myownpetard

In crypto, wash trading usually refers to the practice of exchanges or project creators colluding to trade the same asset back and forth in order to make the volume/liquidity/popularity look greater than it is.

- "Our coin hit $100M daily volume, get on this rocketship before it's too late!"

- "Our exchange does $1B annually, so you know we're trustworthy!"

- "Hey investors, look at the massive demand for our GPUs (driven by the company we invested $100B)!"

MCP is more like a UI that is optimized for LLMs for interacting with a tool or data source. I'd argue that an API is not a user interface and that's not really their intention.

> Regardless, again: if the AI is so smart, and it somehow needs something akin to MCP as input (which seems silly), then we can use the AI to take, as input, the human readable documentation -- which is what we claim these AIs can read and understand -- and just have it output something akin to MCP.

This example is like telling someone who just wants to check their email to build an IMAP client. It's an unnecessary and expensive distraction from whatever goal they are actually trying to accomplish.

As others have said, models are now being trained on MCP interactions. It's analogous to having shared UI/UX patterns across different webapps. The result is we humans don't have to think as hard to understand how to use a new tool because of the familiar visual and interaction patterns. As the design book title says, 'don't make me think.'

That's because attention is all we need.

I disagree. A neural network is not learning it's source code. The source code specifies the model structure and hyperparameters. Then it compiled and instantiated into some physical medium, usually a bunch of GPUs, and weights are learned.

Our DNA specifies the model structure and hyperparameters for our brains. Then it is compiled and instantiated into a physical medium, our bodies, and our connectome is trained.

If you want to make a comparison about the quantity of information contained in different components of an artificial and a biological system, then it only makes sense if you compare apples to apples. DNA:Code :: Connectome:Weights

Evolution is the heuristic search for effective neural architectures. It is training data, but for the meta-search for effective architectures, which gets encoded in our DNA.

Then we compile and run that source code and our individual lived experience is the training data for the instantiation of that architecture, e.g. our brain.

It's two different but interrelated training/optimization processes.

A more fair comparison would be subtract it from the size the of source code required to represent the LLM.

These are two very different things.

One is talking about an improvement made by making control flow changes during inference (no weights updates).

The other is talking about using reinforcement learning to do weight updates during training to promote a particular type response.

OpenAI had previously used reinforcement learning with human feedback (RLHF), which essentially relies on manual human scoring as its reward function, which is inherently slow and limited.

o1 and this paper talk about using techniques to create a useful reward function to use in RL that doesn't rely on human feedback.

He was creating a paper trail for illegal working/billing practices.

Almost every PM I've worked with had an engineering degree and/or previously worked as an engineer. This "Josh" sounds like a strawman or you've had bad luck with PMs in the past.

It also sounds like you have a PM on your team, but they actually have the title of SWE or Eng. Mgr. They probably spend > 50% of their time on the above listed responsibilities rather than engineering. Hopefully they don't get docked in their performance reviews for essentially performing the duties of a PM rather than those of an engineer.

> Word of advice to all those who are chomping at the bit to disrupt pharma with AI.

Literally the first line in the comment that started this thread.

Your sources are talking about the ratio of small molecule vs. large molecule drugs. Even if you're developing small molecule drugs you are likely targeting some aspect of protein signaling/gene expression.

People are being dismissive of your comments because to say that proteins are niche in the context of pharma is like saying advertising is niche in the context of Meta and Google.

I think its use probably has a loose correlation to younger, east coast, Big Lebowski fans.

Which as we know puts you down an irreversible ideological path of... something?

[flagged]

It was a pun about how this thread is being overly semantic in an unnecessary and uninteresting way...

We could also talk about how the word 'executes' implies some kind of agency which computers lack. It's like saying a rock just executes the laws of physics when it rolls down a hill...