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@sanjayjoshi gave ๐Ÿพ to How To Make a Fast Dynamic Language Interpreter , 1ย month, 2ย weeks ago.
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@laura_garcia shared a post, 1ย month, 2ย weeks ago
Software Developer, RELIANOID

๐—›๐—ฎ๐—ฐ๐—ธ ๐—ฆ๐—ฝ๐—ฎ๐—ฐ๐—ฒ ๐—–๐—ผ๐—ป ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฒ

๐Ÿš€ ๐—›๐—ฎ๐—ฐ๐—ธ ๐—ฆ๐—ฝ๐—ฎ๐—ฐ๐—ฒ ๐—–๐—ผ๐—ป ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฒ ๐Ÿ“ Kennedy Space Center ๐Ÿ“… May 6โ€“9, 2026 ๐™’๐™๐™š๐™ง๐™š ๐™˜๐™ฎ๐™—๐™š๐™ง๐™จ๐™š๐™˜๐™ช๐™ง๐™ž๐™ฉ๐™ฎ ๐™ข๐™š๐™š๐™ฉ๐™จ ๐™จ๐™ฅ๐™–๐™˜๐™š ๐™ž๐™ฃ๐™ฃ๐™ค๐™ซ๐™–๐™ฉ๐™ž๐™ค๐™ฃ. Hack Space Con is not your typical event โ€” itโ€™s where cybersecurity, aerospace, and advanced technologies converge to shape the future of security beyond Earth. ๐Ÿ” ๐—ช๐—ต๐—ฎ๐˜ ๐˜๐—ผ ๐—ฒ๐˜…๐—ฝ๐—ฒ๐—ฐ๐˜: - Hands-on techn..

HACKSPACECON2026_florida_RELIANOID
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@varbear shared a link, 1ย month, 2ย weeks ago
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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 ย 

A Couple Million Lines of Haskell: Production Engineering at Mercury
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@varbear shared a link, 1ย month, 2ย weeks ago
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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 ย 

Agentic Coding is a Trap
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@varbear shared a link, 1ย month, 2ย weeks ago
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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 ย 

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@varbear shared a link, 1ย month, 2ย weeks ago
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How To Make a Fast Dynamic Language Interpreter

Zef's AST-walking interpreter posts a 16.6ร— 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 ย 

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@varbear shared a link, 1ย month, 2ย weeks ago
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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 ย 

How We Reduced Median Memory Estimation Error by 99%, With the Help of AI
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@kaptain shared a link, 1ย month, 2ย weeks ago
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v1.36: In-Place Vertical Scaling for Pod-Level Resources Graduates to Beta

Kubernetes v1.36 moves In-Place Pod-Level Resources Vertical Scaling to Beta and flips the feature gate on by default. Operators can patch a Pod's aggregate resource to resize running Pods. Often no container restart is needed. Kubelet breaks the Pod-level change into per-container resize events. It.. read more ย 

LangChain is a modular framework designed to help developers build complex, production-grade applications that leverage large language models. It abstracts the underlying complexity of prompt management, context retrieval, and model orchestration into reusable components. At its core, LangChain introduces primitives like Chains, Agents, and Tools, allowing developers to sequence model calls, make decisions dynamically, and integrate real-world data or APIs into LLM workflows.

LangChain supports retrieval-augmented generation (RAG) pipelines through integrations with vector databases, enabling models to access and reason over large external knowledge bases efficiently. It also provides utilities for handling long-term context via memory management and supports multiple backends like OpenAI, Anthropic, and local models.

Technically, LangChain simplifies building LLM-driven architectures such as chatbots, document Q&A systems, and autonomous agents. Its ecosystem includes components for caching, tracing, evaluation, and deployment, allowing seamless movement from prototype to production. It serves as a foundational layer for developers who need tight control over how language models interact with data and external systems.