6 ms·
I did some work yesterday with Opus and found it amazing. Today we are almost on non-speaking terms. I'm asking it to do some simple stuff and he's making incr
by ericol 6mo ago
I did some work yesterday with Opus and found it amazing.
Today we are almost on non-speaking terms.
I'm asking it to do some simple stuff and he's making incredible stupid mistakes:
This is the third time that I have to ask you to remove the issue that was there for more than 20 hours. What is going on here?
and at the same time the compacting is firing like crazy. (What adds ~4 minute delays every 1 - 15 minutes)
| # | Time | Gap before | Session span | API calls |
|---|----------|-----------|--------------|-----------|
| 1 | 15:51:13 | 8s | <1m | 1 |
| 2 | 15:54:35 | 48s | 37m | 51 |
| 3 | 16:33:33 | 2s | 19m | 42 |
| 4 | 16:53:44 | 1s | 9m | 30 |
| 5 | 17:04:37 | 1s | 17m | 30 |
# — sequential compaction event number, ordered by time.
Time — timestamp of the first API call in the resumed session, i.e. when the new context (carrying the compaction summary) was first sent to the
model.
Gap before — time between the last API call of the prior session and the first call of this one. Includes any compaction processing time plus user
think time between the two sessions.
Session span — how long this compaction-resumed session ran, from its first API call to its last before the next compaction (or end of session).
API calls — total number of API requests made during this resumed session. Each tool use, each reply, each intermediate step = one request.
Bottomline, I will probably stay on Sonnet until they fix all these issues.
- whalesalad 6mo agoI am having a shit experience lately. Opus 4.7, max effort. > You're right, that was a shit explanation. Let me go look at what V1 MTBL actually is before I try again. > Got it — I read the V1 code this time instead of guessing. Turns out my first take was wrong in an important way. Let me redo this in English. :facepalm:
- al_borland 6mo agoThis seems like the experience I've had with every model I've tried over the last several years. It seems like an inherent limitation of the technology, despite the hyperbolic claims of those financially invested in all of this paying off.
- smt88 6mo agoOpus 4.6 pre-nerf was incredible, almost magical. It changed my understanding of how good models could be. But that's the only model that ever made me feel that way.
- whalesalad 6mo agoYes! I genuinely got a LOT of shit done with Opus 4.6 "pre nerf" with regular old out-of-the-box config, no crazy skills or hacks or memory tweaks or anything. The downfall is palpable. Textbook rugpull.
- solenoid0937 6mo agoThere was no nerf - this meme needs to die.
- smt88 6mo agoWhat exactly happened then? How did we all have this collective hallucination?
- solenoid0937 6mo agoCollective hallucinations are common. Mandela effect, people thinking FB is listening to your microphone because they see relevant ads, etc This is a common phenomenon that all humans pattern match to things we expect. When we learn a new vocabulary word you see it everywhere for the next two days. When we think Claude might be nerfed, we overindex on every instance of Claude underperforming. The only way to account for this is credulous, hard data. Like benchmarks over time. To this day no one has provided evidence that Claude Code, when fixed to the same thinking level, has had degraded performance.
- al_borland 6mo agoAre there any good ways to benchmark models over time that don't fall victim to Goodhart's law? It seems that once the benchmark is defined, the AI will train on it, and it will become effectively meaningless. I read many articles about AIs doing extremely well on various tests in graduate or PhD level programs. But these tests are well defined. A professor put the same models though his freshman CS class and most of them failed.
- tremon 6mo ago> I read the V1 code this time instead of guessing Does the LLM even keep a (self-accessible) record of previous internal actions to make this assertion believable, or is this yet another confabulation?
- radlad 6mo agoYes, the LLM is able to see the entire prior chat history including tool use. This type of interaction occurs when the LLM fails to read the file, but acts as though it had.
- cheesecakegood 6mo agoNo they do not (to be clear, not internal state, just the transcript). It’s entirely role-play. LLM apologies are meaningless because the models are mostly stateless. Every new response is a “what would a helpful assistant with XYZ prior context continue to say?”
- ericol 6mo agoMatches what I am experiencing. Makes incredible stupid mistakes. The weird stuff is yesterday I asked it to test and report back on a 30+ commit branch for a PR and it did that flawlessly.
- alphabettsy 6mo agoThe docs suggest not using max effort in most cases to avoid overthinking :shrug:
- whalesalad 6mo agoThey've jumped the shark. I truly can't comprehend why all of these changes were necessary. They had a literal money printing machine that actually got real shit done, really well. Now it's a gamble every time and I am pulling back hard from Anthropic ecosystem.
- geraldwhen 6mo agoIt seems clear that it was a money spending machine, not a money printing machine.
- solenoid0937 6mo agoit's clearly all in your head. 4.6 is just as capable as it used to be. literally no one on the internet has managed to post credulous and real evidence of a nerf this is just another trendy conspiracy theory that people reinforce because of selection/recency bias. you hear "nerf", your brain overindexes on the next time Claude does poorly. it is the same phenomenon when you notice a new vocabulary word all the time.
- ed_elliott_asc 6mo agoIf it isn’t working for you why don’t you choose an older model? 4.6
- aulin 6mo agoThey won't. These are not "issues", it's them trying to push the models to burn less compute. It will only get worse.
- criemen 6mo ago> it's them trying to push the models to burn less compute I'm curious, how does using more tokens save compute?
- shortstuffsushi 6mo agoI think that the idea is each action uses more tokens, which means that users hit their limit sooner, and are consequently unable to burn more compute.
- ryanschaefer 6mo agoWhat?
- bloppe 6mo agoIt could be the adaptive reasoning
- b65e8bee43c2ed0 6mo agoproductivity (tokens per second per hardware unit) increases at the cost of output quality, but the price remains the same. both Anthropic and OpenAI quantize their models a few weeks after release. they'd never admit it out loud, but it's more or less common knowledge now. no one has enough compute.
- cebert 6mo agoDo you have a source for that claim?
- b65e8bee43c2ed0 6mo ago
- cadamsdotcom 6mo ago> he’s making .. mistakes Claude and other LLMs do not have a gender; they are not a “he”. Your LLM is a pile of weights, prompts, and a harness; anthropomorphising like this is getting in the way. You’re experiencing what happens when you sample repeatedly from a distribution. Given enough samples the probability of an eventual bad session is 100%. Just clear the context, roll back, and go again. This is part of the job.
- yokoprime 6mo agoWhy be so upset at someone using pronouns with a LLM?
- ericol 6mo agoYou are being downvoted but I actually agree with your statement.
- yanosh_kunsh 6mo ago[dead]
- losvedir 6mo ago> This is the third time that I have to ask you to remove the issue that was there for more than 20 hours. What is going on here? I don't know if you're giving this as something you've actually given Claude, but I don't think it's a good way of using Claude. It's not a collaborator who's having a bad day where a little empathy might make him feel better and realize his error. It's a token generator based on a prompt which includes all chat history. If you have three examples of the bad approach in the history, in a format that looks like Claude doing work, it will totally pollute it! And even worse with auto-compaction where you don't know exactly what of those false starts is getting summarized into its context. You have to treat this like a tool and understand how it works. If Claude is going down a wrong path it's better to cancel and rewind and improve the previous addition to the prompt. You don't want it to generate a bunch of misleading tokens for itself and leave it in the context window indefinitely!
- j_bum 6mo agoYep I bewilders me when I see instructions like this. Go bad and edit your previous message if you didn’t et what you want! I think this is a direct result of OpenAI and Anthropic humanizing these models too much. I want C-3PO by my side helping me work, not a machine acting emotional. But that’s what they’ve given us, and now a huge fraction of the username treats these tools like a human.
- ericol 6mo ago> I don't know if you're giving this as something you've actually given Claude, but I don't think it's a good way of using Claude. That wasn't the full prompt, I trimmed it for clarity, but I agree with everything you said and that's how I actually use it. I have a proxy logging everything sent to and from Claude in a structured way, which is precisely what let me do that compaction analysis in the first place. When Claude goes off track, I don't tell it "you did something wrong". I ask it to analyze the tool outputs and the exchange so far and let it reconcile the discrepancy itself. That tends to work better than narrating the error to it. The venting messages like that one are honestly for me, not for Claude. I know it's a tool. But it also behaves and communicates like a person, and that's a design choice that comes from Anthropic, not from me. What I've found is that writing something like that and then following it with proper instructions works fine in practice: Claude either ignores the venting or briefly acknowledges it and moves on. The actual output isn't affected. It's just how I process frustration without breaking the workflow.
- solenoid0937 6mo agoThis is not how AI works man. Speaking condescendingly or sternly to it WILL result in worse output. Imagine if you spoke to an intern like that, would they make more or less mistakes after? You should just revert the context and provide more detail and rationale in the message.