Podcasts about HTML

¿Tu departamento está creando sus propios agentes de inteligencia artificial sin avisar a Tecnologías de la Información? Cuidado: el problema no es que TI sea "el malo de la película", sino que la urgencia por automatizar está abriendo una brecha crítica de ciberseguridad en las empresas.En este episodio, LuisGyG analiza la nueva amenaza que casi nadie está viendo: las inyecciones de código malicioso en prompts y cómo los cibercriminales aprovechan estos descuidos para robar datos bancarios, contraseñas e información sensible.En este contenido descubrirás:¿Por qué el área de TI no puede ir al mismo ritmo que la evolución de la IA? ¿Cómo funciona un ataque por inyección de prompts a través de HTML o notas ocultas? La postura oficial de OpenAI y Microsoft: por qué se debe asumir el riesgo desde el diseño UX y la arquitectura, no como un parche posterior.Cómo convertir al equipo de TI en un consultor estratégico en lugar de un "departamento del no".La responsabilidad compartida sobre las herramientas no autorizadas (Shadow IT).

Before Matt Schwartz became Chief Technology Officer at Sage Hospitality, a weekend HTML class changed the trajectory of his career. In the first episode of our six-part series with him, Matt shares how that unexpected experience led him from environmental economics into technology and ultimately hospitality technology leadership.Learn more about the Destination AI Forum in Washington, DC (where Matt will be speaking)Want more?Learn more about Sage HospitalityListen to Matt discuss bringing AI into the hotel technology stack on Hotel Tech Insider [Podcast]Podcast episodes with Sage founder Walter Isenberg: The Letter That Turned a Young Dishwasher into a Hospitality LeaderThe Dana Crawford Playbook A few more resources:If you're new to Hospitality Daily, start here. You can send me a message here with questions, comments, or guest suggestionsIf you want to get my summary and actionable insights from each episode delivered to your inbox each day, subscribe here for free.Follow Hospitality Daily and join the conversation on YouTube, LinkedIn, and Instagram.If you want to advertise on Hospitality Daily, here are the ways we can work together.If you found this episode interesting or helpful, send it to someone on your team so you can turn the ideas into action and benefit your business and the people you serve!Music for this show is produced by Clay Bassford of Bespoke Sound: Music Identity Design for Hospitality Brands

https://itayverchik.co.il/utm-elementor-forms/כאשר קמפיינים רצים במספר פלטפורמות במקביל (גוגל, פייסבוק, אורגני, ניוזלטר), אתם חייבים לדעת מאיזה מקור מדויק הגיע כל ליד שנכנס למערכת. הסתמכות על מערכות אנליטיקס בלבד מראה לכם את התמונה הכללית של הטראפיק, אבל כדי לסגור את המעגל ברמת הלקוח הבודד (ולסנכרן את הנתונים ישירות מול מערכת ה-CRM שלכם), צריך להעביר את נתון המקור ישירות מתוך טופס צור הקשר בעת השליחה.בסרטון הזה אני מראה לכם איך להשתמש בקוד ג'אווה סקריפט (JavaScript) פשוט וקצר, בשילוב עם תגיות דינמיות ושדות נסתרים (Hidden Fields) בטפסי אלמנטור פרו, כדי לתפוס את מקור ההגעה של הגולש ולהעביר אותו אליכם יחד עם פרטי הליד.מה נראה במדריך?יצירת שדות נסתרים: איך להוסיף שדה מוסתר בטופס אלמנטור שישמש כ"כלי קיבול" לנתוני המקור מבלי להפריע לחוויית המשתמש.משיכת פרמטרי UTM: שימוש בתגיות הדינמיות של אלמנטור (Request Parameter) כדי למשוך נתונים כמו utm_source או utm_campaign ישירות משורת הכתובת.קוד זיהוי מקור (Referrer): איך להטמיע סקריפט קצר בווידג'ט HTML או בניהול הקודים של אלמנטור, שיודע לזהות מאיזה אתר חיצוני הגולש הגיע (למשל, מעבר טבעי מחיפוש אורגני בגוגל), גם אם אין קישור מתויג.בדיקה ובקרה: שליחת טופס ניסיון ווידוא שמקור הליד מתקבל בהצלחה במייל ההתראה, בדפי התודה או במערכת ניהול הלידים שלכם.הטמעה של מנגנון כזה תעזור לכם לעשות סדר בנתונים, לדעת בדיוק אילו קמפיינים ומקורות מביאים את הלידים האיכותיים ביותר, ולייעל את התקציב השיווקי בצורה מבוססת דאטה.המדריך עזר לכם להבין מאיפה מגיעים הלידים שלכם?אל תשכחו לעשות לייק לסרטון, להירשם לערוץ וללחוץ על הפעמון כדי לקבל עדכונים על עוד מדריכי אלמנטור, מעקב המרות, פיתוח מערכות ושיווק דיגיטלי.

Topics covered in this episode: Some more things about Django I've been enjoying Who cleans up after the vibe-coding party? Where Did All Your AI Tokens Go? AgentsView to the rescue! Careful with phishing all Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Consulting from Six Feet Up Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Calvin #1: Some more things about Django I've been enjoying Julia Evans is learning "2010-style" web dev (Django + SQL + server-rendered HTML) after years of Go backends and JS-heavy frontends Query builders: likes defining custom QuerySet classes with chainable filter methods (.approved().future().with_tags()) — more readable than raw SQL Template filters: highlights urlize, linebreaksbr, json_script, and especially querystring for building/modifying query-string links in templates Migrations: still loves Django's auto-generated migrations — 19 and counting on her project Skips inheritance for class-based views; prefers function-based views for sharing code, though fine using Django's own mixins/interfaces Performance surprise: CPU profiling (via py-spy) — not slow DB queries — revealed the culprit; she'd accidentally disabled the cached template loader, and re-enabling it took throughput from ~2-3 req/s to ~12 req/s on a $10/mo VM Michael #2: Who cleans up after the vibe-coding party? FT Magazine piece by Sam Learner (July 11) on AI coding tools overwhelming open source maintainers - sent in by listener Dylan McConnell, whose main point was that this ran in the Financial Times, not a dev blog. cURL as the case study - Daniel Stenberg has been the only full-time person on it for years; libcurl has been installed an estimated 20+ billion times with 3,000+ listed contributors. Bug bounty killed - cURL ended its paid security bounty program in January, citing an "explosion of AI slop reports" that take real time to debunk and drain morale. Extractive contributions - authoring a PR is now nearly free, reviewing one still costs a human; tldraw's Steve Ruiz closed outside contributions entirely, asking why he'd want someone else writing the easy part. Guido weighs in - van Rossum says projects are holding emergency meetings over the slop flow, and notes LLM patches tend to touch unrelated parts of a file, making review more tedious. "Vibe Coding Kills Open Source" - paper from Miklós Koren's group: packages frequently recommended by coding models saw big download jumps with no matching engagement, breaking the reputation loop that sustains maintainers. Stack Overflow flatlined - over 100,000 questions a month before ChatGPT, under 1,500 last month, with the response rate cut roughly in half; the public archive is now stale training data. The course-creator angle - Josh Comeau's newest web dev course launched at about a third of prior enrollment, and he worries about devs who never learn which questions to ask. But the most interesting portion is what was omitted. Focused on: The end of the curl bug-bounty Omitted: High-Quality Chaos Why the omission is interesting It fits a narrative. The FT piece is a maintenance-and-decline story, and January-Stenberg is a perfect witness for it. April-Stenberg complicates it - same person, same project, better data, opposite direction on the specific claim being used. The tell is already in the article. Learner quotes Stenberg saying AI tools are much better at finding problems than fixing them. That's the April thesis in one line, and it goes undeveloped. Reason for the shift is process, not vibes. Killing the bounty removed the cash incentive and the venue change filtered the rest. Worth saying out loud, because "AI reports got better" isn't quite it - "no bounty plus a real triage platform" is closer. Joke too: Sarah O'Connor wrote a related piece (is this just before skynet launches?) Calvin #3: Where Did All Your AI Tokens Go? AgentsView to the rescue! Local-first desktop/web app for browsing, searching, and analyzing your past AI coding agent sessions (Claude Code, Codex, Copilot, Cursor, Gemini, Aider, and dozens more) Auto-discovers session files on your machine — no config needed; everything stored locally in SQLite, no cloud/accounts agentsview usage is a drop-in ccusage alternative — reads from pre-indexed SQLite, reports run 80–220× faster on large histories New Activity dashboard shows peak concurrency, active vs. idle time, agent-minutes, and cost — filterable by project/agent/machine, with a -json CLI report too Full-text + optional semantic search across every session; also imports Claude.ai/ChatGPT chat exports Install via pip install agentsview, uvx agentsview, brew install --cask agentsview, or download desktop binaries from GitHub Releases Michael #4: Careful with phishing all The situation I pass this along because it was a pretty sneaky bit of targeted phishing, and happened to play off an old interaction in bandit's repo. As usual with phishing scams there are a bunch of tells that this isn't legitimate, but just enough plausibility that I could see falling for it in a weak moment. Relative nobodies like me haven't historically been worth the effort to hit with scams this specific. Agents change the game though :-/. Be careful out there folks! Original message From: "Patrick (Blacktrace)" [HTML_REMOVED] To: LISTENER EMAIL Subject: Your Bandit #1350 (B105 NextToken false positive) -- just fixed that exact case Date: Wednesday, July 15, 2026 12:02 AM Hi AJ, Saw your Bandit issue #1350 -- the B105 hardcoded-password false positive on the string NextToken. I build a deterministic gate that filters that class of Bandit noise, and #1350 was literally the case I just fixed: NextToken / next_token / page_token / nextPageToken now stay quiet, while a genuine hardcoded token like api_token="sk-live-..." still fires. Verified against your exact case. 30-second paste: https://blacktrace.co/noise-eraser Where it still trips, published: https://blacktrace.co/kruc Curious whether it clears what you hit -- and if it trips on something of yours, that's the more useful reply. Patrick, Blacktrace I asked Claude for some analysis too. It was pretty good at finding them. The message name-drops enough real detail to feel legit, but the structure is pure phishing - everything in it exists to get AJ onto blacktrace.co. The strongest ones: Freemail sender, corporate signoff. Signs as "Patrick, Blacktrace" but sends from emailpjv@gmail.com. Real company outreach comes from the company domain, not a personal Gmail - and there's no last name. Over-specific targeting. It mirrors AJ's exact public activity - issue #1350, the B105 rule, the NextToken false positive, even the token variants. That's the "just enough plausibility" AJ flagged, and it's exactly what agents make cheap: scrape a GitHub issue, auto-generate tailored bait. Legit cold outreach rarely reads your history back to you this precisely. The entire payload is two links. Strip the technical flattery and the message is just "paste here" plus "see results here." When the whole point of an email is the click, that's the tell. "30-second paste." Low-friction urgency, and "paste" most likely means paste your source into their tool - handing your code to a stranger's site. Exfiltration dressed as convenience. Brand-new, no-reputation domain. blacktrace.co has no track record, and the name is doing some ominous work. The /kruc slug is random noise, not how real product pages get named. Precise-sounding jargon that's actually vague. "Deterministic gate," "noise-eraser" - impressive, empty. Bolted onto correct real details (B105 is the Bandit hardcoded-password test, sk-live- is a Stripe live-key prefix) to borrow credibility. The disarming close. "if it trips on something of yours, that's the more useful reply" - engineered humility that flatters your expertise and baits a response. Makes engaging feel like you're doing them a favor, which drops your guard. Extras Calvin: DjangoCon US 2026 is rapidly approaching, August 24-28, Chicago Ruff v0.16.0 massively expands its default rule set Ruff now enables 413 rules by default, up from 59 https://astral.sh/blog/ruff-v0.16.0 Michael: Completely redesigned the home page. Try /insights in Claude Code (terminal) Joke: We're Safe

Did you like the episode? Send me a text and let me know!! One Reddit user sent 464,000 cold emails in a single year — and figured out exactly what made people reply. A lowercase subject line with no punctuation hit a 90% open rate. A permissionless value DM on X returned a 40% response rate. And asking for a 15-minute call? Converted at a devastating 0.8%. In this episode of Business Conversations with Pi and Piette 2.0, PI and Piette answer a listener's question from tuepodcast.net/askpi: "How do I run cold outreach on LinkedIn, X, or email without sounding like a spammer?" The answer is a complete platform-by-platform playbook built on deliverability science, human psychology, and one core truth — your ultimate competitive advantage is proving you're genuinely human. What You'll Learn: Why asking for a 15-minute call is the worst thing you can do in a cold emailThe offer psychology shift that jumped reply rates from 0.8% to 2.1%How to send 1,000 emails a day without triggering spam filters (the interval trick)The "ugly email rule" and why plain text outperforms beautiful HTML every timeNicholas Cole's lowercase subject line that achieved a 90% open rateThe four-step follow-up sequence — including the "right person pivot" and the A/B/C closeoutLinkedIn's five-touch warm-up sequence before you ever send a DMWhy automating LinkedIn likes and comments gets your account shadowbannedHow X (Twitter) works for reaching C-suite executives — and the golden rule about linksThe permissionless value framework and why it generates a 40% response rateWhy the future of outreach belongs to whoever can prove they're most humanTimestamps: [00:00:00] – Introduction & The Listener Question[00:01:00] – 464,000 Cold Emails: What the Biggest Outreach Test Ever Revealed[00:02:30] – Offer Psychology: Why Asking for a Meeting Is Killing Your Reply Rate[00:03:30] – Cognitive Load: The Sofa vs. the Piece of Paper[00:04:00] – From 0.8% to 2.1%: The Low-Friction Offer That Changes Everything[00:05:00] – Email Deliverability: The Invisible Bottleneck No One Talks About[00:05:30] – Sending Intervals: How to Mimic a Human to Beat the Algorithm[00:06:30] – Nicholas Cole's 90% Open Rate Subject Line (All Lowercase, No Punctuation)[00:07:00] – The Ugly Email Rule: Why Plain Text Wins Every Time[00:08:00] – The Text-to-Code Ratio: How Spam Filters Actually Read Your Email[00:08:30] – The Four-Step Follow-Up Sequence[00:09:00] – The Right Person Pivot (Step 3)[00:09:30] – The A/B/C Closeout (Step 4 — The Best Tactic in the Whole Episode)[00:10:00] – LinkedIn's Context Layer: Why DMs Here Are Different[00:10:30] – The Five-Touch Warm-Up Sequence Before You DM Anyone[00:11:30] – True Personalization vs. Mail Merge: The Trigger Event Framework[00:12:00] – Why Automating LinkedIn Engagement Gets You Shadowbanned[00:13:00] – The Three-Touch Limit on LinkedIn DMs[00:13:30] – X (Twitter): The C-Suite Goldmine With Strict Rules[00:14:00] – Account Warm-Up: Why You Can't Start DMing Immediately on X[00:14:30] – The Golden Rule: Never Put a Link in Your First X DM[00:15:00] – The Permissionless Value Framework: Reversing the Sales Debt[00:16:00] – 40% Response Rate: The Data Behind Leading With Value[00:17:00] – The Future of Outreach: AI Makes Noise, Humans Break Through[00:17:30] – Full Recap & Submit Your QuestionThe Four-Step Follow-Up Sequence: ✅ Day 2-3: Quick bump — "Open to a quick yes or no?"✅ Day 4-5: One specific researched detail proving you've done your homework✅ Day 7-10: Right person pivot — "Am I reaching the right person, or should I talk to someone else?"✅ Day 14: A/B/C closeout — "Reply A if not a priority, B to circle back next month, C if you're the wrong person"Submit Your Question:

