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https://hertzbeat.apache.org/blog/2025/08/24/hertzbeat-graduation
Can you believe that you can quickly adapt to a new monitoring type like K8s or Docker with just a simple configuration
https://www.apache.org/dist/httpd/CHANGES_2.4
org) A cross-site scripting vulnerability exists in mod_proxy_ftp's HTML directory list generation in Apache HTTP Server 2
https://apisix.apache.org/docs/apisix/plugins/ai-proxy/
For instance, if you are working with OpenAI, you can configure additional parameters such as temperature, top_p, and stream
https://apisix.apache.org/docs/apisix/plugins/ai-prompt-decorator/
You can optionally save the key to an environment variable as such:
export OPENAI_API_KEY=<YOUR_OPENAI_API_KEY>
If you are working with other LLM providers, please refer to the provider’s documentation to obtain an API key
https://apisix.apache.org/docs/apisix/plugins/ai-prompt-template/
You can optionally save the key to an environment variable:
export OPENAI_API_KEY=<YOUR_OPENAI_API_KEY>
If you are working with other LLM providers, please refer to the provider’s documentation to obtain an API key
https://airflow.apache.org/docs/apache-airflow-providers-common-ai/stable/index.html
ai is built on pydantic-ai, so the model vendor (OpenAI, Anthropic, Google, Bedrock, …) is picked by the connection llm_conn_id points at — switching providers later is a connection change, not a Dag rewrite
https://hertzbeat.apache.org/blog/2025/04/10/hertzbeat-v1.7.0
org/docs/
New Features and Enhancements
Custom Refresh Interval: Supports custom refresh intervals for each group of metrics to meet monitoring needs in different scenarios
https://airflow.apache.org/blog/airflow-registry/
org, is built from the same repo as the providers, and updates automatically when new versions are published
https://hertzbeat.apache.org/blog/2026/02/05/hertzbeat-v1.8.0
Dear Community Members,
We are excited to announce the official release of Apache HertzBeat™ 1.8.0! This major release brings significant enhancements including AI-powered features, expanded monitoring capabilities, improved performance
https://flink.apache.org/2025/03/24/apache-flink-2.0.0-a-new-era-of-real-time-data-processing/
, watermark propagation, timer handling, and key ordering) to ensure that users can adopt the new architecture without worrying about behavioral changes in their applications
https://doris.apache.org/blog/vector-search/
For example, if someone searches for a specific error code or product name, keyword search can return highly precise results very quickly
https://flink.apache.org/2026/02/06/apache-flink-agents-0.2.0-release-announcement/
You can check the list of known issues and their resolution status via the Github Issues
https://flink.apache.org/2026/03/26/apache-flink-agents-0.2.1-release-announcement/
flink</groupId>
<artifactId>flink-agents-integrations-chat-models-openai</artifactId>
<version>0
https://burr.apache.org
Apache Burr (Incubating) makes it easy to develop applications that make decisions, from simple chatbots to complex multi-agent systems. Pure Python, no magic.
chatbot.py
ChatbotMulti-AgentState Machine
from burr.core import action, State
https://communityovercode.apache.org/events/glasgow-2026/lakehouse-day-glasgow
You can register for just the Lakehouse Day event, or receive a special discount if you register for both Community Over Code and Lakehouse Day
https://airflow.apache.org/blog/common-ai-provider/
It’s built on Pydantic AI and supports 20+ model providers (OpenAI, Anthropic, Google, Azure, Bedrock, Ollama, and more) through a single install
https://camel.apache.org/
Define a tool once as a Camel route, and it works with LangChain4j, Spring AI, and OpenAI
https://apisix.apache.org/docs/apisix/plugins/ai-cache/
A streamed response is written to the cache only after it completes — that is, the terminal event for the client protocol is received (data: [DONE] for OpenAI, message_stop for Anthropic, response
https://apisix.apache.org/docs/apisix/plugins/ai-prompt-guard/
You can optionally save the key to an environment variable as such:
export OPENAI_API_KEY=<YOUR_OPENAI_API_KEY>
If you are working with other LLM providers, please refer to the provider’s documentation to obtain an API key
https://apisix.apache.org/docs/apisix/plugins/ai-aws-content-moderation/
user, tool and assistant follow request_check_mode; system is checked on every request (it can be poisoned by malicious ToolCall arguments overwriting the system prompt) and also covers OpenAI’s developer role, which replaces system on newer models
https://apisix.apache.org/docs/apisix/plugins/ai-proxy-multi/
For instance, if you are working with OpenAI, DeepSeek, or AIMLAPI, you can configure additional parameters such as max_tokens, temperature, top_p, and stream
https://apisix.apache.org/docs/apisix/plugins/ai-rate-limiting/
If it is local, the counter is stored in local memory
https://hertzbeat.apache.org/blog/2025/09/06/hertzbeat-v1.7.3
org/zh-cn/docs/
New Features and Enhancements
Monitoring Support Expansion: Added monitoring support for Apache DolphinScheduler(#3656), TDengine(#3678), macOS systems(#3715), Synology NAS(#3721), and HertzBeat(#3641)
https://flink.apache.org/2026/07/25/apache-flink-agents-0.3.1-release-announcement/
flink</groupId>
<artifactId>flink-agents-integrations-chat-models-openai</artifactId>
<version>0
https://apisix.apache.org/cookbooks/redis-ai-cache/
If that copy fails, APISIX still serves the semantic hit and logs the error
https://flink.apache.org/2025/12/04/apache-flink-2.2.0-advancing-real-time-data--ai-and-empowering-stream-processing-for-the-ai-era/
You can create models with specific providers (like OpenAI) and use them to make predictions on your data
https://apisix.apache.org/integrations/redis/
If a header can change the provider response, remove it or map it to a trusted server-side variable and include that variable in cache_key
https://apisix.apache.org/docs/apisix/plugins/prometheus/
You can then create event monitoring and alerting in Prometheus to monitor the health of your API gateway and APIs
https://flink.apache.org/2025/07/31/apache-flink-2.1.0-ushers-in-a-new-era-of-unified-real-time-data--ai-with-comprehensive-upgrades/
The implementation supports both Flink builtin model providers (OpenAI) and interfaces for users to define custom model providers, accelerating Flink’s evolution from a real-time data processing engine to a unified realtime AI platform
https://flink.apache.org/2025/12/09/apache-flink-agents-0.1.1-release-announcement/
The Apache Flink Community is pleased to announce the first bug fix release of the Flink-Agents 0.1 series.
This release includes 11 bug fixes, vulnerability fixes, and minor improvements for Flink-Agents 0.1. Below you will find a list of