Some people love automation, while others want full control over every entry. Where do you fall on that spectrum? Do you trust automated tracking, or do you prefer being hands-on with your finances?
Today, I read in Techcrunch that India has an ambition to "compete" with the US and China in the startup scene:
India has updated its startup rules to better support deep tech companies in sectors like space, semiconductors, and biotech, which take longer to mature.
Wherever you code (online, CLI, or IDE), everyone has some love, hate or horror vibe coding stories, from building a prod-ready app in minutes to the whole repo got wiped. Would love to hear your story.
I ve been using Pencil.dev for a few days and honestly it s a big paradigm shift for how fast you can explore UI. I'm loving it!!
One thing I bumped into: I still need to move some screens into Figma to polish details, collaborate, and keep everything in the same place as the rest of our design work.
Writing on the @1Password blog, Jason Meller says that he found that the top downloaded OpenClaw skill was a malware delivery vehicle:
While browsing ClawHub (I won t link it for obvious reasons), I noticed the top downloaded skill at the time was a Twitter skill. It looked normal: description, intended use, an overview, the kind of thing you d expect to install without a second thought.
But the very first thing it did was introduce a required dependency named openclaw-core, along with platform-specific install steps. Those steps included convenient links ( here , this link ) that appeared to be normal documentation pointers.
Today, the productivity domain in tech is very well developed - there are tools for almost any need!
But at the same time, there s always a feeling that there might be something else, something better. All the time.
What I like about this space is that once people start using tools like Miro, Notion, Trello, ClickUp, etc., they tend to keep testing new things and experimenting with different tools.
Claude just launched Claude Opus 4.6 . This is Claude s newest and most capable model so far. It s designed for deep reasoning, long-running agent workflows, and large codebases, with a 1M token context window in beta and stronger planning and code understanding.
This term has been coined by someone and there are already more than 80 products that you could put in this category. Looking at the numbers, it's growing pretty fast.
Building my app with AI tools, zero coding background. The magic part - I can ship features in hours. The scary part - I have no idea if the code is actually good.
I've built my product around traditional SaaS pricing (monthly tiers), but I m starting to wonder if that model is getting outdated, especially with more AI-powered and compute-heavy tools entering the market. That shift requires real architectural changes, instrumentation, metering, billing logic, and UI changes, not just pricing tweaks. It s something I m starting to seriously think about for my own product.
In particular, AI usage has real COGs (every prompt costs money), and I m seeing more platforms experimenting with usage-based models, or hybrids like SaaS base + usage + overage.
For those of you building AI or compute-intensive tools:
This was a deliberate experiment inspired by my CTO. I wanted to test a simple question: Can a Product Manager ship a real website end-to-end today without handoffs?
I ve been spending a lot of time thinking about how people actually work with prompts while building a tool in this space, and I realized I have way more questions than answers.
New AI models pop up every week. Some developer tools like @Cursor, @Zed, and @Kilo Code let you choose between different models, while more opinionated products like @Amp and @Tonkotsu default to 1 model.
Curious what the community recommends for coding tasks? Any preferences?
I came across Deutsche Bank s latest report on AI, and it sparked an interesting thought experiment: how likely is it that we ll see AGI (AI that thinks and learns like a human) within the next five years?
The report highlights a fascinating divergence: the view from money vs. the view from science.
Money: the probability inferred from trillions poured into data centers, Nvidia chips, and servers. Investors seem to be betting that AGI is inevitable.
Science: the probability inferred from research papers and AI development models. Experts are far more cautious, suggesting the realistic probability is only 20%.
When my wife Noa and I heard that MTV was officially shutting down, it felt like the end of an era. As 90s kids, we missed that specific "linear" experience the joy of just turning on the TV and being surprised by a music video without an algorithm getting in the way.
