Shows
LLM.coPrivate LLM vs ChatGPT Enterprise: Cost, Security, and PerformanceChoosing between a private LLM deployment and ChatGPT Enterprise is one of the most consequential AI infrastructure decisions a company can make right now — and most teams approach it with the answer already baked in. This episode works through the decision analytically, drawing on the full cost, security, and performance comparison published by LLM.co, and gives enterprise buyers a sharper framework for evaluating both paths on their actual merits.
Here's what the episode covers:
What each option actually is: ChatGPT Enterprise is a fully managed SaaS product — you never touch the model weights or inference stac...
2026-10-2105 minLLM.coBuilding a HIPAA-Compliant Private LLM: The Spec You Actually NeedWith healthcare data breaches averaging $7.42 million in 2025 and regulators tightening the screws on electronic PHI, deploying an LLM in a clinical environment is no longer a product decision — it's a compliance architecture problem. This episode of LLM.co cuts past the "should you do it?" debate and gets into the actual build, drawing directly from the full HIPAA private LLM specification that technical leads need to get it right the first time.
The episode walks through the layered decisions that determine whether a private LLM deployment survives a regulatory review or becomes an incident report, covering:
Bu...
2026-10-1405 minLLM.coHow to Replace a Public LLM API Without Breaking ProductionSwitching from a public LLM API to a private deployment sounds straightforward — until Monday morning standup reveals broken outputs and a hasty rollback. This episode of LLM.co breaks down why most first attempts fail and how a structured 30/60/90 migration plan changes that outcome. Drawing from this deep-dive guide on replacing a public LLM API safely, the discussion covers everything from the initial inventory to the final cutover decision, with practical sequencing that engineering and compliance teams can actually follow.
Here's what the episode covers:
Start with an inventory, not a sprint. Ninety days of API lo...
2026-10-0705 minLLM.coWhat to Put in a Private LLM RFP Before You SignSigning a private LLM vendor agreement without the right RFP language is how companies end up locked into data topologies, logging schemas, and termination terms they never agreed to — they simply never required anything different. This episode of LLM.co walks procurement leaders, CISOs, and heads of AI through the six contractual line items that actually determine whether a private LLM deal is enforceable — drawing directly from the private LLM RFP requirements checklist published on LLM.co.
Here's what the episode covers:
Data and weight ownership: Five questions every RFP must force in writing — who owns traini...
2026-09-3005 minLLM.coFixed-Scope, Managed Appliance, or Co-Build: Picking the Right Private LLM ContractWith AI pilot failure rates hovering near eighty percent and tens of billions in enterprise spending producing little measurable return, the contract model behind a private LLM deployment deserves far more scrutiny than most buyers give it. This episode of LLM.co examines the three structures dominating the private LLM market today — drawing on the detailed breakdown of private LLM contract models on the LLM.co blog — and explains how each one allocates risk, ownership, and exit rights in ways that can make or break an enterprise AI program.
The episode covers:
Why contract structure is the...
2026-09-2704 min
LLMLLM Strategies in Light of GPT-6 and OutagesIn this episode, we explore how large language models are adapting strategies following the launch of GPT-6 and the outages faced by platforms like ChatGPT and Claude. We discuss future prospects.
Show LinksGet the top 80+ AI Models for $8.99 at AI Box: https://aibox.aiHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter
See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
2026-09-0420 minLLMAstra and Fable 5.1: New Advances in LLM TechnologyIn this episode, we evaluate advancements in LLM technology driven by OpenAI's Astra and Anthropic's Fable 5.1. We discuss their impact on language models.
Show LinksGet the top 80+ AI Models for $8.99 at AI Box: https://aibox.aiHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter
See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
2026-09-0316 minLLMChatGPT's LLM Insights for iMessage UsersIn this episode, we provide insights into how ChatGPT's LLM facilitates its new iMessage feature. We discuss advantages for everyday users.Chapters00:00 ChatGPT and iMessage02:01 Privacy Concerns05:00 Detection Apps and Meta Glasses07:59 AI Research Accelerating Concerns09:59 Micro One's Growth12:59 Every's AI Clone Innovation
Show LinksGet the top 80+ AI Models for $8.99 at AI Box: https://aibox.aiHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aich...
2026-08-2214 minLLMLLMs and OpenAI's Strategic RetreatIn this episode, we discuss how large language models are affected by OpenAI's recent retreat. We cover what this means for the future of LLM development.Chapters00:00 Introduction00:08 OpenAI's Influencer Retreat00:54 Alibaba's New AI Model04:12 Visa Acquires BioCatch11:53 Reddit's AI Concerns14:06 Apple's Siri Subscription Plan
Show LinksGet the top 80+ AI Models for $8.99 at AI Box: https://aibox.aiHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: http...
2026-08-0415 minLLMLLM Changes: Opus 5 and Voice AgentsIn this episode, we evaluate the changes brought by Anthropic's Opus 5 to large language models. We also examine how OpenAI is enhancing their voice agents.Chapters00:00 Introduction00:04 Clawed Opus 5 Launch00:13 OpenAI's ChatGPT Voice09:16 Meta's Productivity Focus11:01 Silicon Valley's Open AI Push15:22 CRISPR-Cas9 Enhancements16:26 Midjourney Acquires CoStar
Show LinksGet the top 80+ AI Models for $8.99 at AI Box: https://aibox.aiHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Dail...
2026-07-2519 min
LLM SEEDING™ Network podcastsLLM Seeding vs AI Visibility - One Plants Seeds, One Harvests ResultsLLM seeding and AI visibility aren't the same: one is the strategic work, the other is the measurable result. Discover how brands plant seeds in trusted sources and harvest AI mentions, citations, and referrals that drive real business growth. Website: https://llmseeding.io/
2026-07-1800 minLLMAnalyzing Claude Pricing in IndiaIn this episode, we analyze the implications of Claude's localized pricing for Indian users. Understand the broader impact on the LLM community.Chapters00:00 Anthropic's Pricing in India00:58 Apple's Lawsuit Against OpenAI03:02 AI Startups Raising Capital08:18 Meta's Data Center Expansion12:59 Sam Altman vs. Elon Musk
Show LinksGet the top 80+ AI Models for $8.99 at AI Box: https://aibox.aiHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aich...
2026-07-1315 minLLMMicrosoft's AI Billions: A New LLM EraIn this episode, we discuss the potential impact of Microsoft's $2.5 billion investment on large language models. Understand how this shapes AI's future.Chapters00:00 Introduction00:22 Microsoft's AI Investment00:56 Challenges with Implementation03:00 The Behavioral Shift Needed07:00 Role of Leadership in AI Adoption14:35 Conclusion and Recommendations
Show LinksGet the top 80+ AI Models for $8.99 at AI Box: https://aibox.aiHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aich...
