Podcasts about graphs

Using Microsoft Graph and Powershell to Mine for Information - Stale Accounts and Licenses https://isc.sans.edu/diary/Using%20Microsoft%20Graph%20and%20Powershell%20to%20Mine%20for%20Information%20-%20Stale%20Accounts%20and%20Licenses/33264 Using Microsoft Graph and Powershell - Risk Detection Commands https://isc.sans.edu/diary/Using%20Microsoft%20Graph%20and%20Powershell%20-%20Risk%20Detection%20Commands/33266 Keycloak Vulnerability https://github.com/keycloak/keycloak/issues/51833 https://www.keycloak.org/2026/08/keycloak-2672-released CRYPTOGRAPHIC CONTEXT INJECTION ATTACK https://adversa.ai/blog/cryptographic-context-injection-grok-data-theft/ N-able password manager https://amibeingpwned.com/blog/solar-winds-part-2-avoided?_sp=75fd154a-e34f-41d0-8624-7c285776c13d.1787263544340 My Upcoming Classes https://www.sans.org/profiles/dr-johannes-ullrich

I'm joined by Cobus Greyling, AI evangelist at Kore.ai, for a wide-ranging conversation on where enterprise AI actually stands today.We explore why many of the ideas now being presented as breakthroughs, from graph engineering and orchestration to intent detection and state management, have deep roots in conversational AI. We also get into one of the biggest barriers to enterprise AI adoption: data. We highlight why customer experience could become the entry point for much wider organisational transformation. We touch on how forward-deployed engineers could help businesses navigate that change and why security will become increasingly important as AI agents gain access to more systems and information.I close with a buzzword bingo round for Cobus covering fleet engineering, agent swarms, orchestration and universal agents, and Cobus explains why he thinks Open Claw was the biggest red herring of the year.Show notes
Follow Cobus on LinkedIn: https://www.linkedin.com/in/cobusgreylingCobus's website: https://cobusgreyling.meFollow Cobus on Substack: https://cobusgreyling.substack.com/Discover more about Kore.aiArticle - The Untrainable by Sarah Guo: https://saranormous.substack.com/p/the-untrainableVideo - Andrej Karpathy: Software Is Changing (Again): https://www.youtube.com/watch?v=LCEmiRjPEtQFollow Kane on LinkedIn:⁠⁠⁠⁠⁠⁠https://www.linkedin.com/in/kanesimms⁠⁠⁠⁠⁠⁠Find out more about VUX:⁠⁠https://vux.ai⁠⁠Subscribe to VUX World: ⁠⁠⁠⁠⁠⁠⁠https://vuxworld.typeform.com/to/Qlo5aaeW?utm_source=podcast&utm_medium=audio⁠⁠⁠⁠⁠Subscribe to The AI Ultimatum Substack: ⁠⁠⁠⁠⁠⁠⁠https://open.substack.com/pub/kanesimms⁠

Salk created some graphs that show the improvement of the Seahawks running game over the course of the season last year. Brock and Salk discuss how the Offensive Line got better and how it affected the Hawks running game and offense as a whole. They talk about how the Mariners get the train back on the tracks, tonight’s HBO Hard Knocks, and more in Need To Know. They discuss why this year is different from last year for the Mariners, why the expectations have changed, and why the team isn’t meeting those expectations. And Brock talks about what Dan Wilson could learn from Mike Macdonald, Quinnen Williams’ extension in Dallas, and more in Blue-88.

Graph engineering is AI's latest buzzy term—but it offers a useful framework for organizing agents, tools, knowledge and humans into working systems. NLW explains the evolution from prompts to graphs. In the headlines: OpenAI delays Astra, ByteDance trains a massive model, open-weight AI tests revenue sharing and Claude Code embraces Auto Mode.AIDB's AI Summer Adventure: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://summeradventure.ai/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Brought to you by:KPMG – Research from KPMG and the University of Texas at Austin shows the highest-impact AI users treat AI like a reasoning partner — and those skills can be taught at scale. Learn more at ⁠⁠⁠⁠https://kpmg.com/us/Sophisticated⁠⁠⁠⁠Hyperagent - Hire a fleet of always-on agents. New users get $1,000 in inference. ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠hyperagent.com/aidailybrief⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Rackspace Technology- One accountable partner to build, operate and run your full enterprise AI stack ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.rackspace.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Section - Section turns AI investment into workforce transformation and ROI - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.sectionai.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Blitzy - Want to accelerate enterprise software development velocity by 5x? ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://blitzy.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AssemblyAI - The best way to build Voice AI apps - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.assemblyai.com/brief⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Robots & Pencils - Cloud-native AI solutions that power results ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://robotsandpencils.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://pod.link/1680633614⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Our Newsletter is BACK: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://aidailybrief.beehiiv.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Interested in sponsoring the show? sponsors@aidailybrief.ai

In this episode of Being Human, host Steve Cuss speaks with political scientist and pastor Ryan Burge about his book The Vanishing Church. They explore how American Christianity has become increasingly radicalized by political partisanship, the role of social media algorithms in amplifying extreme voices, and the disappearance of moderate congregations. Burge discusses how partisanship now shapes religious identity rather than the reverse, the impact of COVID had on churches, and how the church has become a "hospital for the healthy."  Episode Resources: Ryan Burge's The Vanishing Church: How the Hollowing Out of Moderate Congregations Is Hurting Democracy, Faith, and Us Michael Wear's CT review of Ryan's work on the “big church sort” Pew research: How the Pandemic Has Affected Attendance at U.S. Religious Services A Look at The Donatist controversy Who was Count Zinzendorf? Scripture Referenced: 1 John 1:5 (ESV)  Matthew 20:16; Luke 13:30; Mark 10:31 (ESV) Mark 2:17 (ESV) More from Ryan Burge: Ryan's Graphs about Religion Ryan on X (formerly Twitter) Sign up for Steve's Newsletter & Podcast Reminders Capable Life Newsletter Get the Assets & Liabilities pdf and the Life Giving List Join Steve at an Upcoming Intensive Capable Life Intensives Learn more about your ad choices. Visit podcastchoices.com/adchoices

AI agents can write code for hours, but ask them to do real work in the real economy, and they break. Mitch Troyanovsky is co-founder of Basis, a unicorn AI company whose agents run autonomously for hours — sometimes days — completing complex tax returns end to end. His answer to the reliability problem: stop grading outcomes, and start supervising the process.This is a definitive, reference-style conversation on building long-horizon AI agents. Mitch walks through the full history — from ReAct and the AutoGPT crash to reasoning models and RLVR — and explains why the industry abandoned process supervision in 2023, and why it's now coming back at a completely different scale. We go deep on behavior specs, the open standard Basis just released with Braintrust for defining and evaluating how agents behave across entire trajectories, with no ground truth required.Along the way: why context is really runtime training data, why your documentation must be treated like a codebase, ontologies as "worlds for agents to live in," the judge-as-agent architecture, why Basis hires philosophy majors as Language Architects, deploying agents as "onboarding 300 brilliant alien employees," and Mitch's prediction for when the bitter lesson swallows the harness.(01:09) Why Basis Engineers Whisper to Their Agents(04:12) Accounting as Compression: an Intelligence Layer Over the Economy(06:11) Defining Long-Horizon: When You Exceed the Context Window(08:24) Anatomy of a Multi-Day Autonomous Trajectory(10:19) Handoff Design: Optimizing Output for the Reviewer(11:17) ReAct and Why Reasoning Must Regulate Its Own State(12:33) Large Working Memory, No Long-Term Memory(14:13) Compounding Errors: Why AutoGPT and BabyAGI Broke(15:51) Opus 3, o1, o3: the Three Real Paradigm Shifts(17:07) Titrating Inference Compute Across Easy and Hard Steps(18:23) Process Reward vs. Outcome Reward: "Let's Verify Step by Step"(20:32) RLVR and Why the METR Curve Overstates Reliability(22:09) Verifiable at Runtime: the Real Reason Coding Won(25:14) No Ground Truth, No Cheap Verification, No Data(26:55) Encoding Deterministic Checks From Human Review Process(29:18) Synthetic Data Limits: Generating Artifacts, Not Text(33:16) 100 Evals Pass — Does It Generalize to Production?(35:53) Primary Sources vs. Pre-Training Knowledge(36:37) Behavior Specs: Markdown, Judges, and True/False/N.A.(39:58) Specificity vs. Brittleness in Spec Authoring(42:18) Context as Runtime Training Data(44:21) Judge-as-Agent: Trajectory Maps and Sub-Agent Attribution(46:45) The Move 37 Objection: Reliability Over Optimality(50:02) The Magic Box Model: Building Without Weights Access(52:41) "Nothing Paradigm-Shifting Has Changed Since o3"(54:56) Open-Sourcing the Behavior Spec Standard With Braintrust(01:02:54) Ontology Design: Virtual Filesystems, Graphs, Embeddings(01:04:20) Canonical vs. Non-Canonical: Docs as Codebase(01:06:33) Language Architects and Writing for Runtime Interpretation(01:09:05) Deployed Intelligence: 300 Alien Employees With No Context(01:11:10) Closing the Loop: Signal → Context, Tools, Harness(01:12:50) Context Slop: the Mistake Most Agent Builders Make(01:14:29) Reward Function Design and Credit Assignment Over Trajectories(01:17:01) Will the Bitter Lesson Swallow the Harness?(01:18:46) Business Moats vs. Technical Moats(01:21:03) Paradigm Thinking Over Timeline ADHD

Why You Should Be Using Graphs in Your Appraisal Reports

AI is moving fast in life sciences, but a confident answer is not the same thing as a correct, reproducible, auditable answer. Knowledge graphs, ontologies, and FAIR data practices are the keys to bridging this gap. Knowledge3's Tom Plasterer, CEO and co-founder, and Eric Little, chief data officer, join host Allison Proffitt to discuss what it takes to turn semantics into something practical: a knowledge product mindset, modular delivery, and “semantic ops” that can better manage data systems. Their conversation challenges the hype cycle around context graphs, explains why life sciences are a natural starting point for regulated, high-stakes AI, and shows how layered models can capture the right level of context without building a giant monolith that never takes off. Links from this episode:  Bio-IT World BioTeam Bio-IT World Europe Knowledge3 Bio-IT World's Trends from the Trenches podcast delivers your insider's look at the science, technology, and executive trends driving the life sciences through conversations with industry leaders. 

