The following app on https://fluxtimer.pplx.app is life an publicly accessible It is generated with Perplexity Pro version. Took me 10 minutes for the prompt and the AI 10 minutes to generate a first local draft and another 2 minutes to publish this for the public. So all in all 20 minutes for creating an advanced Workout-Timer similar to the Seconds Pro App with the difference that you can edit and upload your workout-routines in an *.md file rather than painstakinly edit it on your iPhone. But the point here is not the feature. Its what follows afterwards. As my goal with this app is to sell customized workouts for individuals the app must be predictably stable with the option to PREDICTABLY add new features without putting everything in danger customers are already used to use. There must be no change to what has worked so far. And therefore I have to OWN the app code and all the building process the same way I had to won it before AI. So Step 1 was to have the code local on MY machine in MY VSC editor under MY control using MY tools. And it took me 2 hours to find out whether this is doable at all. Step 2: Creating a development environment with associated GitHub Repos to manage the code: another 2 hours (testing and documentation included). Step 3: Building the code as is - failed - off course. Why? because its built and tested for Linux - not Windows. However, the AI has helped a lot and what would have taken my hours or even days figuring out the machine-code level cryptic errormessages where they are coming from, AI was able to point out DIRECTLY in which configuration files I had to change what to tweek it for Windows. Lesson: Either harness your AI that it really builds for your setup (but might not find then a solution that meets your requirements) or ask AI before building your code what it means for porting the code into your context. Step 4. Once the app was compiled successfully on my machine, publishing it with Perplexity AI was a breeze: 5 minutes.

If you had $0 and had to make your first $1,000 online using AI, what would be your plan?

What day of the week would you want to attend a live workshop with Igor & Dean to learn next level AI tactics & strategies?

Whenever I have to wait for the AI for building stuff I am getting up from my desk to work a list of body-work exercises that is now always on my desk. THIS is really transformative for me. I have lost 4 Kilos in 2 weeks and have again a much better body feeling. My wife happy too! AI is great!!

There's a counterintuitive pattern starting to emerge in the communities and conversations we follow closely. It doesn't fit the dominant narrative about AI and professional development, so it tends to get dismissed. But it's consistent enough and specific enough that it's worth looking at directly. The pattern: a growing number of professionals who use AI heavily are reporting, often with some embarrassment, that their ability to think through problems independently, to recall information from memory, to write fluently without AI assistance, feels like it has degraded. Not dramatically. But noticeably. The capability was there before. It's less reliably there now. This is the cognitive atrophy problem. It's real, it's specific, and it's something that smart AI adoption can work against. ------------- Context ------------- Cognitive capabilities are use-it-or-lose-it in a way that's well established in the research. Memory, reasoning, writing fluency, the ability to hold a complex problem in your head and work it through: these capabilities are maintained and developed through exercise and they degrade through disuse. For most of professional history, the nature of knowledge work required these capabilities regularly. Writing required sustained original composition. Research required holding a developing understanding in working memory as new information was integrated. Problem-solving required independent reasoning before any external validation was sought. The work itself was the exercise. AI tools are changing the exercise load. When AI drafts the writing, the composition muscle doesn't engage. When AI does the initial research synthesis, the information integration work doesn't happen. When AI suggests the analysis framework, the independent problem framing doesn't get practiced. Each of these is individually a small reduction in cognitive exercise. Across a day of heavily AI-assisted work, the aggregate reduction is significant. The capability doesn't disappear immediately. It degrades gradually, in a way that's invisible until a situation arises that requires it without AI assistance: a meeting where you need to think on your feet, a client situation where you need to produce analysis quickly without time to brief an AI, a creative challenge where your own perspective needs to show up rather than an AI-assisted version of it. These situations surface the gap.