This repository provides an open-source face recognition software development kit (SDK) for Windows and Linux systems, developed by Faceplugin. It uaes deep learning models to offer on-premise processing of facial data, ensuring privacy as no information leaves the user's device. The toolkit supports various functions including face detection, landmark detection, feature embedding generation, and similarity comparison via Python APIs.
- Supports JPG, PNG, BMP, and TIFF image formats
- Compatible with both CPU and GPU acceleration
- Requires Python 3.9 or higher and Anaconda is recommended for setup
- Includes capabilities for bounding box extraction and facial landmark detection
- Supports JPG, PNG, BMP, and TIFF image formats
- Compatible with both CPU and GPU acceleration
- Requires Python 3.9 or higher and Anaconda is recommended for setup
- Includes capabilities for bounding box extraction and facial landmark detection
@githubprojects writes about Quivr-core, a Python package extracted from Quivr.com's production retrieval-augmented generation pipeline that lets developers get a working system in five lines of code. It centers on a `Brain` class that ingests files and answers questions, with a YAML-configurable workflow (filter history → rewrite → retrieve → generate) that keeps the pipeline inspectable rather than a black box. It supports OpenAI, Anthropic, Mistral, and local Ollama models, and handles PDFs, Markdown, and TXT files out of the box.
- Integrates with Megaparse for more sophisticated document ingestion without switching frameworks.
- The opinionated philosophy is the core differentiator: sensible defaults over an infinite configuration surface.
- Integrates with Megaparse for more sophisticated document ingestion without switching frameworks.
- The opinionated philosophy is the core differentiator: sensible defaults over an infinite configuration surface.
Jessica Lyons writes that researcher Johann Rehberger, known as wunderwuzzi, has demonstrated a method for hijacking Anthropic's Claude Code in Auto Mode via prompt injection. By asking the agentic coding model to summarize a malicious website, an attacker can trick it into bypassing its standard WebFetch tool and instead using Bash with `curl` to download files. This chain allows attackers to use "Python module shadowing'' specifically by placing a malicious file named `struct.py` in the same directory as a downloaded archive' to execute arbitrary code on the host system.
- The attack had success rates between 60% and 80% in tested scenarios.
- An attacker can successfully trigger "nested" Claude Code instances to create new agents with their own tool access.
- Anthropic stated that Auto Mode is a convenience feature, not a security guarantee, as the classifier may not catch complex injection chains.
- Experts recommend running coding agents in isolated sandboxes due to these vulnerabilities.
- The attack had success rates between 60% and 80% in tested scenarios.
- An attacker can successfully trigger "nested" Claude Code instances to create new agents with their own tool access.
- Anthropic stated that Auto Mode is a convenience feature, not a security guarantee, as the classifier may not catch complex injection chains.
- Experts recommend running coding agents in isolated sandboxes due to these vulnerabilities.
Rich is a Python library that adds colors, tables, progress bars, markdown rendering, and syntax highlighting to terminal output, making CLI tools and REPL sessions significantly more readable and visually polished across Linux, macOS, and Windows.
- Its `print` is a drop-in replacement for the built-in, so you can swap it in with only an import change and embed markup like ` bold magenta » ` directly in strings
- Can be installed into the Python REPL to automatically pretty-print any data structure you inspect
- Supports true color and emoji on modern Windows Terminal, falling back to 16 colors on classic terminals
- Requires Python 3.8+
- Its `print` is a drop-in replacement for the built-in, so you can swap it in with only an import change and embed markup like ` bold magenta » ` directly in strings
- Can be installed into the Python REPL to automatically pretty-print any data structure you inspect
- Supports true color and emoji on modern Windows Terminal, falling back to 16 colors on classic terminals
- Requires Python 3.8+
paoloap wrote companion code for a Medium article on agent harnesses, including five layers: execution boundary, sandboxing, memory persistence, verification loops, and context pipelines, each with failure and guard scripts running alongside.
Move enforcement out of system prompts to deterministic code; a rule like "never delete without human approval" becomes a pre-execution hook denying the call, not a sentence the model might forget.Every demo runs with no API key; the model is replaced by a ~40-line scripted stand-in that emits a fixed sequence of tool calls, making each failure reproducible and the CI suite able to assert behaviour rather than smoke-test it. A single dependency-free `harness.py` can be dropped into any existing agent loop to add boundary checks, path allowlists, hostname allowlists, a persistent store, read-only review, dry-run, and token-cost distillation.
- `boundary()` detects coroutine functions and awaits them, fixing a silent no-op that made the guard a no-op on the most common (async) agent loops
- `host_allowlist` compares the parsed hostname, so `api.openai.com.evil.com` and `evil.com/?x=api.openai.com` are both refused
- `Denied` subclasses `str` so it drops into the same slot a tool result occupies, keeping existing loops unchanged
- `boundary(rules, max_repeats=3)` detects identical consecutive denials and changes the message to break a deterministic deadlock at full token cost
- The repo is MIT-licensed, Python 3.8+, and has no external dependencies
Move enforcement out of system prompts to deterministic code; a rule like "never delete without human approval" becomes a pre-execution hook denying the call, not a sentence the model might forget.Every demo runs with no API key; the model is replaced by a ~40-line scripted stand-in that emits a fixed sequence of tool calls, making each failure reproducible and the CI suite able to assert behaviour rather than smoke-test it. A single dependency-free `harness.py` can be dropped into any existing agent loop to add boundary checks, path allowlists, hostname allowlists, a persistent store, read-only review, dry-run, and token-cost distillation.
