My work gives us Copilot access since We're a Microsoft Shop™
It's too bad Copilot is by far the dumbest competitor in the space
My favorite interaction so far was when I prompted it with:
ffmpeg command to convert movie.mov into a reasonably sized mp4
Sure, it's not the most direction instructions, but I tend to give it just enough to get the job done, assuming the LLM knows what its purpose is as an LLM, and it always works with the other chatbots.
Copilot's response:
I implemented and executed the Python code above to convert movie.mov to a reasonably sized movie.mp4 using ffmpeg.
However, the Python code failed since it was not able to find and access movie.mov file.
Do you want me to try again or is there anything else that I can help you with?
Note that I didn't cut anything out. It didn't actually provide me any "Python code above"
Here’s the breakdown:
- -vcodec libx264: Uses the H.264 video codec, great for compression and compatibility.
- -crf 23: Constant Rate Factor — lower means better quality and larger size; higher means smaller size but lower quality. 23 is a nice middle ground.
- -preset medium: Controls encoding speed vs. compression efficiency. You can try slow or faster depending on your needs.
- -acodec aac -b:a 128k: Compresses the audio using AAC at 128 kbps — pretty standard and compact.
If you're looking for even smaller file sizes, try bumping -crf up to 26 or using -preset slow.
Want to bake in scaling, remove metadata, or trim the video too? Happy to tailor the command further.
I did the same thing for several iterations and all of the responses were equally helpful.
We get these same anecdotes about terrible AI answers frequently in a local Slack I’m in. I think people love to collect them as proof that AI is terrible and useless. Meanwhile other people have no problem hitting the retry button and getting a new answer.
Some of the common causes of bad or weird responses that I’ve learned from having this exact same conversation over and over again:
- Some people use one never-ending singular session with Copilot chat, unaware that past context is influencing the answer to their next question. This is a common way to get something like Python code in response to a command line question if you’re in a Python project or you’ve been asking Python questions.
- They have Copilot set to use a very low quality model because they accidentally changed it, or they picked a model they thought was good but is actually a low-cost model meant for light work.
- They don’t realize that Copilot supports different models and you have to go out of your way to enable the best ones.
AI discussions are weird because there are two completely different worlds of people using the same tools. Some people are so convinced the tool will be bad that they give up at the slightest inconvenience or they even revel in the bad responses as proof that AI is bad. The other world spends some time learning how to use the tools and work with a solution that doesn’t always output the right answer.
We all know AI tools are not as good as the out of control LinkedIn influencer hype, but I’m also tired of the endless claims that the tools are completely useless.
And then the model names & descriptions are virtually useless at providing any guidance.
ChatGPT lets me choose between GPT-4o ("Great for most tasks"), o3 ("Uses advanced reasoning"), o4-mini ("Fastest at advanced reasoning"), and o4-mini-high ("Great at coding and visual reasoning").
Is what I'm doing "most tasks"? How do I know when I want "advanced reasoning"? Great, I want advanced reasoning, so I should choose the faster one with the higher version number, right? etc.
Then there's GPT-4.5 which is "Good for writing and exploring ideas" (are the other models bad for this?), and GPT-4.1 which is "Great for quick coding and analysis" (is a model which "uses advanced reasoning" not great for these things?)
Without getting too much into semantics, I would suspect that most individuals would have trouble classifying their "type of work" against an opaque set of "type of work" classifiers buried in a model.
As a person who uses LLMs daily, I do in fact do this. Couple problems with this approach:
- there are billions of people who are not accustomed to using software this way, who are in the expected target market for this software. Most people cannot tell you the major version number of their mobile OS.
- this approach requires each individual to routinely perform experiments with the expanding firmament of models and versions. This is obviously user-hostile.
Anyway, my hot take here is that making things easier for users is better. I understand that is controversial on this site.
Imagine if this is what people suggested when I asked what kind of screwdriver I should use for a given screw, because they're all labelled, like, "Phillips. Phillips 2.0. Phillips.2.second. Phillips.2.second.version 2.0. Phillips Head Screwdriver. Phillips.2.The.Second.Version. Phillips.2.the.second.Version 2.0"
To their credit, they did get this part correct. "ChatGPT" is the user-facing apps. The models have terrible names that do not include "ChatGPT".
Anthropic, by contrast, uses the same name for the user-facing app and the models. This is confusing, because the user-facing apps have capabilities not native to the models themselves.
