This is an important article. I hadn’t realized it was already getting this bad. Like a frog enjoying a nice warm bath ...

> Most people do not switch their operating system or phone provider every week either. But even if you do not utilize that freedom, it matters because it changes the relationship you have with the provider and the provider has with you.

This is why it’s important to utilize your freedoms. Do NOT let yourself get locked into a particular ecosystem (this is why I’m building a phone app for OpenCode).

This article makes me reconsider using my recently acquired Codex sub in my home setup. I never liked that they hide the reasoning, but somehow overrode the cognitive dissonance because the performance is so good. But the inauditability is already a huge problem.

I think the article gives a very good overview of a problem that most users of AI rarely evaluate / have to grapple with.

There really is a surprising amount of coupling that happens with many of the "frontier inference providers", where a lot of the powerful non-LLM extensions (web search, code execution) are packaged as simple "tools" on the surface, that build up a lot of moat. Those are parts that are in theory nicely separable from the inference API, and could be externalized via MCP servers, but are usually not offered as such by the inference providers themselves, and are often only available in a slightly less powerful variant from other providers.

We've faced that issue repeatedly while building a on-premise provider-agnostic Chat UI & platform[0], where even adding something as simple as an in-chat image generation tool for the end-users (which is just a build-in tool in the OpenAI Responses API), becomes a bit of an ordeal (though part of that is due to the MCP spec missing a native file transfer protocol as of today[1]).

I am quite hopeful though, as with recent shifts of interest towards open weight models, there will be more opportunities for companies offering alternative implementations in a easier plug-and-play manner.

[0]: https://github.com/EratoLab/erato

[1]: https://github.com/modelcontextprotocol/modelcontextprotocol...

Will https://buzz.xyz/ not help with this a bit?

Part of the solution here I think is to move things out of band as much as possible. Make subagent invocations into tool calls to that agent. Externalize the tool calls themselves to CLI utils, maybe block native tools completely (unrelated but relevant, I just added the AskUserQuestion tool to the global deny list couple days ago because Claude occasionally forgot to honor my standing order to prefer plain text, and that tool is extremely annoying as it creates a gap in the dialogue) and use 3p alternatives. The /compact degradation is also very annoying, so make an alternative that does the same and saves the summary to a regular file. Maybe it's also worth prompting the LLM to save it's reasoning process to file, even if it takes a few extra tokens and it isn't the actual reasoning tokens.

With that said, I may have something that can already help with at least the subagents/tooling bit. Didn't really have a timeline (or solid intent) on releasing it, but with these shenanigans increasing there's no time like the present.

wow this is such an excellent framing of something that i have not managed to think about or come across in the vast ocean of discourse about LLMs. i had not really thought about how explicitly they are designed to not have histories that are user-interpretable. it is really quite not unlike how Apple locks down iOS and MacOS-- there are UI/UX reasons for the choices the frontier models make, but that really is only part of the story, and these choices undeniably do conspire to make the resource more locked-down than it absolutely has to be. and the analogy to "people don't switch OSes every day, but the ability to switch OSes changes your relationship with the provider" is extremely apt.

I don’t see this as a big deal in practice. Conversations contain a bunch of junk anyway, so removing it from the context is usually good.

In my repo, I have a notes directory. I ask the AI to write a markdown file with what it learned, what work has been done, and what remains. In the next conversation, I can ask another model to pick it up from there. Sometimes I edit the note first.

This is exactly why Pi will win. It lets you hot swap models when one is struggling or straight up refusing the task. And since it works with any sub outside Claude Code, you can use it to try different models on OpenCode Go sub or even OpenRouter.

As far as subagent prompts and results being obfuscated, I just let Pi spawn new agents. Using skills and extensions, I’ve essentially built a software factory using Pi and a custom terminal multiplexer.

It’s a shame apps like T3 Code and other UI for terminal apps don’t support Pi and I’m glad to use the terminal above those that lock me in further.

The state of Agent APIs is bad. completions API (supported indefinitely) but does not support reasoning. OpenAI's reasoning API started out well, but is moving in the direction as described well in the article. Anthropic's messages API is odd. It includes changes such as the recently introduced system messages that can be injected mid turn, but are not supported in all models (sonnet 5). So you are left with a pretty fragmented state. For coding agents, the practical move is to focus on getting it right with one API - eg reasoning, and let the router/provider handle the conversion for you.

Perhaps, there is value in having the de-facto API not be the Reasoning API from OpenAI but something from a neutral party?

Maybe a well defined open standard that facades over these APIs that can gain adoption. For that to happen, the party championing the API needs to have some reasonable traffic capture - OpenRouter perhaps, or a group of such routers coming together? While it wont solve the encrypted payload from the frontier lab problem, it will at least be a backstop in these APIs just becoming a back and forth of encrypted payloads over time.

I have some sort of a "solution" - my process keeps state that really matters (think "JIRA for agents") in a separate database (accessible over MCP). Essentially, I can terminate my session, start fresh one (in a different harness with a different model) and continue work with minimal losses. For subagents - I have a custom dispatch_agent tool which, essentially, just shells out.

>A user should be able to close an account, keep a session, and hand it to another model. The new model may disagree, ask questions, or perform worse.

I think this is a fair contract. I also think a user should ideally be able to easily identify a comparable model in terms of embedding 'signature'. When GPT-4o originally kicked the bucket, I remember reading lots of anecdotes of people desperately searching for models similar in manner and language, so they could pump in their exports and re-find their friend. Other open-ai models just didn't have the same vibe. It was sad to read. This, to me, is the power of open-weights models. They are for perpetuity. You can keep your guide, your friend, your therapist, whatever. No big company can pull the rug.

