☆ Yσɠƚԋσʂ ☆

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Joined 6 years ago
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Cake day: January 18th, 2020

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  • What I keep explaining to you here is that silicon is not inevitable, and that it’s obviously possible to make other substrates work and bring costs down. I’ve also explained to you why it makes no business sense for companies already invested in silicon to do that. The reason China has a big incentive is because they don’t currently have the ability to make top end chips. So, they can do moonshot projects at state level, and if one of them succeeds then they can leapfrog a whole generation of tech that way.

    You just keep repeating that silicon is the best material for the job without substantiating that in any way. Your whole argument is tautological, amounting to saying that silicon is widely used and therefore it’s the best fit.








  • While these policies obviously harm Americans as well, their impact on Canada shouldn’t be dismissed. The reality is that the percentage of the US economy that’s dependent on trade with Canada is far smaller than the reverse. The harm to Canadian exports would be quite significant. Of course, the only rational thing for Canada to do here is to suck up short term pain and continue diversifying. The fact that we allowed our economy to become so dependent on trade with the US is the reason we’re in this mess in the first place.



  • The secret sauce here is how the model was trained. Typically, coding models are trained on static snapshots of code from GitHub and other public sources. They basically learn what good code looks like at a single point in time. IQuest did something totally different. They trained their model using entire commit history of repositories.

    This approach added a temporal component to training, allowing the model to learn how code actually changes from one commit to the next. It saw how entire projects evolve over months and even years. It learned the patterns in how developers refactor and improve code, and the real world workflows of how software gets built. Instead of just learning what good code looks like, it learned how code evolves.

    Coding is inherently an iterative process where you make an attempt at a solution, and then iterate on it. As you gain a deeper understanding of the problem, you end up building on top of existing patterns and evolving the codebase over time. IQuest model gets how that works because it was trained on that entire process.