I’m currently working on a custom, state of the art zero-knowledge proof smart contract framework, and as I’m writing circuits using it Copilot correctly suggests an insane number of lines to me. It feels like I’m cheating.
GitHub Copilot Labs
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Re: GitHub Copilot Labs
#42My favorite copilot moment recently is rails migrations. I write the up migrating n, copilot gets the down migration right 90% of the time. It's wild. And these aren't vanilla "change" commands. It's triggers and check constraints and changing column types and stuff.
Not using Copilot yet, would appreciate if you could provide some real examples on what you typed and what copilot suggested.
Re: GitHub Copilot Labs
#43I’m currently working on a custom, state of the art zero-knowledge proof smart contract framework, and as I’m writing circuits using it Copilot correctly suggests an insane number of lines to me. It feels like I’m cheating.
Perhaps your framework is not as state-of-the-art as you believe?
Re: GitHub Copilot Labs
#44I've been using Copilot for 3 weeks in a typical web project: typescript, react, css. Feedback: - when i declare a variable or function using a suggestive name, it correctly autocompletes what i want 50% of the time and it also takes into account the context; sometimes it just blows my mind how accurate it can be. - sometimes it doesn't autocomplete even if i delete the line to start over - i changed the way i code:…
I love it. It removes the more tedious parts of my work and I've found it to give me time to think about the overall system.
I'm wondering what it will cost me (read my employer) once it is out of beta.
Considering I am willing to bet that it's gonna be a fair bit.
Re: GitHub Copilot Labs
#45Hello! I'm the founder of https://denigma.app , an AI that explains code. It's available now, with a free demo and VS Code extension, with an Emacs extension coming soon. Denigma is a product that has been around for a while. Denigma goes beyond a literal line by line explanation, and explains of programming concepts and deduces the business logic and goal of code. I'm excited to see innovation in the field. Here's a…
Very cool product, I'm curious if you have an offline version that an individual developer could pay for. At my Fortune-100 employer I read a ton of code that is often opaquely written and stored in a private git repository. Unfortunately it's a huge hurdle to approve a SaaS app which receives any data or code from our work environments.
Re: GitHub Copilot Labs
#46What's the use-case for this? Hopefully not for commenting code. This only seems to be able to explain syntax, which seems pretty useless for anyone who isn't new to the language they're using. Am I missing something?
The use case is exactly what is stated on the box. You select a bit of code in a language/syntax that may be unfamiliar to you, and it tries to explain it better in English.
I should try it with decompiled APKs in Smali.
Re: GitHub Copilot Labs
#47Tried Copilot and gave up on it, it was only helping out where it didn't matter, the rare trivial parts. After a few mistakes I concluded it was dragging me down. Maybe I'm a hard customer for it even though I work in ML and have been following closely ever since it started.
Re: GitHub Copilot Labs
#48I've been using Copilot for 3 weeks in a typical web project: typescript, react, css. Feedback: - when i declare a variable or function using a suggestive name, it correctly autocompletes what i want 50% of the time and it also takes into account the context; sometimes it just blows my mind how accurate it can be. - sometimes it doesn't autocomplete even if i delete the line to start over - i changed the way i code:…
I love it. It removes the more tedious parts of my work and I've found it to give me time to think about the overall system. I'm wondering what it will cost me (read my employer) once it is out of beta. Considering I am willing to bet that it's gonna be a fair bit.
There are open versions of GPT so presumably, one could train an open version of Codex. It would be really terrible if Codex was made inaccessible to swaths of people in the name of commercial interests.
Re: GitHub Copilot Labs
#49I’m currently working on a custom, state of the art zero-knowledge proof smart contract framework, and as I’m writing circuits using it Copilot correctly suggests an insane number of lines to me. It feels like I’m cheating.
Perhaps your framework is not as state-of-the-art as you believe?
Why is it so difficult to conceive that what these models produce is novel, and capable of high level technical and semantic correctness?
Transformer models aren't auto-complete engines. They're semantic graphs describing the probabilities of token positions within a sequence of a few thousand tokens. They go deep, with a nuanced and hierarchical representation of concepts. The representation is functional, conditionally altering the behavior of tokens within its sequence context. Because of the hierarchical nature of the internal rules, when the model "learns" things, it extracts meta-patterns from the training data, and can chain a seemingly arbitrary number of such rules together to produce an output.
Auto-complete systems generally fail at the first level of abstraction. If transformers have sufficient vocabulary size and high quality data, they're capable of learning any degree of abstractions represented within the training corpus.
They're not magic, or intelligent like human brains, but they're allowing programmers to model very complex systems and produce novel material. This ranges from constructed languages to the rules of programming to storytelling and poetry to protein sequencing and more.
You should be in awe of gpt-3, or at the very least, far less dismissive. It represents a quantum leap in the power of software, and we're barely scratching the surface of what's possible. Recurrence and episodal memory, along with Moore's law and/or a substantial decrease in the size of the models, could be the thing that achieves human level agi that lives on consumer grade devices.
Re: GitHub Copilot Labs
#50Earlier quoted context omitted.
I love it. It removes the more tedious parts of my work and I've found it to give me time to think about the overall system. I'm wondering what it will cost me (read my employer) once it is out of beta. Considering I am willing to bet that it's gonna be a fair bit.
There are open versions of GPT so presumably, one could train an open version of Codex. It would be really terrible if Codex was made inaccessible to swaths of people in the name of commercial interests.
GitHub sent OpenAI something like 57 terabytes of data from GitHub. Good luck scraping that.
(I helped build The Pile, the largest openly-available text dataset.)
You're right that you theoretically can do this, but doing it in practice requires either funding or time.