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関数型プログラミングについて2016/3時点での私の知識をまとめ、C++にどうやって適応していくと皆幸せになれるかを説明しました。 間違っているところなど、コメントお待ちしています。
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This presentation covers the following topics: What is logging? The purpose of logging: Debugging The purpose of logging: Security The purpose of logging: Stats & analytics Traditional logging Traditional logging: Advantages Traditional logging: Disadvantages The solution: Large-scale logging Large-scale logging: Core principles Large-scale logging: Solution types Large-scale logging: Cloud vs on-prem Large-scale logging: Operational complexity Large-scale logging: Security Large-scale logging: Costs Large-scale logging: On-prem comparison - Elasticsearch - Grafana Loki - VictoriaLogs On-prem comparison: Setup and operation On-prem comparison: Costs On-prem comparison: Full-text search support On-prem comparison: How to efficiently query 100TB of logs? On-prem comparison: Integration with CLI tools VictoriaLogs for large-scale logging VictoriaLogs demo instance - Ingestion rate: 3600 messages / minute - The number of log messages: 1.1 billion - Uncompressed log messages’ size: 1.5TB - Compressed log messages’ size: 23GB - Compression ratio: 47x - Memory usage: 150MB VictoriaLogs CLI integration demo - Which errors have occurred in all the apps during the last hour? - How many errors have occurred during the last hour? - Which apps generated the most of errors during the last hour? - The number of per-minute errors for the last 10 minutes - Status codes for the last hour - Non-200 status codes for the last week - Top client IPs for the last 4 weeks with 404 and 500 response status codes - Per-month stats for the given IP across all the logs Large-scale logging solution MUST provide excellent CLI integration VictoriaLogs: (temporary) drawbacks VictoriaLogs: Recap - Easy to setup and operate - The lowest RAM usage and disk space usage (up to 30x less than Elasticsearch and Grafana Loki) - Fast full-text search - Excellent integration with traditional command-line tools for log analysis - Accepts logs from popular log shippers (Filebeat, Fluentbit, Logstash, Vector, Promtail, Grafana Agent) - Open source and free to use!
Large-scale Logging Made Easy: Meetup at Deutsche Bank 2024
Large-scale Logging Made Easy: Meetup at Deutsche Bank 2024
VictoriaMetrics
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Craft an AI & Machine Learning Pitch with our Editable Professional PowerPoint Template. Ignite your AI & Machine Learning pitch with our cutting-edge PowerPoint template tailored for the industry. Perfect for AI conferences, investor presentations, sales pitches to tech-focused companies, training sessions, and educational programs. - 20+ editable slides: Get a variety of options to choose from for your presentation. - Time-saving solution: Download, replace text/images with a few clicks. - User-friendly customization: Easy to use and personalize. - Modern and attractive design: Captivating visuals, sleek layout. - Tailored to your requirements: Fully alterable for customization. - Well-organized slides: Complete control over content. - Thematic specificity: Reflects healthcare industry with relevant graphics. - Showcase your business idea: Communicate value proposition effectively.
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Data spaces in distributed environments should be allowed to evolve in agile ways providing data space owners with large flexibility about which data they store. Agility and heterogeneity, however, jeopardize data exchanges because representations may build on varying ontologies and data consumers may not rely on the semantic correctness of their queries in the context of semantically heterogeneous, evolving data spaces. Graph data spaces are one example of a powerful model for representing and querying data whose semantics may change over time. To assert and enforce conditions on individual graph data spaces, shape languages (e.g SHACL) have been developed. We investigate the question of how querying and programming can be guarded by reasoning over SHACL constraints in a distributed setting and we sketch a picture of how a future landscape based on semantically heterogeneous data spaces might look like.
Shapes for Sharing between Graph Data Spaces - and Epistemic Querying of RDF-...
Shapes for Sharing between Graph Data Spaces - and Epistemic Querying of RDF-...
