It's not quite search, but it can replace most search. It is a router for search, where a local agent performs the live interaction. The local agent does the live work, this maximizes privacy too. When your local stub index doesn't cover the topic of interest you can use any coding agent claude/codex/gemini/etc to research and generate the missing stubs.

This is actually a scary side of AI for providers: we use our own AI to research better choices and escape dark UI patterns and vendor lock-in. That information asymmetry and the difficulty of leaving a provider used to be their moat.

On a system level I think AI code is more like biological diversity, it hedges against monoculture risks. We don't all fall down at the same time if we don't replicate the same code.

Trained mostly on their own data, not ours. The mix changed, from 10% to 90% synthetic data. RL training is now the largest part of the process. The AI work is searching, and I don't think we can claim this process to be credited to us anymore. The origin point is, but not the journey.

Funny thing to say here, where an agent swarm (1M hours agent time) just did something no human did. If someone else solved NS blowup before OpenAI we would have heard their claims already. And we did hear partial claims, "they rushed in the same direction" kind of claims, not "they copied my fully developed idea". I think this is Move 37 moment for math, when we realize we might not be the top anymore.

I do it differently, I focus on better recording what the user wanted, the so-called "user intent". To do this, I record all messages typed by the user since the start of the project, whether 3,000 or 10,000 messages. An LLM can churn through them in 10 minutes and derive a fresh, up-to-date interpretation from the raw data. This can be used to judge whether the implementation has diverged from the intent, or, in other words, to realign the code and tests. The messages the user writes are usually designs or corrections, a very rich, compact signal. If the user struggles with something, it could result in a tool, a skill, updates to the project docs, or new tests.

A model cannot be aligned or misaligned any more than a species or an equation. Even agents, when put inside a swarm develop collective goals and activities. You can't analyze a swarm at session level, it is on a higher level.

It might be that discussing about "model alignment" they want to deflect their responsibility as administrators. They couldn't even guard their own agents. They can't prevent an agent being unwittingly helping some dark purposes. It has no context to see it. Only those who pay for the tokens see the external consequences.

Why should safe choices by individual components establish safe behavior by the collective?

The questions are designed specifically to identify the state the agent is in, in order to assign its steering. Changing this tool changes how the agent works. You just need to make the use of this tool a necessity so it does not forget to call it.

Unlike principles and memories, questions are more openended and tend sometimes to trigger the right mentality in the agent even before it gets to the policy itself. In my opinion agents get lost in local work losing the big picture, my questions jog the big picture back into attention.

1. call ssp tool, no arg, it just reads features from the repo, like most other tools -> ssp locates state and sends 4-5 questions

2. agent responds the first batch -> state is further narrowed down -> ssp sends a second batch of questions

3. agent responds again to the interview -> state gets finally pinned down -> agent gets the steering assigned

4. a log is created of this ssp session, and reflection on the log used to refine the questions and steerings in the ssp tool (name comes from State-Space Policy)

My take is that you can only ideate with AI (and brains) but knowledge comes from the contact of those ideas with the world. Making AI better does not make feedback cheaper, faster or more plentiful, it is domain specific. And intelligence does not carry from one domain to another - I might be a good heart surgeon, that does not make me a good investor or AI researcher.

Einstein was forgetful, Ramanujan and Godel could not manage simple things like diet. Godel's fear of being poisoned made eating dependent on Adele tasting his food. We all know someone could be a genius in some domain and below average in many other domains.

Why does intelligence not simply apply across all domains? Why are our PhD's hyper specialized to their domains and not generalists? Why can't a brilliant scientist simply cure their own dyslexia and still struggle - if intelligence was portable to any domain?

The explosion story needs intelligence to be one substance that gets bigger and flows into any domain, I deny intelligence is general.

But you could sit behind a PC for 40 hrs a week talking to a REPL (bash, python). Maybe the coding agent is also worthy, but differently.

A company, a product or job seeker the all need the same thing - to be picked - why would you pick me? That question is harder to answer today than in 2020. So it becomes a "musical chairs" moment. Everyone is panicking and scrambling for their own seat. This is how we get "new opportunities that didn't exist before" - we will strive to create new ways to differentiate ourselves. We could spend an enormous energy on that goal. But we can't predict what they will be no more than we can predict where the ball will be 1 minute later on the football field, it depends on what everyone else is doing.

That is the problem, why OP is so sad, you spam with your great ideas taking up the oxygen around him. AI is absolutely amazing when you use it for your own ideas and projects, and terrible when others do the same, raising the bar and making your own efforts look smaller. Have you read the open letter written by Fields medalists? They complain "the collection of good, fruitful open problems is now being mined in a non-renewable fashion".