LLMS

Mastering spaCy: Building Advanced NLP Solutions with Custom Components

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19 March 2026 6h29

Mastering spaCy — Build structured NLP solutions with custom components & models powered by spacy-llm: http://
amzn.to/3QpPfXi v/ @PacktDataML [2nd edition] 𝓦𝓱𝓪𝓽 𝔂𝓸𝓾 𝔀𝓲𝓵𝓵 𝓵𝓮𝓪𝓻𝓷:
Apply transformer models and fine-tune them for specialized NLP tasks
Master

→ View original post on X — @sakanaailabs

Sakana AI and MUFG Deploy AI Agent for Banking Lending

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@sakanaailabs

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19 March 2026 6h24

銀行業務にAIエージェントを実装する https://
sakana.ai/mufg-ai-lendin
g-interview/
… 先日、Sakana AIと三菱UFJ銀行の「AI融資エキスパート」が、実案件での検証フェーズへと舵を切りました。プロジェクトの中心メンバー2名が、インタビュー形式でその技術的背景や取り組みの概要を語りました。

Supercharge Your Coding with GitHub Copilot and GenAI Tools

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19 March 2026 6h16

Supercharged Coding with #GenAI — From vibe coding to best practices using GitHub Copilot, ChatGPT, and OpenAI: http://
amzn.to/3VBH2Su v/ @PacktDataML 𝓚𝓮𝔂 𝓕𝓮𝓪𝓽𝓾𝓻𝓮𝓼: Discover how GitHub Copilot, ChatGPT, and the OpenAI API can boost your coding productivity Push

DeepSeek LLM Guide: Fine-tuning, Distillation, Agents, Prompting

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19 March 2026 6h16

"DeepSeek in Practice: From basics to fine-tuning, distillation, agent design, and prompt engineering of open source LLM" via @PacktDataML at http://
amzn.to/4imbryH Discover DeepSeek's unique traits in the LLM landscape
Compare DeepSeek's multimodal features with leading

New Book: Agentic Patterns for Multi-Agent AI Systems

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19 March 2026 6h15

New release from @PacktDataML at http://
amzn.to/3MaHy8T "Agentic Architectural Patterns for Building Multi-Agent Systems: Proven design patterns and practices for GenAI, agents, RAG, LLMOps, and enterprise-scale AI systems" Contents:
GenAI in the Enterprise: Landscape,

→ View original post on X — @theahmadosman

SGLang and vLLM Recommended Over llama.cpp for GPU Inference

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@theahmadosman

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19 March 2026 6h10

llama.cpp is not what you should base your experience on try Sglang and vLLM with GPUs so you can have a proper baseline

→ View original post on X — @pmddomingos

LLMs maximize likelihood via cross-entropy training

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@pmddomingos

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19 March 2026 5h54

Yes. For one, “Bayesian inference” is not Bayesian. Every (frequentist) n-gram model uses Bayes’ theorem. For another, LLMs have high capacity and are trained to minimize cross-entropy, which is equivalent to maximizing likelihood, so it’s not surprising they produce accurate

→ View original post on X — @aibreakfast

AI generates editable consulting slides from prompts or sketches

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@aibreakfast

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19 March 2026 4h19

you give it a prompt — or even a hand-drawn sketch or a screenshot — and it produces consulting-grade slides with top-down storylines, MECE logic, editable charts, tables, and Gantt charts.

everything is native PPTX and actually editable, not a broken export. pic.twitter.com/rnLMjM90u1
— AI Breakfast (@AiBreakfast) 19 mars 2026

you give it a prompt — or even a hand-drawn sketch or a screenshot — and it produces consulting-grade slides with top-down storylines, MECE logic, editable charts, tables, and Gantt charts. everything is native PPTX and actually editable, not a broken export.