Steffen Staab
In the past six months, the AI landscape has undergone a massive transformation, ushering in a new era of productivity with the latest in Large Language Models (LLMs) and AI technology. This deep dive unlocks how to: Create CustomGPT Models: No coding needed to tailor AI for your unique projects. Integrate your own data, including PDFs and Excel sheets, making information handling a breeze. Plus, discover how to call your own actions/integrations for even more personalized utility. Navigate Advanced Prompting: Overcome AI's memory limits and utilize Retrieval-Augmented Generation for accessing your personalized data, streamlining how you interact with AI. Stay Ahead with AI Trends: Peek into the evolving world of LLMs, featuring newcomers like Google Gemini, Anthropic Claude, Open Sora, and Twitter Grok, and understand what their advancements mean for your productivity. Witness Real-Life Transformations: Through examples and prompt demonstrations, see firsthand how these AI strategies revolutionize routine tasks, from data analysis to content creation. Learn to leverage image output and input for advanced practical use cases, adding a new dimension to your productivity toolkit. No previous coding or AI experience is needed for this talk. Stay ahead in the fast-evolving world of work. Embrace the AI revolution and transform your workflow with advanced LLM techniques. Join us to ensure you're not left behind in the productivity race.
AI Mastery 201: Elevating Your Workflow with Advanced LLM Techniques
AI Mastery 201: Elevating Your Workflow with Advanced LLM Techniques
VictorSzoltysek
OpenChain - The Ramifications of ISO/IEC 5230 and ISO/IEC 18974 for Legal Professionals in 2024
OpenChain - The Ramifications of ISO/IEC 5230 and ISO/IEC 18974 for Legal Pro...
OpenChain - The Ramifications of ISO/IEC 5230 and ISO/IEC 18974 for Legal Pro...
Shane Coughlan
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Harnessing ChatGPT - Elevating Productivity in Today's Agile Environment
Devoxx UK 2024 - Going serverless with Quarkus, GraalVM native images and AWS...
Devoxx UK 2024 - Going serverless with Quarkus, GraalVM native images and AWS...
VTU technical seminar 8Th Sem on Scikit-learn
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Direct Style Effect Systems -The Print[A] Example- A Comprehension Aid
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Shapes for Sharing between Graph Data Spaces - and Epistemic Querying of RDF-...
AI Mastery 201: Elevating Your Workflow with Advanced LLM Techniques
AI Mastery 201: Elevating Your Workflow with Advanced LLM Techniques
OpenChain - The Ramifications of ISO/IEC 5230 and ISO/IEC 18974 for Legal Pro...
OpenChain - The Ramifications of ISO/IEC 5230 and ISO/IEC 18974 for Legal Pro...
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functional programming & c++
1.
Functional Programming 2016/3 C++ y =
cos x/3
2.
Functional? Differences from Imperative in C++ Agenda
3.
Functional?
4.
declarative code higher-order function static
binding lazy evaluation Functional Programming is
5.
C/C++ (Imperative) Declarative code
6.
std::function(), , , callback
, DI, etc. Higher-order function
7.
Static binding
8.
(eager evaluation) C/C++ Lazy evaluation
9.
PROS!
10.
IO CONS"
11.
That is…
12.
13.
C/C++
14.
Imperative vs. Functional C++
Haskell
15.
Imperative vs. Functional https://msdn.microsoft.com/ja-jp/library/bb669144.aspx
16.
Programming Paradigms http://docs.python.jp/3/howto/functional.html
17.
18.
Imperative vs. Functional
19.
Imperative & Functional C++ Haskell
20.
21.
22.
23.
💎 💎 💎 💎 💎 💎 💎
24.
Training in FP,
with C++
25.
Immutable
26.
const / const& const const
(mutating ) Immutable
27.
FP UI In/Out FP Immutable
28.
lambda
29.
30.
31.
API vector<int> function_name([xxx,] vector<int>
&array); result = filter_prime(sort(take3(array))); vector<int> function_name([xxx,] vector<int> &array); result = take3(filter_prime(sort(array)));
32.
OK Program is … disposable code long
life code
33.
immutable
34.
immutable mutable
35.
36.
Recap
37.
C/C++ Recap
38.
Try! FP
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