2026-07-1015 min
おちつきAIラジオ〜生成AIニュースをやさしく解説〜LLM Wikiって何？エンジニアも知らなかった新トレンドを実践 (ep.85)実践型の有料コミュニティで、みんなでAIを触っておちつこう！https://rooom.listen.style/p/ochitsuki-ai?tab=membership※番組の継続をサポート頂ける方の為のサポートプランもご用意しています。ぜひよろしくお願いします。【トーク内容】「LLM Wikiって何？」AIエンジニアのしぶちょーですら知らなかった新トレンドを、リスナーからの一通の質問をきっかけに解き明かします。話題の「LLM Wiki」とは何か、そしてRAGと何が違うのか。アンドレイ・カーパシーが提唱したこの概念を、実際に番組の全トークから「おちつきAI Wiki」を作りながら解説します。RAGのチャンクをナレッジグラフに繋ぎ直す仕組み、知識を「繋げる」力（シュンペーターの新結合）、Obsidianや論文Wikiの活用、そしてClaude Codeを使った作り方と課金の落とし穴まで。後半は「自分専用のWiki」の使い方から、原体験と「今ここ」をめぐる深い対話へと広がります。【目次】0:00:00 オープニング0:01:30 話題の「LLM Wiki」とリスナーからの質問0:04:42 実物公開 おちつきAI Wikiとナレッジグラフ0:07:39 仕組み RAGのチャンクから番組Wikiを生成0:12:27 なぜ「番組専用」のWikiにしたのか0:16:38 お勉強タイム LLM Wikiとは（カーパシーが提唱）0:21:00 そもそもRAGとは（検索拡張生成のおさらい）0:30:01 RAGの限界とLLM Wikiの違い（都度生成と蓄積）0:37:32 LLM Wikiのアーキテクチャ（生ソース→スキーマ→Wiki層）0:44:56 知識を「繋げる」力とObsidian・論文Wiki0:51:11 Wikiはサービスになる？ ジャンルを絞る使い方1:00:01 脱線 原体験と「今ここ」をめぐる対話1:16:48 日記をWiki化する／紙とデジタル1:25:51 実践 Claude CodeでWikiを作る（課金の落とし穴）1:33:19 お便り OpenClawで暮らしにAIを溶け込ませる1:48:32 エンディング（深掘り回のまとめ）【今回の放送回に関連するリンク】karpathyのLLM-wiki.md https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94fAndrej Karpathy氏のLLM Wikiを1ヶ月運用してわかった、LLMの知識を『繋げる力』https://zenn.dev/tsurubee/articles/llm-wiki-connecting-knowledgeおちつきAI wikihttps://ochitsuki-airag.com/wiki/【今回の要チェックキーワード】LLM Wiki（LLM Wiki）：LLMが自律的に情報を要約・統合・相互リンクし、複利的に蓄積・進化していく永続的な個人用ナレッジベースを構築・維持する手法。アンドレイ・カーパシー（Andrej Karpathy）：OpenAIの創設メンバーやテスラのAIディレクターなどを歴任し、「LLM Wiki」の概念を提唱した著名なAI研究者。Obsidian（オブシディアン）：ローカル環境で動作するMarkdownベースのノートアプリであり、LLM Wikiにおいては人間が知識のつながりを視覚化・管理するためのエディタ（IDE）として機能するツール。新結合（New Combinations）：経済学者ヨーゼフ・シュンペーターが提唱した「既存の知識や要素の新しい組み合わせこそがイノベーションを生み出す」という概念。【リアルイベント出展のお知らせ】—----------------------------【番組の概要】日々のAIトピックを、現役のAIエンジニアがやさしく解説する対談番組。AIニュースに驚き疲れたあなたに、おちつきを提供します。AIニュースの、驚くポイント、驚かなくても良いポイントがわかります。★ご感想やコメントは、番組公式ハッシュタグをつけてX（旧Twitter）でポストしていただけるとありがたいです。【番組公式Webサイト】※お便りはこちらからhttps://ochitsuki-ai.com/【公式ツール】・おちつきAI RAGhttps://ochitsuki-airag.com/【番組公式ハッシュタグ】#おちつきAI【番組公式X】https://x.com/ochitsuki_AI【パーソナリティ(MC)】▼しぶちょー
2026-07-091h 54
AI & LLM PrimerPrompt Injection and JailbreaksThis chapter examines prompt injection and jailbreak attacks, which exploit a language model's inherent inability to distinguish between authoritative developer instructions and untrusted user data. It covers the mechanics of direct and indirect injection, categorises common jailbreaking techniques, discusses the limitations of defensive prompt engineering, and outlines a layered mitigation strategy to better defend production systems.Amazon.com: LLM Primer VII AI Security: Defending LLM Systems Against Prompt Injection, Jailbreaks, and Adversarial Threats: 9798185644065: SHIMODA, SHO: Books
2026-07-0845 minAI & LLM PrimerData Security and PrivacyThis chapter examines data security and privacy throughout the LLM lifecycle. It explores the inherent risks of training data, such as copyright issues, personal information (PII) contamination, and data poisoning. Additionally, it details how models can leak sensitive information through memorization and extraction attacks, and outlines operational defenses for securing systems, including input redaction pipelines, encryption, tenant isolation, and data retention policies.Amazon.com: LLM Primer VII AI Security: Defending LLM Systems Against Prompt Injection, Jailbreaks, and Adversarial Threats: 9798185644065: SHIMODA, SHO: Books
2026-07-0732 minAI & LLM PrimerThreat Modeling for LLM SystemsThis chapter adapts traditional threat modeling frameworks (such as STRIDE, PASTA, and attack trees) specifically for the unique vulnerabilities of LLM systems. It guides defenders through identifying AI-specific assets and adversaries, and provides a step-by-step procedure for building a living threat model that can be maintained alongside the system's codeAmazon.com: LLM Primer VII AI Security: Defending LLM Systems Against Prompt Injection, Jailbreaks, and Adversarial Threats: 9798185644065: SHIMODA, SHO: Books
2026-07-0654 minAI & LLM PrimerWhy AI Security Is DifferentThis chapter explains that AI security fundamentally and structurally differs from traditional software security. Instead of finding and patching clear bugs in readable source code, defenders must secure probabilistic models whose behaviors are driven by billions of uninterpretable weights and training data. Consequently, the security focus shifts from ensuring code correctness to managing and restricting an unbounded range of unpredictable inputs and outputs. Amazon.com: LLM Primer VII AI Security: Defending LLM Systems Against Prompt Injection, Jailbreaks, and Adversarial Threats: 9798185644065: SHIMODA, SHO: Books
2026-07-0649 min
AI Post TransformersAIConfigurator for Cross-Framework LLM ServingThis episode explores AIConfigurator, a NVIDIA-led system for optimizing LLM serving configurations across frameworks such as TensorRT-LLM, vLLM, SGLang, and NVIDIA’s internal stack without brute-force benchmarking. It explains why operators should care more about TTFT, TPOT, and goodput than raw tokens-per-second, and unpacks the real serving tradeoffs around prefill/decode disaggregation, hybrid tensor/pipeline/expert parallelism, CUDA graphs, KV-cache sizing, and token limits. The discussion argues that the paper’s main contribution is a calibrated, framework-agnostic performance model built from primitive costs like GEMMs, attention, communication, and memory operations, then combined with backend-specific scheduling behavior to search thousands of depl...
2026-07-0500 min
arxiv生成AI論文解説 by ずんだもん「LLMの心はどこに宿る?」個体性問題からSLIM-RLまで生成AI論文10本【2026/07/03】「LLMに人格を語ること」の哲学的前提を実験で覆す論文、軌跡を切り刻まずリスク予算だけで拡散LLMを強化学習し学習サンプルを半分以下にした「SLIM-RL」、離散と連続の2チャンネルをループさせて多段階推論の壁を破った「DiscoLoop」など、生成AI・LLM・拡散モデル・エージェント関連の最新論文10本をずんだもん＆四国めたんが解説。
▼ 今日の論文ラインナップ
・「LLMの人格」はどこに宿るのか——文脈依存性から個体性問題を捉え直す（arXiv:2607.00006）
・安全な言い換えか暴走する脚色か——創作支援LLMのジレンマを解くLoom（arXiv:2607.00009）
・ステアリングベクトルからモデル較正器へ——LLMの潜在空間を制御と信頼のために活用する（arXiv:2607.00083）
・アラビア語の文化的知識でLLMを試す——人間専門家とのクロス評価フレームワーク（arXiv:2607.00139）
・見えるが直せない——医療LLMのハルシネーションに関するニューロンレベルの証拠（arXiv:2607.00158）
・知識ベースVQAベンチマークの落とし穴を暴く——監査・修復・拡張（arXiv:2607.00159）
・軌跡分割なしで拡散LLMを強化学習——リスク予算型ランダムマスキングSLIM-RL（arXiv:2607.00208）
・学生エッセイへのフィードバックをLLMは書けるか——SEFORAコーパスとUniMatch評価（arXiv:2607.00274）
・会話は変わり続ける——時間発展する証拠グラフで状態を追跡するTRACE（arXiv:2607.00339）
・離散埋め込みと連続隠れ状態をループさせるDiscoLoop——多段階推論の壁を破る（arXiv:2607.00341）
▼ 参考論文（arXiv）
https://arxiv.org/abs/2607.00006 — LLMの人格はどこに宿るのか（個体性問題）
https://arxiv.org/abs/2607.00009 — 創作支援フレームワークLoom
https://arxiv.org/abs/2607.00083 — ステアリングベクトルとモデル較正器
https://arxiv.org/abs/2607.00139 — アラビア語文化的知識のLLMベンチマーク
https://arxiv.org/abs/2607.00158 — 医療LLMハルシネーションのニューロンレベル分析
https://arxiv.org/abs/2607.00159 — 知識ベースVQAベンチマークの監査・修復・拡張
https://arxiv.org/abs/2607.00208 — SLIM-RL: 拡散LLMのリスク予算型強化学習
https://arxiv.org/abs/2607.00274 — SEFORAコーパスとUniMatch評価
https://arxiv.org/abs/2607.00339 — TRACE: 時間発展する証拠グラフ
https://arxiv.org/abs/2607.00341 — DiscoLoop: 多段階推論の2チャンネルループ
#生成AI #ChatGPT #Claude #LLM #AI #人工知能 #arxiv #論文解説 #ゆっくり解説 #ずんだもん #OpenAI #Anthropic #機械学習 #ディープラーニング
2026-07-0300 min
AI Post TransformersSuperInfer: SLO-Aware LLM Inference on SuperchipsThis episode explores SuperInfer, a system for serving large language models on GH200-style superchips by treating memory management as the key lever for meeting latency targets rather than just maximizing compute use. It explains why KV cache growth, HBM pressure, and head-of-line blocking often hurt responsiveness first, then breaks down how the paper’s RotaSched policy proactively rotates request state out of fast memory to protect time-to-first-token deadlines. It also covers DuplexKV, the transfer mechanism that makes this practical by batching fragmented KV data, using bidirectional movement across NVLink-C2C, and overlapping transfers with model execution instead of stalling th...
2026-06-3000 min
AI Post TransformersLLMServingSim 2.0 for Disaggregated LLM ServingThis episode explores LLMServingSim 2.0, a simulator designed to model how large language models behave when they are served on mixed hardware fleets with separated compute, memory, and networking resources rather than a uniform GPU cluster. It explains the practical serving concepts that shape user experience, including prefill versus decode, time to first token, time per output token, prefix caching, KV-cache movement, and why latency problems emerge from interactions among batching, routing, placement, and interconnect contention rather than a single bottleneck. The discussion highlights the paper’s core idea of a Model Serving Group, which combines queueing, scheduling, operation mapping, memory mo...
