prng2021

“Interestingly, what’s making Muse a success also explains why Google has mostly failed to capture the zeitgeist so far. They have the compute and the ad money to fund something like Muse. But they have no product chops to build something simple and understandable in consumer AI.”

Umm what? Having billions of hourly active users across YouTube, Google, Google Maps, and Chrome still doesn’t mean they have the product chops to build another consumer facing app/service? I think it’s inevitable that Muse will get crushed.

Only if these platforms don’t provide anyway to export the text files that contain memories and chat history. If they do allow it, switching it is easy. It’s not like these agents are updating a custom models weights based on your convos. The most important thing they have is the API connections you give them access to so they can access your gmail, calendar, etc.

Can someone clarify the use case for this? What's the benefit over this: https://openai.com/index/introducing-the-agents-api/

You’re taking their shoe example way too literally. It’s just showing they’re catering to the average user, not an engineer. And tons of average Joe users now have very high expectations of how intelligent a LLM is because they’ve interacted with ChatGPT and the like. If there’s a super tiny model that is too dumb to do anything beyond shoe searching and they have to switch to Gemini and google AI search for anything more complex, this whole launch would be an immediate failure.

The inference time results are quite different. These models haven’t just been trained with tons of docs, blog entries and videos about Java 8. They’ve also been trained on tons of code examples from simple to complex real world ones.

You’re not going to get all that just by pointing the LLM to the recently released Java 27 documentation. That information is also potentially adding tons of content to your context, which is already filled with tons of other data (your code, other recently released libraries it has to get documentation for, etc).

In your opinion, what are the top 2 positives and top 2 negatives of humanity inventing AGI?

Although many share your mindset, I’m glad there are also many that don’t. Otherwise we’d still have countries in a race to keep building up their nuclear weapons for the same exact reasons you just described.

What is this conversation even about? I can’t understand any of it.

xAI had $2.5B in operating losses in the past quarter. What savings are being passed on?

Thanks for the irrelevant comment. I’m criticizing these Chinese companies because these aren’t accomplishments. Where did I praise Anthropic?

Are we supposed to be impressed? They distilled Anthropic’s models and pat themselves on the back for it. Pathetic.

“That unlocks something beyond energy savings: the possibility of eliminating water consumption entirely.”

They’ve made this claim numerous times in the article and I really don’t understand it. The building has tons of water being recirculated through it. That water came from somewhere in the surrounding natural world. How is that 0 water consumption?

We’ve got a company of several thousand employees serving hundreds of millions of people arguably the best AI model in the market. Meanwhile you’re asking for a handkerchief for your pool of tears because their product is struggling to do your daily job functions for you, with much of that due to being limited by the worlds supply of silicon, electricity, water, and other resources. Cry me a river.

Yes but my original reply was to someone that seemed to imply that this founder was dumb not to verify that Railway’s API key that should have been limited to managing custom domains, truly was limited to managing custom domains. I’ve never used Railway but my pushback is that no one in the real world exhaustively verifies a key is scoped properly against all 3rd party endpoints. We trust vendors to document how they’re scoped and to actually do that.

“why would you not do the same for APIs you integrate with?”

Who does that? Jira and Salesforce have hundreds of endpoints each. AWS has hundreds of services, and each may have hundreds of endpoints. Who on your team is testing key scopes of every endpoint? Do you do it for each key you generate? After all, that external system could have a bug at any moment in managing scopes. Or they could introduce new endpoints that aren’t handled properly. So for existing keys, how frequently do you re-validate the scope against all the endpoints?

Sorry but are you implying that for every system you integrate with, you verify the scope of an API key by checking each CRUD operation on every API endpoint they provide?

How is anyone predicting timelines for AGI when these systems can’t do basic addition of 2 arbitrary numbers with 100% accuracy?

“I think the decision was made because the people making this decision at Anthropic are well-intentioned, driven by values, and motivated by trying to make the transition to powerful AI to go well.”

Every single one of these CEOs happily pirated unimaginable amounts of copyrighted content. That directly hurt millions of real human beings. Not just the prior creators but also crushing the future potential for success of future ones.

https://www.susmangodfrey.com/wins/susman-godfrey-secures-1-...

“You never needed 1000s of engineers to build software anyway”

What is the point of even mentioning this? We live in reality. In reality, there are countless companies with thousands of engineers making each piece of software. Outside of reality, yes you can talk about a million hypothetical situations. Cherry picking rare examples like Winamp does nothing but provide an example of an exception, which yes, also exists in the real world.

This was a great article. The section “Training for the next state prediction” explains a solution using subagents. If I’m understanding it correctly, we could test if that solution is directionally correct today, right? I ask a LLM a question. It comes up with a few potential responses but sends those first to other agents in a prompt with the minimum required context. Those subagents can even do this recursively a few times. Eventually the original agent collects and analyzes subagents responses and responds to me.

Hard to tell how widespread it is but it’s not just 1 company. https://www.webpronews.com/silicon-valleys-2025-ai-boom-revi...

Thanks for repeating what the author explained.

Where did they say that?

Did they post the knowledge cutoff date somewhere

Yea I guess that's accurate but they also explained that AI capabilities advance every 6-12 months and managing a team of agents buys you a few years. So their proposed solution and conclusion that it keeps you safe for years makes no sense right now. Multi agent orchestration, with an agent doing the orchestrating, is all the craze nowadays.

The author gives this example of the problem and incorrect way to leverage AI:

"Sarah was relieved. She thought she could focus on high-value synthesis work. She’d take the agent’s output and refine it, add strategic insights, make it client-ready."

Then they propose a long winded solution which is essentially the same exact thing but uses the magical term "orchestrate" a few times to make it sound different.