» Tag
machine-learning
185 postsGenRec: Netflix Moves Toward LLM-Native Recommendation Systems
Netflix's GenRec reframes recommendation as generative language modeling, signaling a shift from ranking pipelines to LLM-native architectures.
Netflix GenRec: An LLM-Native Ranker Beyond Feature Engineering
Netflix's GenRec replaces feature engineering with an LLM-native ranker, matching production performance using far less labeled data.
LeRobot v0.6.0 Ships World Models, Reward Models, and New VLAs
LeRobot v0.6.0 adds world model policies, new VLAs, a reward models API, six benchmarks, and faster dataset loading for robot learning.
CommitBrief — AI code reviews, right in your terminal
A provider-agnostic, local-first CLI that reviews your staged changes, a historic range, or a whole GitHub pull request. Zero telemetry, no server. Free and open source.
commitbrief.comAi2's OlmoEarth Platform scales geospatial AI inference to continents
Ai2's OlmoEarth Platform runs geospatial AI inference across continents using partitioned CPU/GPU pipelines and custom metadata indexing at scale.
llama.cpp Adds Lossless F32 Compression via QFX32/QFX16
llama.cpp PR adds QFX32/QFX16 GGUF formats that losslessly compress F32 models by 2.05x with bit-identical weight reconstruction.
SDABench: A New Benchmark Testing LLMs on Scientific Discovery
SDABench evaluates LLMs on six scientific capabilities beyond code execution, exposing major gaps in assumption selection and mechanistic reasoning.
Does the Harness Come Before Pretraining? A Data Flywheel View
An analysis of how AI agent harness design and pretraining are interdependent, shaping data flywheels and model biases in coding agents.
How Airbnb Cut LLM Evaluation Time From Weeks to a Day
Airbnb's four-layer LLM evaluation stack uses deterministic caching and micro LoRA adapters to cut iteration time from weeks to a single day.
FlowOptimizer: Learning to Optimize via Unfolded Flows
MIT and Boston University researchers unveil FlowOptimizer, a flow-based learning-to-optimize framework that outperforms classical and learned optimizers by orders of magnitude.
SensorFM: A Foundation Model for Wearable Health Data
SensorFM is a large sensor foundation model trained on over a trillion minutes of wearable data from five million people, predicting cardiovascular, sleep, metabolic and mental health outcomes.