In episode 297 of our SAP on Azure video podcast we talk about Fabric IQ access to SAP Graph. AI agents are becoming more and more powerful, and in the SAP world one of the big questions is still: how do we give these agents meaningful access to SAP business context without just copying data around? This is where things like SAP Graph, SAP ontologies, knowledge graphs, MCP servers and Fabric IQ become really interesting.Mario has been doing a lot of work in this space. The MCP Server for SAP Datasphere environments that he has developed has gained a lot of popularity and is used by several customers. So for today I am really happy to have Mario de Felipe joining us for the first time, to show us what he has built and to talk about how all of this can help bring SAP context into Microsoft Agents.Find all the links mentioned here: https://www.saponazurepodcast.de/episode297Reach out to us for any feedback / questions:* Goran Condric: https://www.linkedin.com/in/gorancondric/* Holger Bruchelt: https://www.linkedin.com/in/holger-bruchelt/ #Microsoft #SAP #Azure #SAPonAzure #FabricIQ #Fabric #Copilot

Parker Fleming (@Statsowar) has released some fun CFB graphics ahead of the season!

手机拍的 “图”、画布上绘制的 “图”、以及数学公式在坐标系中对应的 “图”……这些不同类型的 “图”，在英语里的说法一样吗？又该如何区分使用 “picture”、“image”、“photo” 和 “graph” 呢？听节目，跟主持人步理和 Phil 一起辨析这四个表示 "图，图像” 的单词之间的区别。

Bun quitte Zig pour Rust en 11 jours à coups de Claude Code, pour 165 000$ payés par Anthropic : la réaction du créateur de Zig ne se fait pas attendre. TypeScript 7 débarque, réécrit en Go, 8 à 12x plus rapide. Entre les deux, Vidocq réimplémente Jakarta EE en souverain, le COBOL met un uppercut aux microservices, et un CTO demande à son équipe combien de temps il lui faudrait pour revenir à sa vélocité antérieure sans Claude Code. De quoi réfléchir avant le prochain rewrite. Enregistré le 17 juillet 2026 Téléchargement de l'épisode LesCastCodeurs-Episode-342.mp3 ou en vidéo sur YouTube. News Langages Est-ce qu'on peut aussi utiliser des double, des longs, ou autre pour gérer les montants monétaires en Java ? https://blog.frankel.ch/bigdecimal-vs-double/ double (IEEE 754) Usage : Calculs scientifiques, métriques, statistiques. Avantages : Très performant (matériel), idéal pour l'approximatif. Risques : Erreurs d'accumulation, égalité (==) trompeuse, NaN / -0.0. Bonnes pratiques : Utiliser une tolérance (epsilon ou ULP) pour comparer ; utiliser des algorithmes de sommation compensée (Kahan/Neumaier) pour la précision. BigDecimal Usage : Finance, comptabilité, fiscalité (précision décimale stricte). Avantages : Contrôle total des arrondis et de l'échelle. Risques : Lent (allocations), immutabilité (risque de mauvaise réaffectation), confusion equals() vs compareTo(). Bonnes pratiques : Initialiser via String ou valueOf() ; utiliser compareTo pour l'égalité. Point fixe (long) Usage : Trading, systèmes haute performance, paiements. Avantages : Très rapide, déterministe, zéro allocation. Risques : Gestion manuelle de l'échelle et des débordements (Math.addExact). Points de vigilance en production Sérialisation (JSON) : Préférer les String pour BigDecimal pour éviter la perte d'échelle. Atomicité : double n'est pas atomique ; utiliser volatile ou DoubleAdder (pour les compteurs). Tests : Toujours définir un delta ou Offset pour les tests de flottants. Bibliothèques recommandées Moneta (JSR 354) : Standard bancaire complet. decimal4j : Optimisé pour le point fixe haute performance. Apache Commons Numbers : Outils robustes pour la précision et les sommations. Typescript 7 est de sortie devblogs.microsoft.com/typescript/announcing-typescript-7-0 Performance majeure : Portage natif en Go offrant des gains de vitesse de 8x à 12x et une consommation mémoire réduite. Architecture optimisée : Utilisation du multithreading (mémoire partagée) et parallélisation native (analyse, vérification de types,émission). Nouvelles options de contrôle : Introduction des flags –checkers, –builders (parallélisation) et –singleThreaded (mode mono-cœur). Nouvel observateur de fichiers : Passage à une solution basée sur @parcel/watcher pour une meilleure réactivité et stabilité du mode –watch. Compatibilité et transition : Compatible avec les bases de code TypeScript 6.0. Utilisation du package @typescript/typescript6 recommandée pour maintenir des outils dépendants de l'ancienne API. Changements de configuration : Durcissement des défauts (ex: strict activé par défaut) et suppression de nombreuses options obsolètes (target: es5, baseUrl, etc.). Amélioration de l'expérience éditeur : Serveur de langage (LSP) plus stable avec une réduction de 80 % des erreurs et 60 % des crashs. Limitations actuelles : Support incomplet pour les frameworks utilisant des plugins de langage (Vue, Svelte, Astro, Angular) en attendant une API stable. "Java, the documentary" est sur YouTube, retraçant l'histoire du langage youtube.com/watch?v=… La vidéo n'était pas encore disponible à l'heure de l'enregistrement. Sortie officielle le 17 juillet. Avec des interviews de James Gosling, Brian Goetz, Venkat Subramaniam, et bien d'autres. Librairies What's New in 8.0 - Hibernate docs.hibernate.org/orm/8.0/whats-new L'intégration de Jakarta Persistence 4.0 apporte des nouveautés majeures comme EntityAgent (qui standardise la StatelessSession), les mappings de result set en SQL natif, et de nouvelles options de configuration de session et de requêtes (Session Creation Options, Query Options). Le support de Jakarta Data 1.1 est ajouté pour les Hibernate Data Repositories, incluant l'intégration avec les requêtes statiques JPA4, les projections @Select, et les repositories asynchrones via Jakarta Concurrency ou Hibernate Reactive. L'introduction du Graph-based Flushing remplace l'ancienne approche basée sur des heuristiques par un modèle de dépendances utilisant les contraintes relationnelles, afin d'améliorer la fiabilité des tris, la gestion des batchs et les performances globales (bien que l'ancienne méthode reste temporairement disponible). L'API ProcedureCall a été améliorée pour faciliter le casting des résultats (asResultSetOutput) et permettre la déclaration paresseuse (lazy) du mapping des ResultSet. Hibernate supporte désormais la sécurité au niveau de la ligne (Row-Level Security) de manière native pour les bases de données compatibles (PostgreSQL, Db2, SQL Server, CockroachDB) afin de gérer la visibilité en contexte multi-tenant. Une nouvelle méthode getReference() permet dorénavant de récupérer la référence d'une entité directement à partir de son natural id. Le mode Safe Mode Validator (hibernate.query.safe_mode_enabled=true) fait son apparition pour bloquer les opérations risquées comme sql(), function() ou column() dans les requêtes HQL et Criteria, ce qui est particulièrement utile pour les applications exposées aux LLMs. La gestion des associations bidirectionnelles lors de la phase de flush peut maintenant être prise en charge automatiquement par Hibernate (hibernate.bidirectionality_management=true), synchronisant la référence côté inverse de l'association. Le Subselect Fetching est considérablement amélioré, supportant dorénavant les associations "to-one" pour le bulk select fetching (au lieu de se limiter aux collections) et devenant une option de premier ordre via FetchMethod.BY_SUBQUERY. Un des papas de Cucumber et Gherkin lance Var, une alternative pour le test et le BDD var.oselvar.com Lancement de Vár : Nouvel outil de test créé pour pallier les défauts de Cucumber. Limites de Cucumber : Syntaxe Gherkin trop rigide, intégration difficile avec les exécuteurs de tests et support éditeur limité. Usage avec l'IA : Conçu spécifiquement pour vérifier que les agents IA respectent les intentions et spécifications de l'utilisateur. Fonctionnement : Utilisation du Markdown plutôt que du Gherkin ; sert à la fois de guide et d'outil de vérification. Développement assisté : Code et documentation générés en grande partie par Claude sous supervision humaine. Appel aux retours : Projet ouvert aux tests et aux critiques de la communauté. Web Une nouvelle méthode HTTP : QUERY https://kreya.app/blog/new-http-query-method-explained/ Méthode HTTP QUERY (RFC 10008) pour les recherches complexes. Problème : GET (limité par l'URL) vs POST (sémantique inadaptée). Avantages : Permet un corps de requête, sûr, idempotent et cacheable. Limites : Support infrastructurel faible, non partageable par lien, cache complexe. Usage : À réserver aux requêtes complexes si l'environnement le permet. Comment je fais du design en tant que dev backend eventuallycoding.com/p/comment-je-fais-du-design-en-tant-que-dev-backend Hugo Lassiège retrace l'évolution de son workflow de création d'interfaces en tant que développeur backend, depuis ses débuts avec Bootstrap jusqu'à l'ère de l'intelligence artificielle. L'article explique comment la structuration des éléments visuels a progressé grâce à l'Atomic Design, l'émergence des design systems et l'adoption des design tokens via un framework comme Tailwind. L'auteur détaille son processus actuel qui s'appuie fortement sur Claude Design pour générer et itérer sur des maquettes à partir d'un brief, d'un screenshot ou d'un design system de référence. Il aborde également le risque de slopification et de standardisation extrême apporté par ces outils, rappelant que si l'IA simplifie la technique, il reste crucial d'injecter de l'identité et de l'originalité pour éviter un web trop aseptisé. Data et Intelligence Artificielle De l'utilisation de SKILL.md et de "loop engineering" pour augmenter sa productivité glaforge.dev/posts/…/of-skills-and-loops-with-ai-assistance Les skills permettent d'encoder une procédure de manière répétable et automatisable Le loop engineering enlève l'humain de la boucle afin que l'agent atteigne un objectif donné de façon plus autonome Pour écrire des Codelabs (sorte de tutoriel guidé pas à pas) Guillaume a transformé une séance de création de codelab avec son agent préféré (Antigravity) en skill réutilisable pour l'écriture de ses prochains codelabs Il a également utilisé l'approche de "loop engineering" à la mode en ce moment pour que son agent IA compile, exécute, teste les instructions et le code de son codelab, pour qu'il soit complètement fonctionnel Gain estimé : passer de 2 jours de travail à moins de 2 heures ! Redeploying Claude Fable 5 anthropic.com/news/redeploying-fable-5 Anthropic a annoncé le rétablissement de l'accès à ses modèles Claude Fable 5 et Mythos 5, qui avaient été suspendus suite à des restrictions d'exportation imposées par le gouvernement américain le 12 juin 2026. Cette suspension faisait suite à un rapport d'Amazon démontrant une méthode pour contourner les garde-fous de Fable 5, lui permettant d'identifier et d'exploiter une vulnérabilité logicielle (un jailbreak). Pour y remédier, Anthropic a renforcé ses mécanismes de sécurité en déployant un nouveau classifieur capable de bloquer cette technique spécifique dans plus de 99 % des cas, acceptant en contrepartie une augmentation des faux positifs sur des requêtes bénignes. Face à l'absence de consensus sur l'évaluation des jailbreaks, Anthropic s'associe à Amazon, Microsoft, Google et d'autres partenaires pour développer un standard industriel évaluant la sévérité de ces failles selon quatre critères : gain de capacité, étendue du gain, facilité d'arsenalisation et découvrabilité. L'entreprise s'engage également à approfondir sa collaboration avec le gouvernement américain, notamment via des évaluations pré-déploiement, un partage rapide d'informations sur les failles, et des ressources dédiées à la recherche conjointe sur la sécurité de l'IA. Outillage La réécriture de Bun en Rust et la réaction du créateur de Zig bun.com/blog/bun-in-rust et andrewkelley.me/post/my-thoughts-bun-rust-rewrite.html Bun, le runtime JavaScript et TypeScript écrit à l'origine en Zig, a été entièrement réécrit en Rust pour des raisons de stabilité et de gestion de la mémoire. Cette migration massive d'un demi-million de lignes de code a été bouclée en seulement 11 jours grâce à l'utilisation intensive de Claude Code fonctionnant en parallèle, pour un coût d'API estimé à 165 000 dollars financé par Anthropic. Andrew Kelley, le créateur de Zig, a réagi publiquement en qualifiant l'ancienne base de code de Bun de "slop" remplie de hacks et de fuites mémoire accumulées par une course aux fonctionnalités. Kelley exprime son soulagement face à ce départ, expliquant que les plantages incessants de Bun devenaient un passif réputationnel toxique pour le langage Zig et sa fondation. Le rachat de Bun par Anthropic fin 2025 avait déjà mis fin aux donations financières de Bun envers la Zig Software Foundation, facilitant cette séparation. La nouvelle version Rust de Bun passe désormais la quasi-totalité des tests, réduit la taille du binaire et est déjà déployée de manière transparente en production dans Claude Code. Nouveautés de Git 2.55 github.blog/open-source/git/highlights-from-git-2-55 Support natif de FSMonitor sous Linux via inotify pour accélérer les commandes comme git status sur les grands dépôts Intégration de la compaction incrémentale MIDX (multi-pack index) dans git repack pour optimiser la réécriture des métadonnées Amélioration drastique des performances de génération des bitmaps et des pseudo-merge bitmaps lors des tâches de maintenance Nouvelle commande expérimentale git history fixup pour intégrer facilement des modifications locales dans un commit antérieur Possibilité d'exécuter des hooks configurés en parallèle pour optimiser le temps de build et de validation Utilisation d'un autostash automatique lors d'un git checkout -m en cas de conflit de fusion pour éviter de bloquer l'espace de travail Nouvelle commande git format-rev permettant de formater rapidement des commits reçus via l'entrée standard (stdin) Support du push simultané vers un groupe de remotes configuré Protection contre l'exécution de séquences de contrôle de terminal malveillantes via les flux de progression distants Vidocq, une réimplémentation souveraine et sans dépendance de Jakarta EE et Microprofile vidocq.dev/posts/vidocq-a-sovereign-jakarta-ee-and-microprofile-runtime Lancement de Vidocq : Runtime Java open source complet, compatible Jakarta EE Core Profile et Souveraineté numérique : Projet européen hébergé sur Codeberg, sous licences EUPL 1.2, EPL 2 et GPL 2.0. Standardisation totale : Implémentation fidèle des spécifications (CDI, REST, JSON, etc.), validée par 5 650 tests TCK officiels. Sécurité radicale : Zéro dépendance externe et aucune bibliothèque tierce. Aucune manipulation de bytecode à l'exécution (« magie » générée à la compilation via JDK 25). Compatible JPMS, AOT, GraalVM et Leyden CDS. Disponibilité : Projet en phase alpha, code et documentation accessibles sur vidocq.dev. Article complémentaire qui revient sur la genèse de Vidocq, en utilisant l'IA et les TCKs pour driver l'aspect spec-driven development vidocq.dev/posts/the-story-of-vidocq Le "selfware" : Guillaume s'est fait plais' en vibe-codant son propre éditeur de texte glaforge.dev/posts/…/selfware-building-my-own-text-editor-without-knowing-swift Concept de « Selfware » : création de logiciels conçus exclusivement pour soi-même, sans monétisation ni contraintes liées aux utilisateurs tiers. Le rôle de l'IA : les agents de programmation (comme Antigravity) suppriment la barrière technique de l'apprentissage des langages (Swift, APIs) pour les non-développeurs. Développement minimaliste : privilégier la performance et l'utilité directe (démarrage instantané, interface native) au détriment des fonctionnalités complexes (plugins, télémétrie, gestion de comptes). Absence de pression : libération des contraintes liées à la compatibilité, à la maintenance logicielle et aux retours utilisateurs ; le logiciel n'a besoin d'être « assez bon » que pour ses propres besoins. Incitation à l'autonomie : encourager la création d'outils sur mesure pour résoudre les frictions quotidiennes plutôt que de subir les limitations des logiciels commerciaux. Architecture Le cobol a donné un uppercut au microservices https://freedium-mirror.cfd/@maahisoft20/your-microservices-lost-to-cobol-let-that-sink-in-8ce2e236d007 Retour d'expérience sur la migration d'un système COBOL vers des microservices cloud-native qui s'est soldée par un retour en arrière après avoir constaté que le traitement batch initial était plus rapide, moins cher et plus fiable Là où le batch COBOL traitait 2.4 millions d'enregistrements en 11 minutes, le système distribué modernisé à base de message queues, retries et Kubernetes prenait 47 minutes et tombait sous la charge COBOL brille par ses caractéristiques conçues spécifiquement pour la finance comme le calcul décimal précis sans floating point errors et l'absence totale d'overhead réseau, de conteneurs ou de cold starts Rappel que distribuer un système multiplie les points de défaillance silencieux et complexifie la gestion de la cohérence transactionnelle par rapport à une exécution locale séquentielle Une invitation à se demander si les projets de décomposition en microservices apportent réellement un gain de performance de bout en bout pour l'utilisateur final ou s'ils optimisent seulement le diagramme d'architecture Méthodologies Ma meilleure question d'entretien Spring beaufume.fr/articles/spring-interview Florian beaufumé partage sa question d'entretien favorite pour évaluer des développeurs Spring de niveau intermédiaire à avancé : "Que pouvez-vous me dire sur le paramètre spring.jpa.open-in-view ?". Ce paramètre détermine l'activation du pattern Open Session In View (OSIV) qui, lorsqu'il est à true (la valeur par défaut dans Spring Boot), maintient l'un EntityManager JPA ouvert durant toute la requête HTTP. Si l'OSIV facilite le développement en évitant les fameuses LazyInitializationException lors de la sérialisation des entités en JSON, il pose d'importants problèmes de performance en provoquant des requêtes SQL non maîtrisées (comme le problème du N+1 select) en dehors de la couche service. Maintenir l'OSIV actif augmente également le temps de rétention des connexions au sein du pool de la base de données, limitant la scalabilité de l'application. La recommandation est de désactiver ce comportement en le positionnant à false, et de gérer explicitement le chargement des données requises au sein des transactions (via des DTOs, des requêtes JOIN FETCH ou des Entity Graphs) pour garder le contrôle sur les accès à la base de données. 10 points à retenir du rapport AI Engineering 2026 : The Acceleration Whiplash faros.ai/blog/ai-acceleration-whiplash-takeaways L'IA a franchi un cap et est devenue l'auteur principal du code : le taux d'acceptation du code généré est passé de 20% à 60% dans les équipes étudiées par Faros AI. La vélocité métier est bien réelle, avec une augmentation de 66% des epics livrées et une hausse de 33,7% du throughput des tâches par développeur. Ce volume cache un code churn massif (+861%), ce qui signifie qu'une quantité énorme de code est supprimée ou remplacée peu après avoir été ajoutée. La qualité en aval se dégrade fortement : les bugs par développeur ont augmenté de 54% et le nombre d'incidents par pull request a explosé de 242,7%. Le processus de code review est complètement saturé, entraînant un temps médian de relecture multiplié par cinq et une augmentation de 31,3% des PRs mergées sans aucune revue. Le système repose de plus en plus sur les développeurs seniors qui subissent une "senior engineer tax", devant relire un volume insoutenable de code à l'apparence correcte mais structurellement fragile. Contrairement à certaines hypothèses récentes de DORA, une forte maturité DevOps ne protège pas les entreprises contre cette détérioration ; le "Acceleration Whiplash" frappe de la même manière les équipes très performantes. En résumé, les outils d'IA inondent les pipelines de livraison avec un volume de code pensé pour un rythme machine, alors que les systèmes de vérification reposent toujours sur un rythme de validation humain. Loi, société et organisation Le coût d'une equipe d'engineering qui ne sait plus ce qu'elle fait dans un contexte d'augmentation de coût des coding agents https://freedium-mirror.cfd/@developer_programmer/i-spent-47-000-on-claude-code-in-90-[…]-asked-me-one-question-and-i-couldnt-answer-it-af3b203f81bb Une équipe de 8 ingénieurs a vu sa vélocité de développement exploser en utilisant Claude Code de manière intensive, jusqu'à recevoir une facture d'API salée de 47 213 $ pour seulement trois mois d'utilisation. Face à cette dépense, la question piège du CTO n'était pas sur le montant, mais sur la dépendance : "Si nous arrêtions Claude Code demain, combien de temps faudrait-il pour que notre vélocité revienne à son niveau initial ?". L'auteur s'est rendu compte qu'il était incapable de répondre car son équipe, en particulier les profils juniors, avait commencé à perdre l'habitude de concevoir et d'implémenter des fonctionnalités complexes sans l'aide permanente d'un agent. Le deuxième risque stratégique soulevé est celui de la dépendance tarifaire et du vendor lock-in : si l'outil devient une infrastructure indispensable au quotidien, l'entreprise perd tout pouvoir de négociation face aux augmentations de prix de l'éditeur d'IA. Pour éviter que l'IA ne devienne une béquille qui atrophie les compétences de l'équipe, l'article suggère de poser des limites budgétaires strictes, d'organiser régulièrement des sprints sans IA ("AI-free sprints") et de concevoir des processus de développement portables. Retour de Nicolas Delsaux sur jqwik qui donne une perspective plus complète concernant jqwik, il me semble que vous oubliez (comme tous les gens qui parlent de LLM dans "l'industrie") que l'auteur n'a pas fait ça juste pour faire chier le monde, mais parce que ces outils ont des externalités incroyablement négatives, ce dont l'auteur s'explique dans son blog (blog.johanneslink.net/2026/06/09/the-jqwik-anti-ai-affair) Vous oubliez également de signaler que le ticket (github.com/jqwik-team/jqwik/issues/708) par lequel un utilisateur se plaint de cette fonctionnalité a été écrit par un agent. N'oubliez pas non plus que l'enthousiasme pour ces technologies n'est en fait pas universel, et que ces technologies sont loin d'être inévitables (les gains de vitesse ne sont, d'après circle CI - circleci.com/resources/2026-state-of-software-delivery, pas des gains de productivité ) OkHttp, Okio, Retrofit et SQLDelight rejoignent Commonhaus ! commonhaus.org/activity/315.html La fondation Commonhaus, via une publication de Andres Almiray, annonce l'arrivée de quatre projets majeurs de l'écosystème Java et Kotlin : OkHttp, Okio, Retrofit et SQLDelight. Ces projets, initialement créés chez Square (devenu Block), sont désormais regroupés et gérés sous la bannière lysine.dev au sein de la fondation. Jesse Wilson et Jake Wharton, créateurs et mainteneurs historiques de ces outils, rejoignent Commonhaus en tant que leaders de lysine.dev. Suite à leur départ de Block, ils expliquent avoir choisi Commonhaus pour offrir à leur immense communauté d'utilisateurs un cadre de gouvernance pérenne, stable et digne de confiance. Conférences La liste des conférences provenant de Developers Conferences Agenda/List par Aurélie Vache et contributeurs : 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 : aMP Day Montpellier 2026 - Montpellier (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 : Campus Agile Grenoble - Grenoble (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) 10 décembre 2026 : DevCon 28 : sécurité | post-quantique | hacking édition 2027 - Paris (France) 14-16 janvier 2027 : SnowCamp 2027 - Grenoble (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/

