#tool

I follow so many #Fediverse profiles I am overwhelmed. Time for a cleanup! Can anyone recommend a tool that can help me spot inactive or deleted users among the ones I follow, possibly also offering a good interface suited for seeing the stats of each profile, in order to assess if I am interested in still following it or not? #MastoHelp #help #MastoAiuto #tool

GitHub - NEPENTHESWEB/nepenthes-py: The first anti-AI tarpit. Rewritten in Python. Traps LLM crawlers in an infinite maze of fake pages and Markov babble. The first anti-AI tarpit. Rewritten in Python. Traps LLM crawlers in an infinite maze of fake pages and Markov babble. - NEPENTHESWEB/nepenthes-py

konterfai - konterfAI is a model-poisoner for LLM (Large Language Models) to generate nonsense("bullshit") content suitable to degenerate these models.

Dieser Artikel hat besonders viele Abonnent:innen interessiert. (+) #mac #Terminal #Tool #Code #script #macos #apple

I WILL NOT BE BULLIED 💎 I received a vicious reply today because I use AI. It wasn't a disagreement. Nor a criticism. It was a full-frontal personal attack! And I’ll admit it affected me more than I expected. I USE AI MAINLY BECAUSE I'M PARALYSED. Typing is physically difficult for me. I have the use of only one hand. So, tapping out a post or email with one finger is exhausting. Voice and AI help me turn my own thoughts into usable writing in a normal amount of time. THAT SHOULD NOT REQUIRE A DEFENCE. You can dislike AI. You can distrust it. You can argue about copyright concerns, energy and water use, labour, culture, or anything else that concerns you. I will listen to you and answer you. But attacking people simply for using AI is not principled. IT'S BULLYING. And disabled people should not have to produce a medical justification every time they use a technology that makes their life easier. AI is a tool. How it is used matters. Who gets harmed matters. Context matters. What does not help is turning “AI user” into a moral accusation and deciding that cruelty is suddenly acceptable. I’m quite happy to discuss the technology with anyone. HOWEVER, I WILL NOT BE BULLIED FOR USING IT. I didn't need to do anything about the attack. Before I could even decide how to respond, both the post and the person disappeared from my notifications. @3goodthings@fedigroups.social @DigitalCoup@fedigroups.social @meditation@fedigroups.social @economics_that_works@fedigroups.social @startrek@fedigroups.social #AI #Accessibility #Disability #AssistiveTechnology #Fediverse #Bullying #PWD #Tool

---------------- 🛠️ Tool =================== Archify is an agent skill that converts a codebase or system description into an interactive, shareable technical map, directly within chat-based development agents. 🔹 Tool Purpose and Capabilities Archify integrates with Raven, Cursor, Claude Code, Codex CLI, and OpenCode. You provide a system description or point it at a repository, and it generates a polished, interactive system map. The current development version is v2.16.0-dev.0. 🔹 Key Features • Five diagram types with four presets, dark/light themes, built-in brand marks, and finite motion • Before / Delta / After snapshot comparison: shows exactly what was added, removed, changed, moved, and rerouted between two validated states • Grounded interactions: search nodes, optionally open revision-verified source, trace upstream/downstream authored reach and exact routes, compare roles, and play guided stories without inventing topology • Single-file output: typed JSON IR and deterministic checks produce self-contained HTML plus PNG, SVG, WebM, and 1200x630 share cards 🔹 Technical Implementation The tool relies on a typed JSON IR (intermediate representation) as its ground truth. Deterministic checks run against this IR to ensure the generated visual artifacts stay consistent with the underlying facts. The IR approach means the map does not hallucinate topology; it only renders what the IR encodes. 🔹 Use Cases • Reviewing architecture changes before merge by comparing validated snapshots • Presenting system architecture to stakeholders via shareable HTML artifacts • Tracing dependency reach and routing within a codebase during onboarding or audits • Generating share cards for documentation or social posts 🔹 Installation npx skills add tt-a1i/archify -g For Cursor users, there is an agent-aware quick start guide on the project page. 🔹 Limitations This is a development version (v2.16.0-dev.0). The source does not provide independent verification of output accuracy across complex or polyglot codebases. Haven't tested personally. License is MIT. 🔹 References Project page: tt-a1i.github.io/archify Scenario guide and Proof Lab available on the project site. 🔹 archify #agent_skills #system_map #codebase_visualization #tool 🔗 Source:

a inject ( #mould / #Mold ) #tool is over 50 times more expensive then #3dprinting and a 3dprinter can make anything you can dream up of within #enginering limit s but tooling like molds can only do one shape #savemoney

Etwas, für das ich ein #Abo brauche, ist kein #Tool, sondern eine #Melkmaschine.

