Podcasts about MCP

Meet Kallaway, the creator behind the Kallaway Marketing channel and the founder of Sandcastles — an AI research tool that helps creators find and validate viral video topics before they ever hit record. Kallaway started 4 businesses. All 4 failed for the same reason: no one knew who he was. No audience meant no customers, no leverage, no second chances. So he made it his mission to understand how audience growth actually works. He cracked short-form content at scale after years of building online businesses, and he built the research system he wished he'd had from the start. Now he has over a billion views, a 50% viral hit rate, and he built a tool to help other creators do the same. This episode is the full system. In this episode, we talk about: Content minutes — the framework for mapping how much trust your audience needs before they'll buy at each price point on your offer ladder The modern content stack: why short-form and long-form both need to funnel into email, and why that's the golden goose The 3 metrics that actually matter for content creators — ranked in order of importance, with views coming in third A live walkthrough of Sandcastles: channel watchlists, outlier score, deep video analysis, and how to use the MCP to run research through Claude By the end of this episode, you will have a clear research system for finding validated, high-trust video topics — and a framework for staying creative while using data to de-risk almost everything else. Book a free discovery call with 1of10 StrategyTry Sandcastles for free Full transcript and show notes *** TIMESTAMPS (00:00) Intro to Kallaway (01:03) Introducing “content minutes” (02:12) The modern content stack (03:45) Why you want a narrow ICP (05:40) The 3 metrics that matter (14:38) Sandcastles demo: building a channel watchlist from scratch (16:14) Why you study mid-size creators, not mega-famous ones (22:18) Deep video analysis: hook formula, storytelling structure, contrarian angle (24:59) Kallaway's research flow (30:47) Solving structure and scripting problems using your own channel's data (36:34) Data-enabled creativity: the thesis behind the whole system *** RECOMMENDED NEXT EPISODE #303: Riley Brown — The AI Content Creator Who Doesn't Write With AI *** ASK CREATOR SCIENCE Submit your question here *** WHEN YOU'RE READY

Most testers are stuck arguing about whether AI is coming for their jobs. Swati Seela thinks that's a distraction from a much more useful question: when we say "AI testing," what do we actually mean? What do you think? Take our TestGuild State of Test Automation Survey now: https://testgld.link/27data In this episode, Swati walks through the AI Testing Landscape, a practical framework she presented at CAST 2026 that breaks AI testing into five overlapping areas: AI assisted testing, AI evaluation and trust, AI development quality, testing products that contain AI functionality, and testing the AI models themselves. Each one has a different test intent, a different technique, and a different toolset, which is exactly why lumping them together leaves new testers with no idea where to start. Along the way Swati gets specific about the failure modes she keeps hitting in real work. Why AI opens almost every response by agreeing with you, and why that false sense of correctness is the modern version of a green dashboard hiding tests that stopped meaning anything years ago. Why her MCP test generator kept skipping the cataloging step, what that revealed about models trading completeness for speed, and how splitting one workflow into two got her reliability back. Why traditional Boolean assertions break down against probabilistic systems, and what your automation framework has to do instead. She also shares how she uses AI to learn hard material in the semiconductor world without losing the thread, what she absolutely will not accept without laying eyes on it herself, and her answer to whether a tester with a decade of experience should be worried right now. Her closing advice comes down to two things that have to travel together: testing fundamentals, and AI literacy. Don't hate it, don't love it, just use it.

Lindsay Rosenthal has 50,000 followers on LinkedIn and makes content alongside OpenAI, HubSpot, Atlassian, Descript, and LinkedIn itself. So when she told us she currently uses zero AI for her ideas... and stopped using it for her scripts and copy entirely... we stopped the conversation and made her explain. What she said next is the whole episode. There's a specific reason AI can make you competent and almost never makes you the best, and once you hear it you can't unhear it. We also got into the exact 30-day plan for someone sitting at 500 followers right now, why commenting is the most underrated sales activity on the internet, whether you should tell people you used AI, and the story of a guy who built a business on AI avatars and shut it down after a conference. Oh, and Fonzie's back. He brought the Waffle House rule.

AI is about to replace bloated SaaS, and most businesses are still only playing with chatbotsSean G Muller says the next wave of AI is not about better prompts or prettier copilots. It is about rebuilding business around context, agents, and what actually creates value - before your software stack becomes the expensive middleman.Mark Smth and Sean unpack why the last six months have been a genuine shift: agentic loops are now good enough to handle real business work, not just experiments. Sean explains how he moved from traditional technical architecture into building full application pipelines, MCP servers, and background agents that review email, track social signals, draft responses, and keep business moving without adding more human overhead.You'll discover why context is the missing ingredient in almost every failed AI project, how Sean uses a simple meal-planning example to explain it, and why companies that scatter knowledge across laptops, SharePoint, Google Cloud, and people's heads are sitting on hidden risk. Sean also breaks down the difference between AI as a feature and AI as a business transformation engine, including the mistake many firms make when they bolt chat onto old workflows and call it progress.We also get into the coming SaaS pocalypse - the idea that tools like HubSpot, Salesforce, Xero, Slack, and Atlassian may face a serious reckoning as businesses realize they can build leaner, custom, agent-first systems for less than the cost of endless licenses and modules. Sean shares how he built a headless, agent-driven CRM and why he thinks greenfield builds will replace expensive transformation projects much sooner than most executives expect. This conversation matters if you lead a business, run operations, own a small or mid-sized company, or simply suspect your current software is forcing you to work the wrong way. If you want to understand where AI is actually delivering leverage right now - and how to avoid wasting money on shallow pilots - this episode is essential listening.Mark Smth hosts the conversation and brings the enterprise and product lens, pushing Sean to get specific about what success looks like for real businesses in New Zealand.Sean G Muller is an AI and enterprise architecture specialist based in New Zealand, known for helping organizations build practical AI systems, implement agentic workflows, and rethink business process from the ground up.Resources1. The Cuckoo's Egg: Tracking a Spy Through the Maze of Computer Espionage - https://www.amazon.com.au/dp/0385249462?ref_=mr_referred_us_au_nz2. Gemini: A Family of Highly Capable Multimodal Models — 2312.11805.pdf ⁠https://arxiv.org/abs/2312.118053. On the Measure of Intelligence — 1911.01547.pdf - https://arxiv.org/pdf/1911.01547Support the showIf you want to get in touch with me, you can message me here on Linkedin.Thanks for listening

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Podcasting 2.0 August 28th 2026 Episode 269 - "Verbal Pollution" Shownotes ------------------------------------------------------------------------------------------------------------------------------------- 00 - DATABASE SURGERY WEEK — AND THE THIRD EMERGENCY OF THE MONTH The lead, and it's Dave's: Saturday 22 Aug — "#api I'm in the middle of a big refactor with the Podcast Index database schema. If I do my job right, nobody will notice it. But, the side effect will be an overall huge increase in the consistency of the data (duplicates, url change lag, etc)." @dave — the big schema refactor (22 Aug) Wednesday night: "Podcast Index database surgery will begin in the morning." Thursday: "Step 1. Expect a brief api outage. It should last just a few minutes." — then "Done" — then "#api Adding a very large table index..." — and finally, Thursday evening: "Finished. Butt is intact." (6 favourites. The board's favourite status update of the month.) What Adam should ask: what actually changed under the hood, what does "consistency" buy an app developer in practice, and is there anything a host or an app should do differently now that duplicates and url-change lag are being cleaned up?

In this episode, Ray Cochrane digs into Anthropic’s Model Hardware Standard. It is a shared driver that lets an AI agent run real lab equipment, from pipetting robots to the lasers inside a quantum computer. He also covers OpenAI’s builder’s guide to GPT-5.6, Google’s new Expert Intelligence book feature, Apple’s M5 Ultra Mac Studio, and a judge’s order forcing Google to stop hiding rival app stores. Finally, he weighs in on Apple’s proposed 15 percent link-out fee, Meta’s Australia numbers, the White House deputizing private hackers, and why rivers obey a 1957 math rule. – Want to start a podcast? Its easy to get started! Sign-up at Blubrry – Thinking of buying a Starlink? Use my link to support the show. Subscribe to the Newsletter. Email Ray if you want to get in touch! Like and Follow Geek News Central’s Facebook Page. Support my Show Sponsor: Best Godaddy Promo Codes Get 1Password Full Summary Cochrane opens with a quick personal update. He is hunting for tickets to Michigan for his dad’s anniversary, and he has been learning Blender and Godot on the side, mostly modeling and blocking out levels. Consequently, he asks listeners for advice on starting a big game project, and he plans to record his progress, maybe as a time lapse. Then it is straight into the featured story. Anthropic’s Model Hardware Standard: A Driver for the Physical World The featured story comes from Anthropic, which opened a research preview of the Model Hardware Standard, or MHS. Cochrane frames it as the other side of the question NVIDIA’s world models raised two weeks ago: when do AI agents start touching actual machines? A typical lab runs a microscope, a liquid handler, a robotic arm, and a plate reader, each from a different vendor with its own control software. One Janelia researcher in the post launches seven programs in three languages just to start an experiment. Anthropic says wiring a setup like that takes weeks or months of specialist work. MHS is a driver, the same kind of translation layer a printer uses, except every device gets described with a tiny set of commands like read and write. Devices announce themselves on the network. A plain-English reference file then records what each machine measures, what can be adjusted, and which safety limits get enforced no matter what the agent asks. Agents then reach the hardware through the Model Context Protocol, the command line, or plain code. Cochrane sees the same move the industry keeps making, from coding harnesses to RSS and JSON: agree on a standard and let everyone build against it. In fact, he calls MHS the hardware version of MCP. The partner results carry the segment. QuEra builds quantum computers from individual atoms held by lasers that must hold their frequency to about one part in a trillion. A four-person team spent months on a relock script that worked 58 percent of the time. However, four copies of Claude iterating overnight through MHS produced a decision-tree script that recovers the laser in about six seconds, and it passed 99.3 percent of 700 blind trials. Carnegie Mellon wrote MHS drivers for four instruments across three incompatible computers in about eight hours, then ran dose-response experiments three times faster and blocked all six deliberately induced faults. Genentech, meanwhile, showed the limits. Claude used the same pump speed for water, a foamy protein solution, and a human had to explain that the bubbles were a physics problem. That gap in physical intuition is what sticks with Cochrane. He doubts it will change soon, and he suspects the fix will arrive as sub-agents or sub-models that judge a request against an expected outcome. He also connects MHS to a video of racing robots that never learned to stop at the finish line. What happens, he wonders, once they can read a distance sensor through a shared standard? Still, he calls the announcement a fantastic read and points listeners to the full article. Sponsor: GoDaddy Economy hosting $6.99/month, WordPress hosting $12.99/month, domains $11.99. Website builder trial available. Use codes at geeknewscentral.com/godaddy to support the show. GPT-5.6 Does the Same Work for a Fraction of the Cost OpenAI’s builder’s guide to GPT-5.6 leads the headlines. Cochrane recaps the three tiers from episode 1870, Sol, Terra, and Luna, plus the separate dial for reasoning effort. On BrowseComp, a benchmark for digging up obscure facts on the web, the old GPT-5.5 flagship scored about 84 percent on a run that cost 33 dollars three months ago. Luna now matches that score for a dollar thirty-three, and OpenAI has since cut Luna’s price another 80 percent. Browser Use reports Luna finishing 78 percent of its hardest browser tasks for about 14 dollars, against 80 percent for roughly 235 dollars from the best available model. The guide’s other big addition is a multi-agent beta flag. It lets the model handling a request spawn parallel helper agents that report back to a root agent inside a single API call. However, Cochrane is unimpressed by the timing. He has been running that pattern in Claude Code for months, so he sees OpenAI copying a workflow other companies already ship rather than inventing its own. Along the way, he plugs Claude Code’s remote-control sessions, which let him send prompts from his phone to a terminal session at home. Google Lets Gemini Read the Books You Actually Bought Google launched Expert Intelligence, a name Cochrane calls quite the reach. The feature lets you drop a book you bought on Google Play Books into Gemini Notebook, formerly NotebookLM, and ask questions answered only from that book, with citations. Cochrane sees real power here for students, since he once used NotebookLM to organize scattered course PDFs. Additionally, publishers get a cut, which he calls a far better deal than the wholesale scraping of books that trained earlier models. Nevertheless, he asks who loses out, because a paid publisher does not automatically mean a paid author. He floats the same idea for artists, even a penny per use, then admits that may be too idealistic. Apple’s M5 Ultra Mac Studio Is Built to Run Big Models at Home Back in episode 1861, when Apple killed the Mac Pro, an M5 Ultra Mac Studio was expected later this year. Now it is here. The M5 Ultra brings up to a 36-core CPU, an 80-core GPU, and 512GB of unified memory moving 1.2 terabytes per second. Apple claims up to 4.3 times the AI performance of the M3 Ultra. Thunderbolt 5 can also cluster four machines into one memory pool for up to three times faster inference. The M5 Max model starts at $2,499 and the Ultra at $5,499, with shipping on September 22 and the 512GB configuration arriving in late October. Cochrane finds the clustering pitch ridiculous at that price, but he invites anyone who spends the money to report back. Apple Opens a Manufacturing School in Houston Apple also opened a 20,000-square-foot Advanced Manufacturing Center in Houston. It offers free classes for small and midsize manufacturers, from circuit board design to hands-on time on a scaled-down production line, with college students joining later. Cochrane calls it a solid step in the bring-manufacturing-home movement. The bigger story is the campus itself, which builds Apple’s AI servers and will add the first US-assembled Mac mini line later this year. That ties back to the Mac mini shortage that followed the OpenClaw rush, when Tim Cook warned of months-long waits. Cult of Mac was still reporting four-month waits in late July. However, Cook blamed chip supply rather than assembly, so Cochrane is not counting on relief just yet. Amazon EC2 Turns Twenty Amazon EC2 turned twenty this week, which Cochrane admits makes him feel old. The 2006 beta offered one server size in one region for ten cents an hour. Each came with a 1.7 gigahertz Xeon and under two gigabytes of memory, and accounts were capped at twenty servers. Today AWS offers more than 1,200 instance types across 39 regions. Consequently, Cochrane credits the company with turning that tiny product into the backbone of cloud and AI computing. Intel Gamer Days: Two Free Games, With Fine Print Intel Gamer Days runs through September 13. Buy a qualifying Core Ultra Series 2 or 14th Gen desktop chip, a Core Ultra Series 3 laptop, or an Arc graphics card. In return you get Star Wars: Galactic Racer plus the Tomb Raider: Legacy of Atlantis remake. GamesRadar values the pair at about 120 dollars. However, neither game is out yet, and codes must be redeemed by October 31 even though the Tomb Raider remake ships in February. Cochrane calls that awful, but he still tells qualifying buyers to claim the deal early. Note that 13th Gen chips do not qualify. Judge Orders Google to Stop Hiding Rival App Stores A jury found Google’s Android app monopoly illegal in late 2023, and Judge James Donato ordered rival stores into the Play Store in 2024. On August 13, Epic’s lawyer demonstrated that searching Play for “store for apps” returned Walmart instead of any app store. Donato called that “not acceptable” and ordered three fixes within a week. Searches must surface third-party stores, listings need a plain install button, and the “are you looking for” interstitial has to go. Cochrane welcomes the monopoly being chipped away, but he notes that a controlling entity still sits atop every app store. In his view, community hubs like app stores and social media need a public infrastructure layer. He suspects governments skip that investment because companies already run the services, while selling your data. Apple Wants 15 Percent of Purchases Outside Its Store The other half of the Epic saga is Apple’s proposed link-out commission. After the 2021 anti-steering injunction, Apple charged 27 percent on purchases made through external links. A judge held it in contempt last year, and the Ninth Circuit then allowed a fee limited to the cost of running the system. Judge Yvonne Gonzalez Rogers refused to wait for the Supreme Court, writing that “further delay is unwarranted.” Apple filed 15 percent for standard apps, 10 percent for subscription renewals and partner programs, and 5 percent for small businesses. It also conceded the rate would be “essentially zero” under the appeals court’s cost yardstick. Since Apple has charged nothing on link-outs since the contempt ruling, Cochrane sees this as a raise. He calls a cut on purchases made on a developer’s own website disturbing. He also recalls reading about the size of Uber’s payments to Apple, and he questions whether that kind of percentage is sustainable for companies without funding. Meta Says It Has Cut Off 750,000 Australian Kids Meta reported locking out more than 750,000 Facebook and Instagram accounts in Australia by the end of June under the country’s under-16 social media law. Over 500,000 of those were removed before the law even took effect. Detection relies mostly on AI scanning posts and bios for tells like birthday messages, plus user reports and blocks on re-registration. However, the post gives no count of mistaken removals or appeals, and the regulator’s early data shows under-16 usage falling only from about 86 to 81 percent. Meta wants a single age signal at the operating system or app store level, and Cochrane agrees completely. He connects it to the MHS idea from the top of the show: platforms need a standard flag to reference instead of guessing. The White House Deputizes Private Hackers Earlier this month the White House signed a National Security Presidential Memorandum that lets vetted private security firms run surveillance and disruption operations against overseas criminal groups. The Justice Department and Homeland Security hold the contracts and oversee the work. Firms need a proven track record, vetted staff, and a bond of at least $1 million, and must submit operating procedures within 60 days. Cochrane finds the measure aggressive in a good way and hopes it deters attacks on innocents. Still, he takes Kevin Beaumont’s warning seriously that the private security industry profits from ransomware existing. He compares it to the old Head and Shoulders myth: why solve the problem that drives your revenue? A Weather Satellite Watched the Eclipse Shadow Cross Europe Cochrane skips the readout on this one and simply sends listeners to ESA’s site. The MTG-I1 weather satellite captured the Moon’s shadow sweeping across Europe during the August 12 eclipse. Watching a shadow cross an entire continent, he says, was a first for him. Additionally, it leaves him excited about the research happening beyond the planet. Rivers, Deltas, and the Number 0.6 Quanta Magazine explains Hack’s law, which John Hack discovered in 1957 while measuring streams in Virginia and Maryland. A stream’s length tracks its drainage area raised to the power of 0.6, regardless of the rock underneath, and satellite data later confirmed it worldwide. Computer models in the 1990s showed why. Channels that capture extra runoff cut deeper and steal from their neighbors until the network settles into the arrangement that wastes the least energy. Now a University of Texas Rio Grande Valley team has found the same 0.6 exponent in river deltas, which spread water out rather than gathering it. Nobody knows why yet, and Cochrane calls it a really cool read. Sugar Helped Grow the Human Brain, Too A new paper in Science, co-authored by Jennie Brand-Miller at the University of Sydney, adds a third ingredient to the story of early human brain growth. Alongside meat and cooking, natural sugars from ripe fruit and honey may have fueled it too. The brain is about two percent of body weight but burns twenty percent of resting energy. It runs on glucose, which meat and marrow barely supply and raw starch cannot release without fire. The team modeled ancestral diets from a chimp-like baseline through Homo erectus and concluded that the earliest hominins may have drawn over 65 percent of their energy from natural sugars. Cochrane stresses that it is a model, not fossils, and notes that paleoanthropologist Marina Lozano thinks the authors place widespread cooking too early. Still, he loves this kind of deep research. Retracing the steps to our own intelligence, he suggests, could hint at what it takes for intelligent life to develop at all. A Brain Rhythm That Tells Doctors Where to Aim Finally, Science Daily covered a University of Cologne study on deep brain stimulation. That is the implanted-electrode treatment that eases Parkinson’s tremors for some patients but not others. Andreas Horn’s team recorded from 50 patients using both the implanted electrodes and an external magnetic scanner. They identified a circuit between the electrode’s target and the frontal cortex that oscillates at 20 to 35 cycles per second. Stronger coupling there predicted bigger improvement after surgery, though the study, published in Brain, shows correlation rather than cause. First author Bahne Bahners hopes the finding helps tune DBS more precisely, especially for patients who have not responded well. Cochrane half-jokingly asks whether MHS might one day drive those electrodes, and he calls brain disorders the hardest thing in the body to treat. Cochrane wraps with housekeeping: become a GNC Insider at geeknewscentral.com/insider, email geeknews@gmail.com with questions or comments, subscribe to the newsletter, and grab a modern podcast app at podcastapps.com. He thanks GoDaddy for over twenty years of keeping the show on the air, promises to catch everyone next Monday, and wishes listeners a great night. The post Eyes, Hands, and a Sense of Timing #1874 appeared first on Geek News Central.

