DField SolutionsMérnöki stúdió · Budapest
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AI news aggregator with bias scoring · launch to 12k MAU in 8 weeks

Built a news aggregator that pulls multiple sources, fact-checks with AI, and scores bias. Launched to 12k MAU in 8 weeks, zero moderation incidents.

Timeline8 weeks
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Reviewed by
0112,000 MAU in week 8.
02Bias-score eval accuracy 91% on the gold set.
03Zero moderation incidents in the first 3 months.
04Featured in 3 Hungarian tech press outlets.
The problem01 / 03
  • 01News aggregation is a commodity · needed differentiation via verifiable bias scoring.
  • 02LLM-based bias scoring had to be auditable, not opaque.
  • 03Multi-source ingestion had to respect every publisher's robots + rate limits.
  • 04Launch window was fixed at 8 weeks; no content until the aggregator was live.
The solution02 / 03
  • 01Article-level bias score with explainability · model outputs the 3 sentences that most influenced the score.
  • 02Source verification via redundant retrieval · at least 3 sources must corroborate a claim for a 'verified' badge.
  • 03Rate-limited ingestion pipeline with publisher-specific backoff.
  • 04Evals-as-code pipeline with 400+ labelled articles as gold set.
The outcome03 / 03
  • 0112,000 MAU in week 8.
  • 02Bias-score eval accuracy 91% on the gold set.
  • 03Zero moderation incidents in the first 3 months.
  • 04Featured in 3 Hungarian tech press outlets.
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