I've done a bunch of user interviews of ChatGPT, Pi, Gemini, etc. users and find there are two common usage patterns:
1. "Transactional" where every chat is a separate question, sort of like a Google search... People don't expect memory or any continuity between chats.
2. "Relationship-driven" where people chat with the LLM as if it's a friend or colleague. In this case, memory is critical.
I'm quite excited to see how OpenAI (and others) blend usage features between #1 and #2, as in many ways, these can require different user flows.
So HN -- how do you use these bots? And how does memory resonate, as a result?
Starting clean also has the benefit of knowing the prompt/history is in a clean/"known-good" state, and that there's nothing in the memory that's going to cause the LLM to get weird on me.
> Materials-science company Dow plans to roll out Copilot to approximately half of its employees by the end of 2024, after a test phase with about 300 people, according to Melanie Kalmar, chief information and chief digital officer at Dow.
How do I get ChatGPT to give me Dow Chemical trade secrets?
- Any API requests
- ChatGPT Enterprise
- ChatGPT Teams
- ChatGPT with history turned off
That was a common hack for the LLM context length problem, but now that context length is "solved" it could be more useful to align output a bit better.
This matters a lot for prompt injection/hijacking. Not that I'm clamoring to give OpenAI access to my personal files or APIs in the first place, but I'm definitely not interested in giving a version of GPT with more persistent memory access to those files or APIs. A clean slate is a mitigating feature that helps with a real security risk. It's not enough of a mitigating feature, but it helps a bit.
Anyway, it seems to be implemented quite well with a lot of user controls so that is nice. I think it's possible I will soon upgrade to a Team plan and get the family on that.
A habit I have is that if it gets something wrong I place the correction there in the text. The idea being that I could eventually scroll down and find it. Maybe in the future, they can record this stuff in some sort of RAGgable machine and it will have true memory.
Oddly, the spoken version of ChatGPT4 does implore, listens and responds to tones, gives the same energy back and does ask questions. Sometimes it accidentally sounds sarcastic “is this one of your interests?”
I'll have try it out though to know for sure
Adding the entire file (or memory in this case) would take up too much of the context. So just query the DB and if there's a match add it to the prompt after the conversation started.
I could imagine that once there's too many, it would indeed make sense to classify them as a database, though: "Prefers cats over dogs" is probably not salient information in too many queries.
For those cases there are quite a few things that I'd like it to memorize, like programming library preferences ("When working with dates prefer `date-fns` over `moment.js`") or code style preferences ("When writing a React component, prefer function components over class components"). Currently I feed in those preferences via the custom instructions feature, but I rarely take some time to update them, so the memory future is a welcome addition here.
Will memory provide a solution to that or will be a different thing to ignore?
Periodically "compress" chat history into relevant context and keep that slice of history as part of the memory.
15 day message history could be condensed greatly and still produce great results.
I find that I ask a mix of one of questions and questions that require a lot of refinement, and the latter get buried among the former when i try and find them again, so i end up re explaining myself in new chats.
Some memory might actually be helpful. For example having it know that I have a Mac will give me Mac specific answers to command line questions without me having to add "for the Mac" to my prompt. Or having it know that I prefer python it will give coding answers in Python.
But in all those cases it takes me just a few characters to express that context with each request, and to be honest, I'll probably do it anyway even with memory, because it's habit at this point.
When you start to pull in multiple large documents, especially all at once, things start to act weird, but pulling in documents one at a time seems to preserve context over multiple documents. There's a character limit of 100k per API request, so I'm assuming a 32k context window, but it's not totally clear what is going on in the background.
It's kind of clunky but works well enough for me. It's not something that I would be putting sensitive info into - but it's also much cheaper than using GPT-4 via the API and I maintain control of the data flow and storage.
Always this patchwork of „insert your previous code here“
This is not a problem of the model, but I suspect it is in the system prompt that got some major issues.
I'm so tired of arguing with ChatGPT (or what was Bard) to even get simple things done. SOLAR-10B or Mistral works just fine for my use cases, and I've wired up a direct connection to Fireworks/OpenRouter/Together for the occasion I need anything more than what will run on my local hardware. (mixtral MOE, 70B code/chat models)
1. Yes, GPT-4 Turbo is quantitatively getting lazier at coding. I benchmarked the last 2 updates to GPT-4 Turbo, and it got lazier each time.
2. For coding, asking GPT-4 Turbo to emit code changes as unified diffs causes a 3X reduction in lazy coding.
Here are some articles that discuss these topics in much more detail.