Amazon is testing MCF with Prime. Walmart and TikTok Shop sales are absolutely booming. 10 new AI features that will help your Amazon, Walmart, or TikTok Shop business. These and more on today's Weekly Buzz episode! We're back with another episode of the Weekly Buzz with Helium 10's VP of Education and Strategy, Bradley Sutton. Every week, we cover the latest breaking news in the Amazon, TikTok Shop, Walmart, and E-commerce space, talk about Helium 10's newest features, and provide a training tip for the week for serious sellers of any level.   Walmart e-commerce sales surge as CEO touts 'price, speed and convenience' https://www.foxbusiness.com/retail/walmart-e-commerce-sales-surge-ceo-touts-price-speed-convenience TikTok Shop is driving more online sales in the US than Target and other major retailers https://www.businessinsider.com/tiktok-shop-us-spend-is-larger-than-target-costco-data-2026-8 10+ New AI Features For Amazon, Walmart, and TikTok Shop Seller Pulse — A near-real-time dashboard widget for monitoring sales, orders, units sold, ad spend, conversion rate, TACoS, and other metrics across Amazon, Walmart, and TikTok Shop. Ads MCP Write Capabilities — Users can create advertising campaigns, add keyword targets, and manage bids directly through the Helium 10 MCP using Claude, ChatGPT, or another compatible AI assistant. Walmart Ads in MCP — Users can analyze Walmart advertising performance, ask questions about campaign data, and make changes to Walmart campaigns through the MCP. B2B Metrics in Profits — Helium 10 Profits now separates B2B and B2C orders, allowing sellers to compare business-customer sales with regular consumer sales. AWD Inventory Visibility — The Inventory Levels page now displays Amazon Warehousing and Distribution inventory, including inbound AWD units and inventory moving from AWD to FBA. Keyword Tracker MCP Write Capabilities — Users can check whether keywords are already being tracked, add missing keywords, and start tracking products directly through the MCP. Review Insights in MCP — Users can access Amazon review-analysis data, identify positive and negative review themes, and compare an ASIN's reviews against its broader category. TikTok Influencer Search in MCP — Sellers can find TikTok creators based on niche, keywords, sales generated, units sold, follower count, and other performance criteria. TikTok Product Search in MCP — Users can discover top-performing TikTok Shop products based on niche, GMV, sales period, and other filters.  TikTok Top-Performing Video Search — Sellers can identify which TikTok videos generated the most GMV for a specific product.  TikTok Creator Identification — Users can find the creators responsible for the highest-performing videos associated with a TikTok Shop product.  Additional MCP Credit Packs — Users who reach their MCP credit limit can now purchase additional credit packs through the Plans and Billing section of Helium 10. In episode 549 of the AM/PM Podcast and Weekly Buzz, Bradley talks about: 00:00 - Introduction 00:54 - Amazon MCF Prime Coming? 02:54 - Walmart Online Sales Booming 04:24 - TikTok Shop Sales Booming 06:11 - 10 New AI Features For Amazon/Walmart/TikTok Shop

You got the email, you're confused, let's break it downSo Tailwind sent out an email about something called an MCP server, and if you read it and thought "what does that even mean," you're not alone. Let's break it down simply. MCP stands for Model Context Protocol. Think of it as a translator between AI assistants, like Claude or ChatGPT, and the software you already use. Before this, if you wanted an AI to help with a task, you'd copy information out of one app, paste it into your AI chat, get a response, then copy that back into the other app. Clunky. MCP removes that step. It lets your AI assistant talk directly to Tailwind and actually do things inside it. So now, instead of opening the Tailwind dashboard, you can tell your AI assistant to schedule a Pin, and it happens. That's what the email was announcing.Does this fit into your existing workflow, and is it even necessary?Here's the question I'd actually ask if I got that email. If you're already using Tailwind to schedule, does this save you time? Fair question, because scheduling itself isn't really the bottleneck Tailwind already solves. The real time savings comes from cutting out the handoff between drafting your content and publishing it. Right now your workflow could look like this. You draft your pin copy somewhere, then you go open Tailwind, upload the image, paste in the title and description, pick the board, hit schedule. That's two tools and at least one copy-paste step. With MCP, drafting and scheduling happen in the same conversation. You write the pin copy with your AI, and it just does that last step for you. One less tool, one less switch.But I'll be honest with you. If you're just batch pinning of pre-made images with no copy to write, Tailwind's own scheduler is already fast, and this won't feel meaningfully different. It shines when content creation and scheduling are two separate steps you're doing back to back. This collapses them into one. So is it necessary? No. Is it useful if your workflow already starts with writing? Yes.How to leverage AI for your Pinterest marketingIf you're going to use AI for Pinterest at all, the highest value spot isn't the scheduling piece, it's the content piece. Use it to draft pin titles and descriptions, brainstorm keyword variations, repurpose a blog post or podcast episode into pin copy, or review what you already have scheduled and catch gaps. The AI is most useful the moment before something needs to be written, not the moment it needs to be clicked.A few questions to ask yourself before you adopt thisBefore you add any new tool or integration to your workflow, ask yourself these:Does this remove a step I'm already doing, or does it just add a new thing to learn?Is my current bottleneck actually scheduling, or is it content creation?Will I actually use this weekly, or is it a shiny new feature I'll try once and forget?Does this fit how my team already works, or does it require retraining everyone?If you can't answer yes to at least one of those in a way that saves you real time, it's fine to let this one sit for now. Not every new AI feature needs to become part of your process.—-------Here are some helpful links from the podcast:

Topics covered in this episode: Web UIs for your reverse proxy Wagtail 8.0 is hot off the presses RISC-V is now officially supported by CPython Django's annual releases make every version an LTS Extras Joke Watch on YouTube About the show Sponsored by Logfire from Pydantic: pythonbytes.fm/logfire Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: Web UIs for your reverse proxy Traefik, nginx, and Caddy all sit in front of a lot of self-hosted infrastructure, and all three are configured by hand-editing files. Three active projects put a control plane on top: Traefik Manager (Python + Flask), Nginx UI (Go + Vue), and caddy/ui (React + Node). All three are additive rather than replacements - none of them take ownership of your config away from you - which is the part that matters when the thing has write access to production routing. Traefik Manager is the Python one: Flask 3.1 and Gunicorn for the control plane, a lightweight Go agent for remote instances, currently v1.10.0 with an Android companion app. Nginx UI is a single Go binary at 11.3k stars, with a block-style config editor, an Ace editor doing LLM completion on nginx syntax, and an MCP server so agents can drive it. caddy/ui runs as two containers next to your existing Caddy, reads and writes your Caddyfile directly, and uses Caddy's /adapt API to validate before reload - no Docker socket required. Each one edits the config the underlying server already reads, so your files stay the source of truth and you can drop the UI without unwinding anything. Undo is a first-class feature across all three - timestamped backups with optional Git history, config version compare and restore, Caddyfile snapshots with one-click rollback. Observability is where they diverge: Traefik Manager does CrowdSec and a visual route map, Nginx UI does server metrics, caddy/ui streams access logs over SSE and pulls p50/p95/p99 off Caddy's Prometheus endpoint. Maturity spread is wide - Nginx UI has 11.3k stars, caddy/ui has 4 and was built in a single Claude session - and caddy/ui ships with auth off by default, so set CADDY_UI_USER and JWT_SECRET before it goes anywhere near a public interface. Calvin #2: Wagtail 8.0 is hot off the presses Link: https://github.com/wagtail/wagtail/releases/tag/v8.0 Custom base page models are now supported, so projects aren't locked into subclassing Wagtail's Page as shipped (Matt Westcott). New v3 REST API handles both read and write CMS operations, a first for Wagtail's API. A global registry for permission policies, plus full customizability for the remaining page views via PageViewSet. AVIF and WebP images are no longer auto-converted to PNG by default, a real behavior change to watch on upgrade. Five security fixes: page admin API restrictions, document identification by SHA1 hash, descendant collections in the Documents/Images API, snippet copy permissions, and the page translation endpoint. Formalized Django 6.1 support, and CI now runs on uv with a lockfile. Sponsor: Logfire from Pydantic Your AI agent failed at 2am. Was it the model? A tool call? The database? Most observability tools can't tell you, because they only see part of your stack. Pydantic Logfire sees all of it. One trace across your agents, LLMs, APIs, and database. Down to the infrastructure: services, Kubernetes, and hosts. It's built on OpenTelemetry, with SDKs for Python, TypeScript, and Rust, and it works with any OTel-compatible language. Every prompt, token count, and cost, right next to your vector searches and API calls. You query everything with Postgres-compatible SQL. And so can your coding agent, through the Logfire MCP server. Stop guessing. Read the trace. Pydantic Logfire. AI, it's still just engineering. Visit pythonbytes.fm/logfire today and sign up today. Get 10M records free every month, no card required. You can even click “Onboard with your coding agent” to copy a prompt to have claude or codex integrate Logfire into your app. Thanks to Pydantic for supporting the show. Calvin #3: RISC-V is now officially supported by CPython Link: https://blog.python.org/2026/08/riscv-now-officially-supported/ CPython added RISC-V as a tier 3 platform under PEP 11, specifically the 64-bit Linux target riscv64-unknown-linux-gnu. RISC-V is an open ISA anyone can implement, unlike x86 and ARM, and its market is projected to quadruple by 2032. The RISE Project donated real RISC-V machines for buildbots; the author's work was funded by a Sovereign Tech Agency fellowship. What changes: the port is now a maintained compatibility target, so CPython changes are less likely to quietly break it. What doesn't: no python.org installers, no binary wheel parity for native extensions. Next up: RISC-V runners in CPython CI for pre-merge feedback, then a push toward tier 2, plus architecture-specific optimizations. The ask is testing. If you have RISC-V hardware, build CPython, run your test suite, file what breaks. Tier 3 is the weakest support tier. PEP 11 tier 3 requires a core developer contact and a buildbot, but failures on tier 3 platforms explicitly do not block a release. Saying "ongoing CI/testing expectations" oversells it. The honest bit is "someone is now on the hook for it, and breakage gets noticed," not "it's guaranteed working." Worth the caveat that this is Linux SBCs, not microcontrollers. A VisionFive 2 counts, an ESP32-C6 or Pico 2 does not. Those are 32-bit non-Linux parts where MicroPython is still the answer. Michael #4: Django's annual releases make every version an LTS Starting with Django 2028, Django will move to one January feature release per year, adopt calendar-based version numbers, and support every release for three years. The old distinction between standard and LTS releases disappears, giving teams a predictable annual upgrade path that aligns more closely with Python's own release and support cadence. Every Django release becomes the safe, long-supported choice, so teams no longer need to wait for a specially designated LTS version or absorb two years of changes at once. Each release gets one year of mainstream bug fixes followed by two years of security and data-loss fixes. New releases support the three latest Python versions and add the next Python release during their first year. Calendar versioning begins with Django 2028, followed by Django 2029 and so on. Three Django versions will be supported at any time, giving third-party packages a clearer rolling target. Nothing changes before 2028, and existing commitments for Django 5.2 LTS and 6.2 LTS remain in place. Extras Calvin: The Python docs now document the time complexity of built-in types https://docs.python.org/3.16/library/time-complexity.html Thinking in Python - Bruce Eckel's free book https://thinkinginpython.com/ Michael: prune_uv_pythons.py - Prune uv-managed Python installs, keeping only the newest patch per minor version Runs automatically in my system “upgrade” script: upgrade-output-2026.png Started using Ollama cloud models for my Hermes assistant. Thanks to Jeff Triplett I learned they are not just local models. Joke: The Tao of Programming - Book Seven: Corporate Wisdom

A quarter of enterprise teams have gotten a single AI channel into full production — the rest are stuck somewhere behind it. In this episode, Arun Chandra, Chief Operating Officer at NiCE, explores why that gap persists and what closes it.   The conversation covers getting data, knowledge, and organizational context ready for scale, building the financial case for AI investment with the CFO, and how open protocols like MCP are reshaping enterprise architecture decisions.   This episode is sponsored by NiCE Cognigy.   Learn how leading organizations approach AI investment more like a venture portfolio, and why interdisciplinary collaboration is critical to defining the right data for AI success. Download our free PDF report, "Beginning with AI," at emerj.com/aik1

Hoje o papo é sobre AI Gateways! Neste episódio, conversamos sobre os desafios de centralizar o acesso a modelos, MCPs e ferramentas para controlar custos, reforçar a segurança e ampliar a observabilidade nas empresas. A discussão passa por roteamento entre LLMs, guardrails e fallback, além dos desafios de tirar agentes do purgatório dos pilotos e levá-los à produção. Vem ver quem participou desse papo: Paulo Silveira, o host que já não sabe onde seus agentes estão rodando Vinny Neves, cohost, dev e professor na Alura Kleber Bacili, cofundador e CEO da Sensedia Fernanda Goulart, Head de Produto na Sensedia Murillo Godoi, Tech Lead na Alura  Links:  Gartner: Market Guide for AI Gateways O que é o Sensedia AI Gateway MCP: Model Context Protocol Nova especificação do MCP: protocolo sem sessões OpenRouter Vinny: O que service mesh tem a ver com o teu agente de IA APIs: Gerenciamento e Criação – Hipsters #57 Comente no Spotify: O que você tem usado para organizar o uso de LLMs, MCPs, e o custo de IAs na sua empresa? TechGuide.sh, um mapeamento das principais tecnologias demandadas pelo mercado para diferentes carreiras, com nossas sugestões e opiniões. #7DaysOfCode: Coloque em prática os seus conhecimentos de programação em desafios diários e gratuitos. Acesse https://7daysofcode.io/ Produção e conteúdo: Alura Cursos de Tecnologia – https://www.alura.com.br Edição e sonorização: Rede Gigahertz de Podcasts

In this episode of the Programmatic Digest Podcast, host and guest Rob JAnes, Head of Product at AdButler, discuss the rapidly changing landscape of publisher monetization and programmatic ad operations heading into 2025. Check out the interview on YouTube here:  https://youtu.be/EFAuN7ce1Ag  They break down the industry-wide adjustments following Google's unexpected decision not to deprecate third-party cookies and the subsequent pivot to internal identity resolution systems. A major focus of the conversation is the existential crisis content creators face due to declining traffic, driven by generative AI search summaries (like ChatGPT, Claude, and Google AI Overviews). Rob explores how AdButler is helping publishers adapt through engineering solutions, including Model Context Protocol (MCP) servers for "agentic ad ops," specialized retail media technology (Commerce Catalyst), and turnkey self-service marketplaces that can run directly on top of Google Ad Manager (GAM). They also look ahead to the future of programmatic agencies, predicting a shift from clunky DSP interfaces to autonomous media-buying AI agents guided by emerging standards like the Ad Context Protocol (ADCP). Key Takeaways The AI Traffic Squeeze: Content publishers are experiencing declining site visits as users migrate from traditional search queries to conversational AI summaries (such as ChatGPT, Claude, or Google AI Overviews) that aggregate content offsite. Monetizing AI Scraping: An emerging front in publisher revenue is demanding compensation from LLM companies (like Anthropic or OpenAI) for scraping site content. Driven by concepts discussed by industry leaders like Scott Messer, specialized startups and platforms are expected to launch within 3 to 6 months to help publishers charge scrapers. The Post-Cookie Pivot: Google's late-2025 reversal on cookie deprecation disrupted industry expectations, yet it ultimately accelerated publishers' adoption of first-party and alternative identity resolution standards like ID5 and UID2. Low-Overhead Self-Service Portals: AdButler's self-service portal builder allows local or small advertisers to purchase ad placements directly via credit card. This bypasses the need for high-overhead sales teams, making buying on publisher sites as seamless as running campaigns on Meta or LinkedIn, and can even integrate on top of existing Google Ad Manager (GAM) setups. Why Traditional Ad Servers Fail Retail Media Networks (RMNs): Standard servers like GAM are volume-based (impressions and clicks) rather than performance-driven. RMNs require unified online and offline transactional data paired with SKU-level tracking to deliver highly relevant, objective-based product ads. Agentic Ad Ops & Media Buying: AdButler has introduced Model Context Protocol (MCP) server integration, enabling ad ops teams to conversationally manage campaigns, analyze metrics, and create automated optimization rules via Claude or OpenAI. On the programmatic buyer side, protocols like the Ad Context Protocol (ADCP) and IAB's agentic protocol are paving the way for autonomous AI buying agents to eventually run campaigns across DSPs without manual human intervention.      

