Updates and recent posts about Gemini 3..

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@ravikyada started using tool Grafana , 3 days, 19 hours ago.

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@ravikyada started using tool Docker , 3 days, 19 hours ago.

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@ravikyada started using tool Amazon Web Services , 3 days, 19 hours ago.

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@varbear shared a link, 5 days, 9 hours ago

FAUN.dev()

Why are top university websites serving p0rn? It comes down to shoddy housekeeping.

Researcher Alex Shakhov found scammers commandeering staleCNAMErecords. They hijack university subdomains (eg.berkeley.edu,columbia.edu,washu.edu) and serve p0rn and scam pages. Shakhov found hundreds of abused subdomains across at least34universities. He counted thousands of hijacked pages indexed .. read more

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@varbear shared a link, 5 days, 9 hours ago

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I Decompiled the White House's New App

A React Native app built withExpo SDK 54runsHermes. It talks to a WordPress REST backend and bundles a 5.5MB Hermes bytecode.Its WebView injects JavaScript to strip cookies, GDPR prompts, and paywall dialogs. The build includes OneSignal's fused-location pipeline, polling at 4.5 and 9.5 minutes and.. read more

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@varbear shared a link, 5 days, 9 hours ago

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The AWS Lambda 'Kiss of Death'

A Galera writer node froze afterInnoDBundo history ballooned. PooledAWS Lambdaconnections left transactions open and pinned MVCC read views. The team killed stalled sessions, enabledinnodb_undo_log_truncate, and cappedinnodb_max_undo_log_size. They also set sessiontransaction_isolation=READ-COMMITTE.. read more

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@varbear shared a link, 5 days, 9 hours ago

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PostgreSQL MVCC, Byte by Byte

PostgreSQL's MVCC stores two 32-bit XIDs per tuple -xminandxmax. The transaction snapshot decides visibility per tuple. Updates append new tuples and mark the old withxmax.VACUUMreclaims versions only when no active snapshot can see them. Long-runningREPEATABLE READsnapshots pin versions and cause b.. read more

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@varbear shared a link, 5 days, 9 hours ago

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How The Heck Does Shazam Work? (An Interactive Exploration)

A phone captures audio and runs aFast Fourier Transform (FFT)on short windows. It builds aspectrogramand extractspeaks. Nearby peak pairs form compacthashes(two frequencies + time delta). Aninverted indexmaps those hashes to songs, and timing validates matches. Most services run lookups onserversaga.. read more

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@kaptain shared a link, 5 days, 9 hours ago

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From public static void main to Golden Kubestronaut: The Art of unlearning

The author left JVM monolith ops forKubernetes. They stacked certs:CKA,CKAD,CKS,KCNA,KCSA,CNCF Golden Kubestronaut. They treatPodsas the atomic deployable. They pick fights:IngressvsNodePort. They warn aboutConfigMapdrift. They spotlight runtime primitives:Horizontal Pod Autoscalerandservice meshfor.. read more

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@kaptain shared a link, 5 days, 9 hours ago

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Building a fault-tolerant metrics storage system at Airbnb

Airbnb built a metrics system that ingests50M samples/s, stores2.5PBof logical time series, and hosts1.3B active series. They use tenant-per-service grouping andshuffle sharding. They enforce per-tenant guardrails and a consolidatedcontrol plane. They shard queries and compaction. They run zone-awar.. read more

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.