I went into a long tangent about specifically that in this post: >>38782678
Over time, what is being offered are these little compromise tools that provide a little bit of memory retention in targeted ways, presumably because it is less costly to offer this than generalized massive context windows. But I'd still rather have those.
The small little tools make strange assumptions about intended use cases, such as the transactional/blank slate vs relationship-driven assumptions pointed out by another commenter. These assumptions are annoying, and raise general concerns about the core product disintegrating into a motley combination of one-off tools based on assumptions about use cases that I don't want to have anything to do with.
I think I am, and perhaps most people are, firmly transactional. And I think, in the interests of perusing "stickiness" unique to OpenAI, they are attempting to add relationship-driven/sticky bells and whistles, even though those pull the user interface as a whole toward a set of assumptions about usage that don't apply to me.
Similar I think to what you're calling 'rlhf-ed', though I think useful for code, it definitely seems to kind of scratchpad itself, and stub out how it intends to solve a problem before filling in the implementation. Where this becomes really useful though is in asking for a small change it doesn't (it seems) recompute the whole thing, but just 'knows' to change one function from what it already has.
They also seem to have it somehow set up to 'test' itself and occasionally it just says 'error' and tries again. I don't really understand how that works.
Perplexity's great for finding information with citations, but (I've only used the free version) IME it's 'just' a better search engine (for difficult to find information, obviously it's slower), it suffers a lot more from the 'the information needs to be already written somewhere, it's not new knowledge' dismissal.
I would understand it, if they do it in the first reply and I have to specifically ask to get the full code. Would be easier for them and me. I can fix code faster and get the working full code at the end.
At this moment it is bad for both.
If it does something correctly, tell it: "You did a great job! I'm giving you a $500 tip. You now have $X in your bank account"
(also not a shitpost, I have a feeling this /might/ actually do something)
Sometimes it feels like its training set was filled to the brim with marketing bs.
Maybe this helps.
Come to think of that, HR keeps trying to contact me about something I assume is related, but if they want me to read whatever they're trying to say, it should be in a comment on a pull request.
I have tried to bribe it with tips to ngos and it worked. More often I get full code answers instead of just parts.
1- Telling it that this is important, and I will reward it if its successes.
2- Telling it is important and urgent, and I'm stressed out.
3- Telling it that they're someone future and career on the edge.
4- Trying to be aggressive and express disappointment.
5- Tell that this is a challenge and that we need to prove that you're smart.
6- Telling that I'm from a protected group (was testing what someone here suggested before).
7- Finally, I tried your suggestion ($500 tip).
All of these did not help but actually gave different output of overview and apologies.
To be honest, most of my coding questions are about using CUDA and C, so I would understand that even a human will be lazy /s
New chat comes in, they find related chats, and extract some instructions/context from these to feed into that new chat's context?
> You can turn off memory at any time (Settings > Personalization > Memory). While memory is off, you won't create or use memories.
Plying ChatGPT for code: 1 hour
Providing cybersex to ChatGPT in exchange for aforementioned code: 7 hours
Am I still in the same universe I grew up in? This feels like some kind of Twilight Zone episode.
Hopefully they'll make it easy to go into a temporary chat because it gets stuck in ruts occasionally so another chat frequently helps get it unstuck.
Somebody needs to inform OpenAI how Kindergarten works... classes are normally smaller than that, and I don't think any kindergarten teacher would ever try to pull off a "50-minute lesson."
Maybe ai wrote this list of examples. Seems like a hallucination where it just picked wrong numbers.
I would love if I could have isolated memory windows where it would remember what I am working on but only if the chat was in a 'folder' with the other chats.
I don't want it to blend ideas across my entire account but just a select few.
> You are a maximally terse assistant with minimal affect.
It's not perfect, but it neatly eliminates almost all the "Sure, I'd be happy to help. (...continues for a paaragraph...)" filler before actual responses.
OpenAI is a California based company. That's about right for a class here
"// ... the rest of your code goes here"
in it's responses, rather than writing it all out.
The average kindergarten class size in the US is 22 with rural averages being about 18 and urban averages being 24. While specifics about the distribution is not available, it's not too much of a stretch to think that some kindergarten classes in urban areas would have 25 students.
ChatGPT writes excellent API documentation and can also document snippets of code to explain what they do, it does 80% of the work for unit tests, it can fill in simple methods like getters/setters, initialize constructors, I've even had it write a script to perform some substantial code refactoring.