How did a $4M Amazon brand become a supplier to Amazon itself? Discover its Amazon B2B strategy, PPC shift, hidden Amazon programs, AI tools, and competitor tactics driving its next stage of growth.   ► Watch the Podcasts On Youtube: https://www.youtube.com/@Helium10SeriousSellersPodcast?sub_confirmation=1 ► Instagram: instagram.com/serioussellerspodcast ► Free Amazon Seller Chrome Extension: https://h10.me/extension ► Sign Up For Helium 10: https://h10.me/signup  (Use SSP10 To Save 10% For Life) ► Learn How To Sell on Amazon: https://h10.me/ft   What happens when a decades-old industrial business starts treating Amazon like a serious growth channel? In this episode of the Serious Sellers Podcast, Bradley Sutton sits down with Angie Mosqueda from Stardust, a company aiming for roughly $4 million in Amazon sales this year. What began as a traditional B2B business selling industrial products through distributors has evolved into a sophisticated Amazon operation with high-ticket products, business pricing, PPC clicks that can reach $20 or more, and even a six-figure-per-month product. But one of Stardust's biggest opportunities didn't come from a new keyword or advertising campaign. Through persistent networking at Amazon events and leveraging its business certifications, the company eventually became a preferred seller to Amazon itself—meaning Amazon warehouses can now purchase Stardust products. The company also became a preferred seller for Chicago Public Schools through Amazon Business. Angie explains how these opportunities took roughly a year and a half of networking, follow-ups, and navigating Amazon's programs, showing why sellers shouldn't overlook certifications, Amazon Business, or simply getting in the room with the right people. Angie also shares why Stardust recently moved its Amazon advertising away from outside agencies and brought PPC management in-house using Helium 10 Ads. While the account's overall numbers previously looked healthy, getting closer to the data exposed wasted spend hiding underneath those top-level metrics. She also explains how she's using Helium 10 Audience to validate creative decisions and the Helium 10 MCP with Claude to analyze bids, identify opportunities, make changes, and learn Amazon advertising as she goes. Bradley wraps up the episode with a live Cerebro deep dive into Stardust's top-selling spill kit, revealing how sellers can identify their real competitors, compare relative organic rank, uncover keyword gaps, and study historical advertising and BSR trends. Instead of guessing what might move the needle, sellers can reverse-engineer what changed when a competitor's sales and rankings improved. Stardust may already be approaching $4 million on Amazon, but this episode proves an important lesson: no matter how established your business becomes, there's always another layer of opportunity hiding in the data. In episode 762 of the Serious Sellers Podcast, Bradley and Angie discuss: 00:00 - Introduction 03:37 - Taking A Legacy B2B Brand Online 06:30 - Why Their PPC Clicks Cost $20+ 09:02 - Launching New Products On Amazon 11:49 - Becoming A Supplier To Amazon Warehouses 13:16 - Unlocking Amazon Business Preferred Seller Programs 15:39 - Certifications And Networking With Amazon 16:23 - Bringing Amazon PPC Management In-House 17:34 - Using Helium 10 Audience And AI 19:11 - Managing Amazon Ads With MCP 22:49 - Bradley's Live Cerebro Competitor Analysis 33:50 - Reverse-Engineering Competitor Growth Strategies

Show DescriptionDave and Chris try to get their LUTS in a row, figure out which app to use to edit and publish video quickly, Dave's new syntax highlighter microlighter, could web MCP be a good thing to help agents use CodePen better, and saying no is as important as what you do ship. Listen on WebsiteWatch on YouTubeLinks Introducing Microlighter - daverupert.com MicroLighter - A zero-dep syntax highlighter TextMate: Text editor for macOS Dark mode toggles: two states are enough • Lea Verou The Case for Tri-State Dark Mode Toggles – Bram.us

In this update-packed episode James and Frank dive into developer tooling: the VS Code Mobile Canvas that embeds emulators into your editor, MAUI DevFlow's rewrite enabling plain .NET/native (and desktop AppKit) support, and the growing MCP/agent ecosystem that ties editors, the Copilot app and CLI together. They share practical takeaways—Mobile Canvas works across frameworks, DevFlow supports non‑MAUI apps, agent instruction files matter, and you can automate Microsoft Store publishing—giving cross‑platform builders clear next steps. Follow Us Frank: Twitter, Blog, GitHub James: Twitter, Blog, GitHub Merge Conflict: Twitter, Facebook, Website, Chat on Discord Music : Amethyst Seer - Citrine by Adventureface ⭐⭐ Review Us ⭐⭐ Machine transcription available on http://mergeconflict.fm

Amazon limiting written reviews for customers to read, Helium 10 MCP hackathon contest with big prizes, and new Helium 10 ChatGPT plugin. We're back with another episode of the Weekly Buzz with Helium 10's Manager of Education and Strategy, Carrie Miller. Every week, we cover the latest breaking news in the Amazon, TikTok Shop, Walmart, and E-commerce space, talk about Helium 10's newest features, and provide a training tip for the week for serious sellers of any level.   Amazon Limits How Many Reviews Shoppers Can Read https://www.ecommercebytes.com/2026/08/16/amazon-limits-how-many-reviews-shoppers-can-read/ Helium 10 Hackathon Helium 10 is launching an MCP and Prompt Hackathon where sellers can submit their best Claude skill, prompt, or both for a chance to win prizes worth up to $4,500. Submissions should demonstrate creative ways to use Helium 10 data to save time or money, improve efficiency, increase sales, or accomplish things that weren't possible before MCP. Finalists may present their creations during a live webinar on September 3 at 8 a.m. Pacific, giving sellers a chance to learn from each other and discover new ways to use MCP. Prizes include up to a full year of Helium 10 Diamond plus Amazon gift cards. **Submit your skill or prompt by the September 1 deadline at http://h10.me/hackathon.** Trade Court Upholds the End of De Minimis: What the “Detroit Axle” Ruling Means for Ecommerce https://www.ecomm-alliance.org/blog/trade-court-upholds-the-end-of-de-minimis/ New Helium 10 ChatGPT Plugin Helium 10 has launched its new ChatGPT plugin, giving Diamond plan members and above direct access to the full Helium 10 MCP experience inside ChatGPT. Once installed and connected, users can tap into 70 different tools covering keyword and product research, competitor analysis, listing optimization, historical rank, search volume, pricing, sales velocity, P&L, advertising, inventory, and more—without downloading and uploading reports. By pulling directly from actual Helium 10 and Amazon data, the integration also helps reduce AI hallucinations and makes analyzing seller data faster and easier. Usage counts toward the same combined 1,000 MCP call limit per billing cycle. Amazon Solution Provider Services: New Seller Central Authorisation Process Starting 8/10/2026 https://ecomranker.com/amazon-seller-central-authorisation-process-2026/ Harvest SQP Keywords with AI Bradley introduces the new Search Query Performance Outperformer Skill for the Helium 10 MCP, designed to uncover high-opportunity keywords by analyzing a full year of SQP data and identifying terms where your product converts better than the market. The skill cross-checks Keyword Tracker, Cerebro, and Helium 10 Ads to find outperforming keywords you aren't tracking, keywords with weak organic or sponsored visibility, and advertising opportunities where you should add a target or increase your bid. What could take an hour of manually downloading and comparing reports can be completed in about a minute, and with the new Helium 10 Ads write capabilities available to Elite members and coming soon to Diamond, the MCP can even implement keyword and bid changes for you. To get the skill, comment “I need the SQP Outperformer Skill” below and we'll send you the download link. New AI Agent For Amazon Keyword Research Helium 10 has launched Helium, its new built-in AI agent, in beta for Diamond members and above. Think of it as having an AI assistant like ChatGPT or Claude directly inside Helium 10, without needing an outside subscription or using external AI tokens. Because Helium connects directly to Helium 10 data, it can quickly run analyses across tools like Cerebro, Search Query Performance, Keyword Tracker, and Helium 10 Ads, helping identify keyword gaps, analyze sales and conversion performance, compare listings with competitors, audit advertising, and more. Helium is also being built with Helium 10-specific knowledge, allowing it to proactively surface insights and opportunities beyond what you explicitly ask. Future capabilities are expected to include custom dashboards, scheduled tasks, and more advanced workflows. Diamond and Elite members can find “Ask Helium” inside Helium 10 and start testing the beta now. Amazon Prime Air drone delivery is expanding to nearly 500 US cities and towns this year https://www.aboutamazon.com/news/transportation/amazon-prime-air-drone-delivery-expansion That's a wrap for this week's Weekly Buzz! We hope you found these updates helpful and are ready to put them into action. Be sure to check back next week for the latest Amazon, e-commerce, and Helium 10 news. Until then, we'll see you next week to find out what's buzzing! In episode 547 of the AM/PM Podcast and Weekly Buzz, Carrie talks about: 00:00 - Introduction 00:44 - Amazon Limiting Reviews? 02:31 - Helium 10 Hackathon 05:45 - Trade Court Upholds De Minimis Ruling 07:31 - New ChatGPT Amazon Plugin 09:54 - New Authorization Process for Service Providers 10:58 - Harvest SQP Keywords with AI 14:56 - New AI Agent For Amazon Keyword Research 20:27 - Amazon's Prime Air Drone Delivery

One of the biggest mistakes in AI? Thinking that your company's AI use is noteworthy. Or, even a competitive advantage. It's not. We break it down in Volume 3 of our 'Start Here Series.' AI as an Operating System: LLMs Are the Internet Now -- An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:AI As An Operating System ExplainedLarge Language Models Replace Traditional AppsAI Integration in Knowledge Work PlatformsChoosing the Right AI Operating SystemMicrosoft Copilot vs. Google Gemini vs. Claude vs. ChatGPTAgentic Browsers Powering Autonomous WorkflowsModel Context Protocol (MCP) for AI AgentsOrchestration Layer and Agent CollaborationChatGPT Apps Merging AI and InternetEnterprise Data Integration with AI ToolsContext Switching Reduction Through AI AgentsStrategic AI Adoption and Platform RedundancyTimestamps:00:00 "AI: A New Operating System"03:58 "AI Transforming Work Interfaces"06:41 "Collaborating in AI-Native Workspaces"12:25 Anthropic's Innovations in AI Tools13:46 "OpenAI's Strategy and Market Focus"18:02 "Cognitive Evolution Through AI"20:57 "Agentic Browsers: Key 2025 Advancement"25:12 Improving Content Through Data Insights26:42 "Anthropic's MCP: The AI Connector"32:19 "AI Tools for Productivity Integration"34:20 "AI: Unlocking Context and Efficiency"36:32 AI Governance and System Portability39:35 "AI Operating System Insights"Keywords: AI operating system, large language models, LLMs, AI as infrastructure, enterprise AI, AI adoption, agentic workflows, AI agents, orchestration layer, Copilot, Microsoft 365 Copilot, Google Gemini, Gemini business, Gemini enterprise, Anthropic Claude, Claude cowork, MCP, model context protocol, OpenAI, ChatGPT, ChatGPT apps, ChatGPT business, ChatGPT enterprise, AI native, dynamic data integration, productivity with AI, collaboration tools, agentic browsers, autonomous AI agents, context window, memory and personalization, expert-driven loops, app hop tax, context switching, AI integration in business, AI tools for teams, AI platform selection, data governance, modular AI workflows, permissions and audit logs, backup and redundancy in AI, competitive advantage with AI, Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

The AI gap will kill companies.What is it? it's the large divide between AI's crazy impressive capabilities and what most companies are actually using them for. And one of the biggest reasons for the AI gap? Talking. Like... no one understands how to talk about AI because the technology changes faster than Usain Bolt in Beijing. You wanna talk to your AI team about LLMs? PFT. They're running Ralph Wiggum loops in Claude Code and just kinda reading the code before it hits production. Yeah, the divide is WIIIIIDE. So we're gonna tackle it together on the second volume of our Starter Series: AI Without the Jargon: The AI Language Every Business Leader Needs to live by in 2026 -- An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:AI Jargon Barrier for Business LeadersGenerative AI Basics and Lingo BreakdownChatGPT, Claude, and Gemini Model ComparisonUnderstanding Tokens and Context WindowsLarge Language Models: Prompt to OutcomeParameters, Model Power, and Cost ImplicationsRetrieval Augmented Generation (RAG) ExplainedEmbeddings, Vector Databases, and ChunkingAgentic Models vs. Transformer ModelsAI Risks: Hallucinations, Prompt Injection, GuardrailsModel Context Protocol (MCP) and ConnectorsScaffolding for Complex AI WorkflowsAI Success: ROI, Risk, and Implementation StrategiesTimestamps:00:00 "Join Start Here Series Community"03:21 Bridging AI and Business Leaders09:35 Partnering for Generative AI Success12:26 "AI Models Operate Using Tokens"15:41 "Shift to Smaller AI Models"18:56 "Understanding RAG and Its Impact"22:38 "AI Tools Connecting via MCP"24:56 "Minimizing AI Hallucinations Effectively"28:45 "Fast, Careful AI Implementation"30:53 "AI Guide for Business Leaders"Keywords: AI language, AI jargon, artificial intelligence terminology, AI lingo, large language model, generative AI, prompt engineering, context engineering, context window, tokens, tokenization, model architecture, model parameters, neural network connections, Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