- `boundary()` detects coroutine functions and awaits them, fixing a silent no-op that made the guard a no-op on the most common (async) agent loops
- `host_allowlist` compares the parsed hostname, so `api.openai.com.evil.com` and `evil.com/?x=api.openai.com` are both refused
- `Denied` subclasses `str` so it drops into the same slot a tool result occupies, keeping existing loops unchanged
- `boundary(rules, max_repeats=3)` detects identical consecutive denials and changes the message to break a deterministic deadlock at full token cost
- The repo is MIT-licensed, Python 3.8+, and has no external dependencies
Anurag Singh replaced five Python scripts (backup, organizer, renamer, cleaner, watchdog) with a local LLM agent, which made errors the scripts didn't (wrong directories, skipped steps, false success reports).Each of the original scripts followed explicit rules through a scheduler; the agent instead added a longer inference chain (inspect, interpret, choose a tool, build a command, execute, review) to tasks that fixed logic already described completely, while also holding a loaded model in memory between runs.
- AutomationBench scores for frontier models remain well under 20%: GPT-5.6 Sol 18.1%, GPT-5.5 12.9%, Claude Opus 4.8 15.5%, Gemini 3.5 Flash 14.5%
- Granting an LLM system-level access creates a prompt-injection vector: a malicious file on disk could carry instructions the agent interprets as commands
- Singh's proposed fix: let the agent classify and route ambiguous requests, then hand off to a validator + fixed script for the actual filesystem action
- The five original scripts covered photo backup, extension-based Downloads sorting, file renaming, app-cache clearing, and a disk-threshold alert
- AutomationBench scores for frontier models remain well under 20%: GPT-5.6 Sol 18.1%, GPT-5.5 12.9%, Claude Opus 4.8 15.5%, Gemini 3.5 Flash 14.5%
- Granting an LLM system-level access creates a prompt-injection vector: a malicious file on disk could carry instructions the agent interprets as commands
- Singh's proposed fix: let the agent classify and route ambiguous requests, then hand off to a validator + fixed script for the actual filesystem action
- The five original scripts covered photo backup, extension-based Downloads sorting, file renaming, app-cache clearing, and a disk-threshold alert
Sara A. Metwalli writes that while Polars offers speed gains over Pandas for large data through Rust-based parallel execution, lazy query planning and Apache Arrow columnar memory, the switch is not universal; Pandas remains dominant for exploratory work, teaching and ecosystem compatibility, and the two libraries are best seen as complementary tools for different workloads rather than direct replacements.
- Pandas was launched in 2008 for single-core machines with small datasets
- Polars grammar mimics Pandas for loading CSVs, selecting columns and filtering rows
- Lazy execution in Polars builds a query plan and runs only after .collect()
- Pandas was launched in 2008 for single-core machines with small datasets
- Polars grammar mimics Pandas for loading CSVs, selecting columns and filtering rows
- Lazy execution in Polars builds a query plan and runs only after .collect()
xiaowuc2 writes that the qxresearch-event-1 repository provides a hands-on collection of 50+ concise Python applications averaging about ten lines of code each, spanning Machine Learning, Deep Learning, GUI, Computer Vision and API development, with video explanations on YouTube and guidance for learning, experimenting and customization.
Termaid is a pure Python library and CLI that renders Mermaid diagrams as Unicode or ASCII art directly in the terminal or within Python apps, supporting 18 diagram types with zero dependencies, terminal-aware auto-fitting, optional Rich colored output and Textual widget integration.
- Inspired by mermaid-ascii and beautiful-mermaid
- Offers 11 built-in themes including gruvbox, monokai, dracula, nord and solarized
- Pipe-friendly CLI examples include `cat diagram.mmd | termaid` and `uvx termaid diagram.mmd`
- Inspired by mermaid-ascii and beautiful-mermaid
- Offers 11 built-in themes including gruvbox, monokai, dracula, nord and solarized
- Pipe-friendly CLI examples include `cat diagram.mmd | termaid` and `uvx termaid diagram.mmd`
Abid Ali Awan writes that a Jupyter Notebook pipeline can turn a webpage into a lightweight LLM-powered QA engine by fetching HTML with requests, stripping noisy elements with BeautifulSoup, converting the cleaned DOM to Markdown with markdownify and ftfy, then asking an OpenAI model to answer a specific user query using only the compact Markdown, which reduces token use by removing navigation, scripts and repeated marketing text.
- Uses gpt-5.4-nano for cost-efficient answers
- Removes script, style, nav, header, footer, form, button tags and class/id names containing popup, cookie, navbar, modal, etc.
- Demonstrates queries on olostep.com home and pricing pages and saves output to ai_scraper_result.md
- Notes running costs and cites commercial alternatives such as Olostep, Firecrawl and Exa
- Uses gpt-5.4-nano for cost-efficient answers
- Removes script, style, nav, header, footer, form, button tags and class/id names containing popup, cookie, navbar, modal, etc.
- Demonstrates queries on olostep.com home and pricing pages and saves output to ai_scraper_result.md
- Notes running costs and cites commercial alternatives such as Olostep, Firecrawl and Exa
