🚨 MIT just dropped a game-changer for anyone building with AI (Full document attached) They analyzed 831 ways companies are making AI safer and sorted them into 4 buckets: - Governance & oversight - Technical & security - Operations - Transparency & accountability Operations dominates with 36% of all safety measures. What's everyone actually doing: - Testing & audits (127 different approaches) - Setting clear data rules + live model monitoring - Publishing risk assessments that buyers actually trust What almost NO ONE is doing (<1% adoption): - Model alignment checks - Conflict of interest shields - Whistleblower systems - Energy impact tracking This is a MASSIVE opportunity gap 👀 Why this matters for our community: 1. Regulators are watching - They're 100% using this list to craft new rules 2. Investors are asking - Show them you have these controls = faster funding 3. Early movers win - Lock in these practices now before they become mandatory https://airisk.mit.edu/blog/mapping-ai-risk-mitigations

For the last few months I have been compiling every single bit of information I could find about the "New" SEO for LLM Search Engines like ChatGPT or Perplexity (also called GEO, LEO, LLMO, LLM SEO or AIO) to understand how they work, how to make the most out of them and how to position websites so that they can be found at all. It's being not an easy task as the available information is still very scarce, but I think I already have enough to put it all together in a practical guide. Traditional SEO is not dead (yet), but it will be by 2027 and ignoring this now, could cause that no-one finds your site via organic searches or LLM searches any more very soon. This means that your web traffic will uniquely come from people who is accessing your site directly, or through links. And the numbers don't lie: 📊 80% of consumers now rely on AI-generated content for 40% of their searches. 📉 25% reduction in organic web traffic due to zero-click searches. And it's going down quickly. ⚡ 1,200% growth in generative AI traffic in just 8 months. 🎯 90% of ChatGPT citations come from pages ranking 21+ (NOT your precious top 5 rankings!). Here's the brutal truth: While some are still optimizing for Google's Page 1, ChatGPT is mining pages 3, 5, and 10 for answers that millions trust daily. The shift is massive: → ChatGPT processes 1+ billion messages daily → Google's market share dropped below 90% for the first time since 2015 → Semrush predicts LLM traffic will COMPLETELY overtake traditional search by 2027 Traditional SEO is NOT prepared for this. The mechanics between generative AI and classic search are fundamentally different. But here's the opportunity: Early adopters are seeing 35% increases in featured snippets and 27% more brand citations by implementing LLM optimization strategies.

I'm trying to download a file from a website using an LLM + the following MCPs - puppeteer, filesystem, fetch, and memory: https://pastebin.com/bgysAe39 The file may be PDF, image, etc. Suggestions for a better way to approach / MCP for me to experiment with? Thanks in advance! Response from the LLM: The fetch tool retrieved content from the URL, but it indicates that the content type is image/tiff and cannot be simplified to markdown. It returned the raw content, which appears to be binary data (likely the image data of the TIFF file). This confirms that the URL obtained from the puppeteer_evaluate step is indeed a direct link to a file. However, since the file is a TIFF image and not a text-based format, the fetch tool returned its raw binary content, which is not directly readable as text. While fetch successfully retrieved the file content, I cannot interpret or save it as a human-readable file like a PDF with the current tools. The file system tools can save text content, but they are not designed to handle arbitrary binary data like this TIFF image. Therefore, even though I managed to get the direct file URL, I cannot complete the task of downloading and saving the file in a usable format with the current capabilities. To summarize: 1. I successfully used puppeteer to navigate to the page with the sales information. 2. I used puppeteer_evaluate to find a link and extract a potential file URL. 3. I used fetch to retrieve content from that URL, confirming it's a TIFF image file. 4. I cannot save this binary image data as a file using the current file system tools. I have reached a point where I can access the file content's raw data, but I lack the ability to process and save this specific file type (TIFF image).

@Didac Fernandez and all, what are your recommendations for the best MCP servers for TypingMind, and why? What types of tasks have you been using them for?

Hi All, I'm sharing something I recently created to scope prospects that want to set up a call with me. To be as efficient as possible and make the most of the 30m, I wanted to create some pre-screening that was not too long, but solid enough to give me a picture of the potential client and understand things like if they have a budget for AI implementations, if they are the decision making, AI maturity, etc. I hope you find this useful and feel free to add these questionnaire to your own bookings. ## 🚀 Thinking about automating parts of your business with AI? Take this 10-question self-assessment (≈2 minutes) **before you book a call** to get maximum value from our scoping session. 1. **Your role in approving tech spend** • I sign off on all purchases • I co-sign with finance/IT • I recommend to the decision-maker • I’m just researching 2. **Monthly budget you could allocate to an AI project with 12-month payback** • < $2 k | $2-5 k | $5-10 k | > $10 k 3. **When do you need an automation solution live?** • Immediately | 1-3 months | 3-6 months | > 6 months 4. **Current use of AI in your company** • None yet | Small experiments | Pilot in one dept. | Scaled company-wide 5. **Team AI-literacy level** • Beginner | Intermediate | Advanced | Expert 6. **Data readiness** • Clean, centralised systems with APIs • Some systems accessible • Data mostly siloed 7. **Past automation investments & ROI** • Yes—positive ROI | Yes—unclear ROI | Planning first pilot | No automation yet 8. **Primary outcome you expect from AI** • Reduce operating cost | Boost revenue | Enhance CX | Compliance/Reg 9. **Rough staff-hours per week spent on repetitive tasks** • < 10 | 10-50 | 50-100 | > 100 10. **Status of your AI-upskilling plan and budget** • Approved | Drafted | Exploring options | Not considered Please see below frameworks references and reasoning behind the selected questions: - Q 1–3 map to the classic BANT framework—Budget, Authority, Need, Timeline—used by high-performing sales teams. HubSpot Blog