Grok Code Fast 1 is versatile across the full stack and is particularly strong at TypeScript, Python, Java, Rust, C++, and Go. Using Grok Code Fast 1, @DannyLimanseta built the following game in a day.

"My primary languages are Rust, Go, Python, TypeScript, Solidity, Cairo, Circom, Lean 4, C++, Java, and Zig. I also have experience with Noir, Move, and Yul." my brother in christ you have experience with none of these

Me in 2012 instead of learning: DSA System Design AI / ML Generative AI LLMs AI Agents RAG Vector Databases Embeddings Fine-tuning Prompt Engineering MCP AI Coding Agents Python Java C C++ Go Rust JavaScript TypeScript HTML CSS React Next.js Vue Angular Node.js Express.js NestJS Spring Boot Django FastAPI REST APIs GraphQL WebSockets SQL PostgreSQL MySQL MongoDB Redis Elasticsearch Kafka RabbitMQ Docker Kubernetes Terraform AWS Azure GCP Linux Git GitHub CI/CD DevOps Microservices Serverless Cloud Cybersecurity Data Engineering Data Science MLOps Data Analytics Blockchain Web3 Smart Contracts Solidity Edge Computing Distributed Systems Observability Prometheus Grafana OpenTelemetry Supabase Firebase Vercel Cloudflare PostgreSQL + pgvector LangChain LlamaIndex PyTorch TensorFlow Hugging Face Ollama OpenAI APIs Claude APIs Gemini APIs Model Context Protocol AI Infrastructure GPU Computing CUDA

# Learning Palantir Foundry 🚀 Bring complex logic that no-code can't reach into your data platform, along with full software-engineering quality control. That's what Code Repositories delivers. 📌 Title and Feature URL Title: Code Repositories (Python Transforms) URL: 📝 Overview Code Repositories is a web-based integrated development environment (IDE) for creating and collaborating on production-ready code within Foundry. It provides a friendly UI over the underlying Git repositories, so teams can work without command-line access. With platform-specific features, you can apply software development practices directly to data engineering. 🔧 How It Works Version control and collaboration are at its core. - Common Git tasks (branching, committing, release tagging) execute through the web UI - Pull requests drive code review, with "highly configurable" permissions that support quality assurance such as mandatory reviews - IntelliSense, linting, error checking, and contextual help dialogs are available across all repository types - Transforms repositories let you author data transformation logic in Python, Java, or SQL with preview and debugging - Functions repositories natively integrate the Ontology and run low-latency business logic in TypeScript or Python 🛠 Practical Usage - Use PySpark to implement billion-row entity resolution and complex business rules in code - Require PR reviews so a second reviewer and CI checks must pass before merge - Add unit tests to guard transform logic against regressions - In Functions repositories, leverage Ontology-data-type autocomplete to write logic safely - Bring machine learning workflows into the platform via model development repositories 🎯 Use Cases - Implementing complex reconciliation and business rules in PySpark that Pipeline Builder can't express - Structurally eliminating "regressions from editing production directly" through mandatory reviews and branch-based workflows - Implementing derived KPIs and validation logic as Functions reused across apps - Managing ML model training and inference code under governance ⚠️ Caveats - The docs note that Japanese translations are machine-generated and unverified, so localized content may have accuracy limitations - Each repository type (Transforms/Functions/Model) supports different languages and purposes, so pick the one that fits your goal - Being a pro-code environment, the quality benefits only materialize if your organization establishes review, CI, and test practices #PalantirFoundry# #DataEngineering#

An official, hands-on curriculum for learning MCP — the common language of agents — from the ground up 📚 And it covers 6 languages. Title: microsoft/mcp-for-beginners URL: 📚 Overview An open-source educational curriculum from Microsoft that teaches the Model Context Protocol (MCP) through practical, hands-on instruction. MCP is positioned as a "universal translator" that standardizes interactions between AI models and client applications. ❓ Challenges Solved MCP is a cutting-edge framework standardizing communication between AI systems and various tools and services. ・But as a new protocol, it lacked a systematic introductory resource ・Learners needed a foundation covering basics, security, implementation, and applications step by step 💡 Content & Structure It's organized into 11 modules across 4 phases. ・Foundation (0-2): introduction, core concepts, security ・Building (3): create your first implementations with 15 practical guides ・Growing (4-5): advanced concepts and real-world applications ・Mastery (6-11): community contribution and specialized topics, with a 13-lab capstone Code samples come in six languages — C#, Java, JavaScript, Python, TypeScript, and Rust — progressing from a basic calculator to advanced database-integrated implementations. 🌍 Use Cases / Audience It's for developers with basic programming knowledge in a supported language who understand client-server models and REST APIs; AI/ML background is optional. It's a reliable starting point for anyone adopting MCP at work. #MCP# #AIAgents#

