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GitHub Copilot Workspace: Technical Preview

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Re: GitHub Copilot Workspace: Technical Preview

#231

Post by rco8786 »

studenthrow3831 wrote: post #3

Student here: I legitimately cannot understand how senior developers can dismiss these LLM tools when they've gone from barely stringing together a TODO app to structuring and executing large-scale changes in entire repositories in 3 years. I'm not a singulatarian, but this seems like a brutal S-curve we're heading into. I also have a hard time believing that there is enough software need to make such an extreme prod…

We’re not dismissing them! They’re just not that good at helping us with our actual work.

I have Copilot on and it’s…fine. A marginal productivity improvement for specific tasks. It’s also great at variable names, which is probably the main reason I leave it on.

But it’s not replacing anyone’s job (at least not yet).

Re: GitHub Copilot Workspace: Technical Preview

#232

Post by thehoneybadger »

studenthrow3831 wrote: post #3

Student here: I legitimately cannot understand how senior developers can dismiss these LLM tools when they've gone from barely stringing together a TODO app to structuring and executing large-scale changes in entire repositories in 3 years. I'm not a singulatarian, but this seems like a brutal S-curve we're heading into. I also have a hard time believing that there is enough software need to make such an extreme prod…

Having written and sold machine learning software for over 15 years, you are definitely over-reacting. There is about a 10 year pattern. Every 10 years AI gets drummed up as the next big thing. It is all marketing hype. It always fails.

This happened in the 70s, 80s, 90s, 00s, 10s, and now the 20s. Without fail. It is a hyped up trend.

Only be concerned when someone is presenting a real breakthrough in the science (not the commercial aspect). A real breakthrough in the science will not have any immediate economic impact.

Convolutional neural networks are absolutely not revolutionary over the prior state of the art. These are incremental gains in model accuracy at the cost of massive data structures. There is no real leap up here in the ability for a machine to reason.

ChatGPT is just snake oil. Calm down. It will come and go.

Re: GitHub Copilot Workspace: Technical Preview

#233

Post by throwaway4aday »

015a wrote: post #145
paradite wrote: post #123

Earlier quoted context omitted.

Locally running LLMs in Apr 2024 are no where close to GPT-4 in terms of coding capabilities.

And GPT-4 is nowhere close to the human brain in terms of coding capabilities, and model advancements appear to be hitting an asymptote. So...

I don't see a flattening. I see a lot of other groups catching up to OpenAI and some even slightly surpassing them like Claude 3 Opus. I'm very interested in how Llama 3 400B turns out but my conservative prediction (backed by Meta's early evaluations) is that it will be at least as good as GPT 4. It's been a little over a year since GPT 4 was released to the public and in that time Meta and Anthropic seem to have caught up and Google would have too if they spent less time tying themselves up in knots. So OpenAI has a 1 year lead though they seem to have spent some of that time on making inference less expensive which is not a terrible choice. If they release 4.5 or 5 and it flops or isn't much better then maybe you are right but it's very premature to call the race now, maybe 2 years from now with little progress from anyone.

Re: GitHub Copilot Workspace: Technical Preview

#234

Post by shepherdjerred »

studenthrow3831 wrote: post #3

Student here: I legitimately cannot understand how senior developers can dismiss these LLM tools when they've gone from barely stringing together a TODO app to structuring and executing large-scale changes in entire repositories in 3 years. I'm not a singulatarian, but this seems like a brutal S-curve we're heading into. I also have a hard time believing that there is enough software need to make such an extreme prod…

AI _will_ take jobs. It's a matter of when and not if. The real question is will that occur in the next 10/50/100 years.

It might not happen in your lifetime, but as you've noted the rate of progress is stunning. It's possible that the latest boom will lead to a stall, but of course nobody knows.

IMO it's way too hard to predict what the consequences will be. Ultimately the best thing you can do are to continue with your degree, and consider what skills you have that an AI couldn't easily replicate. e.g. no matter how good AI gets, robotics still has a ways to go before an AI could replace cooks, nurses, etc.

Re: GitHub Copilot Workspace: Technical Preview

#235

Post by blueboo »

studenthrow3831 wrote: post #3

Student here: I legitimately cannot understand how senior developers can dismiss these LLM tools when they've gone from barely stringing together a TODO app to structuring and executing large-scale changes in entire repositories in 3 years. I'm not a singulatarian, but this seems like a brutal S-curve we're heading into. I also have a hard time believing that there is enough software need to make such an extreme prod…

First of all, yes, this is a provocative prompt that bears engagement. You're right to be concerned.

I share your frustration with the reticence of seasoned engineers to engage with these tools.

However, "structuring and executing large-scale changes in entire repositories" is not a capability that is routinely proven out, even with SOTA models in hellaciously wasteful agentic workflows. I only offer a modest moderation. They'll get there, some time between next week and 2030.

Consider: Some of the most effective engineers of today cut their teeth writing assembly, fighting through strange dialects of C or otherwise throwing themselves against what are now incontestibly obselete technologies but otherwise honed their engineering skills to a much higher degree than their comrades who glided in on Java's wing.

