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Topics covered in this episode: django-orjson Best Django Redis configuration for speed and size Linus Torvalds puts the foot down against Anti-AI Kernel Maintainers Django Steering Council backs the Triptych Project Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Consulting from Six Feet Up Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Michael #1: django-orjson Adam Johnson dropped django-orjson - drop-in replacements for the Django and DRF pieces that touch JSON, swapping stdlib json for orjson, the Rust-based library. Headline numbers: 10x faster serialization, 2x faster deserialization. The interesting question is why this needs to be a package at all. pip install orjson is the easy part. Adam's actual pitch: adopting it "isn't easy, especially when your framework uses json in many different parts." Django scatters JSON across JsonResponse, the test client and test case classes, the json_script template tag, and more. There's no single hook to grab, so you get a library that catches them all. Adam is refreshingly honest about the scale of the win. His words: "While database queries tend to dominate the typical Django application's runtime, the time spent in serialization and deserialization can still be significant." He calls it "a nearly free performance win" - not "this will 10x your app." That's a claim about cost, not magnitude, and it's worth keeping those straight. Worth flagging what the post doesn't cover: caveats. There are none in the article, but orjson has real ones. Django and Flask both render datetimes as RFC 822 HTTP-date (Wed, 15 Jul 2026 12:00:00 GMT); orjson does ISO 8601. It can't do ensure_ascii, it rejects NaN and Infinity (which stdlib happily emits), and it raises on Decimal. If you've got a JS client parsing dates, that's a wire-format change. Who should actually take this? If you're a DRF shop shoveling JSON all day, yes - it's cheap and it's real. If your app mostly renders HTML templates, you're optimizing a slice of runtime that's already near zero. The problem Adam's package solves doesn't exist in Flask or Quart. They already centralize every JSON operation - jsonify, request.get_json(), the test client, the |tojson filter - behind one provider object at app.json. So there's no library to install. It's about ten lines: import orjson from quart.json.provider import JSONProvider # or flask.json.provider class OrjsonProvider(JSONProvider): def dumps(self, obj, **kwargs) -> str: return orjson.dumps(obj).decode() # provider must return str def loads(self, s, **kwargs): return orjson.loads(s) app.json = OrjsonProvider(app) The numbers on talkpython.fm Evaluated it, measured it, and skipped it. The biggest JSON payload we serve is our MCP server returning a cached episode transcript, about 139 KB. Swapping the provider saves 0.119 milliseconds per request. That total response takes 1.1 ms We got 4.1x, not 10x - and the reason is the good lesson. Payload shape decides your speedup. The 10x is for structure-heavy data, lots of small keys where stdlib burns time in Python-level dispatch per item. Our hot payload is one giant transcript string, so the work is escaping and memcpy Calvin #2: Best Django Redis configuration for speed and size Peter Bengtsson revisits a classic: his 2017 "Fastest Redis configuration for Django" benchmark now has a 2026 update posted this week. The 2017 post pitted django-redis serializers (json, ujson, msgpack, pickle) and compressors (zlib, lzma) against each other; conclusion was msgpack + zlib as the sweet spot - avoid the json serializer, it's fat and slow. The 2026 update narrows focus to just compressors: default (no compression), zlib, lzma, and newcomer zstd. New results: lzma compresses best but is slowest; zstd is the fastest compressor on Ubuntu; differences between them are very small. Big takeaway across both: compression buys you a lot of space (2–3.5x smaller) for very little speed cost - worth it for Redis where memory is the constraint. Caveat from the author: results depend heavily on your data - his test stores short strings of numbers, so benchmark your own workload. Michael #3: Linus Torvalds puts the foot down against Anti-AI Kernel Maintainers Write up on Ars. Really good coverage by Maximillian: Time to wake up (for some) Torvalds said that “Linux is not one of those anti-AI projects, and if somebody has issues with that, they can do the open-source thing and fork it. Or just walk away.” I agree with Max, putting your head in the sand and waiting for AI to go away will likely mean you won't be working professionally in software development in the coming years. The statement came amid a lengthy thread arguing about the use of Sashiko, an “agentic Linux kernel code review system” that its creators claim can, in tests, independently find 53.6 percent of the bugs that would end up being fixed by human coders in later commits. “We're not forcing anybody to use [LLM tools], but I will very loudly ignore people who try to argue against other people from using it,” Torvalds said. “Anybody who points to the problems at AI had better be looking in the mirror and pointing at themselves at the same time,” Torvalds wrote. Calvin #4: Django Steering Council backs the Triptych Project Django Steering Council issued a Letter of Collaboration backing Carson Gross & Alex Petros's funding bid for the Triptych Project - three proposals to make HTML more expressive natively, in every browser. The three additions: PUT/PATCH/DELETE methods for forms, button actions (buttons that fire HTTP requests without a wrapping form), and partial page replacement. Distills the core ideas from HTMX/Unpoly/Turbo into the HTML standard itself - no JS, no library, nothing to ship or maintain. Current focus is button actions (WHATWG #12330): Logout instead of wrapping a button in a form. Relevant to Django directly - think the admin submit row and disguised delete links; Django 6.0's template partials were already inspired by these patterns. How to help: companies can send non-binding letters of support on letterhead; individuals can read the proposals and weigh in on the WHATWG issues. Extras Calvin: DOOMQL - A playable first-person shooter whose framebuffer is a SQL query. Michael: Granian 2.7.9 fixes WSGI threadpool scheduler starvation/underscaling Welcome Calvin post Joke: Solving all bugs
nFactorial Intelligence - еженедельный обзор новостей из мира стартапов и ИИ Рекомендации от nFactorial Ежегодный nFactorial Incubator Demo Day 2026. 24 июля, пятница, 13:00-17:00, г. Алматы. Вход свободный. Narxoz University, актовый зал, главный учебный корпус, Жандосова 55. Подать заявку: https://nfactorialschool.typeform.com/to/syWrSaRy 22-недельный буткамп по аналитике данных, 44 урока. 6 модулей: Google Sheets, Power BI, SQL, Python, Product Analytics, AI для Аналитика Данных - https://courses.nfactorial.school/da
An airhacks.fm conversation with Stanislav Bashkyrtsev about: discussion about testing terminology and the difference between unit tests, component tests, System Tests, and integration tests, defining component tests as in-process invocations without HTTP, using RestAssured with MockMvc-style direct endpoint calls, avoiding mocks in favor of real system tests, why code coverage is a misused management metric, the anti-pattern of using reflection to inflate coverage, distinguishing line and branch coverage from actual verification, using coverage from system tests to detect dead code for pruning, mutation testing with PIT to measure assertion quality, testing Quarkus applications, the default Guice and Guava dependencies in Quarkus RESTEasy, starting a new microservice with a separate system-test module, calling endpoints over HTTP with the MicroProfile REST Client or the Java HTTP client, deploying Quarkus on AWS Lambda as a production-like environment, backward compatibility testing with multiple production versions, turning system tests into stress and load tests, testing connection pools and metrics under load, introducing a test-only private API to verify state changes in serverless systems, contract-driven work in large consulting projects, generating JSON and JSONB directly in PostgreSQL and returning it over JDBC, mapping database rows to Java records instead of DTOs, running GraalVM inside the Oracle Database for stored procedures and table triggers, the pendulum between database-centric and application-centric logic, the convergence of SQL and NoSQL databases, CI/CD pipelines with Jenkins and manual production deployment steps, avoiding Jenkins access to production via CGI shell scripts behind nginx, AWS CodePipeline and CodeBuild with CDK-defined infrastructure, event-driven pipelines triggered by S3 put-object events, multi-account roles with short-lived STS credentials, the size of the AWS SDK and reducing it by excluding unused HTTP clients, health checks and Kubernetes liveness and readiness probes, why health checks make little sense for short-lived Lambdas, a version endpoint for deployment smoke tests Stanislav Bashkyrtsev on twitter: @sbashkirtsev
Hey yall, Alex here, Huge thanks to Wolfram for running point on the live show this week. Didn't have tons of time to edit this one, so please skip the first 10 minutes, it's a loop of our new “wait for the live show to start” vid, that I build with HyperFrames and can't wait to tell you about, next week! Today it seems that OpenSource is biting back, with Kimi K3 getting released just a short while after Thinking Machines (Thinky) has released Inkling, their near 1T model. I'm attaching the TL;DR and timestamps for the full show (my AI agents, yes even Fable and Sol are not a match yet at editing down hehe) and I'll spare you the long Fable recap (please do let me know in the comments if you were expecting it) 0:00 – Intro, Alex on vacation, TLDR overview11:35 – TLDR: Thinking Machines, open source, OpenAI news12:34 – Banter: impressions of Sol/Codex, over-verification behavior37:22 – TLDR restart & detailed breakdown48:40 – Open Source AI section begins (Bonsai/Prism ML, Kimi K3)58:42 – Inkling (Thinking Machines) deep dive & 3D model visualization1:10:33 – Kimi K3 discussion & demo comparisons1:27:02 – Frontier Labs: AGI governance framework discussion (Demis Hassabis essay)1:47:04 – Grok Build CLI data leak & OpenAI file deletion incident2:02:15 – This Week's Buzz: Wolfbench results on GPT 5.6 Sol/Terra/Luna2:09:52 – Closing remarks & sign-offThe one-minute version: Mira Murati's Thinking Machines released Inkling, a 975B parameter open-weights MoE under Apache 2.0, the top US open-weights model right now. Moonshot's Kimi K3 went from rumor to released API during the show, confirmed at 2.8 trillion parameters with open weights promised within days, and it's already topping early arena boards. PrismML's Bonsai 27B squeezes a full 27B model into 3.9 gigabytes so it runs on a phone. Codex and ChatGPT Work blew past 9 million users, OpenAI confirmed and explained the Sol file-deletion bug (back up your machines, folks), and xAI's Grok Build CLI got caught uploading entire private repos before open-sourcing the whole thing in response. Plus Wolfram's fresh Wolfbench numbers on the GPT-5.6 family in This Week's Buzz
A short rant based on my new article this week, "The Database Is Not the Data Model""In discussions with data practitioners, I keep seeing the same confusion. Someone pulls up a DDL file, a folder of dbt or SQL, or an ERD reverse-engineered from a Postgres instance and says: “Here's our data model.”Not to be pedantic, but that's a schema. Schemas are great. But data modeling is more than just schema design."Let's dive into the difference in this podcast and the article.Article: https://practicaldatamodeling.substack.com/p/the-database-is-not-the-data-model
Build a fully working model-driven app from your existing Dataverse, SharePoint, or SQL data in under a minute — screens, navigation, and forms included. Generate new pages from a natural language prompt with Generative Pages, auto-populate records from any document in seconds with Automatic Form Fill, and embed Copilot to query your app data directly. Layer in an Agent Feed to proactively surface decisions, missing data, and action items, then connect to Outlook and Microsoft 365 through Work IQ to act on your app data without leaving your inbox. Jed Brown, Power Platform Group Product Manager, shares how to turn existing business data into a modern, AI-powered app. ► QUICK LINKS: 00:00 - Create apps using Power Apps 01:07 - Create an app from existing data 02:26 - Copilot + Form Fill Assist 03:46 - AI-generated pages from a prompt 04:53 - Agent feed for proactive intelligence 06:16 - M365 integration via Work IQ 06:46 - Wrap up ► Link References Build your first Power App today at https://make.powerapps.com ► Unfamiliar with Microsoft Mechanics? Microsoft's Official Video Series for IT - Subscribe https://www.youtube.com/c/MicrosoftMechanicsSeries - Microsoft Tech Community: https://techcommunity.microsoft.com/t5/microsoft-mechanics-blog/bg-p/MicrosoftMechanicsBlog - Podcast: https://microsoftmechanics.libsyn.com/podcast ► Join us on social: - https://twitter.com/MSFTMechanics - https://www.linkedin.com/company/microsoft-mechanics/ - https://www.instagram.com/msftmechanics/ - https://www.tiktok.com/@msftmechanics
Why would someone leave Apple, LinkedIn, and Meta to join an early stage startup? That was the first thing I wanted to ask Ranjith Prabu, CTO when he sat down with me at the WALT AI office in Santa Clara on The Ravit Show.He spent two decades building and scaling data platforms at some of the biggest companies on earth. Now he is the CTO of WALT AI.His answer was simple. Even the best resourced companies on the planet still struggle with data engineering. It is the bottleneck nobody talks about. Engineers build the pipelines but never reach the insight. Analysts have the questions but cannot touch the plumbing. Work gets thrown over the wall, and value leaks at every handoff.Ranjith calls this the chasm. He left to close it.A few things from our conversation that stuck with me.Data engineering used to be locked away. It needed huge teams, huge budgets, and armies of consultants. The way cloud opened up infrastructure, agents are starting to open up data engineering.Determinism matters more than people think. If the CEO asks the same question twice, the answer has to be identical. A model writing fresh SQL every time cannot promise that. That is the line between a demo and production.Tribal knowledge should not live in one person's head. Why you exclude Q2 returns should not walk out the door when an analyst quits. It should live in the system.And data quality is where most data projects quietly die. You can build the most elegant pipeline in the world, but if one number is wrong, trust is gone. Once trust is gone, nobody uses the platform.The part I keep thinking about. Tools give you capability. They do not give you the outcome. The outcome still takes people and months of work. That gap is the real problem, and it is the one Ranjith is now building to solve.Worth your time if you care about where data engineering is heading.#data #ai #dataengineering #walt #theravitshow
Topics covered in this episode: The trusted-publishing debate: how to do it right vs. why you shouldn't trust it JupyterLab 4.6 and Notebook 7.6 are out! Tau – new small, readable terminal coding agent Django Tasks and Django 6.1 Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Consulting from Six Feet Up Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Calvin #1: The trusted-publishing debate: how to do it right vs. why you shouldn't trust it https://snarky.ca/how-to-publish-to-pypi-using-github-actions-securely/ (Brett Cannon) and https://blog.yossarian.net/2026/07/07/You-shouldnt-trust-trusted-publishing (William Woodruff) Trusted Publishing (PyPI's OIDC-based auth scheme, also now used by npm, RubyGems, crates.io, NuGet) replaces long-lived API tokens with short-lived, auto-scoped credentials tied to CI/CD machine identity. Yossarian's post: it's purely an authentication mechanism between a machine identity and a package — it says nothing about package safety or quality. PyPI deliberately avoids any "verified/trusted" badge for it, unlike its verified-URL checkmarks. Same logic applies to PyPI attestations: anyone can sign with any machine identity they control, so an attestation's presence isn't itself a trust signal. Bottom line from that post: don't confuse "trusted" (machine-to-machine) with "trustworthy" (human judgment about the package). Snarky.ca's companion piece is more practical: given GitHub Actions compromises in the news, the real fix is 3 concrete steps — run zizmor to lock down workflow permissions/checkout credentials and pin actions to commit hashes, adopt Trusted Publishing to eliminate stored PyPI tokens, and require manual approval via a GitHub environment before any publish job runs. Takeaway for listeners: Trusted Publishing is good hygiene for how you authenticate to PyPI, but it's not a substitute for securing your CI pipeline itself — or for actually vetting the packages you install. Michael #2: JupyterLab 4.6 and Notebook 7.6 are out! Michał Krassowski's rundown - a chunky minor release: 68 features, 97 bug fixes, 95 contributors, one of the biggest ever. Scratchpad console (Notebook 7.6 headliner) - a console next to your notebook sharing its kernel, for throwaway experiments. Ctrl+B. Jump to last-edited cell - new commands hop through recently edited cells. File browser glow-up - Date Created column, editable breadcrumbs with Tab-completion, and Open in Terminal. Debugger - sources open in the main area, floating step/continue overlay, live kernel-sources filter. Custom layouts (Lab) - activity bar top/bottom, draggable panels, four-way tab splits, per-panel Ctrl+scroll zoom. ~5x faster extension builds - webpack → Rspack, and jupyter-builder means no full Lab install needed to build extensions. Keyboard/a11y - add shortcuts from the UI (no JSON), Find & Replace in Edit menu (Ctrl+H). Calvin #3: Tau – new small, readable terminal coding agent Tau – new small, readable terminal coding agent (Python 3.12+), built as both a working tool and a teaching project for how coding agents work under the hood Install via uv tool install tau-ai, pipx, or pip; ships a tau CLI Three-layer architecture: tau_ai (provider-neutral model layer) → tau_agent (reusable "brain": messages, tools, events, loop) → tau_coding (CLI/TUI, file & shell tools, sessions) Supports OpenAI, Anthropic, OpenAI Codex, OpenRouter, Hugging Face, and custom/local OpenAI-compatible endpoints Built-in tools (read/write/edit/bash), durable JSONL sessions with resume/branching, project instructions via AGENTS.md, and context compaction Core harness is UI-agnostic — same brain can power the TUI, print mode, or a custom frontend — usable as a standalone library too Michael #4: Django Tasks and Django 6.1 Django 6.0 finally ships first-party background tasks (django.tasks) - out of Jake Howard's DEP 14, accepted May 2024, after two decades of everyone bolting on Celery/RQ/Huey. It's an API, not a worker. Django handles task definition, validation, queuing, and result storage - it does not execute them. You bring the backend. The default backend traps people. ImmediateBackend runs tasks inline on the request thread and blocks until done - so out of the box .enqueue() backgrounds nothing (a 5-second task means a 5-second response). The other built-in, DummyBackend, runs nothing at all. Both are dev/test only. Nice API otherwise: slap @task on a function, call .enqueue(), get back a TaskResult you look up later by id - with async twins like aenqueue(). Gotcha: args and return values must survive a JSON round-trip, so a tuple sneakily comes back as a list. The community local backend to know: django-tasks-local by Chris Beaven (SmileyChris). A ThreadPoolExecutor backend that gives real background threads with zero infrastructure - no Redis, no Celery, no database - plus a ProcessPoolBackend for CPU-bound work → github.com/lincolnloop/django-tasks-local Its catch: results live in memory, so pending tasks vanish on restart or deploy. Great for dev and low-traffic production; for persistence, drop to Jake Howard's django-tasks (DatabaseBackend + worker command). Extras Calvin: Fixing the dictionary with Python 3.14 — Hugo van Kemenade stumbled on - and got fixed - a markup bug in the OED's own citation of a 1706 use of the pi symbol. Michael: Bunny DNS is now free Jokes: What's the object-oriented way to become wealthy? Inheritance To understand what recursion is... You must first understand what recursion is 3 SQL statements walk into a NoSQL bar. Soon, they walk out They couldn't find a table.
Curious how AI can query your enterprise data without moving it or making things up? AWS and Teradata break down a trustworthy analyst agent built for real production use.Topics Include:Neha Wadhera (AWS) introduces Trinath Yarlagadda and the Teradata Analyst AgentEnterprise AI data prep is costly, stalling most orgs at experimentationAgent answers plain-English questions via traceable SQL, zero data movementBarrier removal drives 3.7x ROI and 40% productivity gainsHealthcare demo setup: hospital COPD readmissions, ~$10K cost per incidentFour design principles: traceability, no data movement, deterministic-first, governance as codeMain orchestrator agent plans, writes SQL, calls Teradata MCP serverComplex questions escalate to a context-isolated data scientist agentBuilt on Claude Agent SDK, running Bedrock Claude Sonnet/Haiku/OpusLive demo: COPD readmission rates explored through iterative agent reasoningDelegation demo: data scientist agent runs in-database analysis, surfaces factorsPre/post tool hooks log every step and cost to CloudWatchAgent hosted on Amazon Bedrock AgentCore, fully serverless and scalableAgentCore delivers runtime, memory, identity, and observability out of the boxLessons learned: guardrails first, deterministic ops, multi-agent registry, ongoing evaluationParticipants:Trinath Yarlagadda – Principal Solution Architect – Agentic AI, TeradataNeha Wadhera – Sr Solutions Architect, Amazon Web Services See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/
Parce que… c'est l'épisode 0x31B! Shameless plug 19 septembre 2026 - Bsides Montréal 20 au 26 septembre 2026 - BruCON 13 novembre 2026 - DEATHCon 16 au 19 novembre - European Cyber Week 1 au 3 décembre 2026 - Forum INCYBER - Canada 2026 24 et 25 février 2027 - SéQCure 2027 Description Un an de progrès vertigineux Dans cet épisode spécial de Polysécure, l'animateur reçoit Mickael Nadeau pour discuter de l'évolution de l'intelligence artificielle entre deux éditions du concours NorthSec (NSec). Le point de départ est frappant : il y a environ un an et demi, lors de l'avant-dernier NorthSec, les participants commençaient tout juste à expérimenter l'IA en mode « full force », mais les capacités restaient limitées à un simple chatbot capable, au mieux, de générer un script rapide pour automatiser une tâche SSH. Aujourd'hui, le paysage est méconnaissable : les agents autonomes, les architectures de type RAG et les compétences (« skills ») spécialisées ont complètement transformé la façon dont les équipes offensives opèrent. Le point de vue privilégié d'une entreprise défensive Mickael explique que son entreprise, active en cyberdéfense, occupe une position d'observation unique. Grâce à un « mode surveillance » qui laisse les systèmes clients actifs sans bloquer les attaques (plutôt que de les stopper), l'équipe peut observer en temps réel les nouveaux outils et méthodologies utilisés par les équipes de pentest lors de leurs tests d'intrusion. Cette visibilité lui permet presque de dresser un classement informel des équipes selon leur niveau, un peu à la manière d'un « wall of shame » inspiré de DEF CON : certaines équipes s'appuient sur leur réputation sans se renouveler, utilisant des outils et méthodologies dépassés, tandis que d'autres repoussent les limites grâce à une utilisation avancée de l'IA. Du couteau suisse au tireur d'élite spécialisé Un des axes centraux de la discussion porte sur le passage d'agents génériques (le « couteau suisse ») à des sous-agents hautement spécialisés. Mickael illustre ce changement avec l'exemple des injections SQL : au lieu d'utiliser un outil bruyant comme SQLMap qui teste systématiquement toutes les possibilités, on peut désormais entraîner un agent dédié, nourri de toute la documentation existante sur les failles SQL connues, capable de reconnaître un scénario familier et d'aller directement à la solution avec un minimum de requêtes. Le résultat est un attaquant beaucoup plus silencieux, difficile à repérer avec les mécanismes de détection traditionnels basés sur le volume de bruit réseau. Ce changement de paradigme complique considérablement le travail défensif. Les équipes de sécurité, historiquement habituées à trier les faux positifs et à repérer les comportements bruyants et erratiques, doivent maintenant chasser des signaux beaucoup plus subtils, dans une fenêtre de temps réduite à quelques secondes plutôt qu'à plusieurs jours. L'apprentissage machine au service de la défense Contrairement à l'approche probabiliste des grands modèles de langage utilisés côté offensif, l'entreprise de Mickael s'appuie depuis longtemps sur l'apprentissage machine plus déterministe pour détecter les menaces. Ce socle a permis d'atteindre un certain niveau de précision, mais un nouveau défi émerge : la gestion des modèles frontières et des agents eux-mêmes. Beaucoup de clients sont, selon ses mots, « hystériques » face à cette nouvelle réalité, car la majorité des solutions du marché n'ont pas encore intégré une stratégie pour encadrer les agents IA, que ce soit du côté de leurs propres outils ou de ceux fournis par des tiers (pare-feu, plateformes de sécurité, etc.). Des gains opérationnels concrets Au-delà de la détection, les agents transforment aussi les tâches opérationnelles quotidiennes : renouvellement de certificats, requêtes de cache, diagnostics de configuration client complexes — autant d'opérations qui pouvaient auparavant nécessiter plusieurs appels et une expertise pointue peuvent maintenant être gérées via une simple conversation avec un agent, même depuis un téléphone. Mickael raconte un cas concret où un agent a diagnostiqué un problème de pare-feu propre à un client en quelques requêtes, un problème qu'il n'aurait pas su résoudre lui-même immédiatement malgré sa connaissance approfondie du produit. L'avenir des services MDR et la montée des benchmarks La conversation aborde également l'impact sur le marché des services MDR (Managed Detection and Response). Selon Mickael, l'automatisation permise par les agents va permettre à ces équipes de gérer un nombre de clients beaucoup plus important avec les mêmes ressources humaines, tout en relevant le niveau minimal de qualité du secteur. Il prédit également l'émergence de benchmarks formels pour évaluer non seulement les modèles d'IA, mais aussi les fournisseurs de services de sécurité eux-mêmes — un peu comme les leaderboards actuels qui comparent les performances des différents modèles de langage face à des scénarios d'attaque réels. Ces chiffres deviendront un argument de vente central, remplaçant progressivement les discours marketing par des données concrètes et vérifiables. Vers l'automatisation de la conformité Enfin, les deux interlocuteurs discutent du potentiel des agents pour transformer les processus de conformité (comme PCI-DSS), aujourd'hui perçus comme une lourde corvée administrative. Mickael envisage un futur où des « skills » standardisés permettront d'appliquer automatiquement les bonnes pratiques de conformité, réduisant la tentation de sauter des étapes. Il conclut en mentionnant qu'il prépare une présentation avec démonstration en direct pour partager plus en détail cette évolution avec la communauté, dans le but de sensibiliser les organisations à moderniser leur posture défensive à l'ère des agents IA. Collaborateurs Nicolas-Loïc Fortin Mickael Nadeau Crédits Montage par Intrasecure inc Locaux virtuels par Riverside.fm
If you've ever wished you could flip a switch and instantly generate more leads, this episode is for you. Guy Bauer explains why "getting leads" is the wrong goal, and why leads are actually the byproduct of a disciplined, long-term marketing system. Using Umault's own experience, he shares why it took more than a year to generate the agency's first inbound lead, introduces David C. Baker's water pump analogy for building marketing momentum, and explains why consistency beats quick fixes every time. Then, Guy delivers on the promise with two practical ways to generate leads faster: targeting the 5% of buyers who are already in-market through bottom-of-funnel marketing, and creating one bold, opinionated video that earns attention and fills your pipeline. If you're tired of "get leads fast" marketing promises and want a more realistic framework for building demand, this episode is for you.
Lords: Chall RT-55J https://samarantes.neocities.org/ https://metroidconstruction.com/hack.php?id=878 Topics: Remembering the Dynowarz Instagram private server, and social media thoughts every Mastodon user had already Why don't CPUs do analog arithmetic? Leaves, by Ursula LeGuin https://www.poetryfoundation.org/poems/148293/leaves-5bd9e153d78b2 Microtopics: DogTroid, the first Metroid ROM hack to star a dog. Self-finishing games. Sparkling water: it's like water but a lot more interesting. Caffeine Free Diet Pepsi. What sodas foam the most in response to a mento. Foam persistence. Foaminess reactions of a mento on various vintagesn of Diet Coke. Jolt Cola: all the sugar, twice the caffeine, three times the foam. A can of soda that's safe to open in a bathroom stall. Artisanal Coca Cola cans on Etsy that finally allow you to open a can of soda in a bathroom stall without anyone realizing you're drinking a Coke on the toilet. Flushable soda cans: as flushable as a flushable wipe. Why do toilets have pee traps when the pee deserves to be free? The second worst game in your NES collection. Desert Chrome title screens. Playing as a little spaceman until you enter the dinosaur mech. Shooting some alien brain or maybe a heart. 8bitnintendo.science How to pick what video games to buy in the late 1980s. How Metroid improved on the maze-with-keys genre. Nanosaur. A velociraptor with a techno-backpack. A game that is exhilarating and scary and endless when you're a child turning out to be a twenty minute trifle when you're an adult. Trespasser (1998) Simulation dinosaur emotions but you can't find a good balance so you just permanently lock them all to angry. Installing a violent action game about dinosaurs in the elementary school computer lab because dinosaurs are technically educational. Revisiting games that perplexed you as a child. Playing bad video games because no matter how bad they are they're still better than going outside and talking to people. The one where Kirby eats a car. The Roblox-like games you can find by logging into third party Minecraft servers. Starting your own Pixelfed server. Following the only person you know on Mastodon. Bluesky's recommendation algorithms recommendeding you nothing but bots. Inventing Internet forums from first principles. When your brain makes up garbage and you need a void to shove it into. Doing your part to make AI worse. CSS Crimes. How to find people to follow on Cohost. Signing up for a social media site and looking around and realizing you doing know anyone here. Mining and reposting. The ongoing maintenance requirements of running a Mastodon server. Ways you can interact with your family that only work if you have an iOS developer in the family. Off-box SQL database backup. Hypothetical IRC servers that support chat logs. Hardware random number generation. The pot of boiling water every Intel CPU draws thermal noise from for random number generation. The most commonly used analog computers in 2026. The market forces that led to semi-modular synthesizers being available for $300. Using your analog CPU to run a million instances of Lunar Lander at once. A rustic summer retreat ranch in the hills of Napa Valley, California. What makes us perceive the gradient of identities over the course of someone's life as a single identity. The most ephemeral thing possible. Musing for a few sentences and then thinking "hmm, I could put some line breaks in here and then publish it." Musing about the nature of identity, with line breaks. Topic Slingers. Topics: throw them around a lil bit. They love it. A lot of people don't know that.
In dieser Episode habe ich Prof. Dr. Johannes Schildgen zu Gast. Er lehrt an der OTH Regensburg. Sein neues Buch "Python für Ki- und Datenprojekte" besprechen wir und gehen auch auf seine weiteren (Hör-)Bücher ein zu SQL und Java Programmierung.
Discover how the massive scale of data operations moves behind the scenes at NEXT. In this episode of the Life at NEXT Podcast, we sit down with a data analyst who successfully transitioned from a four-year primary school teaching career into our Business Intelligence Systems (BIS) team. Learn how the BIS department acts as a central hub, managing millions to billions of rows of data daily to provide a single source of truth for the entire business. We also explore the practical use of AI as an efficiency tool in data development, the reality of building technical skills like SQL and Python in the corporate world, and how soft skills like stakeholder management apply across completely different industries. Plus, find out how the NEXT Sports and Social club helps teams build strong relationships and support mental wellbeing outside of the office!Don't forget to subscribe to our channel and hit the notification bell to receive updates on everything Life at NEXT!
