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GenAI vs. Agentic AI: What Developers Need to Know

Docker’s getting serious about agent-based AI. It just rolled out tools tailor-made for building modular, goal-chasing LLM systems. Model Runnerlets devs spin up LLMs locally—zero cloud, zero wait.Offloadtaps cloud GPUs when local ones tap out. And theMCP Gatewaypipes in external tools without duct.. read more  

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Building a Redis Clone from Scratch – In-Memory KV Store with TCP

A solo dev just spun up a public build of aRedis-style key-value store in Java—lean, thread-safe, and backed by a custom TCP server. Right now it handlesGET,SET, andDELETEover a socket-level protocol. No HTTP. No bloat. At its core: aConcurrentHashMapdoing the heavy lifting. Fast, in-memory, and de.. read more  

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I used NotebookLM to learn a new programming language, and it actually worked

A CS student taught themselvesSwiftusingNotebookLM, Google’s AI that sticks to sources you feed it. They pulled in handpicked docs, YouTube transcripts, and visual mind maps—all dropped into a custom notebook. No generic guesses. No hallucinated trivia. Just clean, source-grounded answers on syntax .. read more  

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How we discovered, and recovered from, Postgres corruption on the homeserver

PostgreSQL index corruption silently broke the matrix.org homeserver. State groups were corrupted, active data was deleted, and restoring consistency took a week of forensic debugging and reindexing. The root cause? Unclear. Hardware, maybe. But not Postgres or Synapse. The team’s fix involved disab.. read more  

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@laura_garcia shared a post, 9 months ago
Software Developer, RELIANOID

📌 New: netstat Command Cheatsheet

Need to check active connections, monitor listening ports, or debug network issues? The Linux netstat command remains a go-to tool for quick and effective diagnostics. We’ve created a clear, quick-reference cheatsheet with: 🔍 Essential command flags 📊 Real-world use cases ⚙️ Integration tips for REL..

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Building Reproducible ML Systems with Apache Iceberg and SparkSQL

Apache Iceberg +SparkSQLbringsACID transactions,schema evolution, andtime travelto data lakes. That means ML pipelines finally get reproducibility and consistency without the hacks. Iceberg’s snapshot-based guts track every version, handle parallel writes without stepping on toes, and keep training .. read more  

Building Reproducible ML Systems with Apache Iceberg and SparkSQL
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How to Build an Agent

A new framework lays out six sharp steps for building agents that actually ship. It kicks off with a grounded task, locks in SOPs, then tunes high-leverage prompts. The real choke point? LLM reasoning. Everything else—architecture, data flow, testing—is scoped to chase tight, measurable gains there... read more  

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Introducing the Amazon Bedrock AgentCore Code Interpreter

AWS just droppedAgentCore Code Interpreter—a managed box where AI agents can run Python, JavaScript, and TypeScript in isolation. Think of it as a secure playground with autoscaling, controlled file access, and deep hooks into frameworks likeLangChain,LangGraph,Strands, andCrewAI. Big picture: This.. read more  

Introducing the Amazon Bedrock AgentCore Code Interpreter
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Using generative AI for building AWS networks

Amazon Q Developer CLI and Bedrock just leveled up. You can now spin up AWS Cloud WANs and VPCs using plain English. Type what you need—get full deployments, phased migrations, and IaC for both CloudFormation and Terraform. Agents handle the whole stack: network discovery, rollout, and config. No m.. read more  

Using generative AI for building AWS networks
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AWS AgentCore: The Overlooked Privilege Escalation Path in Bedrock’s AI Tooling

AWS Bedrock AgentCore just got a new trick: agents (and anyone IAM-blessed) can now runCode Interpreters. Think arbitrary code execution—with custom or predefined IAM roles. But here’s the kicker: these interpreters skipresource policies, lean on control plane APIs, and don’t log squat—unlessyou fl.. read more  

GPT-5.4 is OpenAI’s latest frontier AI model designed to perform complex professional and technical work more reliably. It combines advances in reasoning, coding, tool use, and long-context understanding into a single system capable of handling multi-step workflows across software environments. The model builds on earlier GPT-5 releases while integrating the strong coding capabilities previously introduced with GPT-5.3-Codex.

One of the defining features of GPT-5.4 is its ability to operate as part of agent-style workflows. The model can interact with tools, APIs, and external systems to complete tasks that extend beyond simple text generation. It also introduces native computer-use capabilities, allowing AI agents to operate applications using keyboard and mouse commands, screenshots, and browser automation frameworks such as Playwright.

GPT-5.4 supports context windows of up to one million tokens, enabling it to process and reason over very large documents, long conversations, or complex project contexts. This makes it suitable for tasks such as analyzing codebases, generating technical documentation, working with large spreadsheets, or coordinating long-running workflows. The model also introduces a feature called tool search, which allows it to dynamically retrieve tool definitions only when needed. This reduces token usage and makes it more efficient to work with large ecosystems of tools, including environments with dozens of APIs or MCP servers.

In addition to improved reasoning and automation capabilities, GPT-5.4 focuses on real-world productivity tasks. It performs better at generating and editing spreadsheets, presentations, and documents, and it is designed to maintain stronger context across longer reasoning processes. The model also improves factual accuracy and reduces hallucinations compared with previous versions.

GPT-5.4 is available across OpenAI’s ecosystem, including ChatGPT, the OpenAI API, and Codex. A higher-performance variant, GPT-5.4 Pro, is also available for users and developers who require maximum performance for complex tasks such as advanced research, large-scale automation, and demanding engineering workflows. Together, these capabilities position GPT-5.4 as a model aimed not just at conversation, but at executing real work across software systems.