---------------- 🛠️ Tool =================== VulHunt Community Edition is an open-source vulnerability hunting framework developed by Binarly's Research team. It is designed to help security researchers identify vulnerabilities in software binaries and UEFI firmware. Built on top of Binarly's Binary Analysis and Inspection System (BIAS), the tool provides a flexible environment for analyzing binaries. It integrates with the Binarly Transparency Platform (BTP) for large-scale vulnerability management, hunting, and triage capabilities. Key Features • Open Source Engine: The Community Edition provides the core VulHunt engine for free, facilitating community-developed rulepacks and integrations. • Multiple Loaders: Supports scanning single binary files (component), BA2 archives (ba2), and Binary Ninja databases (bndb). • MCP Server Mode: Can run as a Model Context Protocol (MCP) server for integration with AI assistants. By default, it starts a streaming HTTP server with SSE transport at http://127.0.0.1:8080 • Output Formats: Supports standard JSON output, human-readable formatting (--pretty), streaming JSONL messages (--stream), and Zstandard compression (--compress). Technical Implementation The framework is built in Rust and can be compiled using cargo-make. It requires a patched version of LuaJIT for static building. On Windows, it uses msvcbuild.bat to compile LuaJIT. The tool accepts directories containing auxiliary data, rules, and modules via command line arguments or environment variables (BIAS_DATA, BIAS_VULHUNT_RULES, BIAS_VULHUNT_MODULES). Use Cases • Automated scanning of firmware images and software binaries for known and unknown vulnerabilities using custom rulepacks. • Integrating binary analysis capabilities directly into AI assistant workflows via the MCP server interface. • Large-scale vulnerability triage when combined with the Binarly Transparency Platform. Limitations Building the tool without cargo-make requires manual setup of a patched LuaJIT, which might introduce friction for some environments. Binary Ninja database scanning requires enabling the bndb feature at build time. 🔹 vulhunt #binarly #uefi #firmware #tool 🔗 Source:

---------------- 🛠️ Tool =================== Conversation Stenography is a proof of concept tool that hides secret messages inside normal-looking chat text using local AI models. 🔹 Key Features • Generates innocent cover text for encrypted messages • Works across any messaging platform like WhatsApp or Signal • Uses local GPT-2 model for generation and decoding 🔹 Technical Implementation The tool is written in Go. It clones from GitHub, builds a binary, and downloads a recommended local AI model on first run. It encrypts the message and then uses the local model to generate a cover text that looks like a normal conversation. A local simulation mode allows testing with two users on one device. 🔹 Use Cases • Educational demonstration of LLM-based steganography • Covert communication research against message scanning 🔹 Limitations The author explicitly states this is a proof of concept with multiple issues. Techniques are already being developed to detect hidden content in text. The author notes they are 18 and not the first to explore this, acknowledging LLM-based steganography has existed for years and may already operate at scale. 🔹 steganography #llm #privacy #tool #gpt2 🔗 Source:

---------------- 🛠️ Tool =================== A security researcher detailed an autonomous vulnerability hunting system built around Claude Code and the Model Context Protocol (MCP). The motivation stems from the overhead of context switching and tool wrangling during manual vulnerability research. Architecture Overview The system wraps standard research tools as callable MCP servers, allowing Claude Code to execute terminal commands with typed inputs and outputs natively. The setup consists of 8 distinct MCP servers distributed across 5 VMs, exposing over 300 tools. Server Breakdown • Lab Controller: Manages SSH/WinRM sessions and Proxmox VMs. Handles basic reverse engineering. • Hunter: Dedicated to patch diffing, attack surface enumeration, fuzzing across 10 domains, and crash triage. • RE Tools: Integrates Ghidra, radare2, and Frida for static and dynamic analysis. • Exploit Dev: Automates shellcode generation, heap sprays, CFG bypasses, and PoC assembly. • Debugger: Maintains persistent WinDbg/GDB sessions that survive across tool calls. • RAG: Provides semantic search over campaign data and prior research. • Infra: Provisions and scales fuzzing VMs on Proxmox. • Reporting: Automates disclosure reports and CVE requests. Technical Implementation All 8 servers run as separate Python processes registered in a single .mcp.json file. When Claude needs to interact with a Windows target, it calls tool_surface_kernel_drivers. For decompilation, it uses tool_re_ghidra_decompile. This structured approach eliminates the need to copy-paste terminal output or switch contexts manually. Analysis By delegating tool execution to the AI, the researcher maintains focus on critical thinking and disclosure writing. While automated fuzzing is not new, integrating it directly with an LLM via MCP provides a structured pipeline from initial mapping to CVE submission. 🔹 mcp #vulnerability_hunting #claude_code #fuzzing #tool 🔗 Source:

---------------- 🛠️ Tool =================== π RuView is a WiFi sensing platform that converts radio signals into spatial intelligence using Channel State Information (CSI) from ESP32 sensors. The system enables presence detection through walls, vital sign monitoring, activity recognition, and environmental mapping without cameras or cloud dependency. How It Works Every WiFi router fills a space with radio waves. When people move, breathe, or sit still, they disturb those waves in measurable ways. RuView captures these disturbances using CSI data from low-cost ESP32 sensors and converts them into actionable spatial intelligence: who is present, what they are doing, and whether they are okay. Key Capabilities The platform senses five primary categories: • Presence and occupancy: detecting people through walls, counting them, tracking entries and exits • Vital signs: breathing rate and heart rate measured contactlessly during sleep or sitting • Activity recognition: walking, sitting, gestures, and falls derived from temporal CSI patterns • Environment mapping: RF fingerprinting to identify rooms, detect moved furniture, and spot new objects • Sleep quality: overnight monitoring with sleep stage classification and apnea screening Technical Architecture The system is built on RuVector and Cognitum Seed. It runs entirely on edge hardware: an ESP32 mesh (approximately $9 per node) paired with a Cognitum Seed for persistent memory, cryptographic attestation, and AI integration. No cloud, no cameras, no internet required. Spiking neural networks learn each environment locally and adapt in under 30 seconds. Multi-frequency mesh scanning operates across 6 WiFi channels, using neighboring routers as free radar illuminators. Each node ships 21 entities: 11 raw signals plus 10 inferred semantic states including someone-sleeping, possible-distress, room-active, elderly-inactivity-anomaly, meeting-in-progress, bathroom-occupied, fall-risk-elevated, bed-exit, no-movement, and multi-room-transition. Smart Home Integration The platform integrates natively with four major ecosystems: Home Assistant via HA-DISCO MQTT publisher (single --mqtt flag), Apple Home and HomePod as a discoverable HAP-1.1 bridge, Google Home and Amazon Alexa via the same Home Assistant bridge or a Matter endpoint. Siri, Google Assistant, and Alexa can voice-report presence and vitals by room with zero custom skills. Three starter Home Assistant Blueprints are included. Considerations The edge-only architecture preserves privacy but limits remote access without additional infrastructure. The CSI approach for spatial sensing is well-established in research, and the $9 per node cost makes broad deployment feasible. Performance in dense urban RF environments with many overlapping networks is not well documented. The technique of using neighbor routers as radar illuminators depends on local RF conditions that vary between deployments. Haven't tested personally. 🔹 wifisensing #esp32 #smarthome #tool #csi 🔗 Source:

---------------- 🛠️ Tool =================== open·kritt is an open-source, self-hosted security research platform that orchestrates AI agents to find vulnerabilities in code. Rather than pointing a model at an entire repository and hoping for results, it breaks research into focused, well-defined tasks, runs them in parallel across AI agents, and combines output into validated, prioritized findings. How it works The core approach is decomposition. Full-repository scans with a single LLM prompt tend to produce noisy, unfocused results. open·kritt chains focused prompts into reusable security research playbooks (workflows). Each workflow defines a sequence of targeted analysis steps. Agents run these steps in parallel, and results are merged with automatic de-duplication and custom severity ranking. Key capabilities • Workflow builder: Chain focused prompts into reusable security research playbooks • Scan execution: Analyze remote or local repositories and dependencies using Codex or Claude Code • Finding validation: Post-scripts verify issues, build proofs of concept, produce reports • Result prioritization: Custom severity rankers, consistent finding schema, automatic de-duplication • Model flexibility: Bring your own model access via Codex, OpenAI, Anthropic, or OpenRouter Technical details The stack runs on Docker with Docker Compose, requiring Node.js 20 or newer. The CLI is repository-local with no separate install step. Default ports bind to 127.0.0.1, and the backend ships without application authentication. The documentation explicitly advises keeping the stack private. Tool-enabled agents run as root inside disposable job containers, with writable repository copies and direct internet access. This allows agents to install tools, compile targets, run tests, and build proofs of concept. The threat model documentation recommends running open·kritt on a dedicated Docker host or VM, especially when scanning untrusted code. Background The Kritt team built this from real security research. Under the researcher name Blockian, they earned over $1,500,000 in bug-bounty payouts across platforms including Immunefi and HackenProof. open·kritt is the open-source version of the internal tool behind that work. Limitations No application-level authentication by default. Agents run as root in containers with internet access, requiring isolation awareness. The tool has not been independently verified for this writeup. 🔹 openkritt #tool #AI #vulnerability #bugbounty 🔗 Source:

Offline Translator An Android translator app that performs text, PDF/ODT documents and image translation completely offline using on-device models. Supports automatic language detection and transliteration for non-latin scripts. There's also a built-in word dictionary. 💬 https://github.com/DavidVentura/offline-translator 📱 https://f-droid.org/packages/dev.davidv.translator #android #fdroid #translater #offline #tool #language #pdf #odt #text #opensource

Ooh, look! I made a thing! A handy little clamp/vice tool thing to help get #PTFE tubing out of couplers. #3DPrinting #tool

---------------- 🛠️ Tool: garak - LLM Vulnerability Scanner =================== garak (Generative AI Red-teaming & Assessment Kit) is an open-source tool developed under NVIDIA's GitHub organization that systematically probes LLMs for security weaknesses. If you know nmap or Metasploit Framework, garak operates on a similar concept but targets language models instead of network services or software vulnerabilities. What garak does The tool probes LLMs and dialog systems for failure modes that security teams care about: hallucination, data leakage, prompt injection, misinformation generation, toxicity output, and jailbreaks. It combines three probing strategies. Static probes use fixed test cases. Dynamic probes generate test cases based on model responses. Adaptive probes adjust their strategy based on intermediate results, which is potentially more effective at uncovering weaknesses that fixed test suites miss because the probing strategy evolves as it learns about the model's behavior. Supported backends • Hugging Face Hub generative models • Replicate text models • OpenAI API (chat and continuation models) • AWS Bedrock foundation models • LiteLLM • REST-accessible endpoints • GGUF models via llama.cpp (version >= 1046) This range means you can run the same probe suite across different providers and compare results directly. That comparative angle is useful for organizations evaluating which model to deploy. Installation Standard install via pip: python -m pip install -U garak Development version from GitHub: python -m pip install -U git+https://github.com/NVIDIA/garak.git@main Recommended Conda environment setup with Python >=3.10, <=3.12. The tool runs as a command-line utility with the general syntax garak . Technical context The project has active CI pipelines for Linux, Windows, and macOS. Code formatting follows Black. An arXiv paper (2406.11036) documents the methodology. DEF CON presentation slides are available. The Discord community is active for discussion. Practical considerations The tool is free under Apache 2.0. It focuses on making LLMs fail in ways we don't want, which is a different posture than typical benchmarking. The adaptive probe mechanism is conceptually interesting. I'm not sure how it performs in practice against commercially deployed models with layered safety filters. Haven't tested personally, so can't speak to performance at scale or coverage completeness against specific model families. Documentation at docs.garak.ai. 🔹 garak #LLM #red_teaming #NVIDIA #tool 🔗 Source: https://github.com/NVIDIA/garak

Earliest #Evidence of #Ivory #Tool Production Uncovered in #Ukrainian Site : Medium #Neutrino’s #Nursery found: the ‘#Shadow #Blaster’ : Nature #Bacteria share #Proteins to survive #Antibiotics : Science Latest #KnowledgeLinks

I'm praying for rain I'm praying for tidal waves I wanna see the ground give way I wanna watch it all go down Mom, please flush it all away I wanna see it go right in and down I wanna watch it go right in Watch you flush it all away Yeah, time to bring it down again Yeah, don't just call a pessimist Try and read between the lines And I can't imagine why you wouldn't Welcome any change, my friend https://youtu.be/CehYA3omb5o #Tool #Adapt #Fitness #Change