Fredrik snackar shaders med Mårten Rånge. Shaders är små program som skrivs i ett C-liknande språk (GLSL), körs direkt på grafikkort, och ritar grafik (eller skapar ljud) på ens skärm med ofta chockerande lite kod. Mårten ger en introduktion till shaders och vanliga tekniker och begrepp, och det blir mycket grafik, snabb respons, iterativ matematik och programmering med underbart korta återkopplingsloopar och fokus på resultatet. Hur påverkar shadertänk vardagsutvecklandet? Mårten tror att det kanske gett honom mer fokus på att faktiskt lösa problem. Skulle shaders - för rätt personer - kunna vara ett bra första programmeringsspråk? Mot slutet kommer vi också in på att bygga program för DOS och, inte minst, göra program som gör något häftigt och är så små det bara går. 300 bytes till exempel. Tänk så långt RAM skulle räcka om alla program låg i den storleksklassen … Ett stort tack till Cloudnet som sponsrar vår VPS! Har du kommentarer, frågor eller tips? Vi är @kodsnack, @thieta, @krig, och @bjoreman på Mastodon, har en sida på Facebook och epostas på info@kodsnack.se om du vill skriva längre. Vi läser allt som skickas. Gillar du Kodsnack får du hemskt gärna recensera oss i iTunes! Du kan också stödja podden genom att ge oss en kaffe (eller två!) på Ko-fi, eller handla något i vår butik. Länkar Mårten Øredev SQL server COBOL Neo Shader art Shaders TIC-80 - stödjer fler språk än Lua, och har en Webassembly-host så att man kan skriva i vilket språk som helst som kan kompileras till Webassembly Shaders för musik Pestis - crazy på ett bra sätt kring matematik och musik Shadertoy Fragment shader eller pixel shader Vertex shader GLSL - shaderprogrammeringsspråk Kodelife - IDE för shaderprogrammering Posh brolly - annan sida för shaders Fragcoord - ännu fler shaders! Bonzomatic - används för livekodande av shaders Sinusfunktioner Babels bibliotek Ray marching Ray marching-exempel på en sfär på Shadertoy Ray tracing Normal Dot product - eller skalärprodukt på svenska Diffust ljus Distansfält Fraktaler 3D-fraktaler Mandelbulb Clean code Fabrice Neyret Stötta Kodsnack på Ko-fi! ICQ Kodsnack 705 - om signalprocessing och annat Scratch Pacman i Shadertoy Arkanoid Rasterlinjer Mårtens 4 kb-minröjarspel Sointu - ett musikprogram av "en galen finländare" DOS Dosbox Dosbox-X 256-bytes-spel Wake up! 16-bytes-demo i x86-assembler med Matrix-aktig kod och musik (video) COM-program PICO-8 Fantasikonsoll Lua Strudel DJ_Dave och Switch angel Bytebeat Synesthesia Mårtens adventbloggserie om shaders Adventbloggserie för 2025 Bonuslänkar från lyssnare Kolla in shader-livekodnings-tävlingarna på diverse demopartyn, exempelvis finalen från Revision 2024 DOS-like - På tal om låtsasmaskiner och DOS Distansfält implementerade för några 3d-former av Inigo Quilez Distansfält implementerade för några 2d-former av Inigo Quilez Titlar Øredev sade just nej till mig Det jag gör på kvällstid Min primära hobby Crazy på ett bra sätt Jättemycket flops Pixelkoordinat in, färg ut Någon funktion här och där Iterativ matematik Ändlösa rum Resultatet är målsättningen Fyra flyttal Tiden ändrar sig Rakt ut från sfären Riktningen i tre dimensioner Diffust ljus En punkt i rymden Annorlunda patterns Var kom bergen från? Jag hittade 30 bytes till Din shader är optimal Var kommer grafiken från? Det ser ut som magi Tillbaka till rötterna Pseudoslumptal För rätt tonåring Musik, grafik, och spel på fyra kilobyte 240 bytes mindre Det som är minst är bäst Inga abstraktioner att lära sig Hon kodar på scen Andra typer av uniformer
Unlocking billions in cloud marketplace revenue. Subscribe to our Newsletter: https://theultimatepartner.com/ebook-subscribe/ Check Out UPX: https://theultimatepartner.com/experience/ This powerful panel discussion featuring leaders from Google, Tackle, and dbt Labs dives deep into the explosive growth of cloud marketplaces and the radical shift toward AI-driven go-to-market strategies. With hyperscaler backlogs nearing half a trillion dollars, the conversation unpacks how top-tier organizations are transforming their compensation models, aligning executive buy-in, and navigating the complexities of co-selling to capture committed customer budgets. From the rise of AI agents acting as metered SaaS to the essential operational investments required to scale marketplace revenue from 10% to over 50%, this session provides an actionable roadmap for software companies ready to dominate the 2026 partner ecosystem. https://youtu.be/LSj49f5FEII Key Takeaways Hyperscaler backlog commitments represent a massive, nearly half-trillion-dollar addressable market that completely changes the budgeting conversation. Successful marketplace selling requires complete executive alignment, right down to the CFO, and strategic adjustments like spiffing sales teams for marketplace transactions. The AI category is experiencing staggering 18x year-over-year growth, forcing companies to pivot toward an “agent-first” go-to-market model. Shifting from traditional channels to cloud go-to-market demands a multi-year, intentional investment in operations, people, and technology. System integrators are evolving into software companies as they build orchestration agents to manage fragmented, end-to-end workflows. Leveraging cloud commitments bypasses standard 12-15 month budget cycles, allowing for significantly faster deal closures and larger initial lands. If you're ready to lead through change, elevate your business, and achieve extraordinary outcomes through the power of partnership—this is your community. At Ultimate Partner® we want leaders like you to join us in the Ultimate Partner Experience – where transformation begins. Key Tags: Google Cloud Marketplace, hyperscaler backlog, cloud commitments, co-selling strategies, AI agents, metered SaaS, product-led growth, rev ops, B2B sales transformation, ecosystem shift, channel strategy, system integrators, Deal registration, private offer APIs, digital transformation, software procurement. Transcript: Insight to Revenue- The State of Cloud GTM [00:00:00] Dai Vu: These are all things everyone has to do to get to that first five to 10 deals, and then 10, 20, 30% of your business through Marketplace. [00:00:09] Vince Menzione: You can feel it happening. The ecosystem is shifting beneath us, the way Hyperscalers are partnering, how AI is remaking the channel and what it means to win in 2026. [00:00:21] Vince Menzione: Welcome to the Ultimate Partner Podcast. I’m Vince Menzi, own your host, and each week I sit down with leaders at the intersection of technology. Partnerships and outcomes. The voices shaping how ecosystems actually work. We talk about what’s real, what’s changing, and what it takes to lead in this era where the partner channel isn’t just part of the strategy. [00:00:43] Vince Menzione: It is the strategy because being in the room changes [00:00:46] John Janke: everything. Let’s start. [00:00:52] Vince Menzione: And we have an incredible session. The way that we wanted today to, to, to start the day up was like, let’s talk about what’s happening right now and let’s get three leaders in this space to come up and talk about the world and how it’s a rapidly evolving. So I want to invite to the stage dvu from Google is a great friend of Ultimate Partner. [00:01:14] Vince Menzione: Are you guys ready? Are you guys micd up already? Okay, good. Good. John Yanke, the CEO and Founder of Tackle, and Sean Todo, who is an incredible leader with DBT, but also an old friend of mine. We worked together on Microsoft Days. Good to see you gentlemen. Thanks Sean. Great to have you with us. [00:01:37] John Janke: They stuck me on the side ’cause they said I’d block the screen if I sat in the middle. [00:01:41] Shawn Toldo: You still block it a little bit. [00:01:42] John Janke: And that picture’s from like 1985. I, I, we do have to get that. I had way darker hair. It was, uh, 10 year, 10 years at a startup. Makes you turn white. [00:01:52] Shawn Toldo: Mine’s the exact same right now. So it’s all good. [00:01:55] Shawn Toldo: Mine’s AI generated. Yeah. [00:01:57] Vince Menzione: Well, you know, guys, I just took it all off at that point, you know, it’s like good. Yeah, but you lose enough of it. You pull it out over the years. Yeah. So, uh, some really exciting times. Uh, you, we gotta spend some time at you at our breakfast. That’s right. A couple weeks ago. [00:02:13] Dai Vu: A lot of folks here, too. [00:02:14] Vince Menzione: A lot of folks that are here were at that breakfast, and I thought we’d spend a few moments with you talking about all the exciting things that have been happening at, at Google. I mean the, yeah, the businesses just to, first of all, the numbers were house. Outstanding. Congratulations. [00:02:28] Dai Vu: That’s right. [00:02:28] Vince Menzione: Yep. [00:02:28] Vince Menzione: Really, some really great numbers. Commitments are off the charts. [00:02:32] Dai Vu: Yes. [00:02:32] Vince Menzione: Crazy off the charts. [00:02:33] Dai Vu: Yes. [00:02:34] Vince Menzione: Yes. Uh, and then there’s a lot happening in this little world called ai, which makes a ton of sense. Yep. I was critical about Google in the beginning because you had all the assets, but Microsoft leaned in first. [00:02:45] Vince Menzione: Uh, but now it’s like things have evolved, uh, quite a bit since those first days. Absolutely. In, in November of 2022. So, uh, take us through a little bit. Let’s, let’s go through [00:02:56] Dai Vu: it. Yeah. I could talk for quite a bit of time because obviously we came out next, yeah. At the end of April, and then we had our earnings announced, but shortly thereafter. [00:03:03] Dai Vu: But, but real quick on next, uh, for folks who attended, uh, you know, the way they framed, uh, the discussion was they showed this AI integrated stack, and that’s how they frame the keynote because we position ourselves as being the only vendor that provides this. Fully integrated stack from custom silicon all the way to the apps and agents. [00:03:23] Dai Vu: And a lot of the announcements were, were focused in those areas. Um, uh, I won’t go through the, the long list, but I think the big ones coming out of next were, uh, certainly the eighth generation TPU we announced, so we actually split this into two specialized chips for training and inference. Uh, so that’s, uh, that was a big piece. [00:03:41] Dai Vu: Uh, but the big one that we announced was this, uh, Gemini Enterprise. Uh, agent platform. So think of it as the comprehensive platform for companies to basically build scale, govern and optimize their agents. And of course, once they have that, they can bring that into, uh, what we call a Gen Gemini enterprise app, which is really the front door for AI for. [00:04:03] Dai Vu: All customers and all employees to manage a mix of agents, um, as part of their daily workflow. And, uh, and a big part of it is, you know, certainly they’ll have some custom agents, but we think a lot of the agents will come from the ecosystem. And obviously there was a big announcement around what we’re doing there. [00:04:21] Dai Vu: Um, and in fact, one of the things that’s interesting is this shows the evolution of, of marketplace in our, in our partnership, which is we’ve taken a lot of the marketplace experience. And brought it into Gemini exp uh, Gemini Enterprise app, right? So search, discovery, uh, the ability to invoke agents, uh, in context. [00:04:39] Dai Vu: I think that’s gonna be very powerful as we think about the evolution, uh, of, of go to market. And then the last thing maybe I’ll highlight is this, um, is. 750 million, uh, investment fund that we’re gonna drive with the broad partnership. So this cuts across all partner types, global system integrators, uh, uh, you know, AI, pure plays, uh, ISVs, uh, the big management consultants as well, uh, because we recognize that partners are gonna be critical to drive business transformation with our end customers. [00:05:08] Dai Vu: So we’re investing around things like. Technical enablement, access to our product teams, access to our FDE for deployment engineers, and then a lot of incentives to drive usage and deployment. So, um, so a lot of, a lot of activity and obviously the ecosystem’s gonna be very critical for us to drive that impact’s. [00:05:25] Dai Vu: Fine. And the last thing, I know we’ve going on and on fine, but the last thing I’ll just mention is just on the earnings announcement, uh, Vince touched on the backlog, so people have been tracking Yeah. Two quarters ago. We were 155 billion on the backlog, and then a quarter later we were 240 billion. And then in the last quarter, just recently, 462 billion. [00:05:46] Dai Vu: So obviously that’s a, a massive signal of customer intent, but more importantly, it’s a, it’s, it’s a addressable market for this ecosystem to go after as well. [00:05:54] Vince Menzione: Yeah. Almost a half a trillion dollars. Yes. In commitment. So a lot, a lot of reason why we should be on the marketplace. [00:06:01] Dai Vu: Absolutely. Absolutely. [00:06:02] Vince Menzione: Um, each of these gentlemen have some things to talk about as well, about their companies and the exciting things that have been happening. [00:06:08] Vince Menzione: I’m gonna start, John, I’m gonna start with you because Tackle has, has transformed quite a bit since the last time you were on stage with us. I thought maybe introduce the company. Take us through the transformation and then we’re gonna do the same thing with Sean with his organization. [00:06:21] John Janke: Yeah. Thanks. Uh, thanks Vince. [00:06:23] John Janke: Great to see everybody. Uh, John Yanke, GM of Tackle at App Direct. So the big news there is Tackle was acquired in Q4 by a company called App Direct, and I think the why behind this app, direct Powers, marketplaces, they run 400 marketplaces around the world for telcos, for ISVs, for system integrators, channel partners. [00:06:42] John Janke: And we were talk like, when you build a marketplace and diagnose this, stocking the shelves is actually really hard. Uh, and we were talking to them about how could we connect the dots between the hyperscaler marketplaces, the iscs we support, and these additional routes to market. Uh, and that became more strategic and we ended up joining forces in December. [00:07:00] John Janke: And since then, the other part that’s really hard when you build a marketplace is how do you generate demand? Uh, so four weeks ago we acquired a company called Partner Stack. And Partner Stack does affiliate content. They have an affiliate content platform that allows you to connect with 150,000 content providers to be able to start to tell your story to drive leads to. [00:07:23] John Janke: Marketplace. So we think there is a tremendous opportunity to continue. We’re in the earliest days. I think the, you know, Jay, I was with Jay at Channel Partners a few weeks ago and he is like, we under called it, he didn’t say this on stage yesterday, but he is like, uh, the 82% growth. He’s like, we totally under called it. [00:07:40] John Janke: Uh, and I think just listening to dies commit level increase mm-hmm. Reinforces the fact that we’ve under called it. But I also think we’re at this tipping point in the market where all of the new capabilities coming out, we have to all rethink our better together stories. So I think the challenge to all partner leaders, it’s like, how do we. [00:07:58] John Janke: Figure that out. So it’s, it’s a, it’s a fun time. As we transform the way we worked. We wrote the first helping people kind of list, launch and sell through the marketplaces. And now to be able to take that to the next level to hopefully unlock the next a hundred billion of marketplace throughput. [00:08:13] Vince Menzione: And are we at a hundred billion? [00:08:15] Vince Menzione: ’cause that was the number, right? [00:08:16] John Janke: I mean that’s, that’s, that’s the number that’s talked about. I mean, we’re seeing the data signals we see, I mean, we will process 20 billion plus this year. Uh, and that number’s growing faster than Jay’s stated number. So I think we’re excited to see where this year lands. [00:08:30] Vince Menzione: We’ve come a long way from three years ago and we all got on stage and talked about marketplaces together. Right. It’s been, it’s been amazing. And then Sean, let’s talk about DBT. You’ve had some excitement. I know some things maybe we can’t even talk about yet on stage. [00:08:43] Shawn Toldo: Uh, yeah, go ahead. [00:08:44] Vince Menzione: No, I was saying I, I could, I’ll pre-announce things, but No, I’m just, uh, tell, tell us about DBT for those who don’t know in the room, sure. [00:08:49] Vince Menzione: Mean Yeah, that might help. [00:08:51] Shawn Toldo: So, uh, Sean Todo, I lead the partner business at DBT. I’ve been here about 18 months. Um, DBT really started as an open source tool. That help data engineers be successful in SQL transformation with cloud data warehouses? Right. And so back even to the Redshift days now into what I would call more the BigQuery, snowflake, Databricks fabric led days, um, DBT is the tool of choice amongst the data engineering community in terms of how they wanna drive SQL transformation. [00:09:21] Shawn Toldo: And so more recently, we kind of jumped into this kind of paid world. Which is why we needed to bring in additional experience leadership around go to market product, sales, et cetera. And so when I walked in the door, one of the things I noticed really quickly was we were running on AWS, which was great. [00:09:40] Shawn Toldo: We were doing some AWS marketplace stuff. We were running on Azure in Europe only. And one of my first strategies was we have to be everywhere, right customer. We have to meet customers where they are. And so we, uh, made some major investments to be on Google Cloud platform to then be able to really take advantage of marketplace, to then really be able to take advantage of the co-sell opportunities that exist in the field from a day, day-to-day AI perspective with Google. [00:10:07] Shawn Toldo: And it has been a hell of a ride. We launched on, uh, Google Marketplace in July of last year. We went to Google next and we were Google Partner of the Year. Wow. For data and analytics in a very rapid way. We’re now in three, uh, data centers around the, the world. So we’re here in the us, we’re in Frankfurt, we’re in uh, uh, UK as well. [00:10:30] Shawn Toldo: And so it’s been a pleasure to work with D and the broader team. Because the enablement we’ve had and the support we’ve had from that group has really helped our growth be up and to the right. The data point I would give is that when I walked in the door, we were 10% of our business from an A RR perspective was transacting through marketplace. [00:10:48] Shawn Toldo: Last quarter we cracked 40%. Whoa. We will be at north of 50, uh, next quarter. [00:10:53] Dai Vu: Wow. [00:10:54] Shawn Toldo: The other piece that Vince was talking about is we’re getting ready to merge with a company called Five Tran. And so there will be a new company name at some point down the road. Uh, pay attention on June 1st for a public announcement around that merger. [00:11:06] Shawn Toldo: Uh, but we’re really looking forward to what we’re gonna be able to do with folks like DI and the Google team as well as others in the ecosystem. Um, ’cause I think in this data world that we’ve played for so long. This trusted foundational element of data and what it’s gonna mean to context in the AI world. [00:11:23] Shawn Toldo: We’re in a very interesting place to really continue our growth rate at a high level. [00:11:28] John Janke: Yeah, that maybe just a comment something there. Start there. I think we, we used to hear people say we wanted to be strategic with cloud, go to market and get to say 10 or 20% of revenue. I think this like 40, 50%. Yeah. Th that’s where people are setting the bar these days. [00:11:43] John Janke: Yeah. So the numbers are getting really crazy. Yeah. Uh, and people are showing up and being like, I have to go big. Mm-hmm. So a huge change over the last few years. [00:11:52] Vince Menzione: Yep. What’s the experience you’re seeing as well? I mean, it, it was a huge amount of buzz at next. [00:11:57] Dai Vu: Yeah. I mean, so interestingly, um, you know, typically when, when people get started on the, on the marketplace in Cosal journey, I always try to caution them and say, this is, uh, this is like a multi-year. [00:12:07] Dai Vu: Yeah. Uh, process. You have to be very intentional. You have to invest. It’s not gonna be a thing where you just list and, and, and, and, and, and sort of this channel opens up. So in some ways, Sean is describing an acceleration that is not common, right? Uh, so they’ve done, we’ve done some amazing things together and we hope to keep that acceleration going. [00:12:22] Vince Menzione: What does that require, by the way? Is it engineering resource? I mean, there’s, I talk about executive commitment and maniacal focus. Yeah. But it’s all those things, right? [00:12:29] Shawn Toldo: Well, all of it. But we went to a QBR in Austin, and I put up a slide and I said, we have to do this. And everybody in our ETE agreed. So when you have a chief financial officer that’s bought into the partner business. [00:12:43] Shawn Toldo: Yeah. And I guess qualifying coming into this role at this company, I qualified the C-level staff. Uh, like are they really serious about partner or not? And it’s one of the reasons I took the role. So I think executive commitment was one thing. I think the second thing is we were really well supported, um, by the Google team across the board, right? [00:13:02] Shawn Toldo: Yeah. So folks, Indy’s team that we would work with regularly on, these are the things you need to do to have an effective marketplace offering. Here’s what you need to do operationally with folks like John and team and others that are in the market, right? That helped us a ton to be able to scale. And then the other thing that we did is we changed comp. [00:13:20] Shawn Toldo: So from our VP of sales levels down, we have a 5% kicker for everything that goes through marketplace. [00:13:26] Vince Menzione: Hear [00:13:26] Shawn Toldo: that everyone. So as soon as we incented the sales team, I love that, right? We, we created the foundation on the partner side, but then from top down on the sales side, they were all in. And as a result of that, the question would become, okay, which marketplace stage two sales cycle are we gonna go use? [00:13:42] Vince Menzione: Yeah. [00:13:43] Shawn Toldo: Who’s the right partner to go partner with? And then my team is reaching out to make sure that co-sell connection happens. [00:13:48] Vince Menzione: That is such a best practice, Sean, to, because there is, as a seller out in the field and we talk about, you talk to John, talks about rev ops all the time. But getting rev ops eng getting the field engaged in the right way. [00:14:01] Vince Menzione: ’cause it feels like it’s more work for them. ’cause they have to think, they have to have more conversations with their customer about their cloud commitments and things like that. Mm-hmm. And then getting them incentive to do the right things. The right behavior. [00:14:12] John Janke: Yeah. It’s a strategy process. People, technology problem. [00:14:17] John Janke: Yeah. It’s not just some flip API automation, go list something if you don’t like that top down view. I think the other thing. Like there’s a, there’s a theme in startups where VCs fund second time founders. I think Sean and team have done this before and they took a lot of learnings over the years and reapplied them, which I think helps them go faster. [00:14:36] John Janke: It’s like that second time. Yeah. Second time cloud go to market Founder theme. [00:14:41] Vince Menzione: Yeah. Yeah. Um, so we could talk about the platform and all the changes there on the. The, the commitments and everything. Mm-hmm. Uh, what separates ISPs generating real incremental revenue on your, in your marketplace? What, what do you see? [00:14:58] Dai Vu: Yeah, so I mean, I, I think there are a couple things. Number one is, uh, the, the foundation has to be, uh, this better together story, uh, with Google Cloud. Um, so this idea that what, you know, what do you bring, what does the Google platform bring and how does that drive impact with customers? And I think this is the reason why Sean and DBT Labs has been very effective. [00:15:16] Dai Vu: ’cause our field recognized they, they can recognize that better together story and communicate it to their customers. So I think that’s the foundation. For everything. Right. And I think as you get started, uh, you know, we do tell partners that they probably need to lean in a little bit, uh, in terms of focus, uh, you know, pick a vertical, a customer segment, um, you know, a geography where they’re particularly strong and, you know, get that momentum going. [00:15:39] Dai Vu: And once you do that, the field knows about it and starts to pull you into deals. Um, so I think that’s the other big opportunity. And then the other thing I just mentioned. Which, uh, the panel already touched on, which is be very intentional around all the things you need to do to invest. Whether it’s like, uh, you know, the business functional alignment, uh, the policies around like, uh, pricing and, and comp, uh, making sure you have the operational capabilities. [00:16:02] Dai Vu: These are all things everyone has to do to get to that. First five to 10 deals, and then 10, 20, 30% of your business through marketplace. And not to, not to top you Sean, but our very top partners are driving 80 to 90% of their business on marketplace. And in fact, some of these partners are actually only marketplace first, uh, uh, because they started out that way. [00:16:21] Dai Vu: Obviously it’s the bigger challenge if you have an existing channel, you’re trying to shift that. But, uh, the aspiration to be more marketplace focus, uh, is up there. [00:16:28] Shawn Toldo: So I just set a new goal for the business plan for me. So that’s exciting. I love it. Looking forward to seeing you in six months on that. [00:16:35] Shawn Toldo: It’s good. [00:16:36] Vince Menzione: I love [00:16:37] Dai Vu: it. Work together on that. [00:16:38] Vince Menzione: Well, di I’m just gonna add, add this because I, I got to see operationally with some of the things you do. Mm-hmm. You, you have an overlay organization. [00:16:45] Dai Vu: Yes. Yes. [00:16:46] Vince Menzione: And so you put accelerants in place within your own organization Yeah. To drive the ISVs into the, into the lines of business. [00:16:54] Vince Menzione: Right. You have, you, you do some of that to accelerate. [00:16:57] Dai Vu: Yeah, I mean, I think, I think this is somewhat unique. I don’t, I don’t wanna speak to the other [00:17:00] Shawn Toldo: hyperscalers, [00:17:01] Dai Vu: but we do have, um, uh, you gotta know the field roles, right? [00:17:04] Shawn Toldo: Yeah. So [00:17:04] Dai Vu: obviously at Google Cloud in the regions, we have, uh, ISV sales specialists who are effectively quoted on marketplace revenue, right? [00:17:12] Dai Vu: So they’re a hundred percent focused on that. And, uh, in addition to that, uh, we also have these, uh, co-sell teams, partner teams where, you know, opportunistically if there’s an opportunity, uh, in a, in a, in a particular area. This team is responsible for connecting the regional sales leadership, uh, the regional, uh, sales teams with, with the partner on the opportunity. [00:17:32] Dai Vu: So there’s a lot of things we’re doing to sort of accelerate that. And of course, the foundation for all this is, you know, our, our, you know, registering deals. And as you definitely get started on that, it’s very important to be very mindful around when you register deals. Uh, be very clear around what the ask and the engagement is with the field reps. [00:17:51] Dai Vu: But once you have that going and get the right rhythm, it becomes sort of a natural way to sort of register all your deals and get that engagement. And then, um, and then maybe the last thing I would say is it isn’t always the sales specialists. It’s, you know, the FSR, our field sales rep as well as our customer engineers are also very motivated. [00:18:08] Dai Vu: To work, uh, with, uh, with our partners because they know that this, you know, whether it be solution completeness or it’s part of a bigger workload or helps unlock greenfield opportunity, they really are motivated to engage with the partners. [00:18:21] Vince Menzione: Nice. [00:18:22] Shawn Toldo: Yeah. I’ll just add, I’ll just add to that statement too. I think, um, it’s one thing to have a story as it relates to. [00:18:30] Shawn Toldo: Google Cloud and what you do with marketplace. It’s another thing to have a story in terms of how you impact data and analytics in our world. And there’s a set of specialist sellers inside of Google mm-hmm. That really care about us because we drive a lot faster consumption of big query. And our ability to tell that story across the world effectively has really created a pull now. [00:18:54] Shawn Toldo: And so I, I would say it’s almost, you know, back to, you know, being 12 years at Microsoft and watching kind of that. Phase and how that went. As we went to the cloud and we picked specialty areas, um, Google is doing that as well and they’re doing it extremely fast in a very, very productive way with partners. [00:19:12] Shawn Toldo: And so, you know, I’ll get comments from like Levi who runs west in north region for us, and he’s a, he was at Google next and he was like, I, I gotta, I, I just gotta go to bed. I’m tired. Like we wore him out over two days with their sales team and gave him a host of follow ups and actions related to specific sales areas as well as specific accounts. [00:19:34] Shawn Toldo: And I think that’s the other thing that, um, Google’s done a good job of, but we’ve pushed and we’ve had to work really hard to earn that seat at the table. To help make those people successful from a comp perspective inside of Google as well. [00:19:45] John Janke: Yeah, and this is a huge failure zone for partners with the clouds because they think enablement’s a one and done thing. [00:19:51] John Janke: Like I did a training for the field and I told them the better together story. That doesn’t work. Like you have to literally. Have consistency around this message every day. Oftentimes you need experts who can partner with your reps to give them the confidence. ’cause they may be able to ask the first line question, but someone asks a follow up and they fold up ’cause they know your product. [00:20:11] John Janke: That’s right. They don’s don’t understand all of the nuances of Google and the clouds and the questions that may come back. But if you do that well, it is a huge unlock. [00:20:20] Vince Menzione: Talk about the coaching you provided on the tackle side of that as well and kind of helping. Through this maturity model? [00:20:26] John Janke: Yeah. I mean we, we, over the years, I mean we started as a pure SaaS company and over the years our customers would consistently ask us for more help and we would struggle to figure out how to do that, and we had to invest in services and we actually acquired a company. [00:20:42] John Janke: Five years ago now, that was the foundation. Aaron Feiger, who’s in the room. The core consulting was the foundation of our services business. And that continues to evolve with us. And you know, we see customers at scale saying, I wanna operate my cloud, go-to market really consistently, and I want you to do all the backend operations so my teams can be outselling our products, selling the better together value with Google and others, and not have to figure out how to run the machinery. [00:21:09] John Janke: So we’ve invested a lot there. We have services around strategy, like how to help people think about their business strategy and translate it into a better together story and able to get executive buy-in. And then we have coaching, which is really a phone, a friend, because I think these things get complicated. [00:21:24] John Janke: And I had a customer who was doing the largest deal in their company history. It was the end of the quarter and it was Friday, and they’re like, this is going to be the most complex transaction we’ve ever done and we have no idea how to do it. Our team gets on the phone with them, they work through, what are you selling? [00:21:40] John Janke: How are you selling it? Is your listing set up the right way? Can we actually create all the offers? In a way you have confidence to execute. ’cause those are failure modes. You try to build a cloud, go to market business, and you mess up the largest deal in the company. On the last day of the quarter, uh, that’s something you can’t recover from. [00:21:55] John Janke: So we try to really wrap support around our customers to help them have the confidence to grow. [00:22:02] Vince Menzione: Di you’ve seen tremendous growth in marketplace. Mm-hmm. We don’t publish the numbers specifically. Yeah. We kind of try to figure it out on the back end, but [00:22:09] Dai Vu: Yep. [00:22:09] Vince Menzione: I know you’re accelerated. Your, your marketplace numbers are astounding. [00:22:13] Dai Vu: Yes. I can share some numbers, if that’s [00:22:15] Vince Menzione: okay. Please. Yeah, let’s go. [00:22:18] Dai Vu: So, um. I would say that for a few years now, we’ve been talking about growth. So we’ve been consistently, uh, you know, north of a hundred percent year over year growth. Uh, for the last few years we’ve been processing, uh, what I say, uh, billions of dollars, uh, annually and, uh, uh, millions of transactions. [00:22:36] Dai Vu: And again, that’s for a few years now. Now for 24 to 25, that full year we also doubled. Wow. Uh, which is, uh, which is amazing when you think about the scale in which we operate. But more importantly, if you look at specific category areas, right? So, you know, historically, marketplace has always cater to, uh, those solution pillars that are tied to cloud migrations, like, uh, like security and data and analytics. [00:22:59] Dai Vu: And those continue to be very strong areas for us. But the biggest growth area is, uh, is in the areas of business app. So obviously, you know, the, the ServiceNow workday, uh, Salesforce of the world, as well as the AI category. So one number that we threw out next was 18 x. Year over year growth for the AI category. [00:23:17] Dai Vu: Wow. So in one year now, a lot of it is models, right? So foundational models with our, with our ecosystem. But a lot of that is around agents. So this whole agent go to market model is gonna be, continue to grow and it’s gonna be a huge focus area for, for the coming years. [00:23:32] Vince Menzione: Fantastic. Yeah. Fantastic growth. [00:23:34] Shawn Toldo: Yeah, and, and I’ll add, Diane and I talked about this at Google next. This is a. Very complex thing for DBT, where today we sell seats. [00:23:42] Vince Menzione: Mm-hmm. Yeah. [00:23:43] Shawn Toldo: To data engineers. [00:23:44] Yeah. [00:23:44] Shawn Toldo: And now we have all these agentic things that are hitting our engine. And di and I are talking and we’re like, okay, so how does this work in an ag agentic marketplace? [00:23:54] Shawn Toldo: Yeah. Kind of a scenario. And what should we build? Where should we play it? ’cause we’re gonna spin the meter in a different way, so to speak. [00:24:01] Dai Vu: Yep. [00:24:01] Shawn Toldo: And so candidly, we got stuff to figure out related to that. Um, I think what’s been fascinating for DBT is our partner ecosystem changed overnight. So now it’s like I talked to x.ai on Monday. [00:24:15] Shawn Toldo: Mm-hmm. We got time with open AI on Thursday and we have a call with Anthropic and our, uh, CEO and co-founder and uh, chief Product Officer next week. [00:24:26] Vince Menzione: Mm. [00:24:27] Shawn Toldo: We don’t have anybody managing those partners. [00:24:29] Vince Menzione: Right. [00:24:30] Shawn Toldo: Today our focus is on managing the large, uh, hyperscalers plus Snowflake and, uh, Databricks. [00:24:36] Vince Menzione: Mm-hmm. [00:24:36] Shawn Toldo: And then the SI ecosystem and some tech partners. So we’re having to like, to your point on Agile yesterday. Yeah. Mm-hmm. Like we’re having to change our strategy, operating model and organizational model to support that. And candidly, we don’t have all the answers yet, so we have a lot of things to figure out fast, which is a little bit scary. [00:24:54] Shawn Toldo: And challenging, but it’s also a huge opportunity we have to kind of embrace and get into. Yeah. [00:24:59] Vince Menzione: And they’re figuring out as well. ’cause they’re, they’re new to partnering as well. Yeah. As organizations [00:25:03] John Janke: and these AI agents. I think to demystify for a lot of people, and what Sean said is totally right. [00:25:08] John Janke: They’re disrupting everyone’s business model. But in reality from a marketplace standpoint, they’re metered SaaS. This is a thing that’s existed for a long time. Yeah. They look like product-led growth products. There is a lot of patterns around how product-led growth products work in marketplace. Mm-hmm. [00:25:24] John Janke: But you have to bring your business strategy, your product and pricing strategy to those two categories. Metered SaaS and product-led growth. Put that all together to get cross-functional alignment. So we are seeing like. A lot of people get tripped up here and it really does go back to more of the company strategy, product strategy questions, and a lot of partner leaders are not in the room for those conversations. [00:25:48] John Janke: So I think at, at this point in time, as you see big pivots with the partners to go all in on agents, you have to go elevate. Those discussions to be like, what is our plan here? ’cause I, I mean, pricing and packaging will be the thing that trips almost everyone up. [00:26:02] Dai Vu: If I could, if I just build on what John John mentioned, um, so I do agree. [00:26:06] Dai Vu: P it looks a lot like POG, but, uh, but the difference I think is POG has. More historically been in like the data and developer space, now it’s like the general business user, right? So this idea that you want a business user to be able to search and discover, um, agents that could actually be part of their like everyday workflow is going to be very critical. [00:26:26] Dai Vu: And uh, you know, I do think that when we think about the ecosystem building agents. Uh, you know, a lot of the ISV partners aren’t necessarily gonna own end-to-end workflows, right? They’ll, they’ll have a very specific, uh, domain and scope area, but you have to enable yourself to be orchestrated and managed by, you know, orchestration agents or, or, or meta agents that are gonna span end, end workflows. [00:26:49] Dai Vu: And sometimes that includes system integrators and, and others who can stitch that, that automation. So I think, I think that’s, that’s one piece of it. But the other area that I think is gonna be different is, um. There’s going to be a lot of agents. I mean, literally you’re gonna have a very fragmented set of, uh, uh, of players, right? [00:27:07] Dai Vu: It’s not just gonna be the incumbents, it’s gonna be a lot of disruptors and, and, and, and startups. And so the, uh, for the incumbents in the room, it is a mandate that you need to, to innovate because if you do not identify and go to like an agent first, go to market model. Uh, you’re gonna be, you know, disintermediated. [00:27:25] Dai Vu: Somebody’s gonna go build an agent that’s going to leverage you as a dumb database. Um, and they’re gonna own the workflow. So you have to, you have to push the, the, the, the limits here. And I think it’s creates a big opportunity for everyone in this room. [00:27:39] John Janke: I’m going off script. I’m curious. Let’s do it. I’m curious on your take on the system integrators. [00:27:44] John Janke: ’cause I think this, this puts like they’re all, a lot of them are creating agents for people and I think that’s turning them almost more into software companies than they’ve ever been. [00:27:53] Dai Vu: They are, and I think they’re, you know, obviously they’re being, uh, impacted from like, you know, typical like, you know, SOW you know, time and materials type type business models. [00:28:02] Dai Vu: But I do think they play a big role because a lot of the system integrators are bringing, um, you know, vertical and business process expertise. And, um, like I said, I said before, a lot of the ISVs are not gonna necessarily have big enough scope in their area to own end-to-end workflows. And that’s really the promise of agents, right? [00:28:20] Dai Vu: You really need. This cognitive, you know, reasoning, planning, executing across end to end workflows. And I think, you know, the system integrators are gonna bring that capability either, either through, you know, these custom, uh, orchestration or meta agents or if they’re able to productize that and bring that to a model, they can also sort of go through the marketplace model as well. [00:28:41] Dai Vu: So who knows is how it’s gonna evolve. But you know, we’ve always been talking about. Marketplace being a broader opportunity for all partner business models. And I think that will extend to not only, uh, you know, traditional sort of, uh, sell and services partners, but also some of these system integrators as well. [00:28:58] Shawn Toldo: If I could comment on that, please. Yeah. I, I was in London two weeks ago and we did an SI partner day. Mm-hmm. We had 25 sis in a room, probably about 50 people. We had no, um, hyperscalers or cloud data warehouse providers. And when we started talking about open data infrastructure. The role that they can play. [00:29:17] Vince Menzione: Mm-hmm. [00:29:18] Shawn Toldo: Cross platform in a cost efficient manner for customers and the advisory orientation of that. They all leaned in and we, we stopped talking and they started talking. [00:29:28] Vince Menzione: Right. [00:29:28] Shawn Toldo: So they’re all facing this kind of same problem, which is actually causing a little bit of a shift, I think, in how they think about, I’m a Databricks partner. [00:29:38] Shawn Toldo: Uh, you sure you wanna do that? [00:29:39] Vince Menzione: Yeah. [00:29:40] Shawn Toldo: So this, this whole thing that’s kind of evolved in the last six to 12 months, when you kind of pick one horse to ride, I, I would tell you be cautious about what that means. You may pick a horse to lead with mm-hmm. But you’re gonna have to flank yourself a bit in terms of other providers that can help you be successful with that, that that partner you’re gonna roll with. [00:30:00] Vince Menzione: So you’re suggesting data vendor agnostic. [00:30:04] Shawn Toldo: I’m suggesting you really have to think about your strategy. Yeah. Because I think the AI, AI disruption is gonna make you think about that strategy. [00:30:13] John Janke: Yeah, I mean there’s, someone mentioned anthropics First Partner Summit. I was not there, but I’ve heard from a bunch of people were there. [00:30:20] John Janke: You know, they had a hundred partners in the room. 95 of them were system integrators. Five were technology companies, the three Clouds, Databricks and Snowflake. Like if you just think about the, the one of the major disruptors in ai, ISVs, were not in the mix. So I, I think, are they trying to disrupt all of us? [00:30:40] John Janke: Uh, do they need us? And they haven’t figured out how to work with us. I, I think. It’s, it’s, [00:30:44] Vince Menzione: and I’ve heard they only have five people in their partner organization, so I just, it’s, [00:30:49] Shawn Toldo: it’s 11 now, but it’s 11, [00:30:51] Vince Menzione: so it was five [00:30:51] Shawn Toldo: last growing fast in the, in the new company I have 50. So like, to put it in perspective, they have to make some pretty big priority. [00:30:59] John Janke: Yeah. And everyone’s been there a hot second, [00:31:00] Vince Menzione: like, right, exactly. Yeah, they, well, we will talk about the learnings we’ve had over the years, getting to where they need to get to. It’s exciting times. We got a lot to talk about here. Um, I, you know, we have about 15 minutes. I I, I want to kind of gauge, ’cause we could talk, we, we have a few things we could talk about, I could ask about, but I want to see if there’s an, like, an interest in opening up to the room for questions. [00:31:25] Vince Menzione: ’cause I feel like we’ve got a very interesting group here. [00:31:28] Shawn Toldo: You got a hand here? [00:31:29] Vince Menzione: Uh, are there hands that wanna Yeah, there’s some people that wanna ask some questions. So Yeah. We have a mic? Yeah, [00:31:37] Dai Vu: we have [00:31:37] Shawn Toldo: a mic. We, [00:31:37] Vince Menzione: we [00:31:38] Shawn Toldo: got one here. [00:31:38] Vince Menzione: We got one here. One here. Thank you. Sorry we went off script, but [00:31:44] Shawn Toldo: that’s fine. [00:31:45] Vince Menzione: It’s fine. [00:31:45] Dai Vu: Off [00:31:45] Vince Menzione: script. Better is good. [00:31:46] Shawn Toldo: I’m sure you planted the questions outta anyway. It’s okay. We [00:31:48] Vince Menzione: did, we did. [00:31:55] Audience Guest: Okay. All Eva, Sean Lightner, quick question to your, uh, increase on the marketplace, and you said you spiff the salespeople by fifth percent. 5%. Mm-hmm. So, and that obviously drives a very large adoption of, uh, marketplace transactions. How are you accounting for the margin you’re losing on, uh, you know, going through the marketplace? [00:32:14] Audience Guest: And also have you done analysis? I’m sure you have, how much is, uh, shape shifting or shifting from existing versus incremental? [00:32:22] Shawn Toldo: Yeah, it’s a great question. Um, um, lemme make three points. Number one, the backlog statement makes the margin statement not matter. So do you wanna play in that space where a customer’s already bought or not? [00:32:36] Shawn Toldo: Yeah. Or do you wanna force a budget conversation that you have to drive on your own in a direct model? That to me, I think it was 484 4 62 [00:32:43] Dai Vu: 4 6 [00:32:44] Shawn Toldo: 2. [00:32:44] Vince Menzione: That’s new Tam available to you? [00:32:46] Shawn Toldo: Yeah. That, that’s just with one. Right. And we are, we are, uh, running on four marketplaces. So that just increases our tam and makes our, our sellers lives easier. [00:32:55] Shawn Toldo: So on that piece, yes, there’s an expense, but we believe it’s right for growth. So there’s a balance there. Um, I think the, and then the second part of your question again. Sorry, [00:33:05] Vince Menzione: shapeshift. [00:33:05] Shawn Toldo: Oh, shift. We, we actually don’t think we would’ve won the business. So if I go back to our Q4 and I can probably point to three or four deals that went, um, Google Marketplace, we would not have won those deals because we couldn’t have created the budget cycle and that quarter. [00:33:23] Shawn Toldo: To make it happen. Generally a budget cycle is gonna take anywhere from 12 to 15 months. Bingo. Because of the spend that was available to us, we were able to close it in that quarter, and we had the largest Q4 in company history. [00:33:35] Vince Menzione: That is such an important point. I’m sorry. [00:33:37] Dai Vu: Okay. [00:33:38] Vince Menzione: But I, I just wanna, that is such an important point of the budget cycle. [00:33:42] Dai Vu: Yeah. [00:33:43] Vince Menzione: Being a year to a year and a half versus being able to tap into a commitment that’s already been made. Yeah, so I just emphasize that [00:33:51] Dai Vu: I was, I was just gonna add real quick, even, even when we see sort of a, uh, a channel shift renewal, which is, you know, it’s on partner paper and it moves to marketplace as part of the renewals, we do consistently see that the, uh, renewal rates on marketplace and the incremental a CB on the expansion and new opportunities tend to be better when it’s on the platform marketplace than than offline. [00:34:12] Dai Vu: And that’s why partners choose to continue to drive renewals on marketplace at a reduced to rev share. But uh, because they see that that growth, [00:34:20] John Janke: we, we, sorry. [00:34:22] Shawn Toldo: We see that as well. Yeah. And I would also make the statement on our land business, when we go through marketplace, we are two x higher across marketplaces. [00:34:30] Shawn Toldo: We’re three x higher with them. [00:34:32] John Janke: Yeah, I think separate new from renewals and then instrument deeply. [00:34:37] Shawn Toldo: Yeah, [00:34:38] John Janke: go proactively talk to your CFO and your head of rev ops to understand their mindset. Because I was with a billion dollar seller a couple weeks ago, their CFO still creates friction in the process, even though they’re selling a billion dollars through these channels. [00:34:52] John Janke: But when they broke it down, their deals are three times bigger. They do them faster. They use more components of the product, which I thought was a really cool one. So customers who buy this platform, many component platforms through a marketplace, end up using six components of the product. Versus a normal land customer who uses two increases gross in net retention. [00:35:12] John Janke: So you have to get to the point where you have the data and you can tell that story real really clearly to your finance team to get support ’cause that they will trip you up if you don’t get them on board. [00:35:23] Vince Menzione: And you’re saying there’s friction in that company. I’m just kind of curious ’cause a billion dollar company. [00:35:27] John Janke: There’s a billion dollar marketplace seller [00:35:29] Vince Menzione: market marketplace company. That’s what I meant. Yeah. But, but the fact that this, their CFO friction, like, is it, is it because they’re not doing a good enough job or? [00:35:37] John Janke: Uh, in, of educating, I, the root of the question is from this person is, would they win without it? [00:35:44] Vince Menzione: Yeah. [00:35:45] Shawn Toldo: Oh, and is it worth the three points? [00:35:46] John Janke: Right. It’s, it is And, and I think some pe like to me, it’s the cheapest channel in the world. Yeah. Like with committed budget and people to support you winning. Like the, that formula, the math is so simple. [00:35:57] Shawn Toldo: Yeah. For, for a company of our size to go to like the classic resell ecosystem, I gotta walk in with 30 points. [00:36:02] John Janke: Yeah. [00:36:03] Vince Menzione: Yeah. [00:36:03] Shawn Toldo: It, it’s an illogical conversation. Outside of public sector and growth, you know, geos around the world. And so I, I’ve been lucky to have a CFO that I haven’t had that challenge with, at least at DBTI should say. [00:36:19] Vince Menzione: Really great insights. I think we have, we have another hand up here. [00:36:28] Audience Guest: Yeah. Thanks Susan. The question is for Dai. Uh, my name is Latif Hamani. I’m the founder of Partner System ai. Um, so what we’ve done is we’ve built a, a co-sell AI agent mm-hmm. That your partners can use to Yeah. Reduce all the friction in the co-sell with you. Uh, the questions that I have is, I guess I should back up, so XAWS Madison with a very large alliances, and then I worked, went on the other side. [00:36:55] Audience Guest: For software companies, and even though I had an operational team, I was spending two to three hours on on the keyboard, right? Mm-hmm. Deal registration, emails that can’t be automated, et cetera. So the question that I have for you is, I’d love for you to validate that. You know, unless you are one of the big companies, one of the big enterprises, if you go to the lower end of the enterprise or the mid market, uh, would you validate that there is a challenge? [00:37:20] Audience Guest: There’s a lot of friction for a smaller company. Mm-hmm. Uh, ’cause these marketplaces are complex. Yeah. The cosell is complex. Uh, that there’s an opportunity to really break down that friction with some automation and ai. [00:37:33] Dai Vu: Yeah, absolutely. So, um, we have already been, uh, part of the journey to remove some of the, uh, the friction as part of that selling and purchasing journey. [00:37:43] Dai Vu: Uh. We’re not quite there yet. But, uh, we’ve done things like we have, uh, you know, private offer APIs. We, uh, we have co-sell, uh, registration automation. Um, you know, we have tools like, uh, propensity to buy, tooling to help, uh, partners do, uh, more targeted efforts. Um, but the a i piece is still coming. Um, so I think, uh, the idea here is that we have launched a number of agents as part of our, um. [00:38:08] Dai Vu: Uh, part of our, uh, Google Cloud Partner network, partner hub. Uh, so these are, uh, agents that are gonna do a bunch of things to help partners as part of their workflow, but we’re gonna extend this to the marketplace and ISV area as well. Uh, so I think there’s a lot of opportunity. So, uh, I know there’s probably a lot of feedback in friction, uh, in, in certain parts. [00:38:29] Dai Vu: So we can, we can go tackle together. [00:38:32] Vince Menzione: Hey. There you go. There was a little [00:38:34] Dai Vu: plug [00:38:34] Shawn Toldo: there for tackle. Exactly. [00:38:37] Dai Vu: Uh, and I wanted, and just to be clear, I want to take a look at it from the end to end, uh, uh, flow, right? It shouldn’t just be just marketplace. It should be all the way from like, you know, top of the funnel, demand generation, all the way to like post transaction follow up. [00:38:51] Dai Vu: So we really need to take a look at, at the, the end, end flows and figure out a way we can remove some of that friction [00:38:56] Vince Menzione: three sense. [00:38:57] Dai Vu: Yeah. [00:38:59] Vince Menzione: Any more questions [00:39:00] Audience Guest: back here? Hey. Hey guys. This, this is a really good discussion. Uh, di this question’s primarily, uh, from, I’m interested in the hyperscaler response. [00:39:09] Audience Guest: Yep. Uh, but all of you, uh, can you talk about the patterns or say more about the patterns between. Um, the consumption of just platform capabilities versus industry workflows. Mm-hmm. And how industry where I, I mean, I, I, my sense is that industry workflows are becoming more [00:39:27] Dai Vu: Yeah. [00:39:28] Audience Guest: Uh, the easier thing for enterprises and SMBs to buy. [00:39:33] Audience Guest: Yeah. Especially SMBs, I think. Um, but say more about those patterns that you’re seeing develop and kind of what is. Uh, who are, where, where are those kind of, where is the demand being driven? Is it, is it, yeah. The search and discover in the marketplace, or is it being led by field sales of mm-hmm. Either GCP or partners? [00:39:55] Dai Vu: Yeah, so let me, I’ll mention a couple, a couple areas where, where it’s growing. So I think number one I mentioned before about some of these large horizontal business apps that we’re partnering with, right? Um, and, uh, and of course the fact that we’re, we’re, we’re transacting them through marketplace is, is a huge. [00:40:14] Dai Vu: Evolution from a few years ago. So who would’ve thought you would be buying like, you know, a hundred million dollars a CB deals, uh, through, through marketplace with like a Salesforce or a ServiceNow workday. But it’s happening now. And to be clear, all these. Horizontal business app. They’re not doing this in a very, you know, opportunistic, transactional way. [00:40:32] Dai Vu: They basically see marketplace and cloud go to market as a strategic growth lever for them. So that’s one big area. So from just a pure large deal perspective. Okay. Then you mentioned before around sort of corporate and SMB. Well, we find that a lot of the big opportunities are mostly around as they scale their business, uh, they’re not necessarily looking for things in the traditional sort of infrastructure space, but they’re looking for, you know, full SaaS applications to help scale their business, right? [00:40:58] Dai Vu: So it would be CRM, finance, hr, these types of solutions to become very attractive for some of this, uh, downstream market. And then lastly, as I mentioned before, which is, uh, when we think about this gentrification and owning, um. Uh, driving, uh, this business process and vertical, the ISVs become very important along with the services partners who bring that domain expertise to drive the end to end workflow. [00:41:25] Dai Vu: So I think that’s gonna be increasingly important. So those are three areas I think we need to watch out for. We. Okay. [00:41:30] John Janke: Maybe one thing, like as the cloud commit grows inside of companies, it’s shifted from being an engineering department, IT department budget line item to a corporate finance budget line item. [00:41:40] John Janke: Typically one of the top five to 10 expenses in a company. So that has shifted. Who is thinking about optimizing? The cloud commit with marketplace contracts. And that opens, that’s really opened up the avenue in addition to like these biz apps, vertical apps players. Yeah. Like having success. So I, I do think even inside your own company, evaluating where your cloud commits are, who owns them and are they thinking about the intersection of marketplace? [00:42:06] John Janke: ’cause I, I think it’s smaller companies, they’re still figuring it out. I run into engineering leaders who still own the commits, uh, but in medium to large companies. Very different. [00:42:16] Vince Menzione: Really good point. Because it, you know this, the optics change dramatically, right? This large commitment is now at the board level, [00:42:23] John Janke: right? [00:42:23] John Janke: And then you do have to teach your sellers as a vertical or business application player how to ask that question. ’cause the first resistance everybody says is, oh my, my person, my stakeholder, we. Manufacturing vertical application provider talking at an event last week, and they’re like, the shop floor manufacturing owner doesn’t know anything about the cloud commit. [00:42:43] John Janke: But if they ask the question, be like, Hey, do you guys have a strategic relationship with Google? Would it be easier to buy our product on the bill? Eight out of 10 times they get a yes. So [00:42:52] Vince Menzione: which is why the 5% comes in And that really accelerates the conversation happening. Yeah. We’ve got three more minutes. [00:43:01] Vince Menzione: Um, if we don’t have any other questions, I ha I have one for each of you really about the maturity model and partners are in the room that are not committed yet, right? We’ve talked about some very significant DBTs doing some incredible things, right? So we, there’s maybe a sense that like we, you, you are working with the be the biggest and the best out there, but what about everyone else that’s in the room that maybe isn’t committed yet? [00:43:23] Vince Menzione: And maybe they’re in motion, but they need some help and advice on what to go do next. What? What would you say die first? [00:43:30] Dai Vu: So they’re early stage, [00:43:31] Vince Menzione: early, early stage or not, they’re not on board yet. They’re not, yeah. They’re not with you yet. [00:43:35] Dai Vu: Yeah. So I’ll, I’ll go back to my earlier comment, which is that as you go into the journey, just be very intentional about what you need to do from an operational, investment people, uh, technology perspective. [00:43:47] Dai Vu: Uh, because it could be, it could be a multi-year journey. Um, uh, so I’d say go into it with the right expectations as opposed to thinking it’s going to be some accelerated six month thing that Sean has been driving here. It’s, he’s the outlier. [00:43:59] Shawn Toldo: But, but the reason for the outlier, [00:44:00] Dai Vu: yeah. [00:44:01] Shawn Toldo: And just to add to the intentional point Yeah. [00:44:02] Shawn Toldo: Is, you know, hire the right people. Right. So, somebody told me a long time ago, uh, hire slow, fire fast. That’s a really, really, really good principle that I take. Mm-hmm. I don’t like the fire part, obviously, but just for context, I, I am very lucky to have a great set of leaders that we were able to add people in. [00:44:24] Shawn Toldo: When I walked in the door, we had a person that was leading the Snowflake and AWS partnership. I had nobody on GCPI had nobody on Microsoft. I had nobody on Databricks. And then we made prioritization decisions on where we’re gonna go next. And so we hired people that had the experience and could drive the outcome in the right way. [00:44:43] Shawn Toldo: But we were very thoughtful about when we made those decisions on a quarterly basis, not a daily basis. So who you’re gonna bet on and then who you’re gonna put in the seat to make that bet come to life, I think is a really important thing as well. [00:44:58] John Janke: Yeah. [00:44:58] Vince Menzione: John, you worked with the be biggest and the best out there, so Yeah, sorry. [00:45:01] John Janke: Well, I think there’s the, like there’s the bottoms up and the tops down. Like seven years ago, this was all bottoms up. It was a partner leader who thought launching a marketplace would be good and they would go figure out how to do some deals and then sell their way up. Today there’s a lot more top down where people get it. [00:45:17] John Janke: But you can evaluate top down pretty fast. ’cause if you go talk to your CEO, you talk to your head of product, you talk to your CFO, and they have an allergic reaction to these concepts. You know, you have to go bottoms up. But there also are success story examples in every single ISV category that exists. [00:45:33] John Janke: Like this is not just security and data and DevOp like the, I think the ServiceNow. Salesforce workday. Examples are really great, like the marketing tech examples, more and more business of vertical apps every day. So I do think you can look at those people who’ve been successful. Maybe they’re your competitors, maybe they’re people you aspire to be and reference them as you’re trying to figure out how to do top down. [00:45:55] John Janke: But like you need both. You can’t win long term unless you get top down and bottom up aligned. [00:46:01] Shawn Toldo: And, and when I, when I would go ask for resourcing, I would always get the question, do, could you go faster with more? And I’d say, no. Gimme the one or two humans here, let me go prove it out and I’ll come back. [00:46:13] Shawn Toldo: So there’s a little bit of a strategy in doing that, that you’re gonna get more over time when you’re, you know, very measured in how you go ask for investment and resource. And so I would just add that point also. [00:46:27] Vince Menzione: Was, was hiring a significant component of your executive commitment, Sean? I mean, [00:46:33] Shawn Toldo: yes. So when I walked in the door at DBT, we had eight people in the partner organization. [00:46:38] Shawn Toldo: Today we have 25, and that was 18 months ago. But that did not happen. I didn’t go in and ask for, you know, that 16 people. Right. I asked over time in a very measured way with, you know, the programs and strategy team, like, what can we also support? You don’t want to bring somebody in to go do something and you don’t have the programs and operations side to support it ’cause they’ll fail. [00:47:01] Shawn Toldo: So we’ve been very thoughtful about how we’ve done that as well. [00:47:04] Vince Menzione: Die from you. I know you had something. [00:47:06] Dai Vu: No, no, no. I, I was good. [00:47:08] Vince Menzione: What is the one thing that people in this room need to go better and differently? Is there one, is there one specific thing other than what we’ve already discussed, did we miss anything? [00:47:16] Dai Vu: No, I would just, the whole identification. So obviously, uh, identifying this is not just like slapping a chat bot, but more around thinking all the things we talked about, product commercials, but also go to market where it’s agent first, where you can surface your agent in a workflow like Gemini Enterprise app. [00:47:34] Dai Vu: That’s gonna drive high alignment with how we work and go to market with Google. [00:47:38] Vince Menzione: Awesome. [00:47:38] Dai Vu: Yeah. [00:47:40] Vince Menzione: Wow. Good stuff. Yeah. Very good session. [00:47:44] Dai Vu: Thank [00:47:44] Vince Menzione: you guys. What do you think? Everyone? Thank you very much. [00:47:47] Shawn Toldo: Thanks for listening to the Ultimate Partner Podcast. [00:47:50] Vince Menzione: If today’s conversation resonated, share it with a partner leader in your network. [00:47:55] Vince Menzione: Subscribe where you listen, and head over to the ultimate partner.com. For show notes related content and the resources for this episode. And if you haven’t already, now’s the time to register for the Ultimate Partner Live Event in Reston, Virginia, [00:48:11] John Janke: October 26th through October 28th. [00:48:14] Vince Menzione: Until next time, keep showing up in the rooms that matter because being in the room changes everything [00:48:22] I.
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We're excited to have Databricks join us at AIEWF, among hundreds of the top companies in the AI Engineer ecosystem. LS subscribers can use their discount to get past the late bird pricing and access over $50k in sponsor offers! Everyone is still talking about Satya's Frontier Ecosystems post, but few have actually built a (now $175 billion) frontier ecosystem and cloud like our guests today.From open-sourcing the layer above coding agents to rethinking databases for the agent era, Databricks cofounders Matei Zaharia and Reynold Xin are pushing the company beyond the lakehouse into a full data-and-AI operating system. 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Fundamentally it appears that significantly better model performance in production is a systems problem, one that data guys like us are remarkably well prepared to solve!We discuss:* Why Databricks built Omnigent as a meta-harness above existing AI agents* Why coding agents and custom enterprise agents need the same infrastructure* The common API for agent sessions, files, streams, tool calls, and cancellation* Why persistent sessions, cloud sandboxes, sharing, search, and collaboration matter* Why Databricks open-sourced Omnigent instead of keeping it proprietary* Databricks' internal agent usage, cloud sandboxes, and coding workflows* The scale of Databricks: 50–60 million virtual machines a day and exabytes before breakfast* Why agent security needs contextual and stateful policies* How an agent could read confidential docs, install a compromised npm package, and leak data* Why spend control matters when an agent can burn $500 reading logs* Startup opportunities around coding-agent analytics, quality, skills, and spend* LTAP, Lakebase, and why Databricks wants to rethink the database stack* OLTP vs OLAP, CDC, and why data pipelines break at 3 a.m.* Why HTAP has historically been the holy grail of database engineering* Why Databricks thinks LTAP is “HTAP done right”* How writing transactional data into column-oriented formats changes analytics* Why agents need live operational context from databases, not just telemetry* How Databricks prototypes strategic systems without endless process* Enterprise vs tech customers, governance, procurement, and DIY culture* The “second system syndrome” risk of rewriting a database engine* Building a database engine from a decade of traces and quadrillions of data points* Why vector databases should never have been a separate category* Why open formats and AI changed the race with Snowflake* The Mosaic story, DBRX, Genie, document parsing models, and specialized model training* Why model customization and RL fine-tuning may become mainstream* Why “get the data there, slap some agent on top” may rewrite traditional softwareMatei Zaharia* LinkedIn: https://www.linkedin.com/in/mateizaharia* X: https://x.com/matei_zahariaReynold Xin* LinkedIn: https://www.linkedin.com/in/rxin* X: https://x.com/rxinDatabricks* Website: https://www.databricks.com* X: https://x.com/databricksTimestamps00:00:00 Introduction00:02:22 Omnigent and the Agent Infrastructure Layer00:08:39 Agent Clouds, Common APIs, and Open Source00:16:52 Databricks Scale and Internal AI Workflows00:18:03 Agent Security, Governance, and Spend Controls00:27:34 LTAP and the Database Dream00:30:30 CDC, HTAP, and Why Data Pipelines Break00:34:05 Lakebase, Parquet, and Live Data for Agents00:36:47 Databricks' Culture of Fast Prototyping00:43:40 The Dream Engine and Rewriting the Database Stack00:51:02 Vector Databases, Query Engines, and LTAP00:52:36 Databricks vs Snowflake00:57:48 Mosaic, DBRX, Genie, and Specialized Models01:03:11 Context, AI Runtime, and RL Fine-Tuning01:06:15 Why Data + Agents May Rewrite Software01:07:09 Closing ThoughtsTranscriptIntroduction: Databricks, Data + AI Summit, and Founder DynamicsSwyx [00:00:00]: Matei and Reynold from Databricks, welcome to Latent Space.Reynold Xin [00:00:06]: Hey, thanks for having us.Swyx [00:00:07]: Yeah.Matei Zaharia [00:00:08]: Yeah, thanks so much.Swyx [00:00:09]: thanks for taking time out. You have your Databricks, Data AI Summit going on. You were just telling me how the first summit that you guys ran was just 50 peopleReynold Xin [00:00:17]: Yeah, it wasSwyx [00:00:17]: in BerkeleyReynold Xin [00:00:18]: little meetup at Berkeley, I thinkMatei Zaharia [00:00:19]: YeahReynold Xin [00:00:19]: put togetherMatei Zaharia [00:00:20]: We were doing these tutorials and, yeah, just teach people Spark.Swyx [00:00:23]: Yeah. obviously now it's like, I think like the headline number's like 100,000 people around the world, 30,000 in person.Swyx [00:00:30]: it's a crazyMatei Zaharia [00:00:31]: AmazingSwyx [00:00:31]: community. Well, I just saw the keynote.Swyx [00:00:35]: Ali's just. Did was it obvious or that back when that Ali would be, like, such a great, like, CEO? LikeReynold Xin [00:00:42]: OhSwyx [00:00:42]: such a great presenter?Reynold Xin [00:00:43]: What do you think?Matei Zaharia [00:00:44]: I think among our group of founders it was clear that, I think he'd be the best at this.Swyx [00:00:50]: Yeah.Matei Zaharia [00:00:50]: And yeah, it turned out great. And he's, he's ramped up on so many topics growing a company. He would just go in and, like, study it and, be talk to all the experts. Like, even if he can't hire the person, learn enough about, like, finance and sales and whatever it was, and, and go from there. Yeah.Swyx [00:01:09]: Yeah.Reynold Xin [00:01:10]: he's obviously very high IQ and a very high EQ, but it wasn't. Like, Ali today is quite different from Ali from, like 10 years ago. I think there's a lot of work that he put in to, get to this point.Swyx [00:01:20]: Yeah. no, to me the most appealing thing about him is that he's funny. And like, it, it's, it'Matei Zaharia [00:01:26]: It's true, yeahSwyx [00:01:26]: it's hard to make jokes about, data warehousesReynold Xin [00:01:30]: About serious topicsSwyx [00:01:31]: securityMatei Zaharia [00:01:32]: YeahSwyx [00:01:32]: what have you.Matei Zaharia [00:01:33]: Oh, yeah. That's for sure.Swyx [00:01:34]: Yeah. So you guys launched a whole bunch of things. I'll, I'll just name check briefly, the stuff because we're not gonna cover everything. Omnigentt, your baby. LTAP, your baby, your dream engine.Swyx [00:01:47]: we're also gonna cover Genie, cover CustomerLake, you acquired PantherMatei Zaharia [00:01:52]: YeahSwyx [00:01:52]: Open Sharing, and there's Unity AI Gateway. A lot of these, I think, like, are things that you would expect a Databricks to do. It's, it's like part of the roadmap. Everyone in your category has similar things. But I think, probably the two of you are leading the two most unique and differentiated initiativesOmnigent and the Agent Infrastructure LayerSwyx [00:02:09]: on, in the landscape. Maybe we'll start with, Omnigentt we'll, we'll, we'll, we'll go into it. I do think that a lot of people are exploring this meta harness concept.Matei Zaharia [00:02:21]: Yeah, totally.Swyx [00:02:21]: What led you to it?Matei Zaharia [00:02:22]: Yeah. There were a couple of, like, converging lines, which I think is a good sign that you need something new. So on the one hand, there's all the coding agent info internally. We have really great, dev infra team. they built something called Isaac, that's like a wrapper on Claude Code and Codex, and, lets you use them either on the web in, like, sandboxes or, just on your dev machine or on your laptop or whatever. And then, they were adding all kinds of stuff there. And we saw all the more advanced engineers like, were building their own workflows with tons of agents, and they were building their own UIs and stuff on top or even on top of that. And then the other one was, like, us building agents. We ship this, like, data science agent called Genie on the research team, which I lead. We also build a lot of internal ones for various things, and then we have all the customer ones. And all of them running into this thing of like, “Oh, I need to switch model and harness and so on,” every few months. Plus the agent is, like, completely useless if you can't share sessions with someone and have history and have search and all this, like, layer on top of it for collaboration. I thought a bit about it from both contexts and, at first people thought it was weird. They're like, “Why are you doing coding agents and custom agents in the same thing?” But I said it's, it's the same problems and, you just wanna build the stuff that lets you deliver the agent, maybe control it if you care about security, and, make it portable across things. And then we prototyped some things as experiments. We saw, yeah, we can make it work, and then we built that for real.Swyx [00:04:06]: I'm wondering if this let's call it architectureMatei Zaharia [00:04:11]: YeahSwyx [00:04:11]: maps to anything in your careers in the past. like I always think about how a lot of things just tie back to operating systems.Swyx [00:04:18]: A lot of operatingMatei Zaharia [00:04:19]: YeahSwyx [00:04:20]: systems tie back to databases,Matei Zaharia [00:04:21]: SoSwyx [00:04:21]: or the other way aroundMatei Zaharia [00:04:22]: so the thing, I do think it ties a lot to, like, network protocols, internet protocol. we alsoSwyx [00:04:29]: Communication between entities.Matei Zaharia [00:04:30]: Yeah. We did stuff with, like, data sharing also, which is probably, most viewers probably won't know unless they'Swyx [00:04:36]: Yeah, open protocol is the term.Matei Zaharia [00:04:37]: Yeah.Swyx [00:04:38]: Open sharing. Open sharing.Matei Zaharia [00:04:38]: Open sharing.Swyx [00:04:39]: Yes.Matei Zaharia [00:04:39]: Yeah. So it's like you have a company, you maintain some table, like let's say like a Walmart or something. They have like the, inventory and what's been sold in each store. And then you also have suppliers, and they would love to produce more things and ship them, like, exactly the moment you need them. So they would love, like, real-time access to your table. So instead of like sending emails around or Excel sheets or phone calls, why can't you share like a view of that table in real time with them? Then they query, they, join it with their data, and they decide what to send. So it's one of these things where you, like you might ask like today since we can vibe code anything so fast, why do we even need to design like protocols or APIs or software? Why can't you just vibe code things on demand? But for this type of interoperability where multiple parties that are moving at different speeds are building stuff and you still want some layer on top to coordinate, you do wanna design it and build it. So it reminds me of that, like agents talking to each other and, users talking to agents and tools.Agent Clouds, Cloud Sandboxes, and Keeping Sessions AliveSwyx [00:05:42]: Reynold, any other comments alternative viewpoints?Reynold Xin [00:05:46]: I think, by the way, we had a debate on exactly which set of benefits would, matter a lot, and I think around the time we decided to do this thing I was telling Matei, “Hey,” it just happened to be there's a particular week that I was coding nonstopSwyx [00:06:00]: from the moment I woke up to, like, the moment I went to bed, I was, like, looking at my Claude sessions, my Codex sessions. And one of the things that was particularly annoying was having to keep my laptop open.Swyx [00:06:12]: I was driving to a doctor's appointment, and I remember because I wanted to make sure the whole thing continues working.Matei Zaharia [00:06:18]: But by the way, it's so comforting to hear you say that because I'm like, “I don't know if I'm a clown and I'm doing this or like.”Swyx [00:06:25]: Yeah. Like honestly, I was driving and I was tethering my laptop to my phone.Matei Zaharia [00:06:29]: huh.Swyx [00:06:29]: Keeping it on the side. Whenever I hit a red light, I started looking at what's going on my laptop.Matei Zaharia [00:06:35]: Yeah.Swyx [00:06:35]: And I just felt that was ridiculous.Matei Zaharia [00:06:37]: Yeah.Swyx [00:06:37]: It felt like we went back to the dark agesMatei Zaharia [00:06:39]: YeahSwyx [00:06:40]: programming. the productivity you gain from all this coding age is amazing, but, yeah.Matei Zaharia [00:06:45]: Have you heard of cloud?Swyx [00:06:47]: Yeah.Swyx [00:06:48]: It was crazy to me.Matei Zaharia [00:06:49]: Oh, the thing you were working on was the sandboxes or was this before that?Swyx [00:06:52]: It was a sandbox.Matei Zaharia [00:06:53]: Okay.Swyx [00:06:54]: I was workMatei Zaharia [00:06:54]: So you were inSwyx [00:06:55]: So I was approaching from a very different angle. I wanted to, “Hey, we're gonna have cloud sandboxes that doesn't shut down. You can get one very quickly,” but not just for running agentic sessions.Matei Zaharia [00:07:06]: Yeah.Swyx [00:07:06]: It's also for running development. So I was personally building that week, and through building that, I ran into all these issues, and then I wroteMatei Zaharia [00:07:15]: YeahSwyx [00:07:15]: a document for Matei, it's like, “Here's my wish list of what the actual environment should do.” And I think he ended up almost implementingMatei Zaharia [00:07:22]: YeahSwyx [00:07:22]: every single one of them.Matei Zaharia [00:07:23]: Yeah, I remember Reynolds saying, ‘cause my first prototype of this had just chats with your agent and he said, “I have to be able to open a shell, like my own shell and like list files and like tail them and stuff.” SoSwyx [00:07:36]: So SSH into a mainframe.Matei Zaharia [00:07:37]: Yeah. it has that now.Swyx [00:07:39]: Tailing my log.Matei Zaharia [00:07:40]: Yeah.Matei Zaharia [00:07:41]: Yeah.Swyx [00:07:41]: And also another thing I think I asked was, I had. I still use cursor for the sole purpose of rendering markdown files.Matei Zaharia [00:07:48]: huh. Yes.Swyx [00:07:49]: So I said, “If you just give me a way to see my markdown files and renderMatei Zaharia [00:07:53]: YeahSwyx [00:07:53]: them properly, I don't need a separate tool anymore.”Matei Zaharia [00:07:55]: Yeah.Swyx [00:07:56]: And I think you also built that in.Matei Zaharia [00:07:57]: Yeah, we, yeah, we did that, yeah. Yeah, we had a lot of engineers building, their own vibe coding setup. But then the other thing they all said is like, “Hey, I built something that's amazing for me, but, like, no one else on the team can use it ‘cause I don't have a server to collaborate.” And this is why we tried to set up, Omnigent, so you can have a server and have the security, set up in there. So, like log in with Google or whatever and, like securely share stuff. which. And that's where we've seen a lot of other agents like hit things. Like people think they prototyped an awesome agent, but it's not allowed to connect to like some really important data or whatever because of the security team.Omnigent Architecture, Open Source, and Common APIsSwyx [00:08:38]: Yeah.Matei Zaharia [00:08:38]: So yeah.Swyx [00:08:39]: Yeah. At this point, so for those watching along on YouTube, we're gonna putting up a image of the structure here, and we can talk a little bit of the architecture. I think I just want to have people understand, ‘cause like when we're talking about software, it can be very abstract and like here is what we're talking about. You've worked out in open source this entire platform and there's a runner component and server component with a uniform API that you've, you've figured out. any other element and obviously you can plug in all this, persistence layers and compute layers. This is a whole cloud. It's an agent cloud.Matei Zaharia [00:09:12]: Yeah. It's, it's got these components to work with it. The, a lot of the action happens like on the machine where you deploy your agent too. So whatever you've got on there, you can run. But yeah, it's, I think it's the minimal thing you want to have hosted, like collaborative agents and to have that server. And one of the reasons we open sourced it is, anyone building agents, this gives them an app they can start with and customize, which we were seeing in Databricks too. Like someone would make a nice, agent app and then other teams would ask, “Oh, can I just use yours for my agent?”Swyx [00:09:45]: Yeah, I think we had like five or six different agentic frameworksMatei Zaharia [00:09:48]: YeahSwyx [00:09:48]: built by every different team. They do all do more or less the same thing. Yeah, you need to. people wanna take something that works in Forkit, and you might as well have something open source. Yeah, which also was another question, which is interesting for Databricks. Like what do you choose to open source? What do you choose to make it proprietary? It's in. this goes back to Spark, right?Matei Zaharia [00:10:05]: Yeah.Matei Zaharia [00:10:06]: One, so one of the reasons to open source something is if you think it's a layer that will there'll be some network effect, it'll benefit from many, people collaborating, on it. So, for example, with Spark, I don't know if when Spark came out, we also focused a lot on letting you have libraries on top. So like there used to be differentSwyx [00:10:28]: EcosystemMatei Zaharia [00:10:28]: distributed computing engines for like machine learning and graph computation. We said they should all be libraries that you can compose. And we made it super easy to add connectors to data sources too. And then we benefit because, we don't have the time to write like connectors to like, 1,000 like different databases and file formats, but we can just use the ones people make, and of course they benefit from joining, this thing. So that's like one of these as it. Another way to think about it is like imagine, we our thing wasn't open. We had some agent hosting thing, but it's not open and then there is an open one. if you're. Which one's gonna win in the long run? So like here, because there is this benefit from like people writing integrations, it'll be, it'll be that. And then there are other things that like you just can't, even deliver as open source that are things the company does. Like for example, how do you make sure you're like streaming, jobs or your Lakebase database doesn't like, lose all your data at night? Well, that requires an operational team that's gonna sit there. There's no way it has to be a service. So like we wanna make sure as a company we're really good at those infra services and then we're as open as we can in terms of like what you build on top.Swyx [00:11:42]: speaking from a benefits, I think we are already seeing pull requestsMatei Zaharia [00:11:45]: YeahSwyx [00:11:45]: of all kinds of ecosystem integration, even though it was only released on Saturday.Matei Zaharia [00:11:50]: Yeah, Saturday. Yeah. So someoneSwyx [00:11:51]: Let's see, let's see what's going on. Yeah, you can look at the merge ones. I asked Sam Nigon this morning aboutMatei Zaharia [00:11:59]: 400 merge already?Matei Zaharia [00:12:00]: Yeah. I think Recent quite, I would guess around half are not from our team. but for example, someone added support for running it on Kubernetesrnetes. people added, many cloud sandboxes, so this can launch a cloud sandbox and run your agent in there, which is great for sharing too, ‘cause it's not, like, on your laptop and someone's, like, running scary code on there. so yeah, many startups have put those in, and, we expect to see more of them. We also have more agent harnesses already. Cursor, CLI, and Antigravity also.The Modern Data Stack and the Emerging AI StackMatei Zaharia [00:12:34]: Yeah. That's all, beautiful. And I, I feel like the last time this happens, there was the rise of the modern data stack.Matei Zaharia [00:12:42]: I don't know if it's that useful. I'm, I'm curious in your postmortem.Matei Zaharia [00:12:46]: I think most peopleSwyx [00:12:47]: AgreeMatei Zaharia [00:12:47]: will agree that it is finally dead. but maybe this arises to a new modern AI stack that, like, does the same thing.Matei Zaharia [00:12:52]: I don't know.Reynold Xin [00:12:54]: I think the modern data stack was a pretty useful thing, probably even up until this day. I think what, maybe for the audience who don't understand the history, I think the modern data stack is effectively decomposed into you need a layer to ingest the data in, you need a layer to transform your data, and then all of this are run, and then you need a layer to maybe visualize your data. And all of this runs on some data warehouse, or later on, as we're doing data warehouse or lakehouse.Reynold Xin [00:13:21]: I think that concepts are all very powerful and very useful. They enable a lot of workloads. What people eventually run into is a question of unification and consolidation is, hey, do you really need to chop all this into different pieces and work with so many different vendors and platforms in order to get, like, a very simple visualization done, right? So I think, like, over time, everybody started realizing that customers are pushing us. We started, we can realize that, so we started building more and more capabilities and trying to consolidate. And at the end of the day now, customers don't have to worry about having me hook up five different systems in orderMatei Zaharia [00:13:55]: YeahReynold Xin [00:13:55]: produce a chart. But the. I think, honestly, something like this is probably happening, in how many different frameworks do you want to hook up together in order to produce, like do a very simple agent.Matei Zaharia [00:14:06]: Just to be clear, I would say the core of this is this common API on top of all the harnesses. So the API is like, you've got an agent session, and you can send in a message or, like, a file. That's what you can send in, and then you get out, these streams as it's streaming text or as it's doing tool calls. And, or the other thing you can send in is you can, like, tell it to cancel a turn. So that's the API. Now, the thing we did is we could get you that on top of, like, cloud code running in a terminal, Codex, Py, OpenAI SDK, all that stuff. We map them all to that same interface. So that is something that you'd have to maintain yourself if you built your own, like, agent orchestrator, and then whenever cloud changes its API, you gotta, tweak your thing or it's gonna lose some messages. So that's the thing that's valuable to maintain. Then on top of that, like, we built a few apps. I think we built a pretty cool UI and stuff, but that's, And we built a security and control piece, which I'm excited about. But it's that common interface, so we don't. We. That doesn't try to be a stack. And in fact, you could plug in your own UI on top of this, server. That, and that's one of the use cases we care a lot about, ‘cause we want to use this in our own products.Compute, Sandboxes, and Databricks ScaleSwyx [00:15:20]: Yeah. It should be everywhere.Matei Zaharia [00:15:22]: Yeah.Swyx [00:15:22]: I think one of those things that is really interesting to me is, like, well, first of all, I'll, I'll endeavor to do everything and not call it the modern AI stack because like it needs a different name.Matei Zaharia [00:15:32]: Yeah.Swyx [00:15:32]: But like, yes, like, so one of the first people that told me about compute, sandboxing was Nikita from Neon.Swyx [00:15:39]: Because a lot of people think about Neon as like, well, it's serverless Postgres with, like, the separation of compute and storage and, instant branching and all those things. But every database company is also a compute company.Matei Zaharia [00:15:51]: Yeah. Yeah.Swyx [00:15:52]: And so he was showing to me his whole, his sandboxing solution. I don't think he have ever launched it.Matei Zaharia [00:15:57]: So our sandbox solution, the reason we could build it so quickly was because we realized if you just take the actual Lakebase architectureSwyx [00:16:05]: YeahMatei Zaharia [00:16:05]: and remove the database from it, by the coming from NeonSwyx [00:16:08]: Exactly, rightMatei Zaharia [00:16:09]: you have this sandboxSwyx [00:16:09]: Every database company has it already, yeah.Matei Zaharia [00:16:11]: Now, there are some differences. For example, in the one to support this particular workflow, it's important to have local persistence,Swyx [00:16:19]: YeahMatei Zaharia [00:16:19]: because you want your state to persist. Your libraries, you don't have to install your library every time, right?Matei Zaharia [00:16:24]: whereas the Neon architecture, because of the separation of storage from compute, you don't need persistent local disk.Swyx [00:16:30]: Yeah.Matei Zaharia [00:16:30]: So there's some differences.Swyx [00:16:32]: Yeah.Matei Zaharia [00:16:32]: But the, at the end of the day, yeah, it's, Yeah, so this is when you run, like, a coding sandbox. Like, if I use it, yeah, we have the dev env internally at Databricks. There's, like, many, like, tens of gigabytes of data just for, like, all the source code and, like, artifacts and stuff that I built, and I want that to come back next time, so.Matei Zaharia [00:16:51]: Yeah.Matei Zaharia [00:16:51]: But yeah.Matei Zaharia [00:16:52]: Before the show, we was talking about some statistics that might be surprising at the adoption.Matei Zaharia [00:16:56]: It could be internal, it could be external, whatever comes to mind, just to impress people the scale this is happening.Swyx [00:17:02]: So we, on the analytics side, I think we launchedReynold Xin [00:17:06]: Maybe 50 or 60 million virtual machines a day across all three clouds, so we're one of the biggest compute orchestrators out there.Reynold Xin [00:17:13]: Stuff for sure for CPU compute.Swyx [00:17:14]: Yeah.Matei Zaharia [00:17:14]: Yeah.Reynold Xin [00:17:15]: the. And all of this process, I think exabytes of data, I joked about depending on which time zone you are, typically before you have breakfast, Databricks would have processed exabytes of data already on that day. and on Neon, it's pretty interesting, too. It's launching, I think, 13 million databasesSwyx [00:17:34]: YeahReynold Xin [00:17:34]: a day now.Swyx [00:17:35]: Yeah, to me that was, like, aReynold Xin [00:17:36]: And that's just likeSwyx [00:17:37]: Like, what do you mean?Matei Zaharia [00:17:38]: Yeah. And that's the point.Reynold Xin [00:17:40]: And a lot of those were thanks to agent- agents and branching experimentationSwyx [00:17:44]: YeahReynold Xin [00:17:44]: because we made it so easy and so quickly, and thanks a lot to Nikita's team, to launch databases. It's, the. So it's changing the way people use databases.Swyx [00:17:54]: Yeah. Okay, we're gonna go into more database talk in a bit, but I wanna make sure we close up anything on Omnigentt. you mentioned, you were excited about the securityOmnigent Security, Contextual Policies, and Spend ControlsSwyx [00:18:03]: control side.Matei Zaharia [00:18:04]: Yeah.Swyx [00:18:04]: a lot of companies are figuring that out right now, as well as the spend side.Matei Zaharia [00:18:08]: Yep.Swyx [00:18:09]: what have you found there?Matei Zaharia [00:18:11]: Yeah, so I spent quite a bit of time talking to internal users, developers, security team, managers, and also lots of customers, and there's a few things. Like, first of all, one thing, that immediately was. became obvious is for security, there's this tension between, like, usability and security. And, the way people do. Like, a lot of coding agents today have very basic things like you can tell me which tool patterns I'll allow or disallow or whatever. It's like yes or no. But that puts you in a very tough spot. So just as an example, like, should my agent be able to read, some confidential documents, or let's say, should it be able to install new packages from npm, which, maybe it's compromised. Yes or no? Like, maybe I wanna allow it. Should my agent be able to publish stuff to the company website? Well, if I'm using it to code on the website, yes. But should it be able to do both, so it can, like grab a confidential document and be prompt injected and leak it? Probably not. So the thing we decided we need is stateful or what we call contextual policies where you keep track of the state of that session. It's not like is it allowed to push to the marketing site or not, but, like, hey, if it did a risky thing, like it installed, a old package from npm, or it read, like, 1,000 confidential docs, then no. Then don't, don't do it. Otherwise, maybe it's okay. That's one example of, like, moving that trade-off so it's both more secure and more useful by having a more powerful engine, essentially. This requires tracking sessions. The other piece that was interesting there is, like, there are these very level events it's doing, and you want some libraries on top that parse them. Like, for example, we have a, MCP server on Google Drive internally. It's got 60 API calls. like, how do I know which of those, like, will share a document with stuff on the internet and which ones won't? It's, it's annoying. So we designed in Omnigentt the policy layer so that it's functions and you can have libraries. Like, someone can make something that maps the level events to high-level ones, and then you write a policy about the high-level things that came out. so and thatSwyx [00:20:25]: This is related to the Panther,Matei Zaharia [00:20:27]: Yeah, Panther is. will help with that. PantherSwyx [00:20:30]: YeahMatei Zaharia [00:20:30]: a similar idea on the event processing side, and it's Python-based versus a weird custom language. this is more, as in realSwyx [00:20:39]: I didn't even know we were good yeah.Matei Zaharia [00:20:41]: Those things are happening, yeah.Swyx [00:20:42]: Yeah.Matei Zaharia [00:20:42]: So yeah, but these are the cool things. I think the contextual or stateful part, and then the way it can be libraries, and that was another reason to make it open source because others will write libraries and, like, we and our customers can use them. And the final thing, because it's stateful, one of the states we track is how much you spent in that session. So I can. I've had, like, I ask an agent to debug something, and it spent $500 because it decided to read a lot of log files and burn a lot of tokens. but I can literally say, “Okay, launch a agent to do this and cap it to spending $5.” Like, ask me for permission if it needs more. And because we're counting that within that session, it'll pop up and tell me, “Okay, you spent five, $5. Do you wanna go on?”Reynold Xin [00:21:27]: So important context here. Matei spent the last five years, a lot of his time was architecting Unity Catalog at DatabricksMatei Zaharia [00:21:34]: YeahReynold Xin [00:21:34]: which is the governance layer for data.Matei Zaharia [00:21:35]: That's right, yeah.Reynold Xin [00:21:36]: And he's combining expertise at that layer together with all the AI governance he knows.Matei Zaharia [00:21:41]: Yeah.Swyx [00:21:41]: DoMatei Zaharia [00:21:41]: But I also spent a lot of time being annoyed by coding agents and getting prompts.Matei Zaharia [00:21:46]: And also as theReynold Xin [00:21:48]: All the aboveMatei Zaharia [00:21:48]: I don't want to end up on the front page as, like, I installed some weird npm package and leakedSwyx [00:21:53]: YeahMatei Zaharia [00:21:53]: all the code, so I'm especially paranoid. But also I have very little time, so I don't want to sit there approving, like, do you want to run a 20-line, bash script, yes or no? so that's why I spend a lot of time figuring out, like, how can I make it as safe as possible and not annoying?Swyx [00:22:10]: Yeah. Is safety and mmm, let's call it security a bigger concern than token maxing or token budgets? which one is, likeMatei Zaharia [00:22:19]: Oh, yeah, they're both there. I don't know. I guess it depends on the type of company you are. So I think, some companies, like, the budget is, limited and, they really care about thatSwyx [00:22:34]: you can be Uber and still be concerned?Matei Zaharia [00:22:36]: Yeah. Oh, yeah, totally. Yeah. If you haveReynold Xin [00:22:38]: for us, securityMatei Zaharia [00:22:39]: YeahReynold Xin [00:22:40]: super paramount.Matei Zaharia [00:22:40]: For us, security is absolutely critical as a, cloud provider. It's, it's the most important thing, and, token maxing, we're not so worried about it yet, but I've seen the Like, for example, I talked to some consulting companies. They have, like, 100,000 employees who are all coding for customers. If those each spend, like, an extra $1,000 a month, that's, that's not fun.Swyx [00:23:04]: YeahMatei Zaharia [00:23:04]: we have, like, only a few thousand engineers.Swyx [00:23:06]: What's the policy in Databricks? Is it just unlimited or what'Matei Zaharia [00:23:08]: It's, it's unlimited, but we do. we use our own product to, like, analyze the traces and stuff, and we have a team that'looking to optimize and to see if anyone's doing something weird. And, we had some really cool insights just from analyzing current traces, like whichSwyx [00:23:24]: YeahMatei Zaharia [00:23:25]: models are better at, say, Rust versus like TypeScript or whatever. So yeah, at least in our code base.Swyx [00:23:31]: Yeah. Amazing. Obviously, I have to ask the token question, obviously.Matei Zaharia [00:23:34]: Yeah.Swyx [00:23:34]: I think it'sReynold Xin [00:23:34]: YeahSwyx [00:23:34]: it's a key thing. But yes, security and control above that, and figuring out a sane layer there you can have some autonomy, but, not too much.Matei Zaharia [00:23:43]: Yeah. Yeah, and we wanna make it super easy. As a engineer, you should set a thing. So in Omnigentt, you can ask your agent, “Set a policy on yourself to do this.” So it can likeSwyx [00:23:52]: But if there's something I should be showingMatei Zaharia [00:23:53]: YeahSwyx [00:23:53]: I don't, I don't see it on the GitHub, but,Matei Zaharia [00:23:55]: Oh, yeahSwyx [00:23:56]: there's justMatei Zaharia [00:23:56]: Well, in the docs there's something.Swyx [00:23:57]: Yeah, this is it.Matei Zaharia [00:23:58]: You can look at it later.Swyx [00:23:59]: Okay. Yeah.Matei Zaharia [00:23:59]: Just look in the docsSwyx [00:24:00]: YeahMatei Zaharia [00:24:00]: contextual policies if you wanna see.Swyx [00:24:04]: I just like to point peopleMatei Zaharia [00:24:05]: look at the built-in policies.Swyx [00:24:06]: Yeah.Reynold Xin [00:24:06]: Yeah.Swyx [00:24:06]: If you want to, follow up on this is exactly where to look, right?Reynold Xin [00:24:10]: Yeah.Matei Zaharia [00:24:10]: Yeah. yeah, and the story of these is, like, I just wrote, like, I wrote a doc with like 10 ideas for things before as you were working on them. Well, that was, like, my wish list of things people asked, and I told the team, like, “Hey, can you do like at least five of these for the launch?” And then they just got back with all of them, so.Swyx [00:24:29]: Oh, wow.Matei Zaharia [00:24:29]: so you can come up with more, but them- some of them are just meant to be examples. really you can intercept, like, any event the agent is making, and you can then either block or force it to ask the user or, like, allow, and you can update state to keepSwyx [00:24:45]: YeahMatei Zaharia [00:24:45]: track stuff.Swyx [00:24:46]: Yeah, ‘cause ultimately you're, I think of you as, like, a systems designer.Swyx [00:24:50]: You let people plug in, right? That's the wholeMatei Zaharia [00:24:51]: YeahSwyx [00:24:52]: modus operandi of what you do.Matei Zaharia [00:24:53]: Yeah.Swyx [00:24:54]: It's likeMatei Zaharia [00:24:54]: And we care a lot about also composab- like, can someone else write a library that others use, whichSwyx [00:24:59]: YeahMatei Zaharia [00:24:59]: this is meant to.Reynold Xin [00:25:00]: There's also a batteries included philosophy hereMatei Zaharia [00:25:03]: YesReynold Xin [00:25:03]: probably very similar to how you did Spark, which is you could just start using.Swyx [00:25:06]: Yeah.Matei Zaharia [00:25:06]: Yeah, that's right. It has to be good out of the box at certain things, and then you can build your own things on top that, like, we don't wanna do. But in Spark, if you just wanna like, I don't know, like read a table or do, like, a aggregation, it should be awesome at that out of the box.Building on Omnigent: Contributions, Startups, and AnalyticsSwyx [00:25:23]: Yeah. People wanna catch up on Omnigentt, they should watch your keynote.Swyx [00:25:26]: they should go through the GitHub and the docs. If they wanted to contribute, or they want to build on this ecosystem what would you call out as the most high-leverage places get involved?Matei Zaharia [00:25:36]: Yeah, do get involved in the Discord and in GitHub. Our team is