2026-06-2900 minLLM SEEDING™ Network podcastsAI SEO vs. LLM SEO vs. LLM Seeding: Which Gets You Into AI Responses?Discover why LLM seeding is emerging as the most powerful strategy for getting your brand cited by AI systems like ChatGPT and Claude—and how it differs from traditional SEO. Website: https://llmseeding.io/
2026-06-2400 min
James Dooley PodcastLLM Selection Rate Optimization (James Dooley Interviews Charles Floate)In this episode of the James Dooley Podcast, James Dooley is joined by SEO expert Charles Floate to discuss Selection Rate Optimisation (SRO) and how it impacts visibility in AI search. They break down how large language models like ChatGPT select sources when generating answers, why only a small number of sources are chosen, and how SEOs can optimise their content to be included in those selections. James Dooley and Charles Floate also explore content chunking, semantic structure, question-based headings, entity signals, and the importance of third-party corroboration across the web. The conversation highlights how building brand authority, trust...
2026-06-1907 min
James Dooley PodcastOptimise AI Search Visibility | AI SEO, LLM SEO, ChatGPT (James Dooley Interviews Dan Petrovic)James Dooley speaks with Dan Petravvic about AI SEO and what it takes to rank in AI overviews and LLM driven search. Dan explains why SEO and GEO are not the same, why “generative engine optimisation” is a misleading label, and why AI SEO is the most practical framing for the industry. The conversation breaks down selection rate optimisation, model bias, and how tools like Treewalker.ai probe probabilistic outputs to find weak confidence points in brand understanding. Dan also explains why traditional SEO still matters because LLMs pull from top search results, why brand familiarity often beats technical hack...
2026-06-1948 min
James Dooley PodcastAI Search Visibility | James Dooley Interviews Dawood Khan from CrowdScale.ai on LLM SEO in 2026In this episode, James Dooley is joined by Dawood Khan to break down AI search visibility and how brands get cited and recommended inside LLMs like ChatGPT, Claude, Perplexity, and Gemini AI Overviews. The discussion moves beyond traditional SEO into omni-channel brand visibility, explaining why platforms such as Reddit, Quora, and Facebook now play a critical role in AI-driven discovery. Dawood explains how LLMs use external citations, how brand mentions influence rankings, and why branded searches and clicks are becoming a dominant signal. The episode connects AI recommendations, brand building, and real commercial outcomes heading into 2026.
2026-06-1920 min
Fatrank PodcastListicle Link Building for LLM Consensus & AI Overviews | James Dooley Interviews Jabez ReubenJames Dooley speaks with Jabz Rubin about listicle link building and how it helps brands build consensus across LLMs, AI Overviews and Google organic search. The discussion explains why structured listicles work because they answer specific query variations, compare entities clearly and give AI systems faster retrieval paths. Jabz shares how effective listicles include competitors, comparison tables, media, key takeaways, FAQs, USPs, awards and supporting evidence. The conversation also covers query fan-out, entity definition, third-party corroboration, daisy chaining listicles and building brand coverage through relevant publications. A key outcome is that brands should not rely on one guest post...
2026-05-1216 min
AI Post TransformersLAPS for Length-Aware LLM ServingThis episode explores LAPS, a serving system for large language models that treats long prompt prefills and short multi-turn re-prefills as fundamentally different workloads instead of batching them together. It explains why user-perceived latency, especially time to first token, suffers when tiny follow-up requests get stuck behind large compute-heavy context loads, and how LAPS models the boundary between compute-bound and memory-bound prefills to separate them more intelligently. The discussion covers LAPS’s dual-queue design, its temporal and spatial disaggregation strategies, and engineering choices like short-request waiting windows, length-aware smart batching, and CUDA Graph execution. Listeners would find it interesting because it...
2026-05-0900 min
AI Post TransformersWhy LLM Serving Needs Mathematical OptimizationThis episode explores a position paper arguing that modern LLM serving has outgrown simple heuristics like FIFO, shortest-queue routing, and LRU eviction. It explains why transformer inference creates harder control problems than standard inference, focusing on continuous batching, KV-cache growth, and the tension between compute-heavy prefill and memory-bound decode phases. The discussion highlights the paper’s central claim that serving systems need explicit objective-driven optimization for routing, admission control, scheduling, and cache management, while also questioning where formal methods would truly outperform today’s stronger heuristic baselines such as vLLM and PagedAttention-inspired designs. Listeners would find it interesting because it conn...
2026-05-0900 minLLMUnderstanding Anthropic's $50 Billion TargetIn this episode, we break down Anthropic's ambitious target of raising $50 billion. Gain insights into how this impacts the LLM landscape.
See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
2026-04-3023 minLLM SEEDING™ Network podcastsLLM Seeding Guide 2026: How to Get Your Brand Cited by ChatGPT & AIDiscover why 40% of searches now happen inside AI assistants—and how LLM seeding helps brands get cited by ChatGPT, with conversion rates often 3x higher than traditional SEO alone. Website: https://llmseeding.io/
2026-04-3000 min
Fatrank PodcastHow LLM Selection Rate Optimisation Works (James Dooley Interviews Charles Floate)What is selection rate optimisation in AI search and why does it matter for SEO in large language models? In this discussion, James Dooley and Charles Flo break down how selection rate optimisation (SRO) works within LLMs such as ChatGPT, Gemini and Perplexity. The conversation explains how AI systems run multiple grounded searches, gather hundreds of potential sources and then select a limited number of trusted documents to build an answer. Charles explains why only a small set of sources are chosen and how content structure, semantic optimisation and authority influence whether a page is selected. They also discuss...
2026-03-1707 min
Tool Use - AI ConversationsHow To Build a Hybrid AI System with Any-LLM (ft Nathan Brake)Join the Tool Use Discord: https://discord.gg/PnEGyXpjaXAre you looking to optimize your AI applications with the best open source Large Language Models? This week, we explore open source AI, rapid model switching, and local self-hosting with Nathan Break, a Machine Learning Engineer at Mozilla AI and the creator of Any-LLM. Any-LLM is an open-source Python SDK that allows developers to seamlessly talk to any LLM provider through a single, unified interface.Nathan demonstrates how developers can instantly swap between cloud-hosted AI providers and local models like...
2026-03-0344 min
Fatrank PodcastHow To Optimise AI Search Visibility | AI SEO, LLM SEO, GEO (James Dooley Interviews Dan Petravich)James Dooley interviews Dan Petravich about AI SEO and what it really takes to win visibility inside ChatGPT, Gemini, Perplexity and other LLM-driven search experiences. Dan Petravich explains why SEO and GEO are not the same, why the industry does not need another acronym, and why “AI SEO” is the cleanest label for what is happening. Dan Petravich breaks down selection rate optimisation, model bias, and how tools like Treewalker.ai expose low confidence points in a model’s understanding of a brand. James Dooley and Dan Petravich also dig into why traditional SEO still matters because AI systems tend t...
2026-02-2448 minAI & LLM Primer2-7-7. Hallucinations and Reliability: Managing Confident ErrorsThis episode covers Chapter 7, examining why Large Language Models confidently generate false information. We discuss the probabilistic nature of "hallucinations," the dangerous gap between fluency and correctness, and practical strategies like calibration and hybrid verification to align model confidence with reality.Amazon.com: LLM Primer VII AI Security: Design Safe and Robust AI System eBook : SHIMODA, SHO: Kindle Store
2026-02-1916 minAI & LLM Primer2-7-6. Retrieval-Augmented Generation Risks: Securing the Knowledge PipelineThis episode covers Chapter 6, focusing on the security implications of connecting models to external data (RAG). We discuss how this introduces new trust boundaries, the dangers of malicious document injection where attackers plant traps in your knowledge base, and the necessity of validating documents before they enter the model's context.Amazon.com: LLM Primer VII AI Security: Design Safe and Robust AI System eBook : SHIMODA, SHO: Kindle Store
2026-02-1934 minAI & LLM Primer2-7-5. Input Validation and Output Filtering: The Defense PipelineThis episode covers Chapter 5, detailing how to build disciplined pipelines around an AI model. We discuss strategies for sanitizing user inputs to catch attacks early, the importance of structured prompting to reduce ambiguity, and why output moderation is essential to catch policy violations that slip through earlier defenses.Amazon.com: LLM Primer VII AI Security: Design Safe and Robust AI System eBook : SHIMODA, SHO: Kindle Store
2026-02-1929 minAI & LLM Primer2-7-4. Prompt Injection and Jailbreaks: Defending the InterpreterThis episode explores Chapter 4, detailing how attackers manipulate model behavior through crafted inputs like instruction overrides. We discuss why prompt injection is an inherent property of instruction-following systems rather than a standard bug. The episode covers jailbreaking techniques like role-playing and obfuscation, and why defense requires architectural layers rather than just better prompts.Amazon.com: LLM Primer VII AI Security: Design Safe and Robust AI System eBook : SHIMODA, SHO: Kindle Store
2026-02-1837 minAI & LLM Primer2-7-3. Data Security and Privacy: The AI LifecycleThis episode breaks down Chapter 3, tracking data risks from training to deployment. We discuss how models can memorize sensitive training data, the subtle dangers of leakage through generated outputs, and the critical importance of treating user prompts and logs as sensitive assets.Amazon.com: LLM Primer VII AI Security: Design Safe and Robust AI System eBook : SHIMODA, SHO: Kindle Store
2026-02-1825 minAI & LLM Primer2-7-2. Threat Modeling for LLM Systems: A Step-by-Step GuideThis episode covers the systematic approach of Chapter 2, moving beyond vague security worries to concrete risk analysis. We discuss how to identify unique AI assets—like prompts, logs, and retrieval indexes—and map the expanded attack surface of API-based systems to build durable defenses.Amazon.com: LLM Primer VII AI Security: Design Safe and Robust AI System eBook : SHIMODA, SHO: Kindle Store
2026-02-1829 minAI & LLM Primer2-7-1. The Probabilistic Shift: Why AI Security is DifferentThis episode dives into Chapter 1, exploring why traditional security measures fail when applied to Large Language Models. We discuss the fundamental shift from deterministic code to probabilistic behavior, how LLMs expand the attack surface from endpoints to context, and why security must be designed into the architecture rather than patched on later.Amazon.com: LLM Primer VII AI Security: Design Safe and Robust AI System eBook : SHIMODA, SHO: Kindle Store
2026-02-1836 minAI & LLM Primer2-1-12. The System Architect — Building Your Own LLM SystemIn this episode, we bring every previous concept together to answer the ultimate practical question: How do you actually build a complete LLM system from scratch? We move beyond the model itself to construct the full production environment—from legal compliance to user interface—required to turn a neural network into a working product.Join us as we:• Secure the Foundation: We tackle Datasets and Licensing, explaining why data governance, provenance tracking, and legal compliance are the non-negotiable starting points of any system.• Engineer the Pipeline: We break down the Training Pipeline, detailing the oper...
2026-02-1738 minAI & LLM Primer2-1-5. The Industrial Pipeline — Training Large ModelsIn this episode, we move from the theoretical blueprint of the Transformer to the operational reality of building a Large Language Model. We explore how an empty mathematical shell is transformed into a capable system through a massive, coordinated engineering process known as training.Join us as we:• Curate the Curriculum: We discuss why "more data" isn't always better, explaining the critical steps of deduplication, filtering, and balancing diverse sources like web text, books, and code.• Minimize the Surprise: We break down the mathematical objective of Cross-Entropy Loss and the optimization algorithm Gradient Desc...
2026-02-1731 minAI & LLM Primer2-1-2 The Statistical Backbone — Probability, Tokens, and TextIf the first episode defined what an LLM is, this episode explains how it actually processes information. We dive into the mathematical framework that transforms human language into structured data, reframing creativity as a probabilistic prediction task.Join us as we:• Decode the Input: We explore how raw text is converted into numerical sequences called "tokens" using subword algorithms like Byte Pair Encoding, balancing efficiency with expressiveness.• Formalize the Objective: We examine the core mechanism of "next-token prediction," revealing how models treat language not as ideas, but as a chain of conditional probabilities.
2026-02-1633 min
LLM入門 - 聞くだけでLLMを理解する2-9 エンジニアの生存戦略 — 「ただ使う」から「作り手」へ理論と仕組みを学んだ今、エンジニアとしてLLMとどう向き合うべきか？最終章となるこのエピソードでは、単なる「利用者」から、責任を持って設計・運用できる「作り手」へとステップアップするための具体的なロードマップを描きます。主なトピック：• 継続的な学習の地図：日進月歩のAI分野において、最新の論文（arXiv）や実装コード（GitHub）、テックブログをどのように読み解き、知識をアップデートし続けるか。• カスタマイズの実践：既存のモデルを特定の業務に適用させるための「ファインチューニング」（LoRA/PEFT）や、ハイパーパラメータ調整などの最適化技術について。• 実装のための武器庫： ◦ フレームワーク：Hugging Face、PyTorch、TensorFlowの使い分け。 ◦ インフラ：Google Vertex AI、AWS SageMakerなどのクラウド基盤。 ◦ 学習リソース：DeepLearning.AIなどのオンライン講座や、Common Crawlなどのデータセット。• 技術と責任：強力なツールを扱うエンジニアとして求められる倫理観と、コミュニティと共に成長する姿勢。
2026-02-1213 minLLM入門 - 聞くだけでLLMを理解する2-8 光と影 — 計算コスト・バイアス・倫理的課題LLMは驚異的な能力を持つ一方で、運用には莫大なエネルギーと、社会的なリスクが伴います。このエピソードでは、高性能なAIを「責任ある技術」として社会に実装していくために直面する、技術的・倫理的な「壁」と、それを乗り越えるための解決策について議論します。主なトピック：• 「賢さ」の代償（コストとエネルギー）：パラメータ数が増えれば性能は上がりますが、計算コストも急増します。このトレードオフを解消するための技術（量子化、プルーニング、知識蒸留）とは？• 効率的なトレーニング：巨大なモデルを学習させるための分散学習や、専用ハードウェア（GPU/TPU）、最適化アルゴリズム（Adamなど）の役割について。• AIの偏見（バイアス）：インターネット上のデータから学習するLLMが、ジェンダーや人種に関するステレオタイプを継承してしまうメカニズムと、その対策（公平性指標やデータの多様化）。• 説明可能なAI（XAI）：「なぜその答えになったのか？」ブラックボックスになりがちなAIの思考プロセスを透明化する技術と、法規制や倫理ガイドラインの今後。
2026-02-1213 minLLM入門 - 聞くだけでLLMを理解する2-7 理論から実社会へ — 翻訳・要約・対話で活躍するLLMの現場これまでの章で学んだ数学的な「理論」が、現実世界でどのように「応用」されているのかをつなぐエピソードです。テキスト生成、要約、翻訳、質問応答といった具体的なタスクにおいて、LLMがどのようにその能力を発揮し、私たちの生活やビジネスに入り込んでいるのかを解説します。主なトピック：• テキスト生成と自動要約： ◦ 生成型要約：元の文を切り貼りするだけでなく、内容を理解して新しい言葉で要約し直すLLMの得意技について。 ◦ 自己回帰モデルの実践：ニュース記事や広告文、物語の執筆支援など、文脈を維持しながら文章を紡ぐ技術。• 質問応答（QA）と対話システム： ◦ NLU（理解）とNLG（生成）の融合：ユーザーの質問の「意図」を汲み取り、自然な言葉で回答するチャットボットの仕組み。• 機械翻訳の進化： ◦ 文脈の翻訳：単語ごとの置換ではなく、アテンション機構（Q・K・V）を使って「文全体の意味」を捉え、自然な翻訳を実現するプロセス。• 社会実装の事例：カスタマーサポート、教育、医療、多言語ビジネスなど、知識へのアクセスを民主化するインフラとしてのLLMの役割。
2026-02-1208 minLLM入門 - 聞くだけでLLMを理解する2-6 学習の舞台裏 — 膨大なデータを「知能」に変える技術理論や数式がわかっても、それだけではLLMは完成しません。このエピソードでは、何テラバイトものテキストデータをAIがいかにして学習しているのか、その泥臭くも重要な「エンジニアリングの実践」にスポットを当てます。主なトピック：• データの「下ごしらえ」（前処理）：Web上のノイズだらけのデータを、AIが学習できる「きれいな素材」にするためのクリーニング、正規化、バイアス対策について。• トークン化（Tokenization）：文章を「モデルが理解できる最小単位」に分解するプロセスと、未知の単語に対応するための工夫（BPEなど）。• ミニバッチ学習の戦略：膨大なデータを一度に読み込ませるのではなく、小さな塊（ミニバッチ）に分けて効率よく学習させる手法と、その計算効率のバランス。• 分散学習とインフラ：1台のマシンでは扱いきれない巨大なモデルを、数百台のGPUを連携させてトレーニングする並列処理の技術。AIが賢くなるために不可欠な「質の高いデータ」と「効率的なトレーニング手法」の秘密に迫ります。
2026-02-1215 minLLM入門 - 聞くだけでLLMを理解する2-5 AIはどうやって賢くなるのか？ — 勾配降下法と「学習」の数学どんなに優れた脳（アーキテクチャ）を持っていても、適切なトレーニングがなければAIは機能しません。このエピソードでは、モデルが膨大なデータから自分の間違いを修正し、徐々に正解に近づいていく「学習（最適化）」のプロセスを、数理的な視点から解説します。主なトピック：• 損失関数（Loss Function）：モデルの予測と正解のズレ（誤差）を数値化する「採点基準」。LLMで標準的に使われる「クロスエントロピー損失」が、どのようにモデルの「迷い」や「間違い」を測っているのか。• 勾配降下法（Gradient Descent）：誤差を最小にするために、パラメータを少しずつ調整して「山を下る」イメージで最適解を探すアルゴリズム。• バックプロパゲーション（誤差逆伝播法）：出力された誤差をネットワークの逆向きに辿り、どのパラメータをどう修正すべきかを効率的に計算する、ディープラーニングの心臓部。• 学習の課題と工夫：学習が止まってしまう「勾配消失」や、計算が不安定になる「勾配爆発」といった問題と、それを防ぐためのテクニック（正則化や最適化アルゴリズム）について。
2026-02-1213 minLLM入門 - 聞くだけでLLMを理解する2-4 トランスフォーマーの数理 — AIの「注意力」を式で書く現代のAIブームの立役者である「トランスフォーマー（Transformer）」。なぜこのモデルは、従来のAIよりも圧倒的に深く言葉を理解できるのでしょうか？このエピソードでは、LLMの心臓部とも言える「アテンション機構」の数学的な仕組みを解剖します。主なトピック：• セルフアテンション（Self-Attention）：「彼はそれを投げた」の「それ」は何を指すのか？AIが単語間の関連度（注目度）を計算し、文脈を読み解くプロセス。• Q・K・Vの概念：単語を「Query（質問）」「Key（索引）」「Value（中身）」という3つのベクトルに変換し、必要な情報を検索・抽出するアルゴリズム。• 数式が語る「意味の重み付け」：トランスフォーマーの核心である数式 Attention(Q,K,V)=softmax(dkQKT)V が、どのようにして言葉の重要度を決定しているか。• マルチヘッドアテンション：文法、意味、感情など、異なる視点で同時に文章を読むことで、人間のような多角的な理解を実現する「マルチヘッド」の仕組み。AIが文脈を「計算」する現場を、数式を通して覗いてみましょう。
2026-02-1215 minLLM入門 - 聞くだけでLLMを理解する2-3 数理モデルの基盤 — 確率とベクトルで描く「AIの思考回路」LLMはどのようにして「言葉」という曖昧なものを計算可能な「数」として扱っているのでしょうか？このエピソードでは、AIの思考を支える2つの数学的柱、「確率統計」と「線形代数」に焦点を当て、その処理の裏側にあるロジックを解き明かします。主なトピック：• 確率論と統計： ◦ 条件付き確率：文脈（B）があるときに次の単語（A）が起きる確率（P(A∣B)）をどう計算するか。 ◦ ベイズの視点：事前の知識（事前確率）を、新しい情報（尤度）でアップデートして正解に近づく「学習」のプロセス。• 線形代数とベクトル空間： ◦ 埋め込み（Embedding）：言葉を「数の並び（ベクトル）」に変換し、コンピュータが理解できる形にする技術。 ◦ 意味の演算：「King - Man + Woman = Queen」のように、言葉の意味を足し算・引き算できる「ベクトル空間」の不思議。 ◦ 類似度の測定：単語同士の意味の近さを「距離」や「角度」で測る仕組み。AIが言葉を「確率」で予測し、「ベクトル」で意味を捉える――その数学的な「思考のしくみ」を直感的に理解しましょう。
2026-02-1215 minLLM入門 - 聞くだけでLLMを理解する2-2 LLMの解剖図 — トークン・パラメータ・トランスフォーマーの正体数学の基礎を終え、いよいよ「モデル」そのものの構造に迫ります。このエピソードでは、言葉という曖昧なものをAIがどのように処理可能なデータに変換し、文脈を理解しているのか、その全体像（アーキテクチャ）を俯瞰します。主なトピック：• LLMの定義：LLMは単なるチャットボットではなく、「次に来る単語」を確率的に予測して文章を紡ぐ巨大な計算機です。• 4つの基礎概念： ◦ トークン：言葉をAIが扱える最小単位（ID）に分解する仕組み。 ◦ 埋め込み（Embedding）：IDを「意味を持つベクトル」に変換するプロセス。 ◦ パラメータ：学習によって調整される、モデルの「知識」の実体。 ◦ アーキテクチャ：情報の流れを決める骨組み。• NLP（自然言語処理）の進化：特定のタスク専用だった従来のモデルから、あらゆる言語タスクをこなす汎用的なLLMへの転換点について。• トランスフォーマー革命：現在のAIブームの火付け役となった「トランスフォーマー」モデルと、文脈を一度に捉える「セルフアテンション」の概念を直感的に解説します。
2026-02-1215 minLLM入門 - 聞くだけでLLMを理解する2-1 LLMの「思考」を数学で読み解く — 確率・情報量・ベクトルの世界大規模言語モデル（LLM）は魔法のように見えますが、その裏側には確固たる数学的構造が存在します。このエピソードでは、書籍の第1章「LLMを理解するのに必要な数学の基礎」に焦点を当て、AIが言葉を扱う仕組みを数学的な視点から紐解きます。主なトピック：• 数式は「言葉の圧縮」：記号（P、Σ、logなど）への苦手意識をなくし、数式を「思考の道具」として捉え直します。• 確率論と予測：AIはどのようにして「次の単語」を選んでいるのか？言葉の曖昧さを確率（P(A∣B)）で捉える仕組みを解説します。• 情報量とエントロピー：モデルの「迷い」や情報の「驚き」を数値化する「エントロピー」の概念について。• 線形代数とベクトル：言葉の意味を「空間上の点」として配置し、計算可能にする「埋め込み表現」の基礎。高校数学を「再起動」し、ブラックボックスに見えるLLMの中身を直感的に理解するための第一歩を踏み出しましょう。
2026-02-1215 minLLMLLM's Role in Standardized TestsIn this episode, we discuss the role of Large Language Models (LLMs) in standardized testing, focusing on Google's free Gemini-powered SAT practice. We examine the technology behind this innovation.Chapters00:00 Intro & AIbox.ai Plug02:00 Google's Free SAT Prep05:09 AI in Education Debate12:14 Impact on Tutors & Google's StrategyIn this episode, we discuss Google's new initiative offering free SAT practice exams powered by Gemini, exploring how this advancement will impact education and the broader implications of AI in learning. We also examine the potential disruption to traditional education companies and...
2026-01-2210 minLLMRunPod Powers LLM SuccessIn this episode, we focus on RunPod's role in enabling the training and deployment of Large Language Models (LLMs) through their powerful GPU cloud. We also discuss how their infrastructure supports advancements in natural language processing.Chapters00:00 RunPod's Origin Story02:38 Bootstrapped to Enterprise08:28 Competitive AI Cloud Market11:25 Modern Startup FundingIn this episode, we explore the incredible journey of RunPod, an AI cloud startup that bootstrapped to $24 million in revenue before raising a $20 million seed round and reaching $120 million in annual recurring revenue. We also discuss how they acquired...
2026-01-2108 minLLMLLM's Energy FootprintIn this episode, we specifically focus on the energy footprint of Large Language Models (LLMs) and the Trump administration's $15 billion power plant proposal. We discuss the infrastructure required to support the massive computational demands of LLMs.Chapters00:00 AI's Power Hunger01:48 Trump Admin's Plan10:07 Energy Source Debate13:30 Accountability for ConsumptionIn this episode, we explore the Trump administration's proposal for tech companies to invest $15 billion in power plants to meet the surging electricity demands of AI and data centers. We also discuss its potential impact on America's power grid, consumer...
2026-01-2111 minLLMMeta's LLM StrategyIn this episode, we explore Meta's strategic pivot from its Metaverse ventures to a concentrated focus on AI, significantly impacting LLM strategy. We discuss the implications for large language models and natural language processing.Chapters00:00 Meta's Metaverse Investment02:05 VR's Limited Appeal06:21 Meta's Financial Losses11:24 AI: Meta's New FrontierIn this episode, we break down Meta's decision to discontinue its ambitious Metaverse project after a colossal $73 billion investment, leading to significant layoffs and the shutdown of several VR game studios. We also explore why the Metaverse failed to gain traction...
2026-01-2013 minLLMLLM-Powered AI Video FutureIn this episode, we connect Higgsfield's $1.3 billion valuation to the broader LLM ecosystem, showing the power of AI in video generation. We also discuss how its specialized focus enhances content creation.
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2026-01-1510 minLLMLLM Brain ConnectionIn this episode, we examine how large language models (LLMs) might integrate with brain-computer interfaces, inspired by Merge Labs and OpenAI. We explore the future of thinking with AI assistance.Resources Mentioned01:49 AIbox.ai02:27 AIbox.ai11:14 AIbox.ai
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2026-01-1511 minLLMMeta AI Wrong Turn: LeCun's LLM Dead End CallWrong turn Meta AI LeCun's LLM dead end call architecturally flawed absent reasoning world models potently radically. Token-chasing prison confines transformers pattern recognition incapable biological fidelity fundamentally. Visionary demands hierarchical perception revolution transcending scale worship disruptively comprehensively.Get the top 40+ AI Models for $20 at AI Box: https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustleSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
2026-01-0708 min
半球观察Vol.21. 【漫谈AGI】AGI成为伪命题的当下，LLM应用流量爆发将如何改写AI Infra生态？在过去的一年里，我们几乎在DeepSeek后几乎都没有再聊过AI。一方面是因为再也没有出现像DeepSeek那样的历史时刻，另一方面，随着豆包和Nano Banana的爆红，LLM应用成为了一个新的强劲用户增长点，可以说，从抖音的AI分身聊天框再到淘宝的AI万能搜，现在的LLM几乎走进了每个人的现实生活。潜藏在这背后的，是LLM的范式在o1后几乎再也未曾改变过，AGI随着时间和LLM应用的大规模发展，逐渐成为了伪命题。LLM App的爆发是恐怖的，过去一年，整个行业的Token消耗量几乎翻了几十上百倍，专门提供LLM Serving的云厂商和AI Infra如雨后春笋般冒出来，大有2021年左右随着Spark和Databrick，Snowflake的上市带来的流处理数据库创业潮之势。随着年中Databrick和Oracle股价的暴涨，再到现在内存条和存储的价格飙升，我们终于不得不后知后觉的意识到，本次LLM Boom的第一枪，终究还是打响在了AI Infra这个赛道上。本期【漫谈AGI】，阿P与P特与您共同聊一聊在AGI成为伪命题的当下，LLM应用流量的爆发将如何改写AI Infra生态。《半球观察》，与您相约～【本期您将听到】PART 1: LLM应用的流量爆发影响到了消费端硬件与开源生态02:18 半导体与芯片不足愈发严重，甚至影响到了消费端05:45 MinIO停止维护，Infra的战火烧到了开源生态PART 2: to C端LLM应用的需求重塑了传统Infra架构07:29 大模型LLM App爆发时代，为什么对象存储（Object Storage）会成为数据的一等公民？TiDB X的发布与Lakehouse架构12:49 为什么模型生成范式影响了整个Infra生态端？大模型应用带来了写密集负载与数据形式多样性的双重挑战。22:16 随着模型智能的提升，RAG已经被消灭了29:16 虽然AGI成为了伪命题，但模型基础能力的提升使LLM App走进了人们的日常生活，重塑了人们的互联网体验PART 3: to B/G 端LLM Infra的范式转变：私域化与基于Token的Infra策略30:47 AI Infra在私域化部署端的需求：MoE架构下，KTransformers使CPU+内存来代替单一显卡推理成为可能36:18 传统的Training和Inference的AI Infra依旧火爆，但已经趋于成熟：投机采样，训练优化，大模型量化，CuTile41:08 AI Infra与传统Infra的区别：本质同样是降本增效，但LLM App相比互联网App，单用户Infra成本变得不可忽视出场人物：阿P，P特剪辑：P特文本：P特欢迎添加两位主播的微信号 philgroup、Peter-Banqiu或者小🍠搜索“半球观察”加入听友群与我们互动～
2025-12-1646 min
AI vinder frem - hvad sker der med SEO?04. Alle vil tracke LLM’er ... men hvordan pokker måler du din synlighed i LLM'er? med Mikkel Wegener NielsenAlle vil tracke LLM’er ... men hvordan pokker måler man sin synlighed i LLM'er?AI fylder mere og mere i søgelandskabet, men ingen er helt enige om, hvordan man egentlig måler sin tilstedeværelse i LLM’er. I denne episode taler jeg med Mikkel Wegener Nielsen fra AccuRanker om den nye disciplin, hvor klassisk ranktracking ikke længere er nok, og hvor prompts ikke opfører sig som søgeord.Samtalen kredser om, hvad virksomheder reelt kan få ud af LLM tracking lige nu, hvorfor efterspørgslen for LLM-tracking eksploderer, og hvordan branchen forsøger at s...
2025-11-2045 min
POR DENTRO DO LLM - PODCASTBate Papo PDLLM #16 - Gustavo Calazans - Vaga Permanente nos EUAGustavo Calazans no Podcast Por Dentro do LLM falando sobre "Vaga Permanente nos EUA"
2025-11-1442 min
POR DENTRO DO LLM - PODCASTBate Papo PDLLM #14: Carol SayegCarol Sayeg no Podcast Por Dentro do LLM.
2025-11-1040 min
POR DENTRO DO LLM - PODCASTBate Papo PDLLM #13: Nicole BrombergNicole Bromberg no Podcast Por Dentro do LLM falando sobre "LLM".
2025-11-0751 min
POR DENTRO DO LLM - PODCASTBate Papo PDLLM #12: Gustavo VaughnGustavo Vaughn no Podcast Por Dentro do LLM falando sobre "LLM"
2025-11-0558 min
POR DENTRO DO LLM - PODCASTBate Papo PDLLM #11 - Karina MirandaKarina Miranda no Podcast Por Dentro do LLM.
2025-11-031h 07
POR DENTRO DO LLM - PODCASTBate Papo PDLLM #10: Luís Henrique e Caio (Cartas de Recomendação)Caio Gabra no Podcast Por Dentro do LLM falando sobre "Cartas e Recomendação"
2025-10-3146 min
POR DENTRO DO LLM - PODCASTBate Papo PDLLM #9: Sylvia Polo (Processo de Admissão do LLM) EM INGLÊSSylvia Polo no Podcast Por Dentro do LLM falando sobre "Processo de Admissão do LLM"
2025-10-2941 min
POR DENTRO DO LLM - PODCASTBate Papo PDLLM #8: Luís Henrique e Caio (Personal Statement) PARTE 2Caio Gabra no Podcast Por Dentro do LLM falando sobre "Personal Statement" (Parte 2)
2025-10-271h 01
POR DENTRO DO LLM - PODCASTBate Papo PDLLM #7: Applications - Parte 1/3 (CURRÍCULO)Caio Gabra no Podcast Por Dentro do LLM falando sobre "Applications - Currículo)
2025-10-241h 10
POR DENTRO DO LLM - PODCASTBate Papo PDLLM #6: Juliana Santoro (Tudo sobre o BAR!)Juliana Santoro Del Pesco no Podcast Por Dentro do LLM falando "TUDO SOBRE O BAR EXAM!"
2025-10-2242 min
POR DENTRO DO LLM - PODCASTBate Papo PDLLM #5: Gustavo Becker (LLM e Carreira Acadêmica)LLM e CARREIRA ACADÊMICA. O bate-papo de hoje é com @gustavobeckerm, que fez o seu LLM na Holanda e agora está cursando o seu Douturado em Luxemburgo. Falamos sobre a importância do LLM para quem quer seguir carreira acadêmica, estrutura de custos, networking acadêmico, diferenças entre Europa e Estados Unidos, formatos de doutorado e dicas para quem está começando e pensa em seguir na academia. É um mundo diferente do que costumamos ver aqui na página, e o Gustavo de forma muito didática respondeu muitas das dúvidas. Vale demais assistir!!
2025-10-2052 min
POR DENTRO DO LLM - PODCASTBate Papo PDLLM #4: Processo de Admissions. Como funciona?E, para falar de ADMISSIONS, conversamos simplesmente com a incrível @danielahbranco, que faz parte da equipe de admissions de @pennlaw e que tem focado sua atuação em Brasil e América Latina. Comentamos sobre como funciona a análise dos application, early application, perfil de alunos desejado pelas faculdades americanas, dicas para quem está se preparando para fazer um LLM no exterior, bolsas de estudo, diversidade, Upenn e vários outros assuntos! E essa conversa marca mais um capítulo bacana da página, em que trazemos alguém de dentro de uma faculdade renomada dos EUA para respo...
2025-10-1747 min
POR DENTRO DO LLM - PODCASTBate Papo PDLLM #3: Trabalho no Exterior Pós LLM!Estágio no Exterior Pós LLM. Esse é o tema do bate papo com a @larissapinhoo. Falamos sobre NYBar. Sobre Job Fair. Sobre Networking. Sobre timing para procurar emprego. Sobre vagas permanentes. E sobre como os planos de vida podem mudar. É muita informação bacana e útil. Obrigado @larissapinhoo, você é uma inspiração!
2025-10-1549 min
POR DENTRO DO LLM - PODCASTBate Papo PDLLM #2: LLM, Estágio no exterior e…MBA!Bate-papo muito bacana com o Marcelo. Marcelo fez LLM no exterior, trabalhou em uma big law nos Estados Unidos e….resolveu largar o direito e fazer um MBA em NY. Vejam a trajetória do marcelo, que tem muita coisa a ensinar sobre novos horizontes, decisões não triviais e aprendizados nesse novo mundo do MBA!!
2025-10-1246 min
POR DENTRO DO LLM - PODCASTBate Papo PDLLM #1: Os diferentes caminhos até o LLMEsse é um daqueles posts especiais! Batemos um papo com a incrível @deuterellen sobre diferentes caminhos para se chegar no LLM. Ellen fez o LLM dela no Max Planck Institute (Munich Intellectual Property Law Center - MIPLC), com bolsa 100%, e contou um pouco de como foi tudo isso! Falamos sobre a bolsa de estudos que ela recebeu, da trajetória profissional dela, da onde surgiu a ideia de fazer LLM, de como foi importante o inglês na vida dela, como é morar fora do país, como é importante ter referências durante todo esse caminho e muito mais!!
2025-10-0546 minLLMJulius: Beyond the Standard LLM PlaybookUnlike typical models, Julius integrates new approaches to reasoning and contextual memory. These advancements could redefine AI’s role. We discuss whether this signals the dawn of smarter AI systems.
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See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
2025-08-0908 min
AIわくわくミニキャスト！LLM社会実装フェーズへ。データ覇権争いとセキュリティリスクが顕在化なのだ！ 2025年6月19日配信日: 2025年6月19日記事一覧:Claude 4.0とGoodfire Ember｜Anthropicが推進する解釈可能なAI戦略が企業のLLM導入を加速【要約】AnthropicはAIの「ブラックボックス問題」を解消し、企業LLM導入を加速する「解釈可能なAI」戦略を推進。Claude 4.0やGoodfire投資で透明なAI活用を促し、説明責任が求められる分野での企業競争力強化を目指す。三菱電機が2つの技術でエッジ動作可能なLLMを高精度化、2026年度にも製品適用へ - MONOist【要約】三菱電機は、エッジデバイス向けLLMを開発。知識獲得とタスク特化の学習で精度を大幅向上させ、オンプレミスでのLLM実行を実現。通信遅延・セキュリティ・コスト課題を解決し、2026年度の実用化を目指す。AIのモデルとインフラの選択肢を広げるvLLM--レッドハットのAI CTOに聞く可能性 - ZDNET Japan【要約】Red Hatは、オープンソースの推論ライブラリー「vLLM」がAIインフラの複雑化やコスト課題を解決すると発表。効率的なメモリー管理「PagedAttention」でLLMサービングの性能とコスト効率を改善し、多様なLLMやインフラをサポート。Googleアプリでの「AI Mode」、音声会話式検索の提供開始（米国で）【要約】Googleアプリで新機能「Search Live」（AI Modeの一部）が提供開始。AIとの音声会話による対話型検索が可能になり、質問への回答と関連リンクを生成。バックグラウンド継続やテキスト表示もでき、AI検索体験を強化。Archaic、日本語特化型RAGシステムAIを開発。製造業分野でトップクラスの正答率【要約】Archaicは、日本語業務文書に特化したRAGシステムAIを開発。図表含む複雑な文書構造を保持し、高精度な回答を生成。独自の文書解析エンジンで構造を理解し、製造業分野でトップクラスの正答率を記録。「構造理解型RAG」で実用性を実証。生成AIによる攻撃の「質」と「量」が増加Zscalerがフィッシングレポートを公開【要約】Zscalerレポートによると、AI活用によりフィッシング攻撃が巧妙化・増加。AIが生成する偽サイトやコンテンツで機密情報窃取が高度化している。従来の防御は不十分で、AIを防御にも活用した包括的なセキュリティ対策の重要性を強調。脳科学者・茂木健一郎、『LLMの知性定量化』で「テレンス・タオの賢さをどう測るのか」問題提起【要約】脳科学者・茂木健一郎氏は、LLMの知性定量化に疑問を呈し、AIが人間の脳の「ゆらぎ」や「身体性」といった複雑な特性には及ばない可能性を指摘。AI進化には限界があるとし、人間の脳の独自性の重要性を強調した。LLMにプロンプトのみで仮想的な強化学習を発生させる方法【要約】LLMがプロンプトのみで仮想的な強化学習を行い、自己改善する研究。人間が調整せずともフィードバックで賢くなる手法を模索し、「学びの力」を引き出すプロンプトの工夫を検証。LLMの使い勝手向上を目指す。Geminiを業務で使いこなす！Google Cloudが指南する「プロンプト入力」4つのポイントは？【要約】Google Cloudがビジネス向けGemini活用法を解説。プロンプト作成には役割・詳細・タスク・フォーマットの4要素が重要。「Gem」「Deep Research」「NotebookLM」機能を紹介し、AIは触って試すことがビジネス活用の第一歩と強調した。レガシーシステムの「塩漬け戦略」もアリ？AIエージェント時代のITシステム構成を考える【要約】AIエージェントが本格活用され、システム改修不要な可能性も。ツールを呼び出しタスク遂行する技術で、複数エージェント連携も。適切な指示が課題だが、レガシーシステムとの連携でデータ活用も進む。ITシステム開発を変革する可能性。OpenAI、スケールＡＩとの協業打ち切りへ－メタ巨額出資で判断 - NewsPicks【要約】OpenAIは、メタが実質支配したスケールAIとの協業を打ち切りへ。競合への機密情報漏洩リスクを懸念。Googleも同様の動きを見せており、AI開発競争において高品質データの確保と内製化戦略の重要性が高まっている。【7/16開催ウェビナー】LLM開発におけるGPUクラウドとオンプレの徹底比較！ ~独自LLMの開発秘話からGPUコストを削減するための具体的なポイントまで一挙大公開~【要約】アイスマイリーが7月16日に無料ウェビナー開催。LLM開発のGPUクラウドとオンプレミスを徹底比較し、コスト削減ポイントや独自LLM開発秘話を公開。GPU環境選択に悩むDX・AI開発部門向けに、最大70％コスト削減手法などを解説する。
2025-06-1909 min
Study for the Bar in Your CarEvidence - Hearsay - Part 1Ready to decode the courtroom? Dive into "Evidence - Hearsay - Part 1," brought to you by "Study for the Bar in Your Car." Your hosts, Ma and Claude, unpack the often-confusing world of hearsay, using Angela’s incredibly detailed notes—the product of an LLM law student and former judicial law clerk. This episode is your essential guide to understanding the foundational definition of hearsay and, crucially, why it's generally inadmissible due to reliability concerns, the lack of oath, and the absence of cross-examination.We illuminate key categories of statements that are NOT hearsay at all, meaning you...
2025-06-1834 min
Study for the Bar in Your CarEvidence - Relevancy and the Exclusion of EvidenceReady to unravel the intricacies of evidence law? Tune into "Study for the Bar in Your Car" episode three, "Relevancy and the Exclusion of Evidence", where hosts Ma and Claude, powered by the incredibly detailed notes of LLM law student and former judicial law clerk Angela, guide you through the fundamental principles that determine what information gets heard in court.This deep dive is your essential resource for understanding how the legal system filters information. We dissect the absolute baseline of relevancy—when evidence has any tendency to make a fact more or less probable and that fa...
2025-06-1538 min
Study for the Bar in Your CarEvidence - Presentation of EvidenceReady to decode the courtroom? Dive into "Evidence - Presentation of Evidence," episode two of "Study for the Bar in Your Car," where hosts Ma and Claude (powered by Angela’s incredibly detailed notes as an LLM law student and former judicial law clerk) break down the crucial rules governing how information actually makes it before a judge and jury.This essential deep dive illuminates the foundational concepts that dictate witness testimony and evidence handling, offering you a shortcut to understanding the practical mechanics of a trial.You’ll uncover insights into:Witness Competency: The...
2025-06-1416 min
Study for the Bar in Your CarEvidence - IntroductionAre you ready to master evidence law? Dive deep into the absolutely fundamental rules that dictate what information makes it into court with "Study for the Bar in Your Car." Your hosts, Ma and Claude, unpack the complexities of evidence using incredibly detailed notes generously provided by Angela, an LLM law student and former judicial law clerk. Angela, a real-life human being and former judicial law clerk who dealt with evidence issues constantly, contributed significantly to the material.This essential podcast acts as your shortcut to understanding the fundamental rules that shape how facts are determined in...
2025-06-1336 min
Study for the Bar in Your CarCivil Procedure - Bonus Episode - Essay QuestionsReady for a unique approach to bar exam practice? Dive into our bonus episode of Study for the Bar in Your Car! Angela, our LLM law student host, pushes the boundaries of bar prep by putting NCBE MEE Civil Procedure questions into AI and having it generate new, original questions based on the patterns.In this special episode, Angela shares and breaks down the first AI-generated essay question, focusing on the critical topic of Subject Matter Jurisdiction. Angela reads the challenging fact pattern involving complex issues of diversity and amount in controversy. Then, her AI...
2025-05-2729 min
Study for the Bar in Your CarCivil Procedure - Wrap up and ReviewReady to conquer Civil Procedure? Join Ma and Claude on Study for the Bar in Your Car for a comprehensive wrap-up of this challenging but crucial subject! Drawing on Angela's detailed notes – informed by her LLM studies and experience as a judicial law clerk who saw these rules in action – this episode is your roadmap from the start of a lawsuit all the way through appeal. It focuses on what's critical for the bar exam and where pitfalls lie.We tackle the foundational building blocks you need to nail the bar and navigate litigation:Jurisdiction & Venue: Mast...
2025-05-2632 min
Study for the Bar in Your Car Civil Procedure - IntroductionLooking for a smart way to maximize your bar exam study time, especially on the go? Welcome to Study for the Bar in Your Car!Created by Angela, an LLM law student who feels like she's been studying forever, this podcast is designed to provide actual bar study content in an audio format. Tired of not finding resources for studying while driving or travelingIn this deep dive episode, your study companion focuses squarely on a foundational area critical for bar success: Civil Procedure. Think of this episode like a roadmap, covering the journey from...
2025-05-1937 min
Seventy3【第180期】LLM-AutoDiff：一个基于梯度的自动化提示工程Seventy3: 用NotebookLM将论文生成播客，让大家跟着AI一起进步。今天的主题是：LLM-AutoDiff: Auto-Differentiate Any LLM WorkflowSummaryThe provided research introduces LLM-AutoDiff, a novel framework for automating prompt engineering for complex Large Language Model workflows. This system extends gradient-based optimization to multi-step and cyclic LLM applications by treating textual inputs as trainable parameters. LLM-AutoDiff constructs a graph representing the workflow, enabling a "backward engine" LLM to generate feedback that guides iterative prompt improvements, even across functional nodes and repeated calls. The framework incorporates techniques like selective gradient computation and two-stage validation to enhance efficiency. Experimental results demonstrate that LLM-AutoDiff outperforms existing methods in accuracy and trainin...
2025-03-2920 minLLMAlpha Alpha's $500 Million Challenge
In this episode, Jaeden Schafer discusses the challenges faced by Alpha Alpha, a German LLM that raised $500 million but struggles to compete with giants like OpenAI and Anthropic. The conversation explores Alpha Alpha's innovative beginnings, their pivot towards enterprise-focused AI solutions, and the competitive landscape of the AI industry.
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Get on the AI Box Waitlist: https://AIBox.ai/
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See Privacy Policy at https://art19.com...
