digitcatphd

Yes, for me when they do this I immediately dismiss the model, don't even bother testing it. If you need to lie with statistics your model isn't good enough.

First, I don’t think it’s widely accepted financial markets are in an AI bubble or there is actually any evidence to support this, most market cap growth is supported by real earnings per share. It is debatable how long that will continue and if it is sustainable, but that isn’t a bubble it’s a cycle. Even the most skeptical investors to bubbles like Buffet and Burry, have large stakes in Google and Microsoft, front line bets on AI.

Second, I agree with the distinction evals are not enough, and this is dangerously making people reckless, I think the conclusion is still quite incorrect.

There is essentially nothing magical about being human. Our primitive brains were trained on logic, reasoning and processes. An LLM is simply this on less efficient hardware, but, improving at a speed faster than human evolution.

I would argue we still have not even really gotten started.

What do we have in the decade ahead? Robotics in every household, models 10x+ faster and more intelligent than today.

Really no significant impact in life sciences, R&D, and 'offline' world / robotics today as of yet, which is where most of the value will live.

I never quite understood the notion trade workers will be exempt for a couple reasons:

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.

Respectfully, the same argument was for Moltbook's controversial posts and it turned out to be humans.

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.

But - it is absolutely hilarious.

This is fantastic! I created a GMAIL for my Clawdbot and Google deleted the account after an hour.

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.

Backtesting is a complete waste in this scenario. The models already know the best outcomes and are biased towards it.

I find it a bit surprising GenAI has made it this far without this benchmark

They will seed in a few dozen influencers and there will be lines out the door

Beatifully said and you are right. I will get mine on Temu.

Perhaps. I guess we will see :)

What do you think is going to happen to their earnings when CAPEX slows?

This isn't even taking into consideration all the manufacturing ramp up, hiring, and all the other things they will need to unwind when orders inventiably slow since revenue isn't recurring.

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.

T-Bills are at 4% which is below the rate of inflation

Irrational exuberance the very thing this indicator was intended to track.

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.

I would argue Dropbox was a new product category rather than a feature and as such, was a much deeper strategic decision to enter that category than add a feature. My only recommendation would be to focus on deep complex workflows (E.g. N8N style) with extensive integrations or build out a developer community so you can build some data lock in, because if they are surface level templates surely these will get easily disrupted.