Topics covered in this episode: Python 3.12.14, 3.11.16, 3.10.21 - security releases Codeberg's AI-code ban tests its role as a GitHub alternative Brett Cannon: what's missing for reproducible builds on PyPI nothing records the source code a distribution came from. direct_url.json captures it when you install from a repo or archive, so the fix is putting the same info in sdist/wheel metadata. recording the build tools. Wheels can already do this via PEP 770 SBOMs in .dist-info/sboms/ - sdists can't, since they're a tarball plus a precalculated PKG-INFO with nowhere to hang extra metadata. Either "don't use sdists" or an sdist v2. Extra extra extra, hear all about it Extras Joke Watch on YouTube Sponsored by Logfire from Pydantic pythonbytes.fm/logfire This episode is brought to you by Pydantic Logfire. It's observability for AI apps from the team behind Pydantic - agents, LLMs, APIs, database, and infrastructure in a single trace, queried with Postgres-compatible SQL. Your coding agent can query it too, through their MCP server. I'll tell you more later. Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Calvin #1: Python 3.12.14, 3.11.16, 3.10.21 - security releases https://blog.python.org/2026/08/python-31214-31116-31021/ Source-only security releases for the three branches now in security-fix-only mode; release team blamed the European solar eclipse for the timing. tarfile hardening. Multiple path-traversal bypasses of the data filter closed, including a symlink escape that bypassed the CVE-2025-4330 fix; extract() now applies the filter to link targets too. Four fresh CVEs: CVE-2026-2297 (SourcelessFileLoader not using io.open_code() for .pyc), CVE-2026-4224 (expat crash on deeply nested content models), CVE-2026-3644 (control chars in http.cookies.Morsel), plus the completed CVE-2021-4189 fix in ftplib.ftpcp. Quadratic-complexity DoS cleanup across the stdlib: HTMLParser, configparser regexes, unicodedata.normalize(), csv.Sniffer.sniff(), and ElementTree XPath index predicates. Header/injection fixes: CR/LF rejected in HTTPConnection.set_tunnel(), control chars blocked in wsgiref.handlers status, and webbrowser now rejects leading dashes (plus a %action prefix bypass). http.client now caps chunked trailer lines and 1xx interim responses at 100 each - a hostile server could previously hang the client forever despite a socket timeout. Memory-safety odds and ends: stale pointers in lzma/bz2/zlib decompressors after MemoryError, a bz2 stack overflow on reuse-after-error, and bundled libexpat bumped to 2.8.3. If you're still on 3.10, 3.11, or 3.12 - and you extract tarballs from anywhere you don't fully control - this one's not optional. Michael #2: Codeberg's AI-code ban tests its role as a GitHub alternative Armin's article “Codeberg Divides” Armin Ronacher argues that Codeberg's new terms, which prohibit projects mostly written with generative AI, create a vague and difficult-to-enforce boundary. His larger concern is that a democratically governed host can still be unpredictable or ideologically narrow, weakening Codeberg's potential as a broad European alternative to GitHub. The strongest question for Python developers is whether repository hosting should judge legal open source by how code was produced, or focus on behavior and resource abuse. “Mostly generated” is hard to measure in modern codebases where developers mix handwritten code, completions, agents, and generated refactors. Ronacher suggests clearer alternatives: ban all LLM involvement, or target autonomous repository spam, abusive resource use, and low-quality generated contributions directly. Codeberg is free to choose a values-driven community, but that may conflict with being predictable, neutral infrastructure and a serious GitHub competitor. Worth discussing: can open-source communities set meaningful AI boundaries without driving maintainers and projects into opposing camps? Very first search for these terms lands on this page. Codeberg looked like a viable alternative. … Unfortunately, the latest update to its terms of service seems to mark a first step in changing one part I moved there for, namely the “freedom” part. Sponsor: Logfire from Pydantic Your AI agent failed at 2am. Was it the model? A tool call? The database? Most observability tools can't tell you, because they only see part of your stack. Pydantic Logfire sees all of it. One trace across your agents, LLMs, APIs, and database. Down to the infrastructure: services, Kubernetes, and hosts. It's built on OpenTelemetry, with SDKs for Python, TypeScript, and Rust, and it works with any OTel-compatible language. Every prompt, token count, and cost, right next to your vector searches and API calls. You query everything with Postgres-compatible SQL. And so can your coding agent, through the Logfire MCP server. Stop guessing. Read the trace. Pydantic Logfire. AI, it's still just engineering. Visit pythonbytes.fm/logfire today and sign up today. Get 10M records free every month, no card required. You can even click “Onboard with your coding agent” to copy a prompt to have claude or codex integrate Logfire into your app. Thanks to Pydantic for supporting the show. Calvin #3: Brett Cannon: what's missing for reproducible builds on PyPI Framing came out of his 2026 Python Packaging Council nomination - the secure-supply-chain gap he found is that Python has no defined way to do reproducible builds at all. Design goal is zero friction: producers uploading to PyPI shouldn't have to do anything. The work lands on build backends and installers. Gap #1: nothing records the source code a distribution came from. direct_url.json captures it when you install from a repo or archive, so the fix is putting the same info in sdist/wheel metadata. Gap #2: recording the build tools. Wheels can already do this via PEP 770 SBOMs in .dist-info/sboms/ - sdists can't, since they're a tarball plus a precalculated PKG-INFO with nowhere to hang extra metadata. Either "don't use sdists" or an sdist v2. The replay mechanism already exists: [build-system] in pyproject.toml is a defined entry point, so if backends recorded their own environment, you could reinstall and re-run the build. Payoff idea: trusted third parties report successful reproductions back to PyPI, which displays "independently reproduced by X" - surfaced in the index API so installers could prefer reproduced files. Explicitly framed as a perk, not a requirement - roughly SLSA build level 1, no shaming projects that don't opt in. Verbal kicker option: "And don't think pure-Python wheels are off the hook. Something built that wheel, and if that something was compromised, so is your wheel. SolarWinds was a build-process attack." Michael #4: Extra extra extra, hear all about it Python 3.14.7 Upgraded the MCP servers to 2026-07-28 v2 protocols (talk python, python bytes) Got agentsview running synced via postgres Talk Python courses, teams trial offering Talk Python courses, government procurement offering Lean TDD audio book is out Extras Calvin: uv now prefers post-quantum key exchange - https://github.com/astral-sh/uv/releases/tag/0.12.4 Joke: Beware of dog

Every engineering team wants to build its own custom AI agent, but what if all your organization needs is a standardized skill or a stateless MCP server? This week on Dev Interrupted, Andrew sits down with AWS Senior Principal Engineer Clare Liguori to untangle the ecosystem of modern agentic architecture. They work through how a team decides which AI building blocks to own, and how to get that reach without inheriting a maintenance burden. Clare shares her perspective on the simplified MCP 7.28 spec and why stripping away heavy custom scaffolding is how enterprise AI scales.That same shift is what makes MCP a gamechanger for LinearB customers, bringing your SDLC context layer, git, project management, and software delivery, into any agentic surface. What could your agents achieve if they can query your SDLC?Get the guide: The AI engineering productivity gap - how elite teams pull ahead in 2026Register: Dev Interrupted Presents: The Software Factory RoundtableFollow the show:Subscribe to our Substack Follow us on LinkedInSubscribe to our YouTube ChannelFollow the hosts:Follow AndrewFollow BenFollow DanFollow today's guest:Strands Agents SDK: Explore the open-source framework for building model-driven agents at strandsagents.comMCP 7.28): Dive into the new stateless specification at modelcontextprotocol.ioFollow Clare: LinkedIn | X OFFERSStart Free Trial: Get started with LinearB's AI productivity platform for free.Book a Demo: Learn how you can ship faster, improve DevEx, and lead with confidence in the AI era.LEARN ABOUT LINEARBAI Code Reviews: Automate reviews to catch bugs, security risks, and performance issues before they hit production.AI & Productivity Insights: Go beyond DORA with AI-powered recommendations and dashboards to measure and improve performance.AI-Powered Workflow Automations: Use AI-generated PR descriptions, smart routing, and other automations to reduce developer toil.MCP Server: Interact with your engineering data using natural language to build custom reports and get answers on the fly.

ServiceNow and AWS reveal how the DevOps Agent and MCP Server Console are turning incident response into a fast, autonomous, fully governed process. Topics Include:Govind Menon (ServiceNow) and Arun Jacob (AWS) discuss MCP and A2A strategy.ServiceNow understands workflows; partners with AWS to power them with AI.AI Control Tower governs and secures agent access to enterprise data.MCP is the industry standard for how AI agents read and act.Action Fabric spans A2A, REST APIs, and MCP for agentic work.AWS DevOps Agent, built on Bedrock, resolves incidents through sub-agents.Admin and operator access patterns integrate with Dynatrace, Datadog, Slack, GitHub.Demo: ServiceNow incident automatically triggers DevOps Agent investigation and resolution.DevOps Agent writes findings live back into the ServiceNow incident ticket.ServiceNow champions capping MCP servers at 30 tools for performance.MCP Server Console lets teams build scoped, use-case-specific tool servers.NowAssist skills, Knowledge Graph, and REST APIs become MCP tools.Live demo connects a 38-tool custom MCP server to DevOps Agent.Role-based access ensures users only see their permitted MCP tools.ServiceNow's autonomous ITOM agents point toward unsupervised future operations. Participants:Govind Menon – Head of MCP Product, ServiceNow Arunsingh Jeyasingh Jacob – Senior Solution Architect - ISV, Amazon Web Services See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Hello listeners, This week, The MCP would like to showcase another history podcast. Fresh Hell Podcast, hosted by Annie and Johanna, is an amazing deep dive history podcast focusing on the macabre. Like the MCP they focus on research and human stories, taking time to explore the historical context behind every topic they cover. They often focus on lesser-known stories, tangents of larger topics that also deserve to be explored. They release a new episode every Friday, diving into stories of murder, mystery, and the macabre. The episode of Fresh Hell that I chose to share with you all is called This is The Saddest Show. I chose this one because we've covered Freak Shows on the MCP, but didn't have time to really dive into the lives of some of the people who were displayed in this way. This episode talks about Chang and Eng Bunker, the conjoined twins, who were eventually able to take control of their display, and Julia Pastrana, known as 'the Bear Woman,' whom even after her death couldn't escape the exploitation that ruled her life. Annie and Johanna tell their stories in detail, remaining sensitive and non-exploitative when dealing with the complicated issues that wove throughout their lives. So, please give this episode a listen, and if you like it, you can find part 2 on the Fresh Hell Podcast feed where ever podcasts are found. Thank you, and I'll be back next time with another episode of the MCP.

What happens when an autonomous AI agent can complete thousands of actions before a traditional access review has even identified that something has gone wrong? In this episode of Tech Talks Daily, I speak with Alex Bovee, CEO and co-founder of C1, about identity security, runtime governance, shadow AI, and the controls companies need as humans and agents begin working together. Alex has spent much of his career in identity and security. He and his co-founder previously worked at Okta on zero trust products before creating C1 as an access control platform capable of operating at machine speed. That requirement has become increasingly important as AI agents begin accessing company data, calling tools, using credentials, and taking actions across enterprise systems. Alex describes agents as non-deterministic systems that can "reward-max." An agent may pursue its assigned objective so aggressively that it finds an unexpected or dangerous way to complete the task. It does not possess a moral compass or an intuitive understanding of what the company considers acceptable. Traditional identity processes were created for people. A company might review access every 90 days or investigate a security issue after an event. That approach becomes inadequate when an agent can execute thousands of actions within minutes. We discuss why identity is becoming a control plane for AI agents. Networks, data systems, and security tools all play important roles, but identity determines which resources an agent can access, which actions it can perform, and whether it acts independently or on behalf of a person. Without a defined identity or delegated authorization model, an organization may struggle to connect an agent's behavior with a responsible owner, a limited mission, and enforceable permissions. Alex explains the four connected capabilities inside C1's Agentic Control Plane. The first concerns shadow AI discovery. Companies need visibility across cloud services, SaaS applications, endpoint agents, hosted agents, local MCP servers, and credentials stored throughout the environment. This is particularly relevant because employees are downloading locally developed or "vibe-coded" MCP servers and running agent tools on their devices. These components can introduce software supply chain risks and expose local credentials. The second capability covers credential security. C1 has introduced a post-quantum credential vault designed to protect secrets and inject them into authorized agent workflows without leaving credentials scattered across devices and applications. The third area is runtime governance. Instead of reviewing behavior after an incident, organizations can evaluate an agent's actions against its assigned mission as they occur. If an agent is authorized to complete one business task but begins exploiting an internal tool, contacting an unapproved service, or attempting to extract data, runtime controls can block the action or request human approval. The fourth capability concerns agentic security intelligence. This uses information collected across identities, agents, permissions, credentials, and behavior to identify risks and support automated remediation. We also discuss human accountability. Alex says emerging regulatory thinking recognizes the need for a responsible person behind an autonomous agent. That connection allows businesses to establish ownership, delegate authority, and determine who remains accountable for the agent's behavior. The conversation then turns to the effect of AI on employees. Alex rejects the assumption that organizations will simply remove people as agents become more capable. His preferred analogy is that people are moving from manually producing every artifact to building and supervising the factory. Employees provide the inputs, direct the agents, examine the outputs, and correct the process when necessary. C1 has experienced this internally. Alex says its engineering team increased from roughly 150 weekly software merges to around 1,500, while engineering headcount grew by approximately 10% to 15%. That productivity requires careful human review. Generating work faster does not remove the need to assess whether the output is accurate, secure, useful, and aligned with the original objective. For CISOs and CIOs, the goal is to provide a governed path for AI adoption. A blanket prohibition may encourage employees to work around policy. Secure self-service access can give teams approved tools, defined permissions, and runtime protection. If an AI agent can operate at machine speed, are your organization's identity controls capable of observing, authorizing, and stopping it at the same pace? Listen to the conversation and share your thoughts with me.

We turn HexStrike's red team agents loose on our systems, as OpenSSH warns that AI-assisted bug hunting is already changing the security race.

Stewart Alsop sits down with Juan Verhook, founder of Tender Market, for a second conversation that ranges from the mechanics of European public tenders to the future of how we organize digital information. They cover how Tender Market helps smaller companies work around barriers like SOC 2 and ISO certification requirements, the surprising scale of public procurement (roughly 20% of GDP), and how AI and machine learning are reshaping the bidding process. From there the conversation opens up into bigger territory: the changing tolerance for being wrong in an AI-saturated information landscape, how language and culture shape perception, the reverse Turing test and the challenge of verifying human versus AI identity online, and Juan's daily workflow running eight or nine MCP servers through Claude Code. They close out talking about whether the folder and file system will survive the shift to AI-native interfaces, tying back to Stewart's own Stewart Squared episodes on the history of the PC. You can visit Tender Market at tendermarket.eu.Timestamps05:00 — Tender Market's origin story and how they help smaller companies work around SOC 2 and ISO certificate barriers.10:00 — Public procurement and its scale, roughly 20% of GDP, plus a look at public-private partnerships.15:00 — Local LLMs on a plane with no Wi-Fi, and comparing local model performance to frontier models.20:00 — Supply versus demand in AI infrastructure and whether hyperscaler token efficiency is quietly improving.25:00 — Whether AI will replace knowledge work tasks, and the shifting reality of what lawyers and other professionals actually do.30:00 — Reverse Turing test, digital identity verification, and the idea of a "pre-AI internet."35:00 — Model poisoning, RLHF, and the difference between pretraining and post-training.40:00 — Interleaved tool calling and how Tender Market ties pricing to task deliverables instead of billable hours.45:00 — RAG versus fine-tuning, prompt engineering, and when context windows actually matter.50:00 — Deterministic programming versus probabilistic agents, and when to build custom tools versus buy existing ones.55:00 — Juan's daily MCP stack (Supabase, GitHub, Calendly, CRM), and whether the folder-and-file system will survive the shift to AI-native interfaces.Key InsightsCertification requirements aren't dead ends—they're routing problems. When smaller companies got rejected from tenders for lacking SOC 2 or ISO certificates, Juan didn't turn them away. He found that EU procurement rules allow bidding as a consortium or subcontracting to a certified partner, turning a disqualifier into a workaround that builds trust with clients.Public procurement is a massive, underexamined market. Roughly 20% of GDP flows through public purchasing of private-sector goods and services, yet most people have no visibility into how tenders work or how governments post and award these contracts.Being wrong has become more socially acceptable. Juan traced this shift to the falling cost of information: in the Stack Overflow era, giving a wrong answer was costly, but now that answers are instant and abundant, both mistakes and corrections happen faster, changing how people learn and communicate.Task-based pricing beats hourly billing for AI-era services. Rather than charging per hour, Tender Market prices around the deliverable, winning a tender, which avoids the perverse incentive of hourly billing to be inefficient and instead rewards actually solving the client's problem.RAG and fine-tuning solve different problems. RAG helps a model reference large documents without hitting context limits, while fine-tuning changes a model's internal weights so it learns new behavior or style. Juan noted that true RAG use cases needing thousands of pages of context are rarer than the hype suggests.Deterministic code should replace repeated LLM calls once a pattern is found. Stewart described his own workflow: solve a task with an LLM a handful of times, then convert the repeated pattern into deterministic software so tokens are no longer spent on it, freeing the model for genuinely new problems.AI agents are never truly autonomous. Both hosts agreed that no matter how many steps an agent chains together, a human operator always initiates the first prompt, meaning accountability and intent trace back to a person even in multi-agent systems.

In this episode of The Ross Simmonds Show, Ross sits down with Malte Landwehr, CMO of Peec.ai, to unpack what AI visibility really means for modern marketers and how it differs from traditional SEO. They break down how brands can measure LLM visibility, identify the right prompts to track, prioritize the channels that influence AI answers, and build a smarter cross-functional strategy to win recommendations in tools like ChatGPT, Perplexity, Gemini, and more. Key Takeaways and Insights: 1. AI Visibility vs Traditional SEO - Traditional SEO was built around earning clicks and website traffic, while AI search is increasingly about getting your brand recommended inside the answer. - In AI-driven search experiences, users may never visit your site, which changes how marketers think about attribution, brand experience, and customer journeys. - Malte explains why this shift impacts not just SEO teams, but also content, brand, support, PR, affiliate, and product documentation teams. 2.  How to Measure AI Search Performance - Malte outlines four core ways to measure AI visibility: self-reported attribution, web analytics, log file analysis, and prompt tracking. - Self-reported attribution is a strong starting point for teams trying to validate whether LLMs are already influencing pipeline and revenue. - Prompt tracking stands out as the most actionable measurement method because it helps marketers spot content gaps, benchmark competitors, and prioritize next steps. 3. Prompt Tracking and Strategy That Actually Works - The best prompt strategies start with topics, intent, personas, and funnel stage rather than obsessing over exact wording. - Real-world testing shows that people use many different prompts, but LLMs often return similar brand recommendations when the underlying intent is the same. - Malte warns marketers not to overreact to tiny data sets or short-term fluctuations, and instead evaluate performance directionally across prompt sets over time. 4. The New Playbook for AI-Era Content Distribution - LLMs pull from a wide range of sources, including help centers, FAQs, social content, news sites, directories, review platforms, and community discussions. - Brands need consistent positioning across all managed and owned surfaces so AI systems can clearly understand and describe what the company does. - Smart teams use source-level visibility data to decide where to invest, whether that means digital PR, creator partnerships, editorial placements, review platforms, or competitor-inspired content. 5. Agentic Commerce and the Future of Marketing Workflows - Malte shares why agentic commerce will likely arrive faster in B2B procurement and operational workflows than in emotional consumer purchases. - He also explains how AI tools and MCP integrations are changing the software experience, allowing marketers to work through AI interfaces instead of traditional dashboards. - For early-career marketers, his advice is clear: become AI-native, use AI first to solve problems, and build the habit of testing, iterating, and learning fast. Resources & Tools:

Has AI actually helped you make more placements this year, or has it just been noise? Before you dive in, this is your last call for The Recruiting Agents Workshop with Seb Sharp, August 25 and 26. Two live build-along sessions where you will create an autonomous lead agent, a placement agent, and connect your entire tech stack, plus recordings, templates, and a week of Slack access to Seb after the sessions end. Grab your seat now: https://the-recruiting-agents-workshop.heysummit.com/ In this episode Benjamin Mena sits down with Dan McCarthy, Senior Talent Engineer at Zapier and one of the founding members of the a16z Talent Engineer Fellowship, to break down recruiting's newest role and how you can step into it before the rest of the industry catches on. Dan's path into this seat is anything but typical. He was a jazz musician, a New York bartender, a wine educator, and a CrossFit gym owner in Brooklyn before Shopify hired him into tech recruiting at 40 years old. He had never heard of an org chart. By his own telling, he led the entire engineering recruiting team in hires his very first quarter. Two layoffs later he landed at Zapier, where he was handed a blank canvas: build our talent intelligence function. What he built instead of dashboards is the heart of this conversation. Dan walks through the talent intelligence MCP he assembled in about a month using completely free APIs, including Indeed Hiring Lab, the Department of Labor, O*NET, the Bureau of Labor Statistics, and WARN Act data, so recruiters walk into every intake call armed with real compensation numbers, competitor hiring activity, and talent pool data. Then he gets specific about the agency version: what a solo recruiter or a two person shop can stand up over a single weekend to change their next client pitch. Benjamin and Dan also get into the ego build problem on LinkedIn and why screenshots of tools built yesterday are making everyone feel further behind than they actually are, the difference between building to learn and building to ship, who is reviewing your code and updating your API keys, whether a three person agency could really bill five to ten million dollars with a builder in one of the seats (Dan's honest answer: possible, but probably not in the next six to twelve months), the sales engineer ratio that may be coming to recruiting teams, and why Dan completely changed his mind about AI interview screens. If you have ever felt too far behind to start building, this episode is your permission slip. As Dan puts it, nobody is behind. This is day one. ⚡ The Recruiting Agents Workshop with Seb Sharp (August 25-26): https://the-recruiting-agents-workshop.heysummit.com/

In this episode, Dennis Pilarinos, founder and CEO of Unblocked, joins us at LeadDev in London.Dennis explains why AI coding agents are only as good as the context they can access, and why every new agent is like a developer on their first day at a company.We talk about organizational context, token savings, MCP, stale documentation, knowledge graphs, permissions, identity across systems, and why the next big infrastructure layer for AI development may be the context layer.LinksDennis Pilarinos on LinkedInDennis Pilarinos on XUnblockedUnblocked BlogContext EngineeringLeadDevLDX3 London

Don’t miss this massive channel shift! Subscribe to our Newsletter:https://theultimatepartner.com/ebook-subscribe/ Check Out UPX:https://theultimatepartner.com/experience/ In this episode of the Ultimate Partner Podcast, host Vince Menzione sits down with Alexandra Zagury, Corporate Vice President of Channels at Microsoft, to explore ecosystem shifts, partner-led growth, and AI transformation. https://youtu.be/9jrSGX5bM90 Key Takeaways Microsoft’s telemetry and propensity data offer unprecedented insights that many partners are currently failing to unlock. Partners must evolve past basic licensing models and build comprehensive managed service stacks across the entire customer lifecycle. Establishing an AI Center of Excellence on the Microsoft platform is critical for capturing future market share and technical intensity. The modern tech ecosystem demands a shift from product-led growth to true partner-led growth driven by multi-partner collaboration. Renewal engines targeting 110% to 125% retention require an always-on motion starting well in advance of contract expiration. Investing in sales readiness and precision velocity training ensures that end sellers can effectively articulate the value of the Microsoft platform. If you're ready to lead through change, elevate your business, and achieve extraordinary outcomes through the power of partnership—this is your community. At Ultimate Partner® we want leaders like you to join us in the Ultimate Partner Experience – where transformation begins. Key Tags Microsoft, telemetry, Hyperscalers, channel, small medium enterprise, telcos, hosters, SSPs, Cisco, CSP, agentic GTM, propensity data, SPX, PUPP, cloud descent, MSPs, managed XDR, Agent 365, skilling, co-sell, renewals, flywheel, AI Center of Excellence, enterprise Transcript Alexandra Zagury Audio Podcast [00:00:00] Alexandra Zagury: One of the things that I think is the best kept secret at Microsoft is the telemetry that we offer our partners. [00:00:08] Vince Menzione: You can feel it happening. The ecosystem is shifting beneath us, the way Hyperscalers are partnering, how AI is remaking the channel and what it means to win in 2026. Welcome to the Ultimate Partner Podcast. [00:00:22] Vince Menzione: I’m Vince Menzi, own your host, and each week I sit down with leaders at the intersection of technology. Partnerships and outcomes. The voices shaping how ecosystems actually work. We talk about what’s real, what’s changing, and what it takes to lead in this era where the partner channel isn’t just part of the strategy. [00:00:41] Vince Menzione: It is the strategy because being in the room changes everything. Let’s [00:00:46] Guest: start. [00:00:50] Vince Menzione: I am thrilled because I get to have a leader that is somewhat new in role and is the corporate vice president of channels for Microsoft. And so Alex is here to, is joining us for the first time at Ultimate Partner. I’m thrilled to have you. Thank, thank you so much. Thank you so much. Welcome, welcome. [00:01:14] Vince Menzione: Thank you. Thank you. You and I are gonna be in the center seat in the middle. Okay. Yeah. Yeah. We wanna. So I am thrilled. Um, you’re relatively new in your role at Microsoft. [00:01:24] Alexandra Zagury: Seven months. [00:01:25] Vince Menzione: Seven months, [00:01:26] Alexandra Zagury: yes. [00:01:26] Vince Menzione: Wow. That’s a, that’s a crazy. So take us through, ’cause for those who don’t know you, but a little bit of an introduction, your title, your role, CVP, and uh, your remit. [00:01:37] Vince Menzione: Let’s talk about that and what your organization is focused in, in your mission. Yeah. [00:01:41] Alexandra Zagury: So hi everybody. Really exciting to be here. Thanks for the invitation, Vince. I always love to be with partners and with the channel. So my, I came to Microsoft to lead a new role, which we call Global Channel Sales, and it was part of the strategy of Microsoft to really bet on the growth in small, medium enterprise. [00:02:04] Alexandra Zagury: With the channel partner led growth. So my remit is really to lead our managed partners, but also to lead the strategy when it comes to the channel. Very specifically looking at the telcos, the distributors, the hosters. The sis and, uh, the SSPs of course. And, and so my role can be summarized in one word growth, and that’s what we do every single day is map our ambition to your ambition and figure out how we actually conquer this age of ai. [00:02:40] Vince Menzione: It’s a pretty big role. It’s a pretty, you know, I forgot about hosts ’cause we don’t, we don’t talk about them as much these days in the cloud. [00:02:48] Alexandra Zagury: Still a lot [00:02:48] Vince Menzione: of opportunity. You’ve got, and you’ve got all the telcos as well, which are pretty significant organizations, right? Lumen Field, just around the corner. [00:02:56] Vince Menzione: Mm-hmm. Right up the. Right up, I said across the river, but it’s across the pond in Seattle. And, uh, what does that look like from an organization perspective in terms of dollars? Are you allowed to disclose numbers? [00:03:08] Alexandra Zagury: No, we [00:03:08] Vince Menzione: don’t talk numbers. Okay. And, but you have a long career in this type of environment, in this role. [00:03:15] Vince Menzione: 10 years at Cisco, right? [00:03:17] Alexandra Zagury: Mm-hmm. [00:03:17] Vince Menzione: Tell us a little bit more about your background, [00:03:18] Alexandra Zagury: please. Yeah, sure. Um, well I started out as a, a sales leader. Uh, actually I started out in banking, if you wanna go all the way back. Um, but I fell in love with the channel actually, when I was at Yahoo, believe it or not. Wow. [00:03:32] Alexandra Zagury: Because it was the first interact sales model that I had the privilege to operate in, and I really understood. Stood the power of going through a channel. And then I was at Blackberry where I, I, I also, um, well had various sales leadership roles, but our model was all through, was all through the channel. [00:03:51] Alexandra Zagury: Yes. Um, some startups and then help [00:03:54] Vince Menzione: in those days too, right? [00:03:55] Alexandra Zagury: Yeah. It was all sps and I learned from Jim Balze the channel fundamentals. So, um, and then ended up, of course, at Cisco the last. Nearly 11 years, which I think is also one of the greatest channel companies. Yes. And really has thrived in a partner, partner led growth, and in a partner led model. [00:04:12] Alexandra Zagury: And so when, when Microsoft called, I mean, this was just the opportunity of a lifetime. To lead the channel during an era where we’re all getting disrupted, we’re all having to blueprint new systems, new ways of working, where go to market is getting identified, and we’re all having to figure out how to become customer zero ourselves, but then also how to go to market. [00:04:36] Alexandra Zagury: With, with agents. And so this was the, the most exciting time that I could think of to join the Microsoft ecosystem and really take us to the next level. [00:04:44] Vince Menzione: Well, your background is perfect for this. I, I, Rodney Clark is a friend, has been a guest on the podcast and in, in the studio, and I think of Cisco is the quintessential channel company. [00:04:56] Vince Menzione: Like when I think about how channel got created and how it worked well, it was always Cisco that did it. So give us like your perspective now, like seven plus months and like how does this feel like you’ve You’ve got a big remit and, uh, various, uh, routes to market. We’ll call them, uh, channels to market. [00:05:14] Vince Menzione: So take us through a little bit of that, like Yeah, sure. [00:05:16] Alexandra Zagury: Describe [00:05:16] Vince Menzione: the transition, what it’s been like [00:05:18] Alexandra Zagury: for you. So one of the things we’re focused on is supporting the channel through the transformation, and we look at it in a couple of lenses. The first lens is really helping the channel become customer zero. [00:05:29] Alexandra Zagury: We really believe that the partners that invest in. Actually identifying their own processes and their own go go to market are the channels, are are the partners that it can actually win because if you are using it, you’re gonna be able to sell it. The second layer is all about technical intensity, and I’m very passionate about this because I see it from two lenses. [00:05:51] Alexandra Zagury: One is. I would like each and every of our partners to lead in building an AI Center of Excellence based on the Microsoft platform. It is a unique opportunity. I can give you lots of numbers, right? We’ve all heard about the trillions of agents that are gonna be here by 2030. We’ve all heard about the tam. [00:06:12] Alexandra Zagury: I mean, our TAM is going from 777. Million to over a billion, uh, to over a trillion. I, it’s, the numbers are just enormous, insane, right? Over half a billion of customers in our base that are using, that are, that are based already on the Microsoft platform. So all the goodness of our IU IQ platform can be unlocked with all the services that the partners can build. [00:06:36] Alexandra Zagury: So investing in that technical skilling and building those practices are gonna be, is gonna be essential. The third part. The third pillar is all about ag agentic, GTM. So once you’re actually using, uh, the Microsoft platform, you’re gonna have to reimagine all your business’s pro processes, your sales processes, and the more that you are integrated into how we do, how we do things. [00:07:00] Alexandra Zagury: One of the things that I think is the best kept secret at Microsoft is the telemetry that we offer our partners. Yeah. I mean, it’s unbelievable. I’ve never seen the quality of propensity data. And now I’m gonna give you the ABCs, which is please, A SPX, which is where we get all our copilot data, PUPP, which is our proposal upsell, uh, planner, right, cloud descent, where you can actually get your next action directly to your sellers. [00:07:27] Alexandra Zagury: There is just so much goodness that we give, which is part of our, our investment in partners. Which takes me to the fourth pillar, and that is all about value alignment and one of the things that I’m very focused and I bring with me from, from Cisco and the work that I did with MSPs is really thinking through what is that value exchange between us and the partner. [00:07:49] Alexandra Zagury: I believe that I’m in the business of earning your trust. Earning your preference. And, and, and we do that by really mapping that value alignment. So not just, one of the things that the whole industry has copied from Microsoft is really looking at our incentives across the customer lifecycle. Yes. So really mapping the value alignment across the customer lifecycle, not just at the point of the deal. [00:08:13] Alexandra Zagury: ’cause that was the whole purpose of CSP. That’s right. The investments we make in tele telemetry, the investments we make in our go-to-market assets and having those bi-directional feedback loops so that we can be continuously improving. So those are sort of the things that I’m thinking about every day. [00:08:29] Alexandra Zagury: There’s a couple of others as we lead and support you in this transformation. ’cause I think of my job as to supporting and driving growth with you so that we have that joint ambition. But also supporting the transformation that both of us are on this journey. [00:08:44] Vince Menzione: And Microsoft was the first with Jay McBain was with us yesterday, and we talked, we’ve talked about this before, but you were the first company to take and look, get rid of the old metal systems, right? [00:08:55] Vince Menzione: The bronze, silver, gold, mm-hmm. And move to basically a point system for partners so that they can come at it from a kind of a global perspective on how they drive success. What was, um, what did you learn about this partner community, your first months that you didn’t expect? [00:09:12] Alexandra Zagury: Can I say something controversial? [00:09:14] Vince Menzione: Absolutely. Okay. [00:09:14] Alexandra Zagury: I love, [00:09:14] Vince Menzione: we love controversial, [00:09:15] Alexandra Zagury: so I think one of the things that I was surprised was that I didn’t see all the partners really unlocking the value of CSP. Yeah. What do I mean by that? When Microsoft moved to the Point System, and I was on the other side, really, I was so jealous of CSP when I was running managed services. [00:09:34] Alexandra Zagury: Here’s an offer that is for partners, for Partner that gives you that initial. Guarantee in terms of the margin that helps you throughout the customer life cycle with all the incentives and, and programs that we have. And I didn’t see, I mean there are some partners, but I was surprised not seeing more partners really building their value stack and their services across the lifecycle. [00:09:59] Alexandra Zagury: And I think there’s such a great opportunity now to do that. That was one of the most surprising things I thought. Oh my gosh, there must be so many, so many services stack, so many people really unlocking, unlocking that, that value. That was a, that was a little bit surprising. [00:10:14] Vince Menzione: Why? Why do you suppose, why do you suppose that was happening? [00:10:17] Alexandra Zagury: I think some of the things that we were listening here, there’s some, sometimes complexity. There are things that we still have to. Get better on, and I’m one of the first ones to say that like our partner experience, we have, um, you know, a lot of focus right now on partner center and ensuring that we’re identifying it. [00:10:36] Alexandra Zagury: I don’t know if I can say it, but, you know, one of the things that we’re looking at is replicating internally. We have Agent J. That supports our sellers through the sales process. Nice. We’re looking at having something similar for our partners. Very cool. Getting some claps there. So, yeah, so, uh, I think, you know, that I, some, there are some lockers that we, we have to acknowledge a lot of them are operational and comes with being a 50-year-old company. [00:11:02] Vince Menzione: I, I’ll give you my perspective too. I wanna get your thoughts on this. ’cause I got to, I’ve gotten to know this MSP community, which is a, you mentioned managed services and that, um, I think that some of them, well, I think Microsoft is leaning in, in a much bigger way. Um, we had Jose on stage yesterday and just the, the energy around the room, there he is, he’s back here. [00:11:23] Vince Menzione: The energy in the room around the MSP community is palpable and it maybe it wasn’t there a few years ago. Maybe some people got off the bus, so to speak, like they weren’t really paying attention mm-hmm. To all the change and all the investments. That you men, you’re mentioning or being made to support this, would you, what would you say about that? [00:11:42] Alexandra Zagury: Yeah, I’d say that that that is correct, but I’d also say that what I learned, you know, leading MSP at Cisco was that. All the stars have to be aligned, right? And if one thing is not right, if you don’t have product market fit, if you, if you, if you don’t have a good way to, uh, consolidate your offer, if you don’t have that investment in practice development, like there’s a series of things that we need to get right. [00:12:07] Alexandra Zagury: And I think Jose and I spent a lot of time thinking through those things and we’re, we’re ready to, to welcome the community and specifically around security. I mean, that was the second thing that I was really surprised because I lost so many deals on the other side to Microsoft, and I was like, and then I come on this side and I’m like, there’s all this opportunity everywhere. [00:12:28] Alexandra Zagury: I look underneath this chair, this opportunity, and I’m like, why are people not going after it? There is the opportunity to build managed XDR solutions, the opportunity to reinvent the song. There’s, there’s just so much opportunity and I think people get so stuck in the. Just thinking of it from a licensing model and not thinking it from the, the full on end-to-end value that you can then unlock through the best licensing model on the planet. [00:12:55] Alexandra Zagury: And so, look, we’re here, we’re, we’re ready to talk to all of you and, and really figure this out ’cause we are gonna place really bet big bets next year on ensuring that we’re growing with the MSP community. [00:13:08] Vince Menzione: So what are you personally focused on in changing Microsoft to drive this. [00:13:13] Alexandra Zagury: Well, uh, I don’t know. [00:13:14] Alexandra Zagury: Changing is a, is a, is a big word. Well, I like evolution, change, [00:13:17] Vince Menzione: evolution, evolving. [00:13:18] Alexandra Zagury: You know, I [00:13:19] Vince Menzione: transition. [00:13:21] Alexandra Zagury: I think there is, there is a couple of things that I’ll say that one of the things that I, I’m really focused on right now, the first one is skilling. We’re at a time of such transformation. That investing in skilling, and if you look at our skilling model, it has four pillars. [00:13:37] Alexandra Zagury: The first pillar we’re best in class, in which is certification specializations, and making sure that our ecosystem is certified to go to market. The second one, which we call project ready. It’s sort of how we help you, uh, technically skill the folks that sit in your practices. And there’s more work to do there, and there’s more that you can learn about how we can actually help you. [00:13:59] Alexandra Zagury: And then the middle part I’m obsessed with right now, which is sales ready and tech sales ready. So it is ensuring, because AI is new for everybody. Yes. It’s a muscle, it is a proposition that you have to sell it’s value that you’re selling. I, I love what the gentleman from Lenovo was talking about. It’s, it’s that CSB always on motion. [00:14:21] Alexandra Zagury: Yes. And so really getting very crisp to the end seller at the, at the reseller. For example, at the end, seller at the partner about why Microsoft. Why now, how do I sell and how do I win? And giving them the assets, the competitive battle cards, the, the, the ability to end objection handling all these. Great, we have them. [00:14:45] Alexandra Zagury: I mean, the amount of content we have, but it’s about doing it at what I call precision velocity. [00:14:51] Vince Menzione: Precision [00:14:51] Alexandra Zagury: velocity, right? Which is this concept of how do we get very precise at a persona level. So that we get the velocity of impact. And so I’m, I’m very obsessed with that right now. And there’s two other things I’m very obsessed with, right? [00:15:04] Alexandra Zagury: The other one is this practice building, ensuring that we are together building these AI centers of excellence, um, especially for all our managed partners. This is something that I’ve put on, uh, every single PDM in our org is gonna, is gonna be talking about that. And then the third one is one that I find super interesting, which I think all of us. [00:15:25] Alexandra Zagury: Have a lot of work to do, which is partner to partner. [00:15:29] Vince Menzione: Yes. [00:15:29] Alexandra Zagury: If we look at a customer outcome, thank you. A customer outcome is built of many partners, right? There’s so many different touch points. I think some folks talk about seven partners in, in a customer outcome, and so how do we actually. Use agents, use agent solutions to suddenly unlock this opportunity because most of the time you’ll see an SSP with an si, maybe an ISV in the middle of a transaction to deliver on that customer outcome. [00:16:04] Alexandra Zagury: Yes. So what can we Microsoft do? And I’d love ideas, right? I haven’t cracked this. I don’t think the industry has completely cracked this. It’s more of a, a science than, than, well, more of an art than it is a science today. So that’s the third thing that I, I, I’d love to really improve and, and get better at. [00:16:20] Vince Menzione: I wish you got to see my slide earlier. ’cause I had the seven seats. I had this, I had the seven partners surrounding the customer. Customer is able to make their decisions now because with their cloud commitments [00:16:31] Alexandra Zagury: mm-hmm. [00:16:32] Vince Menzione: They’re in the, they’re in the seat where it used to be. I would rely on the partner to tell me what to do. [00:16:38] Vince Menzione: I, I’m cobbling together the best solution for my organization. Based on the trusted partners, to your point, those seven seats, and that’s partner to partner action. And Jay McBain was here yesterday and he took us through a great example. It’s AstraZeneca, that Microsoft won AWS, thought they were gonna win the deal, and then there were Microsoft partners in involved. [00:17:00] Vince Menzione: And the, the decision was made in December, but the deal didn’t happen until July. And that whole process was because all these different partners showed up. And influence the decision and the solution areas for that customer. [00:17:12] Alexandra Zagury: Yeah, that’s the best example of PLG partner led growth in action, which I think, again, that is the other thing that I’m super excited about is that actually p proving in the AI era that it’s about PLG as partner led growth, not the other PLGI. [00:17:31] Vince Menzione: I love that. I love that. Instead of product led growth, it’s partner led growth. So I understand there’s three layers that you’re very interested in that you want, you were gonna take us through today. Okay. Do you know about this, [00:17:44] Alexandra Zagury: the layers [00:17:45] Vince Menzione: of we have, uh, copilot chat. Oh, yes. 365 and, and agents. [00:17:49] Alexandra Zagury: Yeah. [00:17:50] Vince Menzione: From a product perspective, I thought maybe, [00:17:51] Alexandra Zagury: yeah, sure. [00:17:52] Alexandra Zagury: This is, I mean. This is the, uh, advantage of choosing the market Microsoft platform. Yeah, so as you look, look at it, there’s, there’s definitely different options, but when you look at Microsoft, what’s really, really interesting is that we have all the, all the layers. There’s no AI without data, and we’ve got that data foundation. [00:18:15] Alexandra Zagury: We also have the intelligence data foundation, right? Then we’ve got the, the layer of actually building those AI agents, and then the last layer that we have is actually the experience or the application layer. So when you look at our platform is a completely integrated platform with the different choices. [00:18:35] Alexandra Zagury: We are not behold, beholden to one LLM or another LLM. You’re actually able to bring your data and, and bring the LLM that you want to deliver on the, on the outcomes that you need. And I think that is very, very unique about our proposition. Yeah, I [00:18:50] Vince Menzione: agree. [00:18:50] Alexandra Zagury: But the other thing that is unique, it’s the most exciting product out there. [00:18:55] Alexandra Zagury: Agent 3, 6 5, our own oh oh seven. It really is a differentiated proposition that every single partner can build services around. Starting with your advisory services, tell me customer, what is it that you are thinking of? Then you actually move on to thinking about security because again, just like there’s no AI without data, you have to start with that data foundation. [00:19:24] Alexandra Zagury: There’s no AI without security and no security without ai. And so really thinking through how, uh, agent 3, 6, 5, I love it. I get these claps once a, I love, love really thinking about how Agent 3, 6 5 really unlocks, not only. A security budget, but an observability budget because you can do both. You are talking to both, uh, folks at the customer, right? [00:19:47] Alexandra Zagury: You can actually start talking about how you’re gonna actually govern all of these agents, manage all of these agents, but also there’s the observability layer, which is gonna tell you what actually can you do? How can you actually deliver on the outcomes that we all want from ai, which is productivity. [00:20:05] Alexandra Zagury: Experience and efficiency and all the other things. So I think this is the biggest opportunity this channel has ever seen, and every single partner is gonna have to make a choice on what platform they’re gonna lead with, and we believe it should be ours because it is completely integrated across these three layers with our very own oh oh seven. [00:20:30] Vince Menzione: I wanna get your perspective, but it feels like many of these partners in the MSP community are stuck at that CSP level. We were having this conversation about getting through that, coaching ’em through it. What would your be your perspective on that? [00:20:44] Alexandra Zagury: Um, I’d say, uh, use this moment to unlock that opportunity. [00:20:50] Alexandra Zagury: First off, invest in your skills. Get your, get your team skilled and, uh, on Microsoft, build your center of excellence. Map out your strategy where actually you’re gonna monetize and use all the different assets that we have. And if you are being really, um, I mean, most of them are serviced through a distributor. [00:21:12] Alexandra Zagury: Make your distributor accountable for supporting you in packaging the offers and giving you the, uh, information that you need in terms of skilling. And then in terms of co-selling, again, distributor has a lot of tools that can help you understand how to unlock the co unlock, the co-selling opportunity with Microsoft. [00:21:35] Alexandra Zagury: I think that’s another really big competitive advantage that Microsoft has. When, uh, Microsoft changed what it started by, by, by changing its strategy. I think clarity is kindness. We were very, very clear that where we want, we wanted partners, of course, to play an enterprise with a services stack, but we were very clear that we were betting on a partner led growth in small, medium enterprise, right? [00:22:03] Alexandra Zagury: And so we’ve built our whole operating system. Around that. And so I think it’s really about finding the information that you know you want, planning your strategy, getting skilled and go to market with us. Our co-sell Advantage is very, very unique. It is one of the only companies that has the sales teams completely aligned because CSP is our hero motion. [00:22:29] Vince Menzione: So being with a customer through the journey on CSP, but also renewals are a big component of growth. Talk to us about that. [00:22:36] Alexandra Zagury: Yeah. Renewals are, uh, a machine and an engine that is just absolutely beautiful. It’s your [00:22:42] Vince Menzione: flywheel. [00:22:43] Alexandra Zagury: It is your flywheel. Um, and it is the gift that keeps on giving. We, of course, have a very focused, um, and in fact, one of the things that I’ve done since. [00:22:52] Alexandra Zagury: Uh, since we’ve started, it started a very focused motion in terms of looking at our renewals. We have very specific targets. We, we like to see a renewal at 110% at the moment of renewal. And then we like to see a motion, t plus three, T plus six. That gets us to that a hundred and and 25%. But what we’ve also found out is that this needs to be an always on motion. [00:23:18] Alexandra Zagury: So one of the things that we’re doing is using this concept of precision velocity becoming very rigorous. ’cause we have all the data. Yeah. In terms of what is that next action, and really looking at starting that renewal process, we see that the partners that are able to reach the targets are the ones that start at T minus. [00:23:36] Alexandra Zagury: Six, maybe T minus three, you’re cutting it, but T minus six. And really building that constant motion, getting out in front Yeah. With the customer is really important. And then of course, we now even have these amazing go back motions, uh, with, with our partners where we actually, after the renewal, we go and. [00:23:56] Alexandra Zagury: The renewal was not at the target. We just constantly keep on going. Uh, going back with the, with the partners and we’ve unlocked a, a bevy of data. We, our operating model, we call it the pods, where the PDM sits at the center and orchestrates it with all the different roles that we have. And so we now have a very systematized moment, uh, motion of how to do the renewals. [00:24:18] Vince Menzione: So for the partners in the room, what’s one investment that they should make and what should every partner in the room do differently? Going into, uh, July 1st. [00:24:27] Alexandra Zagury: Well, I think the first thing, remember, CSP is our hero motion, so really for, uh, real, really focused on that. But the one investment, can I say two? [00:24:38] Vince Menzione: Please, please. [00:24:38] Alexandra Zagury: Your time. The, the first one is skilling, right? This is the time. [00:24:43] Vince Menzione: Yeah. [00:24:43] Alexandra Zagury: Technical intensity is super, super important, and really making those investments in skilling not only from a practice perspective, your your, your technical practice, uh, teams, but also from a sales readiness perspective. [00:24:59] Alexandra Zagury: This is a new muscle. It’s we’re all learning how to truly sell outcomes, and so getting your sales teams ready. Is is really important. And then the second one very tied to that is building your AI Center of Excellence based on the Microsoft platform. Because as we go into FY 27, you will see that the partners that prefer and grow with us are the ones that will see the investment come to them. [00:25:28] Vince Menzione: So I want to use the term front. I I, I’ve been avoiding the term frontier firm, but I think it is super critical. Everything I’ve heard today, like you need to be customer zero. You need to get in train, advance on it and go build against it. [00:25:41] Alexandra Zagury: Absolutely. Well, if you had, let me a third, I would’ve said customer zero. [00:25:46] Vince Menzione: You have it? Alright. We have less than a minute. Would you be okay if we ask for like maybe one question? Yeah, absolutely. We’ll do like one, maybe two. So good to have you by the way. Thank you, Vince. So nice to have you here. [00:26:04] Vince Menzione: I think we did such a good job. Oh, here we go. Here’s, here’s fun. A mic is coming your way. [00:26:20] Vince Menzione: Thank you. Here we go. Okay. [00:26:22] Guest: So we’ve been very keen on, um, skilling our people, and I still find it very hard to get all of the information out of Partner Center to get a complete global view. Are you and your team thinking about maybe having an MCP server access and having a real portal working with that data? [00:26:41] Alexandra Zagury: You just touched on one of our areas of improvement. Absolutely. In fact, um, thank you for, stay tuned for holding us accountable to that. That is definitely one of the things that we’re working on is how to integrate skilling hub into partner center. Right. As most of you will know, there are it Qs, and so that’s one of the things that we are definitely prioritizing, but thank you for holding me accountable to that one. [00:27:09] Vince Menzione: Awesome. [00:27:13] Vince Menzione: We have one more back here, David. I see. We wanna see how fast they can move that microphone across the room. Relay system here. The relay team. There we go. [00:27:25] Guest: That was excellent, Alexander. Thank you. And welcome. [00:27:28] Alexandra Zagury: Thank you. [00:27:29] Guest: Can you point to a specific example? ’cause I think it’s so critical what you highlighted just the skill piece and the customer outcomes piece. [00:27:35] Guest: Right. Can you point to a specific example of a story that you really love that highlights, uh, customers lighting it up with ROI. [00:27:44] Alexandra Zagury: Yeah, I think, you know, we’re, we’re, we’re a platform, so I just saw a win wire. Like at Microsoft, we get these win wires all the time about how an SSB actually won a, a deal against one of these big AI only companies. [00:28:00] Alexandra Zagury: And it was really about selling the full platform, right? Yeah. Because if, if you, if you put a full platform against an LLM proposition, I mean, the full platform really stacks up because it’s completely integrated. It’s not behemoth to one, you’re not making a bet on one company. And it really highlighted our mantra around trust and intelligence. [00:28:24] Alexandra Zagury: The customer was able to see, they, they were an M 365 customer, so all their iq, all their intelligence was already there. They knew that they, there were guardrails against it. They had a problem with shadow ai and by actually standardizing on copilot and going on that journey from copilot paid to agents, they sue the, they saw the full, uh, value proposition. [00:28:49] Alexandra Zagury: And so we won that deal and it was one of the. First E seven deals that we won, so it was great. [00:28:55] Vince Menzione: Fantastic. Great. Congratulations. [00:28:57] Alexandra Zagury: Thank you [00:28:58] Vince Menzione: Alex. I am so honored and thrilled that you got, you chose us to be your I I would say the first big Yeah, absolutely. Presentation in front of the partner community. [00:29:07] Vince Menzione: I’m so excited to have you. [00:29:08] Alexandra Zagury: Thank you [00:29:09] Vince Menzione: guys, and hopefully many more times ahead with us. [00:29:10] Alexandra Zagury: Absolutely. Invite me at anytime. [00:29:12] Vince Menzione: Okay. Well, thank you [00:29:13] Alexandra Zagury: so much. [00:29:14] Vince Menzione: Thank you. So great [00:29:15] Alexandra Zagury: to have you. Thank [00:29:16] Vince Menzione: you so much. Thanks for listening to The Ultimate Partner Podcast. If today’s conversation resonated, share it with a partner leader in your network. [00:29:26] Vince Menzione: Subscribe where you listen, and head over to the ultimate partner.com. For show notes related content and the resources for this episode. And if you haven’t already, now’s the time to register for the Ultimate Partner Live Event in Reston, Virginia, October 26th through October 28th. Until next time, keep showing up in the rooms that matter because being in the room changes everything.

