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If you're still guessing how marketing is really performing, this episode will flip the switch. Spencer breaks down why Sales Qualified Leads (SQLs) are the most important metric you can track and the best leading indicator of future revenue. You'll learn how to clearly define an SQL, why it matters more than raw lead volume, and how tight communication between sales and marketing turns SQLs into better forecasting, smarter ad spend, and higher close rates. If you want fewer “junk leads” and more projects that actually fit your business, this is a must-listen.
In this episode of the Crazy Wisdom podcast, host Stewart Alsop welcomes Roni Burd, a data and AI executive with extensive experience at Amazon and Microsoft, for a deep dive into the evolving landscape of data management and artificial intelligence in enterprise environments. Their conversation explores the longstanding challenges organizations face with knowledge management and data architecture, from the traditional bronze-silver-gold data processing pipeline to how AI agents are revolutionizing how people interact with organizational data without needing SQL or Python expertise. Burd shares insights on the economics of AI implementation at scale, the debate between one-size-fits-all models versus specialized fine-tuned solutions, and the technical constraints that prevent companies like Apple from upgrading services like Siri to modern LLM capabilities, while discussing the future of inference optimization and the hundreds-of-millions-of-dollars cost barrier that makes architectural experimentation in AI uniquely expensive compared to other industries.Timestamps00:00 Introduction to Data and AI Challenges03:08 The Evolution of Data Management05:54 Understanding Data Quality and Metadata08:57 The Role of AI in Data Cleaning11:50 Knowledge Management in Large Organizations14:55 The Future of AI and LLMs17:59 Economics of AI Implementation29:14 The Importance of LLMs for Major Tech Companies32:00 Open Source: Opportunities and Challenges35:19 The Future of AI Inference and Hardware43:24 Optimizing Inference: The Next Frontier49:23 The Commercial Viability of AI ModelsKey Insights1. Data Architecture Evolution: The industry has evolved through bronze-silver-gold data layers, where bronze is raw data, silver is cleaned/processed data, and gold is business-ready datasets. However, this creates bottlenecks as stakeholders lose access to original data during the cleaning process, making metadata and data cataloging increasingly critical for organizations.2. AI Democratizing Data Access: LLMs are breaking down technical barriers by allowing business users to query data in plain English without needing SQL, Python, or dashboarding skills. This represents a fundamental shift from requiring intermediaries to direct stakeholder access, though the full implications remain speculative.3. Economics Drive AI Architecture Decisions: Token costs and latency requirements are major factors determining AI implementation. Companies like Meta likely need their own models because paying per-token for billions of social media interactions would be economically unfeasible, driving the need for self-hosted solutions.4. One Model Won't Rule Them All: Despite initial hopes for universal models, the reality points toward specialized models for different use cases. This is driven by economics (smaller models for simple tasks), performance requirements (millisecond response times), and industry-specific needs (medical, military terminology).5. Inference is the Commercial Battleground: The majority of commercial AI value lies in inference rather than training. Current GPUs, while specialized for graphics and matrix operations, may still be too general for optimal inference performance, creating opportunities for even more specialized hardware.6. Open Source vs Open Weights Distinction: True open source in AI means access to architecture for debugging and modification, while "open weights" enables fine-tuning and customization. This distinction is crucial for enterprise adoption, as open weights provide the flexibility companies need without starting from scratch.7. Architecture Innovation Faces Expensive Testing Loops: Unlike database optimization where query plans can be easily modified, testing new AI architectures requires expensive retraining cycles costing hundreds of millions of dollars. This creates a potential innovation bottleneck, similar to aerospace industries where testing new designs is prohibitively expensive.
Nik and Michael are joined by Lev Kokotov for an update on all things PgDog. Here are some links to things they mentioned:Lev Kokotov https://postgres.fm/people/lev-kokotovPgDog https://github.com/pgdogdev/pgdogOur first PgDog episode (March 2025) https://postgres.fm/episodes/pgdogSharding pgvector (blog post by Lev) https://pgdog.dev/blog/sharding-pgvectorPrepared statements and partitioned table lock explosion (series by Nik) https://postgres.ai/blog/20251028-postgres-marathon-2-009~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith credit to:Jessie Draws for the elephant artwork
In this episode, Chris, Andrew, and David dive into details about refactoring with SQL, updates on new Ruby versions, and share their views on various developer tools including Mise, Overmind, and Foreman. They also touch on standardizing tools within their teams, the benefits of using Mise for Postgres, and the efficiency of task scripts. The conversation also covers encoding issues, Basecamp Fizzy SSFR protection, and rich-text editors like Lexxy and its application in Basecamp. Additionally, there's a light-hearted discussion on the speculative future of AI and Neuralink. Hit download now to hear more! LinksJudoscale- Remote Ruby listener giftRuby ReleasesForeman-GitHubOvermind-GitHubMise versionsUsage SpecificationA Ruby YAML parser (blog post by Kevin Newton)Lexxy-GitHubBasecamp Fizzy SSRF protection-GitHubNeuralinkAndrew Mason-The MatrixHoneybadgerHoneybadger is an application health monitoring tool built by developers for developers.JudoscaleMake your deployments bulletproof with autoscaling that just works.Disclaimer: This post contains affiliate links. If you make a purchase, I may receive a commission at no extra cost to you. Chris Oliver X/Twitter Andrew Mason X/Twitter Jason Charnes X/Twitter
In this episode of RevOps Champions, host Brendon Dennewill sits down with Peter Fuller, Founder of Workflow Academy and a leading expert in revenue operations, CRM systems, and workflow automation. Peter shares his unconventional path from studying Russian literature to building a RevOps consultancy and training ecosystem, and why the “human” side of RevOps will only become more important as AI adoption accelerates.Peter breaks down the three pillars he teaches (ask better questions in plain English, “measure twice cut once” with clear scoping, and only then build), and explains why most AI initiatives fail: not because the tools don't work, but because leaders chase hype instead of focused, high-ROI use cases. He offers a practical approach for 2026: empower your internal tinkerer, carve out time, and prove ROI on one micro-solution before turning AI into a company-wide strategy. The conversation is a grounded, refreshingly contrarian take on where AI actually helps RevOps teams today, especially in reporting, dashboards, SQL, and automation, without sacrificing relationships, trust, and real human context.This episode is essential listening for RevOps leaders, operators, and executives who want to cut through AI noise, prioritize what matters, and deploy automation in ways that genuinely improve performance without distracting the business.What You'll LearnWhere AI is creating real leverage in RevOps today, and where it quietly falls shortWhy the most critical parts of RevOps still depend on human judgment and trustA simple framework for approaching RevOps work without jumping straight to toolsHow to experiment with AI in a way that minimizes risk and maximizes learningHow to separate real opportunity from AI hype and vendor-driven urgencyWhat leaders should prioritize in 2026 to explore AI without derailing core operationsResources MentionedCerebro AnalyticsLovableMoon Knox ChatGPTClaudeWorkflow AcademyAspireshipZohoIs your business ready to scale? Take the Growth Readiness Score to find out. In 5 minutes, you'll see: Benchmark data showing how you stack up to other organizations A clear view of your operational maturity Whether your business is ready to scale (and what to do next if it's not) Let's Connect Subscribe to the RevOps Champions Newsletter LinkedIn YouTube Explore the show at revopschampions.com. Ready to unite your teams with RevOps strategies that eliminate costly silos and drive growth? Let's talk!
Nik and Michael are joined by Radim Marek from boringSQL to talk about RegreSQL, a regression testing tool for SQL queries they forked and improved recently. Here are some links to things they mentioned:Radim Marek https://postgres.fm/people/radim-marekboringSQL https://boringsql.comRegreSQL: Regression Testing for PostgreSQL Queries (blog post by Radim) https://boringsql.com/posts/regresql-testing-queriesDiscussion on Hacker News https://news.ycombinator.com/item?id=45924619 Radim's fork of RegreSQL on GitHub https://github.com/boringSQL/regresql Original RegreSQL on GitHub (by Dimitri Fontaine) https://github.com/dimitri/regresql The Art of PostgreSQL (book) https://theartofpostgresql.comHow to make the non-production Postgres planner behave like in production (how-to post by Nik) https://postgres.ai/docs/postgres-howtos/performance-optimization/query-tuning/how-to-imitate-production-planner Just because you're getting an index scan, doesn't mean you can't do better! (Blog post by Michael) https://www.pgmustard.com/blog/index-scan-doesnt-mean-its-fastboringSQL Labs https://labs.boringsql.com~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith credit to:Jessie Draws for the elephant artwork
professorjrod@gmail.comIn this episode of Technology Tap: CompTIA Study Guide, we explore how proactive detection surpasses reactive troubleshooting in cybersecurity. For those preparing for their CompTIA exam, understanding the subtle clues and quiet anomalies attackers leave behind is essential for developing strong IT skills and excelling in tech exam prep. We dive deep into the critical indicators that help you detect security compromises early, providing practical knowledge essential for your technology education and IT certification journey. Join us as we equip you with expert insights to sharpen your detection abilities and enhance your competence in protecting systems effectively.We walk through the behaviors that matter: viruses that hitch a ride on clicks, worms that paint the network with unexplained traffic, and fileless attacks that live in memory and borrow admin tools like PowerShell and scheduled tasks. You'll learn how to spot spyware by the aftermath of credential misuse, recognize RATs and backdoors by their steady beaconing to unknown IPs, and use contradictions—like tools disagreeing about running processes—as a signal for rootkits. We also draw a sharp line between ransomware's loud chaos and cryptojacking's quiet drain on your CPU and fan.Zooming out, we map network and application signals: certificate warnings and duplicate MACs that hint at man-in-the-middle, DNS mismatches that suggest cache poisoning, and log patterns that betray SQL injection, replay abuse, or directory traversal. Along the way, we talk about building Security+ instincts through scaffolding—A+ for OS and hardware intuition, Network+ for protocol fluency, and Security+ for attacker behavior—so indicators make sense the moment you see them.If you want a sharper eye for subtle threats and a stronger shot at your Security+ exam, this guide will train your attention on the tells adversaries can't fully hide. Subscribe, share with a teammate who handles triage, and leave a review with your favorite indicator to watch—we'll feature the best ones in a future show.Support the showArt By Sarah/DesmondMusic by Joakim KarudLittle chacha ProductionsJuan Rodriguez can be reached atTikTok @ProfessorJrodProfessorJRod@gmail.com@Prof_JRodInstagram ProfessorJRod
In less than 12 months, Shahar went from an idea to a $30M Series A and a team of 40. He didn't sell another AI tool—he built an AI-first service that replaced expensive human consultants in the massive pen-testing market.In this episode, Shahar breaks down the "Service-as-Software" playbook that allowed him to hit $1M ARR in just three months. He reveals how to convert design partners into paying customers before the product is finished, why he refuses to sell to service providers, and how to achieve a 40% SQL-to-Close rate in the enterprise.Why You Should ListenHow to hit $1M ARR in a single quarter with zero marketing spend.Why asking "Would you use this?" is useless and the one question that actually validates demand.Why "Service-as-Software" is the single best business model for AI startupsHow to maintain a 100% win rate against competitors in live bake-offs.The ultimate litmus test for knowing if you have true Product-Market Fit.Keywordsstartup podcast, startup podcast for founders, product market fit, finding pmf, agentic AI, cybersecurity startup, B2B sales strategy, service as software, rapid scaling, Felicis00:00:00 Intro00:04:06 Why Manual Pen Testing is Broken00:15:42 Ideation and The Wallet Test00:22:38 How to Convert Design Partners to Paid00:28:05 40 Percent SQL to Close Rate00:33:14 The Service as Software Business Model00:46:06 Hitting 1M ARR in One Quarter00:48:50 Raising a 30M Series A from Felicis00:50:01 The Turn It Off PMF TestSend me a message to let me know what you think!
What happens when engineering teams can finally see the business impact of every technical decision they make? In this episode of Tech Talks Daily, I sat down with Chris Cooney, Director of Advocacy at Coralogix, to unpack why observability is no longer just an engineering concern, but a strategic lever for the entire business. Chris joined me fresh from AWS re:Invent, where he had been challenging a long-standing assumption that technical signals like CPU usage, error rates, and logs belong only in engineering silos. Instead, he argues that these signals, when enriched and interpreted correctly, can tell a much more powerful story about revenue loss, customer experience, and competitive advantage. We explored Coralogix's Observability Maturity Model, a four-stage framework that takes organizations from basic telemetry collection through to business-level decision making. Chris shared how many teams stall at measuring engineering health, without ever connecting that data to customer impact or financial outcomes. The conversation became especially tangible when he explained how a single failed checkout log can be enriched with product and pricing data to reveal a bug costing thousands of dollars per day. That shift, from "fix this tech debt" to "fix this issue draining revenue," fundamentally changes how priorities are set across teams. Chris also introduced Oli, Coralogix's AI observability agent, and explained why it is designed as an agent rather than a simple assistant. We talked about how Oli can autonomously investigate issues across logs, metrics, traces, alerts, and dashboards, allowing anyone in the organization to ask questions in plain English and receive actionable insights. From diagnosing a complex SQL injection attempt to surfacing downstream customer impact, Oli represents a move toward democratizing observability data far beyond engineering teams. Throughout our discussion, a clear theme emerged. When technical health is directly tied to business health, observability stops being seen as a cost center and starts becoming a competitive advantage. By giving autonomous engineering teams visibility into real-world impact, organizations can make faster, better decisions, foster innovation, and avoid the blind spots that have cost even well-known brands millions. So if observability still feels like a necessary expense rather than a growth driver in your organization, what would change if every technical signal could be translated into clear business impact, and who would make better decisions if they could finally see that connection? Useful LInks Connect with Chris Cooney Learn more about Coralogix Follow on LinkedIn Thanks to our sponsors, Alcor, for supporting the show.
SHOW: 992SHOW TRANSCRIPT: The Cloudcast #992 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET NEW TO CLOUD? CHECK OUT OUR OTHER PODCAST - "CLOUDCAST BASICS" SHOW NOTES:Tonic.ai websiteTonic Validate Product PageTonic Validate GitHubTopic 1 - Adam, welcome to the show. Give everyone a brief introduction.Topic 2: Our topic today is RAG systems, specifically RAG in production. Let's start with customization sources and types. When it comes to customizing off-the-shelf LLMs, RAG is one option, as is an MCP connection to a SQL database, and there is pre- and post-training, as well as fine-tuning. How does an organization decide what path is best for customization?Topic 3 - RAG came on the scene as the savior for organizations that want to use customer AI without the need for fine-tuning and additional training. It has either gone through or is currently still in the trough of disillusionment. What are your thoughts on RAG's evolution and the challenges it faces?Topic 4 - Let's walk through the basics of validation. Once you set up RAG, how would an organization know it works? How is accuracy measured and validated? Are you looking for hallucinations? Context quality?Topic 5 - What is Tonic Validate, and where does it fit into this stack? Is it in band? Out of band? Built into the CI workflow?Topic 6 - Accuracy is one aspect, but we hear more and more about ROI for Enterprises. How should ROI, risk, and compliance be measured?Topic 7 - Where and how does security fit into all of this? Also, your thoughts on synthetic data for training vs. real data?Topic 8 - If anyone is interested, what's the best way to get started?FEEDBACK?Email: show at the cloudcast dot netBluesky: @cloudcastpod.bsky.socialTwitter/X: @cloudcastpodInstagram: @cloudcastpodTikTok: @cloudcastpod
Baris Gultekin, VP of AI at Snowflake, explains how “bringing AI to the data” is reshaping enterprise AI deployment under strict security and governance requirements. PSA for AI builders: Interested in alignment, governance, or AI safety? Learn more about the MATS Summer 2026 Fellowship and submit your name to be notified when applications open: https://matsprogram.org/s26-tcr. He shares the importance of bringing AI directly to governed enterprise data, advances in text-to-SQL and semantic modeling, and why high-quality retrieval is foundational for trustworthy AI agents. Baris also dives into Snowflake's approach to agentic AI, including Snowflake Intelligence, model choice and cost tradeoffs, and why governance, security, and open standards are essential as AI becomes accessible to every business user. LINKS: AWS' Automated Reasoning checks Sponsors: MongoDB: Tired of database limitations and architectures that break when you scale? MongoDB is the database built for developers, by developers—ACID compliant, enterprise-ready, and fluent in AI—so you can start building faster at https://mongodb.com/build Serval: Serval uses AI-powered automations to cut IT help desk tickets by more than 50%, freeing your team from repetitive tasks like password resets and onboarding. Book your free pilot and guarantee 50% help desk automation by week four at https://serval.com/cognitive MATS: MATS is a fully funded 12-week research program pairing rising talent with top mentors in AI alignment, interpretability, security, and governance. Apply for the next cohort at https://matsprogram.org/s26-tcr Tasklet: Tasklet is an AI agent that automates your work 24/7; just describe what you want in plain English and it gets the job done. Try it for free and use code COGREV for 50% off your first month at https://tasklet.ai CHAPTERS: (00:00) About the Episode (03:02) Snowflake 101 and AI (09:25) Text-to-SQL and semantics (19:10) RAG, embeddings and models (Part 1) (19:17) Sponsors: MongoDB | Serval (21:02) RAG, embeddings and models (Part 2) (32:23) Bringing models to data (Part 1) (32:29) Sponsors: MATS | Tasklet (35:29) Bringing models to data (Part 2) (51:14) Designing enterprise AI agents (58:35) Trust, governance and guardrails (01:07:14) Agents and future work (01:15:33) Platforms, competition and value (01:26:04) Enterprise models and outlook (01:40:00) Outro PRODUCED BY: https://aipodcast.ing
The FBI warns of Kimsuky quishing. Singapore warns of a critical vulnerability in Advantech IoT management platforms. Russia's Fancy Bear targets energy research, defense collaboration, and government communications. Malaysia and Indonesia suspend access to X. Researchers warn a large-scale fraud operation is using AI-generated personas to trap mobile users in a social engineering scam. BreachForums gets breached. The NSA names a new Deputy Director. Monday Biz Brief. Our guest is Sasha Ingber, host of the International Spy Museum's SpyCast podcast. The commuter who hacked his scooter. Remember to leave us a 5-star rating and review in your favorite podcast app. Miss an episode? Sign-up for our daily intelligence roundup, Daily Briefing, and you'll never miss a beat. And be sure to follow CyberWire Daily on LinkedIn. CyberWire Guest Today we are joined by Sasha Ingber, host of the International Spy Museum's SpyCast podcast, on the return of SpyCast to the N2K CyberWire network. Selected Reading North Korea–linked APT Kimsuky behind quishing attacks, FBI warns (Security Affairs) Advantech patches maximum-severity SQL injection flaw in IoT products (Beyond Machines) Russia's APT28 Targeting Energy Research, Defense Collaboration Entities (SecurityWeek) Malaysia and Indonesia block X over deepfake smut (The Register) New OPCOPRO Scam Uses AI and Fake WhatsApp Groups to Defraud Victim (Hackread) BreachForums hacking forum database leaked, exposing 324,000 accounts (Bleeping Computer) Former NSA insider Kosiba brought back as spy agency's No. 2 (The Record) Vega raises $120 million in a Series B round led by Accel. Reverse engineering my cloud-connected e-scooter and finding the master key to unlock all scooters (Rasmus Moorats) Share your feedback. What do you think about CyberWire Daily? Please take a few minutes to share your thoughts with us by completing our brief listener survey. Thank you for helping us continue to improve our show. Want to hear your company in the show? N2K CyberWire helps you reach the industry's most influential leaders and operators, while building visibility, authority, and connectivity across the cybersecurity community. Learn more at sponsor.thecyberwire.com. The CyberWire is a production of N2K Networks, your source for strategic workforce intelligence. © N2K Networks, Inc. Learn more about your ad choices. Visit megaphone.fm/adchoices
Happy New Year! You may have noticed that in 2025 we had moved toward YouTube as our primary podcasting platform. As we'll explain in the next State of Latent Space post, we'll be doubling down on Substack again and improving the experience for the over 100,000 of you who look out for our emails and website updates!We first mentioned Artificial Analysis in 2024, when it was still a side project in a Sydney basement. They then were one of the few Nat Friedman and Daniel Gross' AIGrant companies to raise a full seed round from them and have now become the independent gold standard for AI benchmarking—trusted by developers, enterprises, and every major lab to navigate the exploding landscape of models, providers, and capabilities.We have chatted with both Clementine Fourrier of HuggingFace's OpenLLM Leaderboard and (the freshly valued at $1.7B) Anastasios Angelopoulos of LMArena on their approaches to LLM evals and trendspotting, but Artificial Analysis have staked out an enduring and important place in the toolkit of the modern AI Engineer by doing the best job of independently running the most comprehensive set of evals across the widest range of open and closed models, and charting their progress for broad industry analyst use.George Cameron and Micah-Hill Smith have spent two years building Artificial Analysis into the platform that answers the questions no one else will: Which model is actually best for your use case? What are the real speed-cost trade-offs? And how open is “open” really?We discuss:* The origin story: built as a side project in 2023 while Micah was building a legal AI assistant, launched publicly in January 2024, and went viral after Swyx's retweet* Why they run evals themselves: labs prompt models differently, cherry-pick chain-of-thought examples (Google Gemini 1.0 Ultra used 32-shot prompts to beat GPT-4 on MMLU), and self-report inflated numbers* The mystery shopper policy: they register accounts not on their own domain and run intelligence + performance benchmarks incognito to prevent labs from serving different models on private endpoints* How they make money: enterprise benchmarking insights subscription (standardized reports on model deployment, serverless vs. managed vs. leasing chips) and private custom benchmarking for AI companies (no one pays to be on the public leaderboard)* The Intelligence Index (V3): synthesizes 10 eval datasets (MMLU, GPQA, agentic benchmarks, long-context reasoning) into a single score, with 95% confidence intervals via repeated runs* Omissions Index (hallucination rate): scores models from -100 to +100 (penalizing incorrect answers, rewarding ”I don't know”), and Claude models lead with the lowest hallucination rates despite not always being the smartest* GDP Val AA: their version of OpenAI's GDP-bench (44 white-collar tasks with spreadsheets, PDFs, PowerPoints), run through their Stirrup agent harness (up to 100 turns, code execution, web search, file system), graded by Gemini 3 Pro as an LLM judge (tested extensively, no self-preference bias)* The Openness Index: scores models 0-18 on transparency of pre-training data, post-training data, methodology, training code, and licensing (AI2 OLMo 2 leads, followed by Nous Hermes and NVIDIA Nemotron)* The smiling curve of AI costs: GPT-4-level intelligence is 100-1000x cheaper than at launch (thanks to smaller models like Amazon Nova), but frontier reasoning models in agentic workflows cost more than ever (sparsity, long context, multi-turn agents)* Why sparsity might go way lower than 5%: GPT-4.5 is ~5% active, Gemini models might be ~3%, and Omissions Index accuracy correlates with total parameters (not active), suggesting massive sparse models are the future* Token efficiency vs. turn efficiency: GPT-5 costs more per token but solves Tau-bench in fewer turns (cheaper overall), and models are getting better at using more tokens only when needed (5.1 Codex has tighter token distributions)* V4 of the Intelligence Index coming soon: adding GDP Val AA, Critical Point, hallucination rate, and dropping some saturated benchmarks (human-eval-style coding is now trivial for small models)Links to Artificial Analysis* Website: https://artificialanalysis.ai* George Cameron on X: https://x.com/georgecameron* Micah-Hill Smith on X: https://x.com/micahhsmithFull Episode on YouTubeTimestamps* 00:00 Introduction: Full Circle Moment and Artificial Analysis Origins* 01:19 Business Model: Independence and Revenue Streams* 04:33 Origin Story: From Legal AI to Benchmarking Need* 16:22 AI Grant and Moving to San Francisco* 19:21 Intelligence Index Evolution: From V1 to V3* 11:47 Benchmarking Challenges: Variance, Contamination, and Methodology* 13:52 Mystery Shopper Policy and Maintaining Independence* 28:01 New Benchmarks: Omissions Index for Hallucination Detection* 33:36 Critical Point: Hard Physics Problems and Research-Level Reasoning* 23:01 GDP Val AA: Agentic Benchmark for Real Work Tasks* 50:19 Stirrup Agent Harness: Open Source Agentic Framework* 52:43 Openness Index: Measuring Model Transparency Beyond Licenses* 58:25 The Smiling Curve: Cost Falling While Spend Rising* 1:02:32 Hardware Efficiency: Blackwell Gains and Sparsity Limits* 1:06:23 Reasoning Models and Token Efficiency: The Spectrum Emerges* 1:11:00 Multimodal Benchmarking: Image, Video, and Speech Arenas* 1:15:05 Looking Ahead: Intelligence Index V4 and Future Directions* 1:16:50 Closing: The Insatiable Demand for IntelligenceTranscriptMicah [00:00:06]: This is kind of a full circle moment for us in a way, because the first time artificial analysis got mentioned on a podcast was you and Alessio on Latent Space. Amazing.swyx [00:00:17]: Which was January 2024. I don't even remember doing that, but yeah, it was very influential to me. Yeah, I'm looking at AI News for Jan 17, or Jan 16, 2024. I said, this gem of a models and host comparison site was just launched. And then I put in a few screenshots, and I said, it's an independent third party. It clearly outlines the quality versus throughput trade-off, and it breaks out by model and hosting provider. I did give you s**t for missing fireworks, and how do you have a model benchmarking thing without fireworks? But you had together, you had perplexity, and I think we just started chatting there. Welcome, George and Micah, to Latent Space. I've been following your progress. Congrats on... It's been an amazing year. You guys have really come together to be the presumptive new gardener of AI, right? Which is something that...George [00:01:09]: Yeah, but you can't pay us for better results.swyx [00:01:12]: Yes, exactly.George [00:01:13]: Very important.Micah [00:01:14]: Start off with a spicy take.swyx [00:01:18]: Okay, how do I pay you?Micah [00:01:20]: Let's get right into that.swyx [00:01:21]: How do you make money?Micah [00:01:24]: Well, very happy to talk about that. So it's been a big journey the last couple of years. Artificial analysis is going to be two years old in January 2026. Which is pretty soon now. We first run the website for free, obviously, and give away a ton of data to help developers and companies navigate AI and make decisions about models, providers, technologies across the AI stack for building stuff. We're very committed to doing that and tend to keep doing that. We have, along the way, built a business that is working out pretty sustainably. We've got just over 20 people now and two main customer groups. So we want to be... We want to be who enterprise look to for data and insights on AI, so we want to help them with their decisions about models and technologies for building stuff. And then on the other side, we do private benchmarking for companies throughout the AI stack who build AI stuff. So no one pays to be on the website. We've been very clear about that from the very start because there's no use doing what we do unless it's independent AI benchmarking. Yeah. But turns out a bunch of our stuff can be pretty useful to companies building AI stuff.swyx [00:02:38]: And is it like, I am a Fortune 500, I need advisors on objective analysis, and I call you guys and you pull up a custom report for me, you come into my office and give me a workshop? What kind of engagement is that?George [00:02:53]: So we have a benchmarking and insight subscription, which looks like standardized reports that cover key topics or key challenges enterprises face when looking to understand AI and choose between all the technologies. And so, for instance, one of the report is a model deployment report, how to think about choosing between serverless inference, managed deployment solutions, or leasing chips. And running inference yourself is an example kind of decision that big enterprises face, and it's hard to reason through, like this AI stuff is really new to everybody. And so we try and help with our reports and insight subscription. Companies navigate that. We also do custom private benchmarking. And so that's very different from the public benchmarking that we publicize, and there's no commercial model around that. For private benchmarking, we'll at times create benchmarks, run benchmarks to specs that enterprises want. And we'll also do that sometimes for AI companies who have built things, and we help them understand what they've built with private benchmarking. Yeah. So that's a piece mainly that we've developed through trying to support everybody publicly with our public benchmarks. Yeah.swyx [00:04:09]: Let's talk about TechStack behind that. But okay, I'm going to rewind all the way to when you guys started this project. You were all the way in Sydney? Yeah. Well, Sydney, Australia for me.Micah [00:04:19]: George was an SF, but he's Australian, but he moved here already. Yeah.swyx [00:04:22]: And I remember I had the Zoom call with you. What was the impetus for starting artificial analysis in the first place? You know, you started with public benchmarks. And so let's start there. We'll go to the private benchmark. Yeah.George [00:04:33]: Why don't we even go back a little bit to like why we, you know, thought that it was needed? Yeah.Micah [00:04:40]: The story kind of begins like in 2022, 2023, like both George and I have been into AI stuff for quite a while. In 2023 specifically, I was trying to build a legal AI research assistant. So it actually worked pretty well for its era, I would say. Yeah. Yeah. So I was finding that the more you go into building something using LLMs, the more each bit of what you're doing ends up being a benchmarking problem. So had like this multistage algorithm thing, trying to figure out what the minimum viable model for each bit was, trying to optimize every bit of it as you build that out, right? Like you're trying to think about accuracy, a bunch of other metrics and performance and cost. And mostly just no one was doing anything to independently evaluate all the models. And certainly not to look at the trade-offs for speed and cost. So we basically set out just to build a thing that developers could look at to see the trade-offs between all of those things measured independently across all the models and providers. Honestly, it was probably meant to be a side project when we first started doing it.swyx [00:05:49]: Like we didn't like get together and say like, Hey, like we're going to stop working on all this stuff. I'm like, this is going to be our main thing. When I first called you, I think you hadn't decided on starting a company yet.Micah [00:05:58]: That's actually true. I don't even think we'd pause like, like George had an acquittance job. I didn't quit working on my legal AI thing. Like it was genuinely a side project.George [00:06:05]: We built it because we needed it as people building in the space and thought, Oh, other people might find it useful too. So we'll buy domain and link it to the Vercel deployment that we had and tweet about it. And, but very quickly it started getting attention. Thank you, Swyx for, I think doing an initial retweet and spotlighting it there. This project that we released. And then very quickly though, it was useful to others, but very quickly it became more useful as the number of models released accelerated. We had Mixtrel 8x7B and it was a key. That's a fun one. Yeah. Like a open source model that really changed the landscape and opened up people's eyes to other serverless inference providers and thinking about speed, thinking about cost. And so that was a key. And so it became more useful quite quickly. Yeah.swyx [00:07:02]: What I love talking to people like you who sit across the ecosystem is, well, I have theories about what people want, but you have data and that's obviously more relevant. But I want to stay on the origin story a little bit more. When you started out, I would say, I think the status quo at the time was every paper would come out and they would report their numbers versus competitor numbers. And that's basically it. And I remember I did the legwork. I think everyone has some knowledge. I think there's some version of Excel sheet or a Google sheet where you just like copy and paste the numbers from every paper and just post it up there. And then sometimes they don't line up because they're independently run. And so your numbers are going to look better than... Your reproductions of other people's numbers are going to look worse because you don't hold their models correctly or whatever the excuse is. I think then Stanford Helm, Percy Liang's project would also have some of these numbers. And I don't know if there's any other source that you can cite. The way that if I were to start artificial analysis at the same time you guys started, I would have used the Luther AI's eval framework harness. Yup.Micah [00:08:06]: Yup. That was some cool stuff. At the end of the day, running these evals, it's like if it's a simple Q&A eval, all you're doing is asking a list of questions and checking if the answers are right, which shouldn't be that crazy. But it turns out there are an enormous number of things that you've got control for. And I mean, back when we started the website. Yeah. Yeah. Like one of the reasons why we realized that we had to run the evals ourselves and couldn't just take rules from the labs was just that they would all prompt the models differently. And when you're competing over a few points, then you can pretty easily get- You can put the answer into the model. Yeah. That in the extreme. And like you get crazy cases like back when I'm Googled a Gemini 1.0 Ultra and needed a number that would say it was better than GPT-4 and like constructed, I think never published like chain of thought examples. 32 of them in every topic in MLU to run it, to get the score, like there are so many things that you- They never shipped Ultra, right? That's the one that never made it up. Not widely. Yeah. Yeah. Yeah. I mean, I'm sure it existed, but yeah. So we were pretty sure that we needed to run them ourselves and just run them in the same way across all the models. Yeah. And we were, we also did certain from the start that you couldn't look at those in isolation. You needed to look at them alongside the cost and performance stuff. Yeah.swyx [00:09:24]: Okay. A couple of technical questions. I mean, so obviously I also thought about this and I didn't do it because of cost. Yep. Did you not worry about costs? Were you funded already? Clearly not, but you know. No. Well, we definitely weren't at the start.Micah [00:09:36]: So like, I mean, we're paying for it personally at the start. There's a lot of money. Well, the numbers weren't nearly as bad a couple of years ago. So we certainly incurred some costs, but we were probably in the order of like hundreds of dollars of spend across all the benchmarking that we were doing. Yeah. So nothing. Yeah. It was like kind of fine. Yeah. Yeah. These days that's gone up an enormous amount for a bunch of reasons that we can talk about. But yeah, it wasn't that bad because you can also remember that like the number of models we were dealing with was hardly any and the complexity of the stuff that we wanted to do to evaluate them was a lot less. Like we were just asking some Q&A type questions and then one specific thing was for a lot of evals initially, we were just like sampling an answer. You know, like, what's the answer for this? Like, we didn't want to go into the answer directly without letting the models think. We weren't even doing chain of thought stuff initially. And that was the most useful way to get some results initially. Yeah.swyx [00:10:33]: And so for people who haven't done this work, literally parsing the responses is a whole thing, right? Like because sometimes the models, the models can answer any way they feel fit and sometimes they actually do have the right answer, but they just returned the wrong format and they will get a zero for that unless you work it into your parser. And that involves more work. And so, I mean, but there's an open question whether you should give it points for not following your instructions on the format.Micah [00:11:00]: It depends what you're looking at, right? Because you can, if you're trying to see whether or not it can solve a particular type of reasoning problem, and you don't want to test it on its ability to do answer formatting at the same time, then you might want to use an LLM as answer extractor approach to make sure that you get the answer out no matter how unanswered. But these days, it's mostly less of a problem. Like, if you instruct a model and give it examples of what the answers should look like, it can get the answers in your format, and then you can do, like, a simple regex.swyx [00:11:28]: Yeah, yeah. And then there's other questions around, I guess, sometimes if you have a multiple choice question, sometimes there's a bias towards the first answer, so you have to randomize the responses. All these nuances, like, once you dig into benchmarks, you're like, I don't know how anyone believes the numbers on all these things. It's so dark magic.Micah [00:11:47]: You've also got, like… You've got, like, the different degrees of variance in different benchmarks, right? Yeah. So, if you run four-question multi-choice on a modern reasoning model at the temperatures suggested by the labs for their own models, the variance that you can see on a four-question multi-choice eval is pretty enormous if you only do a single run of it and it has a small number of questions, especially. So, like, one of the things that we do is run an enormous number of all of our evals when we're developing new ones and doing upgrades to our intelligence index to bring in new things. Yeah. So, that we can dial in the right number of repeats so that we can get to the 95% confidence intervals that we're comfortable with so that when we pull that together, we can be confident in intelligence index to at least as tight as, like, a plus or minus one at a 95% confidence. Yeah.swyx [00:12:32]: And, again, that just adds a straight multiple to the cost. Oh, yeah. Yeah, yeah.George [00:12:37]: So, that's one of many reasons that cost has gone up a lot more than linearly over the last couple of years. We report a cost to run the artificial analysis. We report a cost to run the artificial analysis intelligence index on our website, and currently that's assuming one repeat in terms of how we report it because we want to reflect a bit about the weighting of the index. But our cost is actually a lot higher than what we report there because of the repeats.swyx [00:13:03]: Yeah, yeah, yeah. And probably this is true, but just checking, you don't have any special deals with the labs. They don't discount it. You just pay out of pocket or out of your sort of customer funds. Oh, there is a mix. So, the issue is that sometimes they may give you a special end point, which is… Ah, 100%.Micah [00:13:21]: Yeah, yeah, yeah. Exactly. So, we laser focus, like, on everything we do on having the best independent metrics and making sure that no one can manipulate them in any way. There are quite a lot of processes we've developed over the last couple of years to make that true for, like, the one you bring up, like, right here of the fact that if we're working with a lab, if they're giving us a private endpoint to evaluate a model, that it is totally possible. That what's sitting behind that black box is not the same as they serve on a public endpoint. We're very aware of that. We have what we call a mystery shopper policy. And so, and we're totally transparent with all the labs we work with about this, that we will register accounts not on our own domain and run both intelligence evals and performance benchmarks… Yeah, that's the job. …without them being able to identify it. And no one's ever had a problem with that. Because, like, a thing that turns out to actually be quite a good… …good factor in the industry is that they all want to believe that none of their competitors could manipulate what we're doing either.swyx [00:14:23]: That's true. I never thought about that. I've been in the database data industry prior, and there's a lot of shenanigans around benchmarking, right? So I'm just kind of going through the mental laundry list. Did I miss anything else in this category of shenanigans? Oh, potential shenanigans.Micah [00:14:36]: I mean, okay, the biggest one, like, that I'll bring up, like, is more of a conceptual one, actually, than, like, direct shenanigans. It's that the things that get measured become things that get targeted by labs that they're trying to build, right? Exactly. So that doesn't mean anything that we should really call shenanigans. Like, I'm not talking about training on test set. But if you know that you're going to be great at another particular thing, if you're a researcher, there are a whole bunch of things that you can do to try to get better at that thing that preferably are going to be helpful for a wide range of how actual users want to use the thing that you're building. But will not necessarily work. Will not necessarily do that. So, for instance, the models are exceptional now at answering competition maths problems. There is some relevance of that type of reasoning, that type of work, to, like, how we might use modern coding agents and stuff. But it's clearly not one for one. So the thing that we have to be aware of is that once an eval becomes the thing that everyone's looking at, scores can get better on it without there being a reflection of overall generalized intelligence of these models. Getting better. That has been true for the last couple of years. It'll be true for the next couple of years. There's no silver bullet to defeat that other than building new stuff to stay relevant and measure the capabilities that matter most to real users. Yeah.swyx [00:15:58]: And we'll cover some of the new stuff that you guys are building as well, which is cool. Like, you used to just run other people's evals, but now you're coming up with your own. And I think, obviously, that is a necessary path once you're at the frontier. You've exhausted all the existing evals. I think the next point in history that I have for you is AI Grant that you guys decided to join and move here. What was it like? I think you were in, like, batch two? Batch four. Batch four. Okay.Micah [00:16:26]: I mean, it was great. Nat and Daniel are obviously great. And it's a really cool group of companies that we were in AI Grant alongside. It was really great to get Nat and Daniel on board. Obviously, they've done a whole lot of great work in the space with a lot of leading companies and were extremely aligned. With the mission of what we were trying to do. Like, we're not quite typical of, like, a lot of the other AI startups that they've invested in.swyx [00:16:53]: And they were very much here for the mission of what we want to do. Did they say any advice that really affected you in some way or, like, were one of the events very impactful? That's an interesting question.Micah [00:17:03]: I mean, I remember fondly a bunch of the speakers who came and did fireside chats at AI Grant.swyx [00:17:09]: Which is also, like, a crazy list. Yeah.George [00:17:11]: Oh, totally. Yeah, yeah, yeah. There was something about, you know, speaking to Nat and Daniel about the challenges of working through a startup and just working through the questions that don't have, like, clear answers and how to work through those kind of methodically and just, like, work through the hard decisions. And they've been great mentors to us as we've built artificial analysis. Another benefit for us was that other companies in the batch and other companies in AI Grant are pushing the capabilities. Yeah. And I think that's a big part of what AI can do at this time. And so being in contact with them, making sure that artificial analysis is useful to them has been fantastic for supporting us in working out how should we build out artificial analysis to continue to being useful to those, like, you know, building on AI.swyx [00:17:59]: I think to some extent, I'm mixed opinion on that one because to some extent, your target audience is not people in AI Grants who are obviously at the frontier. Yeah. Do you disagree?Micah [00:18:09]: To some extent. To some extent. But then, so a lot of what the AI Grant companies are doing is taking capabilities coming out of the labs and trying to push the limits of what they can do across the entire stack for building great applications, which actually makes some of them pretty archetypical power users of artificial analysis. Some of the people with the strongest opinions about what we're doing well and what we're not doing well and what they want to see next from us. Yeah. Yeah. Because when you're building any kind of AI application now, chances are you're using a whole bunch of different models. You're maybe switching reasonably frequently for different models and different parts of your application to optimize what you're able to do with them at an accuracy level and to get better speed and cost characteristics. So for many of them, no, they're like not commercial customers of ours, like we don't charge for all our data on the website. Yeah. They are absolutely some of our power users.swyx [00:19:07]: So let's talk about just the evals as well. So you start out from the general like MMU and GPQA stuff. What's next? How do you sort of build up to the overall index? What was in V1 and how did you evolve it? Okay.Micah [00:19:22]: So first, just like background, like we're talking about the artificial analysis intelligence index, which is our synthesis metric that we pulled together currently from 10 different eval data sets to give what? We're pretty much the same as that. Pretty confident is the best single number to look at for how smart the models are. Obviously, it doesn't tell the whole story. That's why we published the whole website of all the charts to dive into every part of it and look at the trade-offs. But best single number. So right now, it's got a bunch of Q&A type data sets that have been very important to the industry, like a couple that you just mentioned. It's also got a couple of agentic data sets. It's got our own long context reasoning data set and some other use case focused stuff. As time goes on. The things that we're most interested in that are going to be important to the capabilities that are becoming more important for AI, what developers are caring about, are going to be first around agentic capabilities. So surprise, surprise. We're all loving our coding agents and how the model is going to perform like that and then do similar things for different types of work are really important to us. The linking to use cases to economically valuable use cases are extremely important to us. And then we've got some of the. Yeah. These things that the models still struggle with, like working really well over long contexts that are not going to go away as specific capabilities and use cases that we need to keep evaluating.swyx [00:20:46]: But I guess one thing I was driving was like the V1 versus the V2 and how bad it was over time.Micah [00:20:53]: Like how we've changed the index to where we are.swyx [00:20:55]: And I think that reflects on the change in the industry. Right. So that's a nice way to tell that story.Micah [00:21:00]: Well, V1 would be completely saturated right now. Almost every model coming out because doing things like writing the Python functions and human evil is now pretty trivial. It's easy to forget, actually, I think how much progress has been made in the last two years. Like we obviously play the game constantly of like the today's version versus last week's version and the week before and all of the small changes in the horse race between the current frontier and who has the best like smaller than 10B model like right now this week. Right. And that's very important to a lot of developers and people and especially in this particular city of San Francisco. But when you zoom out a couple of years ago, literally most of what we were doing to evaluate the models then would all be 100% solved by even pretty small models today. And that's been one of the key things, by the way, that's driven down the cost of intelligence at every tier of intelligence. We can talk about more in a bit. So V1, V2, V3, we made things harder. We covered a wider range of use cases. And we tried to get closer to things developers care about as opposed to like just the Q&A type stuff that MMLU and GPQA represented. Yeah.swyx [00:22:12]: I don't know if you have anything to add there. Or we could just go right into showing people the benchmark and like looking around and asking questions about it. Yeah.Micah [00:22:21]: Let's do it. Okay. This would be a pretty good way to chat about a few of the new things we've launched recently. Yeah.George [00:22:26]: And I think a little bit about the direction that we want to take it. And we want to push benchmarks. Currently, the intelligence index and evals focus a lot on kind of raw intelligence. But we kind of want to diversify how we think about intelligence. And we can talk about it. But kind of new evals that we've kind of built and partnered on focus on topics like hallucination. And we've got a lot of topics that I think are not covered by the current eval set that should be. And so we want to bring that forth. But before we get into that.swyx [00:23:01]: And so for listeners, just as a timestamp, right now, number one is Gemini 3 Pro High. Then followed by Cloud Opus at 70. Just 5.1 high. You don't have 5.2 yet. And Kimi K2 Thinking. Wow. Still hanging in there. So those are the top four. That will date this podcast quickly. Yeah. Yeah. I mean, I love it. I love it. No, no. 100%. Look back this time next year and go, how cute. Yep.George [00:23:25]: Totally. A quick view of that is, okay, there's a lot. I love it. I love this chart. Yeah.Micah [00:23:30]: This is such a favorite, right? Yeah. And almost every talk that George or I give at conferences and stuff, we always put this one up first to just talk about situating where we are in this moment in history. This, I think, is the visual version of what I was saying before about the zooming out and remembering how much progress there's been. If we go back to just over a year ago, before 01, before Cloud Sonnet 3.5, we didn't have reasoning models or coding agents as a thing. And the game was very, very different. If we go back even a little bit before then, we're in the era where, when you look at this chart, open AI was untouchable for well over a year. And, I mean, you would remember that time period well of there being very open questions about whether or not AI was going to be competitive, like full stop, whether or not open AI would just run away with it, whether we would have a few frontier labs and no one else would really be able to do anything other than consume their APIs. I am quite happy overall that the world that we have ended up in is one where... Multi-model. Absolutely. And strictly more competitive every quarter over the last few years. Yeah. This year has been insane. Yeah.George [00:24:42]: You can see it. This chart with everything added is hard to read currently. There's so many dots on it, but I think it reflects a little bit what we felt, like how crazy it's been.swyx [00:24:54]: Why 14 as the default? Is that a manual choice? Because you've got service now in there that are less traditional names. Yeah.George [00:25:01]: It's models that we're kind of highlighting by default in our charts, in our intelligence index. Okay.swyx [00:25:07]: You just have a manually curated list of stuff.George [00:25:10]: Yeah, that's right. But something that I actually don't think every artificial analysis user knows is that you can customize our charts and choose what models are highlighted. Yeah. And so if we take off a few names, it gets a little easier to read.swyx [00:25:25]: Yeah, yeah. A little easier to read. Totally. Yeah. But I love that you can see the all one jump. Look at that. September 2024. And the DeepSeek jump. Yeah.George [00:25:34]: Which got close to OpenAI's leadership. They were so close. I think, yeah, we remember that moment. Around this time last year, actually.Micah [00:25:44]: Yeah, yeah, yeah. I agree. Yeah, well, a couple of weeks. It was Boxing Day in New Zealand when DeepSeek v3 came out. And we'd been tracking DeepSeek and a bunch of the other global players that were less known over the second half of 2024 and had run evals on the earlier ones and stuff. I very distinctly remember Boxing Day in New Zealand, because I was with family for Christmas and stuff, running the evals and getting back result by result on DeepSeek v3. So this was the first of their v3 architecture, the 671b MOE.Micah [00:26:19]: And we were very, very impressed. That was the moment where we were sure that DeepSeek was no longer just one of many players, but had jumped up to be a thing. The world really noticed when they followed that up with the RL working on top of v3 and R1 succeeding a few weeks later. But the groundwork for that absolutely was laid with just extremely strong base model, completely open weights that we had as the best open weights model. So, yeah, that's the thing that you really see in the game. But I think that we got a lot of good feedback on Boxing Day. us on Boxing Day last year.George [00:26:48]: Boxing Day is the day after Christmas for those not familiar.George [00:26:54]: I'm from Singapore.swyx [00:26:55]: A lot of us remember Boxing Day for a different reason, for the tsunami that happened. Oh, of course. Yeah, but that was a long time ago. So yeah. So this is the rough pitch of AAQI. Is it A-A-Q-I or A-A-I-I? I-I. Okay. Good memory, though.Micah [00:27:11]: I don't know. I'm not used to it. Once upon a time, we did call it Quality Index, and we would talk about quality, performance, and price, but we changed it to intelligence.George [00:27:20]: There's been a few naming changes. We added hardware benchmarking to the site, and so benchmarks at a kind of system level. And so then we changed our throughput metric to, we now call it output speed, and thenswyx [00:27:32]: throughput makes sense at a system level, so we took that name. Take me through more charts. What should people know? Obviously, the way you look at the site is probably different than how a beginner might look at it.Micah [00:27:42]: Yeah, that's fair. There's a lot of fun stuff to dive into. Maybe so we can hit past all the, like, we have lots and lots of emails and stuff. The interesting ones to talk about today that would be great to bring up are a few of our recent things, I think, that probably not many people will be familiar with yet. So first one of those is our omniscience index. So this one is a little bit different to most of the intelligence evils that we've run. We built it specifically to look at the embedded knowledge in the models and to test hallucination by looking at when the model doesn't know the answer, so not able to get it correct, what's its probability of saying, I don't know, or giving an incorrect answer. So the metric that we use for omniscience goes from negative 100 to positive 100. Because we're simply taking off a point if you give an incorrect answer to the question. We're pretty convinced that this is an example of where it makes most sense to do that, because it's strictly more helpful to say, I don't know, instead of giving a wrong answer to factual knowledge question. And one of our goals is to shift the incentive that evils create for models and the labs creating them to get higher scores. And almost every evil across all of AI up until this point, it's been graded by simple percentage correct as the main metric, the main thing that gets hyped. And so you should take a shot at everything. There's no incentive to say, I don't know. So we did that for this one here.swyx [00:29:22]: I think there's a general field of calibration as well, like the confidence in your answer versus the rightness of the answer. Yeah, we completely agree. Yeah. Yeah.George [00:29:31]: On that. And one reason that we didn't do that is because. Or put that into this index is that we think that the, the way to do that is not to ask the models how confident they are.swyx [00:29:43]: I don't know. Maybe it might be though. You put it like a JSON field, say, say confidence and maybe it spits out something. Yeah. You know, we have done a few evils podcasts over the, over the years. And when we did one with Clementine of hugging face, who maintains the open source leaderboard, and this was one of her top requests, which is some kind of hallucination slash lack of confidence calibration thing. And so, Hey, this is one of them.Micah [00:30:05]: And I mean, like anything that we do, it's not a perfect metric or the whole story of everything that you think about as hallucination. But yeah, it's pretty useful and has some interesting results. Like one of the things that we saw in the hallucination rate is that anthropics Claude models at the, the, the very left-hand side here with the lowest hallucination rates out of the models that we've evaluated amnesty is on. That is an interesting fact. I think it probably correlates with a lot of the previously, not really measured vibes stuff that people like about some of the Claude models. Is the dataset public or what's is it, is there a held out set? There's a hell of a set for this one. So we, we have published a public test set, but we we've only published 10% of it. The reason is that for this one here specifically, it would be very, very easy to like have data contamination because it is just factual knowledge questions. We would. We'll update it at a time to also prevent that, but with yeah, kept most of it held out so that we can keep it reliable for a long time. It leads us to a bunch of really cool things, including breakdown quite granularly by topic. And so we've got some of that disclosed on the website publicly right now, and there's lots more coming in terms of our ability to break out very specific topics. Yeah.swyx [00:31:23]: I would be interested. Let's, let's dwell a little bit on this hallucination one. I noticed that Haiku hallucinates less than Sonnet hallucinates less than Opus. And yeah. Would that be the other way around in a normal capability environments? I don't know. What's, what do you make of that?George [00:31:37]: One interesting aspect is that we've found that there's not really a, not a strong correlation between intelligence and hallucination, right? That's to say that the smarter the models are in a general sense, isn't correlated with their ability to, when they don't know something, say that they don't know. It's interesting that Gemini three pro preview was a big leap over here. Gemini 2.5. Flash and, and, and 2.5 pro, but, and if I add pro quickly here.swyx [00:32:07]: I bet pro's really good. Uh, actually no, I meant, I meant, uh, the GPT pros.George [00:32:12]: Oh yeah.swyx [00:32:13]: Cause GPT pros are rumored. We don't know for a fact that it's like eight runs and then with the LM judge on top. Yeah.George [00:32:20]: So we saw a big jump in, this is accuracy. So this is just percent that they get, uh, correct and Gemini three pro knew a lot more than the other models. And so big jump in accuracy. But relatively no change between the Google Gemini models, between releases. And the hallucination rate. Exactly. And so it's likely due to just kind of different post-training recipe, between the, the Claude models. Yeah.Micah [00:32:45]: Um, there's, there's driven this. Yeah. You can, uh, you can partially blame us and how we define intelligence having until now not defined hallucination as a negative in the way that we think about intelligence.swyx [00:32:56]: And so that's what we're changing. Uh, I know many smart people who are confidently incorrect.George [00:33:02]: Uh, look, look at that. That, that, that is very humans. Very true. And there's times and a place for that. I think our view is that hallucination rate makes sense in this context where it's around knowledge, but in many cases, people want the models to hallucinate, to have a go. Often that's the case in coding or when you're trying to generate newer ideas. One eval that we added to artificial analysis is, is, is critical point and it's really hard, uh, physics problems. Okay.swyx [00:33:32]: And is it sort of like a human eval type or something different or like a frontier math type?George [00:33:37]: It's not dissimilar to frontier frontier math. So these are kind of research questions that kind of academics in the physics physics world would be able to answer, but models really struggled to answer. So the top score here is not 9%.swyx [00:33:51]: And when the people that, that created this like Minway and, and, and actually off via who was kind of behind sweep and what organization is this? Oh, is this, it's Princeton.George [00:34:01]: Kind of range of academics from, from, uh, different academic institutions, really smart people. They talked about how they turn the models up in terms of the temperature as high temperature as they can, where they're trying to explore kind of new ideas in physics as a, as a thought partner, just because they, they want the models to hallucinate. Um, yeah, sometimes it's something new. Yeah, exactly.swyx [00:34:21]: Um, so not right in every situation, but, um, I think it makes sense, you know, to test hallucination in scenarios where it makes sense. Also, the obvious question is, uh, this is one of. Many that there is there, every lab has a system card that shows some kind of hallucination number, and you've chosen to not, uh, endorse that and you've made your own. And I think that's a, that's a choice. Um, totally in some sense, the rest of artificial analysis is public benchmarks that other people can independently rerun. You provide it as a service here. You have to fight the, well, who are we to, to like do this? And your, your answer is that we have a lot of customers and, you know, but like, I guess, how do you converge the individual?Micah [00:35:08]: I mean, I think, I think for hallucinations specifically, there are a bunch of different things that you might care about reasonably, and that you'd measure quite differently, like we've called this a amnesty and solutionation rate, not trying to declare the, like, it's humanity's last hallucination. You could, uh, you could have some interesting naming conventions and all this stuff. Um, the biggest picture answer to that. It's something that I actually wanted to mention. Just as George was explaining, critical point as well is, so as we go forward, we are building evals internally. We're partnering with academia and partnering with AI companies to build great evals. We have pretty strong views on, in various ways for different parts of the AI stack, where there are things that are not being measured well, or things that developers care about that should be measured more and better. And we intend to be doing that. We're not obsessed necessarily with that. Everything we do, we have to do entirely within our own team. Critical point. As a cool example of where we were a launch partner for it, working with academia, we've got some partnerships coming up with a couple of leading companies. Those ones, obviously we have to be careful with on some of the independent stuff, but with the right disclosure, like we're completely comfortable with that. A lot of the labs have released great data sets in the past that we've used to great success independently. And so it's between all of those techniques, we're going to be releasing more stuff in the future. Cool.swyx [00:36:26]: Let's cover the last couple. And then we'll, I want to talk about your trends analysis stuff, you know? Totally.Micah [00:36:31]: So that actually, I have one like little factoid on omniscience. If you go back up to accuracy on omniscience, an interesting thing about this accuracy metric is that it tracks more closely than anything else that we measure. The total parameter count of models makes a lot of sense intuitively, right? Because this is a knowledge eval. This is the pure knowledge metric. We're not looking at the index and the hallucination rate stuff that we think is much more about how the models are trained. This is just what facts did they recall? And yeah, it tracks parameter count extremely closely. Okay.swyx [00:37:05]: What's the rumored size of GPT-3 Pro? And to be clear, not confirmed for any official source, just rumors. But rumors do fly around. Rumors. I get, I hear all sorts of numbers. I don't know what to trust.Micah [00:37:17]: So if you, if you draw the line on omniscience accuracy versus total parameters, we've got all the open ways models, you can squint and see that likely the leading frontier models right now are quite a lot bigger than the ones that we're seeing right now. And the one trillion parameters that the open weights models cap out at, and the ones that we're looking at here, there's an interesting extra data point that Elon Musk revealed recently about XAI that for three trillion parameters for GROK 3 and 4, 6 trillion for GROK 5, but that's not out yet. Take those together, have a look. You might reasonably form a view that there's a pretty good chance that Gemini 3 Pro is bigger than that, that it could be in the 5 to 10 trillion parameters. To be clear, I have absolutely no idea, but just based on this chart, like that's where you would, you would land if you have a look at it. Yeah.swyx [00:38:07]: And to some extent, I actually kind of discourage people from guessing too much because what does it really matter? Like as long as they can serve it as a sustainable cost, that's about it. Like, yeah, totally.George [00:38:17]: They've also got different incentives in play compared to like open weights models who are thinking to supporting others in self-deployment for the labs who are doing inference at scale. It's I think less about total parameters in many cases. When thinking about inference costs and more around number of active parameters. And so there's a bit of an incentive towards larger sparser models. Agreed.Micah [00:38:38]: Understood. Yeah. Great. I mean, obviously if you're a developer or company using these things, not exactly as you say, it doesn't matter. You should be looking at all the different ways that we measure intelligence. You should be looking at cost to run index number and the different ways of thinking about token efficiency and cost efficiency based on the list prices, because that's all it matters.swyx [00:38:56]: It's not as good for the content creator rumor mill where I can say. Oh, GPT-4 is this small circle. Look at GPT-5 is this big circle. And then there used to be a thing for a while. Yeah.Micah [00:39:07]: But that is like on its own, actually a very interesting one, right? That is it just purely that chances are the last couple of years haven't seen a dramatic scaling up in the total size of these models. And so there's a lot of room to go up properly in total size of the models, especially with the upcoming hardware generations. Yes.swyx [00:39:29]: So, you know. Taking off my shitposting face for a minute. Yes. Yes. At the same time, I do feel like, you know, especially coming back from Europe, people do feel like Ilya is probably right that the paradigm is doesn't have many more orders of magnitude to scale out more. And therefore we need to start exploring at least a different path. GDPVal, I think it's like only like a month or so old. I was also very positive when it first came out. I actually talked to Tejo, who was the lead researcher on that. Oh, cool. And you have your own version.George [00:39:59]: It's a fantastic. It's a fantastic data set. Yeah.swyx [00:40:01]: And maybe it will recap for people who are still out of it. It's like 44 tasks based on some kind of GDP cutoff that's like meant to represent broad white collar work that is not just coding. Yeah.Micah [00:40:12]: Each of the tasks have a whole bunch of detailed instructions, some input files for a lot of them. It's within the 44 is divided into like two hundred and twenty two to five, maybe subtasks that are the level of that we run through the agenda. And yeah, they're really interesting. I will say that it doesn't. It doesn't necessarily capture like all the stuff that people do at work. No avail is perfect is always going to be more things to look at, largely because in order to make the tasks well enough to find that you can run them, they need to only have a handful of input files and very specific instructions for that task. And so I think the easiest way to think about them are that they're like quite hard take home exam tasks that you might do in an interview process.swyx [00:40:56]: Yeah, for listeners, it is not no longer like a long prompt. It is like, well, here's a zip file with like a spreadsheet or a PowerPoint deck or a PDF and go nuts and answer this question.George [00:41:06]: OpenAI released a great data set and they released a good paper which looks at performance across the different web chat bots on the data set. It's a great paper, encourage people to read it. What we've done is taken that data set and turned it into an eval that can be run on any model. So we created a reference agentic harness that can run. Run the models on the data set, and then we developed evaluator approach to compare outputs. That's kind of AI enabled, so it uses Gemini 3 Pro Preview to compare results, which we tested pretty comprehensively to ensure that it's aligned to human preferences. One data point there is that even as an evaluator, Gemini 3 Pro, interestingly, doesn't do actually that well. So that's kind of a good example of what we've done in GDPVal AA.swyx [00:42:01]: Yeah, the thing that you have to watch out for with LLM judge is self-preference that models usually prefer their own output, and in this case, it was not. Totally.Micah [00:42:08]: I think the way that we're thinking about the places where it makes sense to use an LLM as judge approach now, like quite different to some of the early LLM as judge stuff a couple of years ago, because some of that and MTV was a great project that was a good example of some of this a while ago was about judging conversations and like a lot of style type stuff. Here, we've got the task that the grader and grading model is doing is quite different to the task of taking the test. When you're taking the test, you've got all of the agentic tools you're working with, the code interpreter and web search, the file system to go through many, many turns to try to create the documents. Then on the other side, when we're grading it, we're running it through a pipeline to extract visual and text versions of the files and be able to provide that to Gemini, and we're providing the criteria for the task and getting it to pick which one more effectively meets the criteria of the task. Yeah. So we've got the task out of two potential outcomes. It turns out that we proved that it's just very, very good at getting that right, matched with human preference a lot of the time, because I think it's got the raw intelligence, but it's combined with the correct representation of the outputs, the fact that the outputs were created with an agentic task that is quite different to the way the grading model works, and we're comparing it against criteria, not just kind of zero shot trying to ask the model to pick which one is better.swyx [00:43:26]: Got it. Why is this an ELO? And not a percentage, like GDP-VAL?George [00:43:31]: So the outputs look like documents, and there's video outputs or audio outputs from some of the tasks. It has to make a video? Yeah, for some of the tasks. Some of the tasks.swyx [00:43:43]: What task is that?George [00:43:45]: I mean, it's in the data set. Like be a YouTuber? It's a marketing video.Micah [00:43:49]: Oh, wow. What? Like model has to go find clips on the internet and try to put it together. The models are not that good at doing that one, for now, to be clear. It's pretty hard to do that with a code editor. I mean, the computer stuff doesn't work quite well enough and so on and so on, but yeah.George [00:44:02]: And so there's no kind of ground truth, necessarily, to compare against, to work out percentage correct. It's hard to come up with correct or incorrect there. And so it's on a relative basis. And so we use an ELO approach to compare outputs from each of the models between the task.swyx [00:44:23]: You know what you should do? You should pay a contractor, a human, to do the same task. And then give it an ELO and then so you have, you have human there. It's just, I think what's helpful about GDPVal, the OpenAI one, is that 50% is meant to be normal human and maybe Domain Expert is higher than that, but 50% was the bar for like, well, if you've crossed 50, you are superhuman. Yeah.Micah [00:44:47]: So we like, haven't grounded this score in that exactly. I agree that it can be helpful, but we wanted to generalize this to a very large number. It's one of the reasons that presenting it as ELO is quite helpful and allows us to add models and it'll stay relevant for quite a long time. I also think it, it can be tricky looking at these exact tasks compared to the human performance, because the way that you would go about it as a human is quite different to how the models would go about it. Yeah.swyx [00:45:15]: I also liked that you included Lama 4 Maverick in there. Is that like just one last, like...Micah [00:45:20]: Well, no, no, no, no, no, no, it is the, it is the best model released by Meta. And... So it makes it into the homepage default set, still for now.George [00:45:31]: Other inclusion that's quite interesting is we also ran it across the latest versions of the web chatbots. And so we have...swyx [00:45:39]: Oh, that's right.George [00:45:40]: Oh, sorry.swyx [00:45:41]: I, yeah, I completely missed that. Okay.George [00:45:43]: No, not at all. So that, which has a checkered pattern. So that is their harness, not yours, is what you're saying. Exactly. And what's really interesting is that if you compare, for instance, Claude 4.5 Opus using the Claude web chatbot, it performs worse than the model in our agentic harness. And so in every case, the model performs better in our agentic harness than its web chatbot counterpart, the harness that they created.swyx [00:46:13]: Oh, my backwards explanation for that would be that, well, it's meant for consumer use cases and here you're pushing it for something.Micah [00:46:19]: The constraints are different and the amount of freedom that you can give the model is different. Also, you like have a cost goal. We let the models work as long as they want, basically. Yeah. Do you copy paste manually into the chatbot? Yeah. Yeah. That's, that was how we got the chatbot reference. We're not going to be keeping those updated at like quite the same scale as hundreds of models.swyx [00:46:38]: Well, so I don't know, talk to a browser base. They'll, they'll automate it for you. You know, like I have thought about like, well, we should turn these chatbot versions into an API because they are legitimately different agents in themselves. Yes. Right. Yeah.Micah [00:46:53]: And that's grown a huge amount of the last year, right? Like the tools. The tools that are available have actually diverged in my opinion, a fair bit across the major chatbot apps and the amount of data sources that you can connect them to have gone up a lot, meaning that your experience and the way you're using the model is more different than ever.swyx [00:47:10]: What tools and what data connections come to mind when you say what's interesting, what's notable work that people have done?Micah [00:47:15]: Oh, okay. So my favorite example on this is that until very recently, I would argue that it was basically impossible to get an LLM to draft an email for me in any useful way. Because most times that you're sending an email, you're not just writing something for the sake of writing it. Chances are context required is a whole bunch of historical emails. Maybe it's notes that you've made, maybe it's meeting notes, maybe it's, um, pulling something from your, um, any of like wherever you at work store stuff. So for me, like Google drive, one drive, um, in our super base databases, if we need to do some analysis or some data or something, preferably model can be plugged into all of those things and can go do some useful work based on it. The things that like I find most impressive currently that I am somewhat surprised work really well in late 2025, uh, that I can have models use super base MCP to query read only, of course, run a whole bunch of SQL queries to do pretty significant data analysis. And. And make charts and stuff and can read my Gmail and my notion. And okay. You actually use that. That's good. That's, that's, that's good. Is that a cloud thing? To various degrees of order, but chat GPD and Claude right now, I would say that this stuff like barely works in fairness right now. Like.George [00:48:33]: Because people are actually going to try this after they hear it. If you get an email from Micah, odds are it wasn't written by a chatbot.Micah [00:48:38]: So, yeah, I think it is true that I have never actually sent anyone an email drafted by a chatbot. Yet.swyx [00:48:46]: Um, and so you can, you can feel it right. And yeah, this time, this time next year, we'll come back and see where it's going. Totally. Um, super base shout out another famous Kiwi. Uh, I don't know if you've, you've any conversations with him about anything in particular on AI building and AI infra.George [00:49:03]: We have had, uh, Twitter DMS, um, with, with him because we're quite big, uh, super base users and power users. And we probably do some things more manually than we should in. In, in super base support line because you're, you're a little bit being super friendly. One extra, um, point regarding, um, GDP Val AA is that on the basis of the overperformance of the models compared to the chatbots turns out, we realized that, oh, like our reference harness that we built actually white works quite well on like gen generalist agentic tasks. This proves it in a sense. And so the agent harness is very. Minimalist. I think it follows some of the ideas that are in Claude code and we, all that we give it is context management capabilities, a web search, web browsing, uh, tool, uh, code execution, uh, environment. Anything else?Micah [00:50:02]: I mean, we can equip it with more tools, but like by default, yeah, that's it. We, we, we give it for GDP, a tool to, uh, view an image specifically, um, because the models, you know, can just use a terminal to pull stuff in text form into context. But to pull visual stuff into context, we had to give them a custom tool, but yeah, exactly. Um, you, you can explain an expert. No.George [00:50:21]: So it's, it, we turned out that we created a good generalist agentic harness. And so we, um, released that on, on GitHub yesterday. It's called stirrup. So if people want to check it out and, and it's a great, um, you know, base for, you know, generalist, uh, building a generalist agent for more specific tasks.Micah [00:50:39]: I'd say the best way to use it is get clone and then have your favorite coding. Agent make changes to it, to do whatever you want, because it's not that many lines of code and the coding agents can work with it. Super well.swyx [00:50:51]: Well, that's nice for the community to explore and share and hack on it. I think maybe in, in, in other similar environments, the terminal bench guys have done, uh, sort of the Harbor. Uh, and so it's, it's a, it's a bundle of, well, we need our minimal harness, which for them is terminus and we also need the RL environments or Docker deployment thing to, to run independently. So I don't know if you've looked at it. I don't know if you've looked at the harbor at all, is that, is that like a, a standard that people want to adopt?George [00:51:19]: Yeah, we've looked at it from a evals perspective and we love terminal bench and, and host benchmarks of, of, of terminal mention on artificial analysis. Um, we've looked at it from a, from a coding agent perspective, but could see it being a great, um, basis for any kind of agents. I think where we're getting to is that these models have gotten smart enough. They've gotten better, better tools that they can perform better when just given a minimalist. Set of tools and, and let them run, let the model control the, the agentic workflow rather than using another framework that's a bit more built out that tries to dictate the, dictate the flow. Awesome.swyx [00:51:56]: Let's cover the openness index and then let's go into the report stuff. Uh, so that's the, that's the last of the proprietary art numbers, I guess. I don't know how you sort of classify all these. Yeah.Micah [00:52:07]: Or call it, call it, let's call it the last of like the, the three new things that we're talking about from like the last few weeks. Um, cause I mean, there's a, we do a mix of stuff that. Where we're using open source, where we open source and what we do and, um, proprietary stuff that we don't always open source, like long context reasoning data set last year, we did open source. Um, and then all of the work on performance benchmarks across the site, some of them, we looking to open source, but some of them, like we're constantly iterating on and so on and so on and so on. So there's a huge mix, I would say, just of like stuff that is open source and not across the side. So that's a LCR for people. Yeah, yeah, yeah, yeah.swyx [00:52:41]: Uh, but let's, let's, let's talk about open.Micah [00:52:42]: Let's talk about openness index. This. Here is call it like a new way to think about how open models are. We, for a long time, have tracked where the models are open weights and what the licenses on them are. And that's like pretty useful. That tells you what you're allowed to do with the weights of a model, but there is this whole other dimension to how open models are. That is pretty important that we haven't tracked until now. And that's how much is disclosed about how it was made. So transparency about data, pre-training data and post-training data. And whether you're allowed to use that data and transparency about methodology and training code. So basically, those are the components. We bring them together to score an openness index for models so that you can in one place get this full picture of how open models are.swyx [00:53:32]: I feel like I've seen a couple other people try to do this, but they're not maintained. I do think this does matter. I don't know what the numbers mean apart from is there a max number? Is this out of 20?George [00:53:44]: It's out of 18 currently, and so we've got an openness index page, but essentially these are points, you get points for being more open across these different categories and the maximum you can achieve is 18. So AI2 with their extremely open OMO3 32B think model is the leader in a sense.swyx [00:54:04]: It's hooking face.George [00:54:05]: Oh, with their smaller model. It's coming soon. I think we need to run, we need to get the intelligence benchmarks right to get it on the site.swyx [00:54:12]: You can't have it open in the next. We can not include hooking face. We love hooking face. We'll have that, we'll have that up very soon. I mean, you know, the refined web and all that stuff. It's, it's amazing. Or is it called fine web? Fine web. Fine web.Micah [00:54:23]: Yeah, yeah, no, totally. Yep. One of the reasons this is cool, right, is that if you're trying to understand the holistic picture of the models and what you can do with all the stuff the company's contributing, this gives you that picture. And so we are going to keep it up to date alongside all the models that we do intelligence index on, on the site. And it's just an extra view to understand.swyx [00:54:43]: Can you scroll down to this? The, the, the, the trade-offs chart. Yeah, yeah. That one. Yeah. This, this really matters, right? Obviously, because you can b
This is episode 315 recorded on December 16th, 2025, where John & Jason talk about the Fabric November 2025 Feature Summary part 3 including updates to Data Warehouse, Real-Time Intelligence, and Data Factory. For show notes please visit www.bifocal.show
Nik and Michael discuss the events and trends they thought were most important in the Postgres ecosystem in 2025. Here are some links to things they mentioned: Postgres 18 release notes https://www.postgresql.org/docs/18/release-18.htmlOur episode on Postgres 18 https://postgres.fm/episodes/postgres-18LWLock:LockManager benchmarks for Postgres 18 (blog post by Nik) https://postgres.ai/blog/20251009-postgres-marathon-2-005PostgreSQL bug tied to zero-day attack on US Treasury https://www.theregister.com/2025/02/14/postgresql_bug_treasuryPgDog episode https://postgres.fm/episodes/pgdogMultigres episode https://postgres.fm/episodes/multigresNeki announcement https://planetscale.com/blog/announcing-nekiOur 100TB episode from 2024 https://postgres.fm/episodes/to-100tb-and-beyondPlanetScale for Postgres https://planetscale.com/blog/planetscale-for-postgresOracle's MySQL job cuts https://www.theregister.com/2025/09/11/oracle_slammed_for_mysql_jobAmazon Aurora DSQL is now generally available https://aws.amazon.com/about-aws/whats-new/2025/05/amazon-aurora-dsql-generally-availableAnnouncing Azure HorizonDB https://techcommunity.microsoft.com/blog/adforpostgresql/announcing-azure-horizondb/4469710Lessons from Replit and Tiger Data on Storage for Agentic Experimentation https://www.tigerdata.com/blog/lessons-replit-tiger-data-storage-agentic-experimentationInstant database clones with PostgreSQL 18 https://boringsql.com/posts/instant-database-clonesturbopuffer episode https://postgres.fm/episodes/turbopufferCrunchy joins Snowflake https://www.crunchydata.com/blog/crunchy-data-joins-snowflakeNeon joins Databricks https://neon.com/blog/neon-and-databricks~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith credit to:Jessie Draws for the elephant artwork
In this episode I talk with Mike Bowers, Chief Architect at Faircom, about ISAM—the bare-metal database layer that predates SQL and powers stock trading systems. We cover Faircom's pivot into industrial IoT, their JSON/SQL hybrid approach, and discuss AI, consciousness, and the symbol grounding problem.Links:FairComNonsense Monthly
This is episode 314 recorded on December 15th, 2025, where John & Jason talk about the Fabric November 2025 Feature Summary part 2 including updates to Data Engineering & Data Science. For show notes please visit www.bifocal.show
Journalist Lee Sanders is back with a full breakdown, analysis, and reaction to WWE Monday Night RAW – December 22, 2025 as Austin Theory revealed himself to be the mystery man who attacked CM Punk twice now! Why Austin?! Why?!
