Results for “linpeas”

Language-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences/sec), lightweight (6MB memory), deterministic vocabulary. Used by T5, ALBERT, XLNet, mBART. Train on raw text without pre-tokenization. Use when you need multilingual support, CJK languages, or reproducible tokenization.

Hunt for adversary abuse of Living Off the Land Binaries (LOLBins) by analyzing endpoint process creation logs for suspicious execution patterns of legitimate Windows system binaries used for malicious purposes.

Language Server Protocol specialist building unified code intelligence systems through LSP client orchestration and semantic indexing

Detect abuse of legitimate Windows binaries (LOLBins) like certutil, regsvr32, mshta, and rundll32 using process telemetry, Sigma rules, and parent-child process analysis.

Navigate, manage, and optimize monorepos with tools like Turborepo, Nx, pnpm workspaces, and Lerna. Enables cross-package impact analysis, selective builds and tests, remote caching, dependency graph visualization, and structured multi-repo to monorepo migrations.

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

Train and use SentencePiece tokenizers for multilingual NLP, supporting BPE and Unigram algorithms with raw Unicode text.

LinkedIn connection requests and DM sequences adapted to tool stack

Language-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences/sec), lightweight (6MB memory), deterministic vocabulary. Used by T5, ALBERT, XLNet, mBART. Train on raw text without pre-tokenization. Use when you need multilingual support, CJK languages, or reproducible tokenization.

Detect abuse of legitimate Windows binaries (LOLBins) used for living off the land attacks by monitoring process creation, command-line arguments, and parent-child relationships.

Build lean, opinionated products using the 37signals philosophy from Getting Real, Rework, and Shape Up. Use when the user mentions "Getting Real", "Rework", "Shape Up", "37signals", "Basecamp method", "six-week cycles", "fixed time variable scope", "appetite vs estimates", "betting table", "breadboarding", "fat marker sketch", "build less", "underdo the competition", or "opinionated software". Also trigger when cutting scope to ship faster, running small teams, avoiding long-term roadmaps, or eliminating meetings. Covers shaping, betting, building, and the art of saying no. For MVP validation, see lean-startup. For design sprints, see design-sprint.

Python interface to OpenMS for mass spectrometry data analysis. Use for LC-MS/MS proteomics and metabolomics workflows including file handling (mzML, mzXML, mzTab, FASTA, pepXML, protXML, mzIdentML), signal processing, feature detection, peptide identification, and quantitative analysis. Apply when working with mass spectrometry data, analyzing proteomics experiments, or processing metabolomics datasets.

Bash/Linux terminal patterns. Critical commands, piping, error handling, scripting. Use when working on macOS or Linux systems.

name: iam description: IAM namespace for Netsnek e.U. identity and access management toolkit. Provides user authentication, role-based access control, and session management for web applications. u...