DagKnows, Inc’s Post

This is how you can actually speed up incident response using DagKnows. Build workflows with AI assistance in minutes and trigger them upon Grafana alerts. Troubleshoot and remediate issues, or enrich alerts and cut down your MTTR!

🌟 Unlock Efficiency with Aliases 🌟 Aliases are a simple yet powerful tool, that can streamline your workflow and save you valuable time. By creating shortcuts for frequently used commands, you can minimize repetitive typing and enhance productivity in your day-to-day tasks. To create a persistent alias, you can add it to your shell configuration file (.zshrc or .bashrc depending on your OS). Just use the syntax: alias shortcut='command', save the configuration file, and then run source ~/.zshrc (or the equivalent for your shell) to apply the changes. In the following video, I demonstrate how to set up aliases effectively, including some practical examples and tips to keep in mind for optimal usage. https://lnkd.in/gpDhq9u5 I’d love to hear from you, what aliases you have found most helpful in your workflow? Share your go-to aliases in the comment section, so that we can also learn about them and make use of them! #Aliases #Productivity #CommandLine #TechTips #Workflow #Efficiency #ShellScripting #DevOps #Coding #Automation

Give your #engineers more time to deliver new code and stop wasting time on bugs! Railtown AI’s Root Cause Analysis #RCA tells you which errors to fix and where to find solutions. Learn more on our website: https://lnkd.in/gAP7eCe5

❓ Remember that feature that will detect all of the suspicious words within Confluence and make an alert to that. 👉 That will require an Atlassian guard premium subscription....but if there is a way, at least for the data centre users to have an insight for the issues in Jira if there are suspicious words added? ScriptRunner comes handy here, because it can do exactly that! #AtlassianCreator #Jira #ScriptRunner

TTL implementation refactored using Actor Pattern: Lesson learned: - Not sharing data between actor makes testing extremely easy. - But then, communication between them requires additional input (sender, for example) - setting the right `tick` interval requires performance metric Question: - Conceptually, actor models store its internal state and no shared state is made. So, instead of having Arc[Shard;NUM_OF_SHARD] structure, we may be able to spawn the "stateful actors" and let them store their own data structure which is absolutely lock free, ordering free?

🔒 **Architect a Secure and Scalable Shift-Left CI/CD Pipeline for Enterprise Microservices** I’m excited to share a powerful **prompt** designed to help you architect a **Shift-Left CI/CD pipeline** that transforms your enterprise microservices environment. 🚀 Whether you're looking to reduce costs, accelerate releases, or enhance software quality, this prompt guides you through every step of the process: Here’s what the prompt can do for you: 1. **Shift-Left Strategy Development**:    - Define a strategy that integrates Shift-Left practices early in the SDLC to improve defect detection and security. It also helps you quantify the benefits like faster releases, cost reduction, and better software quality. 2. **CI/CD Pipeline Architecture**:   - Design a comprehensive CI/CD pipeline tailored to microservices. The prompt ensures security checks (SAST, DAST, SCA) and testing (unit, integration, performance) are automated early in the process, with a visual pipeline diagram and tooling recommendations. 3. **Tooling & Implementation Plan**:   - Choose the right tools for CI/CD, testing, and security with justifications for scalability and cost-effectiveness. The structured roadmap helps ensure a smooth implementation. 4. **Risk Assessment and Mitigation**:   - Identify potential risks and propose mitigation strategies like phased rollouts and team training to overcome challenges like cultural resistance and integration complexity. 5. **Evaluation Criteria**:   - Evaluate the clarity, scalability, and adherence to industry standards (ISO 27001, GDPR) of your architecture. This prompt gives you a complete blueprint for transitioning to a **Shift-Left CI/CD pipeline**, ensuring a proactive stance on security and collaboration. #DevOps #ShiftLeft #CICD #Microservices #SoftwareDevelopment #Security #Automation #EnterpriseInnovation #ai #innovation #devops #ciso #cso #shiftleft #devsecops #cicd #microservices

What exactly do we mean by “automated pipeline”? If we mean just a bot fine tuned by a user directly on an LLM, this is spot on. If it is a pipeline with a domain specific expert layer created by a professional team, then nothing beats that. Pasting stuff into ChatGPT is neither consistent in output nor scalable. samvid.ai #sootras #neeyums #essences

A quick mix of low tech and high tech mechanisms.

The recent release of the Mixture-of-Agents paper is timely with the experimentation I have been doing recently with LLM-driven agents. However, it doesn't seem like Mixture-of-Agents is actually about agents? Based on the learnings from the prototypes I've been working on, I propose that the core of an agent is a control loop coupled with an LLM. You will see this loop referred to as a "reasoning loop" in some orchestration frameworks. (There's some interesting lessons we can take from Kubernetes controllers, but I'm still cooking with that idea). Past the core exists the environment that the agent interacts with. These interactions are intermediated by "tools"—a collection of functions (in LLMs that have function calling abilities) that the agent can choose to use to complete its target goal. There's more to it—especially as you get into multi-agent orchestration—with concerns such as planning, scheduling, ensemble generation, "human-needed" interrupts, etc. In any case, I found it curious that these concepts were missing from the Mixture-of-Agents paper. I do think the Mixture-of-Agents pattern can be rather useful (and you should be able to implement such pattern with tools like LangGraph, PromptFlow, etc), but I suspect it will create confusion in discussions about agents. And it looks like I'm not alone, as others have provided similar feedback in the repo (https://lnkd.in/g4c5RXt5). What do you think? How are agents being defined and discussed in your conversations?

Interested in building and optimizing RAG pipelines in production? Join the next ZenML webinar where Alex S. walks through: 🔷The process of ingesting and preprocessing data for your RAG pipeline 🔷The critical role of embeddings in an RAG retrieval workflow, including how to generate and store these embeddings in a vector database for efficient retrieval of relevant information. 🔷How ZenML simplifies the tracking and management of RAG-associated artifacts, ensuring reproducibility and facilitating collaboration. 🔷Strategies for assessing the performance of your RAG pipelines 🔷The use of rerankers to enhance the overall retrieval process in your RAG pipeline We're hosting it at a US-friendly time this time, but if it doesn't work for you, please comment below. With enough interest, happy to host it in a time friendlier to your locality! Link to register in the comments! ⤵ #llmops #rag #mlops