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In this episode, Conor and Bryce chat about CityStrides, graph algorithms, GPT 5.6 Solver, and more!Link to Episode 295 on WebsiteDiscuss this episode, leave a comment, or ask a question (on GitHub)SocialsADSP: The Podcast: TwitterConor Hoekstra: LinkTree / BioBryce Adelstein Lelbach: TwitterShow NotesDate Recorded: 2026-07-13Date Released: 2026-07-17ADSP Episode 149: CityStrides.com, Graph Algorithms and More!Carlo de Lorenzi: Toronto runner with terminal brain cancer runs every street in the cityCarlo's fundraiser for Community Music Schools of Toronto FoundationCityStrides.comOpen Street Mapscity-strides-hacking GitHub RepoHamiltonian PathEulerian PathEpisode 220: Graph Algorithms & 7 Bridges of KönigsbergNVIDIA cuoptIntro Song InfoMiss You by Sarah Jansen https://soundcloud.com/sarahjansenmusicCreative Commons — Attribution 3.0 Unported — CC BY 3.0Free Download / Stream: http://bit.ly/l-miss-youMusic promoted by Audio Library https://youtu.be/iYYxnasvfx8

The Pirates have a very little chance to win the NL Central because the Brewers have created such a gap. But what about a wild card spot? Fan Graphs gives them the best chance of any team outside looking in to make the postseason. The site gives the Pirates just under a 42% chance to make it to October. Are they better than the Cardinals, Diamondbacks and Marlins? Steelers insider Ray Fittipaldo from the PG joined the show. Jon Gruden continues to praise the work of Will Howard as we have yet to see him in a pro game. Ray thinks it's more of the same and added what we have heard so many times from Mike McCarthy so far. Per usual, Ray said the offense in the preseason will be vanilla, so it could come down to what we see in practice. Ray reacted to the idea that 6 Steelers, including Keeanu Benton, would fetch a 1st round pick in return if traded. Ray expects a bounce back year from TJ Watt and thinks he will have a 12-sack season. When will the Joey Porter Jr. contract buzz start to pick up again and when will that deal get done? How long will it take for Max Iheanachor to become the starter at right tackle? Poni took us on a ride through some football preview books.

Read the article here: https://journals.sagepub.com/doi/full/10.1177/30494826261455199

Many corporate marketing strategies face dropping conversion tracking metrics because they rely on closed ad-tech vendors that introduce platform bias into campaign attribution. Mathieu Roche, co-founder and CEO of ID5, breaks down how to construct neutral identity infrastructure to protect cross-channel addressability and secure reliable measurement.He shares his operational playbook for navigating the shift toward agentic advertising, altering data graphs to track automated consumer assistants, and using internal team hackathons to automate routine sales and CRM processes.Key tactical themes covered:Building neutral identity channels to protect unbiased attribution tracking.Modifying data graphs to track verified virtual agents across the purchase funnel.Shifting identity tech priorities from baseline audience targeting to outcome measurement.Linking automated AI transactions directly back to programmatic ad investments.Utilizing structured team sprints to automate routine operational follow-up tasks.Mathieu Roche is the co-founder and CEO of ID5, directing un-siloed identity architecture and multi-platform validation systems for leading digital publishers.Connect with our guest:Follow Mathieu Roche on LinkedIn: https://www.linkedin.com/in/mathieuroche/Explore ID5 Solutions: http://id5.ioOptimize Performance Advertising with Strike Social: https://strikesocial.com/guaranteed-paid-social-media-ads-outcomes/Connect with Host Dylan Conroy: https://www.linkedin.com/in/dylanconroy/

In this episode, Jason and Jordan kick things off with post–Fourth of July stories, from eight-inch mortars and a professional fireworks barge to the unexpected lesson in staffing, leadership, and broken promises that came with a canceled show. They pivot into the green industry and dig into how Jason is onboarding virtual assistants (VAs) to help track lead flow, build out data-driven reporting, and improve budgeting and cash flow visibility. The conversation gets candid around switching to weekly payroll, the hidden cost of payroll advances, and where to draw the line between supporting employees and becoming "the bank." They compare live answering options like Ruby versus AI-backed tools such as RingCentral, and explore how VAs, phone systems, and SingleOps can work together to tighten operations. Jason shares how he's thinking about social media ROI, content strategy with Sky Palm Studios, and why long-term trust-building may beat pure ad spend. Jordan closes with two powerful branding and values stories: how 25 years of Empire Today ads led him to a no-hesitation buying decision, and how an HOA dust-up over flying the American flag at the community boat ramp turned into a real-world case study in leadership, perception, and standing your ground. Connect with Jason and Jordan:

MUKTZAH 5 GREAT GRAPHS

Dan Adams is a 13-year Microsoft 365 and SharePoint veteran who joined Andrew to talk about the often misunderstood world of SharePoint, the shift to Microsoft Graph, and his custom PowerShell module built which can assess and benchmark M365 environments. Dan breaks down why SharePoint gets such a bad reputation (spoiler: it's usually about who built it, not the platform), explains why "everything in M365 is SharePoint" isn't just a meme, and digs into how the Microsoft Graph API is changing the way admins interact with the platform. He also shares his experience going from longtime podcast listener to first-time guest, and closes with some honest advice about reaching out to people in the community. Key Takeaways: SharePoint's bad reputation often comes from poor initial architecture, not the platform itself. Getting the information structure right at the start has downstream effects on everything from Copilot to Purview to legal compliance, and PowerShell automation is only as useful as the metadata you've set up to work with. The Microsoft Graph API is consolidating how everything in M365 is accessed. Where older APIs like the SharePoint client-side object model might count a batch of 100 items as 100 separate API calls, Graph treats that same batch as a single call, which is a meaningful difference for performance and rate limiting. Dan's custom PowerShell module (SP'r Smash Bros Automation) automates M365 permission extraction to produce security assessments against CIS benchmarks and Microsoft Secure Score. Built as a practical tool for consultants and admins, it delivers a fast, standardized baseline of a tenant's security posture, featuring an optional AI sidecar to instantly generate personalized remediation plans. Guest Bio: Dan Adams is a Microsoft 365 and SharePoint Architect with 13 years of M365 consulting experience. Leveraging deep SharePoint and strategic information architecture expertise, he has led teams to deliver over 40 Fortune 500 intranets across industries ranging from healthcare to the NFL. He is the architect behind multiple award-winning portals, including two Ragan "Best Overall Intranet" winners. Dan is also an AI enthusiast, an advanced PowerShell expert, and the creator of the custom "SP'r Smash Bros Automation" module for Microsoft 365. Resource Links: Dan Adams on LinkedIn: https://www.linkedin.com/in/dan-adams-10887650/ Dan Adams on Github: https://github.com/sprsmashbrosautomation Connect with Andrew: https://andrewpla.tech/links PnP PowerShell: https://pnp.github.io/powershell/ PDQ Discord Community: https://discord.gg/pdq The PowerShell Podcast on YouTube: https://youtu.be/OLsTaKC9GmM PowerShell Wednesday Playlist: https://www.youtube.com/watch?v=XQT8lrn8hhU&list=PL1mL90yFExsix-L0havb8SbZXoYRPol0B

Join us as Du'An digs into the real mechanics of running AI locally and in production - from GPU memory math to multi-agent architectures, observability, and the economics of self-hosted inference. Du'An walks through how model weights and KV cache compete for GPU memory, why continuous batching matters when you have more than a handful of users, and how agent architectures like single-agent, workflow, graph, swarm, and supervisor patterns each solve different problems. You will learn how to instrument your agents with Langfuse for observability and cost tracking, when to use Ollama versus vLLM, how prompt caching can cut provider costs by up to 75%, and why GPUs should never sit idle. Episode two of three - the next episode covers deploying at scale. Timestamps 0:00 Welcome & Introduction 1:47 Du'An's New Role at Akamai Cloud 3:10 Data Privacy and the Case for Self-Hosted AI 7:21 Anthropic and OpenAI as the New Cloud Layer 12:48 Local Models for Specific Use Cases - Cancer Detection Example 15:02 GPU Memory Math - Weights, KV Cache, and Context Windows 19:32 Continuous Batching and GPU Time Slicing 20:03 Observability with Langfuse - Live Demo 27:44 Agent Architectures - Single Agent, Workflow, Graph, Swarm, Supervisor 36:36 Token Economics, Prompt Caching, and GPU Cost Planning 45:32 Ollama vs vLLM - Prototyping vs Production How to find Du'An: https://duanlightfoot.com https://www.linkedin.com/in/duanlightfoot/ Links from the show: https://langfuse.com/ https://github.com/akamai-developers/akamai-workshop-solution-architect-agent https://amzn.to/4bvHn1p https://vllm.ai/

What if the biggest barrier to successful AI isn't the model itself, but the lack of context behind every decision your teams make? As AI agents become more capable, how do organisations ensure they understand the people, projects, documentation, and history that shape real work? In this episode of Tech Talks Daily, recorded at Team '26, I'm joined by Taroon Mandhana, CTO of AI and Teamwork at Atlassian. His responsibilities span engineering for products including Jira, Confluence, Loom, and Trello, alongside the company's AI strategy and the development of Rovo. Our conversation explores why Atlassian believes AI should become a teammate rather than simply another chatbot. Taroon explains why enterprise context has become one of the most valuable assets in the AI era. While today's foundation models continue to improve at an incredible pace, they still lack the organisational knowledge that human teams naturally accumulate over time. Atlassian's Teamwork Graph aims to bridge that gap by connecting people, projects, documentation, code, goals, and conversations into a living knowledge network that AI agents can use to produce more accurate, relevant outcomes. We also discuss why Atlassian has chosen an open approach, making its Teamwork Graph available through technologies such as MCP rather than limiting it to its own AI products. Taroon shares why interoperability will become increasingly important as businesses adopt multiple AI platforms and why organisations should be free to use the agents that best suit their needs without losing access to valuable business context. Another fascinating part of our conversation focuses on how Atlassian's own engineering teams are changing the way they build software. Smaller teams, tighter collaboration, AI-assisted development, and faster iteration cycles are allowing products to move from concept to release in weeks rather than months. Taroon explains how AI is changing both software development and the structure of engineering teams themselves. We also examine where AI should take ownership of work inside platforms like Jira, where human judgement remains essential, and why successful organisations are treating AI adoption as an ongoing product journey rather than a one-time technology deployment. If your business is looking beyond isolated AI experiments and wondering how to build AI into everyday work, this conversation offers valuable insight into the role context, openness, and organisational change will play in the next generation of enterprise software. As AI becomes part of every workflow, what do you think will become the real competitive advantage: better models, or better organisational knowledge?

For law firms, artificial intelligence has often arrived as a choice between speed and control. Stephen Costigan, founder of Atlas AI, argues that choice deserves a rethink. In this episode of The Geek in Review, we speak with Costigan about private legal AI infrastructure, knowledge graphs, and why a firm's internal work product may become its most valuable long-term asset.Atlas AI focuses on turning documents, matter history, precedents, clauses, parties, and obligations into a curated legal knowledge graph inside a firm's own environment. Costigan contrasts this approach with standard vector search and retrieval systems, which find text with similar language but often lack context around clients, matters, entities, and relationships. A knowledge graph offers structure, linking people, documents, clauses, and legal concepts in ways closer to how lawyers understand their work.The conversation also explores data quality, a subject with enough baggage to fill a records room. Costigan argues firms no longer need year-long cleanup projects before seeing results. Agent-led curation, entity extraction, duplicate resolution, and ontology mapping reduce much of the manual sorting traditionally associated with knowledge management. Human judgment still matters, especially around practice-area vocabularies and lower-confidence results, but the machines get assigned more of the janitorial work.Security and governance sit at the center of Costigan's model. Rather than asking firms to trust a vendor's assurances around privileged data, Atlas AI runs within a firm's Azure environment, under firm-controlled keys and policies. Costigan frames this as a shift from confidentiality as a contractual promise to confidentiality as an architectural decision. For legal organizations handling sensitive client information, the location of data, embeddings, audit trails, and model interactions matters as much as the interface lawyers see on screen.Looking ahead, Costigan predicts a divide between firms renting generic AI tools and firms building durable knowledge infrastructure from their own experience. As routine drafting, diligence, and review work compress, firms with structured and reusable internal intelligence may productize expertise, offer new fixed-fee services, and rely less heavily on traditional leverage models. The future question, Costigan suggests, will not center on which AI tool sits on a lawyer's desktop. The bigger question will ask who owns the knowledge behind the work.Listen on mobile platforms:  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ |  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠Substack⁠[Special Thanks to ⁠⁠Legal Technology Hub⁠⁠ for their sponsoring this episode.]⁠⁠⁠⁠⁠Email: geekinreviewpodcast@gmail.comMusic: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠Transcript:

Text us your thoughts!In this quick follow-up to last week's episode, we invited our guest (Deborah) to model a short, fun classroom debate. In just a few minutes, you can hear a sample debate that captures the spirit of productive mathematical argumentation. Tune in for a rapid-fire glimpse of what these debates can look like in action as we ask the question:Which is better: bar graphs or pie charts?You can find Dr. Deborah Peart Crayton on social media: @mathersgonnamath Check out her website: MathersGonnaMath.comListened to the episode? Now, it's your turn to share! Find us on Social Media: @DebateMath to share your thoughts.Don't forget to check out the video version of this podcast on our YouTube channel!Keep up with all the latest info by following @DebateMath or going to debatemath.com. Follow us @Rob_Baier & @cluzniak. And don't forget to rate and review us on Apple Podcasts!

Here are the top five insurance products you should be looking to sell during the Medicare lock-in period. The insurance industry never stops moving. Make sure you know the products to keep your business moving along with it.   Read the text version   Get Connected:

Aji and Joël join forces to discuss graph and tree structures, and their connection to the emergent properties, attributes and qualities you can find from a largely connected group of data. Joël dives into their recent graphs and tree work through a contracting system, whilst Aji looks back at when he previously tried to serialise a graph or tree to a database. — Watch Joël's Blue Ridge Ruby talk here, or Matheus' Ruby Internal talk from last year here. There's still time to secure your place at thoughtbot's upcoming UK meet ups over the next month. London Tech Leader Meetup - Tuesday June 23rd Brighton Tech Leader Meetup - Wednesday June 24th Brighton Ruby - Thursday June 25th Evolve - Friday June 26th Your hosts for this episode have been thoughtbot's own Joël Quenneville and Aji Slater. If you would like to support the show, head over to our GitHub page, or check out our website. Got a question or comment about the show? Write to our hosts: hosts@bikeshed.fm This has been a thoughtbot podcast. Stay up to date by following us on social media - YouTube - LinkedIn - Mastodon - BlueSky © 2026 thoughtbot, inc.

