Opus 4.7 consumes approximately 1.3 times as many tokens. The instructions must be very precise. Many are complaining about a "rushed release." In the Bullshit Benchmark, it performs worse than Opus 4.6. The mood is very mixed. Anthropic may have done OpenAI a big favor with
LLMS
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Levangie Labs Enhances Anthropic with Cognitive Architecture
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That isn't the only example. Levangie Labs built a cognitive architecture that sits on top of Anthropic and greatly improves it.
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Opus 4.7 Update Reception: Mixed User Sentiment Analysis
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The mood regarding the Opus 4.7 update has shifted. If I had to guess, I'd say 60% are disappointed with the latest update, while 40% are positive. I'm still undecided myself. Here's a good summary from someone on Reddit. What's your take on it so far?
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Data Viewer Tool Compares 130+ AI Model Variants
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Data viewer is very nice you can see 130+ model variants yourself https://
petergpt.github.io/bullshit-bench
mark/viewer/index.v2.html
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Anthropic Engineer Releases 14-Minute Masterclass on Building Agents
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🚨 This is absolute GOLD.
— Charly Wargnier (@DataChaz) 17 avril 2026
The @AnthropicAI engineer who literally wrote "Building Effective Agents" just dropped a 14-minute masterclass.
saves you months of headaches trying to figure this out alone.
bookmark for the weekend + read @Av1dlive's great guide below 👇 https://t.co/h5TbVmFuEN pic.twitter.com/e4GzZtj9RfThis is absolute GOLD. The @AnthropicAI engineer who literally wrote "Building Effective Agents" just dropped a 14-minute masterclass. saves you months of headaches trying to figure this out alone. bookmark for the weekend + read @Av1dlive
's great guide below -
LLM vs RAG vs AI Agent vs MCP comparison
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#LLM vs. RAG vs. #AIAgent vs. MCP
by @Python_Dv #GenerativeAI #ArtificialIntelligence #MachineLearning #ML -
Opus 4.7 Underperforms 4.6 on BullshitBench Testing
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BullshitBench: Opus 4.7 did WORSE than Opus 4.6 family. The 'Max' thinking version did worse than non-thinking – 74% 'pushback' vs 83% for non-thinking. As always, code, data etc is on github
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Tokenizer Switching: Computational Efficiency in LLM Operations
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It's possible but switching to a new tokenizer is actually very cheap and easy, so it'd be a big waste of compute
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Anthropic’s Safety Filters Align with Their Long-Term Vision
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Anthropic explicitly built around safety from the start. Stronger filters are consistent with what they always said they would do. Being surprised by it now means you weren't reading what they were publishing three years ago.
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Anthropic’s Incremental Releases and Marketing Mismatch
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Incremental releases feeling incremental is not a scandal. The problem is Anthropic's marketing implies otherwise and then users feel misled.