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Posts in categories: "Research" (12 posts)
We swapped a 70B chat classifier for a typed-decision model and got 7x the speed at higher accuracy. The result worth writing down was the one where both models scored exactly the majority-class baseline.
Read More about The Question a Classifier Cannot AnswerSeven of eight major agent benchmarks were driven to near-perfect scores without solving a task — and every one of those runs would have passed a signature check. The unit of trust is not the number. It is the manifest, and a manifest has to be able to state its own weakness.
Read More about What a Benchmark Has to Prove About ItselfA Rust daemon on one MacBook published a dozen sites a day. The same pipeline now runs on Cloudflare, unattended, and publishes about a thousand.
Read More about WWW-RAG: Making the Open Web Chattable, From One MacBook to CloudflareAn e-commerce assistant linked products correctly but recommended the wrong ones. The fix was discovering that decoration, ranking, and recommendation are three different problems — and that prominence is only ever a tiebreaker on relevance.
Read More about Prominence Is Not Relevance: Teaching a Storefront Assistant What to RecommendWe swapped OpenParse for LiteParse in our RAG ingestion pipeline. The headline is 20–50× faster parsing. The useful part is the four ways we got it wrong first.
Read More about We Made Our RAG Pipeline Parse PDFs 20–50× FasterOpen-source Headroom compresses RAG context with an ONNX model. We wired it into our pipeline and raced it against a 50-line in-process extractor. Neither won outright.
Read More about We Tested Headroom Against Our EXIT RAG Compressorz-lab's DFlash drafter on our QLoRA fine-tune captured 92% of the published speedup with no retraining. ~15x faster, ~4x cheaper in prod.
Read More about Speculative Decoding for Free: Pairing DFlash with our DFO-Tuned Gemma 4 31BTwo natively-trained 1-bit LLMs converge on the same activation anomaly without ever sharing weights. A note on convergence under pressure.
Read More about The Two Models That Never Met. Both Measured at the Same Depth.Two natively-trained 1-bit LLMs lose the four-stage circuit that organizes fp16 transformers. Behavior survives; structure dissolves.
Read More about When the Circuit DissolvesA 200-item RAG arena tied at the mean, but two LLM judges only agreed at Spearman ρ=0.55. They aren't measuring the same thing.
Read More about Inside the RAG Arena: When the Judges Don't AgreeA vIndex patch deletes 'Paris is the capital of France' from a frozen LLM. The receipt is a 100-byte JSON file with a SHA-256 checksum.
Read More about Deleting Paris from a Language ModelEvery modern LLM converges on the same four-stage circuit. We map the architecture and what its absence in 1-bit models means.
Read More about The Architecture Every Language Model Converges To