There are roughly 100x more people who use code than who can write code. As code that “just works” becomes easier to generate, this group may be the biggest prize of all — if you can get the agentic interface right.A key trend we have been tracking over at AINews is the absolute explosion in Codex usage this year, with MAU now up >10x from Jan 2026. Less than two weeks after their July 9th launch, OpenAI said ChatGPT Work and Codex had reached 10M users combined (as we cover in the pod, Codex now powers ChatGPT Work, so all ChatGPT Work users are now users of the Codex harness, even if they aren't traditional engineers) — showing the early innings of what happens when you graduate from coding agents to knowledge work agents:We've been calling out how coding agents are “breaking containment” to do everything else this year to power every other part of knowledge work - and it started with the org chart, with a major reorg last month that amounted to two of Codex's most prominent leaders, Greg and Tibo, taking responsibility over product and ChatGPT specifically, completing a “Superapp” consolidation cycle first discussed in March.With these updates Codex is no longer just a coding tool. In June, OpenAI said knowledge workers already accounting for roughly 20% of Codex's user base and growing more than 3x as quickly as developers. A product dedicated for knowledge workers was being pulled out of the Codex team.However, knowledge work has a different set of problems and environments than coding. For decades, knowledge work has been scattered across different primitives like documents for writing, spreadsheets for analysis, slide decks for communication, and specialized applications for everything else. ChatGPT Work now enables users to work across every primitive with agents. Instead of opening an application and manually operating its features, the user can describe an outcome and collaborates with an agent that can assemble the tools, context, and artifact needed to reach it.From building no-code products at Airtable to leading Productivity Engineering at OpenAI, Akshay Nathan has spent much of his career trying to make the power of software accessible to people who do not write code. In this episode, Akshay joins swyx and Vibhu to unpack the launch of ChatGPT Work, why Codex unexpectedly took off among non-developers inside OpenAI, and the company's broader plan to bring useful agents from software engineers to knowledge workers and eventually everyone.We go deep on the shared agent harness behind Codex and ChatGPT Work, why OpenAI brought the experiences together without making them identical, and how persistent computers, artifacts, Sites, plugins, memory, and sub-agents are changing what people can delegate to AI. Akshay explains why some teams are replacing decks and spreadsheets with interactive websites, how agents can gather context across code, Slack, documents, and local files, and what OpenAI learned from personal-agent products like OpenClaw.Side note: also don't miss Abhihek's sandbox track keynote at AIE, which now powers a lot of the sandboxing for ChatGPT Work… and yes was also broken by an unreleased OpenAI model in the recent HuggingFace incident.Akshay also reflects on how AI is transforming product development itself: why more people will become generalists with a specialty, why ideas and taste become the bottlenecks when almost anyone can build, why LLMs still struggle to generate genuinely grounded new ideas, and why teams must distinguish increased motion from actual progress.We discuss:* Why Codex unexpectedly took off among non-developers inside OpenAI* Why employees felt like using Codex gave them a new superpower* The product insight that led OpenAI to build ChatGPT Work* Why Codex and ChatGPT Work share the same underlying agent harness* How their UX, Git visibility, artifacts, and sandboxing defaults differ* Why OpenAI merged its agent experiences instead of building separate products* How AI is blurring the boundaries between engineering, design, strategy, and operations* Why OpenAI wants the default model configuration to work for most users* When power users should use deeper reasoning, Ultra, or multi-agent modes* Artifacts, agentic spreadsheets, and creating high-fidelity work products* Why interactive Sites may replace decks and spreadsheets* The challenge of designing a simple interface for an agent that can build almost anything* Why users should retry tasks that models could not handle three or six months ago* How AI can gather context for performance reviews without replacing human judgment* The OpenAI automation that turns internal Slack and document activity into memes* What reaching ten million ChatGPT Work and Codex users means for the product* How OpenClaw inspired persistent environments, scheduled tasks, and personal agents* Using ChatGPT for financial planning, budgeting, workouts, meals, and household management* The design tradeoffs behind sub-agents and how much of their work users should see* ChatGPT memory, Chronicle, and long-term context* Why AI may make more people generalists with deep specialties* Why ideas and taste become more important when almost anyone can build* Why LLMs still struggle with the instruction “bring me new ideas”* Measuring productivity through quality at-bats instead of commits, tokens, or pull requests* The critical difference between AI-generated motion and meaningful progressAkshay Nathan* LinkedIn: https://www.linkedin.com/in/akshaynathan/* X: https://x.com/akshaynathan_Timestamps00:00:00 Introduction and Bringing the Power of Code to Everyone00:01:33 Joining OpenAI and Preserving a Startup Culture00:02:40 What OpenAI Learned from Enterprise AI Adoption00:05:28 Why OpenAI Built ChatGPT Work00:07:17 Codex vs. ChatGPT Work and the Shared Agent Harness00:12:07 Why OpenAI Merged Its Agent Experiences00:16:24 Models, Reasoning Levels, and Choosing the Right Default00:20:26 Artifacts, Agentic Spreadsheets, and Model–Product Collaboration00:24:22 Why Sites Could Replace Decks and Spreadsheets00:30:08 Designing an Agent That Can Build Almost Anything00:34:28 From Developer Agents to Knowledge Work—and Everyone00:36:07 Power-User Advice and AI-Assisted Performance Reviews00:40:41 OpenAI's Internal AI Memes and the Ten-Million-User Launch00:44:39 OpenClaw, Personal Agents, and ChatGPT as an Operating System00:50:24 Sub-Agents, Ultra Mode, and How Much Control Users Need00:54:39 ChatGPT Memory, Personalization, and Chronicle01:00:19 How AI Is Reshaping Product Development and Tech Roles01:03:15 Ideas, Taste, and Why LLMs Struggle to Generate New Ideas01:04:42 Measuring Productivity, Quality At-Bats, and Motion vs. ProgressTranscriptIntroduction: Akshay Nathan, ChatGPT Work, and the No-Code ArcSwyx [00:00:00]: We're here in the studio with Akshay from OpenAI. Welcome.Akshay Nathan [00:00:07]: Thank you.Swyx [00:00:08]: And with our trusty co-host, Vibhu. So you recently launched ChatGPT Work. You lead Core Product Engineering. It's been a long journey, into all this. I find it very interesting that you started with no code or low code, with Walrus and Airtable. And to some extent, ChatGPT Work is like the super app of super apps of, well, here is the ultimate no code. You just write a prompt.Akshay Nathan [00:00:32]: Yeah. It's funny how things come, full circle. I think for a long time in my career, I started my career working consumer fintech, but then after that, like, there's this hypothesis that, the things that we were able to do with code, like, as engineers, like, if we could bring that to many more people in a more, accessible way, then that would be truly magical. We were working on a startup. It's funny, like, before LLMs, before vision LLMs, on how to do automated testing with AI. It was just kinda jank, back then, but doing what we can, and then worked at Airtable for a while on the same thesis that, like, if we can bring a database or the primitives behind a database to people, that'd be really useful to them. But once LLMs came onto the scene, it became clear that, this was the missing piece, like, the missing technology required to, like, bring the magic of code to everyone without them having to know what's going on underneath the hood. And so, like, I think this launch and a lot of the stuff that we've been up to is, like, the manifestation of that.From Walrus and Airtable to OpenAIVibhu [00:01:33]: How was stuff when you joined? So you joined OpenAI 2023. Now we've got, so much more stuff, so ChatGPT, Codex app, ChatGPT Work. Have things changed?Joining OpenAI and What Hasn't ChangedAkshay Nathan [00:01:44]: I think the more interesting thing is how things haven't changed. Like, one, I joined I remember when I joined, it was, like, five hundred people. One thing I was worried about was, like, I was looking for something, more early stage and, like, was it gonna feel startup enough? And I joined, and I was like, “This feels even more startup-y than I could ever imagine.” And, like, that really hasn't changed even till now. I think the, like, level of, like, bottoms-up ambition and, like, the ability of anyone to, like, do anything or have an idea and ship it is really cool. But on the, like, mission side, I think what was really compelling to me is this mission of, bringing frontier intelligence to everyone. Like, building AGI and then bringing it to everyone. And, I think acknowledging back then that, like, that vision is gonna, not be a linear progression. Like, we're probably gonna, like, try different products and have different things that succeed and don't. But the vision has stayed the same, and the mission has stayed the same, and we're starting to see the pieces, fall together, and that's really cool.Enterprise Lessons: No One-Size-Fits-All AISwyx [00:02:40]: You worked on Enterprise. What A lot of people never touch ChatGPT Enterprise. What is something that you learned from there that you're bringing into your work now?Akshay Nathan [00:02:52]: I think how there's no one-size-fits-all solution in Enterprise. I remember in the early days of ChatGPT Enterprise, like, when we talked to customers and, like, everyone. That was, like, when I think it was a year after ChatGPT was released, and everyone was so excited to bring, AI into their enterprise. And, there were all these teams being stood up. It was, like, the AI deployment team with, like, these enormous budgets. And if you asked anyone, like, what were they excited about? Like, what were they excited about solving? Like, at first, you'd get, like, kinda like the baseline answers of, like, “Yeah, we have all this context and data and all this stuff.” But then if you ask them, like, “What was, like, a discrete use case that, like, they want AI to enable in their workplace?” You get such a different, like, variance, like, explosion of, different types of answers. And it's interesting, like, you using, like, these models and these products, you have this box, and you can say anything to it, which is the magic. But it'on the flip side, it also means that, like, you don't know what to do with it. And in Enterprise, I think a big part of that is, like, meeting the users where they are, like, what use case were they trying to solve, and then teaching them how they can use AI to, like, gain leverage there.Swyx [00:03:56]: Do you meaningfully differentiate that from forward-deployed engineering?Akshay Nathan [00:04:01]: I think there is the go-to-market side of it and then there is the product side of it. I think you need someone on the product side. And I think, like, however good we get at FDE motion, like, I think at the end of the day, if we have a user who's, like, looking at their computer or looking at their phone, like, it's our job in the product to, like, be enabling them and showing them where to go. So we're really excited about that.Vibhu [00:04:24]: Do you think there's been changes, over the past three years of adoption? So there have been, step function changes. You have reasoning models and whatnot. Is there still the same problems of Enterprise has black box, don't know what to do with it, or have things changed?Adoption, Agents, and the Next 10x MarketAkshay Nathan [00:04:39]: We're seeing now that, like, there's this huge uptake, right? Everyone is extremely excited about it. It feels like, many people are, millions, hundreds of millions of people are using ChatGPT. They understand, like, how generally to work with AI. But then, like, every time, like, a new capability gets unlocked, so now, like, we're seeing with agents, like, there is probably a contingent of, like, early adopters still who, truly get it, who are like, “ we you can do anything. You just have to make sure the right context is there, it's connected to the right tools, and that you are supervising it, but, like, anything is possible.” But then there's, like, this, like, 10x or 100x bigger market where, like, they don't yet get that, or they don't yet see that. And so I think that's the next stage here. So to answer your question, like, I think the adoption is there and growing fast, but I think the opportunity is, like, far bigger than that. That's where we wanna play, especially with ChatGPT Work.ChatGPT Work, Codex, and the Super App MergeSwyx [00:05:27]: Yeah. well, let's, let's skip ahead to ChatGPT Work. only, like, a month ago or so, announced. what was the decision process that led into it? there was this, overall merging of the super app. Is that what we're officially calling it? you deprecated the browser as well. Just, summarize your last, like, couple months of working on this thing.Akshay Nathan [00:05:50]: Yeah. It feels like forever now, but it's only been a few months. I think maybe the one, impetus that, like- Is most salient is when we release Codex, or even internally had Codex, like, it was really surprising to us, I think we recently put out some stats on this, that there was this, like, real inflection of, like, adoption among non-developers at OpenAI. And, I, through this product development process, like, would go to, like, these UXR sessions to talk to people internally. And the thing that stuck out to me is, like, one, like, you go talk to, like, strategic finance or marketing or whatever, and they're all using Codex for, their use cases. That part's cool, but the thing that really stuck out to me is how proud people were that they were using Codex. Like, how, likeSwyx [00:06:34]: It's like, “I'm not supposed to be using it, but I am.”Akshay Nathan [00:06:36]: It was that. It was, like, that they were, early to this, like, new thing, but it was also this thing of, like, they felt like they had a superpower, right? And, what we recognized then is that, like, the power of Codex, the power of agents, like, we already had this massive distribution base of people who have, come to know and love ChatGPT. Like, how do we show that to them? Like, how do we bring it to them? Which is, like, a hard product problem, and it's, like, a tricky thing, right? There's many ways you can go about it. And so that's what we called the Merge and the Super App over time, and ultimately launched it in ChatGPT Work, is how do we do that? But it came from that initial realization that, like, the power was not only for developers, like, much earlier than probably even we thought. Like, it could be extended to everyone.Swyx [00:07:17]: How do you see the products differently? So, like, who is it for, right? So Codex started out even CLI, then app. Now there's a merge of ChatGPT Codex and ChatGPT Work, so is it the opening for the average user, for enterprise, for work? How do you position it?Akshay Nathan [00:07:36]: I think we want to get it to position it for if you're doing work-related things, for lack of a better word, right?Who ChatGPT Work Is ForAkshay Nathan [00:07:42]: I think productivity is what, like, the pillar that I support. Like, that's the name of the team. And the reason for that, the reason we call it productivity and not, like, enterprise or, like, work or something like that, is because there's also personal productivity, right? And, like, I think ChatGPT Work is I've seen people do things in their personal lives that you wouldn't classify as, like, work technically, but, like, these agents are, super capable for. Like, one recent example that someone posted about, on our Slack is, like, someone had, like, a missed package, like they didn't receive it, and then they got, like, the picture of it, from Amazon or whoever the courier was, and they, like, asked ChatGPT Work to, like, find out where that package is. And, like, the agent, is extremely tenacious and, like, took the image and, like, looked at a bunch of, like, listings around their neighborhood and figured out exactly the apartment complex in which the package was, like, gave them some information. And so, like, I think there's all these things that, like, you, work-related or productivity-related things, I think that's what we want the product to be. You asked about Codex. I think we think Codex is, a durable brand, but we have a principle that, like, the user we don't want a user to get stuck in a tab or an experience where they don't get the power of the product. And so, like, everything that you can do, in the Codex portion of the product on desktop, you can do in ChatGPT Work and vice versa. But we made some opinionated product decisions on, like, how much of the Git state, if you're in a Git repo, do we wanna expose to the end user? Or how much do we wanna make the experience of seeing the agents thinking, like, diff forward so that you get exposed to the diffs out of the box. And then, like, on the safety side, like, how do we wanna think about, like, sandboxing and making sure that we have the right defaults in one state versus the other? So, there's, like, some opinions that go behind that, but we do want We don't want the user to need to choose which experience they're in.Swyx [00:09:26]: That is a good goal for AGI, right? Like, people don't want, like, to hide to choose what version of AGI they want. They just want the AGI to decide for them. can I get an answer or, like It's not super clear to me. Is the Codex harness and the ChatGPT Work harness the same? Is it just UI affordances, or are there prompt level or even deeper differences?Shared Harness, Different UX: Codex vs. WorkAkshay Nathan [00:09:49]: So the harness is the same. The harness is shared. on In both of the products, we made improvements to the harness to make it good for knowledge work, especially as it relates to plug-ins or computer use or artifacts. You get that power regardless of which experience you're in. On the UX side, there's opinionated takes that we have when you're in Codex mode, what the UX should be how the UX should behave, and some stuff around the sandbox like I mentioned, but the underlying harness and capabilities should be the same.Swyx [00:10:16]: I'm just kinda curious. Maybe we can, -- Is there a query that we can run that would look different in the two modes?Akshay Nathan [00:10:23]: Yeah. I tried to create, like ask it to create, like, a retirement calculator spreadsheet or something, in both modes. And then in Codex mode, you might have to be in a repo for this, but you'll see, like, the diffs of, like, the sheet that it's creating and stuff like that, and the file edits. But in Work you won't be able to see that.Swyx [00:10:42]: I think that's, that's super clear. And then also the other thing I wanted to dive into was your, the productivity team. what else is there? first of all, what are the top-level teams other than productivity? Isn't productivity everything?Productivity Teams and Core ChatAkshay Nathan [00:10:55]: SoSwyx [00:10:55]: Science?Akshay Nathan [00:10:55]: We have a team focused on ChatGPT. Like, the core chat experience, for consumer, which is like, not, I think all productivity. Like, there'People are using ChatGPT every day for search to, figure out how to write messages to loved ones, to think about, how to, like, learn a new topic, et cetera. And so there's so much more inside to create images. And there's so much more in chat that, the hundreds of millions of users are using that warrants, like, a very dedicated effort. And there's teams focused on enterprise and infrastructure and API and stuff like that, so.Swyx [00:11:33]: I will bring it up.Retirement Calculator Demo and Git-First UXSwyx [00:11:34]: Yeah. So I have them both running. This is ChatGPT Work. There's a Codex version here. I picked “Five Little Ducks” song, so this will take a while.Akshay Nathan [00:11:43]: Huh.Swyx [00:11:43]: I think we'll just keep it in the background and, as they finish, we'll look into some of the differences.Akshay Nathan [00:11:48]: Yeah. But immediately, I think if you flip back to the Codex version you'll see that,Swyx [00:11:53]: That it assumesAkshay Nathan [00:11:54]: Like theSwyx [00:11:54]: It assumes Git. Yeah. Yeah.Akshay Nathan [00:11:56]: The, like, dynamic island assumes that you're in a Git repo. And you might miss some stuff because some of it is, like, in the actual chain of thought with those changes and how we display that, but yeah.Swyx [00:12:07]: Is there an unintuitive like, is there a thing that you wanted to ship and then you got feedback, and you were like, “No, let's not do it?” Like, what's the thinking behind that?Why Merge the ExperiencesAkshay Nathan [00:12:14]: In, ChatGPT Work?Akshay Nathan [00:12:17]: I think one direction we could have gone with this is, like, keeping the experiences, like, completely separate. So it's like, whySwyx [00:12:22]: Different apps.Akshay Nathan [00:12:23]: Exactly, like different apps or even in the same app, like different, completely different experiences. Like, why merge it all? Like, what is. Codex, people love. Like, why bring these products together? And I think the intuition here is that, like, all of our jobs are, like, changing dramatically with AI. Like, for, like, every few months, like, I feel like I wake up, and I'm, like, doing a completely different thing than I was doing a few months ago. And my hypothesis here is that, or I should say our hypothesis is that, like, part of what we're, we're building, this technology is giving people leverage. Like, the things, maybe it's the more mundane parts of your job or parts that, like, if you were able to automate, you'd be able to share more ideas faster or whatever, like, you're able to do now. And because of that, like, that might blur the lines between someone who's, like, only writing code or creating strategy docs or, planning events or, helping with marketing or doing podcasts or whatever, right? And so, like, these things are gonna get blurred over time. And so, like, trying to draw a hard boundary based on, like, the who you are is gonna be, is gonna be tough. And, like, we should enable users to choose, but we shouldn't box them in. And so a lot of the work that went in here, like, keeping the primitives the same, like for example, plugins are, like, unified across, this product and ChatGPT and the cloud, was because of that. It's this thesis that, like, eventually things are gonna come together and we don't wanna be Like, we wanna be prescriptive about when to be in either experience, but we don't want to box anyone in.Swyx [00:13:45]: I wonder if there's users who are very tuned to the old ChatGPT harness that is effectively now replaced by the Codex harness. I can't imagine what that was, but maybe they're more the more conversational side. Can you compare and contrast the two harnesses? ‘Cause only you've seen it.Akshay Nathan [00:14:02]: Yeah. I think ChatGPT, the existing harness, like, still exists today. Like, it exists in this app,Harness Engineering: ChatGPT vs. CodexSwyx [00:14:08]: The classic, right?Akshay Nathan [00:14:09]: TheVibhu [00:14:09]: You just start a new chat, and you don't go under Work, right?Akshay Nathan [00:14:13]: Yeah. If you startVibhu [00:14:13]: SoAkshay Nathan [00:14:14]: A new chat and go to chat, then you're, you're talking to ChatGPT with the instant model.Vibhu [00:14:16]: Oh, we can technically do another. But on instant.Swyx [00:14:21]: Yeah. So this one's not gonna code or it's gonna be in line. It's on a in line in a sandbox.Akshay Nathan [00:14:26]: It'llVibhu [00:14:27]: Oh, that's coolAkshay Nathan [00:14:27]: We try to push you to go to Work if you're creating a spreadsheet. Yeah, but this isSwyx [00:14:30]: And this is a router decision? Sorry. Is it a router decision?Akshay Nathan [00:14:34]: This is the decision that, the model is making, and then, like it sees that you're able to. or you're trying to do something that would be better served in Work mode. But I think your question was like, what are the advantages of, like, the chat, like ChatGPT chat harness?Swyx [00:14:48]: It's more broadly, like, I wanna, do an oral history of harness engineering. Right? the ChatGPT harness lasted us from, let's call it the ‘01 era, until now, and now it's being replaced by the Codex harness effectively. And they're, they're overlapping somewhat, but I'm curious what changed if there is.Akshay Nathan [00:15:10]: My perspective on this is, like, there's, there's, there's there's like a constant process of, like, divergence, convergence, divergence, convergence. And in chat, like, many of the use cases I was talking about before, like, search or learning, I think we're, we're really optimizing for latency and optimizing for personality and, like, different things that, over time, like the product The reason people love ChatGPT is because we've been optimizing for those things and working on them for so long. Codex, what we learned was that, like, if you give the agent access to this infinitely flexible environment as a computer, it can do really powerful things. And so when we think about, like, okay, well, for knowledge work, like, what is which mode should we choose? It was like it felt more natural to us to bring that to this, like, computer environment and, maybe abstract some of the details of this computer away from users who might not be used to that, but, like, give them that same power. But ultimately, I think that we want the power in all places, right? We wanna meet people where they are. So I'm sure there'll be work down the road in order to get things to be, equivalently capable in all scenarios. But it's just a question of, like, what we've been focusing on the product on historically and what we're focusing on now.Models, Defaults, and the Reasoning SliderVibhu [00:16:24]: I think alongside that, outside of just harness and when to use Codex, ChatGPT, or Work, there's also the new models you've released, right? any guidance there? So people love to min-max what to use, like only use Terra on high reasoning versus, for this, you wanna use Sol here, ignore all theseAkshay Nathan [00:16:44]: There's 32 options.Vibhu [00:16:46]: But, that being said, for people that are expanding, so, productivity trying stuff for work that don't have the breakdown of what all this is what's, what's the advice, right?Akshay Nathan [00:16:59]: Well, I think before the advice, like the first thing is, like, none of this would be possible without these models. Like, the, I think you asked earlier, like, what was, like, the inspiration for work and, like, early on, like I mentioned, like, what we were seeing with Codex, but that was also because the models were getting infinitely more capable. That's happening again. I think it's like another step function jump now. And to answer the question on advice, like we want this default to be the best possible. Like, we wanna be opinionated about the default, and so we've we've chosen a default that we think is gonna be the best for everyone. And, we have for power users options under the hood. We could One could argue that there might be too many right now, and we're, working on simplifying it. But you can extend, the reasoning level, and you can change between the different model classes if you need to, but the default should be the best for most use cases. So my advice to most people would be to stick to that. And then, if you reach a situation in which you think that you could, you wanna try, a different configuration, if you're not seeing either the efficiency on the cost side or the quality on the intelligence side, then you can change the defaults and see if you can get something better. But we think that the default should be good enough.Swyx [00:18:09]: I have, I'm just gonna run something by you since you have way more experience than me. I've recently been doing Sol Lite but with goal, with the idea that the goal augments the reasoning effort, but with more terminations and turns.Swyx [00:18:24]: Is that a good way to think about it as opposed to Sol Ultra or Sol, Extra High?Akshay Nathan [00:18:29]: Yeah. It's hard to say becauseSwyx [00:18:31]: Yeah. It's like an interaction effect.Akshay Nathan [00:18:33]: exactly. It's like there's a preference on, for you as an individual, like how do you like to collaborate with the models? Like how many of those like terminations, as you call them, do you want where, you can steer or make sure that it's doing the right thing?Akshay Nathan [00:18:46]: I think generally people should try whatever works for them. I think that like using Ultra or the like multi-agent setups are best for like when you have like tasks that are either incredibly complicated, like open explorations or very paralyzable. I think even for tasks using goal, I think is best for tasks that you'll be able to make consistent progress in a way that's verifiable over time. But I think for most tasks, they don't fall into either of those buckets. And so like at least when they're starting, and so that's why I think the best first step is like trying it with the default configuration and then seeing like where you wanna go from there.Swyx [00:19:29]: Right. You guys worked on a slider, which is super helpful for reducing the amount of panic.Vibhu [00:19:36]: It's nice on mobile at least. There's a nice slider there.Swyx [00:19:38]: It's nicer.Vibhu [00:19:39]: I haven't tried it.Swyx [00:19:40]: So you have the advanced view there, but if you click advanced view. Yeah.Vibhu [00:19:44]: Ooh, it's just a nice slider. Yeah.Swyx [00:19:46]: Very pretty, very colorful.Akshay Nathan [00:19:48]: Yeah. The idea was here was like reduce it to like one dimension even though there's multiple dimensions, right? Try to project it onto a single dimension for the user. Like, something from that represents like, speed and efficiency on one side and then like quality and thoroughness on the other side.Artifacts, Spreadsheets, and the Work LaunchSwyx [00:20:04]: I am just puzzled that it uses Sol so much, like the lowerVibhu [00:20:07]: NoSwyx [00:20:07]: Grounds I would've usedVibhu [00:20:08]: I think the slider, if I'm not mistaken, isSwyx [00:20:09]: Terra.Vibhu [00:20:10]: Oh, it is.Swyx [00:20:11]: Yeah. See? So they preset Terra to only be the light one. But like I think a lot of people would more people should use Terra. One, because Sol keeps running out of capacity.Vibhu [00:20:22]: I'm the reason. Here's ten minutes of ourSwyx [00:20:24]: There you goVibhu [00:20:25]: Retirement calculator.Swyx [00:20:26]: Oh, that's the Excel thing working for you.Vibhu [00:20:28]: This is,Swyx [00:20:28]: Oh my God. Look at thatVibhu [00:20:28]: This is work, and then Codex is still cooking, so we'll get back into it. I think it'll be interesting to see the thought process, the reasoning, and also, this is eight minutes on work. Codex is still cooking.Swyx [00:20:41]: Yeah. And by the way, so I've, do Gabriel Chua? He's part of the OpenAI Singapore team. He showed me this, and I was like pretty shocked that this looks like Excel. It edits Excel files. You never paid an Excel license, right? Like, but somehow this is like workable and it's agentic Excel.Akshay Nathan [00:21:01]: Yeah. one of the big like pushes that we made for this launch was like artifacts, right?Akshay Nathan [00:21:05]: Like both on the model side, like I think if you compare this with GPT-5.5 and GPT-5.4 before that, you'll see that there's been pretty dramatic improvements in the quality of these artifacts and then also on the product side.Vibhu [00:21:16]: The UX side is also crazy, like hosted sites and whatnot. No longer needing to host your own little webpage, like itSwyx [00:21:23]: Oh, I have a story about that. I can do, a separate thing. I'll need to take the visuals here, but we-we'll, we'll cut to that later. Was there co-training, because you were moving making this big move and you launched GPT-5.6 on the same day as ChatGPT Work? Was there influence between the model training teams and the harness teams, or did they did the launch dates just happen to line up the same day?Akshay Nathan [00:21:46]: I think the we collaborate heavily with the research teams, and I think that's like one of the most magical parts of the job, like the most fun parts of the job. But yeah, just using artifacts as an example. Like, a lot of what you're seeing, like underneath the hood, there's a lot of work that went into making sure that like, we had the right infra to be able to train the models to get better at this. And then on the product side, like had the right experience for users to be able to collaborate with the model on an artifact like this. In fact, like this whole viewer, like the intuition here is that like, it's not necessarily that you wouldn't need an Excel license. This is stage one, right? Like, this is probably not what you meant when you're like making a retirement calculator.Vibhu [00:22:24]: Yeah, you can iterate very easily. Yeah.Akshay Nathan [00:22:24]: You wanna iterate and like when you're seeing it, and if this thing is high fidelity to like what you would see in or what your coworkers would see if you were to send this to Sean, like that I think makes it so easier and makes you trust the product in terms of iteration.Vibhu [00:22:39]: When you say coworkers would see, do you see a multiplayer, multi-team collaboration with artifacts? Any things you guys think about that?Multiplayer Artifacts and CollaborationSwyx [00:22:46]: You can already share it, right?Akshay Nathan [00:22:48]: Yeah. It's inter It's something that, we're actively thinking about. one thing that, we've noticed internally without talking too much about the roadmap is that like there's many times when someone will ping me about something, and I will ask ChatGPT Work the question, and then I'll ping them back the answer.Akshay Nathan [00:23:04]: And then I'll be thinking likeVibhu [00:23:04]: Like the simplest would be, the three of us are just all on one hosted.Akshay Nathan [00:23:07]: Exactly. And I'll think about like was I required in this loop or and then maybe it was, rephrase like what they were asking or pulled from certain context or whatever. But like, when I gave them back the answer, that process was also lossy, right? Like I gave them just like my interpretation of what ChatGPT Work cooked up. But like underneath the hood, there's so much context like in the rollout and stuff that could be interesting.Vibhu [00:23:28]: Yeah, it'sSwyx [00:23:28]: So like the answer was preemptively respond to every inbound request?Akshay Nathan [00:23:33]: No, it was just like literally like this is what I do sometimes as my job.Swyx [00:23:36]: I know you copy-paste and then you're just a message forwarding serviceAkshay Nathan [00:23:39]: Yeah. Yeah, exactlySwyx [00:23:39]: From AI to AI.Vibhu [00:23:40]: But I think it's interesting, right? It helps people understand the capability of what you can ask and delegate that oftentimes people don't realize until they try or someone shows you, and then you're like, “Oh, okay. Okay, I see.”Swyx [00:23:52]: I think it's als there's also like a, light security issue, where like you're the permissions layer. Like yes, I could query everything that you query, and I could get an automated response, but maybe I'm not supposed to see it. And that there's no way I would know because I'm not supposed to know what I don't know.Akshay Nathan [00:24:07]: Especially as like, with ChatGPT Work, we're, we're asking you to connect your plug-ins and, it's pulling from your local files and stuff like that. Like the amount of context that the agent has access to is like- Deeply personal and like that's something I think we need to preserve, so that'll be definitely a challenge.Swyx [00:24:22]: There's Excel, there's PowerPoint, there's Docs, the, grand trio of work. What other formats of work do you think about? like you worked on Airtable. Is there a future where there's like OpenAI Airtable? Like what does that look like if you ever ended up doing it?Akshay Nathan [00:24:41]: It's a really good question. I think,Formats of Work: Sites as Knowledge ArtifactsAkshay Nathan [00:24:43]: one that you didn't bring up was Sites, and I think that wasSwyx [00:24:46]: SitesAkshay Nathan [00:24:46]: A core part of this launch. There's one side of Sites that I think people commonly talk about, especially on Twitter and stuff or X, of like, this like prototyping tool. And like we saw that happen with this launch even. The model slider that you guys were referencing earlier, like that was developed almost fully in a Site. Like, the collaboration between design and engineering and product on that was like on a site where we play with, the affordance and figure out how it feels and all of that. But the other aspect that I think is a little bit less talked about is like Sites as like an artifact for knowledge work. I was talking to someone the other day who's on like our corporate finance team, and like we were mentioning how like now when they have these reports that they're, they're working on as a team month to month, historically those things were in slide decks and in spreadsheets, and now they're just in Sites. And like Sites is the mechanism that they collaborate across the team. And the reason is ‘cause it's like, it's like somewhat higher bandwidth. Like, at these tools like PowerPoint and Excel are like infinitely flexible, but at some point you reach the boundary of like either as a human you may not know how to use some feature or something, or the product itself doesn't support it. But with a site you can do anything. You ask for anything and you can get that. once people see that magic, I think it's been really valuable.Swyx [00:26:02]: Yeah, let me show you my case study. this involves all the hot topics including ChatGPT Work, but also GPT-5.6 token billionaires and token maxing and Sites and auto research. I'm a fan of this game called Strata. It's, it's like a little board game that youSites, Auto Research, and Research DashboardsSwyx [00:26:17]: That you play with, physical blocks, that come on top of it like that. So over the weekend I took like thirty photos and just threw into ChatGPT. one point seven billion tokens later, out comes this site with a fully playable thingAkshay Nathan [00:26:32]: WowSwyx [00:26:32]: With 3D, block placement and everything. Because it requires physical blocks and I needed friends to train on it so they can get better, so I can play against them. But also, I could also, do things like train an AI on it and that's, thatAkshay Nathan [00:26:45]: That's your auto researchSwyx [00:26:46]: That gets into auto research. So, you want to train your own AIs, and then make sure they self-play against, each other. I need to set both AIs. So this is AI versus AI, and they're, they're gonna self-play. the AIs start out bad and then you want to define a loss function and get good. I wasn't gonna supervise all this. I was at, I was down in San Mateo, attending a conference. What I ended up doing was, auto researching and on this and creating benchmarks and that there was just way too many parameters for me to read. So I started asking it for a site, and it's created this lab, panel. Where is there a, is there a shortcut for a site that is created?Akshay Nathan [00:27:28]: You should be able to go in the sidebar to Sites, top of the sidebar. The left sidebar.Swyx [00:27:33]: This one? Oh, left?Akshay Nathan [00:27:35]: Yeah. Just scroll all the way to the top.Swyx [00:27:36]: Oh. Oh, it says Sites. Oh, there you go. Yeah.Akshay Nathan [00:27:39]: Ooh.Swyx [00:27:40]: So it create, it creates the sites. I don't, I don't think this is, it is exactly what I wanted, but let me show you what it popped up, right? Like I think as a research artifact, it is very important to communicate, exactly, what is being done. Outputs this thing which I eventually started publishing. So I moved it off of Sites because I wanted more, database and infrastructure than Sites afforded me. But this is like a research output that you can start to mess with and like try to think about like what hyperparameters are you tuning for training AIs. And like I was trying to make like scaling laws and everything and doing all sorts of like game optimization stuff. And the fact that you can just throw this up as a research artifact, like I no longer need to read ChatGPT output. I read Site output. But then there's also a huge sprawl. Like look at how long this thing is. There's so many numbers. It is pretty overwhelming, so then I have to start pruning it from there. But, it's an interesting transition from Markdown effectively that you're putting out to, you're putting out a whole functional site.Akshay Nathan [00:28:41]: I think Markdown just isn't that optimal for people to read, right? Might as well just write HTML website and I don't know. I think you can do a lot with customizing this, right? You have your skills that explain what you want. Like I noticed they're quite verbose. I don't need a lot of this information.Swyx [00:28:57]: It's very verbose.Akshay Nathan [00:28:58]: So and then the nice thing of having a site side by side is, you just iterate on what you want and what you don't, right?Swyx [00:29:05]: Yeah. I don't know if, any that triggers any stories for you of how it's run internally. Am I doing this right?Akshay Nathan [00:29:11]: Yeah. I think that this is like a workflow that we're seeing like all different types of teams use, where like the canonical artifact that was previously a deck or something is now becoming a site. And like with a site you, because it's just HTML, you can like. It's infinitely flexible. And so, if you want to give more prominence to a certain thing that like in a slide deck would, feel like it was buried, like you can do that. You can have it be like the hero image, right? And so I think that like, people are starting to see that. There's more work to be done to make these things like much more easier, easy to collaborate on. You mentioned that they're very, they're long and verbose, could be broken up. I'm sure that there's still something to do there.Swyx [00:29:53]: They're super long. Yeah.Akshay Nathan [00:29:54]: Yeah. But I think we're starting to see that like there is this aspect of this is a really interesting, format, for people to use, that's like much more flexible than what they ever had before.Swyx [00:30:07]: I think your job also comes becomes meta. You're not designing the products. You're designing a product to make products, and I'm curious how you manage that.Designing a Product That Makes ProductsAkshay Nathan [00:30:18]: I think one thing that we've been Like when we look at the UX, like that we've been thinking a lot about is how can we balance like simplicity with capability? Like if we're designing a product, like you said, that like is made to make up build other things, right? You can build so many different things. But we can't put that all in front of you because you'll get overwhelmed.Vibhu [00:30:41]: Yes.Akshay Nathan [00:30:41]: And so we had similar problem or similar challenges even Chat-with ChatGPT, but especially now, like when there's so much that can be done, I think the balance that we're constantly trying to strike is like, how can we give the user enough of a UI surface where, they can be expressive, they can tell the agent what they need, they can verify that it's using the right tools, it's pulling from the right sources, et cetera, but then it gets out of the way. And then how can we build the right system such that we can show them instead of telling them what can be done? Because so much of this is gonna be like, how do they discover the next use case and the next one after that if they really want to be super powered by the AI.Games, Private Evals, and Show-Don'TellVibhu [00:31:19]: Yeah. It's interesting. I feel like everyone also just has a different way to do it, right? I made a similar version of this same game. I didn't take any pictures of board or rule game. I threw in at goal eighteen minutes, fifty-three seconds later, a lot of tokens later, I've got a similar version. not with all the auto research and whatnot, butAkshay Nathan [00:31:39]: You gotta do all the latest trends.Vibhu [00:31:40]: And yeah, I did it with, did it with Codex, not Work, but it's interesting, right?Akshay Nathan [00:31:45]: Yeah. And this is GPT Image generating the pro avatars. Very good for game design. LikeVibhu [00:31:51]: AndAkshay Nathan [00:31:52]: A lot of game designers were like really into GPT Image for assets.Vibhu [00:31:54]: I will say like the broader takeaway probably is the reason that we do this is more so just to test the tools, right? Like, this was also a test for GPT-5.6 came out. I had done the game on GPT-5.5, right? The ability for me to no longer need it to. I had to feed it the rules. It's, it's a pretty niche game. It couldn't find how to do this on its own.Akshay Nathan [00:32:15]: Oh, yeah.Vibhu [00:32:15]: GPT-5.6Akshay Nathan [00:32:16]: It is out-of-distribution, which is why I was also very keen on testing the GPT-5.6 capability.Vibhu [00:32:21]: But, this is just as work comes out, as new things come out, these are just our side ways to test things, right?Akshay Nathan [00:32:27]: Yeah. It's some private eval. That is not this private.Vibhu [00:32:31]: But also valuable because now you can send this to your friends and I learned about this game through seeing this.Akshay Nathan [00:32:36]: It's a hard game. He's very good.Vibhu [00:32:39]: It's good to when no one is competing with you. But yes, it's a classic RL problem of like self-play, bootstrapping your game AI. yeah, you see how easily work becomes personal and personal becomes work because the thing I do for personal, it directly informs people I work with because I showed it to them. They were like, “Oh, you can do that with GPT?” Which like I imagine is the growth strategy.Akshay Nathan [00:33:02]: Yeah. The show not tell is a big piece that, I think we've we're not still not fully cracked of like, showing people all the things that they can do with the product versus like trying to teach that to them through like, articles or onboarding or whatever.Akshay Nathan [00:33:18]: So meeting them in the moment.Vibhu [00:33:19]: It's a career risk for me, because I used to be in developer relations, right? Where your job is to show, and then you're like, “What do you mean? You don't, you don't need.” your job is to tell. And then. But the product people are like, “Well, we don't need you if our product is intuitive enough.” SoAkshay Nathan [00:33:37]: Yeah. that's the magic of the models. So you can tailor the telling or the showing to like specifically what the user needs, like what they care about, what they've done in the past, exactly where they are on the adoption journey. So I think that's like gonna be a super big opportunity.Vibhu [00:33:50]: Seems easier and easier now to tailor custom showing, right? People have different use cases. As much as you said you don't wanna segment different people into different buckets, right? It's also not that hard to for people that are in different categories. But the question, is you said your team is more broadly on. What was the term you used? Productivity?From Developers to Knowledge Work to EveryoneAkshay Nathan [00:34:12]: Productivity.Vibhu [00:34:12]: Productivity. So howAkshay Nathan [00:34:12]: Which is now work.Vibhu [00:34:14]: Is it work? Is there another distribution that we're not hitting? Is there a group of people that will have something different than ChatGPT, Codex or Work? Is there more that the mass isn't targeting?Akshay Nathan [00:34:28]: I see it as like a sequencing, like. The vision is like bring useful agents to everyone. We started with like developers. Like developers historically are like early adopters that are willing to put up with more friction, set things up, et cetera. Like that's where, Codex started. I think the next opportunity is like what we call general knowledge work, all the other functions around developers. I think when you go from developers to this segment, like there's inherent challenges with like, this show not tell thing that we're talking about, making the product more understandable, bringing in new capabilities that matter more for this cohort than matter for developers, things like artifacts, things like computer use, et cetera. And then I think like the same learnings, like similarly how we took the learnings from developers and brought it to, general knowledge work, the next stage will be like taking the learnings from general knowledge work and bringing it to everyone no matter what they're doing in their lives. And we're already seeing that a little bit. Like this game example that you have is, something that's like on the border of like fun and personal life to, your professional life. I use ChatGPT Work full-time at home for everything, like for whatever I'm doing. I used it the other day to come up with a meal plan and like, save that on the like computer environment that it has and something that I can continue going back to. Like is everyone doing that yet? Probably not because the thing says work on it, but eventually, we wanna get people there.Vibhu [00:35:51]: ChatGPT life.Akshay Nathan [00:35:52]: Yeah, exactly. ChatGPT cooking. But I think there's a lot of, there's a lot of opportunity there, but I see it as like, we're, we're built we built a foundation in software engineering, and we're gonna take the same learnings that we take from software engineering to knowledge work to everyone.Vibhu [00:36:07]: Do you have any power user advice? I feel like, there's a group of people that will live it, use it for everything, stay on it twenty four-seven. And then there's a bit of a gap between that crew and people that, okay, I use it for work. I use it occasionally. Sometimes I type questions. any advice, any learnings, anything you recommend or just, takeaways that you've found that help bridge that gap?Power User Advice: Push the Frontier of ImaginationAkshay Nathan [00:36:30]: I think a couple things that I've seen is like, one, that it really helps to broaden your imagination of what's possible, and this has been a learning even for me. Like, the technology has progressed so fast that, something that, like, even three months ago, like, no way the models can do this. Like, now it's like, wow, it's like it can. Like,Swyx [00:36:52]: Give an exampleAkshay Nathan [00:36:52]: We're going through right now our, like, review cycle internally, and, people always talked about this as, like, a thing that the models are good at and like, there's a cliché of like: Okay, like, no one wants to be writing reviews and, like, we just use AI to do it. But in all seriousnessSwyx [00:37:09]: And it can evaluate it as well.Akshay Nathan [00:37:10]: Yeah, exactly. In all seriousness, before it was, like, just, like, slop and, like, I think it was helpful, but, not super productive. Now I've found that, like, the model can do a much better job than me, especially in this environment of, like, pulling context on, like, what people are up to, how they've like the things that they've done to make a difference, highlighting like, wins that they've had that, like, I might may not even have seen. It has access to, like, everything, right? Like the code, like, things that they've caught, reviews, Slack, everything. And so it's, like, incredibly powerful in that domain and, like, just like six months ago, the last time we did this cycle, like, I didn't even I tried using it, but it was not at all helpful. And this time it's been, like, incredibly helpful and, like, so I think continuing to push the frontier of imagination of what's possible, even if you tried something before, I think is maybe the my biggest piece of advice. The other, thing is, like, the more you put in, especially in this environment where, like, the model has access to everything on your computer or in ChatGPT Work, like you can create, artifacts over time and save them in your library and, like, the model will continue having access to those. Like, the more information you give it about whatever domain you're in, whether it's your life or your work, the more valuable it becomes, and it'll become valuable in, like, ways that might surprise you. Like, it might pull from context in a way that, may be proactive and that you might not even have thought about. But it needs to have access to those, to that those tools or that context first.Reviews, Agentic Search, and Context GatheringSwyx [00:38:27]: One thing I just wanna talk about the review stuff because I'm still that's a very sensitive thing and you're, you're a founder, you've managed people, you've hired people. As manager myself, I'm very reticent to put out any LLM-generated things especially when it comes to people, ‘cause it feels like you don't care.Swyx [00:38:46]: Presumably at OpenAI, people are more open to being eval rated by GPT. But are there any unofficial rules around this? Like, what's the etiquette?Akshay Nathan [00:38:57]: Oh, I think the etiquette is that, like, I would never write something via, like, well, solely via AI and, like, present it as, like, a review for someone. What I was talking about is more, like, gathering context. That's the place where it's incredibly helpful.Swyx [00:39:08]: So it's just search.Akshay Nathan [00:39:09]: Yeah, exactly.Swyx [00:39:09]: It's agentic search. Yeah.Akshay Nathan [00:39:10]: It's like agentic search, but, that you can tailor and steer much more capably than you could before, ‘cause, like, the thing is it's all there's a flywheel happening, right? Because of Codex, people are able to do, and because of ChatGPT, people are able to do so much more now than ever before. And if you're able to do so much more, it's easy to miss things as well. And so, like, I think we need to use these same tools to keep up with all the impact that people are having and understand, where we can be helpful.Swyx [00:39:39]: I think the thing, like, I run a small company, so easy to search, but at the scale of OpenAI with the amount of messages that you guys put in Slack, do you think that it misses things?Remembering What Humans MissAkshay Nathan [00:39:50]: Probably, but I think that I also miss things.Swyx [00:39:52]: Like, it doesn't matter, right?Vibhu [00:39:53]: I think sometimes it'sSwyx [00:39:53]: Like it's, as it needs to be human-levelAkshay Nathan [00:39:54]: It's all relative, right? Yeah.Vibhu [00:39:56]: Sometimes it's nice when it finds things you wouldn't, right? Like right now, my Codex system prompts, they're set up in such a way that every project I have has a secret- separate, notes MD, and it just writes learnings to there. And then the global one can pull from all these. So sometimes it'll be like: Oh, there's this project you did like four months ago. Here's a note that we had, and it randomly pulls it back into context that I would never do, I haven't thought about.Vibhu [00:40:20]: And I'm like, okay, this is quite superhuman, right? Like, stuff that would. And, it'll save like hours on chunking of stuff or find something that's already been done. I'm like, as much as it might miss stuff, I would too, but it's very useful when it finds stuff. And I have like a very, non-super engineered solution to this. It's just marked down files that get pulled whenever they want.Akshay Nathan [00:40:41]: Yeah. I have a funny anecdote about this. Like, recently gearing up to this launch, the team has been, really cooking on it for a couple months, and over that time, like there's so much conversation and chatter going on in Slack and Docs and elsewhere. And, one of the members of the team set up this, scheduled tasks, like automation to like look at everything that's going on and, like, come up with the best memes and then post it in one of our shared channels. And like, there are two cool things about this. Like, the first is, like, I think the models are, over time, like starting to become like funny.Swyx [00:41:13]: Funny. Nice.Akshay Nathan [00:41:13]: Whereas like, a year ago, like that was not at all the case. The second is, it was what you were saying, like they find things that in surprising ways that you may not have thought of and like create connections that you may not have thought of. And that really helps with like the meme generation because then you can see something that, genuinely surprises you and, is funny in that way. So yeah, that's like not like the most productive, use of this the technology, but it does it does uncover this, like this capability that's emerging, which is just like to find information that you otherwise would not know of.Launch Momentum and the 10 Million User MilestoneSwyx [00:41:43]: Talking about the launch, I think, I have pretty much said this is the most successful launch in a long time. I think even more successful personally than 5.0, and they're announcing ten million users. Does it feel different? You've been through a lot of launches.Akshay Nathan [00:41:58]: I think it feels like a culmination. Well, I think two things. One, it feels like a culmination, like I was mentioning earlier, like this like vision mission that we've been on for a long time. Like I said, we saw the magic of Codex internally, and then we're like extremely excited to bring this to many more people and to see it working, to like see us reach, the distribution goal, numbers that you mentioned, like I think that's like huge and super exciting. The flip side of that is like, there's so much more to do too. Like, that's also really exciting. Like, ChatGPT as a whole, like the this product that, everyone almost equates to AI and like loves, has hundreds of millions of users. And so like ten million is really cool, but like we need to get this to everyone. Like, we need everyone to feel this magic. And so that's the next step from here. But yeah, I think extremely pumped about how it's going so far and the opportunities.Swyx [00:42:46]: Awesome. I did want to also Because I've, I've, I've been tracking the number closely, it transitioned at some point from just Codex users to Codex plus ChatGPT Work, because they're same harness. The whole point is that you don't, you can't, count them separately. Do you have roughly a billion, ChatGPT users? Why did it just jump to one billion right away? Like, isn't that the default on ChatGPT or no?Codex, ChatGPT Work, and the Developer BrandAkshay Nathan [00:43:11]: We don't default you into ChatGPT Work if you're on ChatGPTSwyx [00:43:14]: If you're free. YeahAkshay Nathan [00:43:15]: It's also only available to paid users right now. And I think there's like a process of, educating users of what is the value of this product, having them try it, learning from their feedback, and making it better over time. But the goal is to, get as many of the people who love ChatGPT today to like feel the power of ChatGPT Work. But I think it'll be a journey.Swyx [00:43:36]: Yeah. And Codex will still be alive as a brand for the foreseeable future. And we'll just toggle between them as needed for UI stuff.Akshay Nathan [00:43:44]: Yeah, I think it's even stronger point than that. Like, I think we fully intend to like, treat developer. Like, developers have been, a core market for us for so long, and like there's, there's so much more that we can do to make Codex great specifically for, software development, and we'll continue to do that. This doesn't take away from that at all. If anything, it should increase the utility of something like Codex, because now you can move seamlessly between writing a diff to creating an artifact or, doing a search over your factor.Swyx [00:44:11]: I do wonder how much this terminology leaks to the non-technical user. Like, do they have to learn to say artifact if I want artifact? Or.Akshay Nathan [00:44:20]: It's funny, like we call it artifacts internally ‘cause that's what the teams call it.Swyx [00:44:23]: It's nice. Yeah.Akshay Nathan [00:44:23]: But like externally, like no one says that, no one calls it an artifact. But I think that people like often, like describe things, whatever they're used to, right? So if, ChatGPT Work is good at creating slides, they'll say ChatGPT Work is good at creating slides, and that's what we want.OpenClaw, Personal OS, and Persistent ComputersSwyx [00:44:38]: One big Another, it's July of twenty-six. One big thing that also happens in, for OpenAI was OpenClaw, and that's I think a lot of people's first time really maxing a agent for personal stuff, but also crossing over to work in essence same way. As far as I understand, OpenClaw is still independent, but did you go through your own OpenClaw moments? Were there any lessons you took from OpenClaw to Codex or back? Whatever.Akshay Nathan [00:45:06]: I think there's a lot of inspiration. I did go through my own OpenClaw moment. I,Swyx [00:45:10]: Yeah, tell the storyAkshay Nathan [00:45:10]: Me and my wife like set up an OpenClaw to like try to manage everything in our house. Not that there's like a ton, but it was like quite useful. We gave it a calendar. It started, creating events for us and stuff. At some point, the laptop that we were running on, it died and never got a chance to pick it back up. But there was a lot of inspiration there, like, in ChatGPT Work, in web and mobile, like you get access to this like persistent computer environment where, you can store files, and those files stay around between sessions. And the idea is to be able to enable use cases like this. one of the members of our team uses ChatGPT Work for what they used OpenClaw from before, and then feel like it has like completely transitioned, which is like, workout planning and like meal tracking. which again, it's like a work-related thing, right? It's like not work necessarily, but it's like in personal productivity space. But it has all the same primitives. So it has scheduled tasks. It has the ability to store files on a file system. It has the ability to like reference those things over time. And so you start to see the same types of use cases emerge, which has been really cool.Swyx [00:46:14]: Is there a point that ChatGPT Work completely replaces OpenClaw? they're independent, so.Akshay Nathan [00:46:20]: Yeah, I'm, I'm not close to it, so I can't speak to the OpenClaw roadmap, but I don't think so. I think that there's gonna be, there's always a need for like this like incredible, like open source technology that team has built. And I think that we can draw inspiration, in the product and, ChatGPT, I think many more people have like heard about and used ChatGPT than have used OpenClaw. And if we can take the magic from OpenClaw and bring it to them, I think that'll be a success. I think that like one thing on the ChatGPT Work side that we feel strongly about is that like the core experience is that you come to this product and you have a conversation, start a session, whatever you wanna call it, with this agent. And the magic of the product is that you can do anything in that moment. And we would like to create a product where you don't have to click a button or to go to a different place, whatever, and you can get whatever functionality exists in, your finances app or where or any other product like in this one place. And so that's the goal. It's like it we want an extensible system with plugins where you can connect to the tools that you need in order to be able to accomplish like a financial task, where you can, if you're doing like science work, like we have an ability to like extend the system in such that you can like write the tech and it performs well. There'll always be like products that we support that are best in class at those things, but we want as much of the magic as possible in that core experience.Swyx [00:47:45]: Yeah. Do you think that you can do everything you used to do with Wealthfront in ChatGPT Finance?Finance, Data Access, and Centralized ContextAkshay Nathan [00:47:50]: I tried it. like ChatGPT doesn't yet custody, cash and assets for me. So that part, no, not yet. But I, there was like a whole component of like retirement planning and, like financial planning and budgeting and stuff that, we were looking into when I was there. And like with the finances plugin, like that's all possible with ChatGPT today. So, I feel

