> Copilot is encouraging us to block users unnecessarily, by suggesting obviously flawed code, which is wrong on every level: wrong ethically, wrong legally, and the wrong way to build software.
I share many of the same worries as the author. This is why I think teams need to build and run their own Copilot-like systems, so that they can guide the suggestions they receive. Each developer and team has their own way of building software, and they need to be able to shape and evolve the suggestions they receive to fit their definition of the "right" way: https://blog.continue.dev/its-time-to-collect-data-on-how-yo...
I've been playing around with it for the last couple days on my Windows machine, using it for local tab-autocomplete in VS Code, and it's been just as good as it is on my Mac
Continue (YC S23) | Founding Engineer | ONSITE | Full-time | San Francisco | $130-$170K + 1-2% Equity
At Continue, we are on a mission to make building software feel like making music. We are creating the open-source autopilot for VS Code and JetBrains——the easiest way to code with any LLM (https://github.com/continuedev/continue).
You are likely a good fit if you
- have founded or want to found your own startup one day
- have experience with frontend, backend, ML technologies
- are enthusiastic about AI/LLMs, open source, developer tools
- get excited about supporting users and helping customers
- want to work in-person in SF the majority of the time
Continue | Founding Engineer | ONSITE | Full-time | San Francisco | $130-$170K + 1-2% Equity
At Continue, we are on a mission to make building software feel like making music. We are creating the open-source autopilot for software development—an IDE extension that brings the power of ChatGPT to VS Code and JetBrains (https://github.com/continuedev/continue).
You are likely a good fit if you
- have founded or want to found your own startup one day
- have experience with frontend, backend, and ML technologies
- are enthusiastic about AI/LLMs, open source, developer tools
- get excited about supporting users and helping customers
- want to work in-person in SF the majority of the time
Continue | Founding Engineer | ONSITE | Full-Time | San Francisco | $130-$170K + 1-2% Equity
At Continue, we are on a mission to make building software feel like making music. We are creating the open-source autopilot for software development—an IDE extension that brings the power of ChatGPT to VS Code and JetBrains (https://github.com/continuedev/continue).
You are likely a good fit if you
- have founded or want to found your own startup one day
- have experience with frontend, backend, and ML technologies
- are enthusiastic about AI/LLMs, open-source, developer tools
- get excited about supporting users and helping customers
- want to work in-person in SF the majority of the time
Companies developing or demonstrating an intent to develop potential dual-use foundation models to provide the Federal Government, on an ongoing basis, with information, reports, or records ... (i) any model that was trained using a quantity of computing power greater than 10^26 integer or floating-point operations, or using primarily biological sequence data and using a quantity of computing power greater than 10^23 integer or floating-point operations; and (ii) any computing cluster that has a set of machines physically co-located in a single datacenter, transitively connected by data center networking of over 100 Gbit/s, and having a theoretical maximum computing capacity of 10^20 integer or floating-point operations per second for training AI.
Training large models in the US now requires reporting
I've been surprised that evaluation dataset leakage into training dataset is not discussed more, given that LLM creators often don't reveal their training data