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.

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#

📚 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:

BROADCOM $AVGO EVENT GOING ON RIGHT NOW Broadcom has a 3 day VMWare that started this morning ... Broadcom just announced a bunch of new products and partnerships including: - Broadcom Announces VMware AI Factory, Enabling Faster Time to Production AI and Greater Control Over AI Tokenomics - VMware Cloud Foundation Brings Leading AI Models to the Private AI Cloud - Broadcom Strengthens Spring Security and Adds Coverage of Java, Python, and Node Ecosystems with TrueSource - Broadcom Unveils AI-Ready Data Foundations in VMware Tanzu Platform to Power Secure Enterprise AI Cloud - Broadcom Delivers End-to-End Security, Identity, and Observability for Agentic AI - Broadcom Unveils AgentMinder, An Enterprise Solution for AI Agent Governance and Runtime Control

✍️ Now that AI can write hundreds of syntactically valid lines per second, the developer's job has shifted from "writing" to "reading and verifying." That shift changes what we should want from a programming language. Why Go is an Ideal Language for AI-Assisted Software Engineering 🔍 Overview Google's Go team makes the case for Go as the ideal language for the AI-assisted development era. As the bottleneck moves from code generation to review and verification, the language properties that matter most have flipped — readability and maintainability win over writability and expressiveness. ⚠️ The Problems Being Solved ・LLMs frequently hallucinate type mismatches and non-existent properties ・Dynamically typed languages (e.g. Python) only catch these errors at runtime ・AI-generated code often pulls in stale packages or vulnerable dependencies ・Repeated refactoring passes degrade accuracy (~95% first-pass, declining) 🛠 How Go Addresses Each ・gofmt enforces uniform formatting — AI output is always syntactically predictable ・Static typing + fast compilation: rejects errors instantly, orders of magnitude faster than Java, C#, or Rust ・govulncheck: low-noise vulnerability scanning targeted only at invoked symbols ・Native fuzz testing: continuous discovery of boundary-condition bugs ・15 years of strict backward compatibility: Go 1.0 code runs unchanged today 📌 The Counterintuitive Conclusion "As developers write less code, language choice becomes more critical." Absorbing AI's high-velocity output safely requires deterministic guardrails — and Go was designed for exactly that. #Golang# #AIEngineering#

# Useful but Little-Known Features of ADK 2.0 🌍 Sending the same system prompt and tool definitions to the LLM on every call wastes both money and time. What if the SDK could cache that for you? ADK 2.0's ContextCacheConfig caches repeated context data sent to the LLM, reducing both API costs and response latency. Available with Gemini 2.0+, Python v1.15.0+, and Java v0.1.0+. 📌 Title: Context Cache (ContextCacheConfig) 🔗 URL: 🧩 Overview ContextCacheConfig reduces token consumption by caching context sent to the LLM — system prompts, tool definitions, fixed portions of conversation history, and more. It has three key parameters: min_tokens sets the minimum token threshold for caching to activate (default 0), ttl_seconds controls cache lifetime (default 1800 seconds / 30 minutes), and cache_intervals limits maximum cache reuse count (default 10). Configure it on the App object and caching is applied automatically. 🛠 How to use it Create a ContextCacheConfig and set it on the App. ```python from import App from google.adk.context import ContextCacheConfig cache_config = ContextCacheConfig( min_tokens=1000, # Cache only when context >= 1000 tokens ttl_seconds=3600, # Keep cache for 1 hour cache_intervals=20, # Reuse up to 20 times ) app = App( agent=my_agent, context_cache_config=cache_config, ) ``` Setting min_tokens appropriately ensures that small contexts are sent normally while large contexts benefit from caching. 🏗 Building it into production ・Agents with large system prompts or many tool definitions benefit the most from caching ・Tune ttl_seconds to match your workload pattern (short conversations → shorter TTL, long ones → longer TTL) ・Adjust cache_intervals based on request frequency to balance freshness and cost savings ・Monitor cost reduction metrics and continuously optimize parameters 💡 Use cases 💰 Cut API costs for agents with large, stable system prompts ⚡ Reduce response latency by skipping repeated tool definition transmission 🔁 Optimize token consumption for high-frequency chatbot interactions 📋 Efficiently handle fixed context (rules, guidelines, policies) that rarely changes ⚠️ Watch out This feature requires Gemini 2.0 or later. Context changes won't take effect while a cache is active, so set a shorter ttl_seconds if you frequently update system prompts. When cache_intervals is exceeded, a new cache is created, which can cause cost optimization effects to fluctuate. ✨ Context caching delivers significant cost and performance improvements, especially in scenarios with large, frequently-accessed context. It's a quick win for production deployments. #ADK# #AIAgent#

