Hi HN,

I’m the co-founder of Neutral News AI: a site that tries to answer a simple question:

“What actually happened here, across multiple biased sources, and can we check the claims against the original articles?”

Link: https://neutralnewsai.com Analyzer: https://neutralnewsai.com/analyzer No signup needed to read the news or run a basic analysis.

What it does

• Crawls multiple outlets (left / center / right + wires / gov sites) for the same story.

• Generates a short, neutral summary constrained to those sources (no extra web search).

• Extracts atomic claims (events, numbers, quotes) from the draft.

• Uses an MNLI model to test each claim against the underlying articles:

• entailment → “Supported”

• contradiction → “Refuted”

• neutral → “Inconclusive”

• Surfaces a “receipt ledger” per article: claim text, verdict, quote, source, timestamp.

• Exposes the underlying models on an Analyzer page where you can paste any URL and get:

• political bias score,

• sentiment / subjectivity,

• readability metrics,

• a rough credibility signal.

Stack and models

• Backend: Python, PostgreSQL.

• Crawling / aggregation: scheduled scrapers + RSS + manual curated source lists.

• Bias / propaganda detection: transformer-based classifiers fine-tuned on public political news datasets, plus some hand-engineered features (e.g., source-level priors, readability, sentiment). In offline tests I get 93% accuracy on bias detection(happy to share more detail if people care).

• Claim extraction: sentence segmentation + a lightweight classifier to label check-worthy clauses (counts, quotes, time-bound events, entity claims).

• Fact-checking: MNLI model (currently DeBERTa-based) over (claim, evidence-passage) pairs with some heuristics to merge multiple snippets.

• Frontend: Angular + server-rendered news pages for speed and SEO.

The methodology is documented here with more detail:

https://neutralnewsai.com/methodology

What I’m unsure about

• How far I can push MNLI-style models before needing a more explicit retrieval-augmented system or custom architectures.

• Whether my current claim extraction approach is good enough for high-stakes use, or if I should move to a more formal information extraction pipeline.

• How to expose uncertainty and failure modes in a way that’s actually useful for non-technical readers.

Why I’m posting

I’d like feedback from this community on:

• ML / NLP choices you strongly disagree with.

• Evaluation: what would be a more convincing test suite or benchmark?

• UI/UX for showing “supported/refuted/inconclusive” without overselling model confidence.

I’m very open to critique. If you think this is conceptually wrong or socially dangerous, I’d also like to hear that argument.

Thanks for reading, Marcell