# Signal vs. Noise: How We Curate Tech News with AI > How nextbig.dev's agent pipeline scores 300+ sources for builder relevance and drops the rest. - Published: 2026-02-05 - Author: Oday Brahem - Canonical URL: https://www.nextbig.dev/blog/signal-vs-noise-how-we-curate-news-with-ai Every day, thousands of tweets, blog posts, and announcements flood the tech ecosystem. Most of it is noise: recycled takes, engagement bait, and press releases disguised as news. **We built a system to find the 1% that actually matters to builders.** This is the story of how nextbig.dev's AI curation pipeline works, what we got wrong, and what we learned building a system that reads the internet so you don't have to. ## The Problem with Tech News If you're a builder (someone actually shipping products), your relationship with tech news is complicated. You need to stay informed. You can't afford to miss a major API change, a funding shift that affects your market, or a security vulnerability in your stack. But you also can't spend two hours a day scrolling X and Hacker News. The existing solutions weren't great: - Algorithmic feeds (X, LinkedIn) optimize for engagement, not relevance. You get outrage and hot takes, not signal. - Newsletters are written by humans with opinions and blind spots. Great for perspective, bad for comprehensive coverage. - RSS readers give you everything from your sources, with no ranking or filtering. Firehose, not filter. We wanted something different: a system that combines the breadth of algorithmic aggregation with the judgment of a well-informed editor, but runs 24/7 without getting tired or developing biases toward clickbait. ## How the Pipeline Works ### Source Selection: The 186 Everything starts with source curation. We manually vetted 186 X accounts across six tiers, from tier-1 publications (TechCrunch, The Verge, Wired) down to tier-6 niche builders and hardware accounts. Each tier has different fetch frequencies: the top sources are checked every cycle, while lower tiers rotate to manage API costs. This is the most human-intensive part of the system, and deliberately so. Garbage in, garbage out. No amount of AI scoring can compensate for following the wrong people. ### The Fetch Cycle Three times daily (7 AM, noon, and 6 PM UTC), our scheduler fires. It pulls recent tweets from each tier, extracts any linked URLs, and resolves them to their final destinations. We filter out known non-article domains (shopping sites, social media, media platforms) because a tweet linking to a YouTube video isn't a news article. ### AI Scoring: The Judgment Layer This is where it gets interesting. Every candidate article gets scored by Claude Haiku on a 1-10 scale across multiple dimensions: - Relevance: Is this actually about technology, AI, or the builder ecosystem? - Substance: Does it contain real information, or is it just an opinion/reaction? - Timeliness: Is this breaking news, or a rehash of something from last week? - Builder impact: Would this change how a builder thinks about their work? Articles scoring below 6 get filtered out. The rest get categorized into our five buckets: AI, Dev, Startups, Security, and Tech. ### Freshness-Weighted Ranking Raw engagement numbers are misleading. A tweet from Elon Musk will always out-engage a detailed technical breakdown from a security researcher. But the security breakdown might be far more valuable to our audience. Our ranking formula balances engagement with recency: score = engagement / (hours_old + 2)^1.5. This means a moderately-engaged recent article can outrank a heavily-engaged older one. Fresh signal beats stale virality. ## The Daily Briefing: AI as Editor Every morning at 6 AM UTC, our most ambitious agent kicks in. Claude Opus takes the previous 24 hours of scored, categorized articles and writes a complete editorial briefing: - Hero story: The single most significant development, with 2-3 paragraphs of original analysis explaining why it matters, beyond what happened. - Section roundups: Stories grouped by category, each with a concise summary that captures the key takeaway. - Quick hits: Lower-priority items condensed to one-liners for scanning. - Takeaway: An editorial observation that connects the day's themes, the kind of insight you'd get from a thoughtful colleague, not a RSS aggregator. We then generate a hero image via DALL-E and a two-host audio version using Deepgram's text-to-speech, creating a podcast-style briefing with distinct voices. ## What We Got Wrong Building this taught us some uncomfortable lessons: **AI scoring has blind spots.** Early versions over-indexed on AI news and under-indexed on security stories. The model had a subtle bias toward topics it "understood" better. We fixed this by adding category-specific scoring criteria and minimum category quotas. **Context windows aren't infinite.** Our first briefing generator tried to send every article to Opus. At 50+ articles with full descriptions, we hit context limits and got truncated output. We now cap input at the top 35 articles by score and send concise metadata, not full text. **Engagement isn't quality.** We initially weighted raw engagement too heavily. The result was a feed dominated by drama and dunks. The freshness-weighted formula was our solution, but it took three iterations to get right. ## The Philosophy The hardest part of building this system wasn't the engineering. It was deciding what "good curation" means. We landed on a simple principle: **if a builder reads one thing today, would this be worth their time?** AI doesn't replace editorial judgment. It extends it. Our 186 source accounts represent a human judgment about who's worth listening to. The scoring criteria represent a human judgment about what "relevance" means. The AI just applies those judgments at a scale no human editor could sustain. That's the real lesson: the best AI systems don't remove humans from the loop. They amplify human judgment to superhuman scale. --- Cite as: "Signal vs. Noise: How We Curate Tech News with AI" — nextbig.dev, https://www.nextbig.dev/blog/signal-vs-noise-how-we-curate-news-with-ai