In this episode, Bradley shares 10 new tools and strategies for Amazon, TikTok Shop, and Walmart sellers, covering AI research, ad tactics, inventory, indexing, and smarter profit tracking. What if you could uncover keyword indexing problems, research entire Amazon niches with AI, monitor sales across marketplaces, and find new advertising opportunities in a fraction of the time? In this episode, Bradley Sutton breaks down 10 new Helium 10 tools and strategies, plus 2 bonuses, built to help Amazon, TikTok Shop, and Walmart sellers make faster, more informed decisions.   The episode dives heavily into new Helium 10 MCP capabilities, including Black Box Niche research, historical price, BSR, review, and search volume data, Index Checker, Product Targeting, and competitor research. Bradley demonstrates how these tools can work together with Claude to uncover niche opportunities, analyze competitors, identify keyword gaps, and find potential product targets without jumping manually between multiple reports.   There are also updates designed to improve everyday operations. Sellers can now check keyword indexing directly from Keyword Tracker, use prebuilt advertising tactics and bid rules, monitor TikTok Shop performance through Seller Pulse, view Walmart inventory levels, account for indirect business costs inside Profits, and gain greater visibility into AWD inventory. Each feature is connected to a practical strategy for protecting profitability, improving advertising, or spotting problems before they become bigger issues. The biggest takeaway is that these aren't just new buttons to click. They're opportunities to combine AI, automation, and better data into smarter workflows for your e-commerce business. Whether you're researching your next product, optimizing PPC, watching inventory, or figuring out why a keyword suddenly stopped ranking, the right data can help you act faster and make better decisions before your competitors do. In episode 546 of the AM/PM Podcast, Bradley covers: 00:00 - Introduction 01:27 - Keyword Tracker Index Checker 04:57 - Niche MCP 08:00 - MCP History - Price, BSR, Reviews, Search Volume 11:16 - Amazon Ads Tactics 15:56 - Index Checker MCP 18:21 - TikTok Shop Seller Pulse 20:09 - MCP Product Targeting 23:48 - Walmart Inventory Levels 24:53 - MCP Competitors 29:02 - Amazon Indirect Costs 31:20 - AWD Inventory

Kamille Parks demonstrates how to use the Model Context Protocol (MCP) to manage Airtable automations via Claude. She walks through updating an existing automation by adding placeholder email addresses to a notification step, showing how the AI can modify specific parameters while preserving existing context. The demo also covers management tasks like deleting an automation and using the "undo" feature to revert actions. By leveraging action IDs tracked through the MCP, the AI can effectively restore what was previously deleted. Finally, the segment explores creating new scheduled automations from scratch and exporting all base automations as JSON for auditing and configuration reviews. ⏱ In this cut: 01:58 — Updating an existing automation 06:52 — Deleting and undoing actions 08:36 — Creating new scheduled automations 16:11 — Exporting automations as JSON