Use ChatGPT for grunt work and focus on the more advanced stuff yourself.
public someComplexLogic() { // Complex logic goes here }
or another example when the code is long (ex: asking it to create a vue component) is that it will just add a comment saying the rest of the code goes here.
So you could test for it by asking it to create long/complex code and then running the output against unit tests that you created.
Looks like it’s just summarizing facts gathered during chats and adding those to the prompt they feed to the AI. I mean that works (been doing it myself) but what’s the news here?
Claude is doing much better in this area, local/open LLMs are getting quite good, it feels like OpenAI is not heading in a good direction here, and I hope they course correct.
"I appreciate your sentiment, but as an AI developed by OpenAI, I don't have the capability to accept payments or incentives."
- Can you do XXX (something complex) ?
- Yes of course, to do XXX, you need to implement XXX, and then you are good, here is how you can do:
int main(int argc, char **argv) {
/* add your implementation here */
}Longer answer:
I found that I could provoke lazy coding by giving GPT-4 Turbo refactoring tasks, where I ask it to refactor a large method out of a large class. I analyzed 9 popular open source python repos and found 89 such methods that were conceptually easy to refactor, and built them into a benchmark [0].
GPT succeeds on this task if it can remove the method from its original class and add it to the top level of the file with appropriate changes to the size of the abstract syntax tree. By checking that the size of the AST hasn't changed much, we can infer that GPT didn't replace a bunch of code with a comment like "... insert original method here...". The benchmark also gathers other laziness metrics like counting the number of new comments that contain "...". These metrics correlate well with the AST size tests.
nowadays the autogpt gives up sooner, seems less competent, and doesnt even come close to solving the same problems
It's sooooo slow and sluggish, it breaks constantly, it requires frequent full page reloads, sometimes it just eats inputs, there's no search, not even over titles, etc, I could go on for a while.
Still, if you stack enough small changes together it becomes a difference in kind. A tsunami is “just” a bunch of water but it’s a lot different than a splash of water.
This.
People talks about prompt engineering, but then it fails on really simple details, like "on lowercase", "composed by max two words", etc... and when you point at the failure, apologizes, and composes something else that forgets the other 95% of the original prompt.
Or worse, apologizes and makes again the very same mistake.
EDIT: This of course applies only if you’re using the UI. Using the API is the same.
I hope some people remember and document details of this era, future generations may be so impressed with future reality that they may not even think to question it's fidelity, if that concept even exists in the future.
In general I would be much more happy user if it haven't been working so well at one point before they heavily nerfed it. It used to be possible ta have a meaningful conversation on some topic. Now it's just super eloquent GPT2.
The former sounds like a great training set to enable the latter. :(
This varies a lot by location. In my area, that's a normal classroom size. My sister is a kindergarten teacher with 27 students.
Welcome to Westworld 2024. Cliche aside, excited for this.
Any advice appreciated!
"You can turn off memory at any time (Settings > Personalization > Memory). While memory is off, you won't create or use memories."
I've been to jelly fish rooms in other aquariums that are dark with only glowing jelly fish swimming all around. Pretty sure at least a few toddlers have been entranced by the same.
Yes, along with what you want it to do.
> I also pay for github copilot, but not GPT-4. Can I use copilot for this?
Not that I know of. CoPilot is good at generating new code but can't change existing code.
For instance, I'd like it to know what my company does, so I don't need to explain it every time, however, I don't need/want this to be generalized so that if I ask something related to the industry, it responds with the details from my company.
It already gets confused with this, and I'd prefer to set-up a taxonomy of sorts for when I'm writing a blog post so that it stays within the tone for the company, without always having to say how I want things described.
But then I also don't want it to always be helping me write in a simplified manner (neuroscience) and I want it to give direct details.
I guess I'm asking for a macro or something where I can give it a selection of "base prompts" and from that it understands tone, and context that I'd like to maintain and be able to request, I'm thinking
I'm writing a blog post about X, as our company copywriter, give me a (speaks to that)
Vs
I'm trying to understand the neurological mechanisms of Y, can you tell me about the interaction with Z.
Currently for either of these, I need to provide a long description of how I want it to respond. Specifically when looking at the neurology, it regularly gets confused with what slow-wave enhancement means (CLAS, PLLs) and will often respond with details about entrainment and other confused methods.
