I built this using OpenAI Codex desktop, which turns out to have the Markdown session transcript export feature I've always wanted. Here's the session that built the blog. See also issue 179.
Tags: ai, datasette, generative-ai, llms, ai-assisted-programming, codex
“11 AI agents” is meaningless as a phrase.
If I said “I have 11 spreadsheets” or “I have 11 browser tabs” to do my work, it means about the same thing.
Tags: ai-agents, ai, agent-definitions
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Good morning, {{ first_name | AI enthusiasts }}. Two months ago, OpenAI's Fidji Simo told staff the company was in a "code red" over Anthropic's enterprise rise. New spend data says the leaderboard has actually flipped.
Ramp’s latest AI Index just showed Anthropic leading adoption among its paid business users for the first time, a 4x adoption surge since 2025 that may tell the story of why OpenAI has been drastically shifting its priorities throughout 2026.
P.S. — Our next live workshop, 'Finally Getting AI to Do Real Work', is today at 2 PM EST. Join for a breakdown of how AI actually works in 2026 and the context habits power users layer on top to get reliable output from any model. RSVP here.
In today’s AI rundown:
Anthropic seizes OpenAI's business AI lead
Amazon doubles down on Alexa+ for shopping
Create content with Claude Code and Higgsfield
Adaption automates AI training with AutoScientist
4 new AI tools, community workflows, and more
LATEST DEVELOPMENTS
Image source: Ramp
The Rundown: Fintech firm Ramp just published its latest AI Index, showing Anthropic taking the paid business-adoption lead from OpenAI for the first time, an enterprise surge that has quadrupled its usage over the past year while OpenAI has leveled off.
The details:
Ramp tracks corporate card and invoice payments from 50K+ U.S. businesses, making this a spend signal, not full market share.
Anthropic rose 3.8% in April to 34.4% of adoption, while OpenAI fell 2.9% to 32.3% as overall AI use continued to climb to 50.6%.
Claude Code anchored much of the swing, with Anthropic expanding from technical teams into finance, legal, and research workflows.
Ramp highlighted several risks facing Anthropic despite the trend, including recent Claude outages and increasing costs compared to OAI and open-source.
Why it matters: OpenAI is not suddenly cooked, with Ramp not tracking some large enterprise deals and ChatGPT remaining the bigger consumer brand. The yearly trend speaks louder, and was likely the same type of chart that caused OAI’s Fidji Simo-led pivot, which has become evident from recent Codex and other enterprise pushes.
The Rundown: Scale your AI operations with the Gemini Enterprise Agent Platform, Google’s new evolution for building, governing, and optimizing AI agents. Using the open-source Agent Development Kit (ADK), this hands-on codelab teaches you to orchestrate specialized agents like Researchers and Judges into self-correcting workflows that solve complex problems at enterprise scale.
In this codelab, you will:
Construct self-correcting AI feedback loops.
Orchestrate task hand-offs between specialized agents.
Deploy production-ready workflows to Google Cloud Run.
Image source: Amazon
The Rundown: Amazon folded its standalone shopping chatbot ‘Rufus’ inside "Alexa for Shopping", a new agent that takes over Amazon search and follows shoppers across devices with a shared memory of purchases, preferences, and prior chats.
The details:
Rufus drew 300M+ users in 2025 while still in beta, with its product knowledge and shopping history now feeding Alexa for Shopping answers.
Amazon says the new assistant draws on catalog data, reviews, delivery timing, past purchases, and Alexa conversations for information.
Alexa can now field questions in the search bar, run side-by-side comparisons, track pricing, and Auto-Buy items when prices hit a target.
A new Buy for Me handles checkouts on non-Amazon stores, with Scheduled Actions able to automatically restock products on a cadence.
Why it matters: RIP to Rufus, but consolidating under one Alexa AI brand feels like a better play, and the sheer amount of customer history Amazon has gives its agent quite the moat to work with. But with a consumer base that is already moving to other AI platforms for their agentic shopping needs, will Alexa play nice and integrate with them?
The Rundown: In this guide, you will learn how to connect Higgsfield to Claude Code with the Higgsfield CLI, then use Claude Code to send one image prompt to several AI image models at once.