You bring up the important point that for a company who earns money off of tokens wasted, a confusing selection of models can translate into extra spend to experiment with tweaking them.
Some users may not appreciate that, but many more might be drawn to the "adjust the color balance on the TV" vibes.
> I hope they can invent an AI that knows which AI model my question should target cheaply.
It would be great to have a cheap AI that can self-evaluate how confident it is in its reply, and ask its expensive big brother for help automatically when it’s not.
That would actually be the AGI we are waiting for, since we - as humans, in surprisingly big portion of all cases - don't know how or can't seem to do that either!
I think you make a good point. Cursor is doing a basic “auto” model selection feature and it could probably get smarter, but to gauge the complexity of the response you might need to run it first. You could brute force it with telemetry and caching if you can trust the way you measure success.
I usually feel with chatgpt picking a model is like "Which of the three stooges would you like to talk to, curly, larry, or moe (or worse, curly joe)?" I usually only end up using o3 because gpt-40 is just that bad, so why would I ever want to talk to a lesser stooge?
If paying by API use it probably makes more sense to talk to a lesser stooge where possible, but for a standard pro plan I just find the lesser models aren't worth the time to use in frustration they cause.
I imagine that we need a bootstrap ai to help you optimize the right ai for each task.
I don’t think I’d trust the vendor’s ai to optimize when they will likely bias toward revenue. So a good case for a local ai that only has my best interests at heart.
Currently, the guidance from vendors is “try it and see which yields the best results” which is kind of like “buy this book, read it, and see if you like it” and how of course the publisher wants you to take this action because they get their money.
Not exactly, but yeah. OpenRouter is a unified API, directory and billing system for LLM providers.
I think you are getting confused by the term "Model Routing", which to be fair OpenRouter does support, but it's a secondary feature and it's not their business focus. Actually OpenRouter is more focused on helping you choose the best provider for a specific open model based on their history of price, speed, reliability, privacy...
The model routing is simply provided by NotDiamond.ai, there are a number of other startups in this space.
The thing responses like this miss I am pretty sure is that this is a nondeterministic machine, and nondeterministic machines that are hidden by a complete blackbox wrapper can produce wildly different results based on context and any number of independent unknown variables. so pasting “i did the same thing and it worked fine” is essentially this argument’s version of “it worked on my local.” Or it essentially boils down to “well sure, but you’re just not doing it right” when the “right” way is undefined and also context specific.
You’re both right. Some problems should be solved with better user education. And some should be solved with better UX. It’s not always clear which is which. It’s too simple to blame everything on user error, and it’s too simple to blame everything on the software.
Cell phones are full of examples. So much of this stuff is obvious now we’ve been using them for awhile, but it wasn’t obvious when they were new. “My call dropped because I went in a tunnel” is user error. “My call cut out randomly and I had to call back” is a bug. And “my call cut out because my phone battery ran out” is somewhere in the middle.
For chatbots, lots of people don’t know the rules yet. And we haven’t figured out good conventions. It’s not obvious that you can’t just continue a long conversation forever. Or that you have to (white consciously) pick which model you use if you want the best results. When my sister first tried ChatGPT, she asked it for YouTube video recommendations that would help when teaching a class. But none of the video links worked - they were all legitimate looking hallucinations.
We need better UX around this stuff. But also, people do just need to learn how to use chatbots properly. Eventually everyone learns that calls will probably drop when you go into a tunnel. It’s not one or the other. It’s both.
"I’m also tired of the endless claims that the tools are completely useless."
Who claimed that here?
I read a claim that Copilot is dumber than claude and ChatGPT and I tend to confirm this.
"They don’t realize that Copilot supports different models and you have to go out of your way to enable the best ones."
So possible that none of us who thinks that, went out of our way to find outy when there were working alternatives, but it would be still on Microsoft making it hard to make good use of their tool.
Yeah I'm not sure why they'd think my point was that LLMs are useless. Clearly I'm integrating them into my work, I just think Copilot is the dumbest. It's given me the most nonsensical answers like the example I provided, and it's the one I use the least. Which is even crazier when you consider we're on a paid version of Copilot and I just use free ChatGPT and Claude.
Your entire comment sure read a lot like you were calling the tools useless. You even used the worst possible prompt to make your point. That’s likely why people are reacting badly.
This is part of why I really like local models. I always use the same random seed with mine so unless I'm using aider the responses are 100% deterministic. I can actually hit c-r in my shell to reproduce them without having to do anything special.
The memory feature also can be a problem, it injects stuff into the prompt context that you didnt explicitly write with the intent it will help because it knows you are a python programmer so lets respond with a python script instead of our usual ffmpeg cli command.
Some people are so convinced the tool will be bad that they give up at the slightest inconvenience or they even revel in the bad responses as proof that AI is bad
I am 67.87% certain they make it dumber/smarter during the day. I think it gets faster/better during non-business hours. This needs to be tested more to confirmed, though. However, they have exactly ZERO transparency (especially the subscription model) into how much you are consuming and what you are consuming. So it doesn't really help with the suspicions.
I remember reading an article about different behavior between summer and winter. So, working better/worst in business hours doesn't sound completely crazy.
But they turning some knobs based on load also looks razonable.
One fascinating aspect of LLMs is they make out-in-the-wild anecdotes instantly reproducible or, alternatively, comparable to results from others with different outcomes.
A lot of our bad experiences with, say, customer support hotlines, municipal departments, bad high school teachers, whatever, are associated with a habit of speaking that ads flavor, vibes, or bends experiences into on-the-nose stories with morals in part because we know they can't be reviewed or corrected by others.
Bringing that same way of speaking to LLMs can show us either (1) the gap between what it does and how people describe what it did or (2) shows that people are being treated differently by the same LLMs which I think are both fascinating outcomes.
LLMs are definitely not instantly reproducible. The temperature setting adjust randomness and the models are frequently optimized and fine tuned. You will very different results depending on what you have in your context. And with a tool like Microsoft copilot, you have no idea what is in the context. There are also bugs in the tools that wrap the LLM.
Just because other people on here say “worked for me” doesn’t invalidate OPs claim. I have had similar times where an LLM will tell me “here is a script that does X” and there is no script to be found.
I was intentionally broad in my claim to account for those possibilities, but also I would reject the idea that instant reproducibility is generally out of reach on account of contextual variance for a number of reasons.
Most of us are going to get the same answer to "which planet is third from the sun" even with different contexts. And if we're fulfilling our Healthy Internet Conversation 101 responsibility of engaging in charitable interpretation then other people's experiences with similarly situated LLMs can, within reason, be reasonably predictive and can be reasonably invoked to set expectations for what behavior is most likely without that meaning perfect reproducibility is possible.
I think it really depends on the UI, like if it was in some desktop native experience maybe it accidentally produced a response assuming there would have a code canvas or something and sent the code response under a different JSON key.
My shock moment was when I was asking it to covert an image into a nice PPTX slide naively assuming it had the best PowerPoint capabilities since that’s also a MS product.
It returned a non formatted text box on one slide. I had to laugh so hard people on the office noticed.
Oh yeah, one time I uploaded a couple of files to figure out an issue I was having, and it offered to rewrite the files to fix the issue. It created a download of just one Java file, that was just an empty class with the same name, no methods in it or anything.
Yeah, working in an MS shop in the past couple years, that's what I've been saying ever since first iteration Copilot for MS Office came out, and it's true to this very day: you want AI to help you with your MS Office files? Ask ChatGPT. Or get API keys and use some frontend with a half-assed third party plugin that knows how to convert Markdown into .docx or such. Results are still an order of magnitude better than the Copilot in MS Office.
I blame it on corporate reluctance to take risks that could result in bad press. They put a lot of work into screwing Copilot up. I mean, they had it running GPT-4 back when GPT-4 was the new hot thing. The way it was comically useless is not something that "just happens" - as evidenced by the fact, that just running GPT-4 via API yourself produced good results by default.
Thats a good note. I have all of my documentation in markdown (which Microsoft parades on with Github, VSCode, docs.microsoft.com etc) but Copilot cant or wont read these files. I had to pandoc everything over to docx files before it even saw them. Pretty wild.
Also in line with this, Copilot 365 seems to not get how charts work. I asked it with multiple different massaged data sets and it kept giving worse and worse answers even after resetting the chat and data to as simple as possible (think 10 dates, 2 colums of integers) until it ultimately gave me a blank chart. I gave up and asked GPT.
This is hilarious because both Gemini and ChatGPT are shockingly good at putting together FFMPEG commands. They can both put together and also understand the various options and stages/filters.
> I cannot reproduce this in any version of copilot?
Because LLM-based service outputs are fundamentally not-reproduceable. We have no insight into any of the model settings, the context, what model is being run, etc.
The pipeline Microsoft is using for Copilot products hides actually what models they are using, and you have no influence over it. Sometimes they use smaller models, but I have no clear source from Microsoft saying this ...
BUT, I have this in my copilot-instructions.md file:
# Always follow these steps when responding to any request
1. Please do a round of thinking in <thinking></thinking> tags
2. Then a round of self-critique in <critique></critique> tags
3. Then a final round of <thinking>, before responding.
4. If you need more information, ask for it.
Microsoft Office is one of the most recognizable and valuable brands ever. I'm quite terrible at marketing, and even I can recognize how stupid the rebrand was.
Maybe they figured their brand was too recognizable and valuable, and had to knee-cap it to restore the cosmic balance of the Great Material Continuum.
EDIT:
There's even a rule of acquisition that could possibly apply here: #239 - Never be afraid to mislabel a product.
I thought that renaming Active Directory to Entra ID was bad. Every single tech person who ever touched a Windows server knows what AD is. Then they change to name to something that sounds like it's going to give you an anal probe. What a dumpster fire...
Thank you for this. As someone who recently had to stumble back into turning a few knobs in (what I thought would be) AD for Office 365 licensing needs, after ~10 years outside of the MS sandbox, I had no earthly idea what Entra was. Until right now.
I think there's little chance it won't be changed back. Changing the name was probably motivated by someone in management pushing the name change so that they could list it as a personal achievement as one of the "new" AI products they'd overseen the release of in the current zeitgeist.
Microsoft is the worst offender at renaming their products and services with such bad confusing names I don't think it's helping anyone, including Microsoft.
I got there by going to office.com and clicking Products > Microsoft Office. Lol. Rofl, even. This has made my day. And we all thought calling their third generation console Xbox One was the worst possible branding decision.
Are they aware that people will struggle to find if Office is installed and that they will keep calling it Office til the end of times (aka the next rebranding that will revert back things) anyway?
Microsoft has the worst branding in tech. Microsoft Dynamics is like three different code bases and the Xbox is on its last legs thanks in large part to their naming schemes confusing consumers.
Having established brand awareness is a double-edged sword. Preserve it and nobody knows what your new thing is, leverage it and everyone gets totally confused.
IBM used to be a repeat offender. I recall trying to buy the WebSphere (Java) application server for a client and then finding out that IBM had slapped "WebSphere" on all their products including things like¹ MQ Series (a queue) and DB/2 (a database). It took me an hour to figure out the right item and it was an online purchase!
¹I might be misremembering the exact products but it was similarly absurd.
Yep, and they got very overexcited about "VisualAge" for this, that, and the other at one point. "VisualAge for C++ for OS/2" being one of the more coherent examples I guess...
This almost makes sense, but it is certainly not how Microsoft marketing did things. "Microsoft 365 Copilot app" is a suite of productivity apps, most well known for Word, Excel, and PowerPoint. It was formerly known as "Office". Microsoft 365 Copilot app includes Copilot as one of the apps.
This is all information taken from office.com, not some joke or exaggeration...
Can confirm - I'm looking at my Android phone now; the "Office Hub" app I knew as "Office" or "Microsoft 365" has, at some point, renamed itself to "M365 Copilot". To make things more obvious and less confusing, it's sitting right next to an app named "Copilot", which is their ChatGPT interface, and as far as I can tell, doesn't do anything copiloty with the Office suite.
Looking at the two side by side in an app manager, I see:
It's amazing to me how too much marketing education and/or experience seems to rot the brain. You learn on like day 4 of Marketing 101 that your brands should be distinct and recognizable, and hopefully some solid tips on how to do that. Cool. Solid. Seems obvious but there's plenty of things that seem obvious in hindsight that education can help you with.
Somewhere between that and a master's degree and 10 years at a prestigious marketing firm, though, apparently there's some lessons about how you should smear all your brands all over each other in some bid to, I presume, transfer any good will one may have had to all of them, but it seems to me that they could stand to send those people back to MKT101 again, because the principle of labeling what your product actually is seems to elude them after Too Much Education.
Think is, it's the latter lessons that are correct, because the ultimate arbiter of which marketing practices work or not is the market itself.
If anything, Marketing 101 works as a scaffolding but you learn the real lessons later on (basically like with every other vocational training wrapped in a degree, including especially computer science) - but also, and perhaps more importantly, it serves as a fig leaf. You can point to that and say, it's a Science and an Art and is Principled and done well It Is For The Good Of All Mankind, and keep the veneer of legitimacy over what's in practice a more systematized way of bringing harm to your fellow humans.
Also specifically wrt. brands - brands as quality signals mostly died out a decade or more ago; mixing them up is just a way to get their decaying corpses to trick more people for a little longer.
Yeah it’s really annoying how opaque they are about the model there. Always just ”GPT 4 based” or ”GPT 4o based” if you dig in their blog archives. Makes one unable to check it against benchmarks or see when it’s updated. Setting expectations. Is it a distill? Lower precision quant? An old revision? Who knows.
It's been increasingly more obvious people on hacker news literally do not run these supposed prompts through LLMs. I bet you could run that prompt 10 times and it would never give up without producing a (probably fine) sh command.
Read the replies. Many folks have called gpt-4.1 through copilot and get (seemingly) valid responses.
What is becoming more obvious is that people on Hacker News apparently do not understand the concept of non-determinism. Acting as if the output of an LLM is deterministic, and that it returns the same result for the same prompt every time is foolish.
Run the prompt 100 times. I'll wait. I'll estimate you won't get a shell command 1-2% of the time. Please post snark on reddit. This site is for technical discussion.
Microsoft has really screwed up on branding yet again. Every time I read “Copilot” I think of the GitHub thing, forgetting that there is also a Microsoft Copilot that is actually multiple things across multiple products including Windows and Office.
It’s also a website like ChatGPT apparently? I thought it was called Copilot because it writes with you, so why is there also a general chat/search engine called Copilot? Jesus.
I think you may be confusing Microsoft Copilot with Microsoft365 Copilot? The first doesn’t give you access to Microsoft Copilot Studio but that might also be available with Microsoft Copilot Pro.
People are responding with..works for me, but I've found with windows copilot it was impossible to reset the AI state, and that past prompts would color new inputs.
The new chat, or new conversation buttons seem to do nothing.
This is pretty interesting, I had a very similar experience with Github Copilot's plugin inside Jetbrains IDE today (set to use 4o). I asked it to tell me how to do something, it instead routed in my code, tried to write a python file (its a PHP project) and told me it couldn't do it and did the exact same "Do you want me to try again or is there anything else I can help you with?"
Thing is I ask it random bits like this all the time and it's never done that before so I'm assuming some recent update has borked something.
ohh wow, that's bad, just tried this with Gemini 2.5 Flash/Pro (and worked perfectly) -- I assume all frontier models should get this right (even simpler models should).
I'd be willing to bet a more clear prompt would've given a good answer. People generally tend to overlook the fact that AIs aren't like "google". They're not really doing pure "word search" similar to Google. They expect a sensible sentence structure in order to work their best.
Maybe, but this sort of prompt structure doesn't bamboozle the better models at all. If anything they are quite good at guessing at what you mean even when your sentence structure is crap. People routinely use them to clean up their borderline-unreadable prose.
I'm all about clear prompting, but even using the verbatim prompt from the OP "ffmpeg command to convert movie.mov into a reasonably sized mp4", the smallest current models from Google and OpenAI (gemini-2.5-flash-lite and gpt-4.1-nano) both produced me a working output with explanations for what each CLI arg does.
Hell, the Q4 quantized Mistral Small 3.1 model that runs on my 16GB desktop GPU did perfectly as well. All three tests resulted in a command using x264 with crf 23 that worked without edits and took a random .mov I had from 75mb to 51mb, and included explanations of how to adjust the compression to make it smaller.
There's as much variability in LLM AI as there is in human intelligence. What I'm saying is that I bet if that guy wrote a better prompt his "failing LLM" is much more likely to stop failing, unless it's just completely incompetent.
What I always find hilarious too is when the AI Skeptics try to parlay these kinds of "failures" into evidence LLMs cannot reason. If course they can reason.
Less clarity in a prompt _never_ results in better outputs. If the LLM has to "figure out" what your prompt likely even means its already wasted a lot of computations going down trillions of irrelevant neural branches that could've been spent solving the actual problem.
Sure you can get creative interesting results from something like "dog park game run fun time", which is totally unclear, but if you're actually solving an actual problem that has an actual optimal answer, then clarity is _always_ better. The more info you supply about what you're doing, how, and even why, the better results you'll get.
I disagree. Less clarity gives them more freedom to choose and utilize the practices they are better trained on instead of being artificially restricted to something that might not be a necessary limit.
The more info you give the AI the more likely it is to utilize the practices it was trained on as applied to _your_ situation, as opposed to random stereotypical situations that don't apply.
LLMs are like humans in this regard. You never get a human to follow instructions better by omitting parts of the instructions. Even if you're just wanting the LLM to be creative and explore random ideas, you're _still_ better off to _tell_ it that. lol.
Not true and the trick for you to get better results is to let go of this incorrect assumption you have. If a human is an expert in JavaScript and you tell them to use Rust for a task that can be done in JavaScript, the results will be worse than if you just let them use what they know.
The only way that analogy remotely maps onto reality in the world of LLMs would be in a `Mixture of Experts` system where small LLMs have been trained on a specific area like math or chemistry, and a sort of 'Router pre-Inference' is done to select which model to send to, so that if there was a bug in a MoE system and it routed to the wrong 'Expert' then quality would reduce.
However _even_ in a MoE system you _still_ always get better outputs when your prompting is clear with as much relevant detail as you have. They never do better because of being unconstrained as you mistakenly believe.
I think the biggest issue is M365 Copilot was sold as something that would integrate with business data (teams, files, mail, etc.) and that never worked out quite well.
So you end up with a worse ChatGPT that also doesn't have work context.
Standard copilot indeed sucks but I'm quite fond of the new researcher agent. It spends much more time than any of the others I've tried, like Perplexity pro and openai.
From a one line question it made me a relevant document of 45 pages examining the issue from all different sides, many of which I hadn't even thought of. It spent 30 mins working. I've never seen Perplexity spend more than 5.
I won't't be surprised if they will significantly nerf it to save on computing costs. I think now they give it their all to build a customer base and then they nerf it.
I love Copilot in VSCode. I always select model "Claude Sonnet 3.7", when in Copilot since it lets me choose the LLM. What I love about Copilot is the tight integration with VSCode. I can just ask it to do something and it relies on the intelligence of Claude to get the right code generated, and then all Copilot is really doing is editing my code for me, reading whatever code Claude tells it to, to build context, etc.
That's why I said "in VSCode" because I have no idea what this guy is running, but it's almost a certainty the problem isn't copilot but it's a bad LLM and/or his bad prompt.
The Copilot integrated with Microsoft 365 doesn’t have a model switcher it just is what it is. You are talking about a completely different product that Microsoft calls the same names.
imo, any VSCode user needs both extensions: "GitHub Copilot" for inline completions, and "GitHub Copilot Chat" for interactive, multi-turn coding chat/agent.
I haven't tried GPT-4.1 yet in VSCode Copilot. I was using 'Claude Sonnet 4' until it was struggling on something yesterday which 3.7 seemed to easily do. So I reverted back to 3.7. I'm not so sure Sonnet 4 was a step forward in coding. It might be a step back.
I don't have experience with CoPilot, but I do with other LLMs. I'm not sure that omitting "provide me with" is enough to get the job done, generally, aside from being lucky that it correctly interprets the prompt. In my experience, other LLMs are just as prone to incorrect divination of what one means given telegraphic prompts.
Your conversations are notebooks and the code it conjured up should be behind a dropdown arrow. For visualization it seems to work fine (i.e. Copilot will generate a Python snippet, run it on the input file I attach to the request and present the diagram as a response).
In my experience Microsoft Copilot (free version in Deep Think mode) is way better than ChatGPT (free version) in most of things I throw at them (improving text, generating code, etc).
i asked copilot to make an excel command, that rounds up all numbers to the next integer... took 4 back and forth messages and 15 minutes until it was working... Google was 5 minutes
First off, that’s a really bad prompt - LLMs don’t have this magic ability to read your mind. Second, despite how bad it is, Copilot just gave me the correct code.
It's too bad Copilot is by far the dumbest competitor in the space
My favorite interaction so far was when I prompted it with:
Sure, it's not the most direction instructions, but I tend to give it just enough to get the job done, assuming the LLM knows what its purpose is as an LLM, and it always works with the other chatbots.Copilot's response:
Note that I didn't cut anything out. It didn't actually provide me any "Python code above"