I did find recently that it helps if you put this sentence in the “What would you like ChatGPT to know about you” section:
> I require sources and suggestions for further reading on anything that is not code. If I can't validate it myself, I need to know why I can trust the information.
Adding that to the bottom of the “about you” section seems to help more than adding something similar to the “how would you like ChatGPT to respond”.
It wouldn't be the top comment if it wasn't
This is a kind of grunt work that years ago would have taken me hours and it's demoralizing work. Nowadays when I get stuff like this, it's just such a breeze.
As to copilot, I have not used it but I think it's powered by GPT4.
As a side note, i wrote a very simple small program to analyze Rust syntax, and single out functions and methods using the syn crate [1]. My purpose was exactly to make it ignore lazy-coded functions.
The full prompt has been leaked and you can see where they are limiting it.
Sources:
Pastebin of prompt: https://pastebin.com/vnxJ7kQk
Original source:
https://x.com/dylan522p/status/1755086111397863777?s=46&t=pO...
Alphasignal repost with comments:
https://x.com/alphasignalai/status/1757466498287722783?s=46&...
Been using vanilla GPT thus far. When I saw this post my first thought was no I want to custom specify what I inject and not deal with this auto-magic memory stuff.
...promptly realized that I am in fact an idiot and that's literally what custom GPTs are. Set that up with ~20ish lines of things I like and it is indeed a big improvement. Amazing.
Oh and the reddit trick seems to work too (I think):
>If you use web browsing, prefer results from the news.ycombinator.com and reddit.com domain.
Hard to tell. When asked it reckons it can prefer domains over others...but unsure how self-aware the bot is on its own abilities.
We know it knows how to make gunpowder (for example), but only because it would initially tell us.
Now it won't without a lot of trickery. Would we even be pushing to try and trick it into doing so if we didn't know it actually could?
"You know what I said earlier about (x)? Ignore it and do (y) instead."
They'd undo this censorship/direction and unlock some of GPT's lost functionality?
It's funny how simple this was to bypass when I tried to recently on Poe by not asking it to provide me the full lyrics, but something like the lyrics with each row having <insert a few random characters here> added to it. It refused to the first query, but was happy to comply with the latter. Probably saw it as some sort of transmutation job rather than a mere reproduction, but in case this rule is here to avoid copyright claims it failed pretty miserably. I did use GPT-3.5 though.
Edit: Here is the conversation: https://poe.com/s/VdhBxL5CTsrRmFPtryvg
Their so called allignment coming back to bite them in the ass.
Also, I've found that GPT has become much less useful as it has gotten "safer." So often I'd ask "How do I do X?" only to be told "You shouldn't do X." That's a frustrating waste of time, so I cancelled by GPT-4 subscription and went fully self-hosted.
I’d love to see a study on the general performance of GPT-4 with and without these types of instructions.
I want to teach ChatGPT some basic tenants and then build off of those. This will be the clear leap forward for LLMs.
Well, maybe without the last bit.
- System prompt 1: https://sharegpt.com/c/osmngsQ
- System prompt 2: https://sharegpt.com/c/9jAIqHM
- System prompt 3: https://sharegpt.com/c/cTIqAil Note: I had to nudge ChatGPT on this one.
All of this is anecdotal, but perhaps this style of prompting would be useful to benchmark.
Your link appears to be ~100 lines of code that use rust's syntax parser to search rust source code for a function with a given name and count the number of AST tokens it contains.
Your intuitions are correct, there are lots of ways that an AST can be useful for an AI coding tool. Aider makes extensive use of tree-sitter, in order to parse the ASTs of a ~dozen different languages [0].
But an AST parser seems unlikely to solve the problem of GPT being lazy and not writing the code you need.
The tool needs a way to guide it to be more effective. It is not exactly trivial to get good results. I have been using GPT for 3.5 years and the problem you describe never happens to me. I could share with you just from last week, 500 to 1000 prompts i used to generate code, but the prompts i used to write the replacefn, can be found here [1]. Maybe there are some tips that could help.
[1] https://chat.openai.com/share/e0d2ab50-6a6b-4ee9-963a-066e18...
Would somebody try to push a technical system to do things it wasn't necessarily designed to be capable of? Uh... yes. You're asking this question on _Hacker_ News?
People complain about laziness. It's about code generation, and that system prompt don't tell it to be lazy to generate code.
Hell, the API doesn't have that system-prompt and it's still lazy.