Thanks so much for listening! For the complete show notes, links, and comments, please visit The Grey NATO Show Notes for this episode:https://thegreynato.substack.com/p/380-tgraph2The Grey NATO is a listener-supported podcast. If you'd like to support the show, which includes a variety of possible benefits, including additional episodes, access to the TGN Crew Slack, and even a TGN edition grey NATO, please visit the link below.Support the show

After decades of decline, many church leaders believe that religious life is on the upswing as some younger Americans flock to Christianity — including Vice President JD Vance, whose new book on his Catholic conversion drops this week. But the fuller picture is more complicated. Coming up, we'll talk to religion reporters and a church leader about what may be driving this shift, and what its lasting impacts could be. Guests: Michael O'Loughlin, executive editor, National Catholic Reporter; O'Loughlin has covered the Catholic church for both the Boston Globe and Crux; author, "Hidden Mercy: AIDS, Catholics and the Untold Stories of Compassion in the Face of Fear" Lauren Jackson, deputy editorial director for newsletters and the host of “Believing," The New York Times Ryan Burge, professor of practice at the John C. Danforth Center, Washington University; author, “Graphs about Religion” Danté Stewart, author, “Shoutin' in the Fire: An American Epistle;” an ordained minister at Tabernacle Baptist Church in Augusta, Ga. Learn more about your ad choices. Visit megaphone.fm/adchoices

Media Watch 2026 Episode 19: Musk's army; You're havin' a graph

Jure Leskovec, Professor of Computer Science at Stanford University and Chief Scientist at Kumo.ai, speaks with host Sriram Panyam about relational and graph language models and their transformative impact on enterprise decision-making and predictive modeling. Jure begins by establishing the critical importance of predictive modeling across industries - from fraud detection in financial institutions to customer churn prediction, lifetime value estimation, product recommendations, and healthcare risk assessment. He notes that while AI has made remarkable advances in natural language understanding and computer vision, predictive modeling over enterprise operational data stored in relational databases has been largely left behind, still relying on 30-year-old machine learning approaches that are expensive, time-consuming, and require manual feature engineering. His proposed solution to the fundamental problem with current approaches is relational deep learning and relational transformers. The discussion explores how this approach differs from traditional graph neural networks (GNNs), which Jure pioneered and deployed successfully at Pinterest. Jure concludes with practical guidance for software engineers and data scientists interested in exploring this technology.

Immerse yourself in captivating science fiction short stories, delivered daily! Explore futuristic worlds, time travel, alien encounters, and mind-bending adventures. Perfect for sci-fi lovers looking for a quick and engaging listen each day.

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.

In Season 15 episode 2, Elixir Wizards Sundi Myint and Charles Suggs chat with Micah Cooper to talk about distributed systems, data replication, and what it actually looks like to build these ideas in Elixir.   Micah shares his journey from Ruby to Elixir and walks us through Visor, a library he's building based on the Viewstamps replication algorithm. Inspired by systems like TigerBeetle, Visor explores how you can replicate state across nodes using GenServers, giving you fault tolerance and recovery without relying entirely on traditional database patterns.   We talk about the difference between distributed systems and data replication, where things tend to get misunderstood, and what changes when you start thinking about state this way. The conversation also touches on event sourcing, tradeoffs in system design, and how Elixir's distributed model makes some of these concepts more approachable than you might expect.   Along the way, we talk about building for curiosity, experimenting with new ideas, and how projects like this push the ecosystem forward.   Topics discussed in this episode: Building Visor and working with the Viewstamps replication model Replicating GenServer state across nodes Distributed systems vs. data replication Lessons from TigerBeetle and financial system design Event sourcing challenges and tradeoffs Rethinking database-first architectures Snapshotting, recovery, and fault tolerance The role of Elixir's distributed model Experimentation, learning, and building for curiosity   Links mentioned: Micah's GitHub https://github.com/mrmicahcooper Micah's GitLab https://gitlab.com/mrmicahcooper The Visor repository: https://gitlab.com/mrmicahcooper/visor Visor Hex Package https://hex.pm/packages/visor Ruby on Rails https://rubyonrails.org/ Phoenix LiveView Framework https://www.phoenixframework.org/ Zig Programming Language https://ziglang.org/ TigerBeetle https://tigerbeetle.com/ TigerBeetle internal docs https://github.com/tigerbeetle/tigerbeetle/tree/main/docs/internals The BEAM https://www.erlang-solutions.com/blog/the-beam-erlangs-virtual-machine/ GenServer https://hexdocs.pm/elixir/GenServer.html Apache Kafka https://github.com/apache/kafka RabbitMQ https://www.rabbitmq.com/ Redpanda https://www.redpanda.com/ SQL https://www.ibm.com/think/topics/structured-query-language Kubernetes https://kubernetes.io/ YAML https://yaml.org/ Nomad Workload Orchestrator https://developer.hashicorp.com/nomad Flutter https://flutter.dev/ Commanded https://hexdocs.pm/commanded/Commanded.html Go Programming Language https://go.dev/ Clojure Programming Language https://clojure.org/ Nebulex https://hexdocs.pm/nebulex/Nebulex.html Mnesia https://www.erlang.org/doc/apps/mnesia/mnesia.html Cachex https://hexdocs.pm/cachex/Cachex.html libgraph https://hexdocs.pm/libgraph/Graph.html Horde https://hexdocs.pm/horde/Horde.Registry.html NocFree split keyboard https://www.nocfree.com/ Micah's LinkedIn https://www.linkedin.com/in/micah-cooper-4a737560/ 

On the latest episode of Minor Issues, Mark Thornton opens with a detailed analysis of the gold correction. Is the three-month decline a sign that inflation is over, or a temporary reallocation driven by war? The answer is in the data: the CRB commodity index continues to climb, the money supply is at an all-time high, and there is no evidence of deflation anywhere in the price structure. The inflation regime remains firmly in place, and the gold correction is a normal feature of bull markets whose real-world zigzags get smoothed away on long-term charts.The second half features a panel interview from VRC Media with Rick Rule, hosted by Darrell Thomas. Rule lays out the case for a decade-long commodity super cycle driven by 30 years of underinvestment in productive capacity. He delivers a sobering calculation: $39 trillion in on-balance-sheet federal debt plus $120 trillion in off-balance-sheet unfunded entitlement promises (a combined $160 trillion against $170 trillion in total private American net worth). The only realistic resolution, Rule argues, is a "dishonest default," inflating away the purchasing power of the dollar, just as happened in the 1970s when the dollar lost 75% of its value. Mark concurs, noting that the money supply is growing at record pace even as Washington insists it's being "restrictive."Mark's "Gold vs CRB Index" graph is available here: https://mises.org/MI175_GraphThe original VRIC interview is online here: https://www.youtube.com/watch?v=3kMiiC08TNo20% off listener offer on the new insulated Minor Issues tumbler and three of Mark's books, signed if ordered by the end of April: https://mises.org/MinorIssuesTumbler. Use coupon code Thornton.Be sure to follow Minor Issues at https://Mises.org/MinorIssues

John and Josh look at a graph on how many Huskers have been drafted with the recent coaches.

This week on “Jesuitical,” Ashley and Zac speak with Ryan Burge, author of the “Graphs about Religion” Substack and the new book, The Vanishing Church: How the Hollowing Out of Moderate Congregations Is Hurting Democracy, Faith, and Us. They discuss the polarization of U.S. Christianity and the supposed Gen-Z “religious revival.” In Signs of the Times, Ashley and Zac discuss some highlights from Pope Leo's trip to Africa; what Pope Leo called the not-exactly-accurate media narrative around him and President Trump; and the first anniversary of Pope Francis' death. 00:00 A Gen-Z religious revival? 3:38 Highlights of Pope Leo's trip to Africa 10:05 VP Vance questions Pope Leo's theology 20:37 Remembering Pope Francis 22:50 Moderate Christianity is vanishing 25:49 U.S. religion is coded "conservative" 34:54 Catholic demographic trends 37:15 Political implications 40:53 Are young people going back to church? 48:18 Winner churches 52:56 Gen-Z religious trads 1:04:08 Faith Sharing: Pope Francis' humble tomb Links: ⁠Order Ryan's book, The Vanishing Church⁠ ⁠Graphs about Religion⁠ ⁠Pope Leo walks in the footsteps of St. Augustine in Hippo⁠ ⁠Pope Leo denounces those who use the name God for military gain⁠ ⁠Pope Leo named one of Time magazine's ‘100 Most Influential People of 2026'⁠ ⁠Pope Leo remembers ‘the great gift' of Pope Francis on the first anniversary of his death⁠ You can follow us on ⁠X⁠ and on ⁠Instagram⁠ @jesuiticalshow.   You can find us on Facebook at ⁠facebook.com/groups/jesuitical.⁠  Please consider supporting Jesuitical by becoming a digital subscriber to America magazine at ⁠americamagazine.org/subscribe⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

The Racing Dudes compare Beyer Speed Figures and Thoro-Graph numbers to determine which metric is more predictive when handicapping the 2026 Kentucky Derby. They break down key contenders through both lenses and discuss what bettors should actually trust heading into the Run for the Roses.

Welcome to Episode 426 of the Microsoft Cloud IT Pro Podcast.Ben and Scott are back together this week to talk through Microsoft 365 Copilot Cowork, including how it compares to Claude Cowork and where each one makes sense. The two products share a name but work pretty differently. Claude Cowork runs locally on your desktop and can access files on your machine, supports MCP server connections while M365 Copilot Cowork runs in the cloud, requires files to be in OneDrive, and does not support MCP connectors yet. On the flip side, the Microsoft version runs scheduled tasks without needing your machine to be on, has native access to all your M365 data through Graph, and fits inside your existing compliance and security controls through Purview, which matters a lot for regulated organizations. Your support makes this show possible! Please consider becoming a premium member for access to live shows and more. Check out our membership options. Show Notes Quentin Amaudry – As everyone knows, Cowork is coming within Copilot and it is extremely promising Copilot Cowork vs Claude Cowork: Same AI, Different Worlds Copilot Cowork: A new way of getting work done Cowork overview (Frontier) About the sponsors   TrustedTech is a leading Microsoft Cloud Solution Provider (CSP) specializing in Microsoft Cloud services, Microsoft perpetual licensing, and Microsoft Support Services for medium and enterprise-sized businesses. Our robust team of in-house, U.S-based Microsoft architects and engineers are certified in all 6/6 Microsoft Solutions Partner Designations in the Microsoft Cloud Partner Program. M365 Licensing Consultation M365 Tenant Assessment Copilot Readiness Assessment Your migration and governance solution for Microsoft 365. ShareGate helps your teams simplify tenant migrations, get Copilot-ready, and take control of Microsoft 365 governance. Nasuni is a leading unstructured data platform for enterprises where file data is mission-critical for both people and AI. We power the operational file layer where work happens — helping organizations manage, protect, and activate data so teams can work smarter, reduce costs, and operate securely without limits. Visit nasuni.com to learn more. Would you like to become the irreplaceable Microsoft 365 resource for your organization? Let us know!

Text us your thoughts!This month's debate is a recording from a LIVE debate that took place at the West Virginia Council of Teachers of Mathematics (WVCTM) Conference in March of 2026.In this quick follow-up to last week's episode, we invited each of our guests to model a short, fun classroom debate. In just a few minutes, you can hear a sample debate that captures the spirit of productive mathematical argumentation—thoughtful, curious, and includes reasoning. Tune in for a rapid-fire glimpse of what these debates can look like in action!You can reach Sissy Collins at the Harrison County Board of Education, via email at jecollin@k12.wv.us, or on Facebook.You can find Sarah McGivern via Jefferson County Schools or on Instagram: @SSMcGivernYou can reach Ellen Holt at Summers County HS, via email at eholt@k12.wv.us, or on LinkedInAnd find Jason Massie via Mountain View School.And thanks to the WVCTM Conference for inviting us in!Listened to the episode? Now, it's your turn to share! Find us on Social Media: @DebateMath to share your thoughts.Don't forget to check out the video version of this podcast on our YouTube channel!Keep up with all the latest info by following @DebateMath or going to debatemath.com. Follow us @Rob_Baier & @cluzniak. And don't forget to rate and review us on Apple Podcasts!

Download Nick's free second brain skill -- one-click install that sets up your entire Obsidian vault and ingestion workflow inside Claude Code: https://return-my-time.kit.com/286e11f7e6I brought on Nick Spisak to build a complete second brain live -- start to finish, in under 20 minutes. By the end of this episode, you'll know exactly how to build your own second brain -- and you can grab Nick's free skill to do it in one click.Timestamps00:00 - Intro and Karpathy's viral second brain tweet00:24 - What the second brain concept is03:28 - Obsidian Web Clipper: scraping pages into your vault04:41 - Nick's free skill: wizard, ingest, query, and lint commands05:53 - Live demo: running the setup wizard in Claude Code08:38 - How many vaults to manage: personal vs. business10:30 - Opening the vault and exploring the file structure12:21 - Graph view: seeing connections between your data15:38 - Ingest command: raw data into organized wiki16:29 - Automating ingestion on a cron schedule19:12 - Compounding value and syncing the vault across devices20:19 - Pruning the vault with the lint command21:53 - Your data set as a moat in the AI ageKey Points* Karpathy released a framework for building an LLM knowledge base. Nick turned it into a free Claude skill with a guided setup wizard, ingest, query, and lint commands -- works across Claude Code, Codex, Gemini CLI, and more. One-click install, no coding required.* The system runs on three tiers: raw (brain dump), wiki (AI-organized knowledge base), and outputs (answers from querying). Drop files into raw, run ingest, and the AI maps everything into structured wiki entries with relationship graphs.* You can automate ingestion using Claude Code's loop feature so the vault stays current without manual work. Pair with Obsidian's paid sync tier and a note captured on your phone is indexed before you're back at your desk.* The lint command health-checks your wiki for outdated entries and missing connections -- and tells you exactly what to clip next to close the gaps. The wiki tells you what it doesn't know yet.* Day zero this thing is basic. Day 90 it's a company asset no competitor can copy. Your private knowledge base is the foundation for every agent and skill you build -- and nobody else will have it.Join the Build With AI community - weekly AI implementations, live coaching, and templates built for non-technical entrepreneurs: https://www.skool.com/buildwithai/aboutFIND ME ON SOCIALX/Twitter: https://x.com/coreyganimInstagram: https://www.instagram.com/coreyganim/LinkedIn: https://www.linkedin.com/in/coreyganim/YouTube: https://www.youtube.com/@coreyganimFIND NICK ON SOCIALX: https://x.com/NickSpisak_LinkedIn: https://www.linkedin.com/in/nicholasspisak/YouTube: https://www.youtube.com/@nickspisak_

What I Did as a Child – Co robiłem jako dziecko "Co robiłem jako dziecko" means "what I did as a child," and in this nostalgic micro-lesson you'll say it like you're flipping through old photo albums with your Polish grandmother. First you hear the phrase at native speed, then slowed down so you can master the rolling "r" and the soft "dziecko." We drop it into three memory-lane-ready sentences: – "Kiedy byłem mały…" (When I was little…) – "Lubiłem się bawić." (I liked to play.) – "To było dawno temu." (That was a long time ago.) Repeat-along track included—perfect while you reminisce or share your own childhood stories. Challenge: Tell us in the comments what YOU did as a child—reply in Polish and Ania might sing your answer in the next episode     What we discussed:   0:00 Welcome & QR Code 0:45 "Dziecko" - The Polish Word for Child 1:30 Childhood Bedroom Memories 2:30 Sports & Experiments 3:30 School & Newspapers 4:30 University & Opinions 5:30 Wishes & Dashboards 6:30 Stars & Classrooms 7:30 Time & Dance 8:30 Building & Creating 9:30 Television & Media 10:30 Graphs & Grades 11:30 Comics & Parks 12:30 Photos & Memories 13:30 Games & Play 14:30 Suggestions & Ideas 15:30 Early Jobs & Studies 16:30 Army & Activities 17:30 Toys & Cars 18:30 Sunset & Evening Play 19:30 Mom's Voice & Family 20:30 Growing Up & Yesterday 21:30 Fitness & Sports 22:30 Comfort & Balm 23:30 National & Sharing 24:30 Adult Life & Planning 25:30 Vision & Yesterday's Story 26:30 Short Stories & Patches 27:30 Royal Games & Health 28:30 Memories & Reports 29:30 Calls & Moods 30:30 Sports & Chores 31:30 Swimming & Memories 32:30 Web & Changes 33:30 Books & People 34:30 School Initiatives 35:30 Travel & Problems 36:30 Chats & Opinions 37:30 Goals & School Sheets 38:30 Status & Movies 39:30 Platforms & QR Code 40:30 Your Turn to Practice!   English Polish Pronunciation Guide child dziecko dzyeh-tsoh childhood dzieciństwo dzyeh-cheen-stvo memory wspomnienie vspo-mnyeh-nyeh to remember pamiętać pah-myeh-tahch to forget zapomnieć zah-pom-nyehch to play bawić się / grać bah-veech sheh / grahch toy zabawka zah-bahf-kah game gra grah school szkoła shkoh-wah teacher nauczyciel / nauczycielka now-chi-tyel / now-chi-tyel-kah friend przyjaciel / przyjaciółka psi-ya-chyel / psi-ya-choow-kah family rodzina roh-jee-nah mom mama mah-mah dad tata tah-tah brother brat braht sister siostra syoh-strah home dom dohm room pokój poh-kooy bed łóżko woo-shkoh to sleep spać spahch to eat jeść yeshch favorite food ulubione jedzenie oo-loo-byoh-neh yeh-dzeh-nyeh sport sport sport football/soccer piłka nożna peew-kah nozh-nah ping pong ping pong ping pong swimming pływanie pwih-vah-nyeh bicycle rower roh-ver to read czytać chi-tahch book książka kyohnsh-kah comic book komiks koh-meeks TV telewizja teh-leh-veez-yah cartoon bajka bahy-kah video game gra wideo / gra komputerowa grah vyeh-deh-oh / grah kom-poo-teh-roh-vah to sing śpiewać shpyeh-vahch song piosenka pyoh-sen-kah to dance tańczyć tahyn-chich party impreza eem-preh-zah birthday urodziny oo-roh-jee-ni holiday wakacje / ferie vah-kah-tsyeh / feh-ryeh summer lato lah-toh winter zima zee-mah to grow up dorastać doh-rah-stahch adult dorosły / dorosła doh-roh-swi / doh-roh-swah young młody / młoda mwoh-di / mwoh-dah old stary / stara stah-ri / stah-rah yesterday wczoraj fchoh-rah-y today dziś dzeesh tomorrow jutro yoo-troh long ago dawno temu dahv-noh teh-moo always zawsze zahf-sheh never nigdy neeg-di sometimes czasami chah-sah-mee often często chen-stoh  

Are you still relying on OCR for your enterprise AI? You're losing critical context.In this episode, Anaiya Raisinghani (Sr. Tech. Evangelist, AI Startups & Ventures at MongoDB) sits down with Adityavardhan Agrawal, Co-Founder and CEO of Morphik. They dive deep into how Morphik is helping developers and enterprises understand complex, unstructured data and automate high-leverage workflows.Adi breaks down the limitations of standard RAG pipelines and reveals why they turned to Vision Language Models (VLMs) to process complex documents like architectural floorplans.What you'll learn in this episode:The OCR Trap: Why text extraction is inherently lossy for complex documents and how VLMs generate better embeddings.The RAG Misconception: Why getting high-quality context requires much more than just plain vector search.Database Architecture: Why Morphik hit the limits of Postgres/JSONB for dynamic datasets and how migrating to MongoDB Atlas simplified their multi-tenancy and querying.Massive ROI: How one manufacturing customer used Morphik to slash their quote generation time from 7 days to under 2 minutes.The Future of Knowledge: Building self-healing, self-updating data layers that leverage MQL.(Want to start building? You can use Morphik's API, Python/TypeScript SDKs, or grab the Docker image from GitHub today!)⏱️ Chapter Timestamps00:00 - Intro: Meet Adi and Morphik01:18 - APIs, SDKs, and Getting Started with Morphik02:28 - The Lightbulb Moment: Why Standard AI Fails on Unstructured Data04:44 - The Biggest Misconception About RAG06:24 - Vision Language Models (VLMs) vs. Traditional OCR08:35 - Reducing Entropy: Combining Embeddings with Knowledge Graphs10:13 - Architecture Deep-Dive: Hitting the Limits of Postgres & JSONB12:06 - Why Morphik Migrated to MongoDB Atlas13:24 - Simplifying Multi-Tenancy at Scale15:13 - Ensuring Data Security and Reliability16:33 - Accelerating Growth with MongoDB for Startups18:10 - Real-World Impact: Cutting Quote Generation from 7 Days to 2 Minutes20:15 - The Future: Self-Healing Data Layers and Native MQL

New Data about New Sets! YouTube version: https://youtu.be/_1ZTxcjWXcY MERCH STORE: https://shop.spacecowmedia.com/Decklists: https://archidekt.com/edhrecastExclusive content on Patreon! https://patreon.com/edhrecastGet new cards on Cardsphere! https://www.cardsphere.com/welcome?referrer=edhrecastProud partners with DragonShield: https://www.dragonshield.com/?ref=edhrecastSocials:@EDHRECast@JosephMSchultz@danaroach@mathimus5500:00 Highest Spikes07:22 Biggest Drops09:26 Graph #213:12 Sidebar15:42 Conclusions17:38 Challenge the Stats!Title sequence by Daniel Woodling / MTG Explainers: https://bit.ly/3982yYaCard images courtesy of Scryfall: https://scryfall.com/Elevate by LiQWYD https://soundcloud.com/liqwydCreative Commons — Attribution 3.0 Unported — CC BY 3.0Free Download / Stream: https://bit.ly/liqwyd-elevateMusic promoted by Audio Library https://youtu.be/nwSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

If my podcast has helped, ⁠my new book, The Light Between the Leaves⁠, goes even deeper“I'm a failure.” “I'm worthless.” “I'm behind everyone else.” If you've ever had thoughts like these, I want to show you how your mind might be distorting reality.In this video, I break down how depression uses black-and-white thinking, overgeneralization, and selective evidence to convince you that you're at the bottom. Then I walk you through one of my favorite tools—the Continuum Exercise—so you can actually test those thoughts.In my experience, you're almost never where your depression says you are. And seeing where you actually fall can give you just enough space to start moving forward again.Next Steps:

In this episode, Sid Pardeshi, co-founder and CTO of Blitzy, joins us to discuss building autonomous development systems able to deliver production-ready software at enterprise scale. Sid contrasts AI-assisted coding with end-to-end autonomy, arguing that “code is a commodity” and acceptance is the real metric—security, standards, tests, and maintainability included. We explore Blitzy's hybrid graph-plus-vector approach, which grounds agents and combines semantic signals with keyword search to navigate large repositories efficiently. Sid breaks down context and agent engineering, how effective context windows have plateaued, and why dynamic agent personas, tool selection, and model-specific prompting matter at scale. He details their orchestration of large swarms of AI agents to collaboratively analyze codebases, plan tasks, and execute complex tasks in parallel. We also dig into why Agents.md and flat memories break down, storing feedback in the knowledge graph, and building real-world evals beyond leaderboards to choose the right model for each task. The complete show notes for this episode can be found at https://twimlai.com/go/763.

In this episode of Screaming in the Cloud, host Corey Quinn sits down with Roi Lipman, CTO and co-founder of Falco DB, to unpack the evolving role of graph databases in a world overflowing with data stores. Roi shares his journey from building RedisGraph at Redis to spinning it out into Falco DB, along with his enduring love of the C programming language (dad jokes included). The conversation explores why graph databases remain niche, but powerful, especially for pathfinding problems like supply chains and access management, how vector search became a feature rather than a standalone database, and what AI-assisted development means for modern engineering. Along the way, they tackle open source sustainability, Rust rewrites, AI-generated pull request chaos, and the looming question of where the next generation of senior engineers will come from.Highlights: (00:00) C Language(00:27) Welcome(01:18) Database Landscape Overview(03:17) Why Graph Databases Matter(07:25) AI Built Apps and Data Choices(10:29) How FalcoDB Fits In(12:20) Vector Search as a Feature(16:48) FalcoDB Origin Story(19:54) Open Source Business and Rust Rewrite(25:23) Toy Graph Problems and Closing ThoughtsSponsored by: duckbillhq.com

I sit down with Cody Schneider, growth engineer and co-founder of Graph, for a live, hands-on crash course in GTM (go-to-market) engineering powered by Claude Code. Cody walks through how he runs multiple AI agents simultaneously to handle everything from bulk Facebook ad creation and LinkedIn outreach to cold email campaigns and live data analysis — tasks that used to require a team of dozens. By the end of the episode, you'll have a full understanding of how to set up your own agent workflow, the specific tools involved, and why domain expertise paired with AI is the real competitive advantage right now. Cody's GTM Toolkit: AI/Agent Tools: Claude Code, Perplexity API, OpenAI Codex Marketing & Outreach: Instantly AI (cold email), Phantom Buster (LinkedIn scraping/automation), Apollo API (data enrichment), Million Verifier (email verification), Raphonic (podcast host scraping): Advertising: Facebook Ads API, Facebook Ads Library (competitor research), Nano Banana Pro (AI image generation), Kai AI (bulk image generation), HeyGen API (UGC/video generation) Infrastructure & Deployment: Railway.com (servers, on-the-fly databases/Postgres), Vercel (deployment) Data & Analytics: Graphed / Graphed MCP (data warehouse, live data feeds), Google Analytics 4 CRM & Communication: Salesforce (mentioned as comparison), Intercom, SendGrid API, Slack, Cal.com API Productivity & Design: Notion, Super Whisper (voice transcription), Claude Code front-end design skill, HTML to Canvas (for converting React components to PNGs) Timestamps 00:00 – Intro 02:02 – What Is GTM Engineering? 05:12 – Setting Up Your Agent Workspace & Environment File 07:54 – Live Demo: LinkedIn Auto-Responder 09:56 – Live Demo: Bulk Facebook Ad Generator 12:31 – Live Demo: Cold Email Campaign Automation (Raphonic + Instantly) 14:47 – Live Demo: Creating Notion Documents via Claude Code 16:46 – Live Demo: Bulk Ad Creative Generator 26:05 – Live Demo: LinkedIn Engagement Scraper to Cold Email Pipeline 28:16 – Context Switching Across Tasks 29:19 – Live Demo: Bulk Ad Generator 31:41 – Live Demo: Data Analysis: Turning Off Low-Performing Ads 35:28 – Summary of GTM Engineering Workflow 37:48 – Deploying Agents and On-the-Fly Databases with Railway for Data Analysis 41:28 – The Dream of Autonomous Marketing 48:50 – Building API-First Products and Agent-Native Infrastructure Key Points GTM engineering has evolved from Clay-style data enrichment workflows into full-stack agent orchestration — where one person running multiple Claude Code agents can replace the output of a large team. The practical setup starts with a single folder containing your environment file (API keys for every tool in your stack), transcription software like Super Whisper, and Claude Code. Cody demonstrates running seven or more agents simultaneously across LinkedIn outreach, Facebook ad creation, cold email campaigns, Notion document generation, and live data dashboards. Code-generated ad creative (React components exported as PNGs) costs nearly nothing to produce at scale and allows rapid testing of messaging variations before investing in polished visuals. Deploying proven workflows to Railway turns one-off agent tasks into always-on, autonomous processes that run 24/7. Domain expertise is the real multiplier — the vocabulary you bring from your field determines the quality of output you can extract from these tools. The #1 tool to find startup ideas/trends - https://www.ideabrowser.com LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/ The Vibe Marketer - Resources for people into vibe marketing/marketing with AI: https://www.thevibemarketer.com/ FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/ FIND CODY ON SOCIAL: Cody's startup: https://www.graphed.com/ X/Twitter: https://x.com/codyschneiderxx Youtube: https://www.youtube.com/@codyschneiderx

AI-Powered Knowledge Graph Generator & APTs https://isc.sans.edu/diary/AI-Powered%20Knowledge%20Graph%20Generator%20%26%20APTs/32712 nslookup and ClickFix https://x.com/MsftSecIntel/status/2022456612120629742 Google Chrome 0-Day Patch https://chromereleases.googleblog.com/2026/02/stable-channel-update-for-desktop_13.html TURN Security Threats https://www.enablesecurity.com/blog/turn-server-security-threats/

@GeneSohoForum coming through with the Graphs and walkthrough of the real story in American Labor markets. Are American's underpaid by greedy corporations? Find out on today's episode.