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I Built an Open-Source Multi-Agent Fact-Checker — Here's How It Works

Problem Statement We have a misinformation problem. But more specifically, we have a speed problem. A journalist spots a suspicious claim. They search for sources. Cross-reference databases. Call experts. Write a verdict. Get it edited. Publish, maybe 6 hours later. Maybe 3 days later. Meanwhile, the original claim has been screenshot, reposted, quoted in newsletters, and cited in arguments across five platforms. I wanted to build something that closed that gap. Not a chatbot that guesses. A proper pipeline, one that retrieves real evidence, reasons from it, and tells you why it reached a verdict. That's what Sift is. What is Sift? Sift (Source Inspection & Fact-checking Tool) is an open-source multi-agent AI pipeline that takes any text, extracts every factual claim, retrieves grounded evidence, and returns auditable verdicts — TRUE, FALSE, or UNCERTAIN, with cited sources and full reasoning chains. Paste a news article. A politician's speech. A viral statistic. A WhatsApp forward. Sift breaks it into individual claims and fact-checks each one independently. Why Multi-Agent? The naive approach is to ask an LLM: "Is this claim true?" The problem: LLMs hallucinate. They have knowledge cutoffs. They're confidently wrong in ways that are hard to detect. And critically, they don't show their work. A single LLM call can't reliably handle the full pipeline of: Extracting structured claims from noisy text Retrieving dated, traceable evidence from live sources Reasoning across conflicting evidence without confabulating Adversarially reviewing its own conclusions for overconfidence Finding corrections when something is wrong Each of these is a distinct task that benefits from its own prompt, its own tools, and its own failure modes. That's why I built five separate agents, orchestrated with LangGraph. The 5-Agent Pipeline Agent 1 — Claim Extractor A single paragraph can contain 4-5 distinct factual claims. Generic LLMs miss them or conflate them. This agent uses LLaMA 3.3 70

2026-05-28 原文 →
AI 资讯

The Sovereign Privacy Illusion: Why GDPR Compliance Doesn’t Equal Data Control

When regulation becomes theater and encryption becomes window dressing By Vektor Memory — 20 min read It is raining here in the Southern Hemisphere again. It has been raining for three weeks now, nonstop. I’m sitting with my chai coffee, watching out of the window, and thinking about data sovereignty. It is, genuinely, the kind of thing I think about often. The northern hemisphere is winding up for summer. Europe is getting ready for long evenings and beach holidays. I’m quietly jealous. I’ve always wanted to split the year: six months south, six months north. Endless summer. The perpetual warmth of a life lived chasing the sun. But here I am. Chai. Rain. Data. I’ve been turning over one question in particular: why is it that the moment you mention data sovereignty, people immediately reach for GDPR? It’s reflexive, especially among Europeans. Understandable. GDPR is loud, it’s enforced, it has teeth. French, German, and Dutch visitors make up a large disproportionate share of our site traffic at VEKTOR, and the interest in privacy and sovereignty from that audience is intense and genuine. Northern Europeans, by and large, take this seriously in a way that other markets don’t; they are working on ways to disassociate from the cloud around the world. And yet. How many times have we clicked “Accept All” on a cookie banner in the last week? How many times have you scrolled past a privacy policy that runs to forty-two pages? How many times have you handed over your email address, your location, your device fingerprint, your behavioral patterns not because you wanted to, but because there was no meaningful alternative? GDPR created the most sophisticated legal architecture for data rights the world has ever seen. It also created the most sophisticated ritual of consent theater the world has ever performed. That gap, between the law and the lived reality, is what this article is about. Ubiquitous data centre growth image The Reflex Problem When people think of data sovere

2026-05-28 原文 →
AI 资讯

How LLMs Work, Part 1: How LLMs Process Text

I am a software developer who has been using LLMs extensively at work. I wanted to develop a foundational understanding of LLMs, but have no background in machine learning or statistics. So, I started to read and take notes with the goal to eventually write up a developer's guide to the foundations of LLMs. The article kept growing, so I have split it into four parts. This is the first in the series. Hope this helps! submitted by /u/Normal-Tangelo-7120 [link] [留言]

2026-05-28 原文 →
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