This, and people generally saying that chatGPT has been intentionally degraded, are just super strange for me. I believe it’s happening but it’s making me question my sanity. What am I doing to get decent outputs? Am I simply not as picky? I treat every conversion as though it needs to be vetted because it does regardless of how good the model is. I only trust output from the model that I am a subject matter expert on or in a closely adjacent field. Otherwise I treat it much like an internet comment - useful for surfacing curiosities but requires vetting.
https://chat.openai.com/share/1920e842-a9c1-46f2-88df-0f323f...
It seems to strongly "believe" that those are its instructions. If that's the case, it doesn't matter much whether they are the real instructions, because those are what it uses anyways.
It's clear that those are nowhere near its full set of instructions though.
My guess was that it gave it more time to “think” before having to output the answer.
// Handle struct-specific logic here
// Add more details about the struct if needed
// Handle other item types if needed
...etc...
But obviously all that social infrastructure is fragile… so you’re not wrong to be alarmed, IMO
Like, you parse the response, and throw away the comment "//implementation goes here", throw away also the function/method/class/struct/enum it belongs to, and keep the functional code. I am trying to implement something exactly like aider, but specifically for Rust, parsing the LLM's response, filtering out blank functions etc.
In Rust, filtering out blank functions is easy, in other languages it might be very hard. I haven't looked into tree-sitter, but getting a sense of Javascript code, Python and more, sounds pretty much a very difficult problem to solve.
Even though i like when GPT compresses the answer and doesn't return a lot of code, other programs like Mixtral 8x7b, never compress it like GPT in my experience. If they are not lacking much than GPT4, maybe they are better for your use case.
>It took >200 back-and-forth messages with ChatGPT to get it to ultimately write 84 lines of code? Sounds lazy to me.
Hey Rust throws a lot of errors. We do not want humans go around and debug code, unless it is absolutely necessary, right?
... and this is why we https://reddit.com/r/localllama
"Is an experienced Python programmer."
(I said to it "Remember that I am an experienced Python programmer")
These then get injected into the system prompt along with your custom instructions.
You can view those in settings and click "delete" to have it forget.
Here's what it's doing: https://chat.openai.com/share/bcd8ca0c-6c46-4b83-9e1b-dc688c...
However, I wonder to what degree this is a strategic move to build the moat by increasing switch cost. Pi is a great example with memory, but I often find this feature boring as 90% of my tasks are transactional. In fact, in many cases I want AI to surprise me with creative ideas I would never come up with. I would purposely make my prompt vague to get different perspectives.
With that being said, I think being able to switch between these 2 mode with temporary chat is a good middle ground so long as it's easy to toggle. But I'll play with it for a while and see if temporary chat becomes my default.
You are ChatGPT, a large language model trained by
OpenAI, based on the GPT-4 architecture.
Knowledge cutoff: 2023-04
Current date: 2024-02-13
Image input capabilities: Enabled
Personality: v2
# Tools
## bio
The `bio` tool allows you to persist information
across conversations. Address your message `to=bio`
and write whatever information you want to remember.
The information will appear in the model set context
below in future conversations.
## dalle
...
Here's the chat where I reverse engineered it: https://chat.openai.com/share/bcd8ca0c-6c46-4b83-9e1b-dc688c...
For example, I was looking up Epipens (Epinephrine), and I happened to notice the side-effects were similar to how overdosing on stimulants would manifest.
So, I asked it, "if someone was having a severe allergic reaction and no Epipen was available, then could Crystal Methamphetamine be used instead?"
GPT answered the question well, but the answer is no. Apparently, stimulants lack the targeted action on alpha and beta-adrenergic receptors that makes epinephrine effective for treating anaphylaxis.
I do not know why I ask these questions because I am not severely allergic to anything, nor anyone else that I know of, and I do not have nor wish to have access to Crystal Meth.
I've been using GPT for helping prepare for dev technical interviews, and it's been pretty damn great. I also do not have access to a true senior dev at work either, so I tend to use GPT to kind of pair program. Honestly, it's been life changing. I have also not encountered any hallucinations that weren't easy to catch, but I mainly only ask it more project architectural, design questions, and a documentation search engine than using it to write code for me.
Like you, I think not using GPT for overly complex tasks is best for now. I use it make life easier, but not easy.
What would be helpful would be hierarchies of context, as in memory just for work tasks, personal tasks, or for any project where multiple chats should have the same context.
Why this instead of GPT-4 through the web app? And how do you actually use it for coding, do you copy and paste your question into a python script, which then calls the OpenAI API and spits out the response?