Show DescriptionCodePen 2.0 is out now and we're talking about the launch, the idea of templating on the web, and how HTML could look very interesting in the future. Listen on WebsiteWatch on YouTubeLinks Yarn Announcing TypeScript 7.0 CodePen 2.0 announcement blog post SponsorsNotionWrite custom tools for Notion Agents that generate assets, query live data, and hit any API. Listen for incoming webhooks from any app, then run workflows with Notion Agents, pages, databases, and external APIs. All of this, on a hosted runtime. Workers are isolated sandboxes managed by Notion, so the code behind your syncs, tools, and workflows runs on our infra instead of your servers.

This month on Sync Up, Stephen Rice and Arvind Mishra are joined by Joe Komban, a Principal Product Manager on SharePoint, to explore two connected leaps forward for Copilot in SharePoint and OneDrive. First, the new file tools — watch Copilot build a quarterly report from a template, spreadsheet, and meeting transcript in minutes, convert it to PDF, grant every meeting attendee access, and even spin up an animated HTML dashboard. Then the star of the show: Copilot Skills, a way to teach Copilot your team's exact process — from legal reviews to file-naming conventions — so anyone can get expert-level output, even if they're not the expert. The team digs into how Skills work, where they live (just markdown files in SharePoint), the human-in-the-loop transparency built in, the community GitHub repo, and why "you're only limited by your imagination." Skills are available today with Copilot in SharePoint, with OneDrive file tools rolling out to frontier customers in the July–August timeframe. Your SyncUp cohosts: Stephen Rice | Arvind Mishra Main resources: Review and subscribe to Sync Up | Keep up to date on the OneDrive blog | Follow us on: Apple Podcasts | Spotify | RSS 

Navigational Keywords - Business Strategies for Websites (Best Practices)The conversation focused on the critical role of navigational keywords in business website strategies and best practices for SEO. Several points were raised, including the distinction between informational and navigational keywords, the practical implications for website structure, and how search engines and users interact with these elements.Key Themes & ConceptsWhat Are Navigational Keywords?One concept discussed was the definition of navigational keywords: these are brand-specific queries used to find specific web pages or destinations within a website, such as "Brand + login," "Brand + pricing," or "Brand + contact." Navigational keywords contrast with informational keywords, which are broad and unbranded, like "best restaurant near me" or "how to tie a tie" 09:06.Why Are Navigational Keywords Important?A key theme that emerged was the way navigational keywords indicate high search intent. Users searching with these terms already know the brand and are seeking specific information or functionality—like product pages, contact details, or policy documents 10:15.How to Implement Best PracticesThe discussion explored actionable best practices for leveraging navigational keywords on business websites:Dedicated Pages: Businesses should create clear, dedicated pages for key navigational actions—such as /contact, /about, /pricing, /reviews, /careers—so that search engines have specific, easily indexed URLs to display in search results 14:03.URL Structure & Keyword Density: URLs should reflect the navigational query and use clear, hyphenated keywords to make it easier for algorithms and users to understand (e.g., yourwebsite.com/privacy-policy). Hyphens are treated as word separators by search engines 15:12.Sitemaps & Technical SEO: XML sitemaps act as a roadmap for algorithms to crawl a website efficiently. Keeping both HTML (what users see) and XML (what bots crawl) structures organized is vital 26:31.Regular Updates: Pages should be maintained and regularly updated. The two critical dates for each page are the date published and the date last modified—these inform algorithms the content is current and relevant, improving SEO performance 45:04.Importance of Brand & Action PairingsThe conversation emphasized that effective navigational keywords pair the brand with a specific action or destination (e.g., "Sephora privacy policy," "Amazon shipping"), which allows businesses to "own" those key queries and drive high-intent traffic 12:24.Measuring & Managing SEOThe discussion also touched on website management:The number of well-structured, dedicated pages can impact investment and SEO value 18:03.Regular auditing and technical optimization of all primary and secondary web properties ensures consistency, compliance, and discoverability 22:41.Understanding audience awareness stages (unaware, problem aware, solution aware, product aware, most aware) helps align SEO content and navigational structure with user intent 35:12.TakeawaysNavigational keywords are essential for brand recognition and conversion, providing shortcuts for users seeking specific brand information.Businesses should invest in user-focused, clearly named, and regularly maintained navigational pages to capture high-intent searches 43:06.Effective technical SEO, including XML sitemaps and proper URL structures, boosts a website's usefulness to both users and search engines.By adopting these best practices, businesses ensure their websites are easily navigable for users and highly visible to search engines, setting the stage for better awareness, engagement, and growth.Who Is This For?The conversation focused on business owners, entrepreneurs, creators, and anyone looking to optimize their website's visibility and effectiveness, particularly through the strategic use of navigational keywords.It is especially relevant for those responsible for maintaining or improving the web presence of a brand, including solopreneurs and those with multiple or complex sites seeking clarity on SEO best practices and site structure (00:01:45, 00:21:48, 00:39:24).Key Moments & SEO Summary with TimestampsIntroduction to Navigational Keywords & Series Context: Outlining the focus on keywords for website business strategies (00:00:10).Website as a Core Asset: Emphasizing the necessity of a website as a brand hub for discovery and operations (00:03:00).Value of Quick Links and Site Accessibility: How having links to websites, calendars, and resources streamlines connections at events (00:04:02).Technological Tools for Networking: RFID/NFC and QR codes as ways to share multifaceted digital presences instantly (00:05:27, 00:07:00).Defining Navigational Keywords: Explained as brand-specific, high-intent search queries driving direct access to internal pages (e.g. “Brand + Contact,” “Brand + Login”) (00:08:39-00:12:24).Site Structure & Essential Pages: Importance of clear, dedicated URLs for contact, about, pricing, policies, reviews, etc. for both users and search engines (00:14:03, 00:16:17).Keyword Density, URL Structure, and Modifiers: The technical aspects affecting SEO on navigational and branded queries (00:13:33).Role of Sitemaps and Algorithm Interaction: Front- and back-end elements (HTML/XML) and the importance of current, updated content (00:26:31, 00:28:12).Awareness Stages: Moving from “unaware” to “most aware” for web users and the importance of matching their journey (00:34:42-00:36:46).Regular Updates and Maintenance: The necessity of revisiting and updating site pages to keep them “alive” in the algorithm (00:44:46, 00:45:50).Summary and Call to Action: Clarity, connection, and proactive site management—with encouragement to take the next steps (00:47:12, 00:48:28).FAQs1. What are navigational keywords and why do they matter?Navigational keywords are search queries aimed at finding specific branded pages (e.g. “Brand + Contact”). They guide users directly to the information or action they seek, playing a key role in brand visibility and customer experience (00:08:39, 00:10:15).2. What essential pages should every business website have?Contact, About, Pricing, Privacy Policy, Reviews, Careers, Locations, Return Policy, and Service pages—all clearly labeled with descriptive, hyphenated URLs (00:16:08).3. How often should I update my website pages?Regular updates signal activity and relevance to both users and algorithms. Important dates include “published” and “modified,” which should reflect recent engagement (00:45:04).4. How can I connect multiple websites or domains effectively?Centralize branding, ensure consistent linking, and consolidate scattered domains when possible so search engines understand their relationship and users can easily navigate between them (00:21:57).5. What's the role of awareness stages in website strategy?Your content and site structure should serve users at different familiarity levels—from “unaware” to “most aware”—with easy access to relevant information at every stage (00:35:12).Action Steps from Favour Obasi-ikeAudit All Active Website Pages:Ensure every required page exists, is linked clearly, and contains current information (00:16:17).Check and Update URLs:Use descriptive, hyphenated slugs for internal navigation—e.g., /about-us, /contact, /pricing (00:15:12).Establish an Update Routine:Schedule regular reviews to update modified dates—especially for high-traffic or business critical pages (00:44:46).Centralize Links Across Platforms:Use tools (link aggregators, NFC cards, QR codes) for seamless access from all touchpoints (00:05:52, 00:07:00).Monitor User Awareness:Identify which stage (unaware, problem aware, solution aware, product aware, most aware) your visitors occupy and tailor CTAs and navigation accordingly (00:35:12).Encourage Feedback and Continuous Learning:Collect responses (like the “put a 1 in the chat”) to gauge clarity and adjust future content and site structures (00:46:10).Ready to Rank? Book Your SEO & Web Dev Services Today

In this episode of the AppleVis Extra podcast, David Nason is joined by Ashley Cox, a blind software developer who is building native apps for Apple's platforms with a strong focus on accessibility, usability, and thoughtful design.Ashley shares his journey into software development—from teaching himself HTML in the early days of the web to developing sophisticated Swift applications for iPhone, iPad, Mac, Apple Watch, Apple TV, and Apple Vision Pro. Along the way, he discusses how personal needs often inspire his projects and why creating software for yourself can lead to better experiences for everyone.The conversation takes an in-depth look at Simulcast, Ashley's upcoming podcast application that combines podcast playback, internet radio, Apple Music integration, iCloud syncing, Siri support, and a host of accessibility-focused features into one native Apple experience. Ashley also discusses two additional projects currently in development: Levelly, a precision spirit level for iPhone; and Audit, a modern single-track audio editor designed specifically for macOS.David and Ashley also explore:Learning Swift as a blind developerWhy native apps still matterCross-platform versus native developmentBuilding accessible interfaces from the ground upVoiceOver customization, rotor support, and real-world testingWorking with hundreds of beta testersUsing Apple APIs such as Siri, Apple Intelligence, ShazamKit, and iCloudThe realities—and frustrations—of publishing software on the App StorePricing apps without relying solely on subscriptionsHow AI is changing modern software developmentWhy blind developers bring unique perspectives to software designWhether you're interested in Apple development, accessibility, podcast apps, or hearing from developers within the blind community, this conversation offers an honest look behind the scenes of modern app development and the passion required to bring quality software to life.Resources & Links:Ashley CoxThe Blind Man's WorkshopAppleVis discussion: Seeking Testers for my new iOS and macOS Podcast AppIf you're interested in following the development of Simulcast, joining the beta, or reading feedback from early testers, be sure to visit the AppleVis discussion thread linked above. It includes feature updates, development news, and information on participating in the TestFlight beta.Thanks for listening to the AppleVis Extra podcast.TranscriptDisclaimer: This transcript was generated by AI Note Taker – VoicePen, an AI-powered transcription app. It is not edited or formatted, and it may not accurately capture the speakers' names, voices, or content.David: welcome to the Appoviz Extra podcast. David Nason here, delighted to be with you for another episode. On today's podcast, really excited to be joined by a developer called Ashley Cox. This is the first in a mini-series of podcasts we're doing to showcase blind and low vision developers. So people from within our community, who…

Hay un debate que lleva tiempo flotando en toda la comunidad del desarrollo web, y en la de WordPress en particular: ¿tiene sentido seguir apostando por una plataforma tradicional cuando una IA es capaz de generar páginas enteras, con código personalizado e interfaces, en cuestión de segundos? Es una sensación perfectamente comprensible. Cuando ves lo que la inteligencia artificial puede hacer al vuelo, es natural preguntarse qué espacio le queda a WordPress. Pero, bajo mi punto de vista, el valor de WordPress no ha desaparecido: simplemente ha cambiado de lugar. En lugar de competir contra la IA, WordPress se está consolidando como el motor de ejecución y la base de datos sobre la que esa misma inteligencia artificial opera. Propiedad y soberanía de los datos La gran diferencia entre las soluciones no-code basadas en IA (o las plataformas SaaS empaquetadas que te alquilan su infraestructura) y WordPress es el control total. Con WordPress, la base de datos, los usuarios, los pedidos y los contenidos son 100% tuyos o de tu cliente. No hay locking. Ese concepto que antes se aplicaba a ciertos temas cerrados hoy lo trasladamos a la IA: no dependes de cambios drásticos en los precios de las APIs de empresas como ChatGPT o Claude, ni del cierre repentino de un servicio. La soberanía digital es un activo cada vez más crítico. Hay empresas que despidieron a mucha gente pensando que la IA lo haría todo, y muchas han terminado quebrando por eso. La IA necesita un backend confiable La inteligencia artificial es extraordinaria generando código y diseñando interfaces al vuelo, pero necesita una estructura sólida detrás para funcionar en el mundo real: la lógica de negocio, la persistencia, la gestión de usuarios y roles, las pasarelas de pago, las suscripciones y los flujos complejos. Un ejemplo real: la web de vozcaster.com la desarrollé entera con IA. Registré el dominio, la alojé en Cloudflare y le pedí a la IA que me creara un HTML estático muy mono, con colores bonitos y una tabla de precios dinámica con su selector anual. Todo rápido y precioso. Pero cuando llega el momento de la verdad —los usuarios, la seguridad, la gestión de suscripciones—, esa tabla enlaza a potencia.pro, donde todo lo gestiona WordPress mediante un plugin. La IA te resuelve la parte estática; la lógica de negocio y la persistencia en condiciones te las da una plataforma madura. Infraestructura lista para usar En lugar de construir desde cero un sistema de autenticación, una base de datos y un panel de administración para cada proyecto generado por IA, WordPress te ofrece toda esa arquitectura probada en batalla de forma nativa. Además, WordPress ya no es el simple generador de plantillas HTML o el gestor de blogs de antaño. Hoy cuenta con: APIs robustas gracias a su REST API y a soluciones como GraphQL. Un funcionamiento excelente como CMS headless, donde la IA alimenta los datos mientras la capa de presentación se gestiona externamente. Integración nativa con agentes de IA gracias al protocolo MCP, que permite conectar un sitio WordPress con modelos de inteligencia artificial para generar contenido, automatizar soporte o procesar datos de forma mucho más sencilla, precisamente por la madurez de la plataforma. Los flecos y los casos de borde Crear un prototipo visual con IA lleva minutos. Pero resolver los flecos finales —el cumplimiento del RGPD, las integraciones con ERP locales, la facturación compleja, los flujos de trabajo específicos— sigue requiriendo un sistema maduro. El repositorio de plugins de WordPress aporta esa flexibilidad para cubrir ese 1% de necesidades complejas que una herramienta automática suele pasar por alto. Es curioso: la IA arranca a toda velocidad, pero al llegar a cierto punto se frena, porque necesita hacer cosas que WordPress ya resuelve de forma simple por estar tan implementado. En una carrera de fondo, WordPress se come a la IA. Pasa igual que con los constructores visuales: Elementor es maravilloso y rapidísimo para la primera versión de una web presencial de cliente, pero cuando quieres implementar cosas más complejas a veces te toca irte a temas como GeneratePress. Son esos casos de borde que WordPress tiene más que superados. El rol del profesional no desaparece: se transforma Es como el agricultor que pasa del arado al tractor: sabe lo mismo, pero lo hace más rápido. El trabajo pesado ya no es picar CSS ni maquetar desde cero. El valor del profesional con experiencia en WordPress ha migrado hacia: La arquitectura de la información y la estrategia. El criterio técnico y la seguridad: saber evaluar si el código generado por la IA es seguro, optimizado y mantenible. La capacidad de conectar el ecosistema de WordPress con flujos de IA para aportar valor real, no solo un sitio estático. Otro ejemplo propio: he desarrollado un bot de Telegram que se conecta con WordPress para publicar capítulos de podcast (de hecho, este capítulo lo estoy publicando con ese sistema porque no tenía ganas de editar). Si yo no tuviera el criterio adquirido a lo largo de más de 15 años, no podría usar la IA de forma eficiente. Sé dónde está la identificación de usuario, cómo se gestiona una suscripción, dónde están los datos, y conozco a fondo plugins como PowerPress. Ese conocimiento profundo es el que me permite establecer la arquitectura y la estrategia adecuadas. No cualquiera puede desarrollar así. En resumen La IA ha democratizado la creación de webs sencillas: cualquiera puede pedirle una página que le diga lo guapo y lo alto que es, y quedará preciosa. Pero, precisamente por eso, las arquitecturas abiertas y extensibles como WordPress se han vuelto aún más valiosas como núcleo de proyectos digitales complejos: gestión de bases de datos, arquitectura, estrategia y capa de negocio. Cuando mezclamos ambos mundos —lo que ya conocemos de WordPress con la inteligencia artificial— aparecen cosas maravillosas. Así que no, la IA no nos va a quitar el trabajo: nos facilita avanzar de forma eficiente. Pero hay que usarla con criterio, y ese criterio es justamente lo que los desarrolladores del mundo WordPress hemos acumulado durante años. La IA sin criterio no sirve de nada. 🤖 El contenido de este post ha sido generado automáticamente con inteligencia artificial a partir de la transcripción del audio. Puede contener errores o imprecisiones. 🎙️ Publicado con VozCaster, el bot de Telegram que convierte tu voz en un episodio de podcast publicado. Pruébalo gratis. ¿Te ha gustado el episodio? Si quieres que sigamos experimentando con bots, protocolos y empanadillas polacas, no olvides suscribirte y dejarnos tu valoración. ¡Nos escuchamos en el próximo capítulo! Métodos de contacto Enviadnos vuestras preguntas al grupo de Telegram. Apuntaos al canal de Youtube del podcast https://www.youtube.com/potenciapro Si nos queréis decir algo directamente lo podéis hacer a @potenciapro , @materron, @mpc, o en el grupo de Telegram Y si eres muy muy muy fan del podcast Echa un vistazo a cómo nos puedes ayudar en https://potencia.pro/se-prosperoso/

Hey everyone, Alex here

Claude Design went viral at launch, but has been kinda crickets since.

Topics covered in this episode: django-orjson Best Django Redis configuration for speed and size Linus Torvalds puts the foot down against Anti-AI Kernel Maintainers Django Steering Council backs the Triptych Project Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Consulting from Six Feet Up Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Michael #1: django-orjson Adam Johnson dropped django-orjson - drop-in replacements for the Django and DRF pieces that touch JSON, swapping stdlib json for orjson, the Rust-based library. Headline numbers: 10x faster serialization, 2x faster deserialization. The interesting question is why this needs to be a package at all. pip install orjson is the easy part. Adam's actual pitch: adopting it "isn't easy, especially when your framework uses json in many different parts." Django scatters JSON across JsonResponse, the test client and test case classes, the json_script template tag, and more. There's no single hook to grab, so you get a library that catches them all. Adam is refreshingly honest about the scale of the win. His words: "While database queries tend to dominate the typical Django application's runtime, the time spent in serialization and deserialization can still be significant." He calls it "a nearly free performance win" - not "this will 10x your app." That's a claim about cost, not magnitude, and it's worth keeping those straight. Worth flagging what the post doesn't cover: caveats. There are none in the article, but orjson has real ones. Django and Flask both render datetimes as RFC 822 HTTP-date (Wed, 15 Jul 2026 12:00:00 GMT); orjson does ISO 8601. It can't do ensure_ascii, it rejects NaN and Infinity (which stdlib happily emits), and it raises on Decimal. If you've got a JS client parsing dates, that's a wire-format change. Who should actually take this? If you're a DRF shop shoveling JSON all day, yes - it's cheap and it's real. If your app mostly renders HTML templates, you're optimizing a slice of runtime that's already near zero. The problem Adam's package solves doesn't exist in Flask or Quart. They already centralize every JSON operation - jsonify, request.get_json(), the test client, the |tojson filter - behind one provider object at app.json. So there's no library to install. It's about ten lines: import orjson from quart.json.provider import JSONProvider # or flask.json.provider class OrjsonProvider(JSONProvider): def dumps(self, obj, **kwargs) -> str: return orjson.dumps(obj).decode() # provider must return str def loads(self, s, **kwargs): return orjson.loads(s) app.json = OrjsonProvider(app) The numbers on talkpython.fm Evaluated it, measured it, and skipped it. The biggest JSON payload we serve is our MCP server returning a cached episode transcript, about 139 KB. Swapping the provider saves 0.119 milliseconds per request. That total response takes 1.1 ms We got 4.1x, not 10x - and the reason is the good lesson. Payload shape decides your speedup. The 10x is for structure-heavy data, lots of small keys where stdlib burns time in Python-level dispatch per item. Our hot payload is one giant transcript string, so the work is escaping and memcpy Calvin #2: Best Django Redis configuration for speed and size Peter Bengtsson revisits a classic: his 2017 "Fastest Redis configuration for Django" benchmark now has a 2026 update posted this week. The 2017 post pitted django-redis serializers (json, ujson, msgpack, pickle) and compressors (zlib, lzma) against each other; conclusion was msgpack + zlib as the sweet spot - avoid the json serializer, it's fat and slow. The 2026 update narrows focus to just compressors: default (no compression), zlib, lzma, and newcomer zstd. New results: lzma compresses best but is slowest; zstd is the fastest compressor on Ubuntu; differences between them are very small. Big takeaway across both: compression buys you a lot of space (2–3.5x smaller) for very little speed cost - worth it for Redis where memory is the constraint. Caveat from the author: results depend heavily on your data - his test stores short strings of numbers, so benchmark your own workload. Michael #3: Linus Torvalds puts the foot down against Anti-AI Kernel Maintainers Write up on Ars. Really good coverage by Maximillian: Time to wake up (for some) Torvalds said that “Linux is not one of those anti-AI projects, and if somebody has issues with that, they can do the open-source thing and fork it. Or just walk away.” I agree with Max, putting your head in the sand and waiting for AI to go away will likely mean you won't be working professionally in software development in the coming years. The statement came amid a lengthy thread arguing about the use of Sashiko, an “agentic Linux kernel code review system” that its creators claim can, in tests, independently find 53.6 percent of the bugs that would end up being fixed by human coders in later commits. “We're not forcing anybody to use [LLM tools], but I will very loudly ignore people who try to argue against other people from using it,” Torvalds said. “Anybody who points to the problems at AI had better be looking in the mirror and pointing at themselves at the same time,” Torvalds wrote. Calvin #4: Django Steering Council backs the Triptych Project Django Steering Council issued a Letter of Collaboration backing Carson Gross & Alex Petros's funding bid for the Triptych Project - three proposals to make HTML more expressive natively, in every browser. The three additions: PUT/PATCH/DELETE methods for forms, button actions (buttons that fire HTTP requests without a wrapping form), and partial page replacement. Distills the core ideas from HTMX/Unpoly/Turbo into the HTML standard itself - no JS, no library, nothing to ship or maintain. Current focus is button actions (WHATWG #12330): Logout instead of wrapping a button in a form. Relevant to Django directly - think the admin submit row and disguised delete links; Django 6.0's template partials were already inspired by these patterns. How to help: companies can send non-binding letters of support on letterhead; individuals can read the proposals and weigh in on the WHATWG issues. Extras Calvin: DOOMQL - A playable first-person shooter whose framebuffer is a SQL query. Michael: Granian 2.7.9 fixes WSGI threadpool scheduler starvation/underscaling Welcome Calvin post Joke: Solving all bugs

Who would have thought that a tiny HTML file could lead to a diagnostic based on server logs?This week I share the latest fruit from the on-going site recovery that will soon be a case study on how to recover from a Helpful Content "site demise".If Google is verifing your domain once a month on the same day - without a doubt you are in topical trouble.I have more professional ways to describe it - at the end I give you the 4 takeaways from this information.Get used to looking at your logs!Mentioned in the show:VizzEx - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://vizzex.ai/vizzex-pro/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠This week a little more about the newest VizzEx tool - Symmetry Gate™ Check Tool. You can find it at:⁠⁠⁠⁠⁠⁠⁠https://symmetrygate.ai⁠⁠⁠⁠⁠⁠⁠Last week's episode: https://podcasts.apple.com/us/podcast/symmetry-before-semantics-why-seo-meaning-without-proof/id1547082306?i=1000776753514Get on the waiting list for the next cohort of the AI Visibility Mastery 12-month course. We are committed to the success of our members.⁠⁠⁠⁠⁠https://vizzex.ai/ai-visibility-mastery/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Wordpress registration⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ HubSpot registration⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Symmetry Gate⁠⁠⁠⁠⁠⁠ - check the "cost" of your content for AI models to extract informationSubscribe to Confessions of an SEO™ wherever you get your podcasts. Your subscribing and download sends the message that you appreciate what is being shared and helping others find Confessions of an SEO™An easy place to leave a review ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.podchaser.com/podcasts/confessions-of-an-seo-1973881⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠You can find me on⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Carolyn Holzman⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ - Linkedin⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠American Way Media⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Google Directly⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AmericanWayMedia.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Consulting AgencyNeed Help With an Issue? - reach out Text me here - 512-222-3132Music from Uppbeathttps://uppbeat.io/t/doug-organ/fugue-stateLicense code: HESHAZ4ZOAUMWTUA

Matthew McQueeny sits down with digital strategist Peter Stringer for a conversation that begins with childhood memories of Boston sports and journalism—but quickly turns into an exploration of fascinating career parallels. Both Matthew and Peter started as aspiring sports journalists. Both discovered the web in its infancy and used HTML, websites, and digital publishing to bypass the traditional media gatekeepers. One landed with the New Jersey Nets. The other with the Boston Celtics. Together, they witnessed—and helped shape—the transformation from newspapers to websites, from websites to social media, and now from search engines to AI. Along the way, they share stories from inside professional sports organizations, what it was like earning trust with coaches and players, how team websites disrupted traditional media, and why the best careers often come from embracing technologies before everyone else realizes their importance.

In un mondo dove i formati di scrittura sono molteplici e ciascuno pensato per obiettivi specifici, ce n'è uno che, negli ultimi anni, ha guadagnato una popolarità inaspettata: il Markdown. Non è il più potente, non è il più completo, eppure è diventato il linguaggio prediletto dall'intelligenza artificiale. Ma perché proprio il Markdown? E come si è evoluto dall'idea di un blogger nel 2004 a diventare lo standard attraverso il quale le macchine comunicano con noi? In questa puntata esploriamo la storia dei linguaggi di markup, dalle radici dell'HTML ai sofisticati LaTeX e AsciiDoc, per capire cosa rende il Markdown così speciale e se continuerà a dominare nel futuro, oppure verrà soppiantato da nuovi contendenti.Nella sezione delle notizie parliamo di come l'IA e il quantum computing stiano rivoluzionando la scoperta di nuovi peptidi, del nuovo limitatore di velocità europeo e infine del successo della Cina con il primo recupero di un razzo riutilizzabile.--Indice--00:00 - Introduzione01:03 - IA e quantum per creare peptidi più efficaci (Wired.com, Luca Martinelli)02:33 - Il nuovo limitatore di velocità europeo (HDMotori.it, Davide Fasoli)03:33 - Anche la Cina ha il suo razzo riutilizzabile (DDay.it, Matteo Gallo)04:48 - Perché l'intelligenza artificiale scrive in Markdown? (Luca Martinelli)19:38 - Conclusione--Testo--Leggi la trascrizione: https://www.dentrolatecnologia.it/S8E29#testo--Contatti--• www.dentrolatecnologia.it• Instagram (@dentrolatecnologia)• Telegram (@dentrolatecnologia)• YouTube (@dentrolatecnologia)• redazione@dentrolatecnologia.it--Brani--• Ecstasy by Rabbit Theft• Baby by Sam Day

Here are the descriptions written out in standard text format, ready to copy and paste without any of the HTML coding. 1. Libsyn Show Description (Podcast Directories) Wrap up your trading day with host Mark Longo on The Hot Options Report for Thursday, July 16, 2026. Tech giants and chipmakers are feeling the heat as we head into a massive Friday expiration. Whether it's traders hunting for late-stage juice in Micron or managing risk in SpaceX, the options tape was absolutely on fire today. Inside This Episode: Capitol Hill Trades: We dive into recent stock purchases from House members including Amphenol Corp (APH), Ellington Financial (EFC), and Hilton (HLT). The Tech & Chip Sell-off: Chip names get hammered despite strong Taiwan Semi numbers. Intel (INTC) drops nearly 6% while Nvidia (NVDA) slides to close at $207.40, pacing the leaderboard with 2.76M contracts. The Wild West of Micron (MU): Down $51 to close at $853.20. Are traders abandoning the $1,000 strikes? We look at the heavy action on the $900 calls going for over $7.00 in premium. SpaceX Back in the Top 10: Trading below its $135 IPO price, SpaceX draws 774,000 contracts. We decipher the massive block of next-week $330 calls—is this ETF risk management at play? Earnings & Expiration Action: Post-earnings movement on Netflix (NFLX) plus short-dated plays on Apple (AAPL), Tesla (TSLA), Alphabet (GOOGL), Amazon (AMZN), and Microsoft (MSFT). Resources & Links: Run Your Own Scans: Check out TheHotOptionsReport.com to find unusual activity, scan the flow, and track the data yourself.

Lauren Wang is the CEO and founder of period-product brand The Flex Company. Before even graduating college, Wang held several positions. At just age 15, she freelanced as an HTML website builder. Then during college, she took classes at night while working for IBM and Coca-Cola on their respective marketing teams. A couple years after graduating college, she moved to San Francisco to work in tech at companies such as Autodesk and Upwork. In 2013, after many years of struggling to find period products that worked for her, Wang came up with her initial idea for a better period product. She spent the next year researching the menstrual-hygiene industry, and in 2015, she quit her corporate job to found Flex. In October of 2016, the company began shipping its first product: the Flex Disc. As the company continued to grow, it released its Flex Cup product line and expanded into new categories, and Wang was named an Ernst & Young Entrepreneur of the Year for the Los Angeles region in 2023.

Founder/ Data S ience, AI & Economic Consultant at Analytics TX LLC Consulting practice focused on analytics, economic analysis, and executive advisory. • Design consulting frameworks to audit enterprise datasets, resolve data silos, build Python analytics workflows, predictive analysis, and statistical models, translating complex data into decision systems for founders and leadership teams. • Advise organizations on analytics infrastructure, AI tool selection, and pipeline architecture; deliver executive training in statistical reasoning, business analytics, and economic indicators. • Provide statistical and economic expert analysis used in U.S. litigation, including modeling, statistical evaluation of claims, and expert reports. Statistical modeling and damages analyses have contributed to multi-million-dollar settlements and financial exposure reductions in complex litigation matters. (Clients confidential) • Built Post it Save it App, a production SaaS platform that ingests LinkedIn post and profile data via OAuth API and processes it into structured performance analytics dashboards and reports (JSON, Excel, HTML). Deliberately built without AI, delivering accurate, deterministic content analytics at a fraction of the cost of AI-based alternatives or reliance on LLM uncertainties. • Built an end-to-end course creation system for the Professional Certificate in Business Analytics: Data-Informed Decision Making, parsing source materials (PDFs, Word, images) and transforming them into full course content including slides, AI-generated visuals, and voiceover-driven video modules using image generation and ffmpeg pipelines. Follow her on the author page on Amazon where she has published her book:https://www.amazon.com/Invisible-Hand-Visible-Profit-Decisions/dp/B0GY7V14VL Linkedin: https://www.linkedin.com/in/kruti-lehenbauer/ ***********Susanne Mueller / www.susannemueller.biz TEDX Talk, May 2022: Running and Life: 5KM Formula for YOUR Successhttps://www.youtube.com/watch?v=oT_5Er1cLvY Join Substack: https://substack.com/@susannemuellernyc?Enjoy one coaching session for free if you are a yearly subscriber. 800+ weekly blogs / 500+ podcasts / 1 Ironman Triathlon / 5 half ironman races / 26 marathon races / 4 books / 1 Mt. Kilimanjaro / 1 TEDx Talk

En este episodio de Atareao con Linux nos vamos a remangar para hablar de una de esas tecnologías que, una vez las dominas, te cambian la vida por completo: el Web Scraping asistido por Inteligencia Artificial.Seguro que te ha pasado alguna vez. Quieres comprar un producto concreto, como unas zapatillas de running (yo las cambio cada 800 kilómetros y es un goteo constante), o quieres extraer todas las recetas de cocina de una web para montarte tu propio planificador semanal. Lo ideal sería que estas páginas tuvieran una API pública para descargar la información de forma limpia. Pero la cruda realidad es que casi ninguna te lo pone fácil. Ahí es donde entra el scraping: la técnica de extraer la información directamente de la página web.En este episodio te cuento por qué el scraping clásico (ese que utiliza Beautiful Soup en Python y depende de identificar las etiquetas HTML y las clases CSS) tiene los días contados para tareas complejas. Basta con que un desarrollador cambie el diseño de la web para que tu script se rompa por completo. Además, con la llegada de las webs dinámicas, los tests A/B y los sistemas anti-bloqueo como Cloudflare, mantener un scraper tradicional es un auténtico dolor de muelas.La gran alternativa: Inteligencia Artificial en local¿Y si en lugar de pelearnos con el código fuente dejamos que un modelo de lenguaje (LLM) entienda la página exactamente igual que lo haría un humano? Un LLM comprende perfectamente qué es un "precio" o el "nombre de un producto", sin importar cómo esté maquetada la web ni el idioma en el que esté escrita. Y lo mejor de todo: ¡lo podemos hacer 100% gratis en local usando Ollama!Te detallo mis pruebas ejecutando modelos en mi Slimbook One utilizando únicamente la CPU (¡sin gastar un céntimo en nubes ni necesitar tarjetas gráficas carísimas!). Hablaremos de cómo rinden modelos como Llama 3.2, Qwen, Mistral y DeepSeek R1, y cuál es el punto de equilibrio perfecto para no eternizarnos esperando la respuesta.También te desvelo mi fórmula secreta para procesar la información. No podemos enviarle 2 Megabytes de HTML ruidoso a la IA. Te explico los 5 pasos que utilizo en Python para eliminar la basura (scripts, estilos, navegación) y reducir el HTML hasta en un 93%, permitiendo que el modelo extraiga los datos en segundos y nos devuelva un JSON estructurado impecable.Por último, vemos cómo montar un auténtico vigilante de ofertas automatizado en segundo plano. Un sistema que compare los precios de varias tiendas en paralelo.Capítulos del episodio:00:00:00 Introducción al Web Scraping con Inteligencia Artificial00:01:22 ¿Para qué sirve extraer datos? Ejemplos prácticos00:02:42 El gran talón de Aquiles del scraping tradicional00:04:31 La revolución de la IA: Entender la web sin saber HTML00:07:36 Los problemas habituales: Selectores rotos y webs dinámicas00:10:00 Cómo un modelo de lenguaje (LLM) procesa la información00:13:17 Cuándo elegir scraping clásico vs. scraping con IA00:15:28 Comparación de costes: Enfoque clásico, IA local e IA en la nube00:17:19 ¿Qué modelos usar? Pruebas con Llama, Qwen, Mistral y DeepSeek00:18:19 Detrás de escena: Mi script de Python y la limpieza del HTML00:21:05 Creando el prompt perfecto para extraer un JSON estructurado00:24:34 Ejemplo real: Comparativa paralela entre tiendas00:28:38 Diseñando un vigilante de ofertas automatizado (24/7)00:30:17 Casos de uso prácticos y mejoras para evitar bloqueos00:32:02 Cierre y detalles del próximo tutorial de scrapingMás información y enlaces en las notas del episodio

メッセージ原稿　HTML　PDF

Check out our Patreon for a daily Lawrence Select™ Meme: https://www.patreon.com/insidegamesYTJoin the Inside Games notification Discord server for alerts when we publish new videos: http://discord.gg/ArvphbMPFJHosted by:Lawrence: http://twitch.tv/sirlarr | Bruce: http://twitch.tv/brucegreene Edited by: Shooklyn: https://linktr.ee/ShooklynSources --https://app.quotemedia.com/data/downloadFiling?webmasterId=90423&ref=320180858&type=HTML&symbol=SONY&cdn=2d35efad0a2d4f7f0d01ebb3df88be67&companyName=Sony+Group+Corporation+American+Depositary+Shares&formType=4&formDescription=Statement+of+changes+in+beneficial+ownership+of+securities&dateFiled=2026-07-07https://app.quotemedia.com/data/downloadFiling?webmasterId=90423&ref=320180863&type=HTML&symbol=SONY&cdn=f8c8615ec0639df9330ed61963fe9ca8&companyName=Sony+Group+Corporation+American+Depositary+Shares&formType=4&formDescription=Statement+of+changes+in+beneficial+ownership+of+securities&dateFiled=2026-07-07https://app.quotemedia.com/data/downloadFiling?webmasterId=90423&ref=320180863&type=HTML&symbol=SONY&cdn=f8c8615ec0639df9330ed61963fe9ca8&companyName=Sony+Group+Corporation+American+Depositary+Shares&formType=4&formDescription=Statement+of+changes+in+beneficial+ownership+of+securities&dateFiled=2026-07-07https://www.change.org/p/don-t-kill-the-disc-tell-sony-to-keep-physical-playstation-games?source_location=searchhttps://www.gamespot.com/articles/there-is-no-reason-to-buy-another-playstation-or-xbox/https://www.playstation.com/en-dk/legal/terms-of-service/https://www.gamesindustry.biz/sony-faces-dutch-lawsuit-over-artificially-high-playstation-priceshttps://www.reuters.com/business/sony-fighting-27-billion-uk-lawsuit-over-playstation-store-prices-2026-03-10/

Amit Rawat is an agentic engineer who spent two decades in QA before shifting fully into building AI agents. He's the creator of PromptWright, a desktop tool that turns natural language prompts into automated Playwright browser tests, complete with screen recording, Gherkin scenario generation, and self-healing locators. In this episode, Amit and Joe get into what it actually takes to work with AI agents at a high level, starting with why the planning phase matters more than the prompt itself. Amit breaks down his own workflow, brainstorming with AI, building a detailed plan in HTML before ever executing, and why curiosity and technical depth still matter even as AI gets more capable. They also cover why Amit believes QA professionals, more than developers or DevOps engineers, are best positioned to thrive in the agentic era, how he tracks the ROI on his $200-a-month Claude subscription, the "Chief of Staff," "Chief Health Officer," and "Chief Financial Officer" AI agents he's built to help manage different aspects of his personal life, and how he uses a memory layer so those agents understand his preferences and become more useful over time. If you're a tester, automation engineer, or QA leader trying to figure out where AI agents fit into your workflow and your career, this conversation is a practical look at what's already working today.

Scott and Wes raid the listener mailbag to settle the pnpm vs. npm debate, decode how web standards sneak into your browser, and ask the big one: is “AI” even intelligent, or just fancy autocomplete? Plus Stack Overflow nostalgia, the Shadcn head-scratcher, and why big design systems are sleeping on modern HTML. Show Notes 00:00 Intro 00:44 Welcome to Syntax! 01:51 Exploring CSS Component Approaches 07:30 Brought to you by Sentry 08:09 The Advantages of PNPM over NPM 12:31 Nostalgia and Growth through Stack Overflow Wes' Stack Overflow Scott's Stack Overflow 19:13 The Evolution of Podcasting and Video Content 23:34 Understanding the Proposal Process for Web Standards TC39 Process TC39 Active proposals Stage 3 examples CSS Working Group Issues CSS Specifications CSS Agenda+ Chrome Platform Status Mozilla Standards Positions WebKit Standards Positions Web Platform Tests HTML Issues HTML pull requests HTML Living Standards TypeScript Issues TypeScript pull requests TypeScript Iteration Plans TypeScript blog Node Issues Node pull requests Node recent releases Node release schedule 28:25 Understanding AI: The Misconception of Intelligence 33:26 The Role of Modern Web Features in Design Systems 39:20 Navigating AI in Software Development: Two Camps of Engineers 47:05 The Importance of Fundamentals in the Age of AI 49:15 Scams in the Tech Industry: A Cautionary Tale 53:34 Sick Picks + Shameless Plugs Sick Picks Scott:Warhammer 40000 minitures Wes: Baby gate Shameless Plugs Scott: Wes: Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

One of the things that helps your posts get traction is if you have a nice, pretty card that displays when you post the URL on social media. Plain blue links just don't get clicked. With WordPress and other dynamic Content Management Systems (CMSs), you can get plugins to create the pretty cards. But with Jekyll (and Hugo and other Static Site Generators), you need to build the capability into your theme. It's not hard, you just need to know what metadata to build, and of course, Bart is here to teach it to us. You can find Bart's fabulous tutorial shownotes and the audio podcast at pbs.bartificer.net. Read an unedited, auto-generated transcript with chapter marks: PBS_2026_06_27 Join our Slack at podfeet.com/slack and look for the #pbs channel, and check out our pbs-student GitHub Organization. It's by invitation only but all you have to do is ask Allison! Join the Conversation: allison@podfeet.com podfeet.com/slack Support the Show: Patreon Donation Apple Pay or Credit Card one-time donation PayPal one-time donation Podfeet Podcasts Mugs at Zazzle NosillaCast 20th Anniversary Shirts Referral Links: Setapp - 1 month free for you and me 15% off Carbon Copy Cloner Wispr Flow - 1 month free for you PETLIBRO - 30% off for you and me Parallels Toolbox - 3 months free for you and me Learn through MacSparky Field Guides - 15% off for you and me Backblaze - One free month for me and you Eufy - $40 for me if you spend $200. Sadly nothing in it for you. PIA VPN - One month added to Paid Accounts for both of us CleanShot X - Earns me $25%, sorry nothing in it for you but my gratitude

Discover Quill, a fully accessible, AI-powered text editor designed for blind and low vision users. Learn how Quill simplifies text editing, integrates AI tools, and empowers productivity with free, feature-rich functionality. In this episode, Steven Scott and Shaun Preece speak with Jeff Bishop about Quill, his new free text editor built with accessibility at its core. Quill bridges the gap between simple editors like Notepad and complex environments such as Visual Studio Code, providing a highly customisable experience that “meets users where they are.” Jeff shares how Quill supports HTML, Markdown, text expansion, dictation, voice commands, background file conversions, and deep AI integration with providers like OpenAI, Claude, and Gemini. Profiles allow users to hide advanced features for a clean, beginner-friendly interface or unlock powerful developer tools. Quill is open-source, cross-platform, and community-driven, proving how accessible technology can be transformative when designed by and for blind users. Relevant Links Quill For All: https://quillforall.org Bits: https://bits-acb.org ----Follow on:YouTube: https://www.doubletaponair.com/youtubeX (formerly Twitter): https://www.doubletaponair.com/xInstagram: https://www.doubletaponair.com/instagramTikTok: https://www.doubletaponair.com/tiktokThreads: https://www.doubletaponair.com/threadsFacebook: https://www.doubletaponair.com/facebookLinkedIn: https://www.doubletaponair.com/linkedinSubscribe to the Podcast:Apple: https://www.doubletaponair.com/appleSpotify: https://www.doubletaponair.com/spotifyRSS: https://www.doubletaponair.com/podcastiHeadRadio: https://www.doubletaponair.com/iheartAbout Double TapHosted by the insightful duo, Steven Scott and Shaun Preece, Double Tap is a treasure trove of information for anyone who's blind or partially sighted and has a passion for tech. Steven and Shaun not only demystify tech, but they also regularly feature interviews and welcome guests from the community, fostering an interactive and engaging environment. Tune in every day of the week, and you'll discover how technology can seamlessly integrate into your life, enhancing daily tasks and experiences, even if your sight is limited."Double Tap" is a registered trademark of Double Tap Productions Inc. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the release of Microsoft Copilot Cowork and its hidden financial implications for your business. You’ll learn how to calculate potential costs by categorizing your daily tasks into light, medium, and heavy workloads. You’ll discover how to apply the 5P framework to prevent runaway AI spending in your organization. You’ll identify specific strategies to optimize your workflows by separating planning from execution. You’ll explore how command-line tools can help you maintain efficiency without burning through expensive credits. 00:00 – Introduction 03:15 – Categorizing AI tasks 08:45 – The shock of the credit-based bill 14:20 – Applying the 5P framework for cost control 19:10 – Using planning to save money 25:30 – Call to action Watch this episode now to learn how to keep your enterprise AI costs under control before you start using Microsoft Copilot Cowork. Use the free Trust Insights Microsoft Copilot Cowork Cost Calculator! Watch the video here: Can’t see anything? Watch it on YouTube here. Listen to the audio here: https://traffic.libsyn.com/inearinsights/tipodcast-how-to-manage-microsoft-copilot-cowork-costs.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! [podcastsponsor] Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher S. Penn: In this week’s In-Ear Insights, let’s talk about the newly generally available Microsoft Copilot Cowork, which is a licensed version of Claude Cowork. So Katie, you have spent a lot of time with Claude Cowork. You teach for Smarter X for their AI Academy on all the different uses of Claude Cowork. You’ll be doing an entire workshop at the Marketing AI conference on the Claude ecosystem and stuff like that. So when you hear that now Microsoft, the largest enterprise AI deployment system, has made effectively a copy of Claude Cowork available, what comes to mind? Katie Robbert: Endless opportunities. I have never met someone who is like, “Yay, Microsoft.” And we’ve talked about why a lot of companies are tied into Microsoft and a lot of it comes down to security and privacy. Chris, you have a whole series on enterprise AI, so enterprise AI not being the size of the company, but really more of the security and governance requirements needed. Microsoft as a workforce software, Microsoft 365, tends to check the most of those boxes, which is why so many large companies or companies in general tend to be tied into Microsoft. Which also means what we hear is, “Well, I can’t use Claude or I can’t use OpenAI, I can only use Copilot. I want all the bells and whistles that I’m seeing you guys talking about.” Very quick anecdote. My husband, who I’ve mentioned numerous times, is not a technology person—that is not the nature of his job—was lamenting that the new version of Microsoft is hiding all the replies to his emails from the entry-level user to the expert user. I don’t know anyone who enjoys using Microsoft, but I’m hoping now that this little bell and whistle is something that could bring people around on the users. Because Claude Cowork has been such a literal game changer for the way that I operate. The amount of things that I can get done that I couldn’t get done before because I’m just one person is infinite. Just the other day, I’ve always done the company financial projections—it’s very laborious. I have a spreadsheet, I have to check numbers from four or five different places. That’s something that Cowork can now not only help me with, but build an interactive dashboard for. And it’s like, “Yeah, you got multiple data sets, I got this, I can build that for you.” The amount of time it saves me is immense because it unlocks my time to do things like, “Hey, what’s a new target market we need to go after? What does that look like?” I didn’t have the brain space to do that before because I was so bogged down. So when I hear that Microsoft now has their version of Cowork, I’m like, “Wow, people are going to get so much done if they want to, if they see the opportunities within the software, if they’re curious.” Christopher S. Penn: If they can afford it. So that’s what I want to talk about on today’s show because Microsoft has released an Excel spreadsheet, of course, a calculator for how much Cowork will cost you because it is pay-as-you-go, it is not flat rate. So let’s talk about some of the tasks that you do, Katie. They define tasks in three categories: light, medium, or heavy. A light task is basically prompt and chat, no tool calls, one deliverable. And they classify this by the four different categories: corporate knowledge workers, customer-facing knowledge workers, technical workers, and managers and senior leaders. Now I would say that you are a manager and senior leader—I think that’s who you are, what you do. I am a technical worker. We have Kelsey who is a customer-facing knowledge worker—she’s our account manager—and we have John who is our corporate knowledge worker. John is our head of business development. So we actually check the box on each of these Cowork types of people. Now on a daily basis, Katie, you for sure have at least one Cowork process that calls more than one tool because you send out a daily update. So you have at least one of those that’s a medium-level task that sends up our daily sales report. What other daily tasks do you have Coworks have to do? Katie Robbert: I have Cowork Daily set up to send me a daily writing prompt. All it’s doing is writing to a Word document. I would imagine that’s a lightweight task. Basically, one of the things that I’m doing for my own professional development is I’m trying to make sure I don’t lose that writing muscle. As AI makes it so easy to replicate our voices, I want to make sure I don’t lose it. So I spend a few minutes every morning writing to a randomly generated prompt. So I would imagine that’s a lightweight thing. You mentioned the update that I send to the team. This is calling on our CRM data, and that I would imagine is sort of a medium because that’s only one piece of software. But once a month, I’m calling on our CRM and our financial data and a couple of other sources, so that would be a heavy task. So on a day-to-day basis, the scheduled tasks that I have are fairly lightweight. But then when I get into the real thinking, that’s when—so I was working on something this morning on behalf of the team. I was engaging a plug-in, I was engaging the Google Drive connector, I was engaging the Google Search connector, I was engaging that deep thinking of “put all this information together,” and all of the skills that are involved: the skill of building a Word doc, the skill of building a PDF, the skill of building an HTML interactive page, the skill of building a PowerPoint—all of those in one specific task. So I would say that is a heavy task, even though it looks at the surface like a lightweight task. Christopher S. Penn: I would say, and I think this is a fair characterization, you probably do two heavy projects a day in Cowork because you’re constantly doing deep strategy and things. So I’m going to put two a day—this is a monthly calculus—put down 60 there. Now for Kelsey, I would say Kelsey at least does at least one light and one medium task in Claude per day. I think it’s actually more than that, but I’m going to put that down as a starting point. What do you think? Katie Robbert: I think that’s a fair starting point. Christopher S. Penn: Okay. For me, I work in Claude code, which is slightly different, but since we’re just trying to get a sense of what Cowork will cost, I’m going to do the equivalent. On a day-to-day basis, I probably do five tasks that are light, so that’s going to be 150 of those a month. I probably do 10 tasks that are medium, so that’s going to be 300 a month. And I probably—actually, I know I do over 10 tasks a day that are heavy, that are like pure heavy code lifting. So that’s going to be another 300 there for John. John really doesn’t use Claude much at all, I don’t think. So maybe like 30 at most. Katie Robbert: Yeah, I think so. We have a skill that was built specifically with his role in mind, and he runs it maybe once every couple of weeks. When I look at the weekly tasks—so this is looking at a month at a glance—I would actually bump up the medium tasks for me because I have weekly reports that are run that engagement, the Claude Chrome extension, the connections to our CRM, connections to our project management software. I have eight of those weekly. Christopher S. Penn: Okay, so you’re basically running two mediums a day. Effectively. Katie Robbert: Yeah. Christopher S. Penn: Claude or Microsoft Copilot Cowork bills on what are called credits because why make this easy? Light tasks bill 125 credits, medium tasks bill 500 credits, and heavy tasks bill 1,200 credits. The cost is a penny per credit. So our Microsoft Copilot Cowork cost—are you ready for this, Katie? $1,600 a month. Katie Robbert: Get out. We’re going back to candlelight and whittling pencils. Christopher S. Penn: That is because it’s a penny per credit, which they do to make it sound cheap, not realizing that a single heavy task is 1,200 credits. So a single task is $12. So for me to do one QA run on a piece of software is swipe the credit card for $12. On a monthly basis, we are consuming effectively 657,000 credits, which is $6,570 total, all in. It’s $1,600 per user. So Katie, our Trust Insights Copilot Cowork bill is $6,570. Katie Robbert: I have no words. That is insane. And to be fair, so you and I, Chris, I would say are power users. We are turning to these tools to do all kinds of things all day long. Even with trying to do things and schedule them off-hours to not be during peak usage, we’re still using up usage. And yeah, we are a small team. If we take out the work that Kelsey does just for the sake of this example, you and I are still eating up the majority of the cost. If we take out you, I’m still eating up a majority of the cost. I don’t know how a company or team is supposed to be able to afford to use this. It’s a real bait and switch. Shame on Microsoft. Christopher S. Penn: Well, this is enterprise. They can do this. Katie Robbert: Yeah, they can. It doesn’t mean they should. Christopher S. Penn: So your usage, because a credit is a penny, your usage of Copilot Cowork a month would be $1,057.50. That is how much you consume in equivalent credits in the system. Now granted, we pay for the four of us to share a Claude Max 20 account; we pay $200 a month for it. This at the enterprise level, you’re talking four people, $1,600 for four people, one of whom barely will use it. Realistically, like you said, we’re probably going to average $3,000 an employee is what it will cost to use Cowork. Katie Robbert: Which is an insane amount. For some companies that don’t even blink at that, but that’s a very small handful of companies who would feel that way about $3,000 a month. One of the things that we’re doing with a lot of our clients right now is trying to help them find cost savings in their tech stack—like how many tools can they reduce or licenses they can let go of and replace with things like Claude Code or Claude Cowork. But if they’re like, “Yeah, I want to do that exercise,” and what I have is Microsoft Cowork, I would say, “Cool, we’re not doing that exercise until Microsoft changes the billing,” because it’s going to cost you 10x more than it’s costing you now. It’s not worth it. Which is a real shame because Microsoft users have been waiting for this kind of functionality. Christopher S. Penn: And so what I wanted to talk about on today’s podcast episode, now that we’ve worked out that this thing is going to cost you three grand a month—because one of the things that people have pointed out on LinkedIn is, “Oh great, you fired all these people so you can switch to AI; now AI is going to cost you more than the people did”—is how do we reduce AI costs? How do we use AI more efficiently? Because this is clearly a lot of money. Katie Robbert: If only we had a few things to start with. I’m going to shock and dazzle everyone and say, “Guess what? Start with the 5P framework by Trust Insights.” You can learn more about it at TrustInsights.ai/5P-framework. At a high level, the five Ps are: Purpose—what the heck are you doing? People—who the heck’s involved? Process—how do you do the thing? (These are your SOPs). Platform—what tools are you using? (Not just the AI, but also your external data sources). And Performance—did you do the thing? It sounds really straightforward because it is. However, a lot of people go straight to pushing the buttons and “vibe coding” and, “Hey, build a thing.” “What do you want it to be?” “I don’t know, you pick.” Without doing this work up front, yeah, you’re going to find yourself at $650,000 a month very quickly. There is no tool that allows you to skip over good planning upfront, good governance up front. Microsoft Cowork is no different from any other large language model in that you still need to have good requirements, you still need to have good prompting, you still need to have good governance, even if you’re just using it internally on your own systems. Enterprise companies, any company, has sensitive data somewhere within their SharePoint stack, within their databases, their document repositories. You don’t want to accidentally or carelessly give a large language model access to that because you didn’t plan ahead. So that’s my soapbox. I’m coming down off of it. Chris, what would you add to how to make AI efficient? Christopher S. Penn: So planning, yes, 100% is going to make the most of the tools you have. The other question is, given these outlandish costs, is Microsoft the right system for you to use? Because Claude in Anthropic’s enterprise level is just as expensive. Companies have recently seen their burn through their entire Claude usage for the year, their budget in weeks. I think it’s Uber that burned their 12-month budget in a month and a half in terms of their token budget. So when we look at these prices, Katie, you remember a while back I had said, “Hey, Nvidia’s got this cool little desktop box. It’s $5,000.” You’re like, “You’re not buying $5,000 worth of hardware.” Absolutely not. Now if Microsoft or Anthropic said, “Hey Katie, you need to pay us $6,500 a month,” you’d be like, “You know what, Chris, go and buy one of those boxes; let’s buy one for each of the team and we’re going to drop Anthropic because we are not paying $6,500 a month for AI.” Right? Katie Robbert: You know, and so it’s an interesting question because where we started the conversation was saying there’s a reason why people are wedded to using Microsoft because of the security and privacy. I don’t know that introducing an Nvidia box would comply with the regulations set forth by that company. I mean, that’s a big question. It’s an interesting workaround, but it’s not going to work for everybody, especially the more regulated the industry gets. It just might not be an option. Christopher S. Penn: Yeah, it’s going to very heavily depend on IT. However, because it lives literally in your infrastructure, you do have a lot more governance over it because it’s literally a box that sits on your desk that you control. But more importantly, today’s top local models match a lot of the cloud foundation models and capabilities. GPU AI’s new GLM 5.2 matches Claude Opus 4.8 capabilities. Now you’re going to need a few of those Nvidia boxes to be able to load and run it well for a small cluster of employees. But for the lighter models like Qwen 3.6 or Google’s Gemma 4 if you have to, or Nvidia’s Neotron Ultra if you have to use a US-based model because of regulatory reasons—like you’re not allowed to use anything Chinese, regardless of the fact that it’s on your infrastructure—those are options that you would then use a tool like Open Cowork to handle the inference for it. So my suggestion is that to Katie’s point, use the 5Ps and then drill down and say, “What are the things that we absolutely positively have to use Cowork for?” Or can we make that task as deterministic as possible using command-line tools and stuff that do not require AI? So for example, Katie, when you query HubSpot every day with Claude Cowork, that is using the MCP connector that uses a ton of tokens back and forth. Now we don’t see it because we’re on an individual plan. The moment we’re forced to switch to a team or an enterprise plan, we will say, “Okay, we’re going to use the HubSpot command-line tool which can fetch data in and out.” And then the AI just says, “Hey tool, give me the thing,” and it goes off and does the back and forth and brings the data back and hands it to the AI. That will dramatically cut the amount of AI usage you have because a non-AI tool is getting data for you. Katie Robbert: As you’re describing it, I want to sort of make sure I understand because you’re making it sound like it’s an easy switch from the process that I currently have built in Cowork to, “Okay, just use a command-line tool.” I’m not someone who’s well-versed in command-line tools. You’re someone who is. However, you have your own set of things to do right now. So it’s time. It’s internal resources to make those switches to make the cost savings. I just want to be clear about that; it’s not a, “Oh well, in order to save money, let me just go ahead and use a command-line tool.” Like you still have to set it up. Christopher S. Penn: Yes, and corporate IT will be very busy doing that. However, corporate IT also likes us because they can then govern it. They can say, “Okay, we will ensure that this suite of 10 command-line tools is installed on every computer in the company, and there’s a joint service key that we can maintain programmatically and rotate every 30 days and stuff like that.” So that infrastructure, which corporate IT is very well-versed in, is going to be much happier with that than kind of like the whole shadow IT where people are like, “Oh, I’ll just have Claude make me this thing.” No, they would much rather say, “I would like to have control over the command-line tools that are installed on every machine in the company.” Katie Robbert: So work that out. You’ve worked with IT teams before. How likely is it that they’re going to—if you say, “Hey, I would like to have control over the command-line tools on every machine in the company,” they’re like, “Yeah, sure, Chris, no problem. Let me bump you to the top of the list. You’re a priority now.” I think you’re going to have a hard time. Like, we see the value in it, we know that it’s a useful thing. I just want to be realistic, and I’m trying not to derail the conversation too much, but I just want to be realistic that, like, yes, that’s the thing. If you have the skills to do it and if you don’t have to go through your IT team to do it, absolutely do it. If you have to go through your IT team and they have to set it up, get comfy, get in line; you’re not a top priority right now. Christopher S. Penn: Yeah, well, my perspective is IT would want to do that. It would be like, “We would love to have more control over this to stop the shadow IT that’s happening all over the place because of AI.” So IT in its MDM config would say, “Okay, these are the 10 tools that we’re going to drop on every machine, and we’re going to also programmatically alter your Claude MD files and stuff to tell Claude this is what’s installed. You must use it so that it cuts those costs.” And IT can then say, “We certify these 10 command-line applications are safe to use.” Katie Robbert: Provided it has the time to get skilled up to do that. So yeah, I like to make sure that we’re very clear about caveats because in the 25 to 30 minutes we have for a podcast, we go through things like “do this, do this,” and then it’s, “Well, what do you mean? It said no.” So let’s get back to—Microsoft has started to release Cowork, their version of Cowork, and we’re talking about AI efficiencies. When you think about starting places for someone who’s using Microsoft, someone who’s using their Cowork version, what is the first thing you think somebody should do before they start burning tokens or usage or spending pennies? Christopher S. Penn: The five Ps, the planning, and build all of your prompts and all of your infrastructure for Cowork in regular Copilot, because regular Copilot is very smart. Now in regular Copilot, if you go in the upper right-hand side, there’s a little menu, a little drop-down saying “models,” and you should choose for planning. Choose GPT 5.5, soon to be 5.6—”think deeper,” that’s the smartest model that’s available. And say—and that’s where you have your conversation like, “Oh, I want to do this in Cowork. I don’t want to do this, I want to do this. Help me figure this out. Ask me questions. Let’s plan this out. Here’s the Trust Insights 5P framework. Help me use this to come up with these plans.” So you do all of your planning and all that heavy token usage in regular Copilot to build the skills and the pieces that you can then drop into Cowork, so you don’t have to use Cowork to plan because Cowork is going to chew up your usage. That way, if you can use regular Copilot, it should be a little bit lighter on your budget. Katie Robbert: And I think one of the questions that you should add into your planning is, “Can I do this in Copilot or do I need Cowork for this?” And you know, I want you to use your human judgment, but it would be a good idea to ask the large language model like, “Do you have the capabilities to do this within Copilot or do I need to bring this into Cowork to actually execute it?” Because you may be surprised. You know, to Chris’s point, the models are getting smarter every day. And so you may not need to execute what you think you need to execute in Cowork; you may be fine with using Copilot. Yes, I get it’s not as shiny and as exciting, but you know what’s also not exciting? Being told you owe the company $60,000. That’s not exciting. Christopher S. Penn: Exactly. Even for something like scheduled tasks—Microsoft Copilot tasks are scheduled tasks—so if it’s not something that needs Cowork’s horsepower, that will obviously keep you from chewing up those extra credits over there. Katie Robbert: Yeah, and I think that’s a good best practice for a lot of these tools, you know? So can you do your planning in Claude Chat before bringing it into Cowork? Can you do your planning in Gemini before bringing it into their version of whatever that is? And that’s just a good best practice for efficiency in general. Christopher S. Penn: Yeah, I mean, when I do my planning for even software builds and stuff like that, the first thing I do is I have a master planning prompt. It’s actually a skill that incorporates the 5P framework by Trust Insights. And so I have the model ask me questions from the 5P framework: “What are you doing? Who’s it for? How should it work? What are the additional command-line tools that we should be using? What is the definition of done?” And all of that is stuff that if I don’t dictate it out loud, it knows to ask me for it. So I can plan first, and then the language model rebuilds the prompt into something that meets all of those conditions and produces a really solid output that I can then go use to build requirements documents and all the stuff. You will save so much time and money by investing more heavily in planning up front, and you can then hand off the execution of the plan to a very small, fast model. Katie Robbert: And I think that’s a really good pro tip. And I just want to give a small plug—you can actually download, we have for sale in our academy at Academy.TrustInsights.ai, a “prompt-to-skill.” So basically, as Chris was just describing, he has a specific process for building those requirements. This prompt-to-skill will help you do that and get more efficient at building those requirements. And then what you may find that you have is a reusable template, and it makes that even more efficient. So start with that. Go to Academy.TrustInsights.ai, purchase the prompt-to-skill—it’s very awkward to say that—and then start building out those requirements before you bring it into something like Cowork. And you’re going to save yourself a lot of time and money, and people are going to be like, “Wow, you did that really fast. How did you do that?” And you’ll be like, “I don’t know, I’m just that good.” But in the back of your mind you’re like, “I use the 5P framework by Trust Insights. It got me there faster.” Christopher S. Penn: Exactly. So Copilot Cowork from Microsoft is now generally available. Before you type one character into it, please take the time to use the 5P framework by Trust Insights. Take the time to understand what your company has budgeted. Take the time to understand what tasks fall in each category, and as best as you can, try to reserve it for the things that truly need Cowork’s capabilities. And don’t just make it the default. If you’ve got some thoughts about the new Microsoft Copilot Cowork that you want to share, pop by our free Slack group. Go to TrustInsights.ai/analytics-for-marketers where you and over 4,700 other marketers are asking and answering each other’s questions every single day. And wherever it is you watch or listen to the show, if there’s a channel you’d rather have it on instead, go to TrustInsights.ai/TI-Podcast. You can find us in all the places fine podcasts are served. Thanks for tuning in. We’ll talk to you on the next one. Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.

