How Netflix Slashed Database Read Times from Seconds to Milliseconds

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Netflix Casandra

Netflix engineers detailed how they handle wide partitions in Apache Cassandra for the TimeSeries Abstraction. Two approaches work together: Time Slice re-partitioning tunes future partitions at the table level, while dynamic partitioning detects and splits oversized partitions per TimeSeries ID on the read path. Detection runs via byte counting and Kafka, splits are checksum-validated, and Bloom filters route reads to parallel child partitions. Average read latency dropped from seconds to low double-digit milliseconds, with 500MB+ partitions staying available.

The post Netflix AI Team Cuts Wide-Partition Read Latency from Seconds to Milliseconds by Splitting Cassandra Partitions Per ID appeared first on MarkTechPost.

Kimi Code CLI: AI Coding Agent by Moonshot AI

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Moonshot AI

  Moonshot AI Just Dropped a Coding Robot for Your Terminal 🚀 Imagine having a super-smart friend who lives inside your computer’s black command screen — the scary one most people avoid — and that friend can write, fix, and manage code for you. That’s basically what Moonshot AI has built with Kimi Code CLI. … Read more

NVIDIA Nemotron 3.5 ASR: Real-Time Speech in 40 Languages

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Nvidia Nemotron ASR

NVIDIA Nemotron Just Dropped a Speech AI That Understands 40 Languages at Once — No, Really Imagine hiring a translator who speaks 40 languages, never needs a coffee break, and can write down everything you say while you are still saying it. That is basically what NVIDIA just built. Meet Nemotron 3.5 ASR — a … Read more

DeepSeek’s DSpark Makes AI Text Generation Up to 85% Faster with Speculative Decoding

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DeepSeek open-sourced DSpark, a speculative decoding framework that attaches a draft module to existing DeepSeek-V4 weights. It pairs a parallel draft backbone with a lightweight Markov head to cut suffix decay, then adds confidence-scheduled verification that tailors how many tokens get checked to real-time GPU load. Offline, accepted length rises 16–31% over DFlash and Eagle3; in production it speeds per-user generation 57–85% over the MTP-1 baseline, losslessly. The training repo, DeepSpec, ships under MIT.

The post DeepSeek Releases DSpark, a Speculative Decoding Framework That Accelerates DeepSeek-V4 Per-User Generation 60–85% Over MTP-1 appeared first on MarkTechPost.

Grok Build’s /goal Mode: The AI Coding Agent That Plans, Executes & Verifies Itself

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xAI

xAI introduced /goal in Grok Build, a mode for long-running, autonomous task execution. You hand off one objective, and the agent plans an approach, executes a progress checklist, and verifies the result until the goal completes.

The post xAI Launches /goal in Grok Build, Adding Long-Running Autonomous Execution With Built-In Verification for Multi-Step Coding Tasks appeared first on MarkTechPost.

NVIDIA Canary-1B-v2: AI Speech Recognition & Multilingual Translation Explained

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In this tutorial, we build a multilingual ASR and speech translation pipeline with NVIDIA Canary-1B-v2. We load the model on a GPU-enabled runtime, prepare audio into 16 kHz mono, and run English ASR. We then translate speech into French, German, Spanish, and Italian, and extract word and segment timestamps. We export translated subtitles as an SRT file, test long-form transcription, run batch processing, and benchmark inference speed.

The post How to Use NVIDIA Canary-1B-v2 for ASR, Translation, and Automatic SRT Subtitle Export in Python appeared first on MarkTechPost.

NVIDIA SkillSpector: AI Security Scanning for Safe Skill Deployment

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In this tutorial, we use NVIDIA SkillSpector to evaluate AI skills for security risks before deployment. We build a corpus of benign and deliberately vulnerable skills, then scan them through SkillSpector’s programmatic LangGraph workflow. We organize the risk scores and findings with pandas, then visualize severity and category distributions. We export results in SARIF format, register a custom analyzer, and optionally apply an LLM-based semantic pass.

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