Building with linked_in_api
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The document discusses using the LinkedIn API for building applications. It provides an overview of the JavaScript and REST APIs, noting that the JavaScript API is easier to use but more limited while the REST API provides more flexibility but requires more development effort. Both APIs require user authorization. The document then describes some of the capabilities of the LinkedIn API, such as accessing profile and connection data, performing faceted people searches, and accessing jobs and group data. It also outlines some rules for using the API related to data storage and exposure.
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Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
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Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
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Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
Removing Uninteresting Bytes in Software Fuzzing
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
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Full-RAG: A modern architecture for hyper-personalization
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Building with linked_in_api
- 1. BUILDING WITH LINKEDIN API Pains, constraints and data
- 2. JUST READ THE http://
- 3. BORING STUFF Request API Keys (You want 2
- 4. 2 APIS Javascript and Rest
- 5. JAVASCRIPT API • Easier to respect • Good the TOS ;-) • Comeswith templates and • Less good tags • Client-side
- 6. REST API • Good • Less good • Server-side • Need to know the API • Need to build everything
- 7. BEST OF BOTH You can exchange token from an API to the other says Sarah.
- 8. REQUIRES USER AUTH You can only call the API through an
- 9. POWER TO THE API • Profile API: Standard vs Public • Profile fields: So many it’s insane. • Connections API: Usually way enough info. • People Search: The Power of facets. • Groups API, Social stream, Invitation (friend-only) • Jobs API: Bookmarks and suggestions.
- 10. NO EMAILS E-mail addresses are never exposed
- 11. PLAY BY THE HOFFMAN RULES • NoLinkedIn data can be stored (except profiles if explicit) • Data from one user's LinkedIn account/network may not be exposed to another user • Youcannot use the APIs in conjunction with data scraped by you or other third parties from LinkedIn
- 12. FACETS http://api.linkedin.com/v1/people-search:(facets:(code,buckets: (code,name)))?facets=location <people-search> <facets total="1"> <facet> <name>Location</name> <code>location</code> <buckets total="5"> <bucket> <name>United States</name> <code>us:0</code> </bucket> <bucket> <name>San Francisco Bay Area</name> <code>us:84</code> </bucket> <bucket> <name>France</name> <code>fr:0</code> </bucket> ... </buckets> </facet>
- 13. LIST OF FACETS • Location • Industry • Network • Language • Current company • Past company • School
- 14. FACETED SEARCH • http://api.linkedin.com/v1/people-search:(facets:(code,buckets: (code,name,count)))? facets=location,network&facet=location,us: 84&facet=network,F
- 16. TIPS • Read the TOS • Read the docs • Expect weird return type (private??) • Expect undocumented errors • Monitor your usage
- 17. ANY QUESTIONS? My email is
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