Posted by fokdelafons 1 day ago
The Problem: 95% of legislation goes unnoticed because raw legal texts are unreadable. Media coverage is optimized for outrage, not insight.
The Solution. I built a digital public infrastructure that:
1. Ingests & Sterilizes: Parses raw bills (PDF/XML) from US & PL APIs. Uses LLMs (Vertex AI, temp=0, strict JSON) to strip political spin.
2. Civic Algorithm: The main feed isn't sorted by an editorial board. It's sorted by user votes ("Shadow Parliament"). What the community cares about rises to the top.
3. Civic Projects: An incubator for citizen legislation. Users submit drafts (like our Human Preservation Act), which are vetted by AI scoring and displayed with visual parity alongside government bills.
Tech Stack:
Frontend: Flutter (Web & Mobile Monorepo),
Backend: Firebase + Google Cloud Run,
AI: Vertex AI (Gemini 2.5 Flash),
License: PolyForm Noncommercial — source is available for inspection, learning, and non-commercial civic use. Commercial use would require a separate agreement.
I am looking for contributors. I have the US and Poland live. EU, UK, FR, DE in pipeline, partially available. I need help building Data Adapters for other parliaments (the core logic is country-agnostic). If you want to help audit the code or add a country, check the repo. The goal is to complete the database as much as possible with current funding.
Live App: https://lustra.news
- Friend of mine is Albanian
- Albania wants to join the European Union
- They are required to ensure that their laws don't have "internal conflicts" e.g. one law says something is legal, a different law says it's illegal
- Reviewing by hand would take a lot of work
- Friend uses an LLM to analyze the Albanian laws and find any of these conflicts
Apparently it worked out pretty well
LLMs let you cover more ground but the fundamental problem of “who to trust” still remains. I don’t see how one can ever be used to strip political spin. It’s baked in.
1. No opinion space: the prompt forbids normative language and forces fact to consequence mapping only (“what changes, for whom, and how”), not evaluation.
2. Outputs are framed explicitly from the perspective of an average citizen of a given country. This narrows the context and avoids abstract geopolitical or ideological extrapolation.
3. Heuristic models over reasoning models: for this task, fast pattern-matching models produce more stable summaries than deliberative models that tend to over-interpret edge cases.
It’s not bias-free, but it’s more constrained and predictable than editorial framing.
Couldn't even pay people to read this literally
I think there needs to be like a military style debate globally on education levels it's that bad like actually that bad yeah
Here in Chicago
I'm dealing with probably a solid 70% of adults who don't know how to read correctly try fitting that into the LLM experience I don't know
As someone complaining about how people can't read, it may do you much benefit to learn how to write.