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@sanjayjoshi gave 🐾 to How To Make a Fast Dynamic Language Interpreter , 2 days, 18 hours ago.
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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, 3 days, 14 hours 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, 3 days, 14 hours ago
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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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@varbear shared a link, 3 days, 14 hours 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  

Agentic Coding is a Trap
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@varbear shared a link, 3 days, 14 hours 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  

How We Reduced Median Memory Estimation Error by 99%, With the Help of AI
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@varbear shared a link, 3 days, 14 hours 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  

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@kaptain shared a link, 3 days, 14 hours ago
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From Ingress NGINX to Higress: migrating 60+ resources in 30 minutes with AI

With the March 2026 retirement ofIngress NGINX, teams face an urgent compliance mandate. They must replace unpatched controllers. EnterHigress. Built onEnvoyandIstio. It unifies LLM protocols, enforces token rate limits, caches prompts, hostsMCP, and usesxDSfor zero-downtime. AnAI agentpaired withhi.. read more  

From Ingress NGINX to Higress: migrating 60+ resources in 30 minutes with AI
Gemini 3 is Google’s third-generation large language model family, designed to power advanced reasoning, multimodal understanding, and long-running agent workflows across consumer and enterprise products. It represents a major step forward in factual reliability, long-context comprehension, and tool-driven autonomy.

At its core, Gemini 3 emphasizes low hallucination rates, deep synthesis across large information spaces, and multi-step reasoning. Models in the Gemini 3 family are trained with scaled reinforcement learning for search and planning, enabling them to autonomously formulate queries, evaluate results, identify gaps, and iterate toward higher-quality outputs.

Gemini 3 powers advanced agents such as Gemini Deep Research, where it excels at producing well-structured, citation-rich reports by combining web data, uploaded documents, and proprietary sources. The model supports very large context windows, multimodal inputs (text, images, documents), and structured outputs like JSON, making it suitable for research, finance, science, and enterprise knowledge work.

Gemini 3 is available through Google’s AI platforms and APIs, including the Interactions API, and is being integrated across products such as Google Search, NotebookLM, Google Finance, and the Gemini app. It is positioned as Google’s most factual and research-capable model generation to date.