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@rafidbottler gave 🐾 to Android Architecture: Components, Patterns & Best Practices Guide , 5 days, 1 hour ago.

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@rafidbottler gave 🐾 to Artificial Intelligence vs Machine Learning: What's the Difference? , 5 days, 1 hour ago.

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@anjali5 shared a link, 5 days, 21 hours ago

How to Fix Developer Productivity at 50+ Engineers

You ship a feature. It works. A week later, someone asks why it's not in staging yet, and you realize it's behind an infrastructure request that's still in review. The ticket isn't urgent enough to escalate. It's also not small enough to ignore. So it waits.

That's what a developer productivity problem feels like at 50 engineers.

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@laura_garcia shared a post, 5 days, 21 hours ago

Software Developer, RELIANOID

AI Reliability Engineering: The New Era of SRE

🤖 As AI becomes part of critical business operations, reliability is no longer just an infrastructure concern. From latency and model drift to observability and trust, AI workloads introduce a new set of challenges for modern SRE teams. In our latest article, we look at how reliability engineering i..

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@pluto_native started using tool Terraform , 1 week, 1 day ago.

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@pluto_native started using tool Kubernetes , 1 week, 1 day ago.

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@pluto_native started using tool Google Cloud Platform , 1 week, 1 day ago.

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@eon01 shared a link, 1 week, 1 day ago

Founder, FAUN.dev

A curated list of free AI models, APIs, and tools you can use without paying a cent.

Running AI shouldn't require a credit card. This list curates genuinely free models — open-weight models you can self-host, free API tiers from major providers, and tools to run everything locally.

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@eon01 added a new tool Unsloth , 1 week, 2 days ago.

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.