115 karma · joined September 28, 2010
This is a news for me. And at the same time it isn’t. Without the knowledge of how the models actually work, most of the prompting is guesstimate at best. You have no control over models via prompts.
[1] https://github.com/steveyegge/beads/blob/main/docs/PROTECTED...
Knowing what setting does what in Gmail is becoming difficult by the day.
I even occasionally ask agents to move some learnings back to my Claude.md or Agents.md file.
I'm curious whether complicating this behaviour with a database integration would further abstract the work in progress. Are we heading down a slippery slope?
So is the issue the format or lack of structure which a local database can bring in?
I am not sure I understand—is this a wrapper around SQLite?
At the same time, accepting and acting on feedback is a skill in its own right.
Though I agree with the larger point, there is a critical way to overcome that. The second line of leadership must own the culture at their team's level. This only works if you have direct access to the larger group. An open-door policy where anyone can schedule time with you is essential.
You might not understand their struggle, but you can hear and route it to the right people. Sometimes the best way to show empathy is simply to listen.
In my limited testing, I found that Gemini 3 Pro struggles with even simple coding tasks. Sure, I haven't tested complex scenarios yet and have only done so via Antigravity. But it is very difficult to do that with the limited quota it provides. Impressions here - https://dev.amitgawande.com/2025/antigravity-problem
This behaviour is pretty prevalent worldwide, I believe. Especially the phone plan setup use case happened to me in Bangkok, too. This happens to me in India at gas stations, cafes and even local supermarkets. All want me to install their apps, and the first step is to log in with my mobile number.
With auto-detection of mobile numbers/Google Accounts on Android, it's even easier to create an account in one click.
Instead, the more the words on the internet are written by AI, the less the worth of those words will be. AI self-training itself into oblivion is a problem worth researching.
Sure, it can be overdone. But at the same time, it shouldn't be undersold.
Is it better to specify the parameters and metrics —aka non-functional requirements —that matter for the application, and let LLMs decide? For that matter, why even provide that? Aren't the non-functional requirements generally understood?
It is the specifics that would change—scale to 100K monthly users, keep infrastructure costs below $800K, or integrate with existing Stripe APIs.