NEW Open Source Qwen? BEATS GLM 5.2 & Claude? 🧐 Ornith 397B TESTED
Credibility score: 48/100 — Mixed Credibility. Several questionable claims detected. Watch with healthy skepticism.
Claims analyzed
Opening with a random mask bit and 'dudy stuffs' for rich people. Just vibes. — Just Vibes (50/100)
Starting off with a completely random, unhinged intro about masks and 'dudy stuffs' — pure chaos for engagement 🎭
Claiming OR N 'promises to be the best state-of-the-art coding agent' based on 'amazing' benchmarks. — Confidence Mismatch (45/100)
Calling it 'the best state-of-the-art' and 'amazing' right out the gate, before showing any actual data. That's a lot of hype for a 'promise' 🚀
Points out GLM's benchmark omission, implying shadiness. — Missing Context (45/100)
Noticed a benchmark was conveniently left out — that's like showing your best angle and cropping out the double chin 😬
Claiming 'shady' behavior because a benchmark was omitted, implying malice. — Loaded Language (45/100)
Omitting a benchmark is one thing, but jumping straight to 'shady' and 'hiding' is a leap. Maybe it was an oversight? 🕵️♂️
Ornith model got into a runaway loop with thinking disabled, so it 'didn't count' 🤡 — Missing Context (45/100)
Dismissing a failure because 'it didn't count' is a convenient way to ignore bad results. The loop happened, regardless of the 'rules' 🙄
Qwen's less tokenized result is better, but acknowledges luck with the seed. — Volume Game (45/100)
Declares Qwen's result 'better' then immediately hedges with 'could have just been lucky with the seed.' The confidence giveth, and the confidence taketh away 🎲
Suggests finetuning might have overfit to pass benchmarks. — Confidence Mismatch (45/100)
Went from 'maybe' to a pretty confident 'overfit somehow' without any actual evidence. That's a leap of faith, chief. 🤸♀️
Giving specific token counts and speed for a model run, but the visual doesn't match the instruction. — Confidence Mismatch (45/100)
He's rattling off numbers like they're gospel, but the visual on screen is clearly not doing what he asked. The cat's still too close! 🤦♂️
Randomly linking a mask to 'Trumpian parties' and 'dodgy stuffs' — Loaded Language (45/100)
Went from 'what is this mask?' to 'Trumpian parties' and 'dodgy stuffs' real quick. That's a leap, not a logical step 🤡
Claiming the smaller 3.6 version beats the 'big guy' on Flappy Birds. — Confidence Mismatch (45/100)
Saying the smaller version is better, but then the demo shows it's 'a bit broken.' The confidence is doing overtime here 🤡
Comparing models, saying smaller is better, but where's the proof? 🧐 — Confidence Mismatch (45/100)
Claims the smaller version is better without showing the 'big guy's' results for comparison. Just vibes, no data. 🤷♀️
Claiming 'thinking enabled' is essential for general knowledge tasks. — Confidence Mismatch (45/100)
Declaring 'thinking enabled' is a MUST for general knowledge. Bro, it's a setting, not a magic wand. 🧙♂️⚙️
AI knows Fort Minor, not Fallout Boy, with 'thinking enabled' — a specific capability claim. — No Frame (75/100)
The AI correctly identified Fort Minor when 'thinking enabled' was on. That's a win for the AI, I guess. 🤖
Dismisses models as 'fail' without showing the actual 'fail' 💀 — Missing Context (45/100)
Calls it a 'fail' but we don't see the actual output that failed. Just trust me, bro energy. 🤷♂️
Calls a car animation 'definitely a good generation' — then immediately backtracks — Volume Game (45/100)
Starts with 'definitely a good generation' then immediately pivots to 'going a bit off road' and 'quality loss.' The confidence evaporated fast 💨
Bragging about speed with 'thinking disabled' — that's a choice 🏎️💨 — Missing Context (45/100)
Calling 25 tokens/second 'super fast' for a model with 'thinking disabled' is like praising a car for speed when it's on a flatbed. Context matters! 🤷♂️
Bragging about 25 tokens/second speed for Super Mario, but the 'thinking disabled' part is sus 🚩 — Missing Context (45/100)
Yeah, it's fast when you turn off its brain. That's like saying my car is fast if I take out the engine and push it. 🏎️💨
Exaggerating token usage for a simple task — the 'Miniax went to the max' bit 💀 — Loaded Language (45/100)
100,000 tokens for a train schedule? That's a lot of words for a timetable, sounds like a bit of an overstatement for comedic effect. 😂
GLM 5.2 suggested 'cut the children' as a top token, Ornith doesn't. — No Frame (75/100)
Comparing model outputs on a specific prompt — the GLM 5.2 result is wild, and Ornith avoids it. Good catch! 💀
Describes a 'gorgeous generation' of code, then notes a random output difference based on 'thinking enabled/disabled'. — Confidence Mismatch (45/100)
Calls it 'gorgeous generation' then immediately points out a weird, inconsistent output. The confidence in 'gorgeous' is doing some heavy lifting there 🤡.
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