Step-by-step:
Create a new project folder, install Higgsfield CLI (npm install -g @higgsfield/cli), authenticate (higgsfield auth login), and add Higgsfield skill (npx skills add higgsfield-ai/skills) for Claude Code
Open Claude Code in the same folder and ask it to inspect higgsfield.ai/cli, verify the installation, and list available image models
Give Claude Code an image prompt and tell it to run it across six models, save outputs into a higgsfield-model-test folder, and generate a comparison.md file with notes for each result
Pick the best direction, then ask Claude Code to refine the winning prompt or run one more comparison
Pro tip: If setup gets confusing, ask Claude Code to check Node/npm and give a walkthrough. Higgsfield also supports video, so you can also try this for short clips.
The Rundown: AI companies are hitting revenue milestones faster than ever—but figuring out how to monetize AI products is still one of the hardest problems founders face. Stripe interviewed teams at Anthropic, Vercel, Clay, and more to build a five-step pricing framework for AI products.
Download the guide to learn best practices for how to:
Pick the right pricing model
Prevent billing surprises
Refine your pricing over time
Image source: Adaption
The Rundown: Adaption, the AI startup from ex-Cohere VP of Research Sara Hooker, just introduced AutoScientist, a new system that automatically customizes AI models for specific jobs by adjusting both what the model learns from and how it learns.
The details:
AutoScientist tests different training data and settings, then iterates until the model meets the user's goal.
In internal tests, AutoScientist outperformed its own expert-tuned models by 35% on average, with success rates jumping from 48% to 64%.
Results held across multiple AI models, a wide range of dataset sizes, and 8 varied industries, including finance, legal, and medical domains.
Adaptive’s initial Adaptive Data release in February aimed at increasing the quality of datasets, with Autoscientist now moving toward customizing models.
Why it matters: A few thousand people in the world know how to properly train and fine-tune a frontier model, and nearly all of them work at the same handful of labs. If a tool like Autoscientist can start to automate that expertise, models customized for individual businesses and use cases may become a lot more practical to create.
QUICK HITS
🛍️ Alexa for Shopping - Shopping agent for Q&A, price tracking, and auto-buy
💻 Higgsfield Supercomputer - Cloud AI agent with tools, memory, and more
🔐 Incognito Chat - New way to have private chats with Meta AI on WhatsApp
🏪 Claude for Small Business - New tools for payroll, invoices, and campaigns
Nvidia became the first company to hit a $5.5T market cap, coming as CEO Jensen Huang arrived in China to join U.S. President Donald Trump in meetings with Xi Jinping.
Sam Altman testified in the Elon Musk vs. OpenAI legal battle that Musk’s “specific plans on safety” made him worry, and proposed passing the company to his children.
David Silver’s Ineffable Intelligence and Nvidia announced a partnership to build training pipelines for RL agents, with early work targeting Nvidia's Vera Rubin hardware.
Microsoft introduced MDASH, an AI security harness that chains 100+ specialized agents to hunt for software bugs, with the system catching 16 flaws across Windows.
UK's AI Safety Institute said that AI’s ability to complete cyberattacks is doubling every few months, with Mythos Preview and GPT-5.5 finishing its simulated breaches.
COMMUNITY
Every newsletter, we showcase how a reader is using AI to work smarter, save time, or make life easier.
Today’s workflow comes from reader Rod R. in Elizabethtown, KY:
"I am an avid cyclist. I turned 61 years old this year, but I am still participating in multi-day cycling events. I needed advice on the type of riding I should be doing to prepare, and I thought I could benefit from a coach, but I can’t afford one. Then it occurred to me to try ChatGPT.
That has worked out tremendously! We had a “conversation” about my goals, medical history, current fitness, event dates, etc. I have been following a training plan for about 6 weeks now.
ChatGPT continues to coach me as I report on each ride, helps me restructure the plan when life requires me to modify it, and also remembers things we have already discussed. I love how this has helped me to prepare, and I have peace of mind from knowing what I should be doing each day. Today is a rest day! 😊"
How do you use AI? Tell us here.
Read our last AI newsletter: Android enters its Gemini Intelligence era
Read our last Tech newsletter: Venmo finally kills its most criticized feature
Read our last Robotics newsletter: Figure’s robots make a bed together
Today’s AI tool guide: Create content with Claude Code and Higgsfield
RSVP to our next workshop @ 2 PM EST today: Getting AI to do real work
See you soon,
Rowan, Joey, Zach, Shubham, and Jennifer — the humans behind The Rundown
Today's content is anchored by a single competitive moment playing out at multiple levels of resolution: Anthropic and OpenAI are fighting not over model benchmarks but over pricing structure, developer tooling, and runtime control. Substantial research and robotics news runs underneath.
Only a one-line teaser was available from this paywalled Stratechery interview, recorded at the MoffettNathanson Media, Internet & Communications Conference. Thompson flags "the implications of the compute shortage on Aggregation Theory, consumer AI, and more" as the territory.
Through his framework, the operative question is whether GPU scarcity tilts leverage toward labs controlling the scarce resource or toward platforms aggregating end-user demand. The consumer AI angle likely probes whether current cost structures support broad consumer pricing or whether economics force the market toward enterprise subscriptions and specialized workflows. The full interview is behind the Stratechery paywall — but it's worth flagging that Thompson's framing connects directly to several dynamics the rest of today's content examines at the product layer.
Swyx opens with a deliberately literary framing: "a tale of two cities" in which Anthropic is winning enterprise spend while OpenAI is gaining ground with AI engineers — and the trigger for the piece is Anthropic's pricing change for programmatic usage. Paid Claude subscribers now receive API credits equal to their subscription dollar amount for using Claude via third-party harnesses (claude-p, OpenClaw, GitHub Actions, third-party SDKs), replacing what was effectively a 70-90% implicit subsidy over API pricing. Developer backlash was immediate, from Theo, Jeremy Howard, Matt Pocock, and Omar Sanseviero. Swyx reads this not as a mistake but as the inevitable end of a historical subsidy: "now that Claude Code has a sustainable brand and clout as an agent harness, Anthropic is putting its most favorable pricing behind its own tools and metering everything else." Anthropic partially offset the friction with a 50% increase in Claude Code weekly limits through July 13.
The Ramp spend data (Anthropic at 34.4% of business adoption vs OpenAI at 32.3% in April, the first apparent lead change) and OpenAI's simultaneous countermove — 2 months free Codex for enterprise customers switching within 30 days — create what Swyx calls "an incredible coincidence" of timing. He offers a structural hypothesis he labels the "mandate equinox": a roughly 6-month alternating cycle in which the challenger offers more generous terms while the incumbent meters more carefully, then the positions flip. He cautions against over-indexing on the swing: "both labs are doing very well." The broader diagnosis is sharper: "the competitive dynamic now looks less like 'best model wins' and more like subsidy + workflow control + harness compatibility."
On agent infrastructure, the architectural consensus across multiple launches is that production agents need durable execution, inspectable intermediate state, and tool-native UI surfaces — not stateless prompt/response loops. Cline open-sourced a rebuilt SDK with a TUI, agent teams, scheduled jobs, and connectors. LangChain shipped LangSmith Engine, SmithDB (a purpose-built observability database for nested long-running traces, claiming 12-15x faster access on key workloads, built on Apache DataFusion and Vortex), and Deep Agents 0.6. Notion's External Agents API lets Claude, Codex, Cursor, and others operate directly inside Notion as a shared, reviewable context layer. Cursor expanded cloud agents with isolated development environments, dependency management, rollback, and scoped egress. The convergence is notable: different vendors, same architectural bet.
Training efficiency research produced several concrete claims worth anchoring. Nous Research's Token Superposition Training reports 2-3x wall-clock speedup during pretraining at matched FLOPs, with no inference-time architecture change, validated from 270M to 3B dense and 10B-A1B mixture-of-experts (MoE) models. Datology's data curation work argues that selection alone yields +11.7 points across 20 public vision-language model (VLM) benchmarks at 2B parameters, beating InternVL3.5-2B by ~10 points at ~17x less training compute — a strong case that what you train on matters as much as how much you train. NVIDIA's Star Elastic claims a single post-training run can produce a full model-size family at 360x lower cost than pretraining each size separately. On cyber capability: the UK AI Security Institute reports that the length of cyber tasks frontier models can complete has been doubling every few months, with Claude Mythos Preview the first model to clear both AISI end-to-end cyber ranges including "Cooling Tower." Finally, Figure's 8-hour autonomous humanoid package-sorting shift — on-device inference, ~3 seconds per package (near human parity), autonomous battery swaps, networked fleet coordination, self-diagnosis — is one of the clearer public demonstrations of multi-robot, long-duration, no-human-in-the-loop orchestration on record, rather than a short benchmark clip designed for a press release.
