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- Hacker News
- 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.
- 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.
by bob1029 - 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.
- Maybe we don't take sessions, but take something else that we can fully control. Sessions were always a poor substitute to capture or archive the basis on which the implemented code rested on. They get noisy over time even with compaction, were not always inspectable, no real science behind when to ditch a session and start a new one, and rarely have a clean mapping to the work done. So instead of trying to archive sessions as a means to preserve basis, we should create our own structures to make the agent/model capture basis outside of a session, and archive that instead.by vamsiraju
- 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.
by skybrian - 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.
by skeledrew - 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...
by hobofan - 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.
by solarkraft