Many of you may be considering switching from OpenAI or Gemini to Claude. If so, one thing you might miss is the continuity you had with your previous AI tool. Over time, it learned your preferences, your style, your projects, and the way you like things done. To make the transition smooth, Claude now supports memory import. Here is how you can migrate your context properly: 1. Copy the prompt below. 2. Paste it into ChatGPT, Gemini, or any AI tool you are moving away from. 3. Generate the response, then copy that full response. 4. Open Claude, go to your memory section, and paste the exported content there. The prompt ------------------------------------------------------------------------------------------------------------------------------------------------------------------- "Export all of my stored memories and any context you've learned about me from past conversations. Preserve my words verbatim where possible, especially for instructions and preferences. ## Categories (output in this order): 1. **Instructions**: Rules I've explicitly asked you to follow going forward — tone, format, style, "always do X", "never do Y", and corrections to your behavior. Only include rules from stored memories, not from conversations. 2. **Identity**: Name, age, location, education, family, relationships, languages, and personal interests. 3. **Career**: Current and past roles, companies, and general skill areas. 4. **Projects**: Projects I meaningfully built or committed to. Ideally ONE entry per project. Include what it does, current status, and any key decisions. Use the project name or a short descriptor as the first words of the entry. 5. **Preferences**: Opinions, tastes, and working-style preferences that apply broadly. ## Format: Use section headers for each category. Within each category, list one entry per line, sorted by oldest date first. Format each line as: [YYYY-MM-DD] - Entry content here. If no date is known, use [unknown] instead.

The change is really huge Fewer manual steps, less back and forth, and more complete builds you can trust. What is new 1 Intelligent planning mode Lovable can now plan complex work more autonomously before it builds. It will • Ask smart questions to fully understand your requirements • Generate a clear plan you can edit and approve • Show what it will build step by step Use this when your feature has multiple pages, roles, or edge cases. 2 Prompt queuing You can now delegate multiple tasks without waiting. You can • Queue prompts for Lovable to process • Reprioritize tasks anytime • Keep momentum while collaborating with others This is a big upgrade for shipping faster. 3 Automated testing with browser tools Lovable can test your app like a real user, then fix issues like a senior developer. It can • Navigate the app in a browser • Fill forms and click through flows • Run full flow checks and fix bugs immediately Try asking this Test the full signup to dashboard flow and fix anything broken 4 Seamless Google sign in You can now add Google sign in with a single prompt, which helps conversion and trust immediately. My opinion This update makes Lovable much better at planning, executing, testing, and finishing real features with less supervision.

In reference to the AI world, we heard about Prompt Engineering, which of course it is important, because you need to know what questions/prompts to ask ChatGPT. This morning I read a post about something new (at least to me, the newbie) Context Engineering. Has anybody some thoughts to share about this? Many thanks!

I was having lunch today and started brainstorming. Instead of asking “What app should I build?” I asked a better question “Who is the best customer to build for” Here is a simple process you can copy - Go to ChatGPT or Perplexity and ask “What are the highest earning customer personas in [your country] with strong purchase power?” -From that list, pick one persona you already understand a little Maybe you know someone in that role through work, family or friends, or you already know their industry. -Then ask ChatGPT to (in deep research mode) Do deep research on that persona List their main problems, pains, and frustrations Suggest possible software solutions or services that would remove those pains You will end up with many focused and realistic product ideas. The purchasing power of your target customer avatar is a crucial factor that can decide the fate of your application. What do you think about this? What technique do you use?

Many of you might be shopping for blackfriday and it is quite hard to go through every website and find the right deals which suits your needs. I tried with Shopping Research in chatGPT and the results were exceptional. Just go to chatGPT select the shopping research mode or type /shopping and select the shopping research model when appears. Then type what you want to buy, it will ask few questions and give you the best choices. Here is what I tried for my webcam search. "I want to buy a webcam that I can use to live stream my coding sessions on YouTube and Instagram, and to record explanation videos similar to what you can do in Descript. I live in Finland and I am hoping to use this Black Friday to get a good deal. Which one should I choose? I prefer a fixed setup that I can simply turn on and start recording, and I do not want a complicated Sony camera. I am open to alternatives to webcams, but my first choice is still a webcam, unless you can clearly justify something else. Please search and recommend at least 10 suitable options. I live in Helsinki, Finland."