# Practical and Useful Patterns with ADK ## 🔌 Apply Cross-Cutting Concerns to All Agents at Once with Plugins Setting up callbacks on every individual agent is tedious. ADK **Plugins** let you register once at the Runner level and apply security, logging, and rate limiting across all agents, tools, and LLM calls! 🎯 ## 📌 Title Plugins ## 🔗 URL ## 🧩 Overview Plugins are cross-agent modules that operate at the Runner level. Unlike agent-specific callbacks, **a single registration applies to every agent, tool, and LLM call** in your application. They're ideal for cross-cutting concerns like security guardrails, logging, and rate limiting. Plugins execute **before** agent-level callbacks, making them perfect for global policy enforcement. ADK ships with built-in plugins: Reflect and Retry Tools, BigQuery Analytics, Context Filter, Global Instruction, and Logging Plugin. ## 🛠 How to Use Import `BasePlugin` from `google.adk.plugins.base_plugin` and `InMemoryRunner` from `google.adk.runners`. Define `SecurityGuardPlugin` as a subclass of `BasePlugin`, initializing with `name="security_guard"`. Its `before_model_callback` method extracts user input from `llm_request.contents[-1].parts[0].text`, checks for injection via `detect_injection()`, and returns a rejection `LlmResponse` if detected. Its `before_tool_callback` method checks `is_authorized( and returns an error dictionary if access is denied. Both return `None` to continue when no issue is found. Finally, create an `InMemoryRunner` with `plugins=[SecurityGuardPlugin()]` to apply this guard to all agents automatically. ## 🏗 Practical Usage **"Gemini as a Judge" pattern for injection detection:** `InjectionDetectorPlugin` extends `BasePlugin` with `name="injection_detector"` and stores `self.judge_model = "gemini-flash-lite"` as its judge model. In `before_model_callback`, it extracts user input from `llm_request.contents[-1].parts[0].text` and passes it to `judge_with_flash_lite()` for fast injection detection. If the verdict is `"INJECTION"`, it calls `audit_log()` and returns a rejection `LlmResponse` to block the request. Otherwise, it returns `None`. `RateLimitPlugin` extends `BasePlugin` with `name="rate_limiter"`, accepting a `max_calls_per_minute` parameter (default 60). It tracks call timestamps in ` and in `before_model_callback`, filters out entries older than 60 seconds. If the count meets or exceeds `self.max_calls`, it returns an `LlmResponse` with a rate limit message. Otherwise, it appends the current timestamp and returns `None`. Finally, an `InMemoryRunner` is created with `plugins=[InjectionDetectorPlugin(), RateLimitPlugin(max_calls_per_minute=30)]` to combine multiple plugins for the production app. ## 💡 Use Cases - 🛡️ **Security guardrails**: Centralized injection detection and PII protection across all agents - 📊 **Unified logging**: Aggregate execution logs from all agents with the BigQuery Analytics plugin - ⏱️ **Rate limiting**: Control API call frequency to prevent cost explosions - 🔄 **Auto-retry**: Intelligent tool failure retries with the Reflect and Retry Tools plugin - 📝 **Global instructions**: Inject shared policies or instructions into every agent ## ⚠️ Caveats - Plugins execute **before** agent-level callbacks — be aware of precedence - Returning a value from a plugin skips both the operation and any corresponding agent-level callback - Since plugins apply to all agents, use Callbacks for agent-specific logic - Error handling is available via `on_model_error_callback` / `on_tool_error_callback` hooks - Available in Python v1.7.0+, TypeScript v0.2.5+, Go v0.4.0+, Java v0.3.0+ ## ✨ Closing Plugins deliver "write once, apply everywhere" for cross-cutting concerns. Delegate security, logging, and rate limiting to Plugins, and keep agent-specific logic in Callbacks. This separation improves both maintainability and security! #ADK# #AIAgent#

