Been thinking about this a lot lately. Newer models are genuinely good at reasoning through hard problem, they get the logic, the structure, the WHY. Ask them to explain a complex system and they'll usually nail it.
But hand that same model actual execution. The real input, real constraints, no room for a rounding error or a dropped edge case, and it starts to wobble. Wrong units. A broken format. A fix that quietly creates two new problems.
After reading the World Economic Forum s 2025 Future of Jobs Report, I learned that AI is both replacing and creating jobs. On one hand, AI is leading to job loss. The report mentions that 41% of employers plan to reduce their workforce because of AI automation. On the other hand, AI is also creating new opportunities. According to the same report, 70% of companies want to hire people to work on AI tools, and 62% are looking for workers who can collaborate with AI. This shows that while some jobs are being replaced, there is still a strong demand for new roles in the AI industry. I believe AI will probably create more jobs than it takes away, just like when computers first started appearing in the workforce.
I used to think simple games would be easier to build. After actually making one, I m starting to think the opposite.
When there are only a few mechanics, every little detail matters the timing, feedback, difficulty curve, animations, and even the first few seconds of gameplay.
Curious what other makers have experienced: does keeping a product simple actually make building it harder?
I have been seriously thinking about a tool that could read my recently generated codebase and teach me what is implemented depending on my understanding of the project, my skill level as dev and more other heuristics that could help me actually learn the project. This could be an essential tool for onboarding people into projects they are not familiar with, but I wanna push this for codebases I may have generated in JAVA a language I have some experience with but not enough to build a SaaS with, but in the case I did, I would love to have a tool handhold me through learning everything and being up to par with the code. There does not seem to exist a tool as such, would love to hear your thoughts and get feedback.
The more AI I build, the clearer it becomes for me that the model is rarely the issue. The true bottleneck is context. AI can write fantastic code.
However, it fails when it needs to comprehend:thousands of existing files,project structure,decisions made before,and all the consequences of making one single change.
Have you ever felt frustrated switching between multiple apps just to get work done as a team? One app for chat, another for notes, another for project management it feels fragmented, wastes time, and makes it easy to lose track of things. With that in mind, I m developing a collaborative app that aims to solve these problems by focusing on: Ease of use: A simple, intuitive interface that any team can quickly adopt.
Modular flexibility: Customizable workspaces (we call them containers) where you can add only what you need: public or private channels, notes, mind maps, tasks, and more. It s tailored to your team s roles and workflow.
Security: End-to-end encrypted chats to ensure your data stays safe.
What makes this app different?
Beyond real-time chat with threads and notifications, users can create sessions within channels, including: Collaborative notes: Like Google Keep but with the option to add voice notes that are automatically transcribed.
Mind maps: For visualizing ideas or planning projects.
Task canvas: Similar to Trello but built right into your channels, so you can assign tasks without leaving your workflow.
Questions: Have you faced issues with fragmentation between tools when collaborating with others?
What features would you like to see in a tool like this?
What are your thoughts on this modular, flexible approach?
Any feedback, suggestions, or even criticism would be incredibly helpful. Thanks for reading and sharing your thoughts!
