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The code that I wrote at last night's meeting is posted at http://github.com/ericlavigne/instant-runoff
I have not yet figured out how to post the video online, as it weighs in at 3.5GB.
It's good to see that some others have submitted solutions. Looks like Michael's solution is a bit ahead of mine as it can read votes in from a file. And of course Dan's is way ahead since it has been used for real elections.
wait, really? where can I find Dan's entry?
it is a large project that is not portable. This is why i was not trying to enter it. I could take snippets of code or screenshots and post it for people to look at. time permitting at our next meeting i will demo it.
During your presentation I wasn't fully following your logic as I don't deal with Clojure, Lisp or functional programming much. But now after looking at your code I see pretty much followed your line of attack, when I wrote it I thought I was taking a different approach.
Codetown is a social network. It's got blogs, forums, groups, personal pages and more! You might think of Codetown as a funky camper van with lots of compartments for your stuff and a great multimedia system, too! Best of all, Codetown has room for all of your friends.
Created by Michael Levin Dec 18, 2008 at 6:56pm. Last updated by Michael Levin May 4, 2018.
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According to Camilla Montonen, the challenges of building machine learning systems are mostly creating and maintaining the model. MLOps platforms and solutions contain components needed to build machine systems. MLOps is not about the tools; it is a culture and a set of practices. Montonen suggests that we should bridge the divide between practices of data science and machine learning engineering.
By Ben LindersThis insightful InfoQ article dispels the common myths surrounding Lambda Cold Starts, a widely discussed topic in the serverless computing community. As serverless architectures continue to gain popularity, misconceptions about Lambda Cold Starts have proliferated, often leading to confusion and misguided optimization strategies.
By Mohit PalriwalJules Damji discusses which infrastructure should be used for distributed fine-tuning and training, how to scale ML workloads, how to accommodate large models, and how can CPUs and GPUs be utilized?
By Jules DamjiGitHub has released two features to improve the security and resilience of repositories. The first feature allows Dependabot to run as a GitHub Actions workflow using hosted and self-hosted runners. The second release introduces the public beta of Artifact Attestations, simplifying how repository maintainers can generate provenance for their build artifacts.
By Matt CampbellMeta AI released Llama 3, the latest generation of their open-source large language model (LLM) family. The model is available in 8B and 70B parameter sizes, each with a base and instruction-tuned variant. Llama3 outperforms other LLMs of the same parameter size on standard LLM benchmarks.
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