Product & Engineering, @tempo, @liamihorne on why more onchain infrastructure can mean less integration work⁣ ⁣ "If we have these kind of tokenized deposit assets that you can use those as collateral for stablecoins."⁣ ⁣ "I've seen experiments of that with some stablecoin issuers, and it's really, really useful because then you can programmatically distribute out which assets are backing which stablecoin at different times."⁣ ⁣ "It's kind of this ironic thing where actually, the more stuff you put on chain, the less integration work there is on the back end."⁣ ⁣ "So it actually, weirdly, it goes faster if you do more stuff on chain."⁣ ⁣ Putting the collateral itself on chain lets issuers show which assets back which stablecoin at any given moment, removing the manual reconciliation that normally sits behind reserve reporting.⁣ ⁣ 🎙 Listen to the latest episode on Tokenizedpod(dot)com

You can’t build a software factory unless your team first does at least some of its product engineering through a factory as opposed to local coding agents. This means centralizing your agents into one shared configuration that the team can optimize together. Once work starts flowing through the factory infra, you’ll have a baseline to measure and improve.

What does it take to make AI agents work at scale? Meet Qi Zhou, VP of Product & Engineering, Head of Cloud Native Application Platform, Alibaba Cloud, at #ApsaraConference2026# in Amsterdam to explore how cloud-native technologies can help businesses bring AI agents into production, scale intelligent applications, and turn AI innovation into lasting value. Date & Time: Oct 29, 2026 | 9:30 AM – 12:10 PM CET Location: Beurs van Berlage, Amsterdam Register: #ApsaraAmsterdam# #AgenticEra# #AIAgents# #AlibabaCloud#

“Which vibe-coded apps actually become real businesses?” I asked @amoljain_, @Replit's head of product engineering, this question since he's seen millions of people build apps and websites with AI. Amol shared three examples of apps built by non-coders that reached six figures. He also did a live demo of what builders should think about before taking their app to production, including security, monetization, SEO, and more. 📌 Subscribe to get our full interview tomorrow:

Brought the team together in Lisbon last week for a week of building, creating, and some great bites and views. Join us before our next one. We're hiring across: - Product engineering - Growth engineering - Data science - Design - Creatives/FDCs - GTM specialists - Generalists

A little look back at Dreamforce 2026 Once again, the energy in San Francisco really struck me. Everyone I spoke to was amazed by what AI can do and excited about the possibilities it opens up for them. They want to start companies, pursue research, and shape the future. Dreamforce gave me the chance to take a closer look at the visions behind that excitement. I spoke with Muralidhar Krishnaprasad, widely known as MK, Salesforce’s President and CTO of Product Engineering & Research. He shared his perspective on how Agentforce is already changing healthcare and helping improve the way healthcare teams work. I’m also happy to say we’ll be recording an interview with him soon. We also recorded a video at Dreamforce with Mick Costigan, who leads the Salesforce Futures team, about how AI will evolve in the near future and how an agentic economy is taking shape. To me, 2026 still feels like just the beginning for agentic AI. I finally got to meet so many people I’d been wanting to meet in person, and reconnect with others. I’m grateful that I get to make these trips, and meeting people is something I look forward to every time. I’m also sorry I can never quite manage to see everyone I’d love to catch up with. But hey, I’m flying back to Germany today. For just three days. On Monday, I’m heading straight back to San Francisco for a very exciting event. More on that soon! Much love, Kim "Chubby"

I'm on my way back to Vancouver to be with my mom but wanted to take a moment to celebrate crossing 100K subs on YouTube. Excited to share a lot more practical interviews soon to answer your most burning AI questions: 1. @HamelHusain and @sh_reya (AI evals experts) on how today's AI models have changed evals completely 2. @amoljain_ (Head of Product Engineering at Replit) on which vibe-coded apps have actually become real businesses 3. @ebloch (Product Lead at OpenAI) on how ChatGPT Finance can help you save both time and money 4. @poteto and Roman (SpaceXAI) on how the Grok Bot team uses Grok @bot 📌 Subscribe to get the episodes soon:

Our UK reps didn’t remember to smile 😂 We recently opened a @llama_index office in London 🇬🇧. It is three people strong (Frank, Max, Joe), with two more people starting soon. It also has a beautiful view of the Thames. We’re hiring people across SF, London, and (soon) NYC in a ton of different functions: * Member of Technical Staff: across core product engineering, infra, and AI research * Forward deployed AI engineers * Mid-market and enterprise account executives BDRs * Field marketing and devrel * GTM and technical recruiting We’re growing quickly - we’ve doubled in size in the past few months - and would love to bring more folks to join our journey 🚀 Come check out our careers page:

This summer, we brought in two first-year Waterloo interns (‘rising sophomores’ in 🇺🇸) and tasked them with shipping complex production features that help push the frontier of our document processing and extraction capabilities. Yash created a dashboard that allowed customers to better understand the distribution of documents flowing through the system - and gives us anonymized metrics that we can use to improve our own capabilities. Jishnu helped to do deep research into grounded confidence scores for all of our document extraction capabilities. These scores are a lot more calibrated to real uncertainty about any value being extracted compared to our earlier implementation, allowing customers to trigger HITL in the right places within their document workflow. More on this coming soon 🙂 Outside of work, I have fond memories of watching the Knicks final game 4 with them (the one where the Knicks came back down 29 points). We’ve historically brought on full-time engineers with a bit more experience, but Yash and Jishnu ramped up extremely quickly and exemplified the high-agency, high-energy scrappiness that we look for in anyone joining the @llama_index team. They weren’t old enough to drink but old enough to ship production code 😂. We’re so happy they spent time with us so early in their careers, and wish them the best with the next 4 years of school. If you’re young but cracked, we’d love to chat. We’re hiring across *a lot* of roles across product/engineering/research (as well as growth and GTM). Careers page:

Enterprise vibecoding is a shit show. Most companies subject themselves to one of two failure modes: • Blindly enable vibe coding. It becomes a matter of when (not if) some data security bomb will drop. • Blindly deny vibe coding. The company deprives itself of decentralizing problem solving, and shadow AI (I.e people using personal accounts runs rampant). Which is why customers/prospects ask for some silver bullet to responsible, enterprise-grade vibecoding weekly. The typical question we get: “How can we let non-technical employees build production software without a data security nightmare or millions in wasted tokens?” And now having solved this problem for a number of companies including a multi billion dollar PE firm and F500 retail biz, we have a proven framework you can use within your business. First, why is vibecoding good for business? • Employees are less likely to solve the problems of the business and are more likely to solve their own problems (which in turn as adoption grows, solves parts of a business). • Employees / vibe coders are going to build anyway. Sanctioned / safe / governed tools > shadow IT/AI. • SMEs are the ones who have the deep knowledge on an industry/workflow, not the engineer. Empower the SMEs to build, and they can build useful tools. • 95/5: most apps will fail, your winners gives your business outsized returns. Okay, now let’s assume you’re bought into vibecoding…. Second, you have to be clear about what you’re optimizing for when enabling enterprise vibecoding. 1. Turning SMEs into problem solvers A critique of engineers as old as time is that they don’t sit close enough to the work to truly understand the problem/end user. Whether you agree or not, it’s not a hot take to say that subject matter experts doing the work deeply understand the problems and friction points that need to be solved through product. 2. Enabling multiplayer AI Building an app that runs locally on your computer and solves a clear problem is good. Getting that app to be used & improved by everyone in your department that experiences the same problem is how you get far more leverage. 3. Allowing IT to sleep at night If customer data leaks or an api key is exposed, guess whose ass is on the line. IT. No wonder they’re so paranoid about things like company-wide vibecoding. They’re not anti-vibecoding, though. They’re anti-having the right guardrails in place to prevent the worst day of their careers. 4. Making data access programmatic Pre-AI, IT teams could get 5 data requests a week. Today, that number could be 50/day. As data access goes parabolic because it’s the foundation for performant AI, tools for IT must evolve in-kind. 5. Production-grade by design For product/engineering to take vibecoding seriously, enterprise consistency has to be built into what employees build. If at any point someone has to take over, or review, they are shaped relatively the same, adhere to the eng org’s conventions, and are easy to understand. Now, how do you put this into practice? Third, you have to build a software pipeline that enables secure & responsible, production-grade vibe-coding at scale. We call it the Citizen SDLC. Step 1 is Discovery. Input: An employee works with AI to described their idea, the problem, users, data needs, and desired behavior. Output: This gets turned into structured product requirements doc. Step 2 is Request. Input: PRD becomes one structured request to IT with a named owner, users, data classification, integrations, and scope. Output: Every app enters IT triage on the record before any build work begins. Step 3 is Triage. Input: AI classifies the request along two axes. • Shape: what kind of app is it? artifact generators, workflow automations, CRUD apps, or interactive dashboards. • Blast radius: how much damage could this build do if it went sideways? Output: Three routes out. • Approved: blast radius inside every threshold, complete brief, high confidence. • Reuse: overlaps an app that already exists, so the requester gets routed to that app’s owner • Escalated: A dimension crosses its threshold. IT gets the full request. Step 4 is Provisioning. Input: One approval, the paved road for the app’s shape (architecture, identities, governance, etc), app metadata and owner. Output: platform stamps the app from the road for its shape: a repo, firm sign-in, a deploy identity, a private environment, and its own database, all defined as infrastructure-as-code that IT owns and versions. Step 5 is The Build. Input: The employ prompts their coding agent (Claude Code, Codex, etc) inside the scaffolded architecture, adds product behavior, and opens a PR when ready to publish. Output: Finished app. Everything an engineer would normally carry is carried by the rails instead. Step 6 is Run & Change. Input: New features or app changes made by the original owner or collaborators in the coding agent. Output: Humans do not review routine changes. The checks are the review. What reaches a human is the consequential tail, detected mechanically: destructive schema changes, new dependencies, etc. Hopefully you found this approach to vibecoding at your company helpful! If you want the full breakdown of the six step Citizen SDLC process in excruciating detail, check this link. It’s an end-to-end playbook for responsible, production-grade enterprise vibecoding: