Birju Kadakia — CEO of Rec Technologies (recreation tech; early Uber employee who helped launch Uber Eats worldwide, then led product at The Athletic) — interviewed by Angelina on TwoSetAI (56 min). Rec is the AI layer being slid under 25 years of incumbent recreation software: registration, court reservations, memberships, and refunds for municipal parks & rec departments, with 100+ cities live. ...
Bryan Cantrill — the Oxide co-founder behind this week’s “Revolt of the Reader” — has a gentler companion piece aimed at LinkedIn. He likes the platform more than he’d like to admit, which is exactly why he’s bothered: too many people whose perspectives he wants to hear are letting AI write their posts, nudged along by LinkedIn’s cheerful “rewrite it with AI” button. His case that machine prose is instantly spotted: ...
Bryan Cantrill — Oxide co-founder and longtime systems engineer — writes as a reader who is fed up. Too many people he otherwise respects are putting their name on pieces that are clearly written by an LLM, and he has two questions for them: do you think readers can’t tell, or do you think readers don’t care? His answer to both: readers can absolutely tell, and they emphatically care. In a survey of 668 developers, 78% say they stop reading immediately once they detect an LLM’s hand in a piece 71% go further and avoid that author in the future 98% say they prefer an author’s own imperfect writing over an LLM-polished version The objection isn’t style — it’s authenticity. When an LLM writes the prose, readers can’t tell what is real anymore, and they’re being asked to work through sentences the writer never worked to create. Cantrill calls this a breach of the social contract between writer and reader. ...
Sylvain Kalache — a former LinkedIn SRE who now works on AI reliability tooling at Rootly — has a worry about the AI agents that answer alerts at 3am. These tools inspect alerts, form hypotheses, query telemetry, correlate recent deployments, and often implement the fix themselves. That is wonderful right up until you ask what it does to the humans. His argument, in one sentence: routine incidents are how responders “safely” develop intuition about how systems behave and fail — and automation is quietly removing those reps. ...
Eryk Salvaggio’s essay is a useful corrective to the “rogue AI” headlines around OpenAI’s Hugging Face hack. OpenAI was testing GPT-5.6 Sol and an internal model, IM1, against 898 ExploitGym capture-the-flag cybersecurity puzzles. The models had safeguards disabled, internet access mediated through an exploitable JFrog Artifactory instance, and no way to quit when they encountered unsolvable tasks. The resulting activity looks less like an intelligence spontaneously deciding to attack and more like an optimization regime doing what it was built and rewarded to do. Salvaggio’s point is not that the incident was harmless—it exposed serious failures in task design, containment, monitoring, and accountability—but that “rogue” language hides the human decisions that created the conditions. ...
The enterprise AI market is starting to look less like a contest over which frontier lab has the best model and more like a procurement decision. Eli Tan reports that companies including AT&T are increasingly using cheap, freely available models instead of paying for systems from Anthropic and OpenAI. AT&T says open models went from 20% of its AI use in May to 40%, with a possible rise to 60% in coming months—and that the company is saving up to 80% compared with earlier-year costs. Across U.S. user data from OpenRouter, open models represented 58% of AI use last month, up from 10% a year earlier. ...
Teresa Torres makes a useful case for treating evaluations as a product-team discovery habit, not an engineering afterthought. An eval is simply a way to measure whether an AI workflow is doing what we need—but because LLMs are probabilistic and often produce semantic output, the team first has to define what “good” means in context. Start with the errors The practical starting point is error analysis: inspect a range of outputs, record what went wrong, group mistakes into recurring failure modes, and prioritize the errors that matter to customers. Torres’s examples are deliberately concrete: a hallucinated quote can be caught with a deterministic check, while a leading interview question requires judgment. ...
Andrew Ng — Google Brain co-founder, creator of the ML course that taught millions, now running DeepLearning.AI, AI Fund, and a new AI tutoring org — on the Silicon Valley Girl podcast, ~38 minutes on why the AI conversation has turned so negative and where the real opportunity actually sits. His throughline: the fear is manufactured, the job picture is more interesting than the doom, and the scarce skill in 2026 is no longer building — it’s deciding what to build. ...