📚 Auto-generate documentation for an entire repository, complete with diagrams. CodeWiki handles up to 1.4M lines across 8 languages and beats DeepWiki on quality (ACL 2026). Title: FSoft-AI4Code/CodeWiki URL: 📦 Overview CodeWiki is an AI-powered framework that automatically generates comprehensive documentation for whole software repositories. Beyond simple API references, it produces system-level docs with architecture diagrams, data-flow visuals, sequence diagrams, and prose. It tackles the challenge of keeping documentation current and complete across large, multi-language codebases. ❓ Challenges Solved Traditional documentation tools struggle with scale and context. CodeWiki addresses three core issues: documenting cross-module interdependencies, maintaining architectural context while scaling, and capturing system-level interactions rather than isolated components. 🛠 Methodology & Approach It runs in three stages. ・Hierarchical decomposition: algorithmic clustering inspired by dynamic programming partitions the codebase into coherent modules ・Recursive multi-agent processing: an adaptive agentic system delegates tasks dynamically for complex modules ・Multi-modal synthesis: it integrates text and visual artifacts (Mermaid diagrams), validating diagrams via Node.js ・It supports Python, Java, JavaScript, TypeScript, C, C++, C#, and Kotlin, and can run through Claude Code or Codex CLI with no per-token cost 🎯 Use Cases It fits generating living documentation for large multi-language codebases, onboarding material for new members, and interactive HTML output compatible with GitHub Pages. 📊 Experimental Results ・On its CodeWikiBench, quality scores were 79.14% for high-level languages (Python, JS) and 68.84% for managed ones (C#, Java) ・It improves on DeepWiki by +4.73 points and scored 82.45% on the 229K-line OpenHands project ・It has 1.2k GitHub stars, 193 forks, an ACL 2026 publication, and handles codebases from 86K to 1.4M lines ・The repository's own documentation was generated by CodeWiki, embodying its capability #AIAgents# #DevTools#

Data skew is not always about row counts. Snowpark lets engineers run Python, Java or Scala UDFs natively on Snowflake's query engine. At scale, two workers can process the same number of rows while one does far more compute. DySkew detects that at runtime and redistributes dynamically. No tuning required on your end. Result: ~30% lower P99 UDxF execution time in production, P90 down 27% on large warehouses. How we built it:

The NSA built a full reverse engineering toolkit and gave it away for free. >⁠Ghidra disassembles, decompiles, and graphs compiled code on Windows, macOS, and Linux >⁠Supports a wide range of processor types and executable formats >⁠Scriptable in Java or Python, with room to build your own extensions PAIR IT WITH AN AI AGENT AND ANY BINARY BECOMES READABLE.

🎉 Excited to share that our paper "VEX-Bench: Benchmarking LLM Agents for Assessing Exploitability of Software Supply Chain Vulnerabilities” has been accepted to EMNLP 2026! This work is a collaboration between @PurdueCS and multiple teams from @RedHat (AI Engineering, Ecosystem Engineering, and Product Security). Software supply chains are an increasingly important security challenge. While tools can identify vulnerable dependencies, determining whether a vulnerability is actually exploitable in a downstream application often requires substantial cross-repository reasoning and security expertise. In this work, we introduce VEX-Bench, a benchmark for evaluating LLM agents on this problem: - 75 real-world vulnerability cases mined from GitHub - Expert-labeled across Python, Java, and Go - Evaluation of 9 models across 3 agent harnesses - Going beyond binary “exploitable / not exploitable” decisions to evaluate the reasoning behind exploitability Our results show that frontier LLM agents can perform well on binary vulnerability assessment, but fine-grained exploitability reasoning remains significantly more challenging. This points to an important direction for AI-assisted software security: it’s not enough for an agent to identify that something is vulnerable. We need to understand why it is exploitable, whether it actually matters in context, and how confidently we can trust the assessment. 🙌 Huge thanks to all the co-authors and collaborators who made this possible! @tian_yi_zhang, Jiahao Shi, Yifeng Di, Ilan Pinto, Edward Tsien, Hongjiao Zhang, Ronit Dey, Ilona Shishov, Gal Netanel, Zvi Grinberg, Vladimir Belousov, Bat-Zion Rotman 📄 Paper: #EMNLP2026# #AI# #LLM# #LLMAgents# #Cybersecurity# #SoftwareSupplyChain# #OpenSource# #PurdueCS# #RedHat#