Thank you Armin, Mario and team for the great writeup, absolutely agree and I love what you guys are building

For some reason, my early morning waking brain, expected the blog title to be a segue into existentialism

BTW the web page uses massive iGPU if you don't have a fast machine.

I think a fantastic companion piece to this article is https://gwern.net/complement

im building my own harness and inference tool chain for much of these reasons. theres so much to do that makes a big difference for users. hoping to get things into shape for early alpha as a saas in the next two months.

heres the easiest biggy: compactions should include all user turns albeit with pastes and attached files not inlined. omg does it make a huge difference.

OpenAI has been nudging their users toward the Responses API lately. Chat completion can still do everything if you are clever. The obvious reason to push everyone into the Responses API is that the reasoning process can be locked down and encrypted. The semantics of the chat completion API are fundamentally transparent. 100% of the tokens are visible.

If you have patience and the willingness to endure a little bit of pain, you can still retain autonomy over the entire reasoning process while using the latest 5.6 model family. The only downside is that you are now fully responsible for it.

Consider that when you flip your agent's reasoning level to "xhigh" or whatever, it's not some magical model internals being pushed around. There isn't an actual "try harder" knob on the black box. This is merely orchestration of many instances of one or more model types based upon some proprietary harness logic. The chances you can develop a domain specific reasoning process that outperforms the frontier providers is still very good.

This is exactly what I'm working on. Very rough yet but it's progressing nicely.

https://github.com/pantoniou/fyai

The idea is that your session data are what's important, and what you need to keep yourself, using a model similar to git.

Currently these models have a limited context window, so the value of a session is not that high, at any rate at some point the model will have forgotten things. If in the future the model will truly learn / "change" as a result of the interaction, in any case this will be non portable to another model. So I think this is kinda moot

> “Most People Do Not Switch Models Mid-Session”

I do, quite regularly. Because different models have different strength (for example when producing text for live presentations based on the text for a reading deck). Or when it comes to other aspects of the work. I regularly switch between open wheight models and closed models.

I know, I am a tiny minority here. And this behavior only ever started a few weeks ago. But it quickly became a habbit, to CTRL-L in pi and change the model.

Does there need to be an open standard or file format for context? Open models should align on the same format for portability? Could be sqlite based so it can be queried by other programs?

I think most LLM work will migrate to open weight models in a couple of years for this exact reason (as well as price), unless the big providers manage to have their way and the market gets regulated in a way that favors closed source models. To me it seems though like most models are becoming good enough for most tasks we want them to do so no need to use the latest super intelligent one that knows how to compose operas and do quantum chemistry and have it answer support requests. And with these models transparency is no issue.

It's one thing that the APIs don't return the reasoning tokens in a readable fashion. But then why do I have to send them the whole conversation every time? If they don't allow me to see parts of the conversation then they should have the burden of storing and transmitting it, and shouldn't charge more and more the longer the conversation gets (unless opaque token caching kicks in) like some LLM Shlemil the painter.

Is this a analogous to idempotent behaviour? Of reproducible compilation? If the inputs and outputs aren't sufficient to reproduce one from the other, what is a minimally satisfying description of the middle third, the missing part? The "other" inputs?

I think there are two things going on more generally:

1) providers increasingly adding hidden state that the user or developer cannot inspect, port, or do anything with. This is clearly bad.

2) providers increasingly diverging how they implement certain features and the APIs becoming quite complicated and provider specific.

The former makes porting sessions or transcripts impossible, the latter just more difficult.

IMO, it is fine and expected that provider APIs are starting to diverge and adding features that cannot be easily ported between providers. The time where the OAI completions API functioned as a universal standard is coming to an end. For example, I generally prefer the new responses API (minus the closed/hidden stuff).

The problem is that many product, library, and SDK authors are still pursuing the "unified abstraction across all model providers" ideal. Just stop doing that, and at least #2 is fine. You can still port parts of the session, but not everything.

I mean just think how difficult it is to add a unified abstraction across, say, databases: some products can do it, but the abstraction is still often leaky. Hence, we've come to accept that our data store layer is often quite technology/provider specific. It will be the same with model providers.

(edit: intro sentence)

I get the point of the article and it resonates with my general tendency to support more openness, but older and more cynical me wonders how much of this is a battle worth fighting or a self-serving argument from an harness developer.

I mean, the article says each of the points it is complaining about has “_a basic justification that's trivial for a provider to come up with, along with good arguments for why this is good for the user_“, which to me sounds like implying these are just bs to have people accept them, but is that actually the case?

For instance, this [0] was mentioned here a while ago, and based on that it seems pretty clear why one would choose not to provide the _thinking_ anymore…

[0] https://role-confusion.github.io/

Another comment got downvoted for raising this, but I found it interesting that pangram flags large parts of this blog post as AI generated ( https://www.pangram.com/history/b82cdbf4-bdbc-46ea-8315-bd43... ), while earendil denies it: https://news.ycombinator.com/item?id=49119957

you can port your phone number to any carrier but your AI doesn't know your name until you explain it from scratch

> Encryption for Whom?

This one is not particularly limited to LLMs. The entire cloud infrastructure is built around the trust me bro model.

The AI companies need some way to lock you in. You weren't hoping for a free market were you? Hah. Free markets are for peasants. Investors are always looking for ways to make the market non-free.

If even Earendil is publishing AI generated blog posts...

OpenAI is doing this so that you can't preserve cache when routing between for example GPT 5.6 and DeepSeek V4, or other providers. I'm building a router that easily reaches >99% cache hit rate in Pi when routing between GPT 5.4, Kimi, DeepSeek etc. Working on a big update that's coming out in a week or so.