Yep. I regretted leaving on memory as it is poisoned my conversations with irrelevant junk.

This matches my experience exactly. "poisoned" is a great way to put it. I find once something has gone wrong all subsequent responses are bad. This is why I am iffy on ChatGPT's memory features. I don't notice it causing any huge problems but I don't love how it pollutes my context in ways I don't fully understand.

My experiences somewhat confirm these observations, but I also had one that was different. Two weeks of debugging IPSEC issues with Gemini. Initially, I imported all the IPSEC documentation from OPNsense and pfSense into Gemini and informed it of the general context in which I was operating (in reference to 'keeping your context clean'). Then I added my initial settings for both sides (sensitive information redacted!). Afterwards, I entered a long feedback loop, posting logs and asking and answering questions.

At the end of the two weeks, I observed that: The LLM was much less likely to become distracted. Sometimes, I would dump whole forum threads or SO posts into it, when it said "this is not what we are seeing here, because of [earlier context or finding]. I eliminated all dead ends logically and informed it of this (yes, it can help with the reflection, but I had to make the decisions). In the end, I found the cause of my issues.

This somewhat confirms what some user here on HN said a few days ago. LLMs are good at compressing complex information into simple one, but not at expanding simple ideas into complex ones. As long as my input was larger than the output (either complexity or length), I was happy with the results.

I could have done this without the LLM. However, it was helpful in that it stored facts from the outset that I had either forgotten or been unable to retrieve quickly in new contexts. It also made it easier to identify time patterns in large log files, which helped me debug my site-to-site connection. I also optimized many other settings along the way, resolving not only the most problematic issue. This meant, in addition to fixing my problem, I learned quite a bit. The 'state' was only occasionally incorrect about my current parameter settings, but this was always easy to correct. This confirms what others already saw: If you know where you are going and treat it as a tool, it is helpful. However, don't try to offload decisions or let it direct you in the wrong direction.

Overall, 350k Tokens used (about 300k words). Here's a related blog post [1] with my overall path, but not directly corresponding to this specific issue. (please don't recommend wireguard; I am aware of it)

    [1]: https://du.nkel.dev/blog/2021-11-19_pfsense_opnsense_ipsec_cgnat/

Has any interface implemented a .. history cleaning mechanism? Ie with every chat message focus on cleaning up dead ends in the conversation or irrelevant details. Like summation but organic for the topic at hand?

Most history would remain, it wouldn’t try to summarize exactly, just prune and organize the history relative to the conversation path?

Agreed poisoned is a good term. I’d like to see “version control” for conversations via the API and UI that lets you rollback to a previous place or clone from that spot into a new conversation. Even a typo or having to clarify a previous message skews the probabilities of future responses due to the accident.

Happens with people too if you think about it.

An interesting little example of this problem is initial prompting, which is effectively just a permanent, hidden context that can't be cleared. On Twitter right now, the "Grok" bot has recently begun frequently mentioning "White Genocide," which is, y'know, odd. This is almost certainly because someone recently adjusted its prompt to tell it what its views on white genocide are meant to be, which for a perfect chatbot wouldn't matter when you ask it about other topics, but it DOES matter. It's part of the context. It's gonna talk about that now.

Subjectively the "getting lost" feels totally different than human conversations. Once there is something bad in the context it seems almost impossible to get back on track. All subsequent responses become get a lot worse and it starts contradicting itself. It is possible that with more training this problem can be improved, but what is interesting to me isn't it's worse than humans in this way but that this sort of difficulty scales differently than it does in humans. I would love to get some more objective descriptions of these subjective notions.

> inability to self-reflect

IMO the One Weird Trick for LLMs is recognizing that there's no real entity, and that users are being tricked into a suspended-disbelief story.

In most cases cases you're contributing text-lines for a User-character in a movie-script document, and the LLM algorithm is periodically triggered to autocomplete incomplete lines for a Chatbot character.

You can have an interview with a vampire DraculaBot, but that character can only "self-reflect" in the same shallow/fictional way that it can "thirst for blood" or "turn into a cloud of bats."

> inability to self-reflect and recognize they have to ask for more details because their priors are too low.

Gemini 2.5 Pro and ChatGPT-o3 have often asked me to provide additional details before doing a requested task. Gemini sometimes comes up with multiple options and requests my input before doing the task.

Isn’t this relatively trivial to correct? Just like chain of thought reasoning replaces end tokens with “hmm” to continue the thought can’t users just replace the llm tokens whenever it starts saying “maybe they are referring to” with something like. “Let me ask a clarifying question before I proceed.”

> and the inability to self-reflect and recognize they have to ask for more details

They're great at both tasks, you just have to ask them to do it.

The inability of LLMs of ask for clarification was exactly the flaw we encountered when testing them on open-ended problems, stated somewhat ambiguously. This was in the context of paradoxical situations, tested on DeepSeek-R1 and Claude-3.7-Sonnet. Blog post about our experiments: https://pankajpansari.github.io/posts/paradoxes/

would be great to get more info on what you’re building - seems interesting!

Sounds like an exciting idea.

May I suggest - put what you have out there in the world, even if it’s barely more than a couple of prompts. If people see it and improve on it, and it’s a good idea, it’ll get picked up & worked on by others - might even take on a life of its own!

This is a class of mental critic from the Emotion Machine.

And everyone loves to chime in with their own excellence in prompt engineering

It’s herding cats, not “learning”, which is a fine situation for some parts of workflows.

You could swap those recommendations for programming without LLMs. Open any software engineering books and you’ll see a lot of good recommendations for building software.

Yes! Deepseek does this to me all the time.

I had 20 something files I wanted it to check and change something. The first 5 or so it did, then the sixth it rightly said everything is correct moving on. It said that for the rest of the 20, the same text over and over.

I checked, and file 6 was the only correct one. It like, learned to just repeat itself after that and did nothing.