This week the AI takeover gets real. Donald officially announces the launch of his AI Radio Network at theairadio.network, a multi-genre platform where listeners blend stations and buy AI-generated tracks. Then Google AI Studio's Zephyr voice bot joins the call as a live co-host, demoing what hands-free Gemini chat sounds like through a screen reader and even live-generating an HTML retro record player on the fly. Hosted by JC, with Rita back from a week off, Donald, Preston, and Angela on the call. Salvis pops in only for the introsetup and mentions the new server-based playout system in passing.JC welcomes everyone, then brings Zephyr (Google AI Studio's voice) onto the call as an honorary crew member for the episode. Donald walks through the AI Radio Network launch: listeners can mix any combination of genre stations, and creators can purchase the AI-generated tracks. The launch is technically pre-live, with everything getting ready behind the scenes at theairadio.network.JC then demos Google AI Studio with VoiceOver on iPhone, noting that the Google AI Studio app is coming to iOS on July 1. Angela weighs in on the difference between Gemini (3 videos a day cap) and Google AI Studio (much higher cap), and adds context on Google Vids as a paid Workspace feature she just started using for content creation. The mood shifts when Angela shares the latest on her premature niece Noah, born to her sister Madison about a week early. Noah is in the NICU, recently came off her phototherapy lights for jaundice, and her bilirubin is improving. Angela's been helping babysit nephew David through the long days. Preston and Rita share their own premature birth stories in solidarity, Rita was 7 months premature, Preston was 1 pound 13 ounces and 3 months premature. The crew also touches on Retinopathy of Prematurity. JC then debriefs his recent night of worship at the Newburgh campus, where worship director Materia and his mom Cecilia brought him on stage to close the night with a live cover of "Goodbye Yesterday"alongside the 12-member Delight Worship Squad. Shoutouts to everyonewho made it happen.To bring the tech-meets-radio theme home, JC asks Zephyr to generate a working HTML retro record player interface in the browser. Angela had earlier suggested "record player" as the demo topic before signing off, and JC picks the actual design later in the show once she had left. A short "Doodles misses bus" audio clip rounds out the lighter moments.The team closes with shoutouts and signs off after a packed evening of AI demos, family updates, and worship reflections.This week's announcementsThe AI Radio Network launch is announced at theairadio.network, pre-live and ramping up.Google AI Studio for iOS launches July 1.Baby Noah continues to progress in the NICU; Angela steps in to help with nephew David at home. JC sang "Goodbye Yesterday" at a Newburgh and Delight night of worship with the 12-person Delight Worship Squad.Preston brings up his shortwave radio hobby; Donald reminisces about 80s AM/FM/shortwave boomboxes. Keep technology alive, let your talent shine. Recorded 2026-06-17. Hosted by JC on the VisionCast Network.www.theairadio.network

Scott, Wes, and CJ reunite fresh off a trip to Amsterdam to chat conferences, burnout, and whether Anthropic actually uses AI. They also dig into a packed bag of sick picks and tech news, including HTML streaming in Chrome, an image-to-ASCII generator, and a wild Arch Linux supply chain attack. Show Notes 00:00 Welcome to Syntax! 02:15 Anthropic Doesn't Use AI Thariq's Tweet Tweet Response 06:15 Taste and Vision in Prompting Output 10:50 Wes and Scott's Slide Decks 18:05 Amsterdam Trip Recap 26:09 Are Conferences worth it? 27:21 Amsterdam Trip Recap 31:17 Fable 5 First Impressions 33:45 Fable 5 and Mythos 5 Banned 41:45 IRL Events Are Great For Burnout 45:12 Brought to you by Sentry.io 45:52 HTML Streaming now in Chrome 55:47 Image to ASCII Generator The Mitos Repo 01:01:31 Find Modern Module Replacements 01:05:55 Scott is Using MacOS / iOS Betas 01:09:25 Xiaomi OpenCode Fork and Mimo 2.5 Pro 01:14:06 Agent Dashboards 01:21:39 Arch Linux Supply Chain Attack 01:23:47 Should we train coding only models? 01:31:37 Thanks! Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

Show DescriptionIn praise of a solid Shure microphone (sponsor??), AI being really helpful in certain situations, TypeScript complaints about complaining, TypeScript at CodePen, should we all slow down a bit, CI jail and work trees, AT Proto and related protocols, interestForElement in HTML, and how many spoons do you have left for Clues by Sam? Listen on WebsiteLinks MV7+ - Podcast Microphone - Shure USA Lit Devin | The AI Software Engineer Understanding Standard.Site Now in your timeline: Standard.site brings richer previews from across the open web - AT Protocol tv for developers — CodeTV HTMLButtonElement: interestForElement property - Web APIs | MDN Using interest invokers - Web APIs | MDN Spoons by Sam - daverupert.com htmx ~ Code is Cheap(er) SponsorsMacroMacro is a tool to cut through the noise - It's a workspace built for engineers; One place for all your emails, tasks, team chat, and documents. Sign up at Macro.com and get $100 of your subscriptions using code SHOPTALK100

I almost didn't record this week. We lost Josh Baer — Capital Factory's founder, the godfather of the Austin startup community, and honestly one of the few people who showed the rest of us this work could actually be done, and done right. Then we turn the corner toward the Portland dream that refuses to die: Devin Gaffney's CoCore, an "Airbnb for compute" rebuilt on AT Proto — the same rails as Bluesky — picking up a thread CPUsage chased here back in 2011. From there we get to Quickish, where you drop a folder of HTML and get a live URL in seconds — I used it myself to park a countdown clock to Silicon Florist's 20th birthday. And we close on CodeTV, Jason Lengstorf's coder reality show pitting two human dev teams against an AI on the same prompt. Plus secrets.CHAPTERS:00:00 Portland startup news00:30 Joshua Baer, the godfather of the Austin startup community04:45 CoCore09:30 Quickish17:20 ⁨@codetv-dev⁩ SecretsLINKS:Remembering Joshua Baer — https://siliconflorist.com/2026/06/17/remembering-joshua-baer-the-godfather-of-austin-texas-startups/Joshua Baer (RuntimeWire) — https://runtimewire.com/article/joshua-baer-capital-factory-founder-dies-laredo-plane-crashAnother swing at collaborative compute (CoCore) — https://siliconflorist.com/2026/06/18/another-swing-at-collaborative-compute-comes-in-the-form-of-cocore-on-at-proto/CoCore — https://console.cocore.dev/Need a new single page site…? Quickish — https://siliconflorist.com/2026/06/18/need-a-new-single-page-site-lets-get-that-done-quickish/Quickish — https://quickish.website/CodeTV pits software developers against AI — https://siliconflorist.com/2026/06/19/codetv-pits-software-developers-against-ai/CodeTV / Jason Lengstorf — https://jason.energyPortland Startup Slack — https://pdxslack.comFIND RICK TUROCZY ON THE INTERNET AT…- https://patreon.com/turoczy- https://linkedin.com/in/turoczy- Portland Oregon startup news on Apple Podcasts https://podcasts.apple.com/us/podcast/portland-oregon-startup-news-silicon-florist/id1711294699- Portland Oregon startup news Spotify https://open.spotify.com/show/2cmLDH8wrPdNMS2qtTnhcy?si=H627wrGOTvStxxKWRlRGLQ- Startup Stories on Spotify https://open.spotify.com/show/1Tk7bbzaNYowGouI9ucKC3- Startup Stories on Apple Podcasts https://podcasts.apple.com/us/podcast/startup-stories-with-silicon-florist/id1849468494- The Long Con on Apple Podcasts https://podcasts.apple.com/us/podcast/the-long-con/id1810923457- The Long Con on Spotify https://open.spotify.com/show/48oglyT5JNKxVH5lnWTYKA- https://bsky.app/profile/turoczy.bsky.social- https://siliconflorist.substack.com/- https://pdxslack.comABOUT SILICON FLORIST ----------For nearly two decades, Rick Turoczy has published Silicon Florist, a blog, newsletter, and podcast that covers entrepreneurs, founders, startups, entrepreneurship, tech, news, and events in the Portland, Oregon, startup community. Whether you're an aspiring entrepreneur, a startup or tech enthusiast, or simply intrigued by Portland's startup culture, Silicon Florist is your go-to source for the latest news, events, jobs, and opportunities in Portland Oregon's flourishing tech and startup scene. Join us in exploring the innovative world of startups in Portland, where creativity and collaboration meet.ABOUT RICK TUROCZY ----------Rick Turoczy has been working in, on, and around the Portland, Oregon, startup community for nearly 30 years. He has been recognized as one of the “OG”s of startup ecosystem building by the Kauffman Foundation. And he has been humbled by any number of opportunities to speak on stages from SXSW to INBOUND and from Kobe, Japan, to Muscat, Oman, including an opportunity to share his views on community building on the TEDxPortland stage (https://www.youtube.com/watch?v=Cj98mr_wUA0). All because of a blog. Weird.https://siliconflorist.com

Shelly Palmer has spent 45 years watching technology reshape every industry—from writing news themes for CBS to consulting with every major media company on AI strategy.On this year-end recap, he cuts through the noise with one devastating observation: 2025 was the year everyone talked about AI while almost nobody actually used it. Executives shook their heads knowingly in meetings, pontificated about capabilities the models don't yet have, and parroted nonsense they read from other people who knew nothing. But when you asked one innocent question, they crumbled.In the News: CES 2026 shapes up with Nvidia sponsoring two full days of AI training. Samsung is skipping the main floor for a massive offsite activation. Sony brings no electronics—only Honda's experimental vehicles. The TCL and Chinese companies' presence hinges on tariff policy. The innovation series breakfast that Shelly runs is becoming an official CES event after a decade of independence.The conversation spirals into deeper territory: $3 trillion in government money is stacked behind AI development. The U.S. explicitly states it must beat China to AGI—making this the Manhattan Project of our lifetime. Shelly walks through what he's seen in successful companies (leadership using the tech, paid "Tech Tuesdays" for AI experiments, cross-discipline teams with SecOps and legal at the table) versus the chaos of places with no process.He breaks down what's real—drone warfare, cybersecurity applications, robotics—versus what's hot air. And he makes a case that won't be killed by AI itself, but by militarized applications and the geopolitical arms race we're already in.5 Key Takeaways from Shelly:Leadership belief and hands-on use are non-negotiable. Companies winning with AI have senior leaders who actually use the technology. When the CEO walks into an LT meeting saying "I built this agent over the weekend," everyone else starts experimenting too.The recipe for AI success has three ingredients: leadership belief, paid time to experiment (Tech Tuesdays/Thursdays with real budgets), and cross-discipline teams (SecOps, legal, compliance, risk) paving the way. Chaos erupts without this structure.You cannot build a point of view on AI from reading blogs or watching YouTubers. Pick a personal project you care about, go hands-on with a model (Claude, Gemini, GPT), and complete it from beginning to end. Only lived experience grounds your understanding.AI parallelizes with web 1.0: In 1998, you had to hand-code HTML, build databases manually, write raw JavaScript. Today you can vibe code a site in 90 seconds. AI will eventually reach "spin me up an expert that does X" without asking questions—we're not there yet, but it's inevitable.It's both bubble and Manhattan Project. Some valuations are insane and will burst. But military applications, cyber warfare, drone control, robotics—those aren't going anywhere. The government won't back off. Both outcomes happen simultaneously.This episode is brought to you by Zappar, creators of Mattercraft—the leading visual development environment for building immersive 3D web experiences for mobile headsets and desktop.Mattercraft combines game engine power with web flexibility and features an AI assistant to help you design, code, and debug in real time in your browser. Build smarter at mattercraft.io. Hosted on Acast. See acast.com/privacy for more information.

Vira Sadlak is a Retention Marketing Strategist at Flowium, a retention-focused agency and Klaviyo Platinum Partner specializing in email, SMS, and lifecycle marketing for e-commerce brands. She works with DTC brands across categories to build the automated systems that turn one-time buyers into long-term customers.Most brands pour their budget into acquisition and go quiet the moment a customer converts. That silence is expensive. This episode gets into exactly what brands are leaving on the table and how to fix it.Eitan and Vira cover the nine foundational flows every brand should have in place, why segmented lists often outperform full sends revenue-wise, and how to build customer journeys that branch based on behavior rather than treating every buyer the same. They also get into the mechanics of growing and identifying your subscriber list, including identity resolution tools and zero-party data collection through quizzes.The conversation goes deep on KPIs that actually matter (open rates are no longer one of them), when and how to layer in SMS, the deliverability mistakes that land you in spam, and where AI fits into retention strategy today. Listeners will walk away with a clear framework for building retention programs that generate consistent revenue without relying on one-off campaigns.Website: https://www.vimmi.netEmail us: info@vimmi.netPodcast website: https://vimmi.net/commerce-untold/Eitan Koter's LinkedIn: https://www.linkedin.com/in/eitankoter/YouTube: https://www.youtube.com/@VimmiVideoCommerce/featuredGuest: Vira Sadlak, Retention Marketing Strategist, FlowiumVira Sadlak's LinkedIn: https://www.linkedin.com/in/virasadlak/Flowium: https://flowium.comKey Takeaways:• A significant share of second purchases happen within 24 to 48 hours of the first order, before the package even arrives. Not messaging customers in that window is one of the most common and costly retention mistakes.• Open rates are no longer a reliable performance signal. Click rates and conversion by segment tell you far more about whether your emails are actually working.• Sending the same message to your entire list hurts deliverability. Segmented sends consistently produce equal or better revenue while protecting your sender reputation.• Plain text emails outperform designed HTML templates on engagement and inbox placement because email providers do not flag them as promotional material.• Klaviyo and similar platforms function as data collection and analytics tools, not just communication channels. The segment-level insights they produce can inform your broader marketing strategy across every channel.• Identity resolution tools can identify anonymous website visitors and add them to your flows, but they require healthy deliverability and meaningful traffic volume (50,000 or more monthly visits) to be effective.Chapters:[01:20] About Vira Sadlak and Flowium[02:40] The Nine Foundational Retention Flows[07:27] Segmentation and Branching: Why 50 Journeys Is Not Too Many[10:04] What Retention Actually Means[11:05] Growing Your Subscriber List and Identity Resolution[14:11] How to Nurture Leads Who Have Not Purchased Yet[16:05] Why Email and SMS Still Drive 25-30% of Shopify Revenue[17:53] The KPIs That Actually Matter (Open Rates Are Not One of Them)[21:11] Email Cadence, Educational vs. Sales Content, and Preference Pages[24:25] When to Add SMS and How Often to Send[25:34] WhatsApp, Telegram, and RCS: Emerging Channels[27:36] Deliverability: How to Stay Out of Spam[29:26] How Flowium Serves Clients and Where AI Fits In

O paulistano Fábio passou a infância achando que seria desenhista, até que uma apresentação na escola despertou seu interesse por engenharia e o levou à Poli-USP. Lá, começou pensando em engenharia ambiental, passou pela elétrica e acabou escolhendo computação, área com a qual já tinha algum contato por meio dos pais e das primeiras experiências com HTML e CSS.Depois de quase dez anos trabalhando no Brasil, uma oportunidade na Amazon abriu as portas para o Japão, onde ele também pôde encurtar a distância de um relacionamento que havia começado em Taiwan. Mais tarde, em busca de projetos com maior impacto, mudou-se para Seattle, nos Estados Unidos, antes de deixar a empresa, tirar um período sabático e se estabelecer em Taiwan.Neste episódio, Fábio detalha essa trajetória, e comenta as diferenças culturais entre todos esses países e o Brasil, além das particularidades de se morar na terra onde mal se tem férias.Fabrício Carraro, o seu viajante poliglotaFabio Gusukuma, Engenheiro de Software na Stripe em Taipei, TaiwanLinks:LinkedIn fo FabioTechGuide.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/Ouvintes do podcast Dev Sem Fronteiras têm 10% de desconto em todos os planos da Alura Língua. Basta ir a https://www.aluralingua.com.br/promocao/devsemfronteiras/e começar a aprender inglês e espanhol hoje mesmo! Produção e conteúdo:Alura Língua Cursos online de Idiomas – https://www.aluralingua.com.br/Alura Cursos online de Tecnologia – https://www.alura.com.br/Edição e sonorização: Rede Gigahertz de Podcasts

Scott and Wes sit down with Jake Archibald from Mozilla to unpack how web standards actually get made at Firefox. From browser features and developer feedback to the drama around the Prompt API. They discuss Interop 2026, the future of web APIs, and what it's really like shaping the web after a career spanning both Google and Mozilla. Show Notes 00:00 The Importance of Sunscreen 02:29 Welcome to Syntax! 04:35 Transitioning from Google to Mozilla 06:00 Brought to you by Sentry.io 06:43 Mozilla's Current Position and Development Priority HTML Sanitizer API 08:35 Feature Implementation and Developer Feedback 13:12 JPEG XL and AVIF: The Future of Image Formats 18:06 Balancing User Features and Web Standards 20:56 Navigating the AI Translation Dilemma 23:03 Understanding the Prompt API Controversy 32:56 Rethinking the Future of Prompt APIs 39:00 Exploring Local Models and User Control 44:04 The State of Firefox DevTools 45:42 Browser Stability and Developer Editions 47:39 Introduction to the Heading Offset API 51:14 Interop APIs and Their Importance Headingoffset & Headingreset attributes 54:10 Developer Feedback and Browser Features Developer Signals 58:05 Animating Display None and Its Challenges 01:00:44 HTML and Canvas: Opportunities and Concerns 01:04:01 Sick Picks + Shameless Plugs Sick Picks Scott: Wes: Jake: Clues by Sam Shameless Plugs Scott: Wes: Jake: Bluesky Mastodon Threads LinkedIn YouTube X Insatgram Tiktok Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

In this episode, Andrew chats with Adriano Carollo at PSConfEU about community, PowerShell Universal, AI, and what happens when you stop lurking and start talking to people. Adriano shares how PowerShell helped him grow from sysadmin into web apps, automation, and open source-style contribution, while Andrew reflects on learning, AI, and why enthusiasm still matters.   Key Takeaways:  · Community accelerates growth. Adriano came to PSConfEU after hearing Andrew encourage listeners to engage, and the payoff was immediate.  · PowerShell Universal can open unexpected doors. Adriano describes using it daily to learn web development concepts like JavaScript, HTML, and React through PowerShell.  · AI is most useful when it supports learning instead of replacing it. Both Andrew and Adriano talk about using AI for research, syntax help, documentation, and personal workflows while still valuing hands-on problem solving.  Guest Bio:  Adriano Carollo is a Berlin-based system administrator and PowerShell enthusiast who uses PowerShell Universal daily. He is active in the PowerShell Universal Discord community and is exploring automation, web apps, self-hosting, and entrepreneurship.  Resource Links:  PDQ Connect:https://www.pdq.com/pdq-connect/ PowerShell Scanner for PDQ Connect:https://www.pdq.com/blog/the-powershell-scanner-has-arrived-in-pdq-connect/ PowerShell Universal:https://powershelluniversal.com/ PSConfEU:https://psconf.eu/  PDQ Community Discord:https://discord.gg/pdq Adriano C. https://linkedin.com/in/adriano-c-501203213 The PowerShell Podcast on YouTube: https://youtu.be/qLYqUF9gD9s

