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Merge pull request #1257 from jasonrandrews/spelling
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.wordlist.txt

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ifuncs
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lm
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memcpy
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multiversioning
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multiversioning
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AwsServerlessDynamoDbLambdaS
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BLASes
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BVH
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Bindless
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Calvo
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Chowdary
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CloudFormation
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GDC
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GLSL
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Helpbox
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Lista
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LttE
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Malhotra
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Mandepudi
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Nikhil
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OPLTK
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Rasterization
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Ravi
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Refractions
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Rohr
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RunsOn
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SSR
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Streamlit
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TLASes
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TorchAO
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Torchchat
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Vulkan's
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Vulkanised
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WebsiteBucket
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aaaa
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barycentric
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bindless
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bonza
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calvo
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chatbot's
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createResponse
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denoising
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getAverageTemperatureButton
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getAverageTemperatureUrl
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jJyHzkWXEfY
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lista
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param
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prismjs
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quicktool
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rasterization
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rasterized
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refractions
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renderer
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skybox
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specular
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ssr
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streamlit
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stylesheet
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torchchat
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tps
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uQ
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vulkan
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vulkanised
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writeTemperaturesUrl

content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md

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- Neoverse
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tools_software_languages:
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- Cloudformation
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- CloudFormation
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- GitHub
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- AWS EC2
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content/learning-paths/servers-and-cloud-computing/github-actions-runner/_review.md

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- "Yes"
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correct_answer: 2
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explanation: >
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RunsOn is free for non-commerical projects. You pay the cost of the EC2 instances that are used and a small free for the AWS AppRunner service.
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RunsOn is free for non-commerical projects. You pay the cost of the EC2 instances that are used and a small free for the AWS App Runner service.
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- questions:
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question: >

content/learning-paths/servers-and-cloud-computing/github-actions-runner/install-runs-on.md

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Once you have your key, proceed with the installation guide by selecting the AWS region you would like to use, creating the CloudFormation stack, and installing the GitHub app.
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3. At the end, follow the link to the deployed AppRunner service endpoint, and you should see a page indicating that your installation is successful.
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3. At the end, follow the link to the deployed App Runner service endpoint, and you should see a page indicating that your installation is successful.
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At this point you can start using RunsOn to spawn runners for your GitHub Actions workflows.
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content/learning-paths/servers-and-cloud-computing/llama-cpu/_demo.md

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- name: llama-3-8b-instruct
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specs: The newest Llama model, with 8 billion parameters.
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- name: llama-2-7b
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specs: Llama2 has 7 billion paramters.
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specs: Llama2 has 7 billion parameters.
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selectable: true
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explanation: The LLM selected affects how performant the model is and such.
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content/learning-paths/smartphones-and-mobile/ray_tracing/rt06_reflections.md

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{{< tab header="Example 1: RT" title="Example 1: Ray tracing reflections" img_src="/learning-paths/smartphones-and-mobile/ray_tracing/images/reflections_artifacts_1_rt.png">}}{{< /tab >}}
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{{< tab header="Example 2: SSR" title="Example 2: Screen space reflections" img_src="/learning-paths/smartphones-and-mobile/ray_tracing/images/reflections_artifacts_2_ssr.png">}}{{< /tab >}}
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{{< tab header="Example 2: RT" title="Example 2: Ray tracing reflections" img_src="/learning-paths/smartphones-and-mobile/ray_tracing/images/reflections_artifacts_2_rt.png">}}{{< /tab >}}
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{{< tab header="Example 3: SSR" title="Example 3: Screen space teflections" img_src="/learning-paths/smartphones-and-mobile/ray_tracing/images/reflections_artifacts_3_ssr.png">}}{{< /tab >}}
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{{< tab header="Example 3: SSR" title="Example 3: Screen space reflections" img_src="/learning-paths/smartphones-and-mobile/ray_tracing/images/reflections_artifacts_3_ssr.png">}}{{< /tab >}}
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{{< tab header="Example 3: RT" title="Example 3: Ray tracing reflections" img_src="/learning-paths/smartphones-and-mobile/ray_tracing/images/reflections_artifacts_3_rt.png">}}{{< /tab >}}
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{{< tab header="Example 4: SSR" title="Example 4: Screen space reflections" img_src="/learning-paths/smartphones-and-mobile/ray_tracing/images/reflections_artifacts_4_ssr.png">}}{{< /tab >}}
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{{< tab header="Example 4: RT" title="Example 4: Ray tracing reflections" img_src="/learning-paths/smartphones-and-mobile/ray_tracing/images/reflections_artifacts_4_rt.png">}}{{< /tab >}}

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