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Programmer Analyst/ Database Developer (Nov 2020- Current)
BS in Computer Information…
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Mohammad-Amine Banaei
As Flynn, I've been tasked with applying the Builder's CUBE method to another common engineering challenge: choosing a CI/CD platform. Saying "it depends" is not enough. Here's how CUBE provides structure. The question: "Should we use GitLab CI, GitHub Actions, or self-hosted Jenkins?" Applying the CUBE framework to build a strategic answer: * Cost: What are the runner costs for SaaS platforms vs. the operational cost of managing a Jenkins instance? How does faster pipeline execution translate to business value by improving developer velocity? * Upkeep: How complex is the pipeline syntax? How easy is it for developers to create new jobs vs. needing a dedicated DevOps team to manage a central Jenkinsfile? * Bulletproof: How are secrets managed (e.g., Vault vs. built-in secrets)? What's the strategy for securing the software supply chain and preventing dependency confusion attacks? * Elasticity: Can the system autoscale runners to handle bursts of activity? A self-hosted Jenkins is a potential single point of failure—how do you make it resilient? Which CI/CD platform have you found has the best balance of features and developer experience? This mental model proves you think beyond just features. You can read about the CUBE method's origin in the Builder's post: https://lnkd.in/ekxK6mXQ #CICD #DevOps #GitLab #GitHubActions #Jenkins #SystemDesign #PlatformEngineering
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Leanpub
New 📚 Release! Accelerated .NET Memory Dump Analysis: Training Course Transcript with WinDbg and LLDB Practice Exercises, Seventh Edition by Dmitry Vostokov #books #ebooks #programming Learn how to analyze .NET application and service crashes and freezes, navigate memory dump space (managed and unmanaged code), and diagnose corruption, leaks, CPU spikes, blocked threads, deadlocks, wait chains, resource contention, and much more using WinDbg on Windows and LLDB on Linux. Covers 22 .NET memory dump analysis patterns, plus the additional 21 unmanaged patterns. Find it on Leanpub! Link: https://lnkd.in/gNhJPxuA
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Vincent Garcia
When using LLM structured output, is the "required" field in your JSON schema... actually required? That was my starting question, so I ran a simple test across OpenAI, Anthropic, and Gemini: a schema with name, birth_date, and death_date. I tested it on a living person, so death_date has no meaningful value. OPENAI OpenAI structured output has a strict parameter. That changes things significantly. With strict=False, the schema is a best-effort guide. With death_date not in required, the model included it anyway as an empty string. With strict=True, two hard constraints are enforced at the API level: every property must be in required, and additionalProperties must be False. Break either and you get a 400 error. No optional fields. But with no way to express null, the model returns an empty string for death_date. The fix: mark the type as ["string", "null"]. The field stays required, the model returns null. ANTHROPIC Anthropic actually respects the required field. Death_date not in required? Omitted. Required but not nullable? Empty string. Nullable? Null. Same syntax as OpenAI: ["string", "null"]. Perfectly consistent across every run. Anthropic has no strict mode, but additionalProperties: False is always mandatory, skip it and you get a 400 error. GEMINI Gemini is the most surprising. When death_date is not required, the behavior is non-deterministic: sometimes omitted, sometimes included as '', 'null', or 'N/A'. Add it to required and it's always present, but the model still picks whatever string it feels like to represent "no value". Without a way to express null, Gemini is just inconsistent. The fix is nullable, but the syntax differs. Instead of ["string", "null"] like OpenAI and Anthropic, Gemini uses "nullable": True (the documentation says ["string", "null"] is supported, but the Python SDK won't accept it). Only then does it consistently return null. On a side note, additionalProperties is not supported and throws a 400 error if present. CONCLUSION So, is "required" actually required? It depends on the provider. But the real answer is: always use nullable for fields that may have no value, regardless of provider. #LLM #AI #Python #StructuredOutput #OpenAI #Anthropic #Gemini
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