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@laura_garcia shared a post, 1 month ago
Software Developer, RELIANOID

𝗛𝗮𝗰𝗸 𝗦𝗽𝗮𝗰𝗲 𝗖𝗼𝗻 𝟮𝟬𝟮𝟲

🚀 𝗛𝗮𝗰𝗸 𝗦𝗽𝗮𝗰𝗲 𝗖𝗼𝗻 𝟮𝟬𝟮𝟲 📍 Kennedy Space Center 📅 May 6–9, 2026 𝙒𝙝𝙚𝙧𝙚 𝙘𝙮𝙗𝙚𝙧𝙨𝙚𝙘𝙪𝙧𝙞𝙩𝙮 𝙢𝙚𝙚𝙩𝙨 𝙨𝙥𝙖𝙘𝙚 𝙞𝙣𝙣𝙤𝙫𝙖𝙩𝙞𝙤𝙣. Hack Space Con is not your typical event — it’s where cybersecurity, aerospace, and advanced technologies converge to shape the future of security beyond Earth. 🔍 𝗪𝗵𝗮𝘁 𝘁𝗼 𝗲𝘅𝗽𝗲𝗰𝘁: - Hands-on techn..

HACKSPACECON2026_florida_RELIANOID
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@varbear shared a link, 1 month ago
FAUN.dev()

A Couple Million Lines of Haskell: Production Engineering at Mercury

Mercury runs ~2M lines ofHaskellin production. They choseTemporalto replace cron and DB-backed state machines. Durable workflows replace brittle coordination. They open-sourced aHaskellSDK forTemporal, wired inOpenTelemetryhooks, and pushed records-of-functions plus domain-error types... read more  

A Couple Million Lines of Haskell: Production Engineering at Mercury
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@varbear shared a link, 1 month ago
FAUN.dev()

Agentic Coding is a Trap

AI-driven coding agents are the hot new trend, but beware of the trade-offs: increased complexity, skills atrophy, vendor lock-in, and fluctuating costs. Only skilled developers can spot issues in the vast lines of generated code, but paradoxically, AI tools are impacting critical thinking skills ne.. read more  

Agentic Coding is a Trap
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@varbear shared a link, 1 month ago
FAUN.dev()

When upserts don't update but still write: Debugging Postgres performance at scale

The Datadog team introduced a new upsert query to track inactive hosts, but it unexpectedly increased disk writes and WAL syncs due to row locking. By digging into Postgres's Write-Ahead Logging (WAL) and rewriting the query using a Common Table Expression (CTE), they avoided unnecessary overhead an.. read more  

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@varbear shared a link, 1 month ago
FAUN.dev()

How We Reduced Median Memory Estimation Error by 99%, With the Help of AI

The compaction pipeline at Mixpanel ran into memory estimation issues causing OOMKills. By implementing AI-assisted analysis, they were able to reduce median estimation errorby 99%, leading to a significant improvement in memory estimation accuracy. Through thorough analysis and exploration of alter.. read more  

How We Reduced Median Memory Estimation Error by 99%, With the Help of AI
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@varbear shared a link, 1 month ago
FAUN.dev()

How To Make a Fast Dynamic Language Interpreter

Zef's AST-walking interpreter posts a 16.6× speed-up. The gains come from surgical changes:64-bit tagged values,AST node & RMW specialization,symbol hash-consing,inline caches, and a shapedobject model. Developers built it onFil-C++and later ported it toYolo-C++. The Yolo build adds ~4x speed, at th.. read more  

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@kaptain shared a link, 1 month ago
FAUN.dev()

v1.36: Tiered Memory Protection with Memory QoS

Kubernetes v1.36 rolls out Memory QoS (alpha). Opt-inmemory reservation. Tiered protection by QoS class. Kubelet observability metrics. Kernel-version warnings. It separatesthrottlingfromreservation. A feature gate enables throttling. A kubelet config field controls tieredcgroup v2protection:Guarant.. read more  

Vertex AI is Google Cloud’s end-to-end machine learning and generative AI platform, designed to help teams build, deploy, and operate AI systems reliably at scale. It unifies data preparation, model training, evaluation, deployment, and monitoring into a single managed environment, reducing operational complexity while supporting advanced AI workloads.

Vertex AI supports both custom models and foundation models, including Google’s Gemini model family. It enables organizations to fine-tune models, run large-scale inference, orchestrate agentic workflows, and integrate AI into production systems with strong security, governance, and observability controls.

The platform includes tools for AutoML, custom training with TensorFlow and PyTorch, managed pipelines, feature stores, vector search, and online and batch prediction. For generative AI use cases, Vertex AI provides APIs for text, image, code, multimodal generation, embeddings, and agent-based systems, including support for Model Context Protocol (MCP) integrations.

Built for enterprise environments, Vertex AI integrates deeply with Google Cloud services such as BigQuery, Cloud Storage, IAM, and VPC, enabling secure data access and compliance. It is widely used across industries like finance, healthcare, retail, and science for applications ranging from recommendation systems and forecasting to autonomous research agents and AI-powered products.