The Ultimate Creator Swipe File

8 Choices I'm Making At 34 To Avoid Regret At 74: I recently turned 34, and I came to a few realisations. Who you become is just a result of the choices you make daily. Have you ever fallen for bad habits? Realised that you're slipping? Or perhaps you haven't noticed? They might seem small or insignificant. But habits have a compounding effect, good or bad. With that being said, Here are 8 choices I'm making at 34 to avoid regret at 74: 1. Treating healthy as the highest priority. 🧘🏻‍♂️ ↳ Your body is the car and your brain is the driver. ↳ Don’t take either’s health for granted. 2. Experience new stuff always. 🌿 ↳ Possessions fade, memories last. ↳ Life’s richer when you do more, not own more. 3. Reading, not scrolling. 📖 ↳ You want knowledge that lasts, not a quick dopamine hit.   ↳ Swap social media time for a book. 4. Consistent investing. 💵 ↳ Have a steady, diversified portfolio insead of get-rich-quick schemes.  ↳ Try some of the investments suggested in the carousel. 5. Saying "no" often. 🚫 ↳ Stop stretching yourself so thin.  ↳ Each “no” is a “yes” to something else. 6. Relentless curiosity. 🤔 ↳ Learning shouldn’t stop after school.   ↳ Try setting a growth plan at the start of every new year. 7. Slowing down. 🐌 ↳ ”Savour the little moments. They’re not little.” - Jon Kabat-Zinn.  ↳ Don’t get lost in the grind. 8. Help your former self. 🤝 ↳ What did you used to struggle with immensely?   ↳ Do something to help those in a similar position. What positive choices are you making today? Drop your knowledge in the comments ⬇️  ♻️ Repost this if you found it inspiring. And follow Chris Donnelly for more. If you enjoyed this content, you might like my newsletter, Step by Step: https://lnkd.in/eXSNaDiu I have 30+ free learning resources for you.

How to become indispensable at work. 7 underrated career skills everyone should know: 1️⃣ Be teachable ↳ You don’t know everything, and you’re not always right. The best people to work with are the ones who stay curious and open to learning. 2️⃣ Be easy to work with ↳ Talent is great, but if working with you feels like a chore, people will avoid it. Make life easier for others, and opportunities will follow. 3️⃣ Be quick to admit mistakes ↳ No one expects perfection. Own your mistakes, fix them fast, and move on. 4️⃣ Be reliable ↳ The bar is lower than you think. Follow up, follow through, and show up consistently. 5️⃣ Be generous with credit ↳ Wins are rarely achieved alone. Acknowledge others, and you’ll build goodwill that compounds over time. 6️⃣ Be the one who does more than expected ↳ Get in the habit of going above and beyond. The extra effort is often what sets you apart from the pack. 7️⃣ Be the person who never stops learning ↳ Jobs change. Industries evolve. Those who keep learning and improving never have to worry about staying relevant. The best way to future-proof your career? Become the person no one wants to lose. Shoutout to my friend Rob Dance for the powerful image and message. ♻️ Repost this if it resonated.

SEO, AEO and GEO are NOT the same thing. Knowing the difference is non-negotiable: If you are in the digital marketing space, this is something you can't avoid. The biggest and best agencies already know the difference. They've optimised for all three types of search. And they are able to not only sell SEO, but AEO and GEO too. (This Friday, June 12th, I'm hosting a free live masterclass: "How To Scale An AEO Agency In 2026" It will teach you how to do the exact same at your agency: https://lnkd.in/easeMTaX) But you don't need to be Ogilvy to be seen. You just need to accept that your clients are no longer crawling through blue links. They're asking ChatGPT and taking Google's AI Overviews at face value. The agencies that can sell all three are the ones winning retainers in 2026. Here's a quick breakdown of each layer 👇 (Check the full sheet for in-depth details) 1️⃣ SEO - Search Engine Optimisation ↳ What it is: Ranking your firm's pages on Google. ↳ How it works: Identify high-intent keywords, optimise on-page content, etc. ↳ Pros: Proven, steady traffic driver. ↳ Cons: Slow to show results. ↳ How to rank higher: Focus on high-intent keyword clusters specific to your service. 2️⃣ AEO - Answer Engine Optimisation ↳ What it is: Getting your expertise pulled into Google's AI responses. ↳ How it works: Provide direct answers to the questions clients are asking. ↳ Pros: Instant authority and brand visibility. ↳ Cons: Some users get the answer without clicking through. ↳ How to rank higher: Answer questions in 40 to 60 words under clear headings. 3️⃣ GEO - Generative Engine Optimisation ↳ What it is: Getting your firm cited by name in AI recommendations. ↳ How it works: Publish well-structured thought leadership with citations and data. ↳ Pros: Early-mover advantage in AI search. ↳ Cons: Hard to measure direct impact. ↳ How to rank higher: Create entity-rich content following E-E-A-T principles. Search has changed more in the last year than the last decade. And most agencies still can't explain the difference between these three. The agencies that can sell SEO, AEO and GEO as one package will own the next decade of search retainers. If you run an agency and want the exact playbook to productise and sell all three,  Join me on Friday on the live masterclass: https://lnkd.in/easeMTaX You'll leave with a clear action plan and roadmap. Which of the three are you weakest on right now? Drop it in the comments below. And be sure to save this to refer back to it later ! ♻️ Repost to help other agency owners get ahead of AI search. And follow me Chris Donnelly for more on SEO, AEO and GEO.

[1/2] In the next version of Claude Code, Claude’s WebFetch tool automatically adds Accept: “text/markdown, *” to requests which helps docs sites provide token-efficient docs https://t.co/uYthd1m9RP [2/2] @0xRaduan Summarization is one thing we do to reduce prompt injection risk. Are you running into specific issues with it?

IKEA replaced 8,500 customer service roles with AI. And the company is now $1.4 billion richer because of it... But before you take up arms against IKEA, know this:  They didn't fire a single person in the process. Every single one of those 8,500 humans was retrained,  And became part of an "interior design advisor" service... That pulled in an insane $1.4 billion in revenue. This goes in the face of what every other company is doing,  With AI taking over the standardised tasks and humans redeployed for the rest. And it's probably the most genius approach I've seen. To sum it up, IKEA decided to: ✅ Use AI to clear the low-value work. ✅ Redeploy your people to the high-value work AI can't do. ✅ Build the new service line that humans are uniquely good at. That's how you build defensibility in 2026. We can't pretend AI isn't changing how we work... It already has.  But maybe not by replacing your entire team with chatbots. Instead, we can be honest about what AI is better at,  And ruthless about putting your people where they create the most leverage. AI can replace standardised tasks that don't require much brainpower. But humans need to be where judgement, taste and relationships actually matter. A lot of companies won't make this distinction, or they'll do it too late.  The ones that follow IKEA's example will build decade-defining businesses. Save this post to show your team or boss.  And comment below with how your business is approaching AI. ♻️ Repost to help other founders think smarter about AI. And follow me Chris Donnelly for more on building and scaling in 2026. -- If you want more AI news, frameworks, and resources,   I unpack this kind of shift every week in my newsletter, Step By Step. 200K+ founders read it for the practical breakdowns on how to build with AI. 📌 Click here to get the next edition on Sunday: https://lnkd.in/eUTCQTWb

[1/14] BREAKING: Apple just proved AI "reasoning" models like Claude, DeepSeek-R1, and o3-mini don't actually reason at all. They just memorize patterns really well. Here's what Apple discovered: (hint: we're not as close to AGI as the hype suggests) https://t.co/9A4Lg5aQWO [2/14] Instead of using the same old math tests that AI companies love to brag about, Apple created fresh puzzle games. They tested Claude Thinking, DeepSeek-R1, and o3-mini on problems these models had never seen before. The result ↓ [3/14] All "reasoning" models hit a complexity wall where they completely collapse to 0% accuracy. No matter how much computing power you give them, they can't solve harder problems. https://t.co/nQJWpOjxBz [4/14] As problems got harder, these "thinking" models actually started thinking less. They used fewer tokens and gave up faster, despite having unlimited budget. [5/14] Apple researchers even tried giving the models the exact solution algorithm. Like handing someone step-by-step instructions to bake a cake. The models still failed at the same complexity points. They can't even follow directions consistently. [6/14] The research revealed three regimes: • Low complexity: Regular models actually win • Medium complexity: "Thinking" models show some advantage • High complexity: Everything breaks down completely Most problems fall into that third category. https://t.co/EvkTg4DPq6 [7/14] Apple discovered that these models are not reasoning at all, but instead doing sophisticated pattern matching that works great until patterns become too complex. Then they fall apart like a house of cards. [8/14] If these models were truly "reasoning," they should get better with more compute and clearer instructions. Instead, they hit hard walls and start giving up. Is that intelligence or memorization hitting its limits? [9/14] This research suggests we're not as close to AGI as the hype suggests. Current "reasoning" breakthroughs may be hitting fundamental walls that can't be solved by just adding more data or compute. [10/14] Models could handle 100+ moves in Tower of Hanoi puzzles but failed after just 4 moves in River Crossing puzzles. This suggests they memorized Tower of Hanoi solutions during training but can't actually reason. https://t.co/g8VOH7gzOs [11/14] While AI companies celebrate their models "thinking," Apple basically said "Everyone's celebrating fake reasoning." The industry is chasing metrics that don't measure actual intelligence. [12/14] Apple's researchers used controllable puzzle environments specifically because: • They avoid data contamination • They require pure logical reasoning • They can scale complexity precisely • They reveal where models actually break Smart experimental design if you ask me. [13/14] What do you think? Is Apple just "coping" because they've been outpaced in AI developments over the past two years? Or is Apple correct? Comment below and I'll respond to all. [14/14] If you found this thread valuable: 1. Follow me @rubenhassid for more threads around what's happening around AI and it's implications. 2. RT the first tweet