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TLDR YaFF is Yandex’s open-source zero-copy wire format for Protobuf — Apache 2.0, currently C++, v0.1.0. The .proto file stays the source of truth; only the physical memory layout changes. On Yandex’s benchmarks, the Flat Layout reads hot data ~3.8× faster than FlatBuffers, within 1.2× of a raw C++ struct. Four layouts — Fixed, Flat, […]
In this tutorial, we build an end-to-end forecasting workflow with TimeCopilot. We prepare a panel dataset containing real airline passenger data and a synthetic seasonal series with injected anomalies, then evaluate a diverse collection of statistical, foundation, and optional GPU-based forecasting models. We use rolling cross-validation and multiple error metrics to identify the strongest model,
NVIDIA Research has released SpatialClaw, a training-free framework for spatial reasoning. It targets a persistent weakness in vision-language models (VLMs). These models still struggle to judge where objects are, how they relate, and how they move in 3D. SpatialClaw does not retrain the model. Instead, it changes the action interface the agent uses to call
While recent breakthroughs in AI reasoning have largely been driven by massive scale, pouring in billions of parameters to cross complex cognitive thresholds—VibeThinker-3B is charting a completely different path. Created by researchers from Sina Weibo Inc (China), this 3-billion-parameter model proves that efficiency can punch far above its weight class. Released under an open-source MIT
This week, Liquid AI released two new retrieval models. They are LFM2.5-ColBERT-350M and LFM2.5-Embedding-350M. Both hold 350M parameters. Both are the first bidirectional members of the LFM family. They build on LFM2.5-350M-Base, released in March. The pair targets fast multilingual and cross-lingual search across 11 languages. Their footprint is small enough to run almost anywhere.
In this tutorial, we implement an end-to-end workflow for Salesforce CodeGen. We load a CodeGen model from Hugging Face, prepare it for code generation, and use it to generate Python functions from natural-language prompts. We then move beyond basic inference by adding function extraction, syntax checking, static safety checks, unit-test-based validation, best-of-N candidate reranking, multi-step
Most AI memory remembers the user. It stores your preferences, your tastes, and your role. Perplexity is taking a different path. Today, Perplexity launched Brain, a self-improving memory system for its agent product, Computer. Brain does not focus on remembering you. It remembers what the agent did. That reframes what memory in AI is for.
Long-context large language models (LLMs) face a memory bottleneck that has nothing to do with model weights. During decoding, transformers cache the key and value (KV) vectors for every token at every layer so they don’t have to recompute attention. This cache grows linearly with sequence length and batch size, and at long context with
The KV Cache Compression Race: TurboQuant vs OSCAR vs EpiCache Read More »
Most biology benchmarks ask narrow, fact-based questions with clean answers. Scientists weigh imperfect evidence and make decisions. OpenAI released LifeSciBench and it targets that gap directly. Even the strongest model passes roughly one task in three. The benchmark is far from saturated. What is LifeSciBench LifeSciBench contains 750 expert-authored tasks. They span seven workflows and
In this tutorial, we explore how NVIDIA SkillSpector helps us evaluate AI skills for security risks before they are used in real-world workflows. We build a controlled corpus containing both benign and deliberately vulnerable skills, scan them through SkillSpector’s programmatic LangGraph workflow, and organize the resulting risk scores and findings with pandas. We then visualize
