Riskiana @riskicoder

🚀 Kubernetes Helm — the cheat sheet every DevOps engineer needs! Managing Kubernetes deployments manually can get messy. Helm makes it easier to package, configure, version, and deploy applications using reusable Charts. Here's what every Kubernetes engineer should know 👇 🔹 Charts & Releases 🔹 "Chart.yaml" vs "values.yaml" 🔹 Helm templates 🔹 Essential Helm commands 🔹 Install, upgrade & rollback 🔹 Managing dev, staging & prod environments 💡 Pro tip: Master "helm upgrade" and "helm rollback" before managing production deployments. Save this cheat sheet for your next Kubernetes project! 🔖 #Kubernetes #Helm #DevOps #SRE #CloudNative

4 CORE CONCEPTS TO MASTER AI SYSTEM DESIGN 1. AI Fundamentals, LLMs & Model Architecture 2. RAG, Embeddings, Vector Databases & Knowledge Systems 3. AI Agents, Tool Calling & Multi-Agent Orchestration 4. AI Infrastructure, Scalability, Evaluation & Production Deployment Grab AI System Design Handbook: codewithdhanian.gumroad.com/l/zdluvb

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Interviewer: You have two beans implementing the same interface. Your application fails to start with NoUniqueBeanDefinitionException. How would you resolve this without removing either implementation? When would you use @primary versus @qualifier?

Males banget denger suara sendiri

Handbook on Understanding Harness Engineering for AI Agents [48-page PDF]: drive.google.com/file/d/1PT-P2P…

Uber publicó cómo armó su infraestructura de MCP y hay varias ideas muy buenas acá. imaginate una empresa con miles de servicios internos y muchos equipos conectando agentes a esos sistemas por su cuenta. cuando eso empieza a crecer, terminás con tooling fragmentado, infra duplicada, distintas formas de exponer tools y poca consistencia en seguridad, discovery y operación. por eso Uber hizo un MCP Gateway: una capa central entre los agentes y sus servicios internos. el agente habla MCP y el gateway se encarga de routing, permisos, ejecución y de traducir las llamadas a HTTP, gRPC o TChannel según corresponda. como Uber ya tenía miles de APIs internas, también hicieron AutoCrawler: recorre sus definiciones, detecta métodos y schemas y genera MCP tools automáticamente. incluso usan un LLM para mejorar las descripciones. hoy tienen +800 MCP servers y +5000 tools. y ahí aparece otro problema: no podés cargar todas esas definiciones en el contexto del modelo. para eso crearon Omni MCP. en vez de pasarle miles de tools al agente, este va descubriendo qué server necesita, qué tools tiene disponibles, carga el schema de la correcta y recién ahí ejecuta. me parece un muy buen ejemplo de cómo cambia la arquitectura cuando los agentes pasan de usar unas pocas tools a trabajar con miles. terminás necesitando algo muy parecido a un API Gateway para agentes.

Uber Engineering @UberEng

3 weeks ago

x.com/i/article/2106…

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An article for everyone who ships, buys, or signs off on software built on large language models (LLMs): engineers, product managers, and the CEO. Written in plain language, from the big picture down to the details. x.com/i/article/2106…

FORGET UDEMY. FORGET COURSERA. FORGET PAID COURSES. CLAUDE can now teach you anything for free. Use these prompts and level up your learning in 2026:

Do yourself a favor! – Learn AI Engineering – Build an Agent that solves a real problem – Give it tools, context, and MCP – Use evals to test it actually works – Deploy it and share code on GitHub – Add this to your resume You won’t believe how many job offers you'll get.

Cracking The Backend Interviews... topmate.io/interviewswith…

It’s GOLD for learning Networking for Cloud & DevOps

Tech Fusionist | Kushal Gangil @techyoutbe

2 months ago

Networking Playbook for Cloud & DevOps (12 Days) techfusionist.substack.com/p/12-days-netw…

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if i had 6 months to become an AI engineer from zero i'd do this Stage 1: the boring foundations - learn: python, git, the terminal, http and json, basic sql, fastapi - practice: build a cli tool that calls a public api and writes clean json to disk - why: ai engineering is software engineering first, and every stage below assumes you can ship a working endpoint Stage 2: llm app development - learn: prompting, structured outputs with pydantic, tool calling, streaming, conversation state, token cost - practice: build an invoice parser that returns a typed python object, then give a model 3 tools and let it pick - why: this is the actual job, and most people skip straight past it to agents Stage 3: rag, properly - learn: embeddings, chunking, vector stores, metadata filtering, reranking - practice: ingest 20 pdfs and ship an endpoint that returns a cited answer from the top 5 chunks - why: most rag failures are retrieval failures, so whoever can debug retrieval gets paid Stage 4: agents, workflows and evals - learn: the agent loop, tool descriptions, state, retries, and when an agent is the wrong call - practice: build one agent with no framework at all, just the api, 3 tools and a while loop - why: a chain of 3 fixed calls beats an agent that might make 3 calls, and knowing which one you need is the senior part Stage 5: deployment and reliability - learn: docker, background jobs, auth, rate limits, tracing with langfuse, caching, cost control - practice: containerize your rag app with a vector db and redis, then put a hard daily spend cap on it - why: this is where most people stall, they can demo but they can't survive real traffic at 2am Stage 6: pick one lane - learn: product engineering, applied ml, or business automation - practice: ship 2-3 projects a stranger can open and use without you explaining anything - why: six months makes you dangerous in one direction, not average in three you don't need to train models you need to build systems on top of them, and that's what companies are actually paying for i wrote the full version of this with every resource and link for each stage so, if you're beginner and skipped this article 6 months ago, read below:

Ronin @DeRonin_

7 months ago

x.com/i/article/2033…

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Jev Decision Services Architecture

May you get married to someone who sees you as an Amanah from Allah, not a burden. 🤲🏻

Jev Founder, Diogo Almeida, just released a 12-page PDF on how to use Jev with LLMs It is more useful than most paid AI courses: this is a 10-step blueprint on how to build a faster, cheaper and more controllable AI system around Claude, Codex, Grok or any other LLM: step 1 → split the responsibilities: the LLM generates, Jev makes bounded semantic decisions and deterministic code keeps authority step 2 → build the state: give Jev the current request, relevant evidence, policy and proposed action instead of sending the entire conversation step 3 → choose the right primitive: Choice selects a route, Score evaluates an ordered rubric and Noul returns the probability that a statement is true step 4 → replace giant evaluation prompts with atomic questions: intent, urgency, evidence, risk and scope become separate typed decisions step 5 → put Jev before the LLM: select the context, tools, provider and workflow before paying for an expensive generative call step 6 → give the LLM a bounded job: once Jev selects the route, the model receives only the instructions, files and tools required for that branch step 7 → put Jev after the LLM: check whether the result answers the request, uses sufficient evidence and stays inside the permitted scope step 8 → route by confidence: high-confidence low-risk cases proceed automatically, uncertain cases request more context and consequential actions go to review step 9 → batch independent decisions: ask multiple Choice, Score and Noul questions over one shared state instead of creating another LLM call for every judgment step 10 → record the complete decision receipt: state version, question, probabilities, selected route, model, latency, outcome and human override most AI courses teach you how to write a bigger prompt this 12-page guide teaches you how to build the control system around every prompt the result: smaller contexts, fewer unnecessary LLM calls, safer tool execution and decisions you can actually inspect, test and improve Send this PDF and the original Jev article to Claude Code or Codex and start rebuilding one expensive LLM decision at a time ↓

rari @0xwhrrari

4 weeks ago

Most AI agents waste tokens on decisions that never needed text Jev turns routing, scoring, and verification into a fast decision layer I broke down the architecture most agent builders are still missing ↓ x.com/i/article/2101…

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Anatomy of a Terraform project 🚀 Still confused about Terraform files and their directory structure? Here are the Terraform files you need to understand: • 𝗺𝗮𝗶𝗻.𝘁𝗳: Defines resources and module calls. • 𝘃𝗮𝗿𝗶𝗮𝗯𝗹𝗲𝘀.𝘁𝗳: Declares the input variables for the configuration. • 𝗼𝘂𝘁𝗽𝘂𝘁𝘀.𝘁𝗳: Exposes resource values like IDs and ARNs. • 𝘁𝗲𝗿𝗿𝗮𝗳𝗼𝗿𝗺.𝘁𝗳𝘃𝗮𝗿𝘀: Supplies the actual values for those declared variables. • 𝗯𝗮𝗰𝗸𝗲𝗻𝗱.𝘁𝗳: Configures the remote state backend like S3 or Terraform Cloud. • 𝗺𝗼𝗱𝘂𝗹𝗲𝘀/: Folder that holds reusable pieces like network and compute. • 𝘁𝗲𝗿𝗿𝗮𝗳𝗼𝗿𝗺.𝗹𝗼𝗰𝗸.𝗵𝗰𝗹: Records selected provider versions and checksums. • 𝘁𝗲𝗿𝗿𝗮𝗳𝗼𝗿𝗺.𝘁𝗳𝘀𝘁𝗮𝘁𝗲: Tracks resources managed by Terraform and their state. Once you understand the structure, reviewing changes and debugging the plan output becomes much easier. So when you join a project or pick up someone else's Terraform code, spend time understanding the layout before you run apply. We have a detailed guide to help you understand Terraform modules and how to structure them properly. 𝐑𝐞𝐚𝐝 𝐢𝐭 𝐡𝐞𝐫𝐞: devopscube.com/terraform-modu… 𝗡𝗼𝘁𝗲: Files such as main.tf, variables.tf, and backend.tf are naming conventions. Terraform reads all .tf files in the working directory as part of the same configuration. How do you structure your Terraform projects? #terraform #iac

One GitHub repo can teach you more about Kubernetes than 50 tutorials. Build a cluster by hand instead: github.com/kelseyhightowe… No scripts. No shortcuts. You'll understand what every managed service is doing for you and never fear a cluster again.