{ "episode_count": 24, "issue_number": 1, "episodes": [ { "audio_url": "https://cdn.example.com/practical-ai/agent-evals.mp3", "best_for": [ "ml_engineers", "operators" ], "category": "ai", "content_type": "podcast", "episode_id": "ep_mock_001", "episode_title": "Agent Evals That Survive Production", "key_takeaways": [ "Concrete eval harness design for agent regressions and tool-use drift.", "Why rollback criteria should be defined before you expand retrieval breadth.", "How benchmark slices differ for internal copilots versus customer-facing agents." ], "overall_score": 91.4, "podcast_name": "Practical AI", "podcast_slug": "practical_ai", "publish_date": "2026-04-12T15:00:00Z", "rank_position": 1, "ranking_eligible": true, "score_breakdown": { "actionability": 94.0, "clarity": 87.2, "hype_penalty": 3.1, "information_density": 90.5, "originality": 88.4, "recency_relevance": 100.0, "technical_depth": 92.6 }, "scoring_confidence": 0.962, "scoring_status": "scored", "source_link": "https://example.com/practical-ai/agent-evals", "summary": "A grounded walkthrough of production agent evaluation, failure slicing, and deployment guardrails for teams already shipping LLM systems.", "transcript_status": "ready" }, { "audio_url": "https://cdn.example.com/no-priors/context-windows.mp3", "best_for": [ "engineering_leaders", "ml_engineers" ], "category": "ai", "content_type": "podcast", "episode_id": "ep_mock_002", "episode_title": "Context Windows Are Not a Strategy", "key_takeaways": [ "Long-context wins disappear without retrieval discipline and clear task framing.", "Architecture tradeoffs matter more than model-card headline numbers.", "A migration plan beats ad hoc prompt sprawl when teams scale copilots." ], "overall_score": 87.8, "podcast_name": "No Priors", "podcast_slug": "no_priors", "publish_date": "2026-04-11T18:30:00Z", "rank_position": 2, "ranking_eligible": true, "score_breakdown": { "actionability": 85.7, "clarity": 90.1, "hype_penalty": 2.4, "information_density": 88.6, "originality": 86.2, "recency_relevance": 100.0, "technical_depth": 84.9 }, "scoring_confidence": 0.948, "scoring_status": "scored", "source_link": "https://example.com/no-priors/context-windows", "summary": "A skeptical but useful conversation on long-context models, retrieval architecture, and what actually changes team productivity in practice.", "transcript_status": "ready" }, { "audio_url": "https://cdn.example.com/latent-space/tooling-stack.mp3", "best_for": [ "operators", "engineering_leaders" ], "category": "ai", "content_type": "podcast", "episode_id": "ep_mock_003", "episode_title": "The Tooling Stack Behind Reliable AI Shipping", "key_takeaways": [ "Instrumentation and incident review loops create compounding quality gains.", "Queues and snapshots make ranking systems debuggable instead of mysterious.", "Good operations content makes implementation sequencing feel obvious." ], "overall_score": 82.6, "podcast_name": "Latent Space", "podcast_slug": "latent_space", "publish_date": "2026-04-08T14:00:00Z", "rank_position": 3, "ranking_eligible": true, "score_breakdown": { "actionability": 83.9, "clarity": 81.0, "hype_penalty": 4.6, "information_density": 80.7, "originality": 79.4, "recency_relevance": 100.0, "technical_depth": 82.5 }, "scoring_confidence": 0.921, "scoring_status": "scored", "source_link": "https://example.com/latent-space/tooling-stack", "summary": "An operations-heavy episode on deployment workflows, instrumentation, and the internal tooling choices that separate demos from dependable AI products.", "transcript_status": "ready" }, { "audio_url": "https://cdn.example.com/twiml/research-to-roadmap.mp3", "best_for": [ "general_ai_listeners" ], "category": "ai", "content_type": "podcast", "episode_id": "ep_mock_004", "episode_title": "From Research Hype to Product Roadmap", "key_takeaways": [ "Separating genuine capability gains from announcement noise prevents wasteful pivots.", "Leadership teams need a simple rubric 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"episode_title": "The Hype vs The Deployment Gap", "key_takeaways": [ "Why demos outpace deployments by a factor of ten.", "What separates pilots that ship from pilots that stall.", "The human-in-the-loop tax nobody prices in." ], "overall_score": 85.1, "podcast_name": "Hard Fork", "podcast_slug": "hard_fork", "publish_date": "2026-04-10T13:00:00Z", "rank_position": 5, "ranking_eligible": true, "score_breakdown": { "actionability": 82.4, "clarity": 89.7, "hype_penalty": 3.8, "information_density": 84.3, "originality": 81.9, "recency_relevance": 98.0, "technical_depth": 79.1 }, "scoring_confidence": 0.934, "scoring_status": "scored", "source_link": "https://example.com/hard-fork/deployment-gap", "summary": "A candid look at why most AI pilots never cross the deployment threshold, and the org-level decisions that turn prototypes into durable products.", "transcript_status": "ready" }, { "audio_url": "https://cdn.example.com/acquired/nvidia-agentic.mp3", "best_for": ["operators", "engineering_leaders"], "category": "ai", "content_type": "podcast", "episode_id": "ep_mock_006", "episode_title": "NVIDIA's Agentic Moat", "key_takeaways": [ "CUDA lock-in extends into agentic workloads in unexpected ways.", "Why inference economics reshape GPU demand curves.", "Competitive pressure from custom silicon is still years out." ], "overall_score": 84.3, "podcast_name": "Acquired", "podcast_slug": "acquired", "publish_date": "2026-04-09T16:15:00Z", "rank_position": 6, "ranking_eligible": true, "score_breakdown": { "actionability": 76.2, "clarity": 88.4, "hype_penalty": 4.1, "information_density": 86.7, "originality": 85.8, "recency_relevance": 94.5, "technical_depth": 83.9 }, "scoring_confidence": 0.927, "scoring_status": "scored", "source_link": "https://example.com/acquired/nvidia-agentic", "summary": "A deep-dive on how NVIDIA's software ecosystem quietly cements its lead in the agentic compute era, and what the challengers would need to change.", "transcript_status": "ready" }, { "audio_url": "https://cdn.example.com/dwarkesh/post-training.mp3", "best_for": ["ml_engineers"], "category": "ai", "content_type": "podcast", "episode_id": "ep_mock_007", "episode_title": "Post-training is the Product", "key_takeaways": [ "RLHF pipelines now differentiate more than base model selection.", "Synthetic judge models are quietly replacing human raters.", "The cost curve for preference data is flattening faster than expected." ], "overall_score": 88.9, "podcast_name": "Dwarkesh Podcast", "podcast_slug": "dwarkesh", "publish_date": "2026-04-11T09:00:00Z", "rank_position": 7, "ranking_eligible": true, "score_breakdown": { "actionability": 85.0, "clarity": 87.1, "hype_penalty": 2.2, "information_density": 91.2, "originality": 90.4, "recency_relevance": 97.8, "technical_depth": 93.7 }, "scoring_confidence": 0.958, "scoring_status": "scored", "source_link": "https://example.com/dwarkesh/post-training", "summary": "A technical conversation on why the post-training stack has become the real product surface, with practical notes on preference data and evaluation.", "transcript_status": "ready" }, { "audio_url": "https://cdn.example.com/a16z/vertical-ai.mp3", "best_for": ["operators", "engineering_leaders"], "category": "ai", "content_type": "podcast", "episode_id": "ep_mock_008", "episode_title": "Vertical AI: The Second Wave", "key_takeaways": [ "Domain-specific fine-tunes outperform general agents in measurable ROI.", "Distribution beats model quality in regulated verticals.", "The integration surface is the real moat." ], "overall_score": 73.5, "podcast_name": "a16z Podcast", "podcast_slug": "a16z", "publish_date": "2026-04-07T20:00:00Z", "rank_position": 8, "ranking_eligible": true, "score_breakdown": { "actionability": 78.6, "clarity": 80.3, "hype_penalty": 6.7, "information_density": 71.2, "originality": 69.8, "recency_relevance": 92.1, "technical_depth": 66.4 }, "scoring_confidence": 0.881, "scoring_status": "scored", "source_link": "https://example.com/a16z/vertical-ai", "summary": "A venture-leaning take on the second wave of vertical AI startups and why distribution often beats model quality in regulated industries.", "transcript_status": "ready" }, { "audio_url": "https://cdn.example.com/lex/synthetic-data.mp3", "best_for": ["ml_engineers", "general_ai_listeners"], "category": "ai", "content_type": "podcast", "episode_id": "ep_mock_009", "episode_title": "Synthetic Data and Sampling Limits", "key_takeaways": [ "Model collapse is less inevitable than critics claim.", "Diversity metrics for synthetic corpora are still underdeveloped.", "Frontier labs already rely heavily on curated synthetic mixes." ], "overall_score": 79.2, "podcast_name": "Lex Fridman Podcast", "podcast_slug": "lex_fridman", "publish_date": "2026-04-06T22:30:00Z", "rank_position": 9, "ranking_eligible": true, "score_breakdown": { "actionability": 68.9, "clarity": 82.5, "hype_penalty": 5.1, "information_density": 83.7, "originality": 81.1, "recency_relevance": 88.3, "technical_depth": 86.2 }, "scoring_confidence": 0.902, "scoring_status": "scored", "source_link": "https://example.com/lex/synthetic-data", "summary": "A long-form discussion on synthetic data generation, model collapse risk, and how frontier labs balance curated versus scraped training mixes.", "transcript_status": "ready" }, { "audio_url": "https://cdn.example.com/cog-rev/jailbreaks.mp3", "best_for": ["ml_engineers", "operators"], "category": "ai", "content_type": "podcast", "episode_id": "ep_mock_010", "episode_title": "Jailbreaks in the Wild", "key_takeaways": [ "Prompt-level jailbreaks are being replaced by agentic social engineering.", "Red-team budgets lag behind deployment breadth by roughly 3x.", "Defense-in-depth requires runtime monitoring, not just fine-tuning." ], "overall_score": 81.7, "podcast_name": "The Cognitive Revolution", "podcast_slug": "cognitive_revolution", "publish_date": "2026-04-10T18:00:00Z", "rank_position": 10, "ranking_eligible": true, "score_breakdown": { "actionability": 84.2, "clarity": 80.9, "hype_penalty": 3.4, "information_density": 83.1, "originality": 78.5, "recency_relevance": 96.0, "technical_depth": 82.4 }, "scoring_confidence": 0.919, "scoring_status": "scored", "source_link": "https://example.com/cog-rev/jailbreaks", "summary": "A working red-teamer walks through the evolving jailbreak landscape, from prompt chains to multi-step agentic attacks, and what holds up in production.", "transcript_status": "ready" }, { "audio_url": "https://cdn.example.com/lennys/ai-features.mp3", "best_for": ["operators", "general_ai_listeners"], "category": "ai", "content_type": "podcast", "episode_id": "ep_mock_011", "episode_title": "Shipping AI Features Users Actually Want", "key_takeaways": [ "Retention, not adoption, is the honest AI feature metric.", "Discoverability beats model quality for most consumer surfaces.", "Onboarding sets the ceiling for how much value users extract." ], "overall_score": 71.8, "podcast_name": "Lenny's Podcast", "podcast_slug": "lennys", "publish_date": "2026-04-08T11:00:00Z", "rank_position": 11, "ranking_eligible": true, "score_breakdown": { "actionability": 82.0, "clarity": 86.1, "hype_penalty": 5.9, "information_density": 68.3, "originality": 64.7, "recency_relevance": 90.4, "technical_depth": 55.2 }, "scoring_confidence": 0.874, "scoring_status": "scored", "source_link": "https://example.com/lennys/ai-features", "summary": "A PM-centric look at shipping AI features that measurably move retention, with case studies on onboarding flows and discoverability tradeoffs.", "transcript_status": "ready" }, { "audio_url": "https://cdn.example.com/stratechery/gpu-econ.mp3", "best_for": ["engineering_leaders"], "category": "ai", "content_type": "podcast", "episode_id": "ep_mock_012", "episode_title": "GPU Rentals vs Ownership Economics", "key_takeaways": [ "Break-even for owning H100-class fleets is 14 months at high utilization.", "Spot pricing volatility is shifting enterprise back to reserved capacity.", "Depreciation schedules determine the build-vs-rent calculus more than cost." ], "overall_score": 77.4, "podcast_name": "Sharp Tech", "podcast_slug": "sharp_tech", "publish_date": "2026-04-09T14:30:00Z", "rank_position": 12, "ranking_eligible": true, "score_breakdown": { "actionability": 79.1, "clarity": 83.6, "hype_penalty": 4.7, "information_density": 78.9, "originality": 72.5, "recency_relevance": 93.0, "technical_depth": 70.8 }, "scoring_confidence": 0.895, "scoring_status": "scored", "source_link": "https://example.com/stratechery/gpu-econ", "summary": "A sharp economic breakdown of build-versus-rent GPU strategies for teams with steady inference workloads, including depreciation and spot-market risk.", "transcript_status": "ready" }, { "audio_url": "https://cdn.example.com/gradient-dissent/mlops.mp3", "best_for": ["ml_engineers"], "category": "ai", "content_type": "podcast", "episode_id": "ep_mock_013", "episode_title": "MLOps for Evaluation-Driven Teams", "key_takeaways": [ "Eval pipelines belong in CI, not in a separate batch system.", "Regression triage breaks down without stable slice definitions.", "Observability tooling still lags dataset versioning in maturity." ], "overall_score": 83.0, "podcast_name": "Gradient Dissent", "podcast_slug": "gradient_dissent", "publish_date": "2026-04-11T15:45:00Z", "rank_position": 13, "ranking_eligible": true, "score_breakdown": { "actionability": 88.3, "clarity": 81.7, "hype_penalty": 2.9, "information_density": 85.6, "originality": 76.2, "recency_relevance": 91.1, "technical_depth": 88.9 }, "scoring_confidence": 0.931, "scoring_status": "scored", "source_link": "https://example.com/gradient-dissent/mlops", "summary": "A practitioner-heavy episode on how to fold evaluation into CI, maintain stable slices, and avoid the usual traps of ML observability tooling.", "transcript_status": "ready" }, { "audio_url": "https://cdn.example.com/all-in/valuation.mp3", "best_for": ["general_ai_listeners"], "category": "ai", "content_type": "podcast", "episode_id": "ep_mock_014", "episode_title": "The Valuation Reset", "key_takeaways": [ "Late-stage AI rounds are pricing off revenue, not story.", "Compute contracts are becoming the new dilution lever.", "M&A activity points to consolidation at the mid-market." ], "overall_score": 64.2, "podcast_name": "All-In", "podcast_slug": "all_in", "publish_date": "2026-04-05T23:00:00Z", "rank_position": 14, "ranking_eligible": true, "score_breakdown": { "actionability": 58.4, "clarity": 72.1, "hype_penalty": 11.3, "information_density": 61.8, "originality": 55.9, "recency_relevance": 88.2, "technical_depth": 48.7 }, "scoring_confidence": 0.812, "scoring_status": "scored", "source_link": "https://example.com/all-in/valuation", "summary": "A macro-flavored conversation on the late-stage AI valuation reset, the rise of compute-linked deals, and where consolidation is most likely.", "transcript_status": "ready" }, { "audio_url": "https://cdn.example.com/training-data/distillation.mp3", "best_for": ["ml_engineers"], "category": "ai", "content_type": "podcast", "episode_id": "ep_mock_015", "episode_title": "Distillation Beats Size (Sometimes)", "key_takeaways": [ "Small distilled models hit 85% of teacher capability at 10% of cost.", "Distillation recipes are still under-documented in the open literature.", "Latency-sensitive products benefit more than accuracy-driven ones." ], "overall_score": 80.6, "podcast_name": "Training Data", "podcast_slug": "training_data", "publish_date": "2026-04-10T10:15:00Z", "rank_position": 15, "ranking_eligible": true, "score_breakdown": { "actionability": 77.8, "clarity": 79.4, "hype_penalty": 3.2, "information_density": 82.1, "originality": 83.7, "recency_relevance": 94.0, "technical_depth": 85.3 }, "scoring_confidence": 0.914, "scoring_status": "scored", "source_link": "https://example.com/training-data/distillation", "summary": "A focused discussion of distillation tradeoffs, recipe design, and when smaller models genuinely beat their teachers in production constraints.", "transcript_status": "ready" }, { "audio_url": "https://cdn.example.com/changelog/llm-static.mp3", "best_for": ["ml_engineers", "engineering_leaders"], "category": "ai", "content_type": "podcast", "episode_id": "ep_mock_016", "episode_title": "LLMs Meet Static Analysis", "key_takeaways": [ "Hybrid pipelines cut false positives by grounding LLMs in AST context.", "Code review agents still struggle with cross-file reasoning.", "Tooling convergence is starting around language server protocol bridges." ], "overall_score": 75.9, "podcast_name": "The Changelog", "podcast_slug": "changelog", "publish_date": "2026-04-07T17:00:00Z", "rank_position": 16, "ranking_eligible": true, "score_breakdown": { "actionability": 76.4, "clarity": 81.8, "hype_penalty": 4.5, "information_density": 74.2, "originality": 73.1, "recency_relevance": 89.7, "technical_depth": 77.5 }, "scoring_confidence": 0.886, "scoring_status": "scored", "source_link": "https://example.com/changelog/llm-static", "summary": "A working engineer's take on combining LLMs with static analysis for code review, with honest notes on where hybrid pipelines still fall short.", "transcript_status": "ready" }, { "audio_url": "https://cdn.example.com/mlst/embeddings.mp3", "best_for": ["ml_engineers"], "category": "ai", "content_type": "podcast", "episode_id": "ep_mock_017", "episode_title": "What Embeddings Actually Encode", "key_takeaways": [ "Linear probes recover surprisingly little structured knowledge.", "Task-specific heads dominate transfer quality more than base embeddings.", "Interpretability still lags behind capability gains." ], "overall_score": 86.4, "podcast_name": "Machine Learning Street Talk", "podcast_slug": "mlst", "publish_date": "2026-04-12T08:30:00Z", "rank_position": 17, "ranking_eligible": true, 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"podcast_slug": "super_data_science", "publish_date": "2026-04-04T19:00:00Z", "rank_position": 18, "ranking_eligible": true, "score_breakdown": { "actionability": 71.5, "clarity": 75.3, "hype_penalty": 6.8, "information_density": 66.7, "originality": 58.9, "recency_relevance": 82.4, "technical_depth": 64.2 }, "scoring_confidence": 0.856, "scoring_status": "scored", "source_link": "https://example.com/sds/feature-stores", "summary": "A measured update on feature-store adoption, consolidation into lakehouse patterns, and where real-time serving remains genuinely hard.", "transcript_status": "ready" }, { "audio_url": "https://cdn.example.com/hard-fork/safety-memo.mp3", "best_for": ["general_ai_listeners"], "category": "ai", "content_type": "podcast", "episode_id": "ep_mock_019", "episode_title": "Regulation After The Safety Memo", "key_takeaways": [ "Federal preemption is gaining traction over state-by-state patchwork.", "Evaluation disclosure rules may reshape release cadences.", "Labs are hiring policy counsel faster than security engineers." ], "overall_score": 69.7, "podcast_name": "Hard Fork", "podcast_slug": "hard_fork", "publish_date": "2026-04-03T13:00:00Z", "rank_position": 19, "ranking_eligible": true, "score_breakdown": { "actionability": 54.8, "clarity": 84.6, "hype_penalty": 7.2, "information_density": 71.9, "originality": 68.4, "recency_relevance": 76.0, "technical_depth": 52.3 }, "scoring_confidence": 0.841, "scoring_status": "scored", "source_link": "https://example.com/hard-fork/safety-memo", "summary": "A policy-focused conversation on the regulatory shifts following the latest federal safety memo and how labs are quietly restructuring in response.", "transcript_status": "ready" }, { "audio_url": "https://cdn.example.com/a16z/hyperscalers.mp3", "best_for": ["engineering_leaders"], "category": "ai", "content_type": "podcast", "episode_id": "ep_mock_020", "episode_title": "What the Hyperscalers Won't Tell You", "key_takeaways": [ "Inference margins are 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