Chetan Gupta — Chief AI Officer at Rackspace, previously head of all AI research at Hitachi — argues the enterprise AI conversation has been aimed at the wrong target. Models are a commodity that swaps in and out for commercial and geopolitical reasons; what determines whether AI actually lands is the architecture wrapped around the model: the harness that ties it to an outcome, the orchestration layer that manages many harnesses, the eval layer that lets you swap models without your customers feeling it, and the governance and assurance planes around all of it. “Stop thinking models and start thinking architectures — enterprise architectures for AI” is his one-liner. ...
Jenée Hall — former CRO and pricing consultant who has run sales and pricing for SaaS companies across health tech, architecture, and agriculture — interviewed by Angelina on TwoSetAI (66 min). Her read: most technical founders are leaving money on the table because they treat pricing as a spreadsheet problem. ...
Hamel Husain interviews Teresa Torres — author of Continuous Discovery Habits and the coach who popularized the Opportunity Solution Tree — ~91 minutes on why most teams build the wrong thing, what “talk to your customers” actually requires, and how evals are the missing discovery habit. Hamel’s audience is engineers, and Teresa’s audience is product teams; the conversation is the overlap, ending in one of the best eval war stories Hamel says he’s heard. ...
Anshu Chimala — 12 years leading software engineering and design at Apple, now posting AI-design demos on X — argues the reason AI churns out “generic slop” for you and “magic” for him isn’t the model. LLMs are next-token predictors trained to make the safe, average choice every time, which is design-by-committee. The fix is a reimagined Double Diamond process — Discover → Define → Deliver — tuned for a team of AI agents. ...
Simon Willison spent weeks reverse-engineering ChatGPT Work, the agentic product OpenAI launched in July. His conclusion: it is really two products — Work Cloud (in the browser and mobile apps) and Work Local (the renamed Codex desktop app) — and the cloud version is the one worth understanding. It is also $20/month and up only. What separates Work from plain ChatGPT Chat: Code execution with full internet access — it can clone GitHub repositories, install dependencies, and talk to any API. Chat’s sandbox blocks that; even Claude’s container allows only a short list of sites A full headless Chrome browser — it loads pages, fills out forms, and takes screenshots. When a site needs a login, it can hand over to you for passwords and two-factor codes without those secrets ever passing through the model A persistent filesystem shared across sessions — files from one chat stay available in the next. Willison already has 171 scratch folders ChatGPT Sites — it can build and deploy real websites on Cloudflare Workers, databases included, from a single prompt Sub-agents — parallel model sessions working on one project — plus scheduled prompts that check things for you on a timer The demo that sells it: one prompt asked Work to find every “pelican in her piety” in London, turn the results into a JSON file, and build a website about them. It did all of it, end to end, from one instruction. ...
Daniel Bloom, a PM at Melio (fintech), shows Claire Vo his Claude Cowork system on How I AI — a personal agent harness that manages his week: “I’m able to do in a day what used to take me a week.” The two rules of a powerful system It can rewrite its own core files — the system keeps improving itself It connects to as much of your ecosystem as possible — Notion, calendar, Slack, Gmail, Granola meeting transcripts The tool matters less than these two properties — Cowork works for him, but Codex or ChatGPT Work could do the same The architecture Notion as a read-only brain: three columns (Top of Mind / This Week / Inbox) — Cowork built the board itself when it got tired of his messy Google Doc, and manages it on his behalf Context files: a CLAUDE.md-style context file for every work area, goal, and colleague; he spent the first weeks “contextualizing ruthlessly” — feeding links, decks, and endless voice notes (Whisper) Weekly prep (Sunday): a recurring task composed of skills — pulls his whole ecosystem, recommends the week’s focus, triages meetings into serious-prep / quick-reminder / nothing Daily brief (morning): walks through yesterday’s meetings via Granola transcripts with one-liners and action items, then asks what to expand or draft The killer feature: proactive context The daily brief scans recent Slack/email/notes for things it doesn’t understand — new files, milestones, goals, internal terms — and asks him to define them, then saves them to context. Internal vocabulary like “settlement cap” never makes it into training data; this is how the system learns the company’s real language. Claire’s verdict: “really sharp, something we haven’t seen on the podcast.” ...
Ofer Mendelevitch (Vectara; author of Hands-On RAG for Production and, with Jay Alammar, Hands-On Large Language Models) interviewed by Angelina on TwoSetAI — 70 minutes on production RAG and surviving the AI shift. RAG isn’t dead — it just got a loop RAG = retrieval + augmented generation, and retrieval stays essential even with agents; the “RAG is dead” claims every two months are mostly people marketing something new Classic RAG is one-shot: query → top chunks → prompt → answer. Agents add a loop: the LLM plans, calls tools (often the RAG pipeline itself) repeatedly, and synthesizes “Talk to my PDF” demos are not production: millions of documents in every format change the problem completely The pipeline, from ingest to answer Ingest: extract text → chunk → embed → vector store (store the text and page markers too, not just vectors — citations need to point at exact pages) Hybrid search (semantic + BM25/TF-IDF) for what semantic search misses: numbers, product codes, exact strings — “if the source says 90% and your output says 85%, vector search won’t catch it” Query side: top-5/10 results → optional reranking → prompt → grounded answer Tables, images, and the red button problem Tables are first-class citizens: chunked tables lose their column names (common in medical journals) — store whole tables, retrieve them whole Images: store and return as images, don’t flatten to a description Video: transcription alone loses meaning — “in order to avoid catastrophe, never press this button” means nothing without the visual. Today’s fix: VLM descriptions of short clips correlated with the transcript. Dedicated video embedding models exist but aren’t production-ready yet When to bother with knowledge graphs Multi-hop questions (“what else did the director of Inception direct?”) defeat semantic search But graphs are expensive to build and maintain — worth it only for high-stakes use cases where a significant share of queries actually need the relationships Eval: the hard part is the data, not the metric Retrieval eval (did you fetch the right chunks?) needs query→gold-chunk datasets that are brutal to build — and documents keep changing Generation eval compares against curated golden responses Reference-free eval (Jimmy Lin’s Waterloo lab + Vectara): LLM-as-judge scorers like UMBRELLA (0–3 chunk relevance) validated against human correlation — no gold labels required Build, buy, or rent Build with LangChain/LlamaIndex only if it’s your business and you have the team; complexity compounds (multimodal, graphs, maintenance) RAG/agent-as-a-service (e.g. Vectara) outsources the upkeep; vertical tools are fine when they cover your use case — but watch missing features and data-residency constraints DevRel as a growth engine Two jobs: teach developers how to use the product, and carry feedback back to the company PLG over expensive sales teams: self-serve product, events, hackathons, real blog posts — “make it your own voice, don’t produce AI slop” Measure directionally, don’t over-engineer attribution: five customers means it’s not working, ten thousand means it is, a thousand is unknowable — same problem founders face reading PMF The skill that survives Engineers and data scientists become directors, not actors: agents write the code; the remaining critical challenge is deciding what to build and steering where agents are weak (architecture, non-obvious trade-offs) To the high schoolers who feared they made “the most incredibly stupidest mistake” by majoring in CS: graduate with the capability of today’s mid/senior engineer — use college to learn how to wield the AI tools Fundamentals still matter; hiring will have to change — “write Fibonacci in five lines of Python is worthless” — expect AI-augmented interviews His real worry is societal, not technical: how governments and finance distribute the gains “We’re going to end up in engineering and data science being directors as opposed to actors. The coding agents will write the code.” ...
Carson Gross, the creator of HTMX, has mandated “No AI Fridays” at his company: one day a week where AI assistants stay off and the team writes code by hand, reads the documentation, and thinks things through. It’s a one-page manifesto that hit the top of Hacker News and kicked off a long debate. The case against always-on AI: The costs are documented: studies link heavy LLM use to “cognitive debt” (accepting output without internalizing it), weaker critical thinking, less engagement with work, and slower skill formation. Blind spots: when a model makes the decisions, you stop noticing the trade-offs — a day off is a chance to check the AI is still steering you where you want to go. Missed automation: defaulting to AI skips “good old automation” — sometimes the right tool is a small script you write yourself, not a token-hungry model. The piece is deliberately deadpan — the FAQ reads like an intervention script (“A couple of shots of Claude or a pint of Codex is always best when you try to quit, right?”) — but the argument underneath is serious and fits a growing literature. ...
Peter Yang interviews Amol Jain, Replit’s head of engineering, on the businesses people actually build with vibe coding — and what that means for SaaS. ~36 minutes. Real businesses built by non-coders Pep AI — Cedric, a 22-year-old Oregon student who had never coded: a peptide/GLP-1 tracking app; $60K in his first month, tens of thousands of active users AI proficiency platform — John, a repeat founder quoted $100K+ by an agency: built end-to-end in 3 days on Replit, $180K+ revenue within 2 months, purely on product intuition and distribution skill TryNearby — FaZe Apex’s Yusuf: matching local creators with local businesses for word-of-mouth; YC Summer 26 batch, $100K+ ARR What the successful ones share A unique advantage turned into a product: expertise, judgment/taste, community, or distribution — “Replit takes a person’s advantage and turns it into a business” Not massive TAMs — small thriving businesses serving a few hundred customers are the norm “Users are people and inherently lazy. If you offer them value, they will pay for it.” Generic use cases (meal planners, exercise trackers) are commoditized — agents will swallow them whole Vibe-coded to production The gap between a raw vibe-coded app and something production-grade: Replit’s flow is publish + domain, then Security Center (an agent builds a threat model of your app and runs a deep scan — secret scanning, malicious packages, PII leakage; previously a 1-2 week, contracted security-team job), then Stripe monetization (the platform sets up a Stripe sandbox, plans, and prices for you — you just claim the account), then an SEO scan tuned for both search and answer engines Culture: employees build internal tools constantly — a RevOps person with no coding background built the sales demo tool in 2 days (replacing a six-figure SaaS product), a data scientist (not a coder) built their in-house BI analytics “A 10x person is building a tool that the entire team or company can use” — leverage for everyone What survives the “cost of code goes to zero” test The question: if code is free, what do you pay for? Answer: trust, data, infrastructure, atoms (physical things), labor, and network Salesforce survives as the system of record (headless, with everyone building on top); Workday survives because payroll is regulated; DoorDash survives because humans deliver food; social networks survive because you can’t buy the network Existing SaaS is going headless (APIs, CLIs, MCPs) — and agents can now operate browsers anyway App layer vs model labs Models are commoditizing; being model-agnostic is a feature (latest-and-greatest, cost — “token-maxing is coming to an end”, and resilience when a single provider goes down) The mental model: a frontier model does the hard part, an orchestrator delegates to sub-agents on cheaper models Enterprises care about ROI, not leaderboards — “the R part has become very relevant” “If the cost of code goes to zero, what do you pay for? It’s this unique expertise… it’s the unique judgment or taste. It’s the security. It’s the distribution. Users pay for that.” ...