# Practical and Useful Patterns with ADK 📄 What if you could define agents in YAML instead of code? ADK's Agent Config enables declarative agent definitions with environment-specific switching -- no redeployment needed for prompt or model changes! 📌 Title: Agent Config — Declarative, Code-Free Agent Definitions in YAML 🔗 URL: 🧩 Overview Agent Config lets you build ADK workflows without writing code, using YAML files to define `name`, `model`, `description`, `instruction`, `tools`, and `sub_agents`. Create projects with `adk create --type=config`, then run with `adk web`, `adk run`, or `adk api_server`. For programmatic loading, use `config_agent_utils.from_config()` in Python. This separation of agent definition from code enables prompt changes, model swaps, and environment-specific configurations without redeployment. 🛠 Usage A basic Agent Config YAML: ```yaml # root_agent.yaml name: assistant_agent model: gemini-flash-latest description: A helper agent that answers user questions. instruction: | You are an agent that answers various user questions. Provide accurate and helpful responses. tools: - google_search sub_agents: - config_path: specialist_agent.yaml ``` Create and run a project: ```bash # Create project adk create --type=config my_agent # Run options adk web # Web interface adk run # Terminal execution adk api_server # API server mode ``` Load programmatically in Python: Use `config_agent_utils.from_config()` from `google.adk.agents` to programmatically load an agent from a YAML file path (e.g., `"my_agent/root_agent.yaml"`). 🏗 Practical Patterns **Environment-Specific Configuration**: Maintain separate YAML files for dev/staging/prod and select them via environment variables. ```yaml # config/dev/root_agent.yaml name: assistant_agent model: gemini-flash-latest instruction: | [DEV] Include debug information in your responses. # config/prod/root_agent.yaml name: assistant_agent model: gemini-2.5-pro instruction: | Answer user questions accurately and concisely. ``` Read the environment name with `os.getenv("ENVIRONMENT", "dev")` and dynamically load the corresponding YAML file via `config_agent_utils.from_config(f"config/{env}/root_agent.yaml")`. **Prompt Versioning**: Track YAML files in Git for full prompt change history. Update instructions without code changes and roll back easily when needed. **A/B Testing**: Prepare multiple YAML files with different instructions or models, and switch between them at runtime to compare performance. Call `get_ab_variant(user_id)` to determine the A/B variant (`"a"` or `"b"`), then load the corresponding YAML file with `config_agent_utils.from_config(f"config/variant_{variant}.yaml")` for runtime A/B testing. 💡 Use Cases 🔄 Prompt and model changes without code modifications or redeployment 🌍 Per-environment configuration management (dev/staging/prod) 📊 A/B testing different instructions and models 📝 Git-tracked prompt versioning with easy rollback 🧩 Enabling non-engineers to update agent configurations safely ⚠️ Considerations - Currently only Gemini models are supported. Other model providers are not yet available. - Custom code tools are limited to Python and Java. - `LangGraphAgent` and `A2aAgent` are not yet supported in Agent Config. - API keys and project settings are managed via `.env` files -- be careful not to commit secrets. ✨ Agent Config separates agent definitions from code, enabling non-engineers to safely modify prompts and models while making environment switching and A/B testing straightforward. Use it to maximize operational flexibility! #ADK# #AIAgent#