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@laura_garcia shared a post, 11 months, 2 weeks ago
Software Developer, RELIANOID

🚀 Boost Network Performance with ECMP

Equal-Cost Multi-Path (ECMP) allows multiple paths with the same cost to be used simultaneously — optimizing traffic, increasing redundancy, and improving throughput. 🔹 Key Benefits • Load balancing across equal-cost routes • Seamless failover and redundancy • Better bandwidth usage and lower laten..

kb ECMP Equal-Cost Multi-Path
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@mostafahussein shared a link, 11 months, 2 weeks ago

Kafka Encryption for Cardholder Data: Solving PCI Challenges with Kroxylicious

Kafka

Encrypt Kafka messages at rest without changing app code — using Kroxylicious and OpenBao to meet PCI encryption requirements.

kroxylicious-kafka-integration
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@faun shared a link, 11 months, 2 weeks ago
FAUN.dev()

GitOps in 2025: From Old-School Updates to the Modern Way

GitOpshas taken the throne, withGitas the undisputed oracle for configurations. Welcome to a world whereArgo CDandFluxstrut their stuff. By 2025, this lively dance ofpull-basedmagic reshapes the landscape. GitOps isn't just a tool anymore—it's a full-blown, no-holds-barred platform transformation... read more  

GitOps in 2025: From Old-School Updates to the Modern Way
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@faun shared a link, 11 months, 2 weeks ago
FAUN.dev()

Weaponizing Dependabot: Pwn Request at its finest

GitHub bots like Dependabot might merge malicious code due to "Confused Deputy" attacks, escalating to command injection via crafted branch names. New TTPs reveal clever ways attackers exploit these issues... read more  

Weaponizing Dependabot: Pwn Request at its finest
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@faun shared a link, 11 months, 2 weeks ago
FAUN.dev()

Clarifying Roles in Data and Engineering: Why Specialization Matters

Data Analystssift through the past usingSQLandBI tools. Their goal? Unearthing insights. Meanwhile,Data Scientistsharness the power ofPythonandRto gaze into the future—predicting trends like data-driven oracles. On another front,Data Engineerscraft pipelines. ThinkApache Spark—the stage manager for .. read more  

Clarifying Roles in Data and Engineering: Why Specialization Matters
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@faun shared a link, 11 months, 2 weeks ago
FAUN.dev()

How We Cut Our AWS Bill by Over 20% and Boosted Application Performance

Migrating toAWS GravitonwithJava 21didn't just pinch pennies—it saved 20% and cranked up performance. Why? The magic of ARM64 support and multi-arch CI/CD. They navigated the switch by harnessing dual-architecture EKS clusters and canary deployments. Result? A smoother path with zero hiccups. End us.. read more  

How We Cut Our AWS Bill by Over 20% and Boosted Application Performance
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@faun shared a link, 11 months, 2 weeks ago
FAUN.dev()

Terraform security: 5 foundational practices

Lock downTerraformlike Fort Knox. Verify your module origins. Guard sensitive state data like a nosy neighbor's business. And, please, no hardcoded credentials—rookie mistake. For ironclad security, pin those module versions, tap into private registries, and wield the power of dynamic provider crede.. read more  

Terraform security: 5 foundational practices
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@faun shared a link, 11 months, 2 weeks ago
FAUN.dev()

Why doesn’t Rust care more about compiler performance?

Rust’scompiler speedhas doubled in three years for some benchmarks. Remarkably efficient. But its intricate architecture still wrestles with progress. Stability hogs the spotlight, and new features waltz in, leaving optimizations to grab scraps. Volunteers—Rust’s lifeblood—fuel much of its growth. T.. read more  

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@faun shared a link, 11 months, 2 weeks ago
FAUN.dev()

Debugging memory leaks in Postgres, jemalloc edition

jemallocexcels at sniffing out memory leaks compared toAddressSanitizer, especially when leaks ghost out at program exit. But here's the catch: to dig into profiling with jemalloc, like you're wrangling Postgres, you better cozy up to Linux... read more  

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@faun shared a link, 11 months, 2 weeks ago
FAUN.dev()

Why Environments Beat Clusters For Dev Experience

Developers chasepromotions, not the tedium of deployments. Environments should reign supreme—not just a lone Kubernetes cluster hogging the spotlight.Real-time insights? They zoom past those outdated, siloed CI pipelines... 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.