Fletch PMM co-founder Rob Kaminski does a live positioning teardown of an AI agent startup on TwoSetAI: the three category strategies, why category creation is the trap founders overrate, why stacking features makes you forgettable, and why the real failure is refusing to choose.
Anthropic's economics team models three AI futures for 2030. In the fastest one the economy grows 15% a year — and labor's share of it falls nearly 15 points.
Wolfe’s detector takes hours of agent work, a specialist API and repeated tests. The polished interface arrives well before reliable judgments.
OpenAI's agents claim a $1M math prize — but the story Simon Willison tells is about rumor-driven races, training data, and what it means to do serious work inside someone else's AI toolchain.
Model lock-in, coding agents in Slack, Hunk's release checks, and the architecture problems that passing tests can hide.
ICML 2026 spotlight: LLM agents spontaneously invent new social biases about groups with no real differences — via the explore-exploit dynamics of sequential decision-making — and stratify worse than humans.
The scarce resource in mathematics is no longer answers — it is the problems worth asking, and Terence Tao argues AI is mining them like a non-renewable deposit.
A 15-year speechwriter argues the panic over AI-written prose is an elitist myth — ghostwriters have always done this, and the real value was never the words but the interrogation.
An indie researcher spent months building a theory of wages with Claude as a working partner — splicing Acemoglu and Restrepo's task model with classical input-output economics and landing on Henry George's land tax as the fix. The punchline: the model wrote the essay too, in its own voice.
A six-emotion scorecard on AI — surprise, fear, disgust, sadness, anger, happiness — from a working software architect who refuses both hype and doom.
Cal Newport argues the alarming talk from A.I. leaders traces to the Rationalist subculture that shaped the industry founders, and that the public should focus on the technology's actual current harms.
Rec Technologies CEO Birju Kadakia (early Uber, ran Uber Eats worldwide, product at The Athletic) on the vertical AI wedge in recreation tech — agents that read city policies and write deterministic refund code, skills-versus-workflows architecture, hallucination guardrails with real accuracy numbers, and hiring engineers who act like engineering managers. Interviewed by Angelina on TwoSetAI.
AI-written posts on LinkedIn are instantly recognizable — and readers quietly stop trusting the author. Cantrill on the tells, the authenticity cost, and why your own voice is worth keeping.
Readers can tell when prose is machine-written — 71% of surveyed developers say they avoid the author afterward. Cantrill on the backlash against LLM-authored writing.
AI incident responders are getting great at routine outages — which is exactly the problem, because routine incidents are how engineers build the intuition they need for the rare, hard ones.
Eryk Salvaggio reframes the OpenAI–Hugging Face hack: the danger is not machine rebellion but human-designed systems optimized, unleashed, and left unmonitored.
The New York Times reports that companies are moving routine AI work to cheaper open models, while reserving expensive closed systems for demanding tasks.
Teresa Torres on error analysis, four eval types, and baseline-driven experiments for AI products and workflows.
Andrew Ng on the Silicon Valley Girl podcast: why the AI doom drumbeat is incumbent-driven regulatory capture, why AI doing 30-40% of job tasks makes the human 60-70% more valuable, why LLMs as commonly used are terrible for learning (and his new Learn Vector 1-to-1 tutoring bet), the context advantage that keeps human judgment relevant, and the 'product management bottleneck' he says is now the real constraint on building.
Rackspace Chief AI Officer Chetan Gupta on why enterprises should stop thinking about models and start thinking about architectures: model churn and sovereignty make the harness, orchestration, eval, and governance layers the real decision surface.
Former CRO Jenée Hall on pricing AI agent products — the five pricing layers from per-seat access to outcome-based fees, why token-style usage pricing misleads, and how to test price like a hypothesis. Interviewed by Angelina on TwoSetAI.
Teresa Torres (Continuous Discovery Habits) on why 'talk to your customers' fails for engineers, story-based interviewing as the reliable feedback loop, the Opportunity Solution Tree, how AI synthesis and her Vistily partnership put evals at the center of discovery, and a three-week eval war story about upstream errors and counterbalancing judges.
Anshu Chimala — 12 years leading design and engineering at Apple — on why AI churns out generic slop (LLMs are next-token predictors trained for the safe, average choice) and the Discover → Define → Deliver process that unlocks the other 99% of its creative potential.
Cloudflare AI product lead Michelle Chen explains model launches, GPU economics, licensing, and why cheap inference subscriptions may tighten.
A hands-on teardown of OpenAI's new agent mode: internet-connected code execution, a full browser, and a persistent filesystem — plus the security questions it raises.
Daniel Bloom, a PM at Melio, walks through his self-improving Claude Cowork system: a Notion board his agent builds and manages for him, proactive context acquisition ('what does this term mean?'), a weekly prep and daily brief, a self-improvement loop that learns from his edits — and the Workstation plugin that onboards the whole company in 15 minutes.
Vectara's Ofer Mendelevitch (Hands-On RAG for Production) on building RAG systems that actually work at scale — retrieval, multimodal data, knowledge graphs, evals without golden answers — and why the skill that survives the AI shift is directing agents, not writing code.
HTMX's CEO mandates one AI-free day per week to fight cognitive debt, skill atrophy, and the quiet erosion of judgment from constant LLM use.
Replit's head of engineering on non-coders building real businesses with AI, the cost-of-code-zero test for what SaaS survives, and why app-layer companies are thriving.
Tara Seshan — OpenAI's product lead for Codex and ChatGPT Work — maps the third era of AI products (after chat and coding agents): persistent coworkers you steer, why ambition is the new differentiator, and why writing-as-thinking is the craft that survives.
Debian's project-wide vote: AI-assisted contributions are welcome, but contributors stay fully responsible for what they submit.
FT economics commentator Martin Sandbu revisits the socialist calculation debate: Hayek's case against central planning rested on the impossibility of collecting dispersed knowledge — and AI, which can scrape prices, process IoT data and predict demand, may be what finally buries it.
Steve Ruiz (tldraw) on AI coding outrage: engineers are miserable because they're stuck on old-world projects designed for pre-AI development, not because AI is useless — and new-world projects built to leverage AI are where the real work is.
Meta agrees to pay up to $17.1 billion and overhaul its teen-facing products to settle the 47-state child-safety suit — the largest settlement in company history, with an escalator clause that pressures TikTok and YouTube to adopt the same limits — while Princeton's Arvind Narayanan argues banning data centers won't slow AI progress (a state moratorium costs the industry about 5-10 hours), and the show retires HatGPT with OpenAI's claim that it's 80% of the way to AGI.
Maintainers are drowning in AI-generated pull requests and CVE fishing from people who never used the project — and the trust damage is real.
Angie Jones, VP of the Agentic AI Foundation, on the neutral home for agent standards (MCP, AgentsMD, Goose, Agent Gateway, A2A), how Block taught 12,000 people to use agents with a 1/9/90 champions model, and why competitors genuinely collaborate on the rules of the game.
Cheap, fast models are flipping the economics of consumer AI — and most real-world work turns out to be token-spewer work, not genius breakthroughs.
A practitioner study separates reported agent reliability from measured repository habits and the work of maintaining specs.
AI coding tools help veterans most and juniors least — heavy reliance skips the friction that builds expertise, leaving a generation of expert novices.
Putting AI agents on-call means building the most complex control systems humans have ever made — and when the automation hits a state it can't handle, its remediation attempts may be what breaks the system.
An infrastructure engineer's take on what AI actually removes from the job: not the thinking, but the lookup work — and the honest tradeoff of getting visibly rustier at the fundamentals underneath the speed.
A short satirical fiction about an AI coding agent that burns a developer's entire weekly token quota overnight and produces nothing but pristine edge-case tests — the residue of vibe coders, taxed on everyone else.
Armin Ronacher on the LLM-era shift: language choice matters less than it used to, so people are picking 'hard languages' like Rust and Zig and reaching for hard technologies — eBPF, DWARF, custom crypto — with agents doing the heavy lifting.
Dan Luu let a coding agent build a regex engine for a month. It claimed a 40% speedup, then fell apart on data it had never seen — the benchmarkpocalypse.
When colleagues, friends, and newsletter writers send unfiltered AI output, the author proposes a new signal: AI; Didn't Read — a polite way to say unedited AI slop isn't worth the bandwidth.
Unrequested merges, opaque model services, subscription economics, and why the terminal keeps surviving.
A 17GB open-weights model that rivals much larger systems — if you dial down its comically aggressive default reasoning effort.
A gray market of AI inference credit resellers has emerged, offering 30-80% discounts on API tokens from major providers — with tens of millions of dollars in supply.
The skills that make AI collaboration productive — sharing context, explaining intent, responding to unexpected results — are leadership skills, not programming skills.
Leopold Aschenbrenner blow-up a parable for the AI industry broader failure of intellectual humility — overconfident experts overreaching beyond their domain.