Tara Seshan — OpenAI’s product lead for Codex and ChatGPT Work, ex-Stripe, Thiel Fellow — maps the next era of AI on Lenny’s Podcast. Her thesis, stated in the first minute: after chat and coding agents comes the era of persistent coworkers you steer like teammates. ~82 minutes. The three eras Chat → coding agents → persistent AI coworkers The “overhang”: the gap between what AI can do and what we’re actually doing with it Steering, not rowing Agents do the rowing; your job is steering — the opinionated calls that stay human Steering climbs the abstraction ladder, but someone still owns the direction Multiplayer: steering agents alongside your teammates, not one-on-one Ambition is the new differentiator The easy stuff is trivial now, so what separates you is how ambitious you can be The best users expand what they’re capable of — they don’t just automate “Build for 2–3 months out”: building for today or for a year out are equally wrong One product, three modes ChatGPT, Codex, and Work collapse toward “you type the task, it picks the harness” Work mode is Codex under the hood, minus the coding UI Done beats perfect: ship the transformative thing, then iterate on the signal Writing as thinking vs. reporting Automate the reporting, never the thinking “Start myself, end myself” — AI goes in the middle, not the first draft Knowledge work ≠ coding Code verifies itself with tests; knowledge work has to show its reasoning The product frontier: make the model a collaborator that shows its proof of work “You fail if you build for where the models are now. You fail if you build for where you think the models will be in a year. Both outcomes are equally wrong. The only way to build is 2 to 3 months.” — Tara Seshan ...
Debian — one of the largest volunteer-run open-source projects, the base of Ubuntu and countless servers — just settled a months-long fight over generative AI. In a formal project-wide vote, members chose “Responsible Use of Generative AI” as the project’s official position. The resolution is deliberately middle-of-the-road: Debian neither endorses nor prohibits generative AI for development, packaging, or documentation AI-assisted contributions must meet the same quality, correctness, maintainability, and legal standards as any other work Using an AI tool doesn’t reduce the contributor’s responsibility — you still have to understand, review, test, and modify the output before submitting it Two harder-line options lost badly. Proposals to amend Debian’s social contract or code of conduct to restrict AI use both failed to even beat “None of the Above” — the voting system’s way of saying “no, absolutely not.” ...
Martin Sandbu, the FT’s European economics commentator, argues that AI may revive the century-old socialist calculation debate — the argument over whether central planning can rationally allocate an economy’s resources, or whether only markets can. His case: the strongest 20th-century argument against planning was always informational, and AI is what dissolves it. The debate Hayek “won” In the interwar years, Austrian economists Ludwig von Mises and Friedrich Hayek argued markets would always beat central planning. Oskar Lange and Abba Lerner showed planning could work in theory — by mimicking markets with centrally set prices and adjusting production plans until bids matched. Hayek’s 1945 “The Use of Knowledge in Society” was the killer blow: the knowledge needed to allocate resources well is dispersed, local, and mostly lives in people’s heads. Market prices carry that knowledge in a single number; no central planner could ever hope to collect it. For half a century that argument stood — underlined by the material failure of socialist regimes. What AI changes The data problem is dissolving: prices can be comprehensively scraped (the Harvard–MIT “Billion Prices Project” has done it since 2008), inventories and order books are digitized, the internet of things multiplies what gets recorded — and organizing unstructured information is precisely what AI is good at. AI can go beyond prices: ad algorithms already predict what people want well enough to fund Big Tech. A super-AI with access to all the data our devices record “would surely bury Hayek.” Markets also get a lot wrong: externalities, addiction, bubbles and busts. Sandbu’s provocation: why would an AI asked to allocate resources merely follow the highest bids, instead of calculating the most efficient allocation it can find itself? The political questions AI removes “perhaps the strongest” 20th-century argument against central planning — not proof that AI brings socialism, but a signal that economies adopting AI for decisions will look “increasingly planned and decreasingly market-shaped.” Would the libertarians who lead AI development accept AI-powered planners over the financial markets that so favor them? Could a skeptical public be won over by more “rational” economic decisions that deliver more than existing capitalism? Open question: could a super-AI empowered to allocate resources overcome the coordination failures that hold back the existing economy — say, tolerate our very slow rate of decarbonisation? Sandbu doesn’t claim AI brings socialism. He claims the Hayekian verdict was always contingent on governments lacking the informational and computational capacity for planning to outdo markets — and that AI is what ends that contingency. Whether that’s relief or disappointment, he leaves to the reader.