I don't know why these posts are being treated by anything beyond a clever prompting effort. If not explicitly requested, simply adjusting the soul.md file to be (insert persona), it will behave as such, it is not emergent.
I built something similar to this before Langraph had their agent builder @braid.ink, because Claude Code kept referencing old documentation. But the problem ended up solving itself when Langraph came out with their agent builder, and Claude Code can better navigate its documentation.
The only thing I would mention is that building a lot of agents and working with a lot of plug-ins and MCPs is everything is super situation- and context-dependent. It's hard to spin up a general agent that's useful in a production workflow because it requires so much configuration from a standard template. And if you're not being very careful in monitoring it, then it won't meet your requirements when it's completed, when it comes to agents, precision and control is key.
At first I was reading this like 'oh boy here we go, a marketing ploy by ChatGPT when Gemini 3 does the same thing better', but the integration with data streams and specialized memory is interesting.
One thing I've noticed in healthcare is for the rich it is preventative but for everyone else it is reactive. For the rich everything is an option (homeopathics/alternatives), for everyone else it is straight to generic pharma drugs.
AI has the potential to bring these to the masses and I think for those who care, it will bring a concierge style experience.
I’ve been writing about building Agent-First SaaS and working with teams implementing LangGraph flows.
I’ve noticed a recurring pattern where we get stuck trying to perfectly replicate a human's SOP (e.g., "click this button, then read this PDF"). While reproducing human workflows is great for trust and "human-on-the-loop" auditing, I argue it often traps us in a local optimum.
This post explores the difference between "Replica Agents" (biomimicry) and "First-Principles Agents" (optimizing for the objective function). I draw on examples like Amazon's "Chaos Storage" and AlphaGo to suggest that sometimes the most efficient agent workflow looks nothing like the human one.
Curious to hear how others are balancing "legibility" vs. "efficiency" in their agent designs.
I’ve been writing about building Agent-First SaaS and working with teams implementing LangGraph flows.
I’ve noticed a recurring pattern where we get stuck trying to perfectly replicate a human's SOP (e.g., "click this button, then read this PDF"). While reproducing human workflows is great for trust and "human-on-the-loop" auditing, I argue it often traps us in a local optimum.
This post explores the difference between "Replica Agents" (biomimicry) and "First-Principles Agents" (optimizing for the objective function). I draw on examples like Amazon's "Chaos Storage" and AlphaGo to suggest that sometimes the most efficient agent workflow looks nothing like the human one.
Curious to hear how others are balancing "legibility" vs. "efficiency" in their agent designs.
The biggest issue with Nvidia is their revenue is not recurring but the market is treating their stock as it were, which is correlated with all semi stocks, with a one-time massive CAPEX investment lasting 1-2 years.
Simple as this - as to why its just not possible for this to continue.
It’s never different this time, this is embedded into human nature and people oscillate between fear and greed. That’s it. Not more complicated than that.
It is an odd position because the P/E and Forward P/E are elevated but not extreme. The bigger warning signal for me is when I see people in public talking about stocks or having stock screens open and this happened almost four times in one week. For me that is basically the sign to risk manage.
So users are more detached from their work? How does this correspond with cognitive decline? Wouldn’t it need to be cross referenced in other areas beside the task at hand? Seems a bit of a headline grabbing study to me. Personally I find thinking with an LLM helps me take a more structured and unbiased approach to my thought process
There is likely also a ton of users who were just playing with it and had no true use case beyond asking it to make haikus about cats and got bored. IMO their true product is and always has been the API.
1. Right now trades businesses are profitable because of supply and demand. They are profitable, because they are undersupplied.
2. We are assuming robotics stagnates.