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We test AI tools and share what we learn. Unfiltered. Unsponsored.
Research notes, tool reports, and experiment logs from the lab floor.
We ran both models through 47 real-world tasks across code generation, analysis, and creative writing. The results surprised us — and changed how we think about model selection.
Bigger isn't always better. We tracked token efficiency across five models and found that 128k context windows introduce subtle reasoning degradation after the 40k mark in most production scenarios.
The new multi-file editing is transformative. But the agent mode still hallucinates file paths 12% of the time. We built a benchmark to track this across updates.
Skip LangChain. Skip LlamaIndex. Here's a minimal RAG setup with just an embedding model, a vector store, and 40 lines of Python. Production-ready patterns included.
Hypothesis: with proper fine-tuning and constrained decoding, a small model can outperform GPT-4 on JSON extraction tasks. We ran 1,200 test cases to find out.
After testing eleven voice synthesis APIs, we're convinced the uncanny valley is gone. The implications for customer service, accessibility, and content creation are staggering.
Like a species ID card, but for software. Complete breakdown of capabilities, pricing, quirks, and real-world performance.
Raw observations with margin annotations. What we noticed, what surprised us, what we'd test next.
Like laminated lab instructions. Copy-paste ready, no fluff, tested on fresh environments.
Hypothesis, setup, result, next test. The scientific method applied to AI tooling.
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