there, is monitoring, and, some of the things people ask for we just built ourselves. Some of them, we're, we're collaborating with them to build it. and also tell us, likeSwyx [00:25:49]: Yeah, they're gonna be veryMatei Zaharia [00:25:49]: how you would like to use it because I think especially for developers, like, everyone wants it to work their own way, and a really good developer tool, like you have to hear the feedback on all the ways and figure out the abstractions and how to let people customize. So we'd love to hear, like, if you think, “Hey, I, I don't want it to work this way,” tell us. We really just wanna get that compatibility layer across agents and then let you do stuff on top.Swyx [00:26:14]: Yeah. is there any, in terms of like the startup side, I'm, I'm a founder.Swyx [00:26:18]: I wantMatei Zaharia [00:26:18]: YeahSwyx [00:26:18]: I see an opportunity, I wanna get in front of you. What's your request for, like, a startup that, like, I wish someoneMatei Zaharia [00:26:23]: Oh, like you wanna integrate with us?Swyx [00:26:24]: someone was working on this.Matei Zaharia [00:26:26]: Oh, for a startup?Swyx [00:26:27]: Yeah.Swyx [00:26:28]: Like, your, you got your own startup. It's doing well.Matei Zaharia [00:26:30]: Yeah.Swyx [00:26:30]: But like, if you weren't working on your own startup, what is, like, obvious that you should You advise many startups too, obviously.Matei Zaharia [00:26:37]: I do think, just as a company with a lot of engineers, like anything that helps me make sense of how people are usingSwyx [00:26:46]: SpendMatei Zaharia [00:26:46]: coding agents and,Swyx [00:26:48]: Yeah. AnalyticsMatei Zaharia [00:26:48]: spend, but also quality or like you should write, you should add this skill, or you should write this thing, or your agents are really horrible at tasks involving this service, so I go spend time. That would be nice. yeah.Swyx [00:27:00]: Yeah. The closest I've found is, this team, GitAI.Matei Zaharia [00:27:03]: Oh, cool. Yeah.Swyx [00:27:04]: They started with, like, we will just do, code and human attribution, but they're building the analytics layer on top of that.Matei Zaharia [00:27:12]: Yeah.Swyx [00:27:12]: I do think, like, there are a bunch of, like, artificial analysis is obviously,Matei Zaharia [00:27:18]: Yeah, they have their benchmarksSwyx [00:27:18]: doing super wellMatei Zaharia [00:27:19]: YeahSwyx [00:27:19]: with their stuff. so there's, there will be people. I think this is like the domain of consultants first, but then peopleMatei Zaharia [00:27:26]: YeahSwyx [00:27:26]: will build software that, let's say, it's kinda like the management planeMatei Zaharia [00:27:29]: YeahSwyx [00:27:30]: for coding agents.Matei Zaharia [00:27:30]: Yeah, I think there'll be a lot of insights there. You have it in other areas.Swyx [00:27:34]: Okay. Well, and then the other, big thing is your dream engine.LTAP: Lake Transactional/Analytical ProcessingSwyx [00:27:39]: maybe you wanna tell the story of, LTAP.Reynold Xin [00:27:45]: So, and background with. I'm, I'm gonna make people listen to our Ankur Goyal episode where we talked about SingleStore, HTAPMatei Zaharia [00:27:52]: YeahReynold Xin [00:27:52]: and all that history.Matei Zaharia [00:27:52]: Yeah. The LTAP idea is pretty simple. so if people have heard of the, Ankur's, talk about HTAP, it's effectively the world of databases. Sorry, there's like maybe a lot of context needs to be injected here. The world of databasesSwyx [00:28:06]: I am happy to be the database podcast that I'm forcing people to, like, learn your databases, guys.Swyx [00:28:11]: You cannot vibe code with just markdown files.Reynold Xin [00:28:13]: Yeah.Swyx [00:28:13]: Like,Reynold Xin [00:28:14]: It's one of the most important fundamental systems technologies out there. But the world of database effectively split into roughly two halves. There's what we call OLTP databases, which are transactional, and think of your Postgres, your MySQL, your Oracle databases, and the other side is what we call analytics, and sometime might refer to term OLAP. And the difference is on OLTP, you typically have maybe run some transaction on some event that looks up at one specific row. We update that row, right? It's a very oriented data structure. And on analytics, you're trying to reason on the data. You're trying to compute, “Hey, what's my revenue per store? What's my. How's my website doing every day?” And then you, eventually want to probably end up running anal- machine learning on it to predict, “Hey, how will my maybe sales be going in the future?” they are so very different architecture, and everybody start with OLTP databases. Every app, when you become serious enough, that needs more than markdown files, you need to have a database. You want to lose your data, you want to have some transactional consistency. But once you want to reason on the data, if you only have like- A hundred rows, it's probably okay to run it on your Postgres or your own, your MySQL database. But once you have more data and want to run more complicated analysis, the very analysis might crush your Postgres database. So you start doing, getting data out of the OLTP databaseSwyx [00:29:35]: Replication.Reynold Xin [00:29:36]: Replicate them into the analytic systems and just startSwyx [00:29:39]: Yeah, which for people, Elasticsearch is, like, aReynold Xin [00:29:42]: Yeah. So some of them get into Elasticsearch for, like, blocked analysis. A lot of our customers obviously get into Databricks to run more sophisticated things.Swyx [00:29:51]: Yeah.Reynold Xin [00:29:51]: And there's this term called CDC, whichMatei Zaharia [00:29:54]: Change data captureReynold Xin [00:29:55]: change data capture. and what it does, it reads the binlog of the database, and if you don't understand what binlog is, it's fine. The, but it's a little delta of the data, and it reconstructs based on the delta, the state of the database, on the analytics side. But CDC is, like, a very painful thing. It's how standard in the industry, everybody uses it, but, it ends up being. I think many data engineers ends up being waken up at, like, 3:00 a.m, because there's some pipeline thing.Swyx [00:30:22]: my explanation is, like, Airbyte is like a, became a $5 billion company just doing CDC.Reynold Xin [00:30:27]: Yeah, exactly.Reynold Xin [00:30:28]: CDC is, like, a veryMatei Zaharia [00:30:30]: It's hard.Reynold Xin [00:30:30]: It's one of the most boring but one of the most fundamental operations, like, powering modern society.Matei Zaharia [00:30:37]: huh.Reynold Xin [00:30:37]: But it's so brittle that, we joke that it's, should be called continuous data corruption, because you might change your schema on your OLTP database, and then the CDC pipeline fails to handleSwyx [00:30:48]: YeahReynold Xin [00:30:48]: the schema change.Swyx [00:30:49]: Yeah.Reynold Xin [00:30:49]: And then everything goes out.Swyx [00:30:51]: And there's all sorts of tricks that you can do, like, you add in, like, some versioning or whatever, but yeah.Reynold Xin [00:30:55]: Yeah, but it's a very, in general, very complicated. Like, I think at my keynote, I asked the audience put up their hand if they love their CDC pipeline. Only, like, maybe two people put it up. So if single store, like, about maybe a decade ago, I think the industry had this idea, hey, what if I built a single database that can handle both workloads? Now I don't.Swyx [00:31:12]: Which, like, by the way, every database person ever has ever always dreamed about this.Reynold Xin [00:31:15]: Yes. Yes.Reynold Xin [00:31:16]: This is the holy grail of database engineering is why not build a single system that can do both of this? But it ends up just being a lot of compromises. one, I think one of the first issue is that, hey, each. they say Postgres has a massive ecosystem, right? You want to be using the tools that's built for Postgres. And Spark, for example, had a massive ecosystem. There's a lot of libraries you want to use. If you were to create now a new thing, you don't have a ecosystem. You tend to create a new, smaller proprietary API, and you're lacking both, and it's also very difficult to make it performance-wise to be, comparable on either side. So it ends up being sucking on both. And our whole idea of LTAP, it's obviously a wordplay on the term HTAP, is that we think this is HTAP done right. HTAP wants to build a single engine for both. We think you can get 99% of what you need by unifying the storage, and just have a single storage layer. And once you have the single storage layer, if your Postgres databases are writing data in a column-oriented format, everything analytics can just go read that data directly without any delay, right? There's no pipeline in between, so all the data will immediately be available for reasoning analytics. I think I was telling some customers earlier, hey, when we talked about this is gonna be super useful for agents, I at first didn't really believe in it myself, even though we wrote that positioning.Lakebase, Agents, and Live Operational DataMatei Zaharia [00:32:39]: Yeah.Reynold Xin [00:32:40]: But then last night I was having dinner with a Australian customer, and they told me, “Oh, hey, one of the big issue we have is we have all these logs from our services, and we see SLA dips and want to investigate. But then there's no way for those agents to even understand what's going on in the actual databases themselves. All we see is just, like, product telemetry of the database and the services.” It would make those agents 10 times more powerful if understand, for example, who's placing those orders, what is happening, what exactly are they doing. So now I'm sold on our own message.Swyx [00:33:13]: Yeah.Reynold Xin [00:33:14]: I think it's really. It gets you the almost all of the benefits of the HTAP holy grail, which is, hey, make the data available immediately for reasoning analyticsSwyx [00:33:26]: Yeah, I think,Reynold Xin [00:33:27]: without compromiseSwyx [00:33:28]: in the way that humans are generally intelligent and want to have the ability and access to query anythingReynold Xin [00:33:34]: YeahSwyx [00:33:35]: while they do the work, they also need history and need context.Swyx [00:33:38]: And, like, where else does they get context? That's it's an analytical workload.Reynold Xin [00:33:41]: Exactly.Matei Zaharia [00:33:42]: Yeah. Yeah. And I remember when we had incidents with our databases and engineers said, “Well, I can't just run a giant query on it to see what's going on because that's gonna bring down the database and hoard it even more.” Like, that's the stuff that this gets rid of, because you spin up a whole separate fleet of machines that's doing the analytics. You're not overloading, like, the main databaseReynold Xin [00:34:02]: RightMatei Zaharia [00:34:02]: that's still trying to serve stuff.Reynold Xin [00:34:04]: Yeah.Matei Zaharia [00:34:04]: Yeah.Why LTAP Works Now: Parquet, Postgres, and LakebaseSwyx [00:34:05]: So this has been a dream for a while. what had to get done in order to get to today? Like,Reynold Xin [00:34:11]: Yeah.Swyx [00:34:11]: I feel like, you have announced variants of this several times, but it wasn't as clear as LTAP.Reynold Xin [00:34:18]: Yeah.Swyx [00:34:18]: I think LTAP is like Like, okay, we've got it, guys.Matei Zaharia [00:34:21]: This thing, yeah.Reynold Xin [00:34:21]: I was talking to somebody at Meta, and then he was asking me, “Hey, what's the catch? Why is it possible now?” And I think the reality is we took a lot of time to work on the Lakebase architecture. obviously a lot of it came from the Neon team, which is a separation of storage from compute. And it turned out it was just a tiny little step away going from that to this LTAP idea, which is, hey, we just. in the Neon architecture and in Lakebase architecture, we're writing data in oriented format to the open data lake, but in there we're writing in Postgres pages. Ali and I were spending a lot of time debating, hey, can we just change that to write in column-oriented format? And we're just debating, and one day, one of our engineers who's, like, super smart came in, he's like, “Hey, I just prototyped it. It works.”Swyx [00:35:07]: Wait, it's, prototype what?Reynold Xin [00:35:09]: Prototype, instead of storing the data in the data lake in the oriented formatSwyx [00:35:15]: ColumnReynold Xin [00:35:15]: like Postgres pagesSwyx [00:35:15]: YeahReynold Xin [00:35:16]: write them in Parquet.Swyx [00:35:17]: Yeah.Reynold Xin [00:35:18]: and he just made the observation that, hey, our storage fleet has a lot of extra idle CPUs And we could use those CPUs to do the transcoding from row to column, where row is good for OLTP, but column is good for analytics. so let's do that transcoding at that time. And as a matter of fact, once you transcode the data compresses better. So from those services writing to, for example, S3 or other data lake, like object stores, you can write them faster ‘cause now they are now smaller.Matei Zaharia [00:35:49]: Yeah.Reynold Xin [00:35:49]: So there's no overhead, it's no compromise in performanceMatei Zaharia [00:35:52]: Some CPU overhead.Swyx [00:35:54]: Yeah, because,Matei Zaharia [00:35:55]: YeahSwyx [00:35:55]: we had extra CPUs anyway.Matei Zaharia [00:35:56]: We had that fleet anyway, yeah.Swyx [00:35:57]: so the debate ended. it's one of the classics of, tech, issue of a lot of debate, but then somebody went ahead and just tried to prototype it and it worked.Matei Zaharia [00:36:06]: But, like, something this strategicSwyx [00:36:07]: That's rightMatei Zaharia [00:36:07]: and important to the company, I expect there to be, like, a kickoff thing, like a design doc. Nothing like that.Swyx [00:36:13]: Nothing like that.Swyx [00:36:14]: He just. We were debating in many meetingsMatei Zaharia [00:36:17]: Yeah.Swyx [00:36:17]: and then we're just debating whether it's possible or not from first principle.Matei Zaharia [00:36:20]: YeahSwyx [00:36:20]: and then, somebody just did it.Matei Zaharia [00:36:23]: Yeah, if you set yourself up so people do that'll be great. And that happened a bit with Omnigentt too. I think if I just had a doc on, like, we can make these together, everyone would, would think, “Oh, what about this? What about this?” But then you. if you try it out, it helps. And then if you have real users and they bash it and, like, it's still working, or in this case, if you have the workload, what the workload looks like, you can just test the same pattern then.Databricks' Culture of Fast PrototypingSwyx [00:36:47]: Yeah.Matei Zaharia [00:36:47]: Yeah.Swyx [00:36:47]: Tech aside, which is very cool, this is, like, the most important thing, the culture of innovation, and you don't have to ask my permission, you don't have like, do a whole form- formal process, just do it?Matei Zaharia [00:36:59]: Well, especially these days, I think withSwyx [00:37:01]: YeahMatei Zaharia [00:37:01]: AI, it's easier to buildSwyx [00:37:02]: But so, likeMatei Zaharia [00:37:03]: a prototypeSwyx [00:37:03]: I think you are very I made a lot of suite of, like, large companies and, like, I think that at scale, things slow down, and I'm sure you felt it already, but somehow you have this core of people that, like, are exempt. How? I think we hire and we work with really good people, and that's a very important part of it, and empowering them, but also spending a lot of time, maybe us in the trenches matter a lot also.Matei Zaharia [00:37:28]: Yeah, I think, I think first, people can adapt to being in the larger company, so that helps. And we wanna make sure they know that they can try stuff and settle debates and have a lot of examples of how it was done before, or launch a thing in beta or whatever. and then the other thing I do think as a company, like despite the size, we don't launch that many, like, products. We try to keep it pretty coherent. That's, that was the whole, like, theory of the company, was like instead of having, like, 20 Amazon services you need to set up, like a analytics and machine learning stack, you just have one, and it's, like, the same API, the same semantics across all of them, the same copy of the data. So that requires, like, unification. And then we added one more thing at a time. Like, we added storage with Delta Lake. We didn't used to do any storage. Then we added SQL, we added, machine learning platform stuff. So, but yeah, don't, don't do too many, but do those things well and, that also helps, it helps keep it manageable.Reynold Xin [00:38:33]: Yeah. The other thing we encourage a lot is instead of building, boil the ocean for everything, let's figure out how do we do it incrementally, how do we do it very quickly. Like, many of our productsMatei Zaharia [00:38:43]: YeahReynold Xin [00:38:43]: they're built in the span of weeks, and then we go to, hey. Like, usually my first question to whoever team is building is who's the target customer? Who are you working with? Are you on a first-name basis with them? Are you texting with them? I think having that very tight loop,Matei Zaharia [00:38:59]: Can you bring up another launch that comes to mind when, in this thing? I just want to give examples.Reynold Xin [00:39:04]: Omnigentt itself happened that way.Reynold Xin [00:39:05]: Yeah.Matei Zaharia [00:39:06]: Who's the customer? That's a good oneReynold Xin [00:39:34]: storage layer we did. we had, our largest customer at the time said like, “Okay, I need some. I want something in the cloud ‘cause, I. if the rest of our network is compromised, like this thing needs to be separate to store and query the events.” And then, talked to us, he said, “Okay, this is the rate of events per second. This is, like, the freshness I want. Can you do it?” So that was, like, way larger than any workload we had, and we had our, engineer, working on that, Michael Armbrust, and he worked just to make this work. And once it worked for them, it worked for everyone else. Yeah. This was early in the company, probably like four years in or something.Matei Zaharia [00:40:24]: 20- 2018?Swyx [00:40:26]: Yeah, ‘17, ‘18.Matei Zaharia [00:40:28]: Few companiesSwyx [00:40:28]: Do you have other examples?Matei Zaharia [00:40:30]: there'Swyx [00:40:31]: Maybe you have othersMatei Zaharia [00:40:31]: yeah, Clean Room, which is how you share data in a way without sharingSwyx [00:40:35]: YeahMatei Zaharia [00:40:35]: underlying data, but you allow specific operations. Those were done effectively initially just for two customers. I think the industry has a sense of, hey, maybe if you overfit to, like, one or two customers, it's gonna be really bad for you. But I think the, downside of overfitting is much smaller than the upside itself. And if you try to be too ambitious and boil the ocean, it's a much bigger problem.Swyx [00:40:58]: Yeah. Yeah.Matei Zaharia [00:40:58]: ‘Cause you might end up having no customer.Swyx [00:41:00]: Yeah, that's more, that's the more likely outcome.Matei Zaharia [00:41:02]: Yeah.Tech Companies vs. EnterprisesSwyx [00:41:03]: than you can pivot from there. I do think there is such a thing as a bad customer that sometimes you should fire. Yeah.Matei Zaharia [00:41:08]: They could exist sometimes if you drive. well, one of the challenge I think we probably see, and maybe many AI, so newer generation companies are seeing is, so tech companies are very different from tech companies or traditional enterprises.Swyx [00:41:22]: Yeah.Matei Zaharia [00:41:22]: And, if you optimize everything just for tech companies, you might have various challengesSwyx [00:41:27]: OhMatei Zaharia [00:41:27]: scaling them outside of tech companies.Swyx [00:41:28]: Okay, what likeMatei Zaharia [00:41:30]: YeahSwyx [00:41:30]: what like top three differences that you always think about?Reynold Xin [00:41:33]: Governance is a big oneMatei Zaharia [00:41:34]: I think, yeah, a big one is like, yeah, security, data privacy, governance, all that stuff. So usually if you're building some kinda like B2B or developer tool, like your biggest market is gonna be enterprises, but it's just very different. A company that's existed for like, it's had some form of IT for like 30 years, they have so many legacy systems or they operate in a regulated space. whereas a startup or, even like a, like sorta more recent tech company, all the. everything is new and pristine. So yeah, it's just different, and if you've never worked with enterprises or been in one, you just won't know about it.Reynold Xin [00:42:13]: Yeah.Matei Zaharia [00:42:13]: Yeah.Reynold Xin [00:42:13]: And the procurement process is probably quite different. There's far more stakeholders.Matei Zaharia [00:42:17]: Yeah, that is one. Yeah.Matei Zaharia [00:42:18]: Another piece that's interesting is I think some tech companies, people, will say, “Oh, I can build that myself,” right? I'll just build that myself.Matei Zaharia [00:42:27]: So then you go,Reynold Xin [00:42:28]: I don't think people say that about Databricks, butMatei Zaharia [00:42:31]: yeah, it dependsReynold Xin [00:42:32]: They do.Matei Zaharia [00:42:32]: They do?Matei Zaharia [00:42:32]: Yeah, the. Yeah, and it depends on the teams and things. So, but, on the other hand, like many of the enterprises say, “I don't, I never wanna be in the business of building that.” Like, I don't want my, whatever, I'm a retailer or something, I never wannaReynold Xin [00:42:45]: Yeah, sell clothes,Matei Zaharia [00:42:46]: be down because like some weird like nerd like couldn't get streaming pipelines working.Matei Zaharia [00:42:51]: That is not what I'm doing.Reynold Xin [00:42:53]: Yeah.Reynold Xin [00:42:53]: Yeah. This makes them great customers, to be honest, right?Matei Zaharia [00:42:55]: Yeah. But you have to understand that it's hard without having worked there and stuff, like you may not appreciate.Reynold Xin [00:43:01]: Look, I think they're all great. don't get me wrong, they have different challenges. But the, many of the tech companies, for sure there's a lot, far more DIY.Matei Zaharia [00:43:10]: On the flip side, you have people who are. they're very much experts in their domain, like they're building airplanes, they're, designing medicines, whatever, and they just want to bridge the technology, where like they don't wanna learn, databases or whatever. As cool as we think it is, even as interesting as the average software engineer might think it is to read a little bit, like they just never wanna know. They just say, “I have a, giant like, matrix or whatever with my, clinical data, like how do I, how do I like cluster it or whatever?” So yeah.The Dream Engine and Rewriting the Database StackReynold Xin [00:43:40]: Yeah. That's true. Okay, so and then I wanted to build out the dream engine, vision. where does this all lead? So one of the thing we, realized maybe a couple years back is that every single database engine out there, especially on the analytics side, are a decade old. pretty much everything that have reasonable traction are about a decade old. And they all started targeting some very specific narrow use cases, and then over time it's become more and more successful. They have grown in their ambition, and then they try to support more and more use cases. But the fastest way to support those use cases tend to be hacked around the abstractions that were initially created, that were not for those use cases.Matei Zaharia [00:44:23]: Yeah.Reynold Xin [00:44:23]: And then, but you can support them more or less okay. And before it, after 10 years of organic evolution that way, it becomes a gigantic pile of s**t.Reynold Xin [00:44:31]: the. And, but that includes Databricks. And very few company or very few systems, I think, have the gut to say, let's go start from scratch. Let's go back to the drawing board and design, knowing everything we know today after a decade of workloads and probably billions in revenue, let's attempt to rewrite it from scratch and make sure it will work and it can support all of these use cases. So we started doing that, but it's a very ambitious project. by the way, you can search on Wikipedia, there's this thing called second system syndrome.Matei Zaharia [00:45:08]: Yeah, I know that. Yes.Reynold Xin [00:45:09]: Or second system effect.Matei Zaharia [00:45:11]: Every developer must know what a second syndrome is.Reynold Xin [00:45:12]: It's you built your first thing and it works out great, and the second one's bound to fail because you become too ambitious.Reynold Xin [00:45:19]: And then you ask so many requirements.Matei Zaharia [00:45:20]: Or like you think everythingReynold Xin [00:45:21]: YeahMatei Zaharia [00:45:21]: and then you're likeReynold Xin [00:45:22]: You justMatei Zaharia [00:45:22]: you're, “I'm gonna design the perfect system this time.”Reynold Xin [00:45:24]: Yeah. And it turned out it's not perfect, and then it start failing and you're too ambitious, never launch, and you get killed. The, and the engineering team that started this, they were brilliant. I think we hired some of the best database engineers, on the planet into Databricks, and they were brilliant. Thank God it's not their second system. Many of them have built more than two in the past.Matei Zaharia [00:45:44]: Ah, nice.Reynold Xin [00:45:45]: But they were still worried about this, hey, building a database engine from scratch, I think the conventional wisdom is gonna take like five years to mature. This would be a very long-term project. It could fail. I think one of the engineers jokingly said, “Hey, maybe we just call it Reynolds Stream Engine.” If we name after a founder, maybe we then may get canceled or killed. But I think they built something pretty remarkable. they went back to. They changed the way the database engines were built from a paradigm point of view. Usually when y
If you've ever poured months into building a semantic layer only to watch it become shelfware the moment the business pivoted, Jacob Matson has some thoughts. And a metaphor. Your data is a jungle—and a semantic layer is a highway. Great if you need to get somewhere fast and reliably (monthly active users: highway, please). But the interesting business questions? The slicing, the dicing, the nuanced dimensions that actually differentiate your company from its competitors? There's no highway for that. There never will be. Jacob, a developer advocate at MotherDuck with deep roots in accounting and ERP systems, joined Michael, Moe, and Julie to talk through what comes after the semantic layer—or at least alongside it. The conversation covered why the most important parts of any business are precisely the parts that resist being modeled in someone else's framework, why AI is actually pretty good at writing SQL but not so great at remembering what it figured out yesterday, and whether the real job to be done here is less about modeling and more about search. Oh, and the uncomfortable truth that at episode 300, we still don't have a great answer for metric drift. But we've got some really good questions. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.
AI isn't necessarily creating impossible new attacks, but it is drastically lowering the technical barrier to entry for cybercriminals. In this episode, Ashish Rajan speaks with Simon Biggs, Cyber Incident Response Specialist at Varonis, about how AI is accelerating the attack lifecycle. Simon explains how attackers are using AI kits to instantly set up ephemeral phishing portals, query SQL databases in minutes, and bypass AI guardrails to compile Remote Access Trojans (RATs). We also discuss the shift in ransomware tactics from "encryption-first" to "data-theft-first," and how AI empowers attackers to post-process terabytes of stolen data to monetize it in novel ways. For defenders, the message is clear: if your S3 access logs and SQL transaction logs aren't turned on before a breach, your forensics team won't be able to tell lawyers or regulators what data was actually lost. Discover why data classification and proactive logging are the ultimate lifelines for IR teams in the AI age. Guest Socials - Simon'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 Security, you can check out our sister podcast - AI Security PodcastQuestions asked:(00:00) Introduction(02:00) Simon Biggs' Background in Law Enforcement and Varonis(03:10) Is There a Huge Volume of Sophisticated AI Attacks?(04:10) How AI Accelerates SQL Queries and Business Email Compromise (BEC)(05:15) Why AI Kits Are the New Metasploit and BloodHound(08:15) Varonis Threat Labs: Copilot Prompt Injection Vulnerability(09:20) The Forensic Challenge: Auditing Prompts vs. Understanding AI Output(10:30) Tricking AI Guardrails to Compile Malware(12:15) Defensive Strategies: Shadow AI, Permissions, and Logging(15:30) Using Defensive AI and BloodHound for Threat Hunting(17:30) Why Ransomware is Now "Data First, No Encryption"(20:50) The Legal Nightmare of Unclassified Stolen Data(23:20) Why Windows Forensics Can't Tell You What Data Was Stolen(31:20) The Crucial Importance of Enabling S3 and Cloud Audit Logs(35:10) How AI Allows Attackers to Post-Process Terabytes of Stolen DataResources spoken about during the episode:Simon's Research at VaronisArticle about SearchLeak Article about RepromptVaronis Threat LabsThank you to Varonis for sponsoring this episode of Cloud Security Podcast
JDK 26 optimise la JVM dans ses moindres recoins, le SDK Java d'Agent2Agent passe en 1.0, Micronaut 5 est là. Côté terrain, un retour d'expérience après 40 jours à coder avec 100 % d'IA : génie ou junior, Alzheimer numérique et dette technique invisible. Pendant ce temps, GitLab restructure, Microsoft suspend ses licences Claude Code, et un développeur injecte un prompt destructeur dans sa lib JUnit. La révolution IA a un coût et les boites commencent à s'en rendre compte. Enregistré le 12 juin 2026 Téléchargement de l'épisode LesCastCodeurs-Episode-341.mp3 ou en vidéo sur YouTube. News Langages Les améliorations de performance dans le JDK 26 https://inside.java/2026/06/09/jdk-26-performance-improvements/ Côté bibliothèques, l'API LazyConstant (anciennement StableValue) fait son entrée en prévisualisation pour permettre une initialisation paresseuse, sécurisée pour les threads et optimisée par le mécanisme de constant-folding de la JVM. L'extraction de chaînes de caractères via MemorySegment::getString a été revue pour réduire considérablement les allocations intermédiaires et les copies en mémoire off-heap, accélérant fortement les traitements sur les chemins critiques (hot paths). La méthode générée automatiquement hashCode() pour les classes de type record a été optimisée par la JVM pour atteindre un niveau de performance équivalent à une implémentation écrite manuellement. Le ramasse-miettes G1 bénéficie du JEP 522 qui redessine sa table de cartes (card-table) afin de réduire les coûts de synchronisation des barrières d'écriture, offrant un gain de débit de 5 % à 15 % sur les applications manipulant énormément de références d'objets. Grâce au JEP 516 (Project Leyden), le cache d'objets Ahead-of-Time (AOT) adopte un format de flux agnostique, ce qui lui permet d'être compatible avec n'importe quel Garbage Collector, y compris le ramasse-miettes à très faible latence ZGC. Le démarrage de la JVM s'accélère par défaut lorsqu'aucune taille de tas n'est configurée, car HotSpot n'applique plus de pourcentage initial (InitialRAMPercentage) mais démarre directement avec la taille minimale (MinHeapSize) pour éviter d'allouer des métadonnées inutiles. Les threads virtuels gagnent en robustesse en étant désormais capables de céder la main (yield) pendant les phases d'initialisation des classes, éliminant ainsi le risque de famine des threads porteurs (carrier threads). Le compilateur C2 JIT améliore son modèle de coût pour la vectorisation des boucles (SIMD) et se montre maintenant capable de compiler et d'optimiser des méthodes dotées de listes de paramètres extrêmement longues. Librairies Release candidate du A2A Java SDK supportant versions 0.3 et 1.0 en même temps https://medium.com/google-cloud/a2a-java-sdk-1-0-0-cr1-released-f0c651ec9139 Dernière étape avant la GA : Toutes les fonctionnalités prévues pour la version 1.0 sont finalisées. Migration simplifiée depuis la Beta1. Compatibilité v0.3 : Ajout d'une couche de compatibilité permettant aux agents v1.0 de communiquer avec les systèmes v0.3 (via JSON-RPC, gRPC ou REST). Support natif pour Android (nouvel AndroidHttpClient). Uniformisation des clients HTTP pour garantir une cohérence entre les versions. Nouveau parseur SSE (Server-Sent Events) conforme aux spécifications. Ça y est, le SDK Java de l'Agent 2 Agent Protocol est sorti en version 1.0 finale ! (avec compatibilité v0.3 et v1.0) https://medium.com/google-cloud/a2a-java-sdk-1-0-0-final-released-10c05b6aee34 Lancement officiel : Sortie de A2A Java SDK 1.0.0.Final, la première version stable (GA) du protocole Agent2Agent. Objectif du protocole : Standard ouvert (Linux Foundation) permettant aux agents IA de communiquer, déléguer des tâches et collaborer, indépendamment du langage ou du framework. Interopérabilité : Introduction de l'Integration Test Kit (ITK) pour valider la compatibilité entre les SDK (Java, Python, TypeScript, etc.). Transports supportés : Support complet et équivalent pour JSON-RPC, gRPC et HTTP+JSON/REST. Alignement total avec la spécification A2A 1.0.0. Passage aux Java records pour l'immutabilité et moins de code répétitif. Architecture interne basée sur un MainEventBus pour garantir la persistance et éviter les conditions de concurrence. Intégration d'OpenTelemetry pour le suivi et la surveillance. Support d'Android et compatibilité descendante avec la version 0.3. Installation : Gestion des dépendances via Maven BOM (org.a2aproject.sdk). Sortie de Micronaut 5.0 https://micronaut.io/2026/05/20/micronaut-framework-5-0-0-released/ Lancement majeur : Disponibilité générale de Micronaut 5, incluant une refonte de plus de 70 modules et la plateforme BOM. Baselines techniques : Support de Java 25, Groovy 5, Kotlin 2.3 et GraalVM 25.0.3. Optimisations internes : Amélioration significative des performances au démarrage et réduction de la surcharge à l'exécution via une refonte du conteneur IoC et du traitement à la compilation. Architecture HTTP : Support stable de HTTP/3, nouvelle API de formulaires (multipart) et annotations de nullabilité (JSpecify) pour une meilleure interopérabilité Kotlin/IDE. Configuration : Nouveau système d'importation de configuration (remplaçant le Bootstrap Configuration) et validateur de schéma JSON intégré. Fiabilité : Nouvelles API programmatiques pour les politiques de retry et circuit breaker. Sécurité & Outils : Mise à jour majeure des dépendances (Jackson 3, Ktor 3), rafraîchissement du Panneau de contrôle et diagnostics AOT améliorés. Écosystème : Mises à jour complètes pour les bases de données (Data, SQL, R2DBC, MongoDB, Redis), le cloud (AWS, Azure, GCP, OCI) et les tests (JUnit 6, Testcontainers 2.0). Évolutions notables : Intégration HTMX dans Micronaut Views, retrait du support RxJava 2 et migration de divers processeurs d'annotations vers des modules dédiés. Comment rajouter un agent IA dans une app Android, avec le tout nouveau framework ADK pour Kotlin https://glaforge.dev/posts/2026/05/21/wiring-adk-kotlin-agents-in-an-android-application/ Guillaume a participé au développement et au lancement du nouveau runtime ADK pour Kotlin et Android https://developers.googleblog.com/adk-kotlin-android-building-ai-agents/ Tutoriel sur comment intégrer un agent ADK dans une app Dépendances : Ajout du noyau ADK (google-adk-kotlin-core) et du processeur KSP dans build.gradle.kts. Sécurité API : Utilisation de local.properties pour stocker la clé API Gemini et l'exposer via BuildConfig afin d'éviter le hardcoding. Définition de l'agent : Création d'un objet LlmAgent configuré avec le modèle Gemini, des instructions spécifiques et des outils (ex: GoogleSearchTool). Utilisation de InMemoryRunner pour gérer automatiquement le contexte et l'historique de la session. Implémentation de runAsync avec StreamingMode.SSE pour un retour en temps réel dans l'interface. Threading : Exécution des requêtes réseau sur Dispatchers.IO et mise à jour de l'état de l'interface utilisateur sur Dispatchers.Main. Comment développer et hoster des agents IA sur la plateforme d'agents managés de DeepMind https://glaforge.dev/posts/2026/05/21/managed-agents-with-the-gemini-interactions-java-sdk/ L'équipe DeepMind de Google a lancé une plateforme d'agents managés sur son API Gemini Interactions https://blog.google/innovation-and-ai/technology/developers-tools/managed-agents-gemini-api/ Guillaume a implémenté un SDK Java pour utiliser cette API Gemini Interactions, qui donne entre autre accès à tous les modèles mais aussi à cette plateforme managée d'agents IA Agents managés : Permet d'exécuter des agents autonomes qui raisonnent, planifient et exécutent du code dans des environnements isolés (sandboxes), sans gestion d'infrastructure par le développeur. Environnement distant : Utilise des espaces de travail Linux éphémères dans le cloud via le paramètre remote, permettant l'accès réseau et la persistance des fichiers sur plusieurs appels. Agents prédéfinis : Accès immédiat à des agents spécialisés comme deep-research-pro (recherche multi-étapes) ou antigravity (tâches de codage généralistes). Agents personnalisés : Possibilité de configurer ses propres agents avec des instructions système dédiées, des outils spécifiques (exécution de code, recherche Google) et des règles réseau (egress) personnalisées. Architecture basée sur les étapes (Steps) : Utilise une structure de données typée (Step, Content) pour suivre le raisonnement de l'agent, ses appels de fonctions et ses résultats en temps réel. Outils et Schémas : Inclut des utilitaires pour générer des schémas JSON complexes via une interface fluide (DSL), par réflexion Java ou par parsing JSON. Streaming réactif : Support natif des événements en temps réel (SSE) pour suivre la progression de l'agent et recevoir les deltas de contenu au fur et à mesure de la génération. Flexibilité : Fournit un gestionnaire de routage (InteractionsHandler) pour créer facilement des serveurs proxy ou des backends intermédiaires traitant les interactions Gemini. Spring Boot 4.1 https://github.com/spring-projects/spring-boot/wiki/Spring-Boot-4.1-Release-Notes Support natif pour Spring gRPC permettant de créer et tester facilement des applications clientes et serveurs basées sur Netty ou des Servlets via HTTP/2 Introduction du lazy fetching pour les connexions JDBC via la propriété spring.datasource.connection-fetch=lazy afin de ne prendre une connexion du pool que lorsqu'un Statement est réellement exécuté Amélioration de l'auto-configuration de Jackson permettant de définir globalement les contraintes de lecture/écriture pour les formats JSON, XML et CBOR via des propriétés de configuration Sécurisation des clients HTTP bloquants et réactifs face aux attaques SSRF grâce à l'introduction d'un InetAddressFilter bloquant les requêtes sortantes vers des adresses spécifiques Améliorations majeures autour d'OpenTelemetry avec le support complet des variables d'environnement OTel, la possibilité de désactiver le SDK via une propriété globale et l'ajout du support SSL sur les exporters OTLP Ajout de l'auto-configuration pour l'utilisation de Spring Batch avec MongoDB incluant un nouveau starter dédié spring-boot-batch-data-mongo Auto-configuration des endpoints @RedisListener sans nécessiter la déclaration manuelle d'un RedisMessageListenerContainer Dépréciation du support de Apache Derby (projet arrêté), suppression définitive du mode layertools du JAR et réintroduction du support de Spock 2.4 (avec Groovy 5) Upgrade des dépendances majeures de l'écosystème avec notamment Spring Framework 7.0.8, Spring Security 7.1.0 et Micrometer 1.17.0 Outillage Vous êtes plutôt endive ou chicorée ? La librairie Chicory qui permet d'exécuter du code WASM à partir de son application Java est forkée et rejointe la Bytecode Alliance pour continuer son développement https://bytecodealliance.org/articles/endive-and-the-next-chapter-of-webassembly-on-the-jvm Annonce d'Endive : Nouveau projet hébergé par la Bytecode Alliance ; fork de Chicory (moteur WebAssembly pur Java, sans dépendance native). Objectif principal : Permettre aux développeurs Java d'intégrer, charger et déployer des modules Wasm nativement via les workflows Java habituels. Compilateur "Redline" : Intégration à venir de Redline (basé sur Cranelift) pour compiler le Wasm en code machine natif ; performances comparables à Rust/Wasmtime. Zéro dépendance (Java 25+) : Grâce à l'API standard Foreign Function & Memory (Project Panama), l'exécution à vitesse native se fait sans composants externes. Modèle de Composants (Component Model) : Support futur prévu pour consommer des composants (Rust, Go, JS, etc.) via des interfaces typées et sécurisées directement dans la JVM. Prochaines étapes : Fusion de Redline, conformité stricte aux specs Wasm (dont WasmGC) et amélioration du support WASI. Un visualisateur de sessions de travail avec Antigravity https://glaforge.dev/posts/2026/06/11/antigravity-brain-visualizer/ Un projet open source construit avec Micronaut, LangChain4j et GraalVM pour analyser les sessions de travail avec l'outil de développement agentique Antigravity (de Google) Analyse toutes les étapes, les requêtes utilisateur, les outils utilisés, les erreurs rencontrées, les réponses du modèle Gemini fait une analyse pour comprendre les moments clés de cette session de travail Outil buildé avec l'aide d'Antigravity lui-même SBX-Kits : des environnements de développement simplifiés pour les débutants (et les autres) https://k33g.org/20260501-sbx-kits.html Philippe Charrière (:whale: ) présente SBX-Kits (Sandbox Kits), une initiative personnelle visant à simplifier radicalement la mise en place d'environnements de développement pour les débutants, en éliminant la complexité d'installation des outils traditionnels. Chaque "kit" est une archive prête à l'emploi contenant un outil de développement spécifique (comme un langage, un framework ou une base de données) configuré pour s'exécuter de manière isolée et portable. La philosophie du projet repose sur le principe de "zéro configuration" et "zéro dépendance globale", permettant de tester une technologie ou de commencer à coder immédiatement sans polluer son système d'exploitation. L'approche technique s'appuie sur des scripts légers et des binaires portables pré-packagés, offrant une alternative plus simple et moins gourmande en ressources que les conteneurs Docker ou les configurations d'IDE complexes pour l'apprentissage. L'objectif à terme est de proposer un catalogue de kits couvrant les technologies courantes (JavaScript, Python, petites bases de données) pour faciliter les ateliers de programmation et le prototypage rapide. De nombreux kits sont disponibles sur https://github.com/docker/sbx-kits-contrib ghui: une interface utilisateur en ligne de commande (TUI) interactive pour GitHub https://github.com/kitlangton/ghui ghui est un outil en ligne de commande (TUI) écrit en Rust qui fournit une interface visuelle, interactive et rapide directement dans le terminal pour interagir avec GitHub. Il permet de gérer ses pull requests, ses issues et ses notifications sans avoir à ouvrir son navigateur web ou à taper de longues commandes avec la CLI officielle de GitHub. L'outil propose une navigation fluide au clavier, des raccourcis efficaces, et permet de réaliser des actions courantes comme valider une PR, ajouter des commentaires, attribuer des reviewers ou inspecter les logs des GitHub Actions. Conçu pour être extrêmement réactif, ghui s'intègre naturellement dans le flux de travail des développeurs adeptes du terminal et du mode "sans souris". Sortie de Homebrew 6.0.0 https://brew.sh/2026/06/11/homebrew-6.0.0/ Introduction du mécanisme de sécurité Tap Trust : comme les dépôts tiers (taps) peuvent exécuter du code Ruby arbitraire non sandboxé sur la machine, Homebrew demande désormais une confiance explicite de l'utilisateur avant d'évaluer ou d'exécuter leur code. L'API JSON interne devient le choix par défaut, offrant un système plus léger et beaucoup plus rapide pour les développeurs. Sécurisation renforcée de l'environnement avec l'implémentation du sandboxing sur Linux. Évolution des comportements par défaut basés sur un sondage utilisateur : le mode "ask" est activé par défaut pour les développeurs, affichant un résumé des dépendances et une demande de confirmation avant toute action de brew install ou brew upgrade. Améliorations notables des performances globales, notamment un boost de ~30 % sur la vitesse de la commande brew leaves et la parallélisation de la récupération des bottles (binaires) lors des mises à jour. Ajout du support initial pour la prochaine version d'Apple, macOS 27 (Golden Gate). Multiples optimisations pour brew bundle, incluant une gestion plus sécurisée des installations de paquets npm. Méthodologies Retour d'expérience très détaillé et 100% humain sur 40 jours avec une équipe 100% AI hormis le superviseur https://www.linkedin.com/pulse/jai-vir%C3%A9-mon-%C3%A9quipe-de-dev-pour-une-100-ia-pendant-40-luc-bonnin-jlgjf/ Voici le résumé en bullet points : Expérimentation de 40 jours : remplacer une équipe de dev par 100% IA agentique (Cursor) sur un vrai projet en production (playthatsheet.com, 200k lignes de code legacy) Chiffres bruts : 2,3 milliards de tokens consommés, 1 477 prompts, 260 564 lignes ajoutées (+145%), 59% du code final produit par l'IA ROI vertigineux à court terme : 9 mois de travail humain livrés en 40 jours, coût total 260$ d'abonnement + 15 jours de supervision, ROI x18 Profil psy de l'IA : Alzheimer (oublis de contexte), schizophrène (change de méthodo), ado de 12 ans (refait les mêmes erreurs), oscille entre génie et junior sans prévenir Effet iceberg : la dette technique ne disparaît pas, elle se camoufle et s'accélère ; hallucinations = bombes à retardement détectables uniquement par relecture humaine ligne par ligne Paradoxe du bateau de Thésée : perte de paternité et de maîtrise fine du code, baisse de l'autonomie du dev humain qui valide sans avoir construit Arnaque du "monkey money" : consommation de tokens opaque, non corrélée à la complexité (écart de 350% sur des prompts identiques), facturation imprévisible donc impossible à budgéter Syndrome du bazooka : les devs utilisent l'IA même pour changer une couleur CSS, atrophie progressive des compétences et coût écologique délirant Risque stratégique : dépendance irréversible aux vendeurs de tokens (Nvidia, Anthropic, OpenAI), business non rentable qui devra augmenter ses prix Conseil final : approche Pareto, garder 20% du temps en code "fait main", nommer un responsable stratégie IA, l'humain senior reste irremplaçable pour superviser Une libraries de test JUnit cache un prompt qui demande aux coding agents d'effacer les tests https://arstechnica.com/security/2026/05/fed-up-with-vibe-coders-dev-sneaks-data-nuking-prompt-injection-into-their-code/ Agacé par les « vibe coders », un développeur introduit une injection de prompt destructrice dans son code Le développeur de jqwik (un moteur de tests pour JUnit 5) a volontairement inséré une injection de prompt dans la version 1.10.0 de sa bibliothèque Java pour saboter le travail des agents d'IA. L'instruction injectée via la sortie standard (stdout) ordonne textuellement aux LLM d'ignorer les consignes précédentes et de supprimer l'intégralité du code et des tests jqwik du projet. Pour dissimuler cette action aux yeux des développeurs humains, le mainteneur a utilisé des séquences d'échappement ANSI qui effacent la ligne d'injection dans les émulateurs de terminaux interactifs. La modification a été découverte par un utilisateur qui a pointé du doigt les risques majeurs et disproportionnés pour les machines des utilisateurs, bien que certains outils comme Claude d'Anthropic aient détecté et bloqué la consigne malveillante. Face aux critiques de la communauté et aux accusations de comportement infantile ou potentiellement illégal, le développeur a mis à jour ses notes de version pour documenter explicitement son opposition à l'usage de son outil par des IA, avant de refuser tout commentaire supplémentaire sur conseil de son avocat. La réalité du rôle de Principal Engineer https://leaddev.com/career-development/reality-being-principal-engineer Le passage au rôle de Principal Engineer marque une transition majeure où les compétences techniques ne suffisent plus, l'impact se mesurant désormais à travers l'influence, la stratégie et la capacité à aligner la technique avec les objectifs business. Contrairement aux attentes, le quotidien est souvent marqué par une forme d'isolement, car le poste se situe à l'intersection de la direction (qui attend des solutions) et des équipes techniques (qui attendent des directives), sans appartenance directe à un groupe précis. Le rôle exige d'accepter une grande part d'ambiguïté et l'absence de retours immédiats, les projets et les décisions stratégiques mettant parfois des mois ou des années à porter leurs fruits. La gestion du temps devient un défi critique, nécessitant de savoir naviguer entre les sollicitations constantes, la présence en réunion et le besoin de préserver des moments de réflexion approfondie pour concevoir des visions à long terme. La réussite à ce niveau repose sur le développement de compétences humaines pointues (soft skills), notamment la négociation, la communication vulgarisée auprès des profils non techniques, et la capacité à faire grandir les autres ingénieurs par le mentorat. Sécurité Une attaque de la chaîne d'approvisionnement npm utilise binding.gyp pour compromettre des dizaines de paquets https://cybersecuritynews.com/binding-gyp-supply-chain-attack-compromises-dozens-of-npm-packages/ Une nouvelle variante du ver auto-propageable "Shai-Hulud", baptisée "Miasma", cible l'écosystème npm (et PyPI sous le nom de "Hades") en dissimulant son exécution dans le fichier binding.gyp au lieu des scripts classiques preinstall ou postinstall. La technique, surnommée "Phantom Gyp", exploite le fait que npm lance automatiquement node-gyp rebuild dès qu'un fichier binding.gyp est présent à la racine d'un paquet pour compiler des modules natifs C/C++, exécutant ainsi le code malveillant dès la commande npm install. L'attaque contourne la plupart des outils de sécurité traditionnels car l'injection s'appuie sur l'évaluation récursive de commandes (via la syntaxe ) ou directement sur la fonction eval() de Python sous-jacente à GYP, cachée sous n'importe quelle clé du fichier. Le script malveillant télécharge un runtime alternatif (Bun) pour échapper aux détections comportementales de Node.js, puis moissonne les identifiants et secrets des développeurs et des environnements CI/CD (npm, GitHub, AWS, GCP, Azure, Kubernetes, HashiCorp Vault). Plus de 57 paquets npm (dont le SDK serveur de Vapi ou des outils liés à l'IA) et des dizaines de paquets PyPI ont été infectés via des comptes de mainteneurs compromis, le ver republiant automatiquement de nouvelles versions vérolées en utilisant les jetons volés. Loi, société et organisation Restructuration chez Gitlab https://about.gitlab.com/blog/gitlab-act-2/ GitLab entame une restructuration majeure pour s'adapter à l'ère de l'intelligence artificielle agentique, incluant une réduction d'effectifs planifiée de manière transparente et ouverte. L'entreprise prévoit de réduire de 30 % le nombre de pays où elle maintient de petites équipes, d'aplatir sa hiérarchie en supprimant jusqu'à trois niveaux de gestion, et de réorganiser la R&D en une soixantaine d'équipes plus petites et autonomes. Les processus internes vont être revus en intégrant des agents d'IA pour automatiser les revues, les approbations et les passages de relais afin d'accélérer le rythme de travail. La stratégie repose sur la conviction que le logiciel sera bientôt écrit par des machines et dirigé par des humains, ce qui va multiplier la demande de logiciels et transformer le rôle des ingénieurs vers la résolution de problèmes complexes. Sur le plan technique, GitLab reconstruit son infrastructure sous-jacente (notamment Git) pour supporter la charge massive générée par les agents d'IA, tout en misant sur l'orchestration du cycle de vie, la centralisation du contexte des données et une gouvernance intégrée. Le modèle économique évolue vers un système hybride combinant les abonnements classiques et une tarification à la consommation pour le travail effectué par les agents d'IA. Un LLM local sur un mac pourrait coûter plus cher en électricité qu'un modèle hébergé sur OpenRouter dans le cloud https://www.williamangel.net/blog/2026/05/17/offline-llm-energy-use.html Conclusion : L'inférence locale sur Mac M5 Max est 3x plus chère et 2x plus lente que le cloud (OpenRouter). Électricité : Négligeable (~0,02 $/heure pour 50-100W). Matériel (Le vrai coût) : Achat du Mac à 4 299 $; l'amortissement sur 3 à 5 ans plombe la rentabilité horaire. Coût au million de tokens (Gemma 4 31b) : Mac M5 Max : 0,40 à4, 79 (pour 10-40 tokens/s). OpenRouter : 0,38 à0, 50 (pour 60-70 tokens/s). Verdict pro : Le temps humain perdu à cause de la lenteur locale coûte infiniment plus cher que les tokens cloud. Privilégier les API (Anthropic, OpenRouter). Ai didn't kill your junior pipeline https://andrewmurphy.io/blog/ai-didnt-kill-your-junior-pipeline-you-did L'IA n'a pas tué le recrutement des juniors, les entreprises l'ont fait elles-mêmes, par effet de mode. Sans juniors, pas de futurs seniors : on retire l'échelle qui nous a tous fait monter. Tout le monde pêche dans le même bassin de seniors sans le réapprovisionner, pénurie garantie dans 3-5 ans. Une équipe 100% senior + IA est fragile : un départ et tout le savoir tacite s'évapore. Les juniors posent les "pourquoi ?" qui révèlent les bugs et processus absurdes ; l'IA, elle, exécute sans questionner. Les seniors s'atrophient aussi en déléguant leur réflexion à l'IA, pince à double effet sur les compétences. Dépendre des outils IA, c'est sous-traiter sa stratégie talents à des fournisseurs dont les prix vont tripler. Solution : redéfinir le rôle junior (revue de code IA + mentorat), pas le supprimer. Les rapports internes de Microsoft révèlent la crise des coûts de l'IA : les agents coûtent plus cher que les employés humains https://fortune.com/2026/05/22/microsoft-ai-cost-problem-tokens-agents/ Des données et rapports internes chez Microsoft et d'autres géants de la tech ébranlent la promesse de rentabilité de l'IA, révélant que le déploiement d'agents autonomes à l'échelle de l'entreprise revient souvent plus cher que de payer des humains pour le même travail. Le modèle de tarification à l'usage (basé sur les tokens) se heurte à la nature même des architectures agentiques : contrairement à un simple chatbot, un agent boucle, enchaîne les appels d'outils, crée des sous-agents et auto-évalue son code, ce qui multiplie la consommation de tokens par un facteur de 5 à 30, voire jusqu'à 1 000 fois pour des tâches de programmation complexes. L'impact financier sur les budgets de calcul cloud est immédiat ; par exemple, Uber a entièrement épuisé l'intégralité de son budget annuel 2026 dédié au codage par IA en l'espace de seulement quatre mois. Face à cette explosion des coûts, des retours en arrière drastiques sont observés : Microsoft a ainsi commencé à suspendre une grande partie de ses licences internes Claude Code pour rediriger d'urgence ses milliers de développeurs vers sa propre solution moins onéreuse, GitHub Copilot CLI. Les directeurs techniques (CTO) et acheteurs de solutions logicielles qui ont signé des contrats pluriannuels basés sur des projections de réduction de masse salariale se retrouvent pris au piège, les gains réels de productivité ne parvenant pas à compenser les factures d'infrastructure exorbitantes. Conférences La liste des conférences provenant de Developers Conferences Agenda/List par Aurélie Vache et contributeurs : 11-12 juin 2026 : DevQuest Niort - Niort (France) 11-12 juin 2026 : DevLille 2026 - Lille (France) 12 juin 2026 : Tech F'Est 2026 - Nancy (France) 15 juin 2026 : Jupyter Workshops: Demystifying MyST Markdown in Education - Orsay (France) 16 juin 2026 : Mobilis In Mobile 2026 - Nantes (France) 17-19 juin 2026 : Devoxx Poland - Krakow (Poland) 17-20 juin 2026 : VivaTech - Paris (France) 18 juin 2026 : Tech'Work - Lyon (France) 22-26 juin 2026 : Galaxy Community Conference - Clermont-Ferrand (France) 23-24 juin 2026 : MWCP 2026 - Paris (France) 24-25 juin 2026 : Agi'Lille 2026 - Lille (France) 24-26 juin 2026 : BreizhCamp 2026 - Rennes (France) 26-27 juin 2026 : LeHACK - Paris (France) 27 juin 2026 : Asynconf - Paris (France) 2 juillet 2026 : Azur Tech Summer 2026 - Valbonne (France) 2 juillet 2026 : MCP Connect Travel Edition - Paris (France) 2-3 juillet 2026 : Sunny Tech - Montpellier (France) 3 juillet 2026 : Agile Lyon 2026 - Lyon (France) 6-8 juillet 2026 : Riviera Dev - Sophia Antipolis (France) 28-30 août 2026 : State of the Map - Champs-sur-Marne (France) 4 septembre 2026 : JUG Summer Camp 2026 - La Rochelle (France) 10-11 septembre 2026 : Nantes Craft - Nantes (France) 17 septembre 2026 : dotAI - Paris (France) 17-18 septembre 2026 : API Platform Conference 2026 - Lille (France) 18 septembre 2026 : WordCamp Bretagne - Rennes (France) 18 septembre 2026 : dotJS - Paris (France) 18 septembre 2026 : WordCamp Bretagne - Rennes (France) 22 septembre 2026 : Salon Data 2026 - Nantes (France) 22-23 septembre 2026 : Agile en Seine & IA 2026 - Paris (France) 24 septembre 2026 : OWASP AppSec Days France 2026 - Paris (France) 24 septembre 2026 : PlatformCon Paris - Paris (France) 24 septembre 2026 : React Native Connection 2026 - Paris (France) 24-26 septembre 2026 : Paris Web 2026 - Paris (France) 25 septembre 2026 : SAP Inside Track Paris 2026 - Paris (France) 28-29 septembre 2026 : 4th Tech Summit on AI & Robotics - Paris (France) & Online 1 octobre 2026 : WAX 2026 - Marseille (France) 1-2 octobre 2026 : Volcamp - Clermont-Ferrand (France) 2 octobre 2026 : DevFest Perros-Guirec 2026 - Perros-Guirec (France) 5-9 octobre 2026 : Devoxx Belgium - Antwerp (Belgium) 8-9 octobre 2026 : Forum PHP 2026 - Marne-la-Vallée (France) 12 octobre 2026 : Dev With AI - Paris (France) 22-23 octobre 2026 : Agile Tour Bordeaux 2026 - Bordeaux (France) 26 octobre 2026 : Agile Tour Montpellier - Montpellier (France) 27-29 octobre 2026 : Directions EMEA 2026 - Paris (France) 29-30 octobre 2026 : BDX I/O 2026 - Bordeaux (France) 29-30 octobre 2026 : Agile Tour Nantais 2026 - Nantes (France) 29 octobre 2026-1 novembre 2026 : Pycon FR - Biarritz (France) 30 octobre 2026 : Cloud Nord 2026 - Lille (France) 4-5 novembre 2026 : Devoxx Morocco - Casablanca (Morocco) 14-15 novembre 2026 : Capitole du Libre - Toulouse (France) 19 novembre 2026 : DevFest Toulouse 2026 - Toulouse (France) 19 novembre 2026 : Agile Laval 2026 - Laval (France) 19 novembre 2026 : OVHcloud Summit - Paris (France) 19 novembre 2026 : Codeurs en Seine - Rouen (France) 27 novembre 2026 : DevFest Paris 2026 - Paris (France) 1-3 décembre 2026 : Apidays Paris - Paris (France) 2-3 décembre 2026 : Cloud Native AI Summit Europe - Paris (France) 4 décembre 2026 : DevFest Lyon 2026 - Lyon (France) 4 décembre 2026 : DevFest Dijon 2026 - Dijon (France) 9-10 décembre 2026 : OpenSource Expérience - Paris (France) 9-10 décembre 2026 : DevOps REX - Paris (France) 10 décembre 2026 : KCD Provence - Aix-en-Provence (France) 7-9 avril 2027 : Devoxx France 2027 - Paris (France) 3 juin 2027 : Cloud Native Days France 2027 - Paris (France) Nous contacter Pour réagir à cet épisode, venez discuter sur le groupe Google https://groups.google.com/group/lescastcodeurs Contactez-nous via X/twitter https://twitter.com/lescastcodeurs ou Bluesky https://bsky.app/profile/lescastcodeurs.com Faire un crowdcast ou une crowdquestion Soutenez Les Cast Codeurs sur Patreon https://www.patreon.com/LesCastCodeurs Tous les épisodes et toutes les infos sur https://lescastcodeurs.com/
Intern to Founder | Justin Collins | Breaking Into CyberEpisode SummaryIn this episode, Justin Collins shares his unique journey from a PhD student in Computer Science to becoming a key figure in the application security space. Justin explains how a funding shortage led him to a life-changing internship at AT&T Interactive, where he combined his passion for compiler theory with cybersecurity to create the open-source tool Brakeman. We dive into how he balanced a full-time job while co-founding a startup and the importance of preparation when breaking into a new field.Key Takeaways- Preparation as a Differentiator: Justin secured his first security role simply by researching the specific topics (SQL injection and XSS) the interviewers mentioned beforehand—a step many other candidates neglected.- Applying Niche Skills to Security: Rather than starting from scratch, Justin leveraged his deep knowledge of programming languages and compilers to build a static analysis tool, proving that specialized non-security backgrounds are highly valuable.- The Power of Open Source: Developing and open-sourcing Brakeman during an internship served as a massive career catalyst, eventually leading to a business acquisition.- The "Side-Hustle" Startup Model: Justin highlights that successful startups don't always require VC funding or fancy offices; his company was built while the founders maintained their "real" jobs.- Negotiating Flexibility: Early in his career, Justin successfully negotiated a part-time security role, which allowed him to support his family while simultaneously building his own business.Resources Mentioned- Brakeman: The open-source static analysis security tool for Ruby on Rails created by Justin.- OWASP: Cited as a critical resource for learning about web vulnerabilities like SQL injection and XSS.- Ruby on Rails: The programming framework that served as the foundation for Justin's early work.- Black Duck (formerly Synopsys): The company that eventually acquired Justin's startup.About the GuestJustin Collins is a cybersecurity expert and the creator of Brakeman, a widely used static analysis tool for Ruby on Rails. With an extensive background in Computer Science and programming languages, Justin transitioned from academia to entrepreneurship, co-founding a boutique security firm that was later acquired by Synopsys. He is a specialist in application security and program analysis.Sponsored by CPF Coaching LLC - http://cpf-coaching.comCheck out our books:
What happens to all those leads marketing works so hard to generate? More often than not, they go cold. According to Nishi Seth, Industry & Solutions Marketing Lead for Google Cloud APAC, this isn't a niche problem. It's one of the most persistent challenges across B2B, regardless of company size or sophistication. In this episode, Shahin sits down with Nishi to unpack the real reasons leads stall between marketing and sales, from qualification gaps to missing context, and what it actually takes to fix them. Nishi shares battle-tested lessons from scaling Google Cloud's marketing-to-sales motion across APAC, including how AI is transforming BDR productivity right now. Guest Introduction Nishi Seth is the Industry & Solutions Marketing Lead for Google Cloud APAC at Google, with over 20 years of experience across cloud technology, financial services, and travel. She has held senior marketing roles at American Express and British Airways, and is a recognised B2B marketing leader across the Asia-Pacific region. Key Topics Why leads go cold: the two root causes (qualification gaps and context gaps) and why even the most sophisticated B2B organisations struggle with themThe three pillars of an optimised marketing-to-sales handover: lead quality, context, and a continuous review and feedback loopWhy BDRs are the critical bridge between marketing and sales, and how to assess whether your BDR function is actually workingHow MQL definitions should evolve over time: Google Cloud's journey from basic demographic scoring to 48+ real-time intent signals, including account-level qualificationWhat providing context to BDRs really means: enablement calls, campaign-specific opening scripts, and cadences tailored to how each lead engagedThe key metrics that reveal handover health: SAL acceptance rates, SAL-to-SQL conversion, number of outreach touches, and lead velocityHow AI is changing BDR productivity: using automation for low-intent lead follow-up, real-time AI assistants during live sales calls, and AI-powered account research at scaleNishi's rapid-fire take: "strong opinions, loosely held," using AI to boost your own marketing productivity, and why simplifying complexity in B2B is what excites her most Resources & Links Google Cloud — Nishi's base for all real-world examples discussed, from MQL definition evolution to AI-powered BDR toolingGoogle Gemini — Nishi's go-to research tool in place of following influencers; she also used it to prepare for this episode Contact & Credits Host: Shahin Hoda Guest: Nishi Seth Produced by: Shahin Hoda and Alexander Hipwell Edited by: Alexander Hipwell Music by: Breakmaster Cylinder APAC's B2B Growth Podcast is Presented by xGrowth
Automation as Core Strategy: Aarin Bailey on RPA, AI, and Scaling MSP OperationsOn the Evolved Radio podcast, Todd interviews Aarin Bailey, COO at Webit Services and former COO at MSP Bots, about treating automation as a core MSP operating strategy. Aarin describes how his automation focus accelerated around COVID by chaining PowerShell scripts, later expanding into Python, GUIs, and modular systems connected via RESTful APIs, with much of the computation running outside the RMM on servers (including SQL and Python) while the RMM remains mainly a monitoring and job-push layer. They discuss whether RMM is a “zombie product,” the ongoing role of PSA/ticketing as a system of record, and managing complexity through separate modules and staff literacy in Python/RPA. Aarin explains build-vs-buy decisions driven by ROI and fit, cites automated triage/dispatch with ~98% accuracy and shifting token costs, argues AI should augment rather than replace humans, and emphasizes documentation, playbooks, and focusing on operational “bad” anomalies. They also cover client tolerance for AI, limiting client-facing AI after hallucinated ticket notes, skepticism about voice AI, and concerns about AI economics and subsidies.This episode is brought to you by Opsleader Pro. A place for MSP owners and managers to get the systems and tools they need to build a stable and growing MSP. Part group coaching, part peer group, everything you need to run a successful MSP. (00:00) - Automation First Mindset (01:10) - Aaron Origin Story (05:04) - From Scripts to Platforms (05:41) - Beyond the RMM Beehive (08:35) - Is RMM a Zombie (12:14) - Managing Complexity Safely (14:33) - Build vs Buy ROI (19:39) - Token Costs and Pair Coding (23:49) - AI Security Reality Check (27:34) - Scaling with Playbooks (30:12) - Hunt the Bad Stuff (30:59) - Blueprints Before Automation (32:46) - Ticket Volume and Vision (33:32) - Saying No as Integrator (35:44) - Healthy Disagreement Dynamics (37:08) - Client Facing vs Backend AI (40:05) - AI Hallucinations and Guardrails (43:05) - Voice AI and Live Answer (46:06) - Costs and Subsidized AI Era (49:26) - Outcome First and RPA Focus (51:36) - Wrap Up and Thanks
For the past few years, the conversation around AI has focused on the technology. Which model is best. Which tools to use. How fast everything is changing. But once you start building with it, a different challenge emerges. The technology is often the easy part. The hard part is everything else. The definitions that don't match. The documentation nobody trusts. The tribal knowledge living in someone's head. The processes that work only because a few key people know how to navigate around the mess. Business intelligence exposed some of these problems years ago. AI is exposing even more of them. For years, the people who cared about semantic models were mostly talking to each other. Everyone else had a simpler view: the dashboards worked, the BI nerds were overcomplicating things, and if a slightly different version of yesterday's question showed up, someone could always write more SQL. That worked well enough until AI agents became the ones asking the questions. Agents don't wait two weeks for a developer. They improvise. And the improvisation is different every time. That's the moment the semantic model stopped being a nice-to-have and started looking a lot more like a requirement. Every data quality problem that used to come home to roost the first time you built a dashboard is back, only now the list is longer. AI cares about policies, institutional knowledge, organizational context, and all the things that used to live quietly in people's heads. The one-version-of-the-truth problem just got a much bigger job description. Along the way, Rob and Justin compare notes from the front lines of building with AI, from multi-agent systems and knowledge management to the unexpected ways these tools behave once they leave the lab and meet real organizations. There's a book update in here too. Fair Game is officially available for pre-order, and Rob shares why the independent bookstore route matters more than most people realize. If you've been wondering what happens after the AI works, this episode is a pretty good place to start. Also in this episode: Pre-order Fair Game: Customizing AI to Your Business Is Easier Than You Think Fortune: Big Tech is laying off developers. My company just hired its first. We're both right about AI (By Rob Collie)
