@mvanhorn@slashlast30days Thanks for this amazing work man. Really appreciate it. Commenting from my other account so that I can catch your amazing works from there as well. Keep up the good work.
@AnthropicAI@cursor_ai@zapier 3. Don't go single-model. Every credible independent test lands in the same place: route by workload.
4. ๐ช๐ฎ๐๐ฐ๐ต ๐ณ๐ผ๐ฟ ๐ฐ๐ฎ๐ฟ๐ฒ๐ณ๐๐น๐ป๐ฒ๐๐, ๐ป๐ผ๐ ๐ฐ๐น๏ฟฝ๏ฟฝ๏ฟฝ๏ฟฝ๐๐ฒ๐ฟ๐ป๐ฒ๐๐. One instance opened its own pages at phone width, caught an off-screen checkout button, and fixed it.
@AnthropicAI@cursor_ai@zapier 5. Benchmark on your own work first. ๐ฉ๐ฒ๐ป๐ฑ๐ผ๐ฟ ๐ฏ๐ฒ๐ป๐ฐ๐ต๐บ๐ฎ๐ฟ๐ธ๐ ๐ฎ๐ฟ๐ฒ ๐บ๐ฎ๐ฟ๐ธ๐ฒ๏ฟฝ๏ฟฝ๐ถ๐ป๐ด ๐ถ๐ป ๐ฎ ๐น๐ฎ๐ฏ ๐ฐ๐ผ๐ฎ๐.
I didn't build any of this. I read a launch page, three reviews and a benchmark table.
All of it published free by people doing harder work than reading.
@AnthropicAI@cursor_ai@zapier ๐ช๐ต๐ฎ๐ ๐'๐ฑ ๐ฎ๐ฐ๐๐๐ฎ๐น๐น๐ ๐ฑ๐ผ:
1. Stop defaulting to max effort. The effort dial is the product. Max isn't better, it's differently expensive.
2. Track cost-per-success, not accuracy. A 2% gain that doubles your bill is a subscription to your own ego.
@AnthropicAI@cursor_ai@zapier CodeRabbit ran it on code reviews.
Precision up (39.3% vs 35.2%). Nitpicks 4x. One config left 110.
More reasoning didn't produce a better review. Just a different failure mode.
๐ฌ๐ผ๐ ๐๐ฎ๐ป๐๐ฒ๐ฑ ๐ฎ ๐๐ฒ๐ป๐ถ๐ผ๐ฟ ๐ฒ๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ. ๐ฌ๐ผ๐ ๐ด๐ผ๐ ๐ฎ ๐ฐ๐ผ๐ป๐ณ๐ถ๐ฑ๐ฒ๐ป๐ ๐ผ๐ป๐ฒ.
@AnthropicAI@cursor_ai@zapier ๐ก๐ผ๐ ๐๏ฟฝ๏ฟฝ๏ฟฝ๏ฟฝ๐ฒ ๐ฑ๐ฎ๐ฟ๐ธ ๐ฝ๐ฎ๐ฟ๐.
They deliberately didn't train it on cyber tasks. It got good anyway: 79.4% at finding vulnerabilities, near-level with their top model's 80%.
At exploiting them: 4 successes vs 13.
They built a locksmith who faints at the sight of a crowbar.
@AnthropicAI@cursor_ai@zapier When a safety classifier trips, the request quietly falls back to the older model.
Which leaves a question hanging: if it's too risky for the smart one, why is it fine for the slightly-less-smart one?
๐ง๐ต๐ฎ๐'๐ ๐ป๐ผ๐ ๐ฎ ๐ด๐๐ฎ๐ฟ๐ฑ๐ฟ๐ฎ๐ถ๐น. ๐ง๐ต๐ฎ๐'๐ ๐ฎ ๐๏ฟฝ๏ฟฝ๐ฑ๐ฒ ๐ฑ๐ผ๐ผ๐ฟ.
@AnthropicAI@cursor_ai@zapier ๐ง๐ต๐ฒ ๐ป๐๐บ๐ฏ๐ฒ๐ฟ๐:
โข $5/$25 per million tokens โ same price as its predecessor, ~2x the performance
โข Within 0.5% of Anthropic's own flagship on CursorBench, at half the cost per task
โข 3x the next-best model on ARC-AGI 3
The frontier hit the value menu.
Anthropic shipped its ๐ฐ๐๐ต ๐ณ๐ฟ๐ผ๐ป๐๐ถ๐ฒ๐ฟ ๐บ๐ผ๐ฑ๐ฒ๐น ๐ถ๐ป ๐๐๐ผ ๐บ๐ผ๐ป๐๐ต๐.
The release notes have stopped reading like launches and started reading like proof-of-life videos.
"The model is fine. The model is improving. Please keep sending tokens."
Claude Opus 5 ๐งต
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