DanTheAIMan

👀 I went through 9 months of @semrush AI Visibility Index source data comparing ChatGPT and Google AI Mode across Business Services, Consumer Electronics, Software, Fashion and Finance: The top outcome isn't just that the two platforms rely on different source ecosystems, but that they're constantly moving. A few patterns stood out: 1. ChatGPT is much more diverse. In April 2026, ChatGPT needed 1,621 brands to reach 80% share of voice Overall. Google AI Mode needed 299. That is a 5.4x gap. 2. YouTube is far more prominent in Google AI Mode (what a surprise!). It became AI Mode’s #1 Overall source in April, jumping from 16.0 to 46.9 in Semrush’s source score from March to April. 3. Wikipedia has been a major ChatGPT source, but April changed the story. It was ChatGPT’s #1 or #2 Overall source from Aug 2025 to Mar 2026, then dropped to #5 in April. 4. Alibaba is one of the most interesting new entrants: It has zero Google AI Mode top-10 appearances in this dataset, but became ChatGPT’s #4 Overall source in April and #1 in Consumer Electronics. 5. The platforms overlap, but not enough to treat them the same. April top-10 source overlap ranges from 4/10 to 6/10 by vertical. Reddit is the strongest April cross-platform constant, appearing in the top 10 for every analysed vertical in both platforms. Forbes is also highly consistent Overall, appearing in both platforms’ Overall top 10 in 7 of 9 months. The takeaway: AI search visibility shouldn't be only about optimizing your own site, but about understanding which external sources each platform repeatedly surfaces as evidence in your vertical, and tracking how that source layer changes over time. This data doesn't prove which sources cause visibility, but it does show where brands should prioritize third-party visibility, reviews, community presence, video, marketplace representation, digital PR, and entity corroboration by platform and vertical... and one AI visibility strategy for every platform is not enough. If you haven't yet, check Semrush AI Visibility Index: ai-visibility-index.semrush.com 👀 Thanks for the data 💪

Ajay just landed a Data Engineering intern role. He found the opportunity through the @DataTalksClub community and credits the community for helping him get there. This is what makes building @DataTalksClub worth it: seeing people learn, connect, and land roles. Congratulations, Ajay! 🎉

Here's the quickest way to migrate your Claude Code setup to the Codex CLI, with backwards compatibility:

'Where are companies' binding obligations to guard our data, our livelihoods, from misuse?' GDPR made that question expensive to ignore. AI is raising the stakes again. Cyber agencies already named it: AI-component SBOMs are the new supply-chain floor.

Anthropic pays $750K+ for engineers who understand how LLMs think. Karpathy just explained it at Sequoia 2026 in one sentence: “Software 3.0 is programming through prompts.” You don’t need the salary. You need 30 minutes and these 10 prompts.

Curated list of Blender add-ons, tools, and tutorials github.com/agmmnn/awesome…

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THE CEO OF Y-COMBINATOR JUST SAID SOMETHING THAT SHOULD MAKE EVERY PROMPT ENGINEER UNCOMFORTABLE. "When someone asks how I prompt my AI, the answer is: I don't. The skills are the prompts." Garry Tan is not talking about better prompting. He is talking about replacing prompting entirely. Here is what he means and why it changes everything. A prompt is something you write every time. A Skill is something you write once and call forever. The difference sounds small. The compounding effect is enormous. Every hour you spend rewriting the same complex prompt from scratch is an hour you could have spent building the Skill that eliminates that prompt permanently. The builders operating at the highest level are not better at prompting. They have stopped prompting entirely. They have a library of Skills that handle every repeating workflow automatically. Type one word. The Skill runs. The output appears. Same quality every time. Here is the 7-day path Garry laid out: Day 1: Read the Skillify 11-item checklist. Day 2: Watch "Don't Build Agents. Build Skills Instead." Day 3: Read "Designing, Refining, and Maintaining Agent Skills at Perplexity." Day 4: Clone GBrain. 30 battle-tested Skills ready to deploy. Day 5: Add GStack. 23 slash-command Skills drop right in. Day 6: Do one workflow. Type /skillify. Watch it become permanent. Day 7: Everything you do more than once is now a Skill. Prompting is the manual labor of the AI era. Skills are the automation layer. The people who make this shift in the next 30 days will not be prompting in 2027. They will be operating. Bookmark this. Follow @cyrilXBT to master every Claude skill system that compounds over time.

Anthropic quietly dropped a free workshop showing how AI agents can divide tasks, execute processes, and run operations — without you micromanaging every step. This isn't "use AI to write emails" advice. This is infrastructure-level thinking for the next era of business. Whether you're a solo founder, a builder, or just someone who sees where this is heading — this 30-minute watch might reframe everything. Bookmark this. You'll want to come back when you're ready to build.

Jensen Huang (CEO of Nvidia) : "AI is not likely to replace you, but someone using AI better than you might." two types of people right now: type 1: using AI to write emails. treating it like a search box. type 2:building skills. powering agents. sold it for $100,000. not get replaced I wrote the exact playbook for type 2. bookmark this. build this weekend ↓

The javascript industry needs to find a solution to make NPM safe.

Your data may be telling you the exact opposite of the truth. This can occur because of Simpson’s paradox, one of the most important and potentially dangerous pitfalls in data analysis. It refers to situations where an observed association reverses or disappears after the data are divided into relevant subgroups, often due to the influence of a confounding variable. If these subgroup structures are ignored, the resulting conclusions can be seriously misleading. The animation below illustrates this effect. When all observations are combined, the data suggest one overall relationship between the variables. However, when the data are separated into relevant subgroups, the relationship changes dramatically. This is why it is essential to explore subgroup patterns and not rely only on aggregated results. In a recent Statistics Globe Hub module, you will learn step by step how Simpson’s Paradox arises, how to spot it in your data, and how to analyze it in R using visualizations and regression models. The Statistics Globe Hub is an ongoing learning program focused on practical skills in statistics, data science, AI, and programming with R and Python. More info: statisticsglobe.com/hub #rstats #statistics #datascience #datavisualization #regression #analytics #statisticsglobehub

Cooldowns will help reduce supply chain risk, but also makes you less able to patch 0-day attacks. There has to be a better way.

“In the best case, this is actually true. In many cases, it is hilariously false.” It can be either true or not true

Great essay by Tobi. Building an AI-native company? Go read it now. I couldn't resist visualizing it with my artifact generator. Biggest takeaway for me: "The risk isn't that AI does the work. It's that nobody learns from it."

So many companies are trying to kill the SaaS model, that for the first time I think we might have a shot at creating something different. Literally everyone wants to replace 12 apps with a single, integrated interface that does everything we need. AI can make this happen.

🚨 SOMEONE JUST KILLED THE COACHING INDUSTRY a developer spent 22,000 hours building a Personal AI Operating System on top of Claude Code now anyone with a terminal can install it for FREE it knows your goals, remembers every decision you've made, and prepares your morning briefing while you sleep [ the numbers are insane ]: - hours of dev work in it: 22,000 - sessions logged: 6,000 - time saved per day: 2-3 hours - GitHub stars: 12,100 - skills built in: 45 - workflows wired up: 171 - safety hooks: 37 - cost to install: $0 [ the science is wild too ]: no embeddings, no vector databases, no AI magic you can't read every memory, decision, and context lives in plain markdown files you read it with cat, search it with ripgrep, version it with git 4 memory types compound over time: - work memory (active projects, open decisions) - knowledge memory (domain expertise, research) - people memory (contacts, companies, relationships) - learning memory (patterns, mistakes, what works for YOU) every complex task routes through a 7-step cycle: OBSERVE → THINK → PLAN → BUILD → EXECUTE → VERIFY → LEARN privacy is enforced by CODE, not prompts a hook called ContainmentGuard physically blocks sensitive data from being written outside designated zones [ the grift opportunity is even wilder ]: freelancers are already charging $500-2,000 per personal AI setup for executives, founders, and busy operators one person + one weekend = a consulting business that didn't exist 6 months ago every AI productivity app you're paying $30/month for is replaceable by 4 hours of setup work and this one repo REPO: github.com/danielmiessler… 100% OPEN SOURCE, FREE

This works really well btw, at the end of your query ask your LLM to "structure your response as HTML", then view the generated file in your browser. I've also had some success asking the LLM to present its output as slideshows, etc. More generally, imo audio is the human-preferred input to AIs but vision (images/animations/video) is the preferred output from them. Around a ~third of our brains are a massively parallel processor dedicated to vision, it is the 10-lane superhighway of information into brain. As AI improves, I think we'll see a progression that takes advantage: 1) raw text (hard/effortful to read) 2) markdown (bold, italic, headings, tables, a bit easier on the eyes) <-- current default 3) HTML (still procedural with underlying code, but a lot more flexibility on the graphics, layout, even interactivity) <-- early but forming new good default ...4,5,6,... n) interactive neural videos/simulations Imo the extrapolation (though the technology doesn't exist just yet) ends in some kind of interactive videos generated directly by a diffusion neural net. Many open questions as to how exact/procedural "Software 1.0" artifacts (e.g. interactive simulations) may be woven together with neural artifacts (diffusion grids), but generally something in the direction of the recently viral x.com/zan2434/status… There are also improvements necessary and pending at the input. Audio nor text nor video alone are not enough, e.g. I feel a need to point/gesture to things on the screen, similar to all the things you would do with a person physically next to you and your computer screen. TLDR The input/output mind meld between humans and AIs is ongoing and there is a lot of work to do and significant progress to be made, way before jumping all the way into neuralink-esque BCIs and all that. For what's worth exploring at the current stage, hot tip try ask for HTML.

Runs professional MMD animation editing entirely within a browser. No heavy software installs. No complicated setup process. Just open the tab and start animating instantly. Supports real-time editing, rendering, and MMD workflows directly online. Feels like a full desktop animation studio inside your browser. Built for creators who want speed without sacrificing control. Github link : github.com/AmyangXYZ/reze…

AI didn't make code cheap. it made bad code lethal. Matt Pocock: "Software fundamentals matter more than ever" AI in a good codebase ships gold. AI in a bad codebase ships garbage. framework mastery doesn't compound. fundamentals do. if you're starting with AI agents, build these 5 patterns before anything else: > Tool Use Design Pattern. tool calling is the core of every useful agent. > Agentic RAG. grounded answers over hallucination. maps to real products. > Planning Design Pattern. agents that break a goal into steps before acting. > Multi-Agent Design Pattern. orchestrator-subagent is the only shape that ships in production. > Building Trustworthy AI Agents. reliability, safety, knowing when not to use one. this article covers what to learn, build, and skip in AI agents in 2026. bookmark and read this today. repo with 500 more in comments