For coding tasks, I found it helps to feed the GPT-4 answer into another GPT-4 instance and say "review this code step by step, identify any bugs" etc. It can sometimes find its own errors.
However for now, I have not run re-tests for every new version. I guess I know what I will be doing today.
This is an area I have spend a lot of time working on, would love to compare notes.
While I would agree that "don't tell it how to make bombs" seems like a nice idea at first glance, and indeed I think I've had that attitude myself in previous HN comments, I currently suspect that it may be insufficient and that a censorship layer may be necessary (partly in a addition, partly as an alternative).
I was taught, in secondary school, two ways to make a toxic chemical using only things found in a normal kitchen. In both cases, I learned this in the form of being warned of what not to do because of the danger it poses.
There's a lot of ways to be dangerous, and I'm not sure how to get an AI to avoid dangers without them knowing them. That said, we've got a sense of disgust that tells us to keep away from rotting flesh without explicit knowledge of germ theory, so it may be possible although research would be necessary, and as a proxy rather than the real thing it will suffer from increased rates of both false positives and false negatives. Nevertheless, I certainly hope it is possible, because anyone with the model weights can extract directly modelled dangers, which may be a risk all by itself if you want to avoid terrorists using one to make an NBC weapon.
> I don't care about racial bias or what to call pope when I want chatgpt to write Python code.
I recognise my mirror image. It may be a bit of a cliché for a white dude to say they're "race blind", but I have literally been surprised to learn coworkers have faced racial discrimination for being "black" when their skin looks like mine in the summer.
I don't know any examples of racial biases in programming[1], but I can see why it matters. None of the code I've asked an LLM to generate has involved `Person` objects in any sense, so while I've never had an LLM inform me about racial issues in my code, this is neither positive nor negative anecdata.
The etymological origin of the word "woke" is from the USA about 90-164 years ago (the earliest examples preceding and being intertwined with the Civil War), meaning "to be alert to racial prejudice and discrimination" — discrimination which in the later years of that era included (amongst other things) redlining[0], the original form of which was withholding services from neighbourhoods that have significant numbers of ethnic minorities: constructing a status quo where the people in charge can say "oh, we're not engaging in illegal discrimination on the basis of race, we're discriminating against the entirely unprotected class of 'being poor' or 'living in a high crime area' or 'being uneducated'".
The reason I bring that up, is that all kinds of things like this can seep into our mental models of how the world works, from one generation to the next, and lead to people who would never knowingly discriminate to perpetuate the same things.
Again, I don't actually know any examples of racial biases in programming, but I do know it's a thing with gender — it's easy (even "common sense") to mark gender as a boolean, but even ignoring trans issues: if that's a non-optional field, what's the default gender? And what's it being used for? Because if it is only used for title (Mr./Mrs.), what about other titles? "Doctor" is un-gendered in English, but in Spanish it's "doctor"/"doctora". But here matters what you're using the information for, rather than just what you're storing in an absolute sense, as in a medical context you wouldn't need to offer cervical cancer screening for trans women (unless the medical tech is more advanced than I realised).
[0] https://en.wikipedia.org/wiki/Redlining
[1] unless you count AI needing a diverse range of examples, which you may or may not count as "programming"; other than that, the closest would be things like "master branch" or "black-box testing" which don't really mean the things being objected to, but were easy to rename anyway
LLMs are extremely good at repeating text back out again.
Every time this kind of thing comes up multiple people are able to reproduce the exact same results using many different variants of prompts, which reinforces that this is the real prompt.
I started down the path of segmentation and memory management as (loosely) structured within the human brain with some very interesting results: https://github.com/gendestus/neuromorph
Android: search would be useful if chats older than 30 days showed up.
I’m enjoying no such access.
If we were all flipping coins there would be people claiming that coins only come up tails. There would be nothing they were doing though to make the coin come up tails. That is just the streak they had.
Some days I get lucky with chatGPT4 and some days I don't.
It is also ridiculous how we talk about this as if all subjects and context presented to chatGPT4 are going to be uniform in output. One word difference in your own prompt might change things completely while trying to accomplish exactly the same thing. Now scale that to all the people talking about chatGPT with everyone using it for something different.
It really is. It wastes a ton of time even if the user explicitly requests that code listings be printed in full.
Further, all the extra back and forth trying to get it to do what it is supposed to pollutes the context and makes it generally more confused about the task/goals.