> 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.
Exactly :-)
Call me naive but I think dark patterns are a short term strategy for winning and I'm optimistic that in the long run they will be replaced with those that are more respectful and oriented to the greater good (granted, the long run might take more time than one hopes for).
Given that the pendulum can sometime swing back fast enough to be able leverage it, it might be a good time to focus on models that are both open weights and economically viable to run to build the next great thing.
This is definitely not the way most software has been going in the past decades. It's rather the opposite: Companies play friendly to obtain a user base, then start applying more and more dark patters to further increase their profit. Facebook, Evernote, Instagram, Komoot... The list goes on and on.
Sorry even if your platform is the greatest thing ever, but I’ll find a different tool. I’ve read one too many stories about Google (or Apple!) closing the entire account over some bullshit unnecessary reason like “fraudulent” gift card issues or whatever. I’m certain the affected people would’ve preferred to just pay back the amount in question instead of losing their entire Google Drive, or their 20 years of iCloud Photos or whatever.
[1] https://tailscale.com/docs/integrations/identity/custom-oidc
I was going to give it a shot, but the only choice was a Google login.
So very weird.
This one is not particularly limited to LLMs. The entire cloud infrastructure is built around the trust me bro model.
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...
That said, I don't really see a problem with hosted tools being offered by providers. They're like impulse items at checkout.
You shouldn't implement image generation as MCP: just write your own tool. There are plenty of image/media inference providers (e.g. Fal), web search or deep research providers, etc.
Yes, and we as "harness providers" don't want to have to hard-code integrations for each of them, and would rather provide an out-of-the box baseline in the form on an MCP server that our customers can either extend or replace with MCP servers by third-party providers. That helps us focus on the core of our offering, while also giving our customers good flexibility, which so far has been working out great.
That's exactly what standards/protocols like MCP are for. You likely nowadays also wouldn't suggest that someone builds bespoke logging/tracing integrations for each provider on the market while the good generic solution in building OTEL logging/tracing exists.
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.
The real thing that will take this alive though is people pushing back a bit against some of these newfangled APIs. Now that there is real competition from the new generation of Chinese models which have much fewer of those restrictions, I think there might be a moment.
And I have been using Chinese models with it. It was not anything ideological. Those models are both excellent and cheap. Hard to beat that combo.
But on top of that, the sort of output I am getting in Pi in relation to what I got in CC is refreshing, in that nothing seems to be hidden from me.
And yeah, the ability to switch models mid-session is an interesting one, especially with branching; where I can branch to switch to a different model and still go back to the original branch with the original model.
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.
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.
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.
FWIW, if you have some tokens to spend, you might want to test Responses vs Completions in intelligence. Since GPT-5 models, we've consistently seen small, but statistically significant and reproducible improvements in intelligence with Responses API vs Completions.
However it works underneath the hood, it's real.
On a purely functional level, yes. However for interactive use cases, the Completions API, as provided by OpenAI or Azure, if paired with reasoning effort of any kind, provides an awful user experience, as you will have a perceived delay of 10+ seconds until the first tokens stream in.
If using other providers that are exposing their thinking traces, this is less of an issue, as they've just extended the Comletions API format to have delta events with reasoning_content.
Encrypted reasoning content is one of the things we keep as blobs in the transcript, even though we can only send it back to the original provider that created them, as otherwise we cannot keep caches live.
{'continue_conversation_id': 123, 'message': 'another message?'}
Your idea though is the core of the responses API with store where everything is stored on their side and then you append to it.
For the LLM there is no difference btw. In either way you need to locate the cache and use it.
The cost of generating token N is O(N) with KV cache so it's unrealistic to expect to not be charged more the longer the conversation is if you are looking for the minimum price.
Also, isn't it without caching even something like O(N^2) because you have to replay the whole conversation on every request to reach the same internal state? My point was I shouldn't have to pay for cache misses if hitting the cache is not deterministic. Give me a guarantee that you keep the session "hot" for N minutes, and cached on disk for M months, charge a little bit more on average, but then the pricing is at least transparent.
[1] Except the general market pressure to keep total cost for the same problem solved lower than the competition.
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…
I tried to outline that in my sibling comment[0], but I think the article also gives a good example in the "hidden searches" paragraph: There is really no good reason not to expose details about many of the builtin tools, or provide them as standalone services, other than protect their moat.
I don't think that "role confusion" attacks could be meaningful prevented by not showing reasoning traces. Prevention of reasoning traces is about as futile as prevention of system prompt leakage. And here you would just have to manage to exfiltrate a small number of reasoning traces per model family in order to distill the writing style of thinking traces.