Observe that months of hand-sculpted assembly has turned into a single Python call. AI is yet another tier of abstraction.

Another lens is application -- AI for X domain, for X group, for X age, for X culture. Lots to do there.

Finally, there's empowerment. If this technology is so powerful, do you concede that power to others? Or are you going to be a part of the group that ensures it benefits all?

FYI, OpenAI published a labor market study suggesting professions that are more or less exposed to AI. Take a look.

Re: GitHub Copilot Workspace: Technical Preview

#236

Post by [deleted] »

sixhobbits wrote: post #20
studenthrow3831 wrote: post #3

Student here: I legitimately cannot understand how senior developers can dismiss these LLM tools when they've gone from barely stringing together a TODO app to structuring and executing large-scale changes in entire repositories in 3 years. I'm not a singulatarian, but this seems like a brutal S-curve we're heading into. I also have a hard time believing that there is enough software need to make such an extreme prod…

Accountants thought spreadsheets would kill their profession, instead demand for them exploded. Compilers made it much easier to code compared to writing everything in Assembly. Python made it much easier to code than writing C. Both increased the demand for coders. Code is a liability, not an asset. The fact that less technical people and people who are not trained engineers can now make useful apps by generating mi…

[deleted]

Re: GitHub Copilot Workspace: Technical Preview

#237

Post by epolanski »

rco8786 wrote: post #231
studenthrow3831 wrote: post #3

Student here: I legitimately cannot understand how senior developers can dismiss these LLM tools when they've gone from barely stringing together a TODO app to structuring and executing large-scale changes in entire repositories in 3 years. I'm not a singulatarian, but this seems like a brutal S-curve we're heading into. I also have a hard time believing that there is enough software need to make such an extreme prod…

We’re not dismissing them! They’re just not that good at helping us with our actual work. I have Copilot on and it’s…fine. A marginal productivity improvement for specific tasks. It’s also great at variable names, which is probably the main reason I leave it on. But it’s not replacing anyone’s job (at least not yet).

Copilot is great at boilerplate and as a super autocomplete.

Useful when needing to recall some api without having to open the browser and google too.

But honestly writing code is nowhere near the hard part of the job, so there's 0 reasons to fear LLMs.

Re: GitHub Copilot Workspace: Technical Preview

#238

Post by freedomben »

whatever1 wrote: post #218
singluere wrote: post #112

While I've not used this product, I've created somewhat similar setup using open source LLMs that runs locally. After having used it for about three months, I can say that debugging LLM prompts was far more annoying than debugging code. Ultimately, I ended up abandoning my setup and going in favor of writing code the good old fashioned way. YMMV

That is my struggle as well. I need to keep pointing out issues of the llm output, until after multiple iterations it may reach the correct answer. At that point I don't feel I gained anything productivity wise. Maybe the whole point of coding with llms in 2024 is for us to train their models.

Indeed, and the more niche the use case, the worse it gets.

Re: GitHub Copilot Workspace: Technical Preview

#239

Post by lispisok »

imiric wrote: post #221
lispisok wrote: post #194

Earlier quoted context omitted.

You are extrapolating from when we saw huge improvements 1-2 years ago. Performance improvements have flatlined. Current AI predictions reminds me of self-driving car hype from the mid 2010s

> Performance improvements have flatlined. Multimodality, MoE, RAG, open source models, and robotics, have all been/seen massive improvements in the past year alone. OpenAI's Sora is a multi-generational leap over anything we've seen before (not released yet, granted, but it's a real product). This is hardly flatlining. I'm not even in the AI field, but I'm sure someone can provide more examples. > Current AI predict…

You make claims of massive improvements but as an end user I have not experienced such. With the amount of fake and cherrypicked demos in the AI space I dont believe anything until I experience it myself.

>Ironically, Waymo's self-driving taxis were launched in several cities in 2023. Does this count?

No because usage is limited to a tiny fraction of drive-able space. More cherrypicking.

Re: GitHub Copilot Workspace: Technical Preview

#240

Post by freedomben »

packetlost wrote: post #52
jonahx wrote: post #48

Earlier quoted context omitted.

Serious answer to a legitimate question: 1. Good senior developers are taking the tools seriously, and at least experimenting with them to see what's up. Don't listen to people dismissing them outright. Skepticism and caution is warranted, but dismissal is foolish. 2. I'd summarize the current state of affairs as having access to an amazing assistant that is essentially a much better and faster version of google and…

I think latency is the biggest reason I killed my Copilot sub after the first month. It was fine at doing busy-work, like 40%~ success rate for very very standard stuff, which is a net win of like... 3-5%. If it was local and nearly instant, I'd never turn it off. Bonus points if I could restrict the output to finishing the expression and nothing more. The success rate beyond finishing the first line drops dramatical…

Interesting, latency has always been great for me. If it's going to work, it usually has suggestions within a second or two. I use the neovim plugin though so not on the typical VS-code based path.
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