LinkedIn and the Nightmare of Connection Requests

Monday, Jan 7, 2019 | 2 minute read

Ricardo Ferreira

Is this just me or LinkedIn became this annoying social network where recruiters and people you never worked with keep poking you for new connections?

Like other professional social networks, LinkedIn was designed to connect people around the globe and empower them with the ability to get jobs, hire talents and be the backbone for professional communication. However, that concept has been exploited because there are people that leverage this concept for their own benefits.

I don’t have a problem in making new connections, friendships, and acquaintances, but I personally can’t approve a connection request from someone that I never meet in my life, nor one that I never worked with. The former seems to be even more relevant on LinkedIn which is supposed to be for professionals. In this context, I simply cannot understand the need for people to connect with someone they never meet.

Some friends think that is unpolite denying a connection requests on LinkedIn, and they naturally disagree with my way of thinking. I don’t blame them. Perhaps I might be a little old fashion in this regard. But hey, we all can have our reservations right? For instance, I simply rate this concept of TL;DR that most people tend to use while they write, but I prefer not to criticise.

Nonetheless, it would be cool if LinkedIn would offer a way to create something – such as a challenge or a task – in which people would only be allowed to send an invitation after completing that. For Software Engineers like me that could be a simple PR filed in a project that I am working on GitHub. Rejected PRs wouldn’t count, though.

© 2018 - 2026 Ricardo Ferreira

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Open Source

I contribute to LangChain4j, an idiomatic open source Java library for building LLM-powered applications on the JVM. Recent work includes adding native vector search embedding stores so developers can build RAG, recommendation engines, and AI memory systems.

I also ported RedisVL to Go, an open source, AI-native client that brings vector search, semantic caching, LLM memory, semantic routing, rerankers, and an MCP server to the Redis ecosystem for Golang developers.

Public Speaking

I’ve been speaking at conferences since 2008 and doing it full time as part of my work with DevRel since 2018. My talks go deep on the systems I build with: distributed systems and event streaming, AI engineering and vector search, and the data infrastructure that has to hold up when the demo ends and production begins. Some of the events I’ve spoken at include AWS re:Invent, Microsoft Ignite, Google Cloud Next, KubeCon, Oracle OpenWorld, QCon, Strange Loop, Kafka Summit, Pulsar Summit, JavaOne, DevNexus, JFokus, JNation, and All Things Open.

Ricardo Ferreira presenting on the main stage at AI DevWorld
On the main stage at AI DevWorld

You can find my upcoming and past talks on my speaking calendar. Recordings also live on my YouTube channel, and the code I write for talks, demos, and workshops is on my GitHub.

Consulting and Professional Services

If you’d like to hire me as a consultant for your projects, speak at your event, or lead a hands-on workshop for your team, contact me at riferrei@riferrei.com. I can understand the scope of your request and provide a free estimate.

Who am I?

I work at the intersection of AI, data infrastructure, and distributed systems, turning complex technology into things developers can understand and products users love.

Lately, that means hands-on AI engineering: building vector search, semantic caching, agent memory, and RAG into the data layer, and figuring out how to make AI agents secure enough to ship. I contribute to open-source projects like LangChain4j and RedisVL for Golang.

The AI-native work isn’t a pivot. It draws on the same systems-design foundation I’ve built for 20+ years: designing data systems for scale, moving data fast, watching where systems break; now applied to vectors and agents. I have worked on RDBMS and Big Data at Oracle; event streaming with Apache Kafka and Apache Flink at Confluent; observability at Elastic; AI and developer tooling at AWS; and NoSQL and vector stores at Redis. That foundation is exactly what separates AI demos that work on stage from AI systems that survive production.

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