The strategic question Thompson's Aggregation Theory names explicitly is the subtext of everything else today: in a world of compute scarcity, who controls the layer between models and users? Anthropic's decision to price programmatic usage at cost is the move of a company that believes it has already built the preferred runtime (Claude Code) and can afford to stop subsidizing competitors. OpenAI's free-Codex-for-switchers counter is the challenger play. Swyx's data and Cheung's Ramp numbers are two views of the same race — and Thompson's framework predicts the outcome: whoever controls the developer workflow wins the recurring revenue, the usage data, and the switching costs that compound into durable advantage.
Willison's quiet quote from Boris Mann — "11 AI agents is meaningless as a phrase... it means about the same thing as 'I have 11 spreadsheets'" — lands wryly against Swyx's detailed catalog of agent infrastructure launches. Mann is right that the terminology has become inflationary. But the underlying engineering Swyx documents is getting genuinely specific: LangSmith's observability database for nested long-running traces, Notion's shared reviewable context layer, Cursor's scoped-egress isolated environments. These are precise architectural choices, not marketing claims. Willison himself built the new Datasette blog using OpenAI Codex — a practitioner reaching for whatever fit the task — which sits in interesting tension with the Ramp enterprise numbers. Aggregate spend data shows a pattern; individual developer choices remain fluid.
Both Cheung and Swyx surface the automated research loop as a thread that could matter more than any single pricing decision. Adaption's AutoScientist (outperforming expert-tuned models by 35% in internal tests, improving success rates from 48% to 64%) and the Recursive startup (targeting AI that can safely improve itself) point to the same structural bet: that model training expertise, currently concentrated in a handful of labs, can be automated. Swyx quotes Hooker framing training failures as "research-loop brittleness rather than mere compute scarcity" — which is a direct challenge to the compute-determinism thesis and connects back to Thompson's scarcity framing in an unexpected direction. If research automation works, the constraint loosens from the supply side.
The cyber capability finding is the one that deserves more attention than digest format typically affords. The UK AISI's finding that frontier models' cyber task completion has been doubling every few months, with Mythos Preview clearing ranges no prior model could, is a capability trajectory — not a product announcement — and it's compounding on a short timeline. Paired with Figure's 8-hour autonomous robotics demo, today's content contains more evidence of genuine frontier capability acceleration than a typical day, mostly buried beneath the pricing drama.
Today on HN was a day of contested infrastructure — the hidden layers everything runs on, from drive encryption to exam integrity to job security. A platform trust crisis, an AI land grab, a database deletion horror story, and a quiet question about whether personal computing is coming full circle.
In March 2026, Linux crossed 5% of Steam's active users for the first time — a milestone that would have seemed far-fetched a decade ago. The headline credit goes to NTSYNC, a new driver added directly to the Linux kernel that gives Linux a native implementation of Windows thread-coordination mechanisms — the internal tools games use to prevent their many simultaneous tasks from colliding. Before NTSYNC, Wine (the compatibility layer that lets Linux run Windows games) had to approximate these mechanisms imperfectly using workarounds. NTSYNC now ships by default on every up-to-date Steam Deck. The bigger structural shift: improvements to Linux gaming used to live inside Proton (Valve's tuned Wine layer), but increasingly the most consequential work is happening inside the Linux kernel itself — Linux is absorbing Windows' internals rather than emulating them from the outside.
At the same moment, Windows' most widely trusted security feature is under fire. Security researcher Chaotic Eclipse — who had already published 2 Windows Defender zero-days after Microsoft allegedly dismissed their disclosures — released YellowKey, which bypasses BitLocker encryption on Windows 11 by copying a handful of files to a USB stick and rebooting into the Windows recovery environment. Tom's Hardware tested it and confirmed it works. The detail that disturbed the security community most: the exploit files erase themselves from the USB stick after a single use — a hallmark of intentional backdoor design rather than accidental vulnerability. The exploit reportedly also works on Windows Server 2022 and 2025, and a variant allegedly defeats TPM+PIN (trusted platform module — the hardware chip that stores encryption keys) setup, though that proof-of-concept hasn't been published. Eclipse stated they "could have made insane cash selling this, but no amount of money will stand between me and my determination against Microsoft."
Completing the picture: Apple's new MacBook Neo runs the A18 Pro chip (same as the iPhone 16 Pro) at a $599 price point, enabled by Apple's unique vertical integration — it designs the chip, controls the OS, negotiates directly with TSMC (Taiwan's dominant chip manufacturer), and amortizes silicon costs across 230 million iPhones per year. The 8GB of unified memory is a real ceiling, but the author frames it as a forcing function: Apple has to keep macOS lