DeepSeek just open-sourced their ENTIRE production inference stack 🏭 The whole factory: 🔥 DeepInfer — production inference engine (Apache 2.0) • 25K tok/s/GPU decode on 64 GPUs • DSpark speculative decoding • NVIDIA SM100 + Ascend 950 NPU 🛠️ deepseek-recipe — official tokenization matched to API behavior Plus the stack they already dropped: ⚡ FlashMLA, DeepGEMM, DeepEP, 3FS, DeepSeek Harness, Engram #OpenSourceAI #DeepSeek #LLM #AIInfrastructure

A weaker model with a better harness beat a stronger model with its original harness. GLM-5 + Harness²: 75.7 Gemini 3.5 Flash as shipped: 72.4 WHAT'S HARNESS² The model is a chef. The skill is trained in. You can't change it. The harness is the kitchen around him: where the recipes sit, how the knives are laid out, the prep list, how the sous-chefs coordinate, what gets cooked first. Same chef. Different kitchen. Very different dinner service. That's the 75.7 vs 72.4. A slightly worse chef in a well-organized kitchen beats a better chef in a messy one. Until now, a human stood next to the chef, watched him fail, and rearranged the kitchen. Slow, expensive. Harness² lets the chef rearrange it himself, mid-service. The hard part: a new dish has no answer key. How do you know the new layout is better? So it cooks the same dish twice, two deliberately different layouts, and compares the process. Not "does it taste right." "Did it stop getting stuck." The square in the name is the second level: after service, it writes down "knives on the left worked better," and reads that note before the next one. Rearranging the kitchen is one level. Rearranging how you rearrange it is the second. The counterintuitive part. Let the chef rearrange freely, no comparison, no notes, and the food gets worse. 73.4 → 63.2. The point isn't that it edits itself. It's that it compares before it edits. #harness #Google