EchoBridge

Welcome to the future of news with EchoBridge. We have often been denied the nuanced, balanced, and thoughtful narratives we crave. The complex process of distilling large documents into meaningful stories was often too expensive, resulting in an oversimplification or sensationalization of information. However, with EchoBridge, we leverage advanced AI and large language models (LLMs) to revolutionize this process, making the production of unbiased, engaging narratives both affordable and efficient.

EchoBridge isn't merely an AI-driven solution; it's a catalyst for a new era in journalism. By harnessing AI, we can take complex documents like legislative bills and transform them into narratives that are not only factual but also provide meaningful insight into how these issues will impact our lives. In an era where information is abundant but comprehension is often limited, EchoBridge seeks to bridge this gap, giving us the comprehensive understanding we need, at a cost society can afford.

Deliverables
The final deliverable has two low-friction components:

• A substack that people can subscribe to.
• A set of prompts that will be open-source available.

LLM and entirely low-code usage
We used Anthropic's Claude model with a 100k token context window.
Its unique capabilities make EchoBridge possible.
We use MacWhisper to transcribe user interviews.
We use Claude-100k to deliver the project to curious experienced end-users.
We use Replit to convert PDF to text. We use Substack, a low-code way of delivering to a maximal amount of end-users.
We use ChatGPT-4 in some way in nearly every aspect of the project to describe, create and present all what you are seeing here today.

Footnote: To Describe this to others
To make matters short, the chain of journalists and experts and interpreters that go from the source doc all the way to whatever news you're actually reading ends up creating a game of telephone that amplifies the extreme aspects of the opinion in the bias and really downplays the core important points from the source text. We use a LLM to provide unbiased and relatable narratives, that allows people to have a baseline understanding of what's actually there.

So instead of reacting to the reaction, they're actually able to engage with and interpret complex documents in a way that emphasizes understanding rather than just emotional outbursts.

Built With

• anthropic's-claude-with-100k-token-context-window
• chatgpt-4
• macwhisper
• replit
• substack