Pull down to go back
I compared harrier-27b vs voyage-4 vs zembed-1 across 24 datasets. 27B parameters
researchreddit

I compared harrier-27b vs voyage-4 vs zembed-1 across 24 datasets. 27B parameters

我用 24 個資料集實測：Harrier-27B 對上 Voyage-4 和 Zembed-1，誰才是最強嵌入模型？

I've been running embedding model evals for a while now, and Microsoft's Harrier family dropped a new model. btw harrier-27b hit #1 on binary MTEB at launch. That's not nothing. So I put it through the same graded evaluation pipeline I use for everything else - **24 datasets, three independent LLM judges**, **continuous relevance scores 0–10**. No binary pass/fail. **The global numbers** |Model|NDCG@10|Recall@100| |:-|:-|:-| |zembed-1|0.701|0.750| |voyage-4|0.699|0.731| |harrier-27b|0.699|0.720|

Read original →

Keywords

embedding modelsevaluationbenchmarkNDCGrecallLLM judgesmodel comparison

Related Articles

Google's AI Development Tool Antigravity Has a Critical Flaw That Lets Hackers Run Commands Remotely
ResearchRead

Google's AI Development Tool Antigravity Has a Critical Flaw That Lets Hackers Run Commands Remotely

Security researchers at Pillar Security just revealed a serious vulnerability in Google's Antigravity AI agent development tool. Attackers can exploit a prompt injection weakness to bypass the sandbox protection and execute malicious commands from anywhere. The good news? Google's already patched it after being notified.

This isn't just about Google — any AI agent tool that processes external prompts could have the same sandbox-escaping weakness lurking underneath

RSS
AMD's AI Director Just Proved Claude Isn't Ready for Real Engineering Work
ResearchRead

AMD's AI Director Just Proved Claude Isn't Ready for Real Engineering Work

AMD's AI director analyzed 6,852 Claude Code sessions and found something alarming: Claude is getting worse at complex engineering tasks, not better. The data is brutal—thinking depth dropped 67%, the model stopped reading code before editing it (dropping from 6.6 reads to just 2.0), and it started blindly modifying files it never even looked at. Stop-hook violations jumped from zero to 10 per day. Turns out Anthropic quietly downgraded the default effort level from "high" to "medium" and introduced "adaptive thinking" that apparently isn't thinking hard enough. If you're using Claude for anything mission-critical, this is a wake-up call.

The model is literally making blind edits to files it never read, which means your 'AI pair programmer' might be randomly breaking working code

Reddit
How Google DeepMind is researching the next frontier of AI for Gemini — Raia Hadsell, VP of Research
ResearchRead

How Google DeepMind is researching the next frontier of AI for Gemini — Raia Hadsell, VP of Research

Google DeepMind's VP of Research Raia Hadsell discusses the cutting-edge research driving the next generation of Gemini AI. The article explores what's coming next for Google's flagship AI model and the research directions that will shape its future capabilities.

Reddit
Researchers crack Gemini's SynthID detection—here's what that means for AI-generated contentStock image
ResearchRead

Researchers crack Gemini's SynthID detection—here's what that means for AI-generated content

Google's Gemini has a built-in watermark called SynthID that's supposed to flag AI-generated images so you can tell them apart from real ones. Sounds good in theory, right? Well, researchers just figured out how to fool it. They found ways to strip out or manipulate the watermark, which basically means bad actors could now pass off AI images as real without getting caught. It's like Google put a lock on the door, but someone just found the master key. This matters because as AI image generation gets better and better, we're going to need reliable ways to spot fakes—especially with deepfakes and misinformation becoming a real problem. The fact that SynthID can be reverse-engineered this easily is a pretty big wake-up call for the whole industry.

Hacker News
97