2024-12-1409 min
Biznes MyśliBM132: LLM i prawo, możliwości, wyzwania, narzędziaCzy duże modele językowe (LLM) to rewolucja, czy zagrożenie dla prawników? W tym odcinku przybliżam możliwości dużych modeli językowych (LLM) w automatyzacji procesów prawnych, tworzeniu dokumentów, tłumaczeniach prawniczych i compliance. To, co wydaje się przyszłością, dzieje się już teraz – ale czy to na pewno oznacza koniec klasycznego prawa?Partnerem podcastu jest DataWorkshop.🎯 W tym odcinku dowiesz się:- Jak LLM może wspierać pracę prawników- Jakie są praktyczne zastosowania AI w prawie- Dlaczego człowiek pozostanie kluczowym elementem procesu
2024-11-0658 minBiznes MyśliBM131: Praktyczny LLMCzy cały szum wokół LLM to tylko marketingowa bańka? 🤔 Choć szum wokół LLM powoli cichnie, ich prawdziwy potencjał LLM dopiero się ujawnia. Kluczem do sukcesu nie jest ślepe podążanie za trendami, ale świadome i ustrukturyzowane podejście, oparte na zrozumieniu zarówno możliwości, jak i ograniczeń tych modeli. W tym odcinku podcastu Biznes Myśli kontynuję wątek o praktycznym zastosowania LLM w biznesie.Partnerem podcastu jest DataWorkshop.Dowiesz się:- Czym różni się myślenie specjalisty od ML od programisty i dlaczego to kluczowe w pracy z LLM?- Jakie są największe wyzw...
2024-10-231h 03
共识粉碎机讨论会03|LLM保险销售(对谈暖哇莫子皓）大家好，欢迎来到共识粉碎机AI颠覆软件讨论会系列的第三期播客！本篇播客讨论于9月初，距今只有1个月时间，但莫子皓老师仍然说有了很大的变化。共识粉碎机是AI圈的老社群了，从去年3月份开始我们就坚持举办AI颠覆软件讨论会系列，最近也开始同步播客信息！与过去大家听到的AI播客不同，我们同时会讨论场景、应用，也会直接进入到技术细节。讨论节奏会非常快速，信息量非常密集，相信每一期对于听众都是一次信息大爆炸。【本期嘉宾】莫子皓：互联网保险暖哇科技合伙人欢迎关注莫子皓老师的公众号《过程即奖励》，以及莫子皓老师的文章《Workflow Based 企业内部大模型落地 Roadmap》【共识粉碎机主持人】周默：共识粉碎机公众号主理人，前美元对冲基金投资人，前腾讯/微软战略与投资经理。共识粉碎机目前也承接投研咨询合作与AI战略转型/大模型技术落地项目，欢迎戳公众号了解。同时我们的新书《大模型启示录》也上架啦，欢迎戳这里购买【参考资料】本期的内容已经抽取成文字纪要，请查阅共识粉碎机公众号的文章：《EP20：非常好的LLM保险销售案例(对谈暖哇)》【本期内容】本次讨论主要围绕LLM保险销售，有特别多的落地细节，从企业微信落地到电销落地。以下是我们聊得：00:01:06 莫子皓与暖哇介绍00:03:47 Sales Agent与Copilot00:06:59 企业微信LLM销售：用户生命周期的产品设计00:09:29 企业微信LLM销售：群发前的服务Agent00:10:55 企业微信LLM销售：为什么拆生命周期00:12:38 企业微信LLM销售：完成闭环00:15:04 企业微信LLM销售：是否需要线索分层00:17:03 企业微信LLM销售：拆SOP00:19:38 企业微信LLM销售：SOP用不同大模型00:23:01 行业Knowhow的意义00:24:24 怎么做SFT数据标注00:31:02 数据标注的人工要求00:37:25 企业微信LLM销售：ROI00:43:16 行业Knowhow公司vs标准化SaaS公司00:46:05 电销vs企业微信00:48:38 电销LLM销售：延迟与座席反应速度00:50:08 电销LLM销售：实现情况00:53:00 电销LLM销售：关单率与时间节省00:57:57 电销vs客服00:59:12 电销LLM销售：提升方向01:01:14 电销LLM销售：延迟解决方法01:05:15 行业横向拓展：保险内与保险外01:14:04 LLM销售产品落地会有打磨期吗01:16:48 未来的预期01:20:20 QA环节：数据标注能否使用第三方01:22:00 QA环节：不同SOP模型会合并成一个模型吗
2024-09-251h 23
Ürün Odaklı MühendisYerli ve Milli LLM Çalışmaları! Trendyol ve Commencis LLMÜlkemizdeki LLM çalışmalarıyla ilgili son durumu aktardım. İyi seyirler.
Video tercih edenler için: https://youtu.be/sLS2uzjDkuc
Yapay zeka ve teknoloji gelişmeleri için abone olmayı unutmayın!
Trendyol LLM: https://huggingface.co/Trendyol
Commencis LLM: https://huggingface.co/Commencis/Commencis-LLM
FinLLM'ler: https://arxiv.org/abs/2402.02315
Bölümler
00:00 Giriş
00:22 LLM nedir?
01:15 Ülkemizde LLM
01:24 Trendyol LLM
02:01 Commencis LLM
02:46 Yatırım tavsiyeleri
LLM’ler yani Büyük Dil Modelleri (Large Language Model), genellikle büyük miktarda metin verisi üzerinde eğitilmiş yapay zeka sistemlerini ifade eder. Bu modeller, doğal dil işleme (NLP) görevlerinde ku...
2024-05-1305 minLLMThe LLM Project: Apple's Vision for AI ToolsExplore Apple's visionary approach to AI-integrated tools through their innovative LLM project, offering a glimpse into the future of technology.
Get on the AI Box Waitlist: https://AIBox.ai/Join our ChatGPT Community: https://www.facebook.com/groups/739308654562189/Follow me on Twitter: https://twitter.com/jaeden_ai
See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
2024-04-0511 minLLMLeading Compliance Transformation: Patronus AI Debuts LLM Evaluation Tool for Regulated SectorsJoin the forefront of compliance transformation with Patronus AI's debut of its LLM evaluation tool designed for regulated sectors. Explore how this innovative solution revolutionizes compliance practices, offering organizations unprecedented efficiency and insight into regulatory compliance.
Get on the AI Box Waitlist: AIBox.aiJoin our ChatGPT Community: Facebook GroupFollow me on Twitter: Jaeden's Twitter
See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
2024-02-1004 minLLMShaping the Future: Patronus AI's LLM Evaluation Tool Transforms Compliance in Regulated IndustriesExplore how Patronus AI is shaping the future of compliance management in regulated industries with its innovative LLM Evaluation Tool. Learn how this tool empowers organizations to achieve regulatory compliance with efficiency and precision.
Get on the AI Box Waitlist: AIBox.aiJoin our ChatGPT Community: Facebook GroupFollow me on Twitter: Jaeden's Twitter
See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
2024-02-0704 minLLMTech Reshaping: Unveiling the World's Largest Open-Source LLM Dataset - Navigating Language Model AdvancementsJoin the conversation on tech reshaping as the world's largest open-source LLM dataset, featuring 3 trillion tokens, is unveiled, offering insights into navigating advancements in language models. In this episode, explore the potential impact on industry standards, the technological advancements, and the ongoing dialogue between artificial intelligence and the future of language understanding. 🌐💡 #TechReshaping #OpenSourceLLM
Get on the AI Box Waitlist: https://AIBox.ai/
Join our ChatGPT Community: https://www.facebook.com/groups/739308654562189/
Follow me on Twitter: https://twitter.com/jaeden_ai
See Privacy Policy at https://art19.com/privacy and California Pri...
2024-02-0108 minLLMRevolutionizing Data Security: DynamoFL's $15.1M Funding Transforms LLM Data Leak DefenseJoin the revolution in data security as DynamoFL's $15.1 million funding transforms the landscape of defending against Large Language Model (LLM) data leaks. In this episode, explore the potential impact on safeguarding sensitive information, the technological advancements, and the ongoing dialogue between artificial intelligence and the future of data security. 🔄🔐 #DataSecurityRevolution #DynamoFLDataLeakDefenseTransformation
Get on the AI Box Waitlist: https://AIBox.ai/
Join our ChatGPT Community: https://www.facebook.com/groups/739308654562189/
Follow me on Twitter: https://twitter.com/jaeden_ai
See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art...
2024-01-3108 minLLMGoogle's Bard & LLM PaLM 2 Explored: What's Fresh?Explore the freshness in Google's Bard and LLM PaLM 2, discovering the latest elements reshaping the landscape of AI language models.
Get on the AI Box Waitlist: https://AIBox.ai/
Join our ChatGPT Community: https://www.facebook.com/groups/739308654562189/
Follow me on Twitter: https://twitter.com/jaeden_ai
See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
2024-01-0715 min
Scaling Tech - The Blueprint for Successful Tech TeamsLLM Transformation Is the New Digital Transformation with Dan MasonLLM fluency: what is it, why do tech teams need it, and why should we care about it? LLM transformation is the new digital transformation, and Dan Mason, a principal at Stride Consulting, is today's guest to help us understand all we need to know about Large Language Model transformation.Dan is a prolific product and engineering team leader who's led teams and products at ESPN, People Magazine, Viacom, and more! Dan has put together a course on LLM fluency and so is uniquely positioned to offer tech leaders out there valuable insight on how they can u...
2023-06-1327 min
Derecho Laboral de la Empresa LLM UCUna breve mirada al recurso de nulidad laboralDentro del contexto de los aspectos procesales del Derecho del Trabajo, en este episodio los estudiantes del LLM UC, curso Derecho Laboral de la Empresa II Semestre 2022, abogados Saúl Cancino (mención Derecho de la Empresa), Mario Pérez (mención Derecho del Trabajo y Seguridad Social), Estefanía Ropero (mención Derecho General) y Juan Pablo Spoerer (mención Derecho Regulatorio) abordan desde un punto de vista crítico y reflexivo el panorama actual del recurso de nulidad laboral y, en particular, la forma de interposición de las causales en este recurso de derecho estricto.
2022-11-2940 min