---------------- 🛠️ Clankerusecase — Threat-led Detection Library =================== Clankerusecase is a threat-led detection library providing detection rules across four platforms: Microsoft Defender KQL, Azure Sentinel KQL, Sigma, and Splunk SPL. The core value is reducing latency between threat intelligence publication and deployable detection content. 🔹 Two Content Tiers Generic use cases are rule files stored in use_cases/*.yml that activate when an article mentions a known trigger keyword. For example, an article referencing psexec fires the rule UC_LATERAL_PSXEC. These are broadly applicable but lack specificity to individual campaigns. They provide baseline coverage for well-known techniques and tools. AI-badged use cases represent a higher-fidelity tier. The pipeline feeds the source article to Claude, which generates bespoke detection logic targeting the exact campaign, threat actor, or malware family described. These rules are pinned to the specific IOCs and TTPs mentioned in the article. AI-badged use cases sort to the top of article cards and the matrix drawer to surface the highest-quality content first. 🔹 Cross-Verification Process AI-generated detections undergo a cross-verification step via web search against authoritative vendor advisories: Microsoft Threat Intelligence, Mandiant, CrowdStrike, MITRE ATT&CK, and abuse.ch. Each AI-badged rule includes "Cross-checked against:" references linking back to these verification sources. This adds a validation layer that pure LLM-generated detection rules typically lack. 🔹 Platform Coverage and Filtering Detection rules target four platforms, each with its own query language. The interface provides filter groups organized by Source, Content, Platform, Target, and Splunk category. On mobile viewports, the filter toolbar collapses behind a "Filters ▾" toggle to keep article cards above the fold. 🔹 Practical Considerations For detection engineers, the AI-badged use cases offer campaign-specific hunting logic without starting from scratch. The cross-check against vendor advisories provides some confidence, though this does not replace manual validation in production. The generic rules provide baseline coverage for known patterns, while AI rules address the gap for novel or recently reported threats. The quality of AI-generated rules depends on Claude's ability to accurately extract IOCs and TTPs from source articles. 🔹 detection_engineering #KQL #sigma #splunk #tool 🔗 Source:

---------------- 🛠️ Tool =================== zsazsa CTI is a cyber threat intelligence program management and production platform built around MISP. It links collection, triage, analyst workflows, requirement management, publishing, and stakeholder delivery in a single integrated workflow. The platform targets teams that treat threat intelligence as an operational capability rather than a collection of loose documents and disconnected scripts. Analysts move from source events to validated intelligence products, align output to PIR and GIR priorities, distribute to stakeholders, and feed response back into program maturity signals. Key functional areas: Dashboard provides a live snapshot: active PIRs and GIRs, stakeholder counts, analyser freshness, the last 24 hours of processing, and scraper events awaiting triage. Stakeholders records who receives output, with role, organisation, TLP clearance, product subscriptions, and notification channels. Includes a power and interest matrix for engagement planning. Requirements (PIR and GIR) hold the intelligence questions driving collection, with scope, ownership, and distribution. Adding scope to a requirement highlights matching events in the data collection view. RFIs handle one-off requests from intake to closure, with SLA, owner, linked PIR or GIR, response confidence, attachments, notes, and feedback. Data collection is the cached view of everything arriving from the scraper MISP, other MISP servers, and manual or newsletter sources. Analysts browse and triage events, enrich them with scope from MISP galaxies, generate AI summaries, and start a product straight from a source event. Products form a searchable catalogue: Flash Intel Alerts, Vulnerability Advisories, Daily Threat Briefings, Threat Landscape Reports, Indicator Feeds, and Threat Actor Profiles. Statistics cover pipeline and program metrics, RFI and feedback figures, and a scope coverage view. A CTI-CMM maturity panel maps the program against levels CTI0 to CTI3. MISP integration All operational data resides in MISP using events, object templates, attributes, and event reports. This preserves auditability and allows teams to inspect raw records directly in MISP. The MISP event history serves as an audit trail for every change to a product, stakeholder, or requirement. Product content and supporting context sit together, so analysts move from collection evidence to published output without losing traceability. The built-in reference panel helps teams apply common intelligence concepts consistently, including the Admiralty Scale, TLP, and CTI evaluation criteria. Considerations The platform assumes you are already running or willing to adopt MISP as the backbone of your CTI stack. Teams without an existing MISP instance face additional deployment overhead. The AI summary feature in data collection is mentioned but the underlying model or service is not specified. Not independently verified. For CTI teams struggling with fragmented workflows, zsazsa provides a structured path from collection to delivery with built-in maturity measurement. The MISP-native design eliminates data silos between stages. 🔹 CTI #MISP #threat_intelligence #tool #zsazsa 🔗 Source: