantage The early mover window is real, and it'll close faster than most expect. FutureProof will be sharing the best practices & tools to help you create winning ChatGPT ads as soon as we have them! Source: https://searchengineland.com/chatgpt-hits-100-million-in-ad-revenue-and-is-opening-self-serve-access-in-april-472797

I ranked in Google AI in under 24 hours using LinkedIn articles & Claude. Watch the below video to see how I did it & let me know if you have any questions below 👇

Most people working on LLM visibility right now are making one big assumption that fixing a citation works the same way as fixing something on Google. It doesn't. Not even close. Let me break down what's actually happening with citation accuracy in LLMs and why you need to rethink your timelines. THE CITATION ACCURACY PROBLEM Here's the reality LLM citations are not reliable to begin with. Research published in Nature Communications in 2025 and papers on arXiv that analyzed over 366,000 citations across ChatGPT, Claude, and Perplexity found that 50 to 90% of LLM citations fail to fully support the claims being made. We're talking fabricated references, outdated sources, and heavy bias toward older papers that were overrepresented in training data. So the first thing you need to understand is the citations you're seeing in LLM outputs right now? A huge chunk of them are already inaccurate. WHY FIXING A CITATION ISN'T INSTANT Now let's say you spot a wrong citation about your client or your brand. You reach out to the source, you get it corrected. On Google, you can request a recrawl, check the status, and within a reasonable time, the updated version is live in search results. It's not instant, but it's relatively fast and trackable. With LLMs, that's not how it works. Imagine how come chatGPT is reading 50+ landing pages processing it under 10 seconds and giving you summary. There are many questions left on the table. What if the website is slow? How consensus is verified within such short amount of time. Without a cache version this action isn't possible for LLMs to perform. To compare you can give 10 newly published articles links to chatgpt and ask it to summarise it. Now compare the time taken yourself. LLMs run on what's basically a cached version of the web. Crawlers like GPTBot and ClaudeBot are scraping the web, but they're not doing it in real time. GPTBot recrawls roughly every 30 to 60 days. And here's a wild stat ClaudeBot has a crawl-to-refer ratio of about 38,000 to 1. That means it's scraping constantly but rarely sending traffic back or updating its understanding of those sources.