For show notes please visit www.bifocal.show
Doug Green, Publisher of Technology Reseller News, spoke with Raj Darji, Founder & CEO of Aarav Solutions, about the company's launch of two generative AI accelerators—InsightForge and Omni360—designed to help communications service providers modernize billing operations, sales workflows, and customer engagement. Aarav Solutions is a long-standing Oracle Communications implementation partner with more than a decade of domain expertise across Oracle BRM and related telecom platforms. Darji explained that this deep operational knowledge is embedded directly into Aarav's GenAI accelerators, enabling CSPs to adopt AI without disrupting existing infrastructure. “We are not experimenting with AI—we are applying it where telecom operators feel the most pain, inside billing and operations,” said Darji. InsightForge is a GenAI accelerator purpose-built for Oracle BRM that allows business, finance, and operations teams to query complex billing data using natural language—without writing SQL or relying on back-office specialists. By translating plain-language questions into database queries, InsightForge delivers real-time visibility into invoices, balances, taxes, and discrepancies, significantly reducing operational dependencies and response times. Omni360 extends this capability with an AI-driven CRM and CPQ platform tightly integrated with BRM. Designed for mid-market CSPs, MVNOs, and enterprise connectivity providers, Omni360 unifies CRM and billing into a single pane of glass and enables sales teams to generate products, pricing, and quotes through natural-language prompts. Introduced at Mobile World Congress, both solutions drew strong interest for demonstrating how GenAI can deliver immediate, practical value rather than remain a conceptual buzzword. Learn more about Aarav Solutions at https://www.aaravsolutions.com/. Software Mind Telco Days 2025: On-demand online conference Engaging Customers, Harnessing Data
Lois Houston and Nikita Abraham explore how Oracle APEX integrates with AI to build smarter low-code applications. They are joined by Chaitanya Koratamaddi, Director of Product Management at Oracle, who explains the basics of Oracle APEX, its global adoption, and the challenges it addresses for businesses managing and integrating data. They also explore real-world use cases of AI within the Oracle APEX ecosystem Oracle APEX: Empowering Low Code Apps with AI: https://mylearn.oracle.com/ou/course/oracle-apex-empowering-low-code-apps-with-ai/146047/ Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. --------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:25 Lois: Hello and welcome to the Oracle University Podcast! I'm Lois Houston, Director of Communications and Adoption with Customer Success Services, and with me is Nikita Abraham, Team Lead: Editorial Services with Oracle University. Nikita: Hi everyone! We hope you've been enjoying these last few weeks as we've been revisiting our most popular episodes of the year. Today's episode is the last one in this series and is a throwback to a conversation on APEX with Chaitanya Koratamaddi, Director of Product Management for Oracle APEX. 00:57 Lois: We began by asking Chaitanya what Oracle APEX is and why it's so widely used. So, let's jump right in! Chaitanya: Oracle APEX is the world's most popular enterprise low code application platform. APEX enables you to build secure and scalable enterprise-scale applications with world class features that can be deployed anywhere, cloud or on-premises. And with APEX, you can build applications 20 times faster with 100 times less code. APEX delivers the most productive way to develop and deploy mobile and web applications everywhere. 01:40 Lois: That's impressive. So, what's the adoption rate like for Oracle APEX? Chaitanya: As of today, there are 19 million plus APEX applications created globally. 5,000 plus APEX applications are created on a daily basis and there are 800,000 plus APEX developers worldwide. 60,000 plus customers in 150 countries across various industry verticals. And 75% of Fortune 500 companies use Oracle APEX. 02:19 Nikita: Wow, the numbers really speak for themselves, right? But Chaitanya, why are organizations adopting Oracle APEX at this scale? Or to put it differently, what's the core business challenge that Oracle APEX is addressing? Chaitanya: From databases to all data, you know that the world is more connected and automated than ever. To drive new business value, organizations need to explore and exploit new sources of data that are generated from this connected world. That can be sounds, feeds, sensors, videos, images, and more. Businesses need to be able to work with all types of data and also make sure that it is available to be used together. Typically, businesses need to work on all data at a massive scale. For example, supply chains are no longer dependent just on inventory, demand, and order management signals. A manufacturer should be able to understand data describing global weather patterns and how it impacts their supply chains. Businesses need to pull in data from as many social sources as possible to understand how customer sentiment impacts product sales and corporate brands. Our customers need a data platform that ensures all this data works together seamlessly and easily. 04:00 Lois: So, you're saying Oracle APEX is the platform that helps businesses manage and integrate data seamlessly. But data is just one part of the equation, right? Then there's AI. How are the two related? Chaitanya: Before we start talking about Oracle AI, let's first talk about what customers are looking for and where they are struggling within their AI innovation. It all starts with data. For decades, working with data has largely involved dealing with structured data, whether it is your customer records in your CRM application and orders from your ERP database. Data was organized into database and tables, and when you needed to find some insights in your data, all you need to do is just use stored procedures and SQL queries to deliver the answers. But today, the expectations are higher. You want to use AI to construct sophisticated predictions, find anomalies, make decisions, and even take actions autonomously. And the data is far more complicated. It is in an endless variety of formats scattered all over your business. You need tools to find this data, consume it, and easily make sense of it all. And now capabilities like natural language processing, computer vision, and anomaly detection are becoming very essential just like how SQL queries used to be. You need to use AI to analyze phone call transcripts, support tickets, or email complaints so you can understand what customers need and how they feel about your products, customer service, and brand. You may want to use a data source as noisy and unstructured as social media data to detect trends and identify issues in real time. Today, AI capabilities are very essential to accelerate innovation, assess what's happening in your business, and most importantly, exceed the expectations of your customers. So, connecting your application, data, and infrastructure allows everyone in your business to benefit from data. 06:54 Oracle University is proud to announce three brand new courses that will help your teams unlock the power of Redwood—the next generation design system. Redwood enhances the user experience, boosts efficiency, and ensures consistency across Oracle Fusion Cloud Applications. Whether you're a functional lead, configuration consultant, administrator, developer, or IT support analyst, these courses will introduce you to the Redwood philosophy and its business impact. They'll also teach you how to use Visual Builder Studio to personalize and extend your Fusion environment. Get started today by visiting mylearn.oracle.com. 07:35 Nikita: Welcome back! So, let's focus on AI across the Oracle Cloud ecosystem. How does Oracle bring AI into the mix to connect applications, data, and infrastructure for businesses? Chaitanya: By embedding AI throughout the entire technology stack from the infrastructure that businesses run on through the applications for every line of business, from finance to supply chain and HR, Oracle is helping organizations pragmatically use AI to improve performance while saving time, energy, and resources. Our core cloud infrastructure includes a unique AI infrastructure layer based on our supercluster technology, leveraging the latest and greatest hardware and uniquely able to get the maximum out of the AI infrastructure technology for scenarios such as large language processing. Then there is generative AI and ML for data platforms. On top of the AI infrastructure, our database layer embeds AI in our products such as autonomous database. With autonomous database, you can leverage large language models to use natural language queries rather than writing a SQL when interacting with the autonomous database. This enables you to achieve faster AI adoption in your application development. Businesses and their customers can use the Select AI natural language interface combined with Oracle Database AI Vector Search to obtain quicker, more intuitive insights into their own data. Then we have AI services. AI services are a collection of offerings, including generative AI with pre-built machine learning models that make it easier for developers to apply AI to applications and business operations. The models can be custom-trained for more accurate business results. 09:47 Nikita: And what specific AI services do we have at Oracle, Chaitanya? Chaitanya: We have Oracle Digital Assistant Speech, Language, Vision, and Document Understanding. Then we have Oracle AI for Applications. Oracle delivers AI built for business, helping you make better decisions faster and empowering your workforce to work more effectively. By embedding classic and generative AI into its applications, Fusion Apps customers can instantly access AI outcomes wherever they are needed without leaving the software environment they use every day to power their business. 10:32 Lois: Let's talk specifically about APEX. How does APEX use the Gen AI and machine learning models in the stack to empower developers. How does it help them boost productivity? Chaitanya: Starting APEX 24.1, you can choose your preferred large language models and leverage native generative AI capabilities of APEX for AI assistants, prompt-based application creation, and more. Using native OCI capabilities, you can leverage native platform capabilities from OCI, like AI infrastructure and object storage, etc. Oracle APEX running on autonomous infrastructure in Oracle Cloud leverages its unique native generative AI capabilities tuned specifically on your data. These language models are schema aware, data aware, and take into account the shape of information, enabling your applications to take advantage of large language models pre-trained on your unique data. You can give your users greater insights by leveraging native capabilities, including vector-based similarity search, content summary, and predictions. You can also incorporate powerful AI features to deliver personalized experiences and recommendations, process natural language prompts, and more by integrating directly with a suite of OCI AI services. 12:08 Nikita: Can you give us some examples of this? Chaitanya: You can leverage OCI Vision to interpret visual and text inputs, including image recognition and classification. Or you can use OCI Speech to transcribe and understand spoken language, making both image and audio content accessible and actionable. You can work with disparate data sources like JSON, spatial, graphs, vectors, and build AI capabilities around your own business data. So, low-code application development with APEX along with AI is a very powerful combination. 12:51 Nikita: What are some use cases of AI-powered Oracle APEX applications? Chaitanya: You can build APEX applications to include conversational chatbots. Your APEX applications can include image and object detection capability. Your APEX applications can include speech transcription capability. And in your applications, you can include code generation that is natural language to SQL conversion capability. Your applications can be powered by semantic search capability. Your APEX applications can include text generation capability. 13:30 Lois: So, there's really a lot we can do! Thank you, Chaitanya, for joining us today. With that, we're wrapping up this episode. We covered Oracle APEX, the key challenges businesses face when it comes to AI innovation, and how APEX and AI work together to give businesses an AI edge. Nikita: Yeah, and if you want to know more about Oracle APEX, visit mylearn.oracle.com and search for the Oracle APEX: Empowering Low Code Apps with AI course. Lois: We hope you've enjoyed revisiting some of our most popular episodes of the year. We always appreciate your feedback and suggestions so do write to us at ou-podcast_ww@oracle.com. That's ou-podcast_ww@oracle.com. We're taking a break next week and will be back with a brand-new season of the Oracle University Podcast in January. Happy holidays, everybody! Nikita: Happy holidays! Until next time, this is Nikita Abraham... Lois: And Lois Houston, signing off! 14:34 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.
In this episode of the SaaS Sessions podcast, Sunil Neurgaonkar sits down with Jigar Dafda, Chief Technology & Product Officer at Fynd, to unpack how AI is fundamentally reshaping e-commerce in India.From conversational commerce and hyper-personalization to autonomous back offices and AI-driven customer support, this conversation cuts through the hype to explain what's actually changing, what's overblown, and what founders must build for if they want to survive the next decade of commerce.Key Takeaways -1. Commerce Is Shifting From Interfaces to Conversations-Traditional storefronts and search-driven UX are being replaced by conversational buying surfaces.- SEO is giving way to GEO (Generative Engine Optimization) as ChatGPT-like interfaces become the new entry point.- Merchants will still own fulfillment and data—but discovery will increasingly happen outside their websites.2. Hyper-Personalization Is No Longer Optional—It's Infrastructure- Customer Data Platforms (CDPs) are the backbone for AI-driven personalization across online and offline channels.- AI enables real-time personalization without armies of data scientists or analysts.- The real win isn't better targeting—it's higher conversion with less customer effort.3. Dynamic Pricing and Forecasting Are Moving Into the Back Office- Pricing, inventory planning, and demand forecasting are becoming autonomous systems.- Decisions that once took days (via SQL and dashboards) now happen in real time.- AI shifts teams from “executors” to “validators” of system-generated decisions.4. Customer Support Is the Lowest-Hanging AI Opportunity- 60–80% of customer queries are repetitive and easily automated.- AI agents now deliver 24/7, multilingual, context-aware support at scale.- The real challenge is no longer conversation—it's clean integration across OMS, WMS, and logistics systems.Lightning Round Insights:- Fastest way to learn today: Use ChatGPT as a personalized tutor—summarize, question, and iterate.- Hardest leadership lesson: Systems are easy. People are not.- Founder advice: Build for where the market is going, not where it is today—today's solution will expire faster than you expect.About Fynd:Fynd is one of India's leading unified commerce platforms, powering brands across online, offline, marketplaces, and quick commerce. From storefronts and PIM to OMS, WMS, and omnichannel integrations, Fynd enables end-to-end retail operations on a single stack.Chapters:00:10 – Introduction00:50 – Jigar's decade-long journey at Fynd05:20 – AI before vs after ChatGPT08:10 – Conversational commerce & GEO13:40 – Hyper-personalization and CDPs19:40 – Dynamic pricing and demand forecasting30:30 – AI in customer support37:20 – Predictions for the future of e-commerce39:40 – Lightning roundVisit our website - https://saassessions.com/Connect with me on LinkedIn - https://www.linkedin.com/in/sunilneurgaonkar/
Journalist Lee Sanders is back with a full breakdown, analysis, and reaction to WWE Monday Night RAW – December 15, 2025 live from The Giant Center in Hershey, Pennsylvania
Nik and Michael discuss a listener question about archiving a database. Here are some links to things they mentioned: Listener request to talk about archiving https://www.youtube.com/watch?v=KFRK8PiIvTg&lc=UgyiFrO37gEgUaVhRgN4AaABAg Our episode on “Is pg_dump a backup tool?” https://postgres.fm/episodes/is-pg_dump-a-backup-tool ~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith credit to:Jessie Draws for the elephant artwork
Episode 312 - Power BI November 2025 Feature Summary by John White & Jason Himmelstein
Ian and Aaron discuss how Aaron's orchestrating fly.io boxes for Database School, why sometimes you need to use AI to help AI, and why Costco is truly a great place. Plus an all-time Ian resume rant and so much more.Sponsored by Bento, Flare, Ittybit, tldraw, OG Kit, Tighten, and NusiiInterested in sponsoring Mostly Technical? Head to https://mostlytechnical.com/sponsor to learn more.(00:00) - Classic Little Kid Disease (04:14) - Weekend Update (18:29) - Ian's Resume Rant (33:57) - Advent of SQL (46:50) - Using AI to Help AI Links:Google ClassroomNorthPark CenterDanbury Railway MuseumMelissa & DougThomas & FriendsAdvent of SQL on Database SchoolCodeRabbitStratechery
In this episode of The Effortless Podcast, Amit Prakash and Dheeraj Pandey dive deep into one of the most important shifts happening in AI today: the convergence of structured and unstructured data, interfaces, and systems.Together, they unpack how conversations—not CRM fields—hold the real ground truth; why schemas still matter in an AI-driven world; and how agents can evolve into true managers, coaches, and chiefs of staff for revenue teams. They explore the cognitive science behind visual vs conversational UI, the future of dynamically generated interfaces, and the product depth required to build enduring AI-native software.Amit and Dheeraj break down the tension between deterministic and probabilistic systems, the limits of prompt-driven workflows, and why the future of enterprise AI is “both-and” rather than “either-or.” It's a masterclass in modern product, data design, and the psychology of building intelligent tools.Key Topics & Timestamps 00:00 – Introduction02:00 – Why conversations—not CRM fields—hold real ground truth05:00 – Reps as labelers and the parallels with AI training pipelines08:00 – Business logic vs world models: defining meaning inside enterprises11:00 – Prompts flatten nuance; schemas restore structure14:00 – SQL schemas as the true model of a business17:00 – CRM overload and the friction of rigid data entry20:00 – AI agents that debrief and infer fields dynamically23:00 – Capturing qualitative signals: champions, pain, intent26:00 – Multi-source context: transcripts, email threads, Slack29:00 – Why structure is required for math, aggregation, forecasting32:00 – Aggregating unstructured data to reveal organizational issues35:00 – Labels, classification, and the limits of LLM-only workflows38:00 – Deterministic (SQL/Python) vs probabilistic (LLMs) systems41:00 – Transitional workflows: humans + AI field entry44:00 – Trust issues and the confusion of the early AI market47:00 – Avoiding “Clippy moments” in agent design50:00 – Latency, voice UX, and expectations for responsiveness53:00 – Human-machine interface for SDRs vs senior reps56:00 – Structured vs unstructured UI: cognitive science insights59:00 – Charts vs paragraphs: parallel vs sequential processing1:02:00 – The “Indian thali” dashboard problem and dynamic UI1:05:00 – Exploration modes, drill-downs, and empty prompts1:08:00 – Dynamic leaves, static trunk: designing hierarchy1:11:00 – Both-and thinking: voice + visual, structured + unstructured1:14:00 – Why “good enough” AI fails without deep product1:17:00 – PLG, SLG, data access, and trust barriers1:20:00 – Closing reflections and the future of AI-native softwareHosts: Amit Prakash – CEO and Founder at AmpUp, former engineer at Google AdSense and Microsoft Bing, with extensive expertise in distributed systems and machine learningDheeraj Pandey – Co-founder and CEO at DevRev, former Co-founder & CEO of Nutanix. A tech visionary with a deep interest in AI, systems, and the future of work.Follow the Hosts:Amit PrakashLinkedIn – Amit Prakash I LinkedInTwitter/X – https://x.com/amitp42Dheeraj PandeyLinkedIn –Dheeraj Pandey | LinkedIn Twitter/X – https://x.com/dheerajShare your thoughts : Have questions, comments, or ideas for future episodes?Email us at EffortlessPodcastHQ@gmail.comDon't forget to Like, Comment, and Subscribe for more conversations at the intersection of AI, technology, and innovation.
Journalist Lee Sanders is back with exclusive predictions and breakdowns for WWE Saturday Night's Main Event — December 13, 2025! Tonight isn't just any SNME — it's the emotional farewell of one of the greatest in WWE history: John Cena. After more than 20 years at the top, Cena wrestles one last time against Gunther in what's being billed as his final WWE match ever.
In this bonus episode of Banking on Fraudology, powered by Safeguard , Hailey Windham talks with Ben Graf, a self-taught AI expert in the neobank space. Ben embodies the spirit of curiosity and courage driving the next wave of fraud-fighting transformation.The conversation dives into what it really looks like to learn AI from the ground up, emphasizing that the future of fraud prevention isn't about replacing people, but empowering them through technology.Key Takeaways: AI, Innovation, and Fraud-Fighting EmpowermentUsing AI to Learn AI: Ben explains how he used varying LLM chats (like ChatGPT, Claude, and Gemini) as a coach or mentor, experimenting for hours to understand their capabilities, consistency, and how to effectively prompt them.This approach helped him translate technical language and practices (like data analysis, SQL, and JavaScript) into actionable knowledge for his team, breaking down communication barriers.The hardest part was knowing where to start, but the key was realizing that "something is better than nothing" and compounding knowledge quickly breaks down barriers.Practical AI Applications for Eliminating Busy Work: AI should be used to make teams more efficient and help professionals focus strategically.Automating Document Verification: AI can use OCR to pull data, flag inconsistencies, and serve up summaries for identity, business, and income documents, which are often the most time-consuming parts of a review.Data Retrieval and System Silos: AI can help team members write their own SQL queries to retrieve data from data warehouses, dramatically reducing requests to the data team.Product and Feature Proposals: AI tools can mock up full dashboard concepts and even provide code snippets to give engineers a visual and break down communication barriers between fraud and technical teams.The Power of Empowerment and Buy-In: Leadership should create a culture where fraud fighters are empowered to explore and innovate.The magic of time savings lies in filling the time freed from "busy work" (like false positives) with new, high-impact tasks, whether that's cost savings in fraud loss or better customer retention.Teams are advised to keep proprietary or PII information out of the loop and find safe spaces to explore, remembering that everyone is still figuring out what AI can do.Get in the mood of being grateful for the fraud-fighting community, and be reminded of how strong the fraud-fighting community truly is. About Hailey Windham:As a 2023 CU Rockstar Recipient, Hailey Windham, CFCS (Certified Financial Crimes Specialist) demonstrated unbounding passion for educating her community, organization and credit union membership on scams in the market and best practices to avoid them. She has implemented several programs within her previous organizations that aim at holistically learning about how to prevent and detect fraud targeted at membership and employees. Windham's initiatives to build strong relationships and partnerships throughout the credit union community and industry experts have led to countless success stories. Her applied knowledge of payments system programs combined with her experience in fraud investigations offers practical concepts that are transferable, no matter the organization's size. Connect with Hailey on LinkedIn: https://www.linkedin.com/in/hailey-windham/
Journalist Lee Sanders is back talking WWE RAW 12/8/25 full show review, results, highlights as we break down tonight's episode live from the T-Mobile Center. RAW 12/8/25 Lineup: AJ Styles & Dragon Lee (c) vs. The War Raiders — WWE World Tag Team Championship Match Rey Mysterio vs. Finn Bálor Lyra Valkyria vs. Roxanne Perez Live appearance from Stephanie Vaquer Promo segment from GUNTHER following his tournament win as he faces John Cena in Cena's final match this Saturday December 13th in Washington, D.C. at SATURDAY NIGHT'S MAIN EVENT Prayers up to CM Punk and AJ Lee as their dog Larry passed away Lee reflects on the passing of his dog Maggie over a year ago and the best piece of advice for first time pet owners Prayers up for a friend of the family who's about to lose his battle to cancer in the coming days.——————————————————————
The 'D' Got Deleted: How VC Funding Broke the Innovation EcosystemLast week's whitepaper isn't production-ready. But someone's already pitching it to your board. Kence Anderson has deployed 100+ autonomous AI systems for Fortune 500 companies—and watched venture capital create a research-to-PR pipeline that skips development entirely. The 'D' in R&D got deleted. Hype cycles got amplified.Rule-based AI—systems encoding expertise as decision logic—was the 1980s breakthrough. Overhyped, then abandoned when it couldn't do everything. But engineers kept deploying it where codified rules excel: industrial controls, diagnostics, compliance. It's running critical infrastructure today. Every AI wave follows this arc. For leaders, the lesson: stop asking which technology wins. Ask what each does well—and build modular systems that match capabilities to tasks. The fix: if AI can learn, someone should teach it the right way. Machine teaching—goals, scenarios, strategies—creates modular agents that compound capability through orchestration.Paradigm Shifts:
This Podcast is sponsored by Team Simmer.Go to TeamSimmer and use the coupon code DEVIATE for 10% on individual course purchases.The Technical Marketing Handbook provides a comprehensive journey through technical marketing principles.Sign up to the Simmer Newsletter for the latest news in Technical Marketing.Latest content from Simo AhavaRun Server-side Google Tag Manager On Localhost Article Latest content from Juliana JacksonThe sin, the oil, the crack and the crisis ArticleSign up for a the very first Google x Jellyfish Cloud Talks event, 15th of January, Copenhagen - The very first event focused on the use of Google Cloud Technology & AI for Marketing.Huge thanks to TRKKN & Analytics Summit for having us over to do this in front of a fantastic audience, we had an amazing time.TRKKNAnalytics Summit This podcast is brought to you by Juliana Jackson and Simo Ahava.
In this week's Security Sprint, Dave and Andy covered the following topics:Warm Open:• TribalHub Magazine, Winter 2025: A Publication For Technology Minded Professionals In Tribal Government Tribal Health, Tribal-Gaming And Non-Gaming Tribal Enterprises. Includes Tribal-ISAC happenings!• React2Shell: Risky Bulletin: APTs go after the React2Shell vulnerability within hours & Critical Security Vulnerability in React Server Components • We discussed our daily SUN and Weekly Ransomware & Data Breach Digest available via Gate 15's GRIP: Join the GRIP! Gate 15's Resilience and Intelligence Portal (GRIP) utilizes the robust capabilities available in Cyware's Collaborate platform to provide the community with technology-enhanced, human-driven analysis products. Further, our team supports the implementation and use of Cyware Collaborate at the Enterprise level. Main Topics:FinCEN Issues Financial Trend Analysis on Ransomware. The U.S. Department of the Treasury's Financial Crimes Enforcement Network (FinCEN) is issuing a Financial Trend Analysis on ransomware incidents in Bank Secrecy Act (BSA) data between 2022 and 2024, which totaled more than $2.1 billion in ransomware payments… Previous FinCEN Financial Trend Analyses have focused on reported ransomware payments and incidents by the date the activity was filed with FinCEN. Today's report shifts the focus to the incident date of each ransomware attack and offers greater visibility into the activities conducted by ransomware actors.• Reported Ransomware Incidents and Payments Reach All-Time High in 2023• FinCEN Data Shows Ransomware Payments Top $2.1B in Just Three Years• Financial Services, Manufacturing, and Healthcare were the Most Impacted Industries• The Onion Router (TOR) was the Most Common Communication Method Reported• ALPHV/BlackCat was the Most Prevalent Ransomware Variant Between 2022 and 2024• FinCEN analysis shows scope of ransomware problemFive-page draft Trump administration cyber strategy targeted for January release; The six-pillar document covers a lot of ground in a short space, and could be followed by an executive order implementing it, according to sources familiar with the draft. America 250: Presidential Message on the Anniversary of the Monroe Doctrine• Here's what the new National Security Strategy says about threats to critical infrastructure• New US National Security Strategy reveals Trump administration's latest stance on TaiwanFBI PSA: Criminals Using Altered Proof-of-Life Media to Extort Victims in Virtual Kidnapping for Ransom Scams. The Federal Bureau of Investigation (FBI) warns the public about criminals altering photos found on social media or other publicly available sites to use as fake proof of life photos in virtual kidnapping for ransom scams. The criminal actors pose as kidnappers and provide seemingly real photos or videos of victims along with demands for ransom payments… Criminal actors typically will contact their victims through text message claiming they have kidnapped their loved one and demand a ransom be paid for their release. Oftentimes, the criminal actor will express significant claims of violence towards the loved one if the ransom is not paid immediately. The criminal actor will then send what appears to be a genuine photo or video of the victim's loved one, which upon close inspection often reveals inaccuracies when compared to confirmed photos of the loved one. Examples of these inaccuracies include missing tattoos or scars and inaccurate body proportions. Criminal actors will sometimes purposefully send these photos using timed message features to limit the amount of time victims have to analyze the images.Quick Hits:• US leader of global neo-Nazi terrorist group signals retribution for arrests• ASD: Information stealers are on the rise, are you at risk? • UK NCSC: Prompt injection is not SQL injection (it may be worse)
Jason Ash, Chief of Data at Symetra, joins the show to unpack how a mid sized insurer is rebuilding its data stack and culture so business and technology actually pull in the same direction. He shares how his team brings actuaries, product leaders, and engineers into one data platform, and why opening that platform to non technical contributors has been a turning point. If you work in a regulated industry and are trying to move faster with data, this conversation gives you a very practical view of what it takes.Key takeaways• Business and tech only work when they share context and trustJason has sat in both seats, first as an actuary and now as a data and engineering leader. That dual background helps him translate between risk, regulation, and modern data practices, and it shapes how he frames projects around shared business outcomes rather than tools.• Put data leaders inside business line leadership, not on the outsideSeveral of Jason's managers sit on the leadership teams for Symetra's life, retirement, and group benefits divisions. They hear priorities and constraints at the same time as product and distribution leaders, which lets them frame data as a value add for new products instead of a back office cost.• Treat the warehouse as a shared product and measure contributors, not just tablesSymetra's dbt based warehouse started with about five contributors. Over three years they grew that to more than sixty, and half of those people sit outside the core data team. Business users learn to contribute SQL, documentation, and domain knowledge directly into the repo, which spreads ownership and reduces bottlenecks.• Shift stakeholders away from big bang launches to steady deliveryJason pushes his teams to think like software engineers. Rather than promising a perfect data product on a single date, they deliver an early slice of data, have partners use it right away, collect feedback, and improve every month. That builds trust and avoids the usual disappointment that comes with one big release.• Use maturity as a guide for where to investEarly on, his group picked a few strong champions who were willing to accept slower delivery in exchange for building real infrastructure. Now that the platform and practices are in place, the focus is on scale, reuse, and getting more people to build on the same foundation, including as AI capabilities start to reshape the work.Timestamped highlights00:53 Jason explains what Symetra actually does and how their product mix makes data work more complex than the company size might suggest02:19 From actuary to Chief of Data, and what sitting on both sides of the fence taught him about business and technology expectations08:08 Why mixing data engineers, data scientists, actuaries, and analysts on the same problems leads to stronger solutions than any single discipline alone13:44 How embedding data leaders into each business division's leadership group changed when and how data enters product discussions16:38 The dbt story at Symetra, and how more than sixty people across the company now contribute directly to the shared data warehouse26:22 Moving away from big bang data launches and setting expectations around early value, continuous feedback, and ongoing quality improvements32:06 The tension between safety and speed as AI advances, and what Jason worries about most for established insurers that move too slowlyPractical moves you can steal• Put data leaders on business line leadership teams so they hear priorities and constraints in real time, not after the roadmap is set• Track how many unique people contribute to your data warehouse and make that a visible success metric across the companyStay connectedIf this episode helped you think differently about data leadership in regulated industries, share it with a colleague who owns product, data, or actuarial work.
Lee & Tammy Sanders are back! The holidays roll on and on this episode of WRESTLING with the TOPICS, the gang talk holiday shopping stress, gift ideas, and the growing popularity of Ancestry DNA kits—including what people really need to think about before gifting personal genetic data in 2025.From there, we shift into D.C. Mayor Muriel Bowser officially stepping down, the early media framing surrounding her exit, and what this moment means for the city going forward. Tams also sounds off on her Washington Commanders, the season so far, and the emotional rollercoaster that continues to define the franchise.Then we move into the viral side of the internet with reactions to the latest public freakout dubbed Cinnabon Karen, and why these moments continue to dominate social media culture. We also discuss a disturbing national headline involving a Kansas teacher accused of misconduct with a student, and the ongoing concerns about accountability, safety, and systemic failures that keep showing up in stories like this.From real-world chaos to wrestling business moves, we break down TNA WRESTLING officially landing a major television deal with AMC, what it means for live programming, production upgrades, long-term growth, and how the WWE–TNA relationship factors into this next chapter. We react to TNA President Carlos Silva's detailed comments on audience expectations, creative integration, and how AMC plans to embed itself into the product.We also tackle the developing controversy surrounding **Sarah Stock's public accusations against AEW, including claims of corruption, unsafe priorities, and talent neglect, along with the sharply divided reactions from wrestlers and fans as AEW remains silent.On the media business side, we dive into Netflix announcing plans to acquire Warner's premium studio assets, including HBO and Warner Bros. Studios, while spinning off the traditional cable networks into a separate publicly traded company pending regulatory approval—and what that means for wrestling, sports, and entertainment distribution.From boardrooms to the ring, **John Cena reveals his original retirement vision was a massive 220-date farewell tour, before WWE scaled it down to just 36 appearances. We also break down the **new federal lawsuit involving WWE, TKO, and John Cena** over the long-standing “The Time Is Now” theme song, with claims of unlicensed sampling and copyright infringement.As always, it's an unfiltered mix of wrestling, media, controversy, and cultural conversation—all in one place.——————————————————————
Matan Yungman is our special host for today, who came especially to talk about his special project called "Rapido", which is capable of automatically tuning SQL queries at scale. Perhaps there is some inspiration in it for us? Relevant links: Matan Yungman | LinkedIn Matan Yungman (@MatanYungman) / X SQL Server 2025 is Now Generally Available | Microsoft Community Hub Intelligent Query Processing - SQL Server | Microsoft Learn
Podcasting 2.0 December 5th 2025 Episode 243: "Nuts & Logs" Adam & Dave poddy training, junie, major dev talk and more! ShowNotes We are LIT NYC? Alby Hub? AI stats analysis Alt Enclosure Video New aggregatory open build GitHub - Podcastindex-org/feedparser: The XML parser that converts saved podcast feeds into intermediary files for SQL ingestion. TTS Podcasts on OP3 Cloudflare Outage Decentralization Transcript Search What is Value4Value? - Read all about it at Value4Value.info V4V Stats Last Modified 12/05/2025 14:32:55 by Freedom Controller
Nik and Michael discuss max_connections, especially in the context of increasing it to solve problems like migrations intermittently failing(!) Here are some links to things they mentioned: max_connections https://www.postgresql.org/docs/current/runtime-config-connection.html#GUC-MAX-CONNECTIONSTweet about deployments vs connections issue https://x.com/brankopetric00/status/1991394329886077090Nik tweet in response https://x.com/samokhvalov/status/1991465573684027443Analyzing the Limits of Connection Scalability in Postgres (blog post by Andres Freund) https://www.citusdata.com/blog/2020/10/08/analyzing-connection-scalability/Exponential Backoff And Jitter (blog post by Marc Brooker) https://aws.amazon.com/blogs/architecture/exponential-backoff-and-jitter/~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith credit to:Jessie Draws for the elephant artwork
Journalist Lee Sanders is back with your WWE RAW 12/1/25 Review, results, and full fallout from WWE Survivor Series: WarGames 2025!We are 48 hours removed from an explosive Survivor Series, and Monday Night RAW is bringing the heat with championship action, tournament semifinals, and major storyline developments — including CM Punk DEMANDING answers about the mysterious masked attacker! Plus, what's NEXT for THE VISION after their huge Survivor Series moment? We're breaking everything down with analysis, reactions, and honest insight you can only get from THE RCWR SHOW.
In this episode, Cliff Crosland, CEO & co-founder of Scanner.dev, shares his candid journey of trying (and initially failing) to build an in-house security data lake to replace an expensive traditional SIEM.Cliff explains the economic breaking point where scaling a SIEM became "more expensive than the entire budget for the engineering team". He details the technical challenges of moving terabytes of logs to S3 and the painful realization that querying them with Amazon Athena was slow and costly for security use cases .This episode is a deep dive into the evolution of logging architecture, from SQL-based legacy tools to the modern "messy" data lake that embraces full-text search on unstructured data. We discuss the "data engineering lift" required to build your own, the promise (and limitations) of Amazon Security Lake, and how AI agents are starting to automate detection engineering and schema management.Guest Socials - Cliff's Linkedin Podcast Twitter - @CloudSecPod If you want to watch videos of this LIVE STREAMED episode and past episodes - Check out our other Cloud Security Social Channels:-Cloud Security Podcast- Youtube- Cloud Security Newsletter If you are interested in AI Cybersecurity, you can check out our sister podcast - AI Security PodcastQuestions asked:(00:00) Introduction(02:25) Who is Cliff Crosford?(03:00) Why Teams Are Switching from SIEMs to Data Lakes(06:00) The "Black Hole" of S3 Logs: Cliff's First Failed Data Lake(07:30) The Engineering Lift: Do You Need a Data Engineer to Build a Lake?(11:00) Why Amazon Athena Failed for Security Investigations(14:20) The Danger of Dropping Logs to Save Costs(17:00) Misconceptions About Building Your Own Data Lake(19:00) The Evolution of Logging: From SQL to Full-Text Search(21:30) Is Amazon Security Lake the Answer? (OCSF & Custom Logs)(24:40) The Nightmare of Log Normalization & Custom Schemas(28:00) Why Future Tools Must Embrace "Messy" Logs(29:55) How AI Agents Are Automating Detection Engineering(35:45) Using AI to Monitor Schema Changes at Scale(39:45) Build vs. Buy: Does Your Security Team Need Data Engineers?(43:15) Fun Questions: Physics Simulations & Pumpkin Pie
This is episode 311 recorded on November 25th, 2025, where John & Jason talk with Stephanie Bruno, Data Platform MVP & Data Witch, about the news coming out of Microsoft Ignite. For show notes please visit www.bifocal.show
Destaney talks with Gibby, BTR Media's Head of DSP, to break down how he uses AMC, DSP, and AI tools like ChatGPT to build custom reports and audiences that actually prove incremental value. Gibby shares how he went from zero coding experience to writing SQL for AMC, how to turn black box data into actionable insights, and why strong prompts and strategy matter more than ever in Amazon Ads.Connect with Gibby on Linkedin: linkedin.com/in/gibby-h-2132b71a4Connect with Destaney on Linkedin: linkedin.com/in/destaney-wishonSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
After a brief hiatus, we are back to discuss the evolution of a modern trading workflow. In this episode, we cover the transition from standard spreadsheets to custom, AI-built Python tools—including a new voice-to-text trade logger and qualitative scanners based on "Rule Breaker" investing principles.We also dive deep into current market mechanics, blending old-school sentiment analysis with new-school data. We break down the "Investor Day" top in Nvidia, why the disappearance of the MicroStrategy (MSTR) premium removes a critical bid for Bitcoin, and why China and commodities might be the next contrarian play.Key Topics:Workflow Evolution: leveraging Claude to build SQL databases and voice-logging tools.Market Psychology: How "Investor Days" and retail euphoria signal tops (The NVDA example).The Crypto Thesis: Why MSTR losing its ability to issue equity at a premium signals a potential drop for Bitcoin to the $50k–$60k range.Global Macro: A contrarian look at China and commodities amidst the AI arms race.
Journalist Lee Sanders is back with your WWE Survivor Series 2025 POST SHOW & REVIEW! We're breaking down all the big moments from San Diego including John Cena defending the Intercontinental Championship, the All-Star Men's War Games match, and the stacked Women's War Games showdown. Full results, reactions, analysis, and standout performances from tonight's premium live event. Plus backstage notes, crowd reactions, and the full fallout heading into RAW and SmackDown.WWE Survivor Series 2025 — Match Card-Men's WarGames Match: Cody Rhodes, CM Punk, Roman Reigns, Jey Uso & Jimmy Uso vs. Brock Lesnar, Drew McIntyre, Logan Paul, Bron Breakker & Bronson Reed-Women's WarGames Match: AJ Lee, Alexa Bliss, Charlotte Flair, IYO SKY & Rhea Ripley vs. Becky Lynch, Asuka, Kairi Sane, Nia Jax & Lash Legend-Intercontinental Championship: John Cena (c) vs. Dominik Mysterio-Women's World Championship: Stephanie Vaquer (c) vs. Nikki Bella
Happy Thanksgiving week friends! Today we're celebrating a turkey and pie overload by sharing another fun tale of pentest pwnage! It involves using pygpoabuse to hijack a GPO and turn it into our pentesting puppet! Muahahahahaah!!!! Also: This week over at 7MinSec.club we looked at how to defend against some common SQL attacks We're very close to offering our brand new LPLITE:GOAD 3-day pentest course (likely in mid-January). It will get announced on 7MinSec.club first, so please make sure you're subscribed there (it's free!) Did you miss our talk called Should You Hire AI Run Your Next Pentest? Check it out on YouTube!
John and Bobby broke down how RAG actually works and why the real battle isn't the LLM, it's the chaos sitting in PDFs, Excel files, post-job reports, random folder structures, and handwritten scans. They showed how extraction, chunking, and embeddings fall apart when data is messy, and why clean structure, good metadata, and consistent organization matter way more than people want to admit. They also hit on how tools like MCP and text-to-SQL let AI pull from WellView, production systems, and databases in one place, instead of everyone living in 20 different apps. The takeaway was simple: AI gets powerful fast when the data is ready, but if the inputs are junk, you'll just get faster junk back.Click here to watch a video of this episode.Join the conversation shaping the future of energy.Collide is the community where oil & gas professionals connect, share insights, and solve real-world problems together. No noise. No fluff. Just the discussions that move our industry forward.Apply today at collide.ioClick here to view the episode transcript. https://twitter.com/collide_iohttps://www.tiktok.com/@collide.iohttps://www.facebook.com/collide.iohttps://www.instagram.com/collide.iohttps://www.youtube.com/@collide_iohttps://bsky.app/profile/digitalwildcatters.bsky.socialhttps://www.linkedin.com/company/collide-digital-wildcatters
Journalist Lee Sanders is back with his WWE RAW November 24th, 2025 results and post-show review, live from Oklahoma City as we head into Survivor Series: WarGames (2025). Tonight's show features:-Roman Reigns opening the program ahead of War Games. -The quarterfinal matches of the “Last Time Is Now” tournament:-Gunther vs. Carmelo Hayes -Penta vs. Solo Sikoa-Rey Mysterio vs. JD McDonagh — another announced match added for tonight. -A segment where Dominik Mysterio will send a direct message to John Cena ahead of their showdown. -Becky Lynch appears to address her controversial loss and what's next for her. Plus, the headline round-up:-Lee gives his rating & ranking for AEW Full Gear-Thoughts on Tony Khan's follow-up interview with Ariel Helwani-A quick wrap on the referenced weekend joint event between WWE & AAA (Alianza)-What's happening with the Washington Commanders: injuries, coaching changes, and next steps-The entertainment update: how the movie Wicked is doing at the box office
Over his 15-year journey through healthcare and financial services security, Ken Bowles, now Director of Security Operations at GreenSky, has collected a plethora of practical strategies for prioritizing crown jewels, managing cloud over-permissions, and building SOCs that scale effectively. He reflects on transforming security operations through AI and intelligent automation and discusses how AI is reducing analyst investigation time dramatically. Ken also asserts the importance of auditing security controls before they silently fail. The conversation touches on the evolving role of the MITRE framework, the concept of signaling versus alerting, and why embracing AI might be the best career move for security professionals navigating rapid technological change in cloud environments. Topics discussed: Building security operations programs around crown jewels and scaling outward to manage the most critical assets first. Managing over-permissions in cloud environments that have snowballed across multiple administrators without proper governance. Using AI to reduce analyst investigation time from 30 minutes to seconds through intelligent data enrichment and context. Creating true single-pane-of-glass visibility by connecting security tools and data sources for more effective threat detection. Training new security analysts with AI assistance to bridge knowledge gaps in SQL, SOAR platforms, and log analysis. Documenting institutional knowledge while encouraging analysts to trust their intuition when something doesn't look right. Understanding the limitations of impossible travel alerts and using AI to establish user behavior baselines for accurate detection. Applying the MITRE framework as a guideline rather than gospel, adapting detection strategies to specific organizational needs. Implementing signaling approaches that label security-relevant events without creating alert fatigue for security operations teams. Auditing security controls regularly to catch configuration drift and ensure protective measures remain effective over time. Listen to more episodes: Apple Spotify YouTube Website
This is episode 310 recorded on November 21st, 2025, where John & Jason talk about the Power BI & Fabric Feature Summaries from October 2025 in preparation for the big news releases from Ignite in November.
Jack Herrington talks with Will Madden about how Prisma ORM is evolving in v7, including the transition away from Rust toward TypeScript, less magic, and a new Prisma config file for more predictable good DX. They dig into Prisma Postgres, improvements to Prisma Studio, better support for serverless environments, and how JavaScript ORM tools like Prisma as an object relational mapper will fit into future agentic coding workflows powered by LLMs. Links LinkedIn: https://www.linkedin.com/in/willmadden Resources ORM: https://www.prisma.io/blog/orm-6-12-0-esm-compatible-generator-in-preview-and-new-options-for-prisma-config https://www.prisma.io/blog/why-prisma-orm-generates-code-into-node-modules-and-why-it-ll-change https://www.prisma.io/blog/from-rust-to-typescript-a-new-chapter-for-prisma-orm https://www.prisma.io/blog/try-the-new-rust-free-version-of-prisma-orm-early-access https://www.prisma.io/blog/rust-free-prisma-orm-is-ready-for-production Prisma Postgres: prisma.io/postgres We want to hear from you! How did you find us? Did you see us on Twitter? In a newsletter? Or maybe we were recommended by a friend? Fill out our listener survey (https://t.co/oKVAEXipxu)! https://t.co/oKVAEXipxu Let us know by sending an email to our producer, Elizabeth, at elizabeth.becz@logrocket.com (mailto:elizabeth.becz@logrocket.com), or tweet at us at PodRocketPod (https://twitter.com/PodRocketpod). Check out our newsletter (https://blog.logrocket.com/the-replay-newsletter/)! https://blog.logrocket.com/the-replay-newsletter/ Follow us. Get free stickers. Follow us on Apple Podcasts, fill out this form (https://podrocket.logrocket.com/get-podrocket-stickers), and we'll send you free PodRocket stickers! What does LogRocket do? LogRocket provides AI-first session replay and analytics that surfaces the UX and technical issues impacting user experiences. Start understanding where your users are struggling by trying it for free at LogRocket.com. Try LogRocket for free today. (https://logrocket.com/signup/?pdr) Chapters
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