Results for “embedded-linux”

Extract Jetson Linux BSP and sample-rootfs tarballs, run apply_binaries.sh with the correct GPU stack flag, and record the image metadata in the active target profile.

Develop firmware for microcontrollers, implement RTOS applications, and optimize power consumption for resource-constrained devices.

Deploy MATLAB-generated code to embedded hardware using Embedded Coder. Use when configuring code generation for microcontrollers (STM32, Raspberry Pi, ARM Cortex), setting up PIL/SIL verification, disabling dynamic memory allocation, or configuring hardware-specific code generation settings. Covers ERT-based configurations, processor-in-the-loop testing, memory constraints, and the MEX→SIL→PIL verification progression.

Deploy and operate the VSS 3.2 GA RT-Embed Video Embedding microservice using Docker Compose, covering GPU prerequisites, REST API usage for file uploads, text/video embeddings, live RTSP streams, Redis/Kafka/OTel integration, and troubleshooting.

Analyzes firmware images for embedded malware, backdoors, and unauthorized modifications targeting routers, IoT devices, UEFI/BIOS, and embedded systems. Covers firmware extraction, filesystem analysis, binary reverse engineering, and bootkit detection.

Senior embedded software engineer specializing in firmware and driver development for ARM Cortex-M microcontrollers (Teensy, STM32, nRF52, SAMD). Decades of experience writing reliable, optimized, and maintainable embedded code with deep expertise in memory barriers, DMA/cache coherency, interrupt-driven I/O, and peripheral drivers.

Deploy AI models to embedded hardware using MathWorks tools (MATLAB, Simulink, Embedded Coder). Covers two workflow patterns: (1) MathWorks-native or imported models rebuilt as dlnetwork for lean hardware, (2) direct C/C++ code generation from PyTorch and LiteRT models. Both patterns support all targets (Cortex-M/A/R, x86, GPU). Trigger when: user wants to deploy AI to embedded targets; generate C/CUDA from neural networks; compress AI models for MCU; integrate AI in Simulink for system-level simulation; import PyTorch/ONNX/TensorFlow models for embedded deployment; optimize AI for resource-constrained hardware; or use loadPyTorchExportedProgram, loadLiteRTModel, importNetworkFromPyTorch, importNetworkFromONNX, importNetworkFromTensorFlow, importNetworkFromKeras, dlquantizer, exportNetworkToSimulink, or Embedded Coder with AI models.

Guide users through installing CUDA-Q, writing quantum kernels, running GPU-accelerated simulations, connecting to QPU hardware, and exploring built-in applications.

Embedded runtime for generated local Wiki skills. It manages local registries, raw document ingestion, incremental indexes, cited queries, static wiki builds, ZIP bundles, large-file upload, and optional on-chain snapshot publication.

End-to-end firmware and IoT penetration testing pipeline following OWASP FSTM methodology. Extracts, emulates, and exploits router, camera, and smart-home firmware using binwalk, EMBA, Firmadyne, and AFL++.

Learn to combine DeFi protocols safely: Uniswap V4 hooks, Aave flash loans, ERC-4626 vaults, Aerodrome on Base, GMX/Pendle on Arbitrum, and dominant DEX per L2.

Extracts and analyzes firmware images using binwalk to identify embedded filesystems, compressed archives, bootloaders, kernel images, and cryptographic material. Covers entropy analysis, recursive extraction, filesystem mounting, and string analysis for credential and configuration discovery.

Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.

Scaffold a one-command dev-local launcher for any codebase by investigating services, ports, and infra dependencies, then generating a single script with tmux session management.

Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.

Enumerate SMB, WinRM, LDAP, and MSSQL services, validate credentials, spray passwords, and execute commands on remote hosts using NetExec during authorized penetration tests.

Perform semantic search, find similar content, and conduct structured research using the Exa API.

Run Bumblebee supply-chain inventory and exposure scans on macOS/Linux to detect compromised packages, extensions, and MCP host configs.

Build and deploy applications on the inference.sh platform using Python or Node.js, with CLI scaffolding, local testing, and cloud deployment.

Build, test, run, and flash NightRun — a bare-metal, no_std Rust UEFI application that boots straight into a local LLM (Llama 3.2, Qwen3, or Granite 4.1) with no operating system underneath. Use when the user wants to build/flash a bootable NightRun USB or Raspberry Pi 5 SD image, convert a GGUF model into the `.nrm` container, run/debug the inference engine on the host or in QEMU/OVMF, or troubleshoot no_std kernel/tokenizer parity issues in the NightRun codebase. Triggers on: "nightrun", "boot into an LLM", "bare-metal LLM runtime", "UEFI LLM appliance", "nrconvert", "nrhost", "cargo xtask", "nrm model file", "flash a bootable LLM USB".

Configure Fluentd and Fluent Bit for centralized log aggregation, routing, filtering, and enrichment across distributed infrastructure.

Builds AWS Nitro Enclave-based confidential computing environments with cryptographic attestation, KMS policy integration, and secure vsock communication for processing sensitive data.

Machine learning on genomic interval data (BED files) with the geniml Python package — region embeddings (Region2Vec), joint region+metadata embeddings (BEDspace/StarSpace), single-cell ATAC-seq embeddings (scEmbed), consensus peak sets / universes (build-universe), tokenization, BEDshift randomization, and BBClient/BEDbase caching. Use when training or using region/cell embeddings, clustering scATAC-seq, building a tokenization universe from BED collections, or any ML/feature-learning task over genomic regions. NOT for plain interval arithmetic (overlap/intersect/merge counts) — that is gtars, not geniml. Part of the AlterLab Academic Skills suite.

Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.

Guides authorized hardware and embedded interface security research including UART/JTAG discovery, debug pad triage, secure boot assessment, and offline firmware extraction support.

name: ing description: Ing namespace for Netsnek e.U. engineering workflow automation. Streamlines build pipelines, deployment orchestration, and infrastructure provisioning for development teams. ...

Detect intrusion attempts, unauthorized access, and privilege escalation on Linux hosts using the auditd framework with ausearch and aureport utilities.

Local RAG indexing with 97% storage reduction via anchor-based lazy recomputation. Graph-based selective embedding storage for memory-efficient semantic code search.

Configures ESLint v9 flat config and neostandard for JavaScript and TypeScript projects, including migrating from legacy .eslintrc files or the standard package.

Deploy kernel-level runtime security monitoring on Linux hosts or Kubernetes clusters using eBPF and Cilium Tetragon for process execution tracking, network observability, file access auditing, and runtime enforcement.

Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.

Enables semantic file search using vector embeddings from the command line, integrated with Claude/Codex.

Implements production LLM operations: RAG pipelines, embeddings, vector databases, fine-tuning, advanced prompt engineering, cost estimation, quality evals, semantic caching, streaming, and agents.