Using Deep Learning to Humanize Social Media Automation – Our Approach with Linkeme
Hi everyone,
We’ve been working at EasyLab on a project called Linkeme, a deep learning-based platform that automates and personalizes social media communication.
Most existing automation tools focus on scheduling and repetitive posting. They work well for timing, but often fail to generate content that actually resonates with real people. Our goal with Linkeme is different: we’re building a system that understands tone, adapts to context, and generates more relevant and natural content — at scale.
Under the hood, Linkeme uses custom fine-tuned LLMs to generate content adapted to specific industries, voices, and current events. We also leverage multi-modal inputs (text, topics, previous posts, engagement patterns) and integrate them into a deep learning pipeline that refines suggestions before publication.
Here are a few things we’re exploring:
- Instruction tuning to control voice/tone across different social platforms
- Lightweight adapters (LoRA/PEFT) to create client-specific models without full retraining
- Engagement prediction models to anticipate how a post might perform based on topic/formulation
We’re launching publicly on May 7th on Product Hunt, and we’re particularly interested in feedback from people who work on NLP, content generation, and applied deep learning.
If anyone here is experimenting with similar challenges (LLMs for copywriting, social content optimization, or attention modeling in NLP pipelines), I’d love to connect and exchange insights.