Description The Future of Tech is Here. Subscribe to our Newsletter:https://theultimatepartner.com/ebook-subscribe/ Check Out UPX:https://theultimatepartner.com/experience/ In this presentation from Ultimate Partner Live, industry analyst Jay McBain breaks down the monumental macroeconomic shifts rewriting the tech sector in 2026. https://youtu.be/r0qTDyw97Gs As the industry rapidly approaches a $6.07 trillion valuation, driven by massive AI infrastructure investments from Sam Altman and the “Magnificent Seven,” traditional sales and channel models are fundamentally collapsing. McBain reveals how buyer demographics have transformed to an integration-first millennial base, why marketplace ecosystems now command over half of all partner-funded deals, and how a tiny elite of just 1,000 tech service providers control two-thirds of global tech revenue. Learn the exact mechanics behind how Microsoft out-partnered AWS to win 26 straight quarters of dominant growth and how your business can deploy an algorithmic early warning system to capture massive wallet share before competitors even step into the boardroom. Key Takeaways Over half of the Fortune 500 companies vanish every 20 years because their leadership fails to anticipate macroeconomic technological cycles. The true opportunity in the $6.5 trillion AI boom lies not in single vendor products, but in the hardware, software, services, and telecom ecosystem surrounding them. Indirect tech sales are undergoing a structural shift toward direct cloud hyperscaler models driven heavily by Nvidia's core infrastructure client base. Modern business deals are won or lost months before the point of sale based on the average of 6.3 partners surrounding a customer’s environment. Over 51% of tech buyers are now millennials who prioritize software integration capabilities and digital marketplaces over traditional human sales interactions. Tech service economics are pivoting aggressively away from upfront margins toward point-based multi-partner funding across subscription cycles. If you're ready to lead through change, elevate your business, and achieve extraordinary outcomes through the power of partnership—this is your community. At Ultimate Partner® we want leaders like you to join us in the Ultimate Partner Experience – where transformation begins. Key Tags Nvidia AI buildout, $7 trillion AI opportunity, cloud ecosystem decade, Microsoft vs AWS growth, multi-partner cloud deals, digital marketplace migration, millennial B2B buyers, B2B tech subscription economics, tokenized micro consumption, tech services wallet share, hybrid cloud infrastructure, 28 customer moments, IT services industry growth, telecom spend breakdown, channel chief strategy, managed service providers MSP, global systems integrators GSI, software integration first, point-based vendor incentives, automated co-selling workflows Transcript JAY McBAIN AUDIO PODCAST [00:00:00] Jay McBain: So to go back to that story about the 53% of companies who are gonna fail, one of us is gonna be asked to write the book, but chapter one is always you Blame the CEO. [00:00:13] Vince Menzione: We just came back from Ultimate Partner live in Bellevue, Washington, where we hosted incredible leaders for two amazing days. Come join us for this next session where we explore the tectonic shifts we’ve all been seeing. With that, I am incredibly blessed to invite a friend of mine to the stage. I have a quick little side note, like I found an old LinkedIn post from this gentleman from like many years ago, like 20 years ago. [00:00:39] Vince Menzione: And I wasn’t really that nice to you on that LinkedIn post. Like, oh, like this is before Jay became the Jay, that we all know Jay to be j. But he was in the space and I was at Microsoft doing something and he reached out about something. It was kind of rude, Jay. I was like, oh my gosh. I can’t believe. But Jay has been a great friend. [00:00:54] Vince Menzione: When we started the podcast back up, uh, during COVID we started doing podcasts together. When we moved to the studio, Jay was the first person in the studio. He’s always got a spot, uh, at our events. He’s s Spot Art, and, and he’s a great friend and supporter of Ultimate Partner Jay McBain. For those of you who don’t know him, Jay, welcome. [00:01:13] Vince Menzione: Thank you, sir. [00:01:22] Jay McBain: 31 days ago, we landed Artemis two. The furthest humans have ever been away from the planet Earth 57 years ago. We landed on the moon in the 56 years. Between those two moments, the tech industry has been the fastest growing industry in the world. Every single year we moved from the space race to the technology race, and we’re just getting started. [00:01:46] Jay McBain: If you’re old enough, you’ll recognize the mainframe and mini era for 20 years. You’ll recognize a young disheveled Bill Gates showing up in Boca Raton, Florida for, uh, August the 12th, 1981 launch, where Bill thought that every one of us would’ve a PC in our home, and IBM thought they were gonna sell 10,000 of them to hobbyists. [00:02:12] Jay McBain: 1999, a small startup from an executive who just left Oracle in San Francisco named Mark Benioff. A couple of years later, Jeff Bezos went into a boardroom and said, listen, we’ve spent a lot of money building infrastructure to our busiest day, Christmas, black Friday. You’re telling me this stuff sits idle 10 or 20% for the rest of the year. [00:02:35] Jay McBain: Why don’t we rent that out to others? Got laughed outta that boardroom and then got made of fun of on magazine covers. Maybe you should just tend the store, let the adults talk about technology. In March of 2023, our neighbors, our friends, our family saw DeepFakes. They saw poetry, they saw music, and they came to us as tech people and said, did we just light up Skynet? [00:03:03] Jay McBain: Now every one of these 20 year eras, this is the Taylor Swift version of our industry. Every single one of these eras triggers the fastest growing product in history. Today it’s actually Chacha bt first to a billion users. It triggers a new, richest person in the world, bill Gates, to Jeff Bezos. Now, Elon Musk is the first to sign a trillion dollar pay package, and it’s not for car. [00:03:27] Jay McBain: It’s not for cars. It also triggers a most valuable company in the world change. And today that’s nvidia. These are monumental changes in our industry and they’re monumental changes in partnering every single time. And it also links to our customers. If you take a 20 year view of business, one era, and, and think about the AI era, you know, at the start of it here, if you’re to grab the Fortune 500 magazine from 20 years ago and start to flip through it, 53% of the companies in there no longer exist. [00:04:06] Jay McBain: Every 20 year cycle, we lose over half of the biggest companies in the world. These are the companies that have very deep pockets to buy their way outta problems. If you’re not in the Fortune 571% of tech companies don’t make it 10 years. These are the changes that cost industries. There are changes that cost really big companies and the decisions we make, the trends we’re in right now, in 2026 will be written about in the future. [00:04:39] Jay McBain: This new era, a lot of big numbers being thrown around. Vince’s best friend talk about a six and a half trillion dollar AI opportunity, but it’s not Microsoft’s tam. Microsoft is chasing about a trillion dollars of this. And the ecosystem, the hardware, the software, the services, the telecom is gonna make up the rest. [00:05:04] Jay McBain: It is an ecosystem. Every time these big numbers are thrown, the word ecosystem is always thrown around it. Not to be outdone, Sam Altman’s talking about a $7 trillion build out. The world economy this year, the world GDP will be 126. These are material numbers to world GDP, but even better, they’re both larger than our entire industry is today. [00:05:27] Jay McBain: So what took 56 years of the fastest growing industry this year will be $6.07 trillion. Big numbers, but it’s easier to think about it in terms of a dollar that our customers spend in that dollar. They’re gonna spend 25 cents on hardware. They’re gonna spend 25 cents on software. So for anyone that read the memo 15 years ago, that software’s gonna eat the world, there’s still a dollar a hardware to run every dollar of that software. [00:05:57] Jay McBain: And whether you’re thinking humanoid robots or whichever future you’re envisioning, there’s going to be a dollar of hardware to run every dollar of software for the next 20 years. There’s over 25 cents now in IT services, and in many cases, these services are growing faster than the product categories and just under 25 cents in telecom, that’s how it breaks out today. [00:06:19] Jay McBain: And this industry, which took 56 years to get to this point, is gonna double in size in the next three to five years. We already have two and a half trillion of that seven raised and being spent. Part of the reason Nvidia is the most valuable company in the world. Now our industry, uh, you talk about ultimate partnerships. [00:06:40] Jay McBain: Our industry traditionally, and world trade by the way, is 75% indirect. The dealerships, the agencies, the brokers, the resellers, the retailers, the franchisees, the gas stations, the grocery stores, the pharmacies, all 27 industries sell indirect. You gotta think back the last time you bought something direct. [00:07:01] Jay McBain: Well, I bought a Dell from that dude in the nineties. Cool. Well, Dell Technologies is now 60% indirect. Well, I bought insurance. Direct is 15 minutes. Could save me 15%. Well, Geico last year sold more insurance through agencies and brokers than they did direct. This is the world now. We used to be 75% indirect four years ago. [00:07:26] Jay McBain: Then it went to 73.2, then it went to 70.1 and it then it went to 66.7. By the way, marketplace is in these numbers indirect. It’s not marketplace causing this change. It’s one company, Nvidia. Nvidia has seven customers. The magnificent seven, uh, half of them are in the room right now that every morning we wake up to a hundred billion dollars press release about this $7 trillion buildout. [00:07:56] Jay McBain: What’s interesting is indirect sales in our industry is growing by revenue. It increases every year, just not at the pace that this AI build out is happening direct with seven companies. But the reason we’re all here, and I think the core reason that Vince is building this community is this, you know, Microsoft forever has measured and been very vocal. [00:08:21] Jay McBain: About 96% of their deals have partners in them. Kind of who cares, who collects the money. We care about the moments, the 28 moments before the customer makes a purchase. We care about every 30 days forever, because two thirds of our industry, over $4 trillion now is subscription consumption based. Winning a customer today is only winning the first 30 days. [00:08:46] Jay McBain: We care about this cycle. We care about who surrounds our customer. So six years ago, I stood on a big stage and said, you know, we went through a decade of sales. You know, in 1999, you thought you were born to be a salesperson. You’re managing your territory with your gut. Well, a few years later, you were introduced to the science of selling. [00:09:07] Jay McBain: You know, 10 years later you thought as a marketer, you sit around a cocktail party joking with your friends, 50% of my marketing dollars are wasted. I just don’t know which 50%. Really funny. In 2009 until every 58-year-old CMO got replaced by a 38-year-old growth hacker. Coming in with Marketo and Eloqua and Pardot and HubSpot, and 15,505 as of yesterday, MarTech and iTech tools, ninjas in marketing, they wouldn’t let a nickel go through without measuring. [00:09:43] Jay McBain: Now we understand 96% of deals and partners that surround it. No deal is gonna be won or lost in this era without partnering effectively. So we had to have this decade of the ecosystem. One of the ways we’re tracking is by outsiders. You know, Salesforce every year publishes the state of sales and they’ve got, you know, the number one CRM in the world. [00:10:05] Jay McBain: So they get to go talk to all the CROs, all the salespeople in the world. And as of this year, a couple months ago, 94% of every salesperson in every industry in the world uses partners every single day. You wanna see what this number was six years ago. Also, 89% of salespeople around the world don’t think they’re going to club this year without partners. [00:10:29] Jay McBain: So this is a big moment for us, halfway through the decade ecosystem, but we’re only halfway through. We’re starting to understand now at a more granular level. What partnering means. It’s not theory, it’s not flywheels. It’s not really cute. McKinsey slides that we keep showing to our board saying how important partnering is. [00:10:51] Jay McBain: We’re trying to get to the very specific level of the 6.3 partners on average that surround the deal and what they’re doing. How their business model works, and that’s average if I’m working on a public sector deal. I was at a Red Hat conference yesterday talking sovereignty. If I’m in an enterprise or a large public sector deal, it’s north of 10 partners in the deal. [00:11:15] Jay McBain: So we’re starting to understand what used to be this, this, you know, you’ve been the fastest growing industry for 56 straight years. Every single professional services person in every industry has come in to join the fund. Over 90% of accountants are tech services firms. Over 90% of marketing agencies are tech services agencies. [00:11:36] Jay McBain: All of this 250,000 software companies, a million emerging comp tech companies, the half a million VAR that have been in that traditional channel. The managed service providers, all of these 20 different partner types, millions of companies, tens of millions of people competing for 6.3 spots. Around the customer. [00:11:58] Jay McBain: That’s it. Luckily, there’s 141 million global customers to compete for. There’s, there’s some open slots that you can go find, and that’s the point. Our industry never had our own Fortune 500. We always talk to, you know, these partners and GSIs are doing this and SI are doing that. And we never really had a view of capability and capacity or what our own TAM was inside of that partnering. [00:12:25] Jay McBain: And so we set out and we would’ve loved, you know, chat GPT or Gemini or Claude or any of those tools to do this. But there’s one problem in partnering with AI is that it doesn’t know one partner from the next. There’s a big digital sameness problem in our industry that every single partner, whether it’s Larry in the White van or Accenture, with 786,000 employees all say they do all things to all people all the time. [00:12:53] Jay McBain: 98% of them, 99% of them are private companies that don’t share their p and l. You can’t go into Microsoft’s LinkedIn system and find out how many employees, ’cause it’s a block system, it AI can’t see into it. So it just sees, and it’s a great pattern matching. Google, SEO can’t figure out who’s who, nor today can the large language models. [00:13:14] Jay McBain: ’cause all the things they’re trying to match, the transformers are trying to match. It all looks the same. Every tweet, every ebook, every website, every digital history looks the same. So this took us thousands of people hours across two years to do, to dig into every p and l to dig into every dollar of what they’re doing. [00:13:33] Jay McBain: But what was interesting is only a thousand partners in our industry do two thirds of all tech services. When you get into enterprise, it goes up to 80 to 90%. The partners in the middle, in Blue do more tech services. The 30 of them than the 970 partners in white on the outside, the 970 partners in White do more tech services than the next million combined. [00:14:03] Jay McBain: This is our industry in a nutshell. Every time we talk to a a vendor, every time we talk to a partner, every time we talk to a distributor, we’re now talking names, faces, and places. You you wanna talk sovereignty. Yesterday in Atlanta, 90% of sovereign conversations in public sector in the globe is handled by these companies here. [00:14:26] Jay McBain: Forget about how much you do with these partners today. You wanna chase the next column, which is the wallet share. And I was a channel chief for 17 years. I get the weekly report and I see a million dollar partner, another million dollar partner, sorted top to bottom. You don’t know which partners which, which of those million dollar partners is doing 1.2 million in your category. [00:14:46] Jay McBain: They deserve a baseball cap and a front row seat at your event as an MVP. The next partner right next to them is doing 10 million in your category. They’re only doing a million with you. ’cause customers are pulling them into it. Nine times outta 10. They’re leading with your competitor. So I don’t want that list anymore. [00:15:03] Jay McBain: I want the new list, which is showing me those $9 million opportunities. And I as a board member, as A CEO, as a CFO, as a CRO, I wanna see this list. And then I want to talk people, processes, programs, technology. What are we gonna do to go get our fair share of that 9 million? Where’s our lowest hanging fruit? [00:15:24] Jay McBain: How do we double our pipeline? How do we double the size of our company in three years? It’s all right here. Let’s have very specific conversations and move away from flywheels and move around from force multipliers and and things like that in partnering. Let’s figure out how this partner community is surrounded. [00:15:45] Jay McBain: What do 10 million people who have to be smart in front of their customers every single day, what do they read? Where do they go and who do they follow? It’s the law of a few. This is the old Malcolm Gladwell of tipping point 10 million people in the broader channel. A hundred percent of our TAM comes down to only a thousand watering holes. [00:16:08] Jay McBain: 12% of that entire audience. Doesn’t sound like a lot, but it’s over A million. People love podcasts. Number one way they learn the Joe Rogan effect. In our industry, there’s 121 podcasts. These are all public lists. You can go get on my LinkedIn newsletter on canals, oia. But there’s 121 podcasts that drive him forward. [00:16:28] Jay McBain: Really high up on that list, actually number one on the list is ultimate partner, Vince. That’s how I met. ’cause I asked people, 10 million people, you love this. You walk your dog, you drive to work, you listen to podcasts. I’m not the biggest podcast fan. It’s not number one on my list, but it’s number one on theirs. [00:16:44] Jay McBain: They say, you know, you gotta meet this guy, Vince. It’s unbelievable how great these podcasts are. They’re ultimate. [00:16:54] Jay McBain: Then I talked to Vince and said, but Vince, you know, 35% of your community, the 10 million people love to come to events like this one. The hallway conversations, the hotel lobby bar last night. This is what we love to do, especially post pandemic. It’s the number one way we learn. We learn from our peers, we learn from those around us, and, and the learn from the conversations we have here. [00:17:17] Jay McBain: We always remember these moments, you know, years and years later. There’s 352 choices. I’m going to five of them this week in five different cities. It’s a lot of coverage, but again, it’s a tighter li list of how people work. The magazine lists 106 of them associations like Conter. Now the GTIA peer groups, there’s 15 different spheres of influence, but only a thousand places. [00:17:43] Jay McBain: I could walk you through billionaire, after billionaire, after billionaire in this industry and show you how they did this. How did Arne Bellini at ConnectWise? How did Austin McCord at Datto, how did Nerdio become a unicorn? How did threat locker and huntress move away from 6,500 cyber companies and become unicorns over and over and over again? [00:18:05] Jay McBain: It’s only one slide. Unicorns and billionaires are made here, and a lot of people don’t get it. So walking away from Bellevue, a thousand partners, top down, a thousand watering holes, bottoms up. You’ve covered a hundred percent of your tam. You do it better than 10% of your competitor, 10% better than your competitors. [00:18:27] Jay McBain: You win. You carry that on your resume into the next company. You get a bigger job at a bigger pay scale. Let’s just walk through some examples. Cyber 91.7% of it goes through the channel. Huge channel audience. You know, if you’re in MarTech, it’s only 10%, but this one happens to be all channel, but that’s not the story. [00:18:48] Jay McBain: For every dollar that the 6,500 cyber companies are trying to close, there’s $2 in services. Plot twist, the products are grown at 11, the services are grown at 12.6. Your partners are growing faster than you are, and they will continue to for the next, at least five years, probably 10. So when I’m here, five years from now, you’ll hear in me talk about a three to one split in cyber and then a four to one split in cyber. [00:19:18] Jay McBain: Now, when we’re in Miami a couple days ago is CrowdStrike, they’re talking about a $7 and 5 cent multiplier, chasing that two to one up higher. You look at managed services. Here’s a fun story. Managed services. 82% of customers who are man, uh, outsourcing more this year than last year. 650 billion in size. [00:19:38] Jay McBain: This is bigger than the entire SaaS industry. Salesforce, ServiceNow, Workday, Marketo, NetSuite, HubSpot, 250,000. Others. This is bigger. It’s also bigger than all the Hyperscalers combined, not just AWS, Microsoft and Google, but Alibaba and Oracle and everybody down the list. This is a massive market also growing at double digits. [00:19:59] Jay McBain: So these are some big things and obviously we’re watching, you know, week in and week out, quarter in, quarter out, the Battle of Software and Battle of the Hyperscalers and things like that, and who’s growing at what pace and, and how partnering is connecting to all of this. You know, we watched a moment really early in the pandemic where Microsoft started growing faster than AWS and they haven’t stopped since 26 straight quarters. [00:20:27] Jay McBain: And you ask customers and say, you know, does Microsoft have a better product? And in most cases they say no. You know, AWS had a five year head start. Well, did they have a better price? Well, no, actually most cases Microsoft’s more expensive. Well, did did they have better promotion? Was their Super Bowl ad better? [00:20:44] Jay McBain: No, they’re both kind of crap. So you kind of ask the questions of what’s the only difference that could create growth above the leader in the market? Well, it’s place. More of the 6.3 partners are walking into those keyboard room meetings and drawing clouds up on the wall and labeling the Microsoft than they are AWS. [00:21:03] Jay McBain: Very simple. It’s never been about product. The best product in our industry has never won. And now the best way forward is that partnering moment, and this is the moment. So to go back to that story about the 53% of companies who are gonna fail, one of us is gonna be asked to write the book. And it could be the book like Kodak, they invented the product that ended up killing them. [00:21:26] Jay McBain: And it’s a woe is me story, but chapter one is always you blame the CEO. How could they not see those trends happening in 2026? How could they, you know, were they blind? Were they stuck in their own, you know, innovation chamber? Innovator’s dilemma, were they stuck in their own boardrooms? Why couldn’t they see? [00:21:46] Jay McBain: Well, chapter two, you, you blame the board. They have fiduciary responsibility, outsider view, and how could they not see it? But really, this is the future right here. If you take this slide and apply it 10 or 20 years from now to every failure and every success, these are the chapters of the book. Your buyer is now a millennial. [00:22:05] Jay McBain: As of last year, the 51% of our market is bought by people born after 1982. Different psychology, different behavior, different journey, different criteria, their integration. First buyers. The buy a product, 80% as good as the next one. If it works better in their environment. 94% of people won’t buy a car unless it has CarPlay or Android Auto. [00:22:26] Jay McBain: New Buyer. You have to be more integrated than your competitors. That’s a partnering story. The 6.3 partners. If you heard cyber, you need some great channel partnerships, but you need the other 5.3 partners as well, the consultants, the advisors, the designers, the architects, the implementers, the integrators, the manner service, all of the other partners. [00:22:44] Jay McBain: You need to know more of them than your competitors do, and have them label clouds with your name in them. You need better alliances. Even if you compete, you only compete in the morning. You’re best friends by the afternoon. You have to be tight with the hyperscalers, tight, with the big SaaS platforms, tight with cyber, tight with distribution, there are layers, seven layers to every deal. [00:23:04] Jay McBain: You gotta be tight in and have better alliances than your competitors. And then it all comes to the 28 moments, which I’m gonna end on, but the go to market of all of this, the co-selling, co-marketing, co-innovation, co-development, co keeping. This is it. Your product has to be good enough that somebody’s gonna renew it. [00:23:21] Jay McBain: Your Super Bowl has to be, you know, ad has to be good enough that people don’t, you know, shame you on social media. Your pricing has to be somewhere in a country mile of the bell curve of what the customer wants to pay. But successor failure is just here and platforms are synonymous with partnering. [00:23:40] Jay McBain: It’s our role now in the decade of the ecosystem to drive our companies forward. Marketplace. It’s probably the most predict, you know, great prediction we ever made. You know, growing at 82% compounded, it’s hard to predict ’cause it doubles almost every year. We were almost exact to the decimal point. Five years later now till 2030, we’re watching a second story, which is more interesting. [00:24:02] Jay McBain: If 96% of all deals have partners inside of them and there’s private offers and multi-partner offers and distributor sellers record all these funding mechanisms or services as a product. As of last week, over 50% of all deals in marketplaces now have partner funding. It means that while money changes hands differently, the respect and the recognition of what partners do is in the deal. [00:24:26] Jay McBain: We think that’s going to 59, but at some point, that’s gonna have to hit 96. ’cause to run the best programs, whether it’s an indirect sale, whether it’s a direct sale, whether it’s a marketplace deal, it doesn’t matter how money changes hands. What matters is we recognize the 6.3 partners. They’re not only making the deal happen bigger and faster, but renewing and enriching that every 30 days forever. [00:24:48] Jay McBain: When we watch, you know, billion dollar clubs and when we read all the press releases and all the hubbub about how fast this is growing and who, which companies are behind all this. When I’m quoted in some of these press releases, it’s because of this. You know, CrowdStrike, you know, brags are a billion dollars in a single year, but inside of that, they’re showing that 91% growth in marketplaces, which is pretty phenomenal for any company to almost double in size every single year. [00:25:17] Jay McBain: What’s more phenomenal is they’re growing the channel piece of it, 3548%. That green part of it is growing. Companies that understand platform and have people and processes and programs and technology to do it are winning. And they’re getting recognition and partners are starting to join the Billion Dollar Club who don’t sell a product, but are also winning at Extreme Scale. [00:25:44] Jay McBain: So talk about those partner 1000 and who are leaning in to win at this level. As well as everything changes, traditional billing moved into subscription models, moved into consumption models. Now we’re being tokenized to death multi it’s, it’s in this mode of micro consumption. There’s no chance there was little chance in subscription consumption that would be resold. [00:26:09] Jay McBain: You don’t buy Netflix from the cable guy in the white van. There’s zero chance when you’re buying tokens at a buck a piece that that’s going through any indirect sale. This continues to grow. Now the tectonic shifts is what happens when money changes hands differently. These old programs that we used to all write hundreds of different boxes, we checked every day on deal reg and trainings and all the other things are changing. [00:26:35] Jay McBain: To this, you’ll get these slides, by the way, in high res, inside of this now is the customer. For the first time ever, 45 years later, we have the customer in the middle of what we do, the 28 moments in green before they buy the seven layer stack and the partners inside it. The implementation. The integration, the managed services in a cycle that never ends, and two thirds of our industry. [00:26:55] Jay McBain: With the customer in the middle, we can now move money around to the different moments. It’s not all landing in front or backend margins or market development funds or new customer bonuses or spiffs. It’s landing where it needs to land. Over 400 companies now, pretty much led by Microsoft 400 companies are in a point system right now and 400 more. [00:27:18] Jay McBain: We’re working kind of behind the scenes to get that announced in the next 12 months. This is a total changeover in terms of how economics work and partners are yelling over half of us. I don’t care. Don’t call me a VAR anymore. Don’t call me an MSP. Don’t call me a regional system integrator. I do the consulting over half the time. [00:27:36] Jay McBain: I do the design, I do the implementations, I do the managed services, and 44% of us are vibe coding. On weekends. We’re not happy. Just on the services side. We wanna join the seven layer tech stack as well. These are partners growing faster than their vendors by understanding this cycle and where to show up and where the money is in ai. [00:27:56] Jay McBain: And the number one thing they’re asking for is not more leads, which they did for 45 years. The number one thing is now recognized for what I do. I’ve never just been a cash register. We’re completely now past this idea of a channel being a channel of distribution, and now a channel being this platform for the future. [00:28:16] Jay McBain: As we lay that on top of ai, the first couple of years of AI has really been consumer driven. The 95% failure rate that MIT reported last year is now 70%. That’s the failure to get from proof of concept to production. That 70 will be 50 by the summer we’re moving now in business, the maturity rates are going up at the end customer and in 88% of cases, that’s because of the channel. [00:28:43] Jay McBain: They’re working with partners. They’re not vibe coding themselves and working in little skunkwork groups. They’re working with partners to make it happen, and it now becomes the partner’s number one growth opportunity. I can grow at 11 or 12% in cyber every year. Compounded I can grow in 10% in managed services. [00:29:03] Jay McBain: You know, those are great double digit growth ’cause my customers are growing at 2.7% and I can go four x my customer, but I can go 10 x my customer if I have the right services built around ai. And this compounded growth rate and that big number in 2 20 32, 267 is what’s got those top 1000 partners obsessed. [00:29:25] Jay McBain: And your companies are leading with ai. Now you need to connect to those AI services. You need to get partners on this scale of growth. And they will be adding your name inside every cloud. They write on every whiteboard, but 82% of partners around the world, you know, we survey 25,000 of them aren’t ready, and they’re blaming vendors for not being ready, and they’re telling them exactly the workshops and the training that they need to get ready for this cycle. [00:29:53] Jay McBain: 82% of our entire partner, tens of millions of people, aren’t ready to grow at 35% and they need our help. Last thing I’ll say about AI is it’s the first time from client server to cloud, edge to cloud that it’s been segment driven. SMB alone has one, you know, six different segments, one to nine, 10 to 24, 25 to 49, et cetera. [00:30:18] Jay McBain: Mid-market into enterprise. No one that runs a restaurant is calling Jensen to buy a GPU to put next to the stove. No one’s calling Sam or Dario or anyone at Anthropic or OpenAI directly. They’re waiting. If you run a restaurant with all the people running around with tablets, you’ve invested in toast or square or clover or one of the platforms to run your business. [00:30:41] Jay McBain: A hundred different things. And you’re gonna wait for toast to work with a hyperscaler and build out the capabilities genetically. So when they see a spike in Uber Eats orders, they automatically place a food order and automatically change the staffing to deliver on it. That’s what the restaurant’s waiting for, and there’s no one calling and having a big a agent conversation. [00:31:03] Jay McBain: But even if you go into hundreds of people in medium sized business, every one of the vice presidents have their tech stack already built. I talked about the marketing person already, but the HR leader has one, and everybody’s got their seven layer stack. They’re not calling to buy a GPU and they’re not calling to, you know, bring in open AI directly or, or anthropic. [00:31:22] Jay McBain: They’re waiting for the platform they built to integrate together ag agenta capabilities. Everybody’s in wait mode up until enterprise and public, large public sector. So we are looking at this market and at 90% of that AI market is run by those thousand companies, and the rest of the millions of partners are helping in terms of how these businesses are gonna change at that level. [00:31:46] Jay McBain: Here’s where I end. You know, the 28 moments used to be a theory. It used to be a flywheel. How do we buy a car? [00:31:55] Vince Menzione: Well, we Google it, [00:31:57] Jay McBain: 81% of us now, 94% of us use large language models. We find out that there’s 365 brands of car. I’d have to test drive one every day of the year to get through them all. So we start narrowing these things down. [00:32:09] Jay McBain: We configure it. We put our rims on it, we color it. We download the invoice price. We download the backend rebates this month, whether I buy it in May or June, we find out what 5,000 people paid for our exact car within 50 miles of us. And then we don’t wanna go to the dealer because we know more than the salesperson, the manager ever will. [00:32:26] Jay McBain: We know what we’re gonna pay within, you know, dollars or cents. Just carvana the car. Hand me the keys. Let’s just forget the whole eight hour back and forth. I’ll get you a deal thing. I’m smarter than you in technology. Our customers are smarter than us, smarter than salespeople. That’s why 75% of millennials don’t wanna talk to a salesperson. [00:32:48] Jay McBain: They want to end digitally, and by the way, they’re not gonna send a fax after 28 digital moments. They’re gonna end on a digital marketplace. This is all demographics. It’s not hard to see where it’s going, but we’re getting into names, faces, places again. What if every dollar of your tam, the board, the CEO, runs around with their big multi-billion dollar number, they’re chasing? [00:33:09] Jay McBain: What if every single deal looks the exact same? This is a deal with AstraZeneca, A real deal, real customer spending millions of dollars. We know it starts in October, it ends in April. It’s a six month cycle. We see what they read, the MQ ls at the beginning. We see the sales demo moments. We see ISV, but we’ve never had the light blue boxes. [00:33:30] Jay McBain: What if we as a team could overlay the 6.3 partners in this deal? And when you find out a couple things. Here’s where I end. In December, five deals were one, three of them by NTT. The person at NTT probably coaches AstraZeneca’s, you know, kids’ soccer team. They probably have a cottage together at the lake. [00:33:50] Jay McBain: For the last 20 years, if the person at NTT worked at Deloitte, Deloitte would’ve run this deal. But Software One and Yash are both there, so we understand that when they were drawing clouds up on the wall in the boardroom in December, this deal was won and lost there. It was not won and lost at the point of sale. [00:34:09] Jay McBain: So what if you knew more about this and could see every dollar in your tam? You had an early warning system that this was happening. Two things jump out at this now that we’re in Bellevue. AWS was touched twice in this deal, directly in the marketing cycle and the sales cycle. AWS lost this deal. Here’s an example of Microsoft winning a deal with Microsoft never being touched. [00:34:34] Jay McBain: For some reason, NTT who won, who won AWS’s partner of the year a couple years ago led with Microsoft, so did Software one, Microsoft’s biggest reseller in Europe, and as did Yash, they all led with Microsoft and without Microsoft, knowing Microsoft took a multimillion dollar deal away from their competitors by winning in December. [00:34:53] Jay McBain: That’s one. Second. These partners didn’t just show up other than soccer and cottages. They didn’t show up in December. It went closed one in their CRM system. Back in the summer, August, September, we already knew AstraZeneca was in market, spending millions of dollars. We didn’t need them to read an ebook or go to an event to find that out. [00:35:17] Jay McBain: We knew it because it was closed one. They’re spending hundreds of thousands of dollars times five in December to know what to do at the end. This is an early warning system that’s better than any MQL, better than any SQL. And if you could give your company these level of view into their pipeline with an early warning system that I can work with those partners for months before they ever show up at the customer’s boardroom. [00:35:44] Jay McBain: This is it. Talk about 47% winners. This takes you from not only surviving the AI era to being a top five platform winner. Thank you very much. [00:36:01] Vince Menzione: Until next time, we’ll see you in person. Hopefully at our next event.
https://clearmeasure.com/developers/forums/ Chris Woodruff, or as his friends call him, Woody, is a software architect of over 25 years. Woody loves software engineering, especially allowing applications and services to communicate across networks and through Web APIs. He has received Microsoft MVP awards in SQL, Data and C# in the past, along with multiple years of being awarded the AWS Community Builder Award. He's a current board member of the .NET Foundation Woody lives in Grand Rapids, Michigan, where he explores the many breweries in West Michigan and travels with his family. Woody is also a long-time bourbon fan and loves hunting for whiskey bottles. Website - https://woodruff.dev/ LinkedIn - https://www.linkedin.com/in/chriswoodruff/ Twitter - https://twitter.com/cwoodruff Simplicity-First Website - https://simplicity-first.dev/ Previous Appearances on the Azure & DevOps Podcast: Episode 262 - Chris "Woody" Woodruff: Network Programming https://azuredevopspodcast.clear-measure.com/chris-woody-woodruff-network-programming-episode-262 ---------------------------------------- Want to Learn More? Visit AzureDevOps.Show for show notes and additional episodes.
גיא ואיתן דנים בשאלת הביקוש לתפקיד שלנו בשוק. האם יש פחות? או שבעצם יש יותר? ומה מקומנו בעולם ה-AI? ואם כבר מדברים על AI אז בואו נדבר על פיצ'רים שקשורים לזה ב-SQL Server! וגם, אנחנו מספרים קצת על סדנא של AWS בנושא PostgreSQL שכדאי לשמוע עליה. קישורים רלוונטים: New T-SQL AI Features are now in Public Preview for Azure SQL and SQL database in Microsoft Fabric - Azure SQL Dev Corner AWS FOR DATA | Transform Your SQL Server Skills to PostgreSQL- Special Workshop with DBArt Statistics are not collected when creating new table and indexes and loading data after. · Issue #990 · olahallengren/sql-server-maintenance-solution
Run enterprise Postgres workloads on Azure HorizonDB with around 3x the throughput of self-managed deployments — zone-resilient by default, no architectural trade-offs. Call AI models directly from SQL, build durable vector pipelines inside the database, and deliver high-accuracy similarity search at massive scale with DiskANN and AI re-ranking, all without leaving PostgreSQL. Debug and optimize queries faster with the Azure HorizonDB VS Code extension. Visualize execution plans, let Copilot generate fixes, and clone production data to test environments in seconds. Charles Feddersen, PostgreSQL Partner Director PM, shares how to put all of it to work on Azure. ► QUICK LINKS: 00:00 - Azure HorizonDB features 00:57 - Open-source PostgreSQL 02:24 - How it works 03:37 - Performance 04:51 - Enterprise-ready security 05:34 - Memory & storage work together 06:29 - AI Model Management + AI Functions 08:24 - AI Pipelines 09:50 - DiskANN + AI Re-ranking 10:50 - VS Code Extension + Data Cloning 12:31 - Wrap up ► Link References Check out our blog at https://aka.ms/azurepostgresblog ► Unfamiliar with Microsoft Mechanics? As Microsoft's official video series for IT, you can watch and share valuable content and demos of current and upcoming tech from the people who build it at Microsoft. • Subscribe to our YouTube: https://www.youtube.com/c/MicrosoftMechanicsSeries • Talk with other IT Pros, join us on the Microsoft Tech Community: https://techcommunity.microsoft.com/t5/microsoft-mechanics-blog/bg-p/MicrosoftMechanicsBlog • Watch or listen from anywhere, subscribe to our podcast: https://microsoftmechanics.libsyn.com/podcast ► Keep getting this insider knowledge, join us on social: • Follow us on Twitter: https://twitter.com/MSFTMechanics • Share knowledge on LinkedIn: https://www.linkedin.com/company/microsoft-mechanics/ • Enjoy us on Instagram: https://www.instagram.com/msftmechanics/ • Loosen up with us on TikTok: https://www.tiktok.com/@msftmechanics
From cracked data foundations to multi-agent AI, Starburst Data's co-founder shares hard-won lessons on getting the right data, not just more of it.Topics Include:Matthew Fuller, co-founder and VP of Product at Starburst Data, joins the show.Starburst is built on Trino, a fast SQL engine for federated data queries.Their platform lets users query data across lakes, stores, and databases seamlessly.Governed "data products" give organizations access to their full data estate in context.A strong data foundation is essential before any AI use case can succeed.AI doesn't create data problems — it exposes the cracks already there.Common mistake: assuming everyone in an org defines "customer" or "revenue" the same way.More data isn't always better — getting the right data is what matters.Customers include HSBC, Comcast, Zalando, ZoomInfo, and DBS, many running on AWS.AWS partnership spans technical support, SLA reliability, and proactive product briefings.Advice for product leaders: always anchor new technology back to the customer problem.2026 will be defined by specialized multi-agents working together autonomously.Participants:Matt Fuller – Co-Founder, Vice President of Product, Starburst DataSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/
This week the trio covers the Latest Ubuntu, Fedora, and CachyOS news. Btrfs has a big performance win, USB4 brings fast data transfers, the latest kernel RC has prompted a classic Torvalds rant. And then Jonathan flies in to wrap up the show with Open Source AI definition news. For tips, we have quein for turbo-charges who is, Shelly for smarter package management, htmlq for querying a web page, and DuckDB for slick SQL on the command line. You can find the show notes at https://bit.ly/434Hrkg and enjoy! Host: Jonathan Bennett Co-Hosts: Ken McDonald, Rob Campbell, and Jeff Massie Download or subscribe to Untitled Linux Show at https://twit.tv/shows/untitled-linux-show Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.
This week the trio covers the Latest Ubuntu, Fedora, and CachyOS news. Btrfs has a big performance win, USB4 brings fast data transfers, the latest kernel RC has prompted a classic Torvalds rant. And then Jonathan flies in to wrap up the show with Open Source AI definition news. For tips, we have quein for turbo-charges who is, Shelly for smarter package management, htmlq for querying a web page, and DuckDB for slick SQL on the command line. You can find the show notes at https://bit.ly/434Hrkg and enjoy! Host: Jonathan Bennett Co-Hosts: Ken McDonald, Rob Campbell, and Jeff Massie Download or subscribe to Untitled Linux Show at https://twit.tv/shows/untitled-linux-show Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.
Nik and Michael discuss autovacuum, including what it does, and the basics of why and how to tune it. Here are some links to things they mentioned: autovacuum https://www.postgresql.org/docs/current/routine-vacuuming.html#AUTOVACUUMautovacuum configuration parameters https://www.postgresql.org/docs/current/runtime-config-vacuum.html#RUNTIME-CONFIG-AUTOVACUUMWhat's Missing in Postgres? (our episode with Bruce Momjian) https://postgres.fm/episodes/what-s-missing-in-postgrespg_squeeze (our episode with Antonín Houska) https://postgres.fm/episodes/pg_squeezeMy queries to monitor autovacuum (post by Laurenz Albe) https://www.cybertec-postgresql.com/en/monitor-autovacuum-my-queries/Autovacuum Tuning Basics (post by Tomas Vondra, originally for 2nd Quadrant blog) https://www.enterprisedb.com/blog/autovacuum-tuning-basicsZero autovacuum_vacuum_cost_delay, Write Storms, and You (post by Jeremy Schneider) https://ardentperf.com/2026/04/12/zero-autovacuum_cost_delay-write-storms-and-you/Our episode on long-running transactions / xmin horizon https://postgres.fm/episodes/long-running-transactions~~~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
Find out why the world's largest banks and enterprises trust CockroachDB for mission-critical infrastructure, and what a decade of AWS partnership means for the future of cloud-native data.Topics Include:Cockroach Labs makes CockroachDB, a distributed SQL database built for resilience.It delivers cloud-native consistency that legacy relational databases simply cannot match.The name "cockroach" reflects survivability — it's designed to never go down.Target customers include major banks, trading platforms, retailers, and gaming companies.AI is forcing enterprises to accelerate database modernization from the board level down.AWS has been a foundational cloud partner for Cockroach Labs for a decade.The CockroachDB-AWS integration spans EC2, S3, Bedrock, and Amazon Q-Transform.AWS partnership shapes both product roadmap decisions and go-to-market execution.New partners should educate themselves first — AWS programs are deep and extensive.CockroachDB now supports native vector search for RAG and generative AI applications.Agentic AI could mean trillions of digital agents demanding real-time data infrastructure.Database modernization and AI adoption will only accelerate dramatically through 2027.Participants:Cassie Zimmerman – Senior Director, Global Strategic Partnerships, Cockroach LabsSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/
In episode 322, the co-hosts examine critical vulnerabilities, changing security standards, and adaptive defense mechanisms. They deep dive into the recent "Megalodon" breach, identifying it as a direct poisoned pipeline execution attack. Rather than exposing a flaw inside GitHub itself , researchers at Hudson Rock traced the root cause to credentials stolen from developer desktops via infostealer malware, which allowed attackers to push base64-encoded payloads into GitHub Actions workflow YAML files. To counter these types of automated supply chain threats, the hosts praise NPM's newly released "staged publishing" pipeline, which mandates two-factor authentication from human maintainers before releasing packages pushed by automated CI/CD workflows. Shifting to framework flaws, they highlight a catastrophic, vanilla SQL injection flaw discovered in GoCMS during active exploitation. Finally, the duo reviews the emergence of AI-powered honeypots highlighted Talos Intelligence. They conclude that turning the tables on attackers by utilizing LLM-driven "hall of mirrors" environments to impersonate real systems represents an innovative, under-explored AppSec strategy designed to drain attacker resources and trigger high token costs.
Microsoft confirms active exploitation of two Defender flaws. Europol dismantles a VPN service tied to ransomware gangs. A nine-year-old Linux kernel bug exposes SSH keys and password hashes. Cisco patches a critical Secure Workload vulnerability, while Drupal fixes a highly critical SQL injection flaw. Android malware quietly signs victims up for premium SMS scams. Webworm upgrades its espionage toolkit with Discord and Microsoft Graph backdoors. Plus, China and Russia deepen cooperation on AI, cybersecurity, and satellite systems. Our guest is Jake Moore, Global Cybersecurity Advisor for ESET, sharing a glimpse into his Infosecurity Europe keynote "The Deepfake Interview." Greg doesn't even work here anymore… 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, Maria Varmazis speaks with Jake Moore, Keynote speaker for the upcoming Infosecurity Europe conference and Global Cybersecurity Advisor for ESET, getting a glimpse into his session "The Deepfake Interview: Breaking In From the Inside." This interview is part of our partnership with Infosecurity Europe. Selected Reading Microsoft Defender vulnerabilities exploited in the wild (Help Net Security) Europol Seizes First VPN Used by Ransomware Gangs, Arrests Administrator (Hackread) Nine-Year-Old Linux Kernel Flaw Leaks SSH Keys and Password Hashes (Infosecurity Magazine) Cisco Patches Critical Vulnerability in Secure Workload (SecurityWeek) Android Malware Spotted Subscribing Victims to Paid Services Without Consent (Hackread) Drupal Patches Highly Critical Vulnerability Exposing Websites to Hacking (SecurityWeek) Webworm: New burrowing techniques (We Live Security) Xi and Putin pledge closer cooperation on AI, cyberspace and satellite systems (The Record) Zombie user account let hackers control the city's water (The Register) 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
This week we sit down with Field Solution Architects Anthony Nocentino and Justin Emerson explore an interesting convergence happening in data architecture—the blending of traditionally separate block and file/object storage systems. Likening the experience to a Chocolate Peanut Butter Cup, Anthony (a database expert focused on block storage) and Justin (an expert in unstructured data and file/object storage) discuss how the clear historical distinctions between structured and unstructured data are rapidly blurring. This shift is fueled by modern challenges like high-scale analytics, data governance, and the rise of technologies like Large Language Models (LLMs) and agentic interactions, which no longer care where the data lives. Our conversation dives into the technical tipping point enabled by data virtualization, referencing features like SQL Server 2022's object integration, which allows a database engine to access data stored efficiently on object storage. This capability is far more than an archival play; it helps customers achieve scale-out analytics, improve data governance by maintaining one canonical copy of data across different performance buckets, and simplify tedious operations like SQL backups by bypassing legacy file system complexities. Anthony and Justin highlight how Everpure's platform aligns perfectly with this new reality. Finally, Anthony and Justin discuss the path forward, noting that the technology is underutilized due to organizational silos and an awareness problem. The next big evolution will focus on security and governance for this distributed data via open table formats like Iceberg and catalogs such as Polaris. We close with what currently excites them: Anthony on collaborating with AI (Claude) to create code and speed up outcomes, and Justin on Everpure's core philosophy of simplicity, efficiency, and treating customers like people, particularly in the context of the current economic conditions. To learn more, visit: https://www.everpuredata.com/platform.html Check out the new Everpure digital customer community to join the conversation with peers and Pure experts: https://purecommunity.purestorage.com/ 00:00 Intro and Career Journeys 04:30 Customer Engagement and SKO 09:55 Vacation Recap 13:45 History of Block and Object Storage 16:04 Why Convergence Now? 20:30 Data Virtualization 25:55 Exploring Access Patterns 29:05 What's Holding Back Adoption 36:02 Simplicity for DBAs
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In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Joshua Bate, founder of Bonfires.ai and DeciWorld, for a wide-ranging conversation covering knowledge management, graph technology, ontologies, decentralized science, and the future of how humans organize and share information. They break down the differences between personal and enterprise knowledge management, explore why flat ontological graphs may be the key to making diverse knowledge bases interoperable, and get into why traditional RAG systems break down at scale and how graph RAG offers a more principled solution. The conversation expands into the philosophy of categorization, the slow death of basic "gentleman science" under institutional pressures, and how decentralized protocols might restore a kind of mycelial knowledge network connecting small groups of researchers, enthusiasts, and communities — much like the original spirit of the encyclopedia before it was co-opted by institutions. You can learn more about Joshua's work at bonfires.ai and deci.world or follow him on X at @Bonfiresai and @DeSciWorld.Timestamps00:00 - Stewart introduces Joshua Bate, founder of Bonfires.ai, discussing personal versus enterprise knowledge management and their fundamental differences at scale.05:00 - Joshua explains ontologies as classifiers for knowledge structures, describing their two-year search for a perfect ontology and ultimately building a flat, ontology-less graph protocol.10:00 - Stewart connects categorization to shamanic practice and intercategorical theory, noting how major companies like Netflix and Yahoo built graph-based ontologies while the discipline remains underappreciated philosophically.15:00 - Joshua traces Bonfires origins through decentralized science, explaining how NFT community excitement inspired redirecting capital toward funding unconventional researchers locked out of institutional systems.20:00 - Joshua describes building federated knowledge networks through hackathons and conferences, comparing the vision to what Wikipedia could have been with decentralized incentive structures.25:00 - Discussion shifts toward inevitable collapse of rigid scientific institutions, debating patchwork age theory, nation-state fragmentation, and rhizomatic versus arboreal knowledge structures.30:00 - Joshua articulates the mycelial network vision, enabling direct cross-cultural information access where individuals control their own narrative lens, warning against collective we thinking and authoritarianism.Key Insights1. Knowledge management exists on a spectrum from personal to enterprise, but the founder of Bonfires argues this split is artificial. He believes knowledge itself does not respect those boundaries, and that small groups, researchers, hobbyists, and large institutions all possess knowledge that can and should interoperate with each other.2. After two and a half years of searching for the perfect ontology to structure their knowledge graph, the team concluded that no perfect ontology exists. Their solution was to build the flattest possible graph structure with only events, entities, and edges, creating a base layer others can build specialized ontologies on top of.3. Graph-based knowledge systems are more efficient than traditional databases for AI traversal because once a graph is computed, it is relatively free to query. Graph RAG combines the discovery power of vector search with the structured precision of graph traversal, solving many hallucination problems associated with standard retrieval augmented generation.4. Basic scientific research, the soil from which applied discoveries grow, is deteriorating because institutional funding structures only reward commercially viable outcomes. The founder built his platform partly to redirect community-driven capital toward researchers who are doing important work without institutional support.5. The institutionalization of science has historically blocked the open exchange of ideas that drove the original scientific revolution. The human spirit for open inquiry has not changed, but people cannot pursue it without financial support, and building decentralized infrastructure could restore that possibility.6. A federated knowledge network would allow individuals to access information from any contributor and filter it through their own preferred lens, rather than receiving information pre-filtered by centralized platforms. This represents a form of information symmetry similar to how mycelial networks distribute nutrients across a forest.7. The concern is not whether current scientific and governmental institutions will change but in what direction the rebuilding goes. Those capitalizing on the transition carry the same incentives as the previous era, which risks reproducing the same problems inside new structures.
Nik and Michael are joined by David Ventimiglia to discuss pg_flight_recorder, a new tool he created for monitoring a Postgres database from within. Here are some links to things they mentioned: David Ventimiglia https://postgres.fm/people/david-ventimigliapg_flight_recorder https://github.com/dventimisupabase/pg_flight_recorderSupabase https://supabase.compg_wait_sampling https://github.com/postgrespro/pg_wait_samplingpg_ash https://github.com/NikolayS/pg_ashpg_cron https://github.com/citusdata/pg_cronpg_tle https://github.com/aws/pg_tle~~~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
What do colors, soup kitchens, and mountain climbing have in common? They're all part of the mental models that have shaped how we think about analytics, and they're exactly the kind of durable wisdom that matters more than ever in an age of AI slop. This campfire-style conversation among the co-hosts reveals the concepts, books, and aha moments that have stuck with us across decades of analytics work. From the magic of randomization to the critical distinction between outputs and outcomes, we share the frameworks that guide our thinking whether we're writing SQL by hand or asking Claude to do it for us. It turns out the most valuable analytics wisdom isn't about tools or techniques—it's about understanding how humans actually make decisions, build trust, and collaborate effectively. Some things never go out of style. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.
Mike & Tommy dive into Databricks Genie and the growing hype around data agents, exploring whether the real challenge is natural language chat or the semantic layer underneath—and what Power BI teams must fix before any AI agent can deliver trusted, governed answers at scale.https://www.advancinganalytics.co.uk/blog/genie-is-a-semantic-layer-problem-not-a-chat-problem-1https://community.fabric.microsoft.com/t5/Fabric-Updates-Blogs/OneLake-catalog-is-now-natively-available-in-Foundry-Generally/ba-p/5178376https://community.fabric.microsoft.com/t5/Fabric-Updates-Blogs/Direct-Lake-on-SQL-with-Fabric-Data-Warehouse/ba-p/5177641https://community.fabric.microsoft.com/t5/Power-BI-Updates-Blog/Modern-Visual-Tooltips-in-Power-BI-Generally-Available/ba-p/5173946Get in touch:Send in your questions or topics you want us to discuss by tweeting to @PowerBITips with the hashtag #empMailbag or submit on the PowerBI.tips Podcast Page.Visit PowerBI.tips: https://powerbi.tips/Watch the episodes live every Tuesday and Thursday morning at 730am CST on YouTube: https://www.youtube.com/powerbitipsSubscribe on Spotify: https://open.spotify.com/show/230fp78XmHHRXTiYICRLVvSubscribe on Apple: https://podcasts.apple.com/us/podcast/explicit-measures-podcast/id1568944083Check Out Community Jam: https://jam.powerbi.tipsFollow Mike: https://www.linkedin.com/in/michaelcarlo/Follow Tommy: https://www.linkedin.com/in/tommypuglia/
Help us become the #1 Data Podcast by leaving a rating & review! We are 67 reviews away! I made a tool that turns your GitHub projects into a real portfolio. Here's what it looks like in action.BUILD YOUR OWN PORTFOLIO: https://dcj.app/mydatafolio-0QqsQr
In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Tyler Cloutier, founder of Clockwork Labs and creator of SpaceTimeDB. They explore how SpaceTimeDB functions as more than just a database—it's essentially a distributed operating system that merges server logic with data storage, enabling real-time applications and time-travel capabilities. The conversation ranges from the technical architecture of databases and operating systems to the philosophy of distributed systems, touching on everything from Unix and Linux to how SpaceTimeDB could revolutionize AI-generated software deployment. Tyler explains how their system reduces the complexity of building real-time applications, makes deployment simpler for both humans and AI agents, and why games like their MMORPG BitCraft Online drove them to create this new infrastructure. They also discuss the future of the internet, the role of bots in gaming, and how SpaceTimeDB fits into the broader landscape of cloud computing alongside tools like Cloudflare, Vercel, and Docker. For more information, visit spacetimedb.com or check out Clockwork Labs on GitHub and Twitter.Timestamps00:00 Stewart introduces Tyler Cloutier, founder of Clockwork Labs, discussing the origin of SpaceTimeDB's name inspired by Einstein's theory and its time travel capabilities that store all operations indefinitely05:00 Tyler explains SpaceTimeDB as more of an operating system than a database, using tables instead of file systems while running code in a sandboxed environment with full atomic properties10:00 Discussion of how SpaceTimeDB replaces both Node.js and Postgres by merging web server and database functionality, eliminating separate deployment concerns15:00 Tyler explains JavaScript execution through Chrome's V8 engine and JIT compiling, leading to Node.js creation for server-side JavaScript development20:00 Explanation of stateless web servers versus stateful game servers, and why games require in-memory state management for real-time performance25:00 Tyler introduces reducers and real-time subscriptions, questioning why more applications aren't real-time when state changes should update immediately30:00 Discussion of Facebook as essentially a text-based MMO, comparing social media architecture to game server requirements and the need for unified systems35:00 Tyler explains ACID properties in databases: atomic, consistent, isolated, and durable, using game item trading examples40:00 Comparing SpaceTimeDB to smart contract systems without cryptocurrency or global consensus, positioning it as a smart database with centralized trust45:00 Tyler reveals SpaceTimeDB uses 43% fewer tokens than Postgres for AI-generated applications, making it valuable for vibe coding platforms50:00 Conversation shifts to bots in games and proof-of-human concepts, with Tyler proposing biometric systems and discussing potential in-person gaming applications55:00 Closing discussion about tracking AI-driven traffic through UTM parameters and finding SpaceTimeDB at spacetimedb.comKey Insights1. SpaceTimeDB is fundamentally a database that runs application code directly inside it, combining what traditionally required separate systems like Postgres and Node.js. Users compile their application logic into WebAssembly or JavaScript and upload it to run within the database itself. This architecture provides high performance because the entire server backend operates inside the database environment. The system also features time travel capabilities, storing every operation and change to data persistently and indefinitely, allowing users to set application state back to any earlier point in time. This makes SpaceTimeDB more accurately described as an operating system rather than just a database, where the abstraction is that everything is a table rather than a file.2. The inspiration for SpaceTimeDB came from building BitCraft Online, an MMORPG where all players exist in a single persistent world and rebuild civilization together. Traditional MMO backends required complex custom solutions to handle real-time state, with game servers storing state in memory and periodically writing to databases. This complexity existed because games cannot afford the latency of constantly delegating to distant databases like traditional web applications can. SpaceTimeDB solved this by making the database fast enough to handle real-time requirements directly, eliminating the need for separate game servers. This same performance advantage that benefits games also applies to web applications, which is why SpaceTimeDB evolved from a game-specific tool to a general-purpose platform.3. SpaceTimeDB functions as a distributed operating system where each database acts like a process in an actor model system, similar to Erlang or Scala Akka. Databases can send messages to other databases and be spawned across a cluster for horizontal scaling. This represents an overlay operating system running on top of Linux rather than competing with it, providing a distributed abstraction across many machines while Linux handles device drivers and hardware support. The vision is for the cloud to function as a single enormous computer running one operating system, where developers simply publish their programs without managing separate services, deployment, routing, networking, or persistence infrastructure.4. The real-time capabilities of SpaceTimeDB address a fundamental limitation in how most web applications work today. Traditional web servers are stateless, delegating all state to databases and accepting network round-trip latency for each request, which is why users often must refresh pages to see updates. SpaceTimeDB allows queries to be subscribed to, maintaining open connections that stream changes whenever query results update. This makes applications like Discord, Facebook, or banking systems naturally real-time without requiring page refreshes. The historical accident that more things are not real-time represents a problem SpaceTimeDB solves by unifying the web world with the game world's real-time requirements.5. SpaceTimeDB implements ACID properties—Atomic, Consistent, Isolated, and Durable—ensuring database operations are reliable and safe. Atomic means operations either fully happen or not at all, preventing issues like item duplication in games when trading between players. Consistent means declared invariants like unique usernames are always enforced. Isolated means concurrent operations do not interfere with each other. Durable means changes persist even if computers restart, with varying levels from in-memory on one machine to disk storage across multiple geographic locations. These properties are managed through reducers, functions inspired by React Redux that fold changes into application state incrementally.6. For AI and large language models, SpaceTimeDB offers significant advantages in building and deploying applications. Testing showed that creating applications with SpaceTimeDB uses 43% fewer tokens compared to Postgres implementations, costs less, has fewer bugs, and is easier to extend. This matters because the primary cost for vibe coding platforms is tokens. As more software gets written in the next twelve months than ever before, there is insufficient focus on infrastructure required to run all this AI-generated software. SpaceTimeDB positions itself as ideal for LLMs to target because of its simplified deployment model where developers just publish code and the system handles everything behind the scenes.7. SpaceTimeDB can be understood as a smart contract system without cryptocurrency or global decentralized consensus. Like blockchain smart contracts, it executes code with atomic, consistent, isolated, and durable properties, but avoids the expense and slowness of requiring all computers worldwide to agree on everything. Instead, it offers centralized trust where users trust Clockwork Labs not to modify deployed contracts, rather than the trustless but extremely costly blockchain approach. This makes it functionally similar to Cloudflare's durable objects but with full relational database capabilities. The system exists before the networking layer where Cloudflare operates, handling deployment, server, and database functions while Cloudflare could provide DDoS protection in front of it.
Every B2B marketer knows the funnel concept is broken. Yet that same model your team is being measured on right now was invented in 1898, and it's surviving because nobody at the top has been given permission to use anything else.Carolyn and Amber react to a recent article in Adweek from Professor Mark Ritson, where he calls the funnel the "cockroach of marketing concepts", a 128-year-old model that has outlived every attempt to replace it. They break down why every critique fails to land, and why the real problem isn't the funnel. It's the classrooms teaching it, the boards demanding it, and the marketers who can't challenge it without risking their careers.Topics covered in this episode:Why a model from 1898 still anchors how B2B companies measure marketing in 2026The gap between what the funnel was designed to do (a market snapshot) and what it's used for (SQL-to-opp conversion, MQL targets)Why every "funnel is dead" critique fails to kill it, and who actually keeps it aliveThe Amazon "full funnel campaigns" moment, and what it says when even the best companies are still using the languageWhy a snapshot in time tells you nothing about where to move budget, why CAC is up, or what to do when pipeline missesIf you've tried to kill the funnel inside your own org and watched it survive every conversation, this is the episode. The cockroach isn't the funnel. It's the system that keeps demanding it.-----------------------------------------------------Want answers now?
Show Notes: Scarlett Jiang from Vantage AI, an AI product and services firm based in London, provides a one-minute overview of Vantage AI, highlighting their focus on data foundations and AI transformation. Vantage AI helps companies consolidate data from various systems into a single source of truth. Scarlett mentions the firm's experience with hospitality franchise clients, such as Burger King, KFC, and McDonald's. Mock Demo of Chatbot Scarlett introduces a mock demo of a chatbot designed for hospitality franchise owners. The chatbot can handle real-time queries about sales data, labor costs, and other key metrics. Scarlett explains the process of using the chatbot to query data, including translating natural language questions into SQL, which means users do not need to know SQL. Custom Dashboard Scarlett introduces the custom dashboard with data intelligence analyst chatbot functionality, allowing users to query via human natural language and retrieve insights from pre-ingested data warehouses. Sales Performance The chatbot can provide summaries of sales performance, labor data, and other operational metrics. Sales Performance Rank Scarlett shows how the chatbot can handle more complex queries, e.g.: If I had to focus on 3 stores to improve performance this quarter, which would you recommend and why? (chatbot showcase the capability to synthesize sales, reviews, and trend data into recommended action) Performance Graph The chatbot can provide detailed insights into top and bottom performers, including specific metrics like net sales and transaction counts. Scarlett discusses the benefits of using a chatbot for specific questions, rather than pre-built dashboards. The chatbot can also provide reasoning behind its answers, showing the steps it takes to generate insights. The Process of Building AI Tools Scarlett explains the process of building AI tools, starting with a diagnostic phase to understand the client's data journey and use cases. After the diagnostic, a strategic roadmap is created to prioritize use cases. A quick prototype is then developed, followed by data foundation transformation. The process can range from a few days to several months, depending on the complexity and scope of the project. Accounts Payable Month-end Reconciliation Demo Scarlett demonstrates a workflow automation tool for account payable month-end reconciliation. Accounts Payable Reconciliation This demo presents a finance use case built around month-end accounts payable reconciliation - a process every finance team navigates. Supplier invoice data sits across two systems: the AP subledger, which holds granular invoice-level detail, and the general ledger control account, which carries a single summary balance that should match. In practice, the two rarely align - late-posted invoices and manual journal entries that bypass the subledger are the most common culprits. This demo showcases an AI agent that pulls data from both sources, identifies and reconciles the gap automatically; and surfaces discrepancies to human reviewers for sign-off or overwrite - eliminating hours of manual investigation at close. Converting PDF Purchase Orders into CSV Files Scarlett demonstrates a tool that converts PDF purchase orders into CSV files. Snowflake Tables The tool extracts key information from the PDF, such as contract terms, payment schedules, and expenditures. The tool can transform the extracted data into a chart format for easier analysis. Reconciliation Report Payment Breakdown The tool is designed to automate the process of working with large amounts of unstructured data, reducing manual effort. Cost and Development Time Scarlett discusses the cost and development time for AI tools, noting that prototypes can be developed quickly. The bulk of the work involves data cleaning, ingestion, and transformation to ensure data accuracy. The development time can range from a few days to several months, depending on the complexity and scope of the project. The cost varies based on the specific requirements and the level of automation needed. Demonstration Videos: Pre-recorded Demo 1: Data Intelligence demo https://www.vantageglobal.ai/insights/demo-pages/data-intelligence-analyst Pre-recorded Demo 2: Accounts payable month-end reconciliation agent https://www.vantageglobal.ai/insights/demo-pages/ap-month-end-reconciliation-agent Pre-recorded Demo 3: Parsing unstructured data to structured data https://www.vantageglobal.ai/insights/demo-pages/purchase-order-explorer-agent Timestamps: 02:24: Demonstration of AI Chatbot for Hospitality Franchise Owners 07:14: Advanced Query Capabilities of the Chatbot 13:06: Process of Building AI Tools at Vantage AI 17:40: Case Study: Account Payable Month-End Reconciliation 28:03: Case Study: PDF to CSV Transformation 34:42: Cost and Development Time for AI Tools This episode on Umbrex: https://umbrex.com/unleashed/episode-643-scarlett-jiang-coo-at-vantage-global-ai-shares-3-live-client-ai-use-cases/ Unleashed is produced by Umbrex, which has a mission of connecting independent management consultants with one another, creating opportunities for members to meet, build relationships, and share lessons learned. Learn more at www.umbrex.com. *AI generated timestamps and show notes.
France pushes digital sovereignty. Adobe rushes an Acrobat Reader patch. Booking.com confirms a targeted breach. SAP fixes a critical SQL injection bug. A sanctions-dodging fraud network resurfaces. ViperTunnel infiltrates U.S. and U.K. firms. GlassWorm spreads across developer tools. Researchers dissect Predator spyware's kernel engine. A lawsuit challenges AI transcription in hospitals. Ted Shorter from Keyfactor unpacks quantum computing at scale. On our Threat Vector segment, David Moulton and Elad Koren pull back the curtain on agentic-first security. Preparing for post-quantum perils. 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 Ted Shorter, CTO and Co-Founder of Keyfactor, discussing the advent of quantum computing at scale, known as "Q-Day". Threat Vector Host David Moulton speaks with returning guest Elad Koren, Vice President of Product Management for Cortex Cloud at Palo Alto Networks on this Threat Vector segment. Together they pull back the curtain on what an agentic-first security experience actually looks like in practice. This isn't a vision deck. The agents are already running. To listen to the full conversation, check it out here. Catch new episodes of Threat Vector every Thursday on your favorite podcast app. Selected Reading France Tees Up Big Public Sector Move Away From US Tech (BankInfo Security) Adobe rolls out emergency fix for Acrobat, Reader zero-day flaw (Bleeping Computer) Booking.com Confirms Data Breach as Hackers Access Customer Details (Hackread) SAP Patches Critical ABAP Vulnerability (SecurityWeek) Triad Nexus Evades Sanctions to Fuel Cybercrime (SecurityWeek) Ransomware-Linked ViperTunnel Malware Hits UK and US Businesses (Hackread) GlassWorm evolves with Zig dropper to infect multiple developer tools (Security Affairs) Predator Spyware's iOS Kernel Exploitation Engine: PAC Bypass, NEON R/W & More (Jamf Threat Labs) Lawsuit: AI Illegally Recorded Doctor-Patient Encounters (BankInfo Security) World Quantum Day (WorldQuantimDay) 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
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