#large-language-models
174 episodes · Page 3 of 8
#3127: Crafting AI Characters That Feel Alive
Move beyond system prompts with structured character bibles that give AI personalities real inner lives.
#2672: When a Startup Claims to Break the Quadratic Wall
A startup claims linear attention scaling at 12M tokens, beating GPT-5.5 on retrieval benchmarks.
#2664: Can You Trust an LLM's Raw Knowledge?
Why pre-trained knowledge isn't reliable for facts — and what actually makes models useful.
#2651: AI Training Itself: Student, Teacher, and Grader
Can models generate their own training data and judge their own outputs? The promise and pitfalls of fully AI-led pipelines.
#2650: How to Catch an LLM's Bad Writing Habits
A practical guide to analyzing podcast transcripts for repetitive language and dialogue patterns — from Python word counts to embedding clustering.
#2622: How Transformers Actually Work: Attention, Tokens, and Context
How one architectural change unlocked chatbots, image generation, and protein folding — explained without the jargon.
#2488: Hybrid Pipelines for Entity Resolution
Classic NLP pipelines vs. lightweight LLMs for handling Hezbollah’s half-dozen spellings.
#2464: Batch APIs: The 50% Discount You're Probably Misusing
Batch inference APIs offer 50% off — but only for the right workloads. Here's when they actually make sense.
#2461: How Claude Code's Conversation Compaction Actually Works
The three-tier system, what survives, what dies, and why you shouldn't rely on auto-compact.
#2426: Why DeepSeek V4's Prose Feels More Vivid Than Claude or GPT
A million-token context window at 2% the KV-cache cost — and prose that actually breathes. Here's what makes V4 different.
#2410: How Researchers Actually Measure Censorship in Chinese LLMs
Beyond headlines: the actual benchmarks, methodologies, and pitfalls in detecting political refusal in Chinese language models.
#2403: Choosing Your LLM Eval Framework
An architectural shootout of four major LLM evaluation harnesses — where each shines and where each breaks down.
#2374: How Granular Can MoE Experts Get?
Exploring the limits of expert granularity in Mixture of Experts models—how narrow can segmentation go before efficiency or accuracy suffers?
#2355: Why Open-Weight Models Are Winning
Discover how Cogito v2.1 leverages process supervision and MoE architecture to redefine reasoning efficiency in open-weight AI models.
#2314: One Model or Three? Inside Claude's Architecture
What makes Claude’s Haiku, Sonnet, and Opus different? Discover how architecture shapes their unique strengths and weaknesses.
#2311: Danish AI: Bridging the Localization Gap
How does AI handle Danish? Explore the challenges and progress in making AI tools work for small-language populations.
#2309: Blind Ranking AI's Best Podcast Scripts
How do 15 AI models handle controversial podcast prompts? We rank their scripts blind and reveal the surprising winners.
#2307: Inside Frontier LLM Training: Stages, Costs, and Checkpoints
Discover the multi-stage process of training frontier large language models, from pretraining to post-training, and why checkpoints are the key to ...
#2306: Can LLM Councils Truly Capture Diverse Worldviews?
Exploring whether LLM councils can achieve genuine worldview diversity or if alignment processes erase meaningful differences.
#2243: What Enterprise AI Pricing Actually Negotiates
Enterprise customers rarely get the deep discounts they expect from AI APIs. What they actually negotiate for—and why the ramp-up requirement exist...
#2242: AI as Your Ideation Blind Spot Spotter
How to use AI not to answer questions you already know to ask, but to surface possibilities your expertise has made invisible to you.
#2233: Who Actually Wants AI to Slow Down?
Daniel argues AI development should slow down for expertise and stability. But who in the industry actually shares this philosophy beyond the obvio...
#2214: The Three Failure Modes of AI News Systems
When a conflict changes hourly, AI systems built for yesterday's information fail. Here's how to architect pipelines that actually keep up.
#2190: Simulating Extreme Decisions With LLMs
LLMs fail at the exact problem wargaming was built to solve—simulating irrational, extreme decision-makers. A new study reveals why.