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The Trick Behind the AI Magic: Explain AI to Your Manager in Plain English

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Maybe you already know what AI is. But then your manager, a friend, or your mom asks you to explain it, and suddenly it becomes harder than expected. This article is my attempt to explain AI in plain words — the way I would explain it to someone who does not care about tokens, weights, gradients, or architecture diagrams. So, let me try. We want to speed up our businesses with AI. We trust it to help with […]

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308 Blog Posts To Learn About Founder Stories

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Let’s learn about Founder Stories via these 308 free blog posts. They are ordered by HackerNoon reader engagement data. Visit the Learn Repo or LearnRepo.com to find the most read blog posts about any technology. Founders, very often, solve problems that we don’t know we have yet. 1. Building a Gaming Metaverse on 750 Acres of Land in Costa Rica Alóki is based on an intricate relationship with the 750 acres of jungle in Costa Rica. 2. The […]

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Anthropic's run-rate revenue hits $47 billion

Anthropic’s run-rate revenue hits $47 billion

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The most interesting thing about Anthropic’s $65B Series H announcement is this line (emphasis mine): Since our Series G in February, adoption has continued to grow across global enterprise customers, and our run-rate revenue crossed $47 billion earlier this month. Anthropic have made a bit of a habit of sharing their “run-rate revenue” in this kind of announcement, which is an annualized projection of their current revenue – typically calculated by taking the most recent month and multiplying […]

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Claude Opus 4.8: "a modest but tangible improvement"

Claude Opus 4.8: “a modest but tangible improvement”

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Anthropic shipped Claude Opus 4.8 today. My favourite thing about it is this note in the release announcement: Users will find Opus 4.8 to be a modest but tangible improvement on its predecessor. There’s still more to be done: we’re working on developing and releasing models that provide many of the same capabilities as Opus at a lower cost. It’s so refreshing to see an AI lab honestly describe a release as a minor incremental improvement over the […]

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llm-anthropic 0.25.1

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Release: llm-anthropic 0.25.1 New model: Claude Opus 4.8 (claude-opus-4.8). New -o fast 1 option for fast mode, for organizations with that feature enabled on their account. Default max_tokens for each model now defaults to that model’s maximum output rather than 8,192. #72 See also my notes on Opus 4.8 – I used this new release of llm-anthropic to generate the pelicans.

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Liquid AI Releases LFM2.5-8B-A1B: An On-Device MoE Model With 8.3B Total and 1.5B Active Parameters

Liquid AI Releases LFM2.5-8B-A1B: An On-Device MoE Model With 8.3B Total and 1.5B Active Parameters

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Liquid AI just shipped LFM2.5-8B-A1B. It is an on-device Mixture-of-Experts (MoE) model built for tool calling. The model holds 8.3B total parameters but activates only 1.5B per token. That sparsity is what lets it run on consumer hardware. The release follows LFM2-8B-A1B, which Liquid AI team published earlier. LFM2.5 is a new family of hybrid models for on-device deployment. This version adds a 128K context window, reasoning, and scaled-up training. What is LFM2.5-8B-A1B The model uses a sparse […]

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Anthropic Ships Claude Opus 4.8 Alongside Dynamic Workflows and Cheaper Fast Mode, With Workflows Capped at 1,000 Subagents

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Anthropic just launched Claude Opus 4.8. Also, there two Claude Code updates shipped with it. Dynamic workflows run many subagents in parallel. Fast mode now supports Opus 4.8 at a lower price. Both are research previews. What Dynamic Workflows Actually Are A dynamic workflow is a JavaScript script that orchestrates subagents at scale. Claude writes the script for the task you describe. A runtime then executes it in the background. Your session stays responsive while agents work. The […]

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Training Azerbaijani language models on Amazon SageMaker AI

Training Azerbaijani language models on Amazon SageMaker AI

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This solution builds on open source tools including PyTorch, Hugging Face Transformers, and Liger Kernels. The authors would also like to thank Aiham Taleb, Arefeh Ghahvechi, Manav Choudhary, Rohit Thekkanal, Daz Akbarov, Jamila Jamilova, Ross Povelikin, Almas Moldakanov, Christelle Xu, and Ivan Khvostishkov for their contributions in making this project possible. Azercell Telecom LLC, Azerbaijan’s leading telecommunications provider, wanted to build an Azerbaijani large language model (LLM) on Amazon SageMaker AI for telecom use cases and a customer-facing […]

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