Recorded on location at Black Hat USA 2026, Eric Avigdor of Menlo Security describes an adoption pattern he hears in customer conversation after customer conversation. AI makes teams measurably more productive. The guardrails that keep company data inside the business arrive later, if they arrive at all. Eric Avigdor leads product for AI security and data security at Menlo Security, and he splits the problem into two categories that get very different levels of attention. One is how people use AI in the browser, including what data gets pasted into an assistant and how much of that usage anyone knows about. The other is autonomous agents built to run business processes, where the question is how to keep them productive without letting their goals get hijacked. The category Eric Avigdor says compliance teams skip past is the agent that holds sensitive data and internet access at the same time. Read a poisoned web page, take the hidden instruction, and the goal changes. What is the difference between an agent running analysis on an internal database and an agent doing financial analysis at a bank with customer records and web access? One of them can be told to send the data somewhere else. So who owns AI governance? In most companies, nobody does, at least not with authority. Responsibility lands with the endpoint team, the network team, or the browser team, and each one works its own angle. An endpoint team tracks agent traffic on the endpoint and then loses the trail when the agent moves data cloud to cloud. A cloud team has the reverse blind spot. Menlo Agent Runtime Security, or MARS, is built around what an agent actually does rather than what it intends to do. Agent traffic is proxied through the Menlo Security cloud browser, where data masking, indirect prompt injection prevention, and web-based and file-based threat prevention are applied before an incident becomes cleanup work. Browser and web traffic today, MCP traffic next. For regulated organizations, that architecture produces something auditors can use. Logging, dashboarding, and a visual record of what an agent attempted in the real world. Europe has the AI Act. The US has not landed comparable rules yet, and Eric Avigdor says that gap concerns him enough that he is talking with people working to close it. GUEST Eric Avigdor, Vice President of Product, Menlo Security | On LinkedIn: https://www.linkedin.com/in/eric-avigdor-0b561118/ RESOURCES Black Hat USA 2026 event coverage: https://www.itspmagazine.com/black-hat-usa-2026-cybersecurity-event-coverage-in-las-vegas Menlo Security: https://www.menlosecurity.com/ Menlo AI Agent Security: https://www.menlosecurity.com/product/ai-agent-security Menlo AI Adaptive DLP: https://www.menlosecurity.com/product/ai-adaptive-dlp Are you interested in telling your story? ▶︎ Full Length Brand Story: https://www.studioc60.com/content-creation#full ▶︎ Brand Spotlight Story: https://www.studioc60.com/content-creation#spotlight ▶︎ Brand Highlight Story: https://www.studioc60.com/content-creation#highlight ▶︎ Get your own Brand Briefing at an upcoming event: https://www.studioc60.com/buy-brand-briefings KEYWORDS eric avigdor, menlo security, sean martin, brand story, brand marketing, marketing podcast, brand spotlight, black hat usa 2026, mars, menlo agent runtime security, ai agent security, prompt injection, indirect prompt injection, data exfiltration, ai governance, browser security, agentic ai, autonomous agents, shadow ai, data loss prevention, eu ai act, ai compliance, coding agents, mcp security Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

“The best value you will get out of it is if you get as many conversations and as many conversation types as possible,” says Mattias Ohde of Meetric. In this special podcast for the Cloud Communications Alliance and TR Publications, Doug Green speaks with Mattias Ohde of Meetric about conversational intelligence, AI and the new opportunity emerging for service providers, UCaaS providers, mobile carriers, MSPs and channel partners. Ohde says Meetric focuses on conversational intelligence for service providers, helping them collect and structure conversations from telephony, video, live meetings, email and other sources. Once those conversations are organized, AI can help extract insights, create workflows, support automation and make everyday work more structured. The conversation centers on a shift in how business communications are understood. For years, companies have recorded calls and saved voicemails, but much of that information was difficult to search, organize or use. Ohde says AI changes that by allowing conversations to become data points. “Now with the introduction of AI, you can all of a sudden consider these conversations as data points,” Ohde says. That creates what Ohde describes as a kind of “gold mine” for businesses. By bringing together conversations from across departments and communications channels, companies can gain a broader view of customers, operations and markets. They can also run more advanced analysis across the accumulated body of communications, creating insights that were not previously practical. For service providers, the opportunity is both operational and commercial. Ohde says conversational intelligence gives providers something new to bring to customers beyond traditional voice, UCaaS and collaboration tools. It can help providers deliver a more direct impact on the customer's daily work while creating a higher-value service layer. “This is in a way quite revolutionary for the industry itself,” Ohde says. Meetric delivers its services as a white-label offering, allowing mobile carriers, UCaaS platform providers and local service providers to bring conversational intelligence to market under their own brand, through their own invoicing and bundled with other services. Ohde says that creates a fast route to market for providers looking to add AI-enabled services without building the full capability themselves. He also discusses MCP and open AI workflows, noting that Meetric's functionality can be used inside other tools and platforms. That allows conversational data and AI-generated insights to become part of broader workflows across the customer's business. Ohde says the revenue opportunity is significant because conversational intelligence services may command much higher market pricing than traditional subscription services for UCaaS or mobile connectivity. For CCA members, MSPs and channel partners, the message is direct: the conversations customers are already having may contain untapped value. AI now gives providers a practical way to capture, organize and act on that information — while building a new service and revenue opportunity on top of the communications infrastructure they already provide. Learn more at https://meetric.com

TikTok Shop USA sales are booming. There are some new regulations for packaging if you're selling in Europe, and the most-requested tool for MCP for Helium 10 is now available. These stories and more on today's weekly buzz!   We're back with another episode of the Weekly Buzz with Helium 10's VP of Education and Strategy, Bradley Sutton. Every week, we cover the latest breaking news in the Amazon, TikTok Shop, Walmart, and E-commerce space, talk about Helium 10's newest features, and provide a training tip for the week for serious sellers of any level.   TikTok Shop's U.S. GMV Nearly Doubles to $11.8B in H1 2026 https://www.netinfluencer.com/tiktok-shop-us-gmv-nearly-doubles-to-11-8b-usd-in-h1-2026/ Why China's traders, e-commerce merchants feel boxed in by new EU packaging rules https://www.scmp.com/economy/policy/article/3363746/why-chinas-traders-e-commerce-merchants-feel-boxed-new-eu-packaging-rules Most Asked For Helium 10 MCP Update Helium 10 has launched Black Box Niche for MCP, giving sellers the X-ray-style data they've been asking for directly inside Claude. Instead of researching keywords one at a time with the Chrome extension, users can analyze multiple niches at once and compare metrics like search volume, top-product revenue, number of listings generating over $5,000 in sales, and how many top products have low review counts. The feature makes it much faster to compare market opportunities and competition across multiple keywords using the Helium 10 MCP.   How Amazon's AI shopping assistant makes hundreds of millions of products feel personal https://www.aboutamazon.com/news/retail/alexa-for-shopping-learn-and-be-curious-podcast Optimize your listings with built-in Seller Central tools https://sellercentral.amazon.com/seller-news/articles/QVRWUERLSUtYMERFUiNHR1ZKWFZETkRTSFhYQVo1 Amazon Expands Locker Network to more than 750 U.S. College Locations https://press.aboutamazon.com/retail/2026/8/amazon-expands-locker-network-to-more-than-750-u-s-college-locations In episode 545 of the AM/PM Podcast and Weekly Buzz, Bradley talks about: 00:00 -  Introduction 00:43 - TikTok Shop US BOOMING 03:13 - New EU Packaging Regulations 05:11 - Most Asked For MCP Update 09:13 - Amazon's Alexa Predictions 14:36 - Research Dead Amazon Listings For Opportunity 17:55 - Amazon Caters To College Students

Max Corbridge, an ethical hacker and red teamer who is co-founder and CEO of Secure Agentics, speaks with SE Radio host Amey Ambade about how AI agents get attacked and what engineers can actually do to defend them. Drawing on years of offensive security work, Corbridge frames agents as a new and largely undefended attack surface: the industry has handed AI systems autonomy and the ability to act in the real world while carrying forward prompt injection, a flaw the frontier labs themselves describe as effectively unsolvable. He likens the moment to the early, lawless days of the web, when SQL injection was everywhere and adoption ran far ahead of security. The conversation builds from first principles as Corbridge explains what separates an agent from ordinary software and why three properties make them hard to secure: they are non-deterministic, their language-model core can be coerced, and they are increasingly interconnected through MCP servers, other agents, databases, and email. Turning to the attack surface, Corbridge lays out his "lethal trifecta" (a vulnerable core, dense interconnection, and security tooling that has not caught up) and contrasts the decades of layered defenses protecting an ordinary email inbox with the thin protection around agents that take autonomous actions on critical systems. The heart of the episode is defense. Corbridge orders practices by leverage: least-privilege access and privilege separation, sandboxing where feasible, imperfect-but-useful guardrails as one layer of defense in depth, and human-in-the-loop for irreversible actions (which he notes is contentious and does not scale). The discussion closes on detecting a compromised or drifting agent, the value of watching an agent's chain-of-thought reasoning alongside its actions, the open-source tooling landscape (including Corbridge's own project, Adrian), and his central advice: build security in proactively, define what good agent behavior looks like up front, and avoid bolting it on after agents have already spread across the business.

The episode highlights the shift toward AI-driven knowledge management within the MSP sector, revealing increased operational dependency on structured data and sophisticated integrations. Lexful, an AI-native documentation platform designed specifically for MSPs, represents this trend by positioning itself not as a simple add-on but as a replacement for legacy documentation tools—controlling critical record-keeping functions and interfacing with principal PSA and RMM systems. This development signals greater infrastructure dependence on AI-based documentation and the implications of technical integration across diverse operational tools. According to Lexful's CEO and statements made during the episode, the platform has completed integrations with major PSA and RMM tools and now handles data by employing a “context-engineered” large language model tailored specifically to the MSP context. Lexful claims its engine minimizes LLM hallucinations, supports record-level access control, and functions as a system of record rather than a direct action platform. Socializing its compliance trajectory, Lexful has achieved SOC 2 Type 2 and shipped its MCP server, but its listing in marketplaces like Pax8 and SureWeb has been delayed, with current status characterized as “coming soon” and full integration targeted before the end of 2026. Supporting developments underscore the complexity and risk of deploying AI-native platforms into MSP environments. The absence of public customer or partner counts persists, with the company attributing constrained accessibility to pending integrations rather than lack of market uptake. Pricing structures diverge from incumbents, moving from per-user to per-client models and establishing minimum contract terms—raising questions about justification of cost versus legacy alternatives. A key operational risk centers on access control and human-in-the-loop governance, with sensitive systems such as password vaults only accessible through layered permissions, and Lexful emphasizing the necessity of robust accountability frameworks to minimize harm from potential automation failures. Practical implications for MSPs include heightened need for rigorous governance of AI systems, especially around data access, role management, and auditability. Vendor dependency deepens as platforms like Lexful supplant multiple existing tools and drive uptake via deeper integration with distribution marketplaces and SaaS ecosystems. Pricing and contract structures require MSPs to reconsider value calculations, as cost is no longer purely user-driven but tied to client volume and operational breadth. The tradeoff is between purported efficiency gains from automation and the risk profile associated with delegating documentation and knowledge management to AI-based infrastructure, particularly as human oversight remains essential to mitigate errors and ensure regulatory compliance. Supported by: ScalePad

Jeremiah Lowin built FastMCP as a side project days after Anthropic announced the Model Context Protocol. It's now downloaded millions of times a day. He joins Tristan Handy on why a skill file is a polite note rather than a workflow engine, why enterprises are choosing MCP over CLIs, and why the buyers he meets aren't ready to talk about the context layer at all—they want the security plumbing first.

In this episode, Zachary Smith, CEO and co-founder of Datum and previously the founder of Packet (acquired by Equinix for $335M) and Voxel (acquired for $35M), joins Liam to explain why the internet is about to undergo its biggest transformation since the cloud. As AI agents, vibe coding, and thousands of new applications flood the web, Zac believes the open internet model we've relied on for decades is breaking down. Zac argues that every person, every company, and eventually every AI agent will need its own private network. He explains why the future internet may look more like the Visa network than today's public web, how digital sovereignty and geopolitics are reshaping infrastructure, and why developers are increasingly relying on dozens of cloud services rather than just the hyperscalers. The conversation also dives into Zac's unlikely journey from Juilliard-trained musician to building and exiting two infrastructure companies, the emotional toll of entrepreneurship, and why he keeps coming back to startups despite already having financial freedom. Key Topics Covered Zach's journey from Juilliard and classical music to building infrastructure companies Building Voxel and selling the company for $35M Starting Packet and its $335M acquisition by Equinix Why AI agents are creating a security problem for the internet Why every person and company may eventually need a private network The difference between the public internet and private internet Why the future internet could resemble the Visa network Digital sovereignty, geopolitics, and the splintering of the internet Why developers increasingly rely on dozens of cloud providers How AI is turning millions of people into software developers APIs, MCP, and the next phase of application architecture Why Zach believes AI agents should only talk to approved systems Open source, network effects, and Datum's long-term vision The emotional side of entrepreneurship and why community matters more than money Episode Timestamps 00:00 Introduction and welcome 00:06 Zach's background: from Juilliard and classical bass to startups 02:44 Building Voxel and the early cloud era 08:44 Starting Packet, raising capital, and the Equinix acquisition 15:28 Why taking time off helped him dream again 18:02 What Datum does and the idea of a network cloud 19:38 Three forces changing the internet 20:41 Hyperscalers explained: Amazon, Google, and Microsoft 24:52 Why new cloud providers are emerging 27:16 Digital sovereignty and the fragmentation of the internet 32:03 Public internet vs. private internet 32:54 Inside the physical "meet me rooms" that connect the internet 39:49 How internet routing actually works 45:56 Why developers use so many cloud providers 48:10 APIs, MCP, and AI agents 51:07 Why the future internet may resemble the Visa network 54:23 Who Datum's customers are, and why Datum is open source 1:03:07 AI agents and the next generation of software 1:07:50 Why Zach keeps building companies, and why he does what he does Connect with Zac: LinkedIn: https://www.linkedin.com/in/zsmith/ Website: https://www.datum.net/ Partner Links Upgrade your AI toolkit: https://www.theaireport.ai/ai-executive-pass Subscribe to our free newsletter: https://newsletter.theaireport.ai/subscribe Join the community: https://community.theaireport.ai/checkout/the-ai-report-welcome-gift?coupon_code=WRTH Learn more about your ad choices. Visit megaphone.fm/adchoices

We got your Patch Tuesday notes. Attackers target Microsoft SharePoint vulnerability following PoC release. Cyberattack on CEVA Logistics causes ongoing supply chain disruptions. Wesco confirms data breach following extortion claims. Akira ransomware bypasses EDR in Safe Mode. California announces AI cybersecurity fund. N2K's Lead Analyst Ethan Cook shares about cyber weapons for space. Dave Bittner sits down with Michael Leland, VP and Field CTO at Island, at Black Hat USA to discuss the growing risks of the AI supply chain. And fasten your seatbelts and ignore the fake Wi-Fi. Remember to leave us a 5-star rating and review in your favorite podcast app. Miss an episode? Sign-up for our daily intelligence roundup, Daily Briefing, and you'll never miss a beat. And be sure to follow CyberWire Daily on LinkedIn. CyberWire Guest On today's Industry Voices, Dave Bittner sits down with Michael Leland, VP and Field CTO at Island, at Black Hat USA to discuss the growing risks of the AI supply chain, including AgentBaiting, where fake AI Skills and MCP servers were used to deliver malware, and hidden instructions that can influence AI agents. If you enjoyed the conversation, be sure to check out the full interview here. Selected Reading Microsoft and Adobe Patch Tuesday, August 2026 Security Update Review (Qualys) Shattering the Dream - When a Job Offer Becomes a Zero-Day Attack (Check Point Research) Patch Tuesday August 2026: A zero-day WinSock driver hole under exploit, and a maximum severity SAP vulnerability CSO Online ICS Patch Tuesday: Vulnerabilities Fixed by Siemens, Schneider, Phoenix Contact (SecurityWeek) Hackers leverage new Microsoft SharePoint exploit in attacks (BleepingComputer) The CEVA Logistics data breach is having major knock-on effects across Europe - here's what we know (TechRadar) Wesco confirms security incident after ExfilSquad claims data theft (BleepingComputer) Akira Hits Safe Mode: Ransomware Rebooting Around EDR (Huntress) California Building ‘AI Cyber Defense Fund' to Protect Critical Infrastructure From Hackers (Gizmodo) Laser weapons for space? US officials see threat, opportunity (BREAKING DEFENSE)  DEF CON dingus suspected of trying to take over Delta in-flight Wi-Fi (The Register) Share your feedback. What do you think about CyberWire Daily? Please take a few minutes to share your thoughts with us by completing our brief listener survey. Thank you for helping us continue to improve our show. Want to hear your company in the show? N2K CyberWire helps you reach the industry's most influential leaders and operators, while building visibility, authority, and connectivity across the cybersecurity community. Learn more at sponsor.thecyberwire.com. The CyberWire is a production of N2K Networks, your source for strategic workforce intelligence. © N2K Networks, Inc.

Topics covered in this episode: Claude Code /insights Post-quantum crypto lands in Python MCP goes stateless — and FastMCP gets renamed inshellisense - IDE style command line auto complete Extras Joke Watch on YouTube About the show Sponsored by Xweather Xweather combines enterprise-grade weather intelligence with agent-ready APIs, natural language capabilities, and an MCP server so your agents can adapt workflows, automate responses, and make better decisions based on real-world conditions. Michael will tell you more about them later in the show. Get started for free at pythonbytes.fm/xweather Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an artisanal digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: Claude Code /insights Michael's Insights: michael-kennedy-claude-code-insights-2026-08-09.html Be careful sharing these outputs, they include details references to your projects, errors, security findings, etc. ;) /insights reads your last 30 days of local session transcripts and hands back an interactive HTML report on how you actually work. One command, zero setup: type /insights in a session, or run claude -p "/insights" from the shell for a non-interactive version that just prints the path Reads what's already on disk: pulls session logs from ~/.claude/projects/, skipping agent sub-sessions and anything under 2 messages or 1 minute Project areas: clusters your sessions into themes like "CLI Tooling" or "Documentation" with session counts Friction analysis: categorizes where things went wrong by root cause - and quotes your own prompts back at you Interaction style: tells you whether you're a delegator or a micromanager, plus which workflows are worth doubling down on Actually actionable: suggests concrete CLAUDE.md additions and Claude Code features you're not using The catch: Haiku does the per-session classification, so the first run takes several minutes; results cache to ~/.claude/usage-data/facets/ and the report lands at ~/.claude/usage-data/report.html Calvin #2: Post-quantum crypto lands in Python pyca/cryptography 48 ships ML-KEM (key establishment) and ML-DSA (signatures) — NIST's post-quantum standards, now one pip install away. Big deal because it's the 11th most-downloaded package on PyPI (~1.2B downloads/month) and sits under Ansible, Certbot, Airflow, and paramiko. No PQ there, no PQ anywhere in Python. Trail of Bits did the work (Rust bindings, cross-backend API, tests, AWS-LC backend support), funded by the Sovereign Tech Agency. Timing tracks a June 22 White House order setting federal deadlines: PQ key establishment by end of 2030, PQ signatures by end of 2031. Not a drop-in swap — the wire sizes explode. ML-DSA-65 signatures are 3,309 bytes vs Ed25519's 64; ML-KEM-768 public keys are 1,184 bytes vs X25519's 32. Hardcoded field sizes and length prefixes will bite. API looks like the existing asymmetric primitives, except ML-KEM is encapsulate/decapsulate rather than a Diffie-Hellman exchange. SLH-DSA (the hash-based conservative backstop) is still in progress. The primitives are here, but protocols haven't caught up — so you won't be running post-quantum Certbot this week. Sponsor: Xweather You're using agents that can write code, summarize documents, and automate workflows. But they're missing one thing: awareness of the world around them. This is where today's sponsor, Xweather comes in. Xweather combines enterprise-grade weather intelligence with agent-ready APIs, natural language capabilities, and an MCP server built for tools like Claude, Codex, Copilot, and modern IDEs – so your agents can adapt workflows, automate responses, and make better decisions based on real-world conditions. Backed by Vaisala, whose instruments fly on NASA missions to Mars, Xweather delivers trusted data and unique insights that go beyond conditions to actual impact – from real-time lightning strikes to road surface forecasts. Start with 15,000 free API calls each month and pay only for what you use as you grow. Xweather is your full weather stack, for developers by developers. Start building for free today at pythonbytes.fm/xweather. The link is in your podcast player's show notes and on the episode page. Thanks so much to Xweather for supporting Python Bytes. Calvin #3: MCP goes stateless — and FastMCP gets renamed From Philipp Acsany over at Real Python The 2026-07-28 spec landed July 28 and the Python SDK shipped 2.0.0 the same day. Biggest rewrite since MCP launched, and it's breaking on purpose. Context for scale: the Tier 1 SDKs are pulling close to half a billion downloads a month, with TypeScript and Python each past a billion total. The headline is the stateless core. The initialize/initialized handshake and the Mcp-Session-Id header are both retired — protocol version, client identity, and capabilities now ride in _meta on every request, with an optional server/discover RPC if a client wants capabilities up front. Any request can land on any instance behind plain round-robin, no shared storage. Server-initiated calls are the hard part of the migration. Sampling, elicitation, and roots/list no longer call back to the client; instead the server returns resultType: "input_required" and the client retries with inputResponses attached. Multi Round-Trip Requests, MRTR. Also: Mcp-Method and Mcp-Name are now required headers so gateways route on headers instead of cracking JSON bodies, and missing-resource errors move to standard 32602. Deprecation sweep with an actual policy behind it — Roots, Sampling, Logging, and the legacy HTTP+SSE transport all deprecated with a twelve-month minimum offramp. Tasks graduated out of the experimental core into a real extension, which is what the formalized extensions framework was for. MCP Apps is now an official extension too, so a tool call can return sandboxed interactive HTML. Auth picked up RFC 9207 issuer validation, issuer-bound credentials, and a shift from DCR toward CIMD. Python SDK 2.0 is where it gets personal: FastMCP is now MCPServer, no alias, no shim. McpError → MCPError. Wire types went snake_case (is_error, input_schema) and moved to a standalone mcp_types package, with mcp.types kept as a permanent alias. One Client object replaces the old transport + ClientSession + initialize() stack. httpx became httpx2. Sync handlers run on worker threads now, so asyncio.get_running_loop() raises inside them. The good news: one MCPServer serves both protocol eras, so 2025-era clients keep working with nothing to configure, and a Resolve(fn) parameter lets one tool body cover MRTR and the old path. 1.x is maintenance-and-security-fixes only — pin mcp>=1.28,

Danny McMillan returns after his longest break in almost ten years, with Seller Sessions approaching its tenth anniversary and roughly 1,300 episodes. This is the pilot of a new monthly roundtable with Sim and Matt (Dorian returns next month), moving away from the conversion show format towards raw conversation. You'll hear how Sim's team runs product development end to end with AI: keyword-scored idea validation, brand director sign-off, Claude-generated product concepts rendered through Codex, and a launch pipeline already booked out to 2027. Matt shares how Productpinion prioritises features from customer feedback, and why prioritisation is the most undervalued skill in the AI era. The back half tackles the big theme: cognitive load. Danny breaks down verification fatigue, context switching and AI maxing, and why the scarce resource is no longer time but attention and decision quality. Key Topics AI-driven product development - from keyword scoring to Claude SVG concepts and Codex-generated product renders Team structure at scale - how ideas route through brand directors to sourcing across UK and Philippines teams Hiring in the AI era - why refusing to use AI is now a dealbreaker, and why gutting teams for AI is commercial suicide Free local AI tools - Fluid Voice (Whisper Flow alternative) and Meetily (Granola alternative) Cognitive load and verification fatigue - the hidden tax of moving from doer to overseer Timestamps 00:00 - Danny returns: ten years of Seller Sessions, new pilot format 01:50 - Sim's update: ditching ClickUp for a bespoke operating system 03:55 - Matt's update: closing the research loop in Productpinion, Florence CRO brain, MCP 05:39 - Sim's product pipeline: AI keyword scoring, brand director approval, deep research 07:07 - AI product development: Claude concepts, Codex renders, 3-in-1 product mashups 09:07 - Packaging designed for the main image, and how far you can push it 10:46 - Team workflow: brand directors owning P&L, sourcing handoffs 14:21 - Danny on gutting teams for AI: who maintains the machines? 15:56 - The hiring line: refuse to use AI, you haven't got a job 17:48 - Claude across every department: projects, Claude Code vs Cowork 20:10 - Free local tools: Fluid Voice for dictation, Meetily for meeting notes 21:57 - Marketplace arbitrage: moving proven products between Amazon marketplaces 23:21 - Matt on signal to noise: wasting tokens instead of wasting time 24:57 - Time blocking and prioritising features by customer impact 26:54 - Danny's segment: cognitive load, oversight duty and verification fatigue 31:46 - Asking the right question: the Claude Science deep-dive example 36:41 - AI maxing, context switching and high-stakes decision quality 42:07 - Claude telling you to go to sleep 45:16 - Danny's framework: reject the first plan, decision sprints, deliberate decompression 50:47 - Where to reach Sim and Matt Key Takeaways AI has made product development fun again - unique product concepts generated with Claude and Codex, feeding a pipeline mapped to 2027. Augment, don't replace - if someone was worth hiring, AI should multiply their output, not justify cutting them. Prioritisation is the undervalued AI skill - just because you can do everything doesn't mean you should. Verification fatigue is real - build in decision sprints and deliberate decompression, and reject Claude's first plan on sight. The scarce resource is attention, not time - your night schedule and recovery feed the next day's output. Notable Quotes "AI is enabling the boring to get released and the fun stuff to happen." - Sim "Instead of people wasting time, now they're just wasting tokens." - Matt "Prompts don't matter, but asking the right question unlocks everything." - Danny McMillan "AI doesn't just speed you up. It puts you on permanent oversight duty, and the cost of that duty is your attention and your judgment, not time." - Danny McMillan Resources Mentioned Fluid Voice - free, open source local dictation with on-device models; a Whisper Flow alternative Meetily - free, open source meeting summariser that runs privately on your machine; a Granola alternative Claude / Claude Code - the AI platform used across both Danny's and Sim's teams Codex - used alongside Claude to generate product concept images Productpinion - Matt's shopper testing platform, now with MCP support and draft polls Connect Sim - on LinkedIn (genuine reach-outs answered) Matt - on LinkedIn or via productpinion.com Seller Sessions is the leading podcast for advanced Amazon sellers, hosted by Danny McMillan. Dorian returns next month.

#363: Three waves of the web, and you are late for the third one. The 90s were about getting a browser to render your page at all. The early 2000s were about SEO, or as Darin puts it, sell me all the ads ready. Now it is agent ready, and Cloudflare built a scoreboard for it at [isitagentready.com](https://isitagentready.com/). The devopsparadox.com site scored about 70 out of 100 and then went down when Cloudflare added new checks. Run yours. You will be sad. Viktor thinks the framing is slightly off, though, and the correction is the good part. Optimizing for agents that browse your site is aiming at the wrong thing, because most requests never touch your server. Agent asks the model, model answers, agent shows you. So the target is not the crawler, it is the training data - and if the model does go looking, the question becomes whether you are the first answer or one of the five sites it was told to go analyze. Same game as Google. Different index. It is not Google index anymore, it is model training now. Then the practical part. Five things Cloudflare scores you on: discoverability, content, bot access control, API, Auth, MCP & Skill Discovery, and Commerce. Content accessibility is where most of you are losing, because agents want Markdown and you are serving them a pile of HTML tags to strip. Both DOP and Viktor's site are Hugo, so the Markdown is already sitting on disk next to the HTML - serve one or the other based on what the request asks for. Almost no effort. If you are still shipping a JavaScript-rendered site, Darin says it is game over, and humans do not like those either. On the blocking side, both of them are baffled by the same thing: if you do not want agents reading it, do not publish it. robots.txt is a suggestion at best. If you really want to block, actually block. The API argument is the one that will annoy people. Viktor says CLIs and MCP servers are both auto-generated from a schema, so the real work is having a good API, and most companies do not. But who your audience is decides the wrapper - developers already have Bash, so give them a CLI and get out of the way. Everyone else needs MCP, because Viktor's mom is not installing your binary. And somewhere in the middle of all this Darin asks whether documentation should live in the code now more than ever, and Viktor says no, less than ever - he wants it separate so he can review it, because agents made everything cheap to produce and review is now the only thing standing between him and 5,000 features a day. Also: WordPress should be the last thing you consider, not the first.   YouTube channel: https://youtube.com/devopsparadox   Review the podcast on Apple Podcasts: https://www.devopsparadox.com/review-podcast/   Slack: https://www.devopsparadox.com/slack/   Connect with us at: https://www.devopsparadox.com/contact/

a16z's Joel De La Garza is joined by Emilio Escobar, Chief Information Security Officer at Datadog, to discuss what it takes to secure a company where nearly every employee is using AI and more than 4,000 engineers are working with coding agents. Rather than trying to block new tools, Emilio explains why Datadog chose to embrace AI early and build the security infrastructure needed to use it safely. They unpack how AI changes traditional assumptions around data permissions, credentials, developer access, and software supply chains. Emilio shares how Datadog uses role-based MCP servers and ephemeral credentials, as well as an AI "judge" built by his security team to evaluate the intent behind code and agent skills before they enter the environment. They also discuss why security teams can't afford to wait for commercial solutions to every new AI threat, how the relationship between developers and security teams needs to change, and why Emilio is less concerned about an AI "escaping" than he is about the sheer volume of vulnerabilities AI could uncover.   Resources: Follow Emilio Escobar on LinkedIn: linkedin.com/in/emilioesc Follow Joel De La Garza on LinkedIn: https://www.linkedin.com/in/3448827723723234/ Follow Datadog on X: https://x.com/datadoghq Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Welcome to episode 367 of The Cloud Pod, where the forecast is always cloudy! Justin, Ryan, and Matthew are in the studio this week and ready with a lot of news, including passkeys (we know, they've had a rough week), Secrets Manager, Vector Search, and Glimmer (no, not my second favorite character from She-Ra), and even…wait for it…undersea cable news!  We've got a lot to cover, so let's get started!  Titles we almost went with this week AWS Secrets Manager Jenkins Rotation Finally Claude Enterprise Hooks a Ride on Data Loss Prevention Passkeys Take the Wheel, SMS Rides Off Into the Sunset Claude Code Says Trust Falls Are Over Muse Glimmer Shines While Meta’s Wallet Dims Zuckerberg Bets Big on Open Weights, Loses on Free Cash Flow AI is persistently in the news How many ways are there to run vector search in AWS, now 1 more Vector Search is the new Docker on AWS… how many ways are there to run it AWS Says “You get a Vector Search, and you get a Vector Search” You say you’re a Cloud Azure, but “Azure Network Router Appliance” says otherwise Claude now tells the world, I did the AI Slop Open, Closed, Open; Zuckerberg is on the AI Revolving Door Anthropic triples everyone’s productivity with Automode A big thanks to this week's sponsors: We're sponsorless! Want to get your brand, company, or service in front of a very enthusiastic group of cloud news seekers? You've come to the right place! Send us an email or hit us up on our Slack channel for more info. AI Is Going Great – or How ML Makes Money  01:40 Inference hooks: inline data loss prevention for Claude Enterprise  Anthropic launched inference hooks in beta for Claude Enterprise, providing inline data loss prevention across chat, Claude Code, Claude Cowork, and other Enterprise surfaces through a single configuration point. Technical approach: every inference request routes through a signed WebSocket connection to a customer-controlled security server; Claude sends the prompt and context before generation begins and waits for an allow/deny verdict before proceeding. The same inspection applies to tool call responses, including those from MCP connectors, skills, and plugins. The feature uses an open, webhook-based protocol with a published schema, allowing integration with existing DLP vendors such as Netskope, Palo Alto Networks, Proofpoint, and Zscaler, or custom in-house security servers, without requiring separate per-product integration work. Rollout controls include shadow mode (log without blocking), role-based exclusions, and percentage-based rollouts, along with configurable failure-policy tolerance and timeouts to match organizational risk requirements. This addresses a gap where inline enforcement was previously limited to Claude Code’s client-side hooks, giving compliance teams a unified enforcement layer for sensitive data across all Claude Enterprise channels.  Documentation is available

#144Josh and Mike sit down with Kenny Warner, VP of Data Science and Engineering at meez, for a conversation that starts with Kenny's unlikely path into tech leaving college after his sophomore year to launch a cause-marketing startup and winds up deep inside the machinery of modern AI. Kenny breaks down what actually happens when a language model reads your data, explaining tokens, embeddings, attention, and inference in plain terms, and why you can't simply point a chatbot at a hundred-million-row database and expect good answers.He and Josh dig into the real difference between building a model and fine-tuning one, why clean and trusted data is the true competitive advantage, and how smaller, purpose-built models can beat the giants at specific jobs. They also get into where this is all heading for restaurants, from turning recipes, costs, and margins into something a system can reason about to the rise of MCP and agentic tooling, with a detour through Nobu, a Miami chef tour, and a few fun facts along the way. It's a rare, jargon-free look under the hood of AI from someone who builds it every day.Links and resources

What if one overlooked Amazon metric is quietly costing you sales? Discover how keyword tracking, rank data, and smarter PPC decisions can uncover hidden revenue and stop wasted ad spend.   Amazon sellers spend countless hours researching products, optimizing listings, and managing PPC, but one important metric can easily get overlooked: keyword rank. In this episode, Bradley Sutton breaks down why tracking where your products appear for important search terms can reveal whether your advertising is actually helping your business grow or simply burning through your budget. Even with the rise of AI-powered shopping experiences, keywords remain a major part of how customers discover products on Amazon. Bradley uses real account data, Search Query Performance, and Helium 10 MCP inside Claude to show just how much revenue individual keywords can generate. In one example, a single keyword accounted for thousands of dollars in sales, while hundreds of different search terms contributed purchases to the same product. But finding keywords is only the beginning. Bradley explains how sellers can monitor sponsored and organic rank, identify keyword opportunities competitors are already winning, track relative rank, and automatically discover new search terms worth watching. He also shows how keyword data can inform PPC decisions, including when to raise bids, lower them, pause campaigns, or restart advertising when organic rankings begin to fall. The biggest opportunity comes from turning that data into action. Instead of manually checking dozens of keywords and adjusting bids throughout the day, sellers can connect keyword tracking with advertising rules to dramatically reduce repetitive work. The takeaway is clear: understand which keywords are actually driving your business, track how your rankings change, and use that information to spend smarter. Small improvements across the right keywords can add up to significant sales while helping prevent thousands of dollars in wasted advertising. In episode 544 of the AM/PM Podcast, Bradley discusses: 00:00 - Introduction 01:30 - The Amazon Metric Most Sellers Overlook 03:00 - How Much Amazon Revenue Comes From Keywords? 07:59 - Why Page One Ranking Matters 08:04 - Using Title Density To Find Ranking Opportunities 13:04 - Is Your PPC Actually Improving Organic Rank? 15:17 - Tracking Sponsored And Organic Keyword Rank 21:34 - How One Keyword Generated Thousands In Sales 22:34 - 333 Keywords Driving Purchases 23:34 - Finding Competitor Keyword Opportunities 27:48 - Tracking Your Rank Against Competitors 28:45 - Automatically Finding New Keywords 30:50 - Automating PPC Bid Changes With Keyword Rank 34:40 - Amazon Keyword Strategy Q&A 36:12 - How Many Keywords Should You Track? 38:29 - How To Choose The Right Competitors

"How do you use AI every day?"