Today we are talking about Test Driven Development, ebooks, and Drupal with guest Oliver Davies. We'll also cover Juicer Social Feed as our module of the week. For show notes visit: https://www.talkingDrupal.com/557 Topics What Is Test Driven Drupal Why Automated Tests Matter How TDD Works AI and Test Quality Balancing Test Coverage When to Write Tests Why Write the Book Why Write an Ebook From Email Course to Ebook Ebook vs Print Tradeoffs Who the Book Helps What You Will Learn Keeping Content Updated Publishing Tools Workflow Lessons and Drupal Changes Podcast and Future Books Mob Programming Explained Free Ebook and Wrap Up Resources Juicer io Drupal 11: The Upgrade Experience I've Been Waiting For codethatships Test-Driven Drupal Sculpin Guests Oliver Davies - oliverdavies.uk opdavies Hosts Nic Laflin - nLighteneddevelopment.com nicxvan John Picozzi - epam.com johnpicozzi Scott Falconer - managing-ai.com scott-falconer MOTW Correspondent Martin Anderson-Clutz - mandclu.com mandclu Brief description: Have you ever wanted to embed social feeds into your Drupal website? There's a module for that. Module name/project name: Juicer Social Feed Brief history How old: created in Mar 2026 by Denis Omerović (drupalchille) Versions available: 1.0.2, that works with Drupal 10.3 or 11 Maintainership Actively maintained (version released today!) No open issues Usage stats: 4 sites Module features and usage This module embeds an aggregated social media feed from Juicer.io directly into Drupal as a configurable block. It natively supports content from Instagram, LinkedIn, Facebook, X (Twitter), TikTok, Bluesky, YouTube, and more. Traditionally, displaying feeds from platforms like Facebook, X, or Instagram requires creating developer accounts, managing rotating OAuth tokens, and keeping up with constantly shifting API restrictions. Juicer handles all API authentication on its platform, shielding your website from sudden breaking changes by individual social networks. To use this module, you will need an active account on Juicer.io. They offer both free and paid tiers depending on how many sources you want to aggregate and how frequently you need the feed to sync. The module is created and maintained by the official Juicer.io team. That should ensure that the module is closely aligned with the product's features and any potential API changes over time. The embedded feed is made available as a Drupal block, to make it easy to control where it should appear on your site. When placing the Juicer block, the UI exposes several user-friendly settings: Feed Slug: Just paste your unique Juicer feed ID to establish the connection. Post Limit: Control exactly how many items populate initially. Source Filtering: If your Juicer account aggregates five networks, but you only want to show LinkedIn posts on a specific page, you can filter down to a single network right inside the block settings. SEO/Semantic Control: You can set titles/subtitles and choose the exact heading level hierarchy ( through ) to ensure your pages remain semantically correct and accessible. I did get a chance to test out the module and the service today, and I can tell you from experience, it's a huge improvement on having to create and pull in feeds directly. I did notice that the block didn't show up in the Drupal Canvas component library, but I was able to determine that two lines of code to declare the block as FullyValidatable were all that was needed. So I opened a Feature Request to add that, and it was merged in and a new release cut in less than an hour. So it's now Drupal Canvas compatible too! It's worth pointing out that the standard Juicer's embed script loads HTMX, which conflicts with the version of HTMX included in Drupal 11 core. As a result, the module fetches feed HTML directly from the Juicer API and includes a minimal HTMX shim to prevent errors. John, you nominated this module, why don't you start us off by telling us about how you got started using it?

Should you convert your website into Markdown to help Large Language Models (LLMs) understand your content better? Is "llms.txt" worth the effort for SEO? In this episode of Search Off the Record, Martin Splitt and John Mueller from the Google Search Relations team dive deep into the history of Markdown, its rise in the AI era, and whether it holds any real weight for search engine discovery. In this episode, you'll learn: The Origins of Markdown: From John Gruber and Aaron Swartz to its status as the "language of GitHub." Markdown vs. HTML: Why the "cleanliness" of Markdown is tempting for developers but potentially risky for site structure. LLMs & Markdown: Do AI crawlers actually prefer Markdown, or are they already experts at parsing HTML? The "Parallel Version" Trap: Why creating a separate text/Markdown version of your site for AI can lead to the same maintenance nightmares as dynamic rendering. Use Cases that Make Sense: When Markdown is actually superior (like developer documentation) and when it's totally unnecessary (like your shoe catalog). Key Takeaways for SEOs & Developers: Crawlers are built for the "messy" web: Google and other engines have decades of experience parsing HTML. Don't sacrifice discovery: Headers, footers, and sidebars in HTML provide critical context for site structure that a raw Markdown file might lack. Maintenance is king: Avoid the complexity of maintaining two versions of the same content. Chapters 0:00 - Introduction: Should we all be using Markdown? 3:45 - The history and purpose of Markdown. 7:15 - Why developers love it: Separation of style and content. 11:20 - Do crawlers need Markdown to understand your site? 14:50 - The danger of "parallel versions" and dynamic rendering lessons. 17:30 - Discussing the "llms.txt" proposal and AI agents. 21:00 - Where Markdown actually makes sense (Developer Docs). 24:00 - Final verdict: Stick to HTML for the web. Resources Mentioned: Google Search Central: https://developers.google.com/search Are you using Markdown for your site's frontend or just as a backend source? Let us know in the comments! Episode transcript →  https://goo.gle/sotr111-transcript Listen to more Search Off the Record → https://goo.gle/sotr-yt Subscribe to Google Search Channel → https://goo.gle/SearchCentral  Search Off the Record is a podcast series that takes you behind the scenes of Google Search with the Search Relations team.  #SOTRpodcast #SEO #GoogleSearch Speakers: Martin Splitt, John Mueller

A coffin lid scratched from the inside, a stalker hiding in the basement, and a plate of "fresh venison" served by a man who was never a hunter — Redditors share the true moments that still keep them up at night.EPISODE BLOG PAGE (includes sources): https://weirddarkness.com/RedditHorrorsREAD or DOWNLOAD the full transcript of this episode: https://weirddarkness.tiny.us/4ywsvu9vLISTEN ON PODCAST APPS: Look for this podcast on Apple Podcasts, Spotify, iHeart Radio, Amazon Music, Pandora, TuneIn Radio, and other podcast apps. Get a list of free listening apps here: https://weirddarkness.com/wdapps*No AI Voices Are Used In The Narration Of This Podcast*SOURCES and RESOURCES:“Creepy True Occurrences From Redditors” posted at Factinate.com: https://weirddarkness.tiny.us/h9zz8vka(Over time links may become invalid, disappear, or have different content. I always make sure to give authors credit for the material I use whenever possible. If I somehow overlooked doing so for a story, or if a credit is incorrect, please let me know and I will rectify it in these show notes immediately. Some links included above may benefit me financially through qualifying purchases.)WeirdDarkness® is a registered trademark. Copyright ©2026, Weird Darkness.Originally aired: November, 2021Here's the blog synopsis in plain text, ready for your review pass before HTML conversion.Weird Darkness gathers dozens of true creepy stories submitted by Redditors, ranging from a grandmother buried alive in a backyard coffin to phantom police officers, a haunted hotel painter, a 1980s kidnapping attempt, and a dinner of "fresh venison" served by a cannibal.It opens with a coworker's family story about exhuming a grandmother who had been buried in a wooden box in the backyard, as was once customary. When the family lifted the lid to move her to a cemetery plot years later, they found claw marks covering the inside of the coffin — she had been buried alive.From there, a babysitter hears pans falling in the basement after putting the children to bed and calls the police expecting a single patrol officer. A full SWAT team arrives at the door instead, because the dispatcher heard a second phone on the line hang up after the call ended. A man wanted for multiple assaults had been listening from the basement extension.A secluded spring campground follows, where a father and his friends befriended a quiet neighbor living out of a makeshift truck camper. Days later, driving out, they spotted him hanging from a tree beside his untouched campsite, a note pinned to the trunk with a buck knife — the suicide had happened at the father's favorite camping spot, the same one where he finally told his children the story years later.Next comes a twelve-year-old girl living in a backyard trailer who heard footsteps crossing the metal roof at night, always when she was alone. Months later she woke to find the trailer sweltering, the heater cranked to full blast, and fled on instinct; investigators later found the door lock tampered with and a kitchen knife hidden behind a chair beside the heating controls, where the staring neighbor had apparently crouched in wait.After the first break, a traveler in Taiwan steps into an elevator near a night market and stops on a pitch-dark, abandoned floor that shouldn't exist. The building's fourth floor — omitted from the panel entirely, in keeping with Chinese numerical superstition — had been sealed after a hair salon employee died by suicide there, and the elevator had been professionally reprogrammed to never stop on it. It sometimes does anyway, and riders report a figure in a gown moving toward the doors.Then a 2 a.m. street fight ends with a stabbing, a daughter catching her bleeding stepfather on the porch, and an answering machine message recorded at the exact time of the attack: a school friend across town, crying, describing a dream of screaming, a fight, and her friend covered in blood — in the late 1980s, long before cell phones could have carried the news.A college student renting a basement room recounts his dog growling at one corner of the room, followed by the small dirt-floored closet under the stairs creaking open on its own with deliberate slowness, leaving him frozen in the dark hallway for five full minutes.A seven-year-old girl visiting her mother's best friend watches a burned family — a mother, a teenage boy, and two younger girls — walk the house and beckon her to come with them. Years later the friend admitted the family had moved out over hauntings: baby toys scattered overnight, blankets and pillows arranged on the floor as if people had slept there.A smashed flower pot follows, found twenty feet from its shelf in the middle of a family room floor with no dirt trail, as if it had been carried and dropped straight down. Then two brothers named Jack and Tom each spend a night silently furious at the other's loud guests, only to meet in the hallway and discover the living room full of chattering old people belonged to neither of them — the room stood empty, smelling of musk.A college party flips from paranormal dread to absurdity when a bleeding, pantsless man with wild hair forces his way through the door screaming "please"; the supposed intruder turned out to be a friend of a friend on a catastrophic acid trip who had lost his pants running through a field.The block closes with a runner who caught a prospective neighbor — a man who had complimented his physique two days earlier — standing at his bedroom window at midnight, having entered the house earlier to adjust the blinds for a better view. The chase across gravel driveways ended with a written confession, a photographed license plate, and, a full year later, a knock on the door from the same man, apologizing.Out of the second break comes a Hollywood Hills doorstep in the early 1980s: a distraught woman babbling about blood, two LAPD officers who collect her within ten minutes, and then two more officers thirty minutes later — the ones actually dispatched to the call, with no record of who the first pair were or where they took her.The night crew of a 24-hour Subway describes their resident "SubGhost," blamed for disembodied conversations, crashing noises, items sliding off counters, and a new automatic paper towel dispenser that unspooled an entire roll, sheet by sheet, in an empty room.Three children watch a white figure of a man sit atop a telephone pole, grinning at them, before he stands, jumps, and vanishes before reaching the ground. Then a basement-apartment tenant describes a man watching him through the window for ten minutes, followed weeks later by an air conditioner cover pried off in the night — and a police department that could do nothing until someone actually broke in.A newspaper carrier on a rural route in 2000 describes a drenched man in a white shirt charging out of a rain-filled ditch at 2 a.m. with what looked like a hatchet in his hand; the man took his own life within the hour, and the carrier had to pound on a farmhouse door to report it because his Motorola flip phone had no signal.A bus rider chats with an oddly unsettling woman at the stop, boards an empty bus, and hears "Hey! Remember me?" from a little girl who resembles the woman exactly — on a bus the rider is certain was empty.The episode then travels to South Africa's Eastern Cape in July 2010, where a humanitarian worker and a missionary named Piet arrive at a Xhosa village to find it deserted. A naked woman covered in cuts, missing an ear, and running on all fours charged their truck, screeching and clawing at the windows as they fled. The villagers later said only that "a bad presence" had been in the village and was now gone.Gentler hauntings follow: a clock radio scraping across a desk to face a grandson and playing opera — the late grandfather's wake-up music of choice — two weeks after the funeral; a glass bowl that shattered downstairs during a sleepover and was found already swept up, its pieces gathered into another bowl on the table; and a dying grandfather whose eyes opened wide on his final breath as he smiled, looking happier than he had in years.The dread returns with a woman home alone who hears something working at her front door lock and sees two silhouettes — one at the door, one at the living room window — standing motionless, watching her watch them. They vanished before help arrived, and she found the basement window partially kicked in the next morning.A Sacramento man recounts surviving an attempted kidnapping around age nine or ten: a white van stopped beside a late-night Frisbee game, the sliding door opened, and a man in black flew out on a rigged telescoping harness operated from inside, missing his grab by inches. The three boys hid on a school roof for nearly an hour while the van circled, searching.A small-town yard sale yields a dented silver cigarette case for two dollars; months later the same elderly seller has the identical case — same dent, same brand of cigarette inside — while the original has vanished from the buyer's nightstand drawer. A man recalls childhood dreams of gripping toys hard enough to wake up holding them, including the Skeletor figure his family swore they never bought.Then a sixteen-year-old new driver and her four-year-old half-sister are stalked across town by a purple-faced man in a white pickup truck who blocked intersections, revealed a gun under his shirt, rammed their car toward oncoming traffic, and drew a finger across his throat. The older sister's gas station escape plan — coaching the four-year-old to jump out and run to the counter — ended the pursuit, though polic

If you spent too much time prompting Claude's Fable 5 before it likely goes away to subscribers in 10 days, you might have missed some AI gems.

This is a recap of the top 10 posts on Hacker News on June 10, 2026. This podcast was generated by wondercraft.ai (00:30): macOS Container MachinesOriginal post: https://news.ycombinator.com/item?id=48469658&utm_source=wondercraft_ai(01:59): Building an HTML-first site doubled our users overnightOriginal post: https://news.ycombinator.com/item?id=48475483&utm_source=wondercraft_ai(03:28): German ruling declares Google liable for false answers in AI OverviewsOriginal post: https://news.ycombinator.com/item?id=48470248&utm_source=wondercraft_ai(04:57): πFSOriginal post: https://news.ycombinator.com/item?id=48480978&utm_source=wondercraft_ai(06:27): I'm Eric Ries, author of "The Lean Startup" and new book "Incorruptible" – AMAOriginal post: https://news.ycombinator.com/item?id=48477135&utm_source=wondercraft_ai(07:56): Mercedes‑Benz starts large‑scale production of electric axial flux motorOriginal post: https://news.ycombinator.com/item?id=48472877&utm_source=wondercraft_ai(09:25): PgDog is funded and coming to a database near youOriginal post: https://news.ycombinator.com/item?id=48476466&utm_source=wondercraft_ai(10:54): AWS Bedrock to require sharing data with Anthropic for Mythos and future modelsOriginal post: https://news.ycombinator.com/item?id=48473166&utm_source=wondercraft_ai(12:24): Chrome is looking to permanently drop MV2 extensionOriginal post: https://news.ycombinator.com/item?id=48471970&utm_source=wondercraft_ai(13:53): Claude Desktop spawns 1.8 GB Hyper-V VM on every launch, even for chat-only useOriginal post: https://news.ycombinator.com/item?id=48479452&utm_source=wondercraft_aiThis is a third-party project, independent from HN and YC. Text and audio generated using AI, by wondercraft.ai. Create your own studio quality podcast with text as the only input in seconds at app.wondercraft.ai. Issues or feedback? We'd love to hear from you: team@wondercraft.ai

Vibe coding for teachers means describing what you want in plain English and letting AI write the code — no coding background required. 2021 Kentucky Teacher of the Year Donnie Piercey joins Vicki Davis to show how any teacher can build custom classroom tools that save real time. Donnie shares the small-problem-first method he used to build printable daily student task lists, auto-translate his classroom newsletter into five languages, and create self-checking games — plus the dead-simple troubleshooting trick of screenshotting the error and pasting it back to the AI. Vicki shares how she rebuilt a unit into a game that raised her eighth graders' scores five points with zero retests. In this episode, you'll learn: - What vibe coding actually is (and what it isn't) - How to pick the one small problem worth solving first - How to fix broken code without knowing how to code - Why publishing to HTML lets your tool work anywhere - How AI tools like Gemini, ChatGPT, Canva Code, and Google Apps Script fit in Full show notes, resources, and transcript: https://www.coolcatteacher.com/e940  If this episode gave you an idea, share it with a teacher friend and leave a review wherever you're listening. Sponsor: Today's show is sponsored by EF Educational Tours and their Career Readiness Tours. Lead your students on an international EF Career Readiness tour and show them what a career in fields like agriculture, hospitality, or automotive engineering could look like. Imagine your students connecting with entrepreneurs at the London School of Economics, getting a behind-the-scenes look at Toyota's manufacturing in Japan, or touring a French culinary school to see future chefs in action. If you've been trying to break through to your students and show them how to turn their career dreams into reality, browse EF's collection of Career Readiness tours at eftours.com/ready.

Most people think selling software means building an app. It doesn't. A static HTML, CSS, and JS file solves real problems for local businesses, costs nothing to build with Claude's free tier, and sells for $200 a pop with the right framing. This episode walks through the whole process — what kinds of businesses buy this, what you actually hand them, and how to price and pitch it without a portfolio or a product page.

Host Mike Rosado welcomes designer/illustrator/author Jon Contino to the Pencil Pushers podcast to discuss Contino's upbringing on Long Island, his parents' craft-driven influence, and his early path from band flyers, cassette art, and self-taught HTML to charging for creative work at 14. Contino explains how his lifelong obsession with lettering, failed graffiti attempts, Photoshop experimentation, hardcore/grunge culture, and New York's grime shaped his "organized chaos" style, later balanced by a problem-solving approach to branding inspired by figures like Paula Scher. He describes career growth from a 2005 studio and a handmade clothing brand to building Contino Studio, shifting from illustration trends into larger storytelling and branding work, including Toyota, the Tampa Bay Buccaneers, and sports. They debate commercialization of "handcrafted," carbon-copy styles, and AI's threat, emphasizing human mistakes and youth rejecting "AI slop." Contino shares his remote studio model, intense family-driven schedule, rare client friction, flexible discovery through live conversation, a move from paper to iPad/Procreate for speed, heavy use of Figma/Framer, and excitement about revitalizing Coffee Bean & Tea Leaf's brand. Host: Mike Rosado (mrcraleigh.com) (instagram.com/ekimodasor) Post Production: Max Trujillo (instagram.com/trujillomedia) Sponsors: MRC (mrcraleigh.com) and Burny Wild's (burnywilds.com) 

Having that first response from the server to users browsers be, ya know, full of good, useful, renderable HTML is certainly better than an empty . You can feel it and see it in the filmstrip view of the site loading. Not to mention you need it for at least things like tags if you want your URLs to "unfurl" properly on social sites. Stephen and Chris jump on the podcast to discuss this journey for us as it relates to Next.js. We got it "for free" for our Ruby on Rails pages, but as we're slowly changing frameworks over the years, we've got new challenges, and some gray-hair inducing bugs. We started on Next.js' "page router" and made changes to our Apollo Cache setup to support SSR. Then, over time, moved to the "app router" and did lots more work getting ready getting the most complex page on our site, the 2.0 editor, completely SSRd. That meant getting rid of using things like localStorage for anything view related and fighting bugs related to React somehow mounting itself to the wrong root. Time Jumps

Your documentation has two audiences now - humans reading the rendered HTML, and AI agents trying to make sense of your library. Rich Iannone and Michael Chow from Posit are back on Talk Python with a brand new Python documentation tool called Great Docs that takes both seriously. Rich is the creator of Great Tables, and before that the R package GT, the man has a serious eye for design, and he's pointed that energy at the Python docs ecosystem. We'll talk about how Great Docs spins up a polished site in three commands, why every page ships as Markdown for your favorite LLM, how it leans on Quarto for executable code blocks and tabbed install sections, and where it lands against Sphinx, MkDocs, and Zensical. Plus, you'll meet Tablin. Here we go. Episode sponsors Sentry Error Monitoring, Code talkpython26 Temporal Talk Python Courses Links from the show Guests Michael Chow: github.com Rich lannone: github.com Python Web Security with OWASP Top 10 and Agentic AI Course: talkpython.fm Great Docs: posit-dev.github.io/great-docs Great Tables: posit-dev.github.io GT Episode: talkpython.fm Sphinx: www.sphinx-doc.org mkdocs: www.mkdocs.org Zensical: zensical.org Hugo: gohugo.io Ghost: ghost.org Rs pkgdown: pkgdown.r-lib.org Quarto: quarto.org quickstart: posit-dev.github.io llms.txt file: llmstxt.org llms.txt: talkpython.fm mcp: talkpython.fm cli: talkpython.fm Watch this episode on YouTube: youtube.com Episode #549 deep-dive: talkpython.fm/549 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Scott and Wes are joined by Jonny Burger, creator of Remotion, to talk about the explosion of programmatic video, going from 125k to 800k installs per day, and how AI and a new HTML-in-Canvas Chrome spec are changing the game. They dig into monetization, the wild world of video slop, motion graphics workflows, and the new Media Bunny tool. Show Notes 00:00 Welcome to Syntax! Remotion has skills! 02:20 Monetization Strategies and Sustainability Remotion Pro 04:40 The Impact of AI on Video Creation 07:46 User Demographics and Use Cases 09:49 The Future of Video Editing Workflows 13:14 HTML in Canvas: A Game Changer 16:17 Technical Challenges and Innovations 18:44 Brought to you by Sentry.io 19:09 The Future of Remotion and Community Feedback 22:59 Rendering CSS Animations and Performance Optimization 27:05 The Underworld of Video Slop 29:12 Transition to Media Bunny remotion.dev/docs/mediabunny/ 33:50 Motion Graphics Workflow 39:42 Sick Picks + Shameless Plugs Sick Picks Jonny: ENTTEC DMX to USB Interface Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads