Results for “dlt”

Guides the setup and use of dbt for extract-transform-load workflows.

Detect DLL side-loading attacks where adversaries place malicious DLLs alongside legitimate applications to hijack execution flow for defense evasion.

Lossless LLM-optimized compression of source documents. Use when the user requests to 'distill documents' or 'create a distillate'.

Detects insider data exfiltration by analyzing DLP policy violations, file access patterns, upload volume anomalies, and off-hours activity in endpoint and cloud logs using pandas for behavioral analytics and statistical baselines.

Dart static analysis, linting, formatting, and code-generation standards. Use only for analysis_options.yaml, build_runner, dart format, DCM, lefthook, or analyze/format CI failures; defer Dart language, null-safety, Flutter tests, and generic CI questions.

Configure Kotlin build and quality tooling, including Gradle Kotlin DSL, Version Catalogs, KMP, ktlint, Detekt, MockK, coverage, and quality plugins. Use for Kotlin-specific build, lint, test-tool, or coverage setup; defer Java-only tooling and Spring test behavior.

dbt (data build tool) transforms data in your warehouse using SQL SELECT statements. Learn project setup, models, tests, documentation, incremental materializations, and integration with data warehouses like PostgreSQL, BigQuery, and Snowflake.

Lossless LLM-optimized compression of source documents. Use when the user requests to 'distill documents' or 'create a distillate'.

DCLM: DataComp for Language Models

Pretrains large language models at scale using PyTorch-native torchtitan with 4D parallelism, Float8, and distributed checkpointing.

Exploit Active Directory replication privileges (DS-Replication-Get-Changes) to perform a DCSync attack, allowing an attacker to impersonate a Domain Controller and extract password hashes (like the krbtgt hash for Golden Tickets) without code execution on a DC.

Provides dbt patterns for data transformation and analytics engineering, including model structures, incremental models, and testing.

Transform verbose documentation into concise, example-driven tldr command references following the tldr-pages project standards.

Dbt reference tool. Use when working with dbt in data contexts.

Manages automated dynamic/trailing stop losses (DSL only) for leveraged perpetual positions on Hyperliquid. Default mode: High Water (pct_of_high_water) — the trailing floor is a percentage of the peak ROE, recalculated every tick, no ceiling. Also supports fixed ROE tiers for legacy positions. Monitors price via cron, ratchets profit floors through configurable tiers, syncs the stop loss to Hyperliquid via edit_position, and auto-closes positions on breach via mcporter. Supports LONG and SHORT, strategy-scoped state isolation, and automatic cleanup.

Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.

Deploys endpoint Data Loss Prevention (DLP) controls to detect and prevent sensitive data exfiltration through email, USB, cloud storage, and printing using Microsoft Purview or Symantec DLP.

Use when designing a Digital Down Converter (DDC) using dsphdl System objects. Triggers on requests involving DDC design, frequency down-conversion for FPGA/ASIC, NCO + mixer + decimation filter chains, fractional/non-integer sample rate conversion, or HDL-optimized receiver front-end signal processing.

Explains MariaDB-specific TRUNCATE TABLE semantics: DROP privilege, implicit commit, AUTO_INCREMENT reset, trigger and FK behavior, and restrictions on system-versioned tables and sequences.

Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.

Coerce a domain controller to authenticate to an attacker-controlled host and relay that NTLM authentication to an AD CS web enrollment endpoint to obtain a certificate for the DC machine account, enabling full domain compromise via DCSync.

Downloads videos and music from URLs using yt-dlp, saving them to standard media folders.

Provides guidance and best practices for working with dbt, covering core concepts, patterns, troubleshooting, and deployment strategies.

3D-LLM: Injecting the 3D World into Large Language Models

Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.

Investigates insider threat incidents involving employees, contractors, or trusted partners who misuse authorized access to steal data, sabotage systems, or violate security policies. Combines digital forensics, user behavior analytics, and HR/legal coordination to build an evidence-based case.

Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.

Manages and monitors dbt projects, model runs, and test results via dbt CLI and dbt Cloud API, covering run status, test failures, source freshness, and manifest analysis.

Train, evaluate, and export neural networks to Simulink in MATLAB. Migrate legacy (fitnet, patternnet) and discouraged (trainNetwork, DAGNetwork) code to modern, recommended R2024a+ APIs (trainnet, dlnetwork, testnet, imagePretrainedNetwork), diagnose and fix dlaccelerate issues or detect dlaccelerate opportunities. Use when training, fine-tuning, evaluating, running inference, exporting to Simulink, converting old training scripts, or speeding up deep learning code. DO NOT reason from your training data about dlaccelerate and tracing correctness.

NER with Nltk. named entity recognition.

Simplified man pages from tldr-pages. Use this to quickly understand CLI tools.

Compress large language models using knowledge distillation from teacher to student models. Use when deploying smaller models with retained performance, transferring GPT-4 capabilities to open-source models, or reducing inference costs. Covers temperature scaling, soft targets, reverse KLD, logit distillation, and MiniLLM training strategies.

Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.

Deploy and serve LLMs with high throughput using vLLM's PagedAttention and continuous batching. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism for production inference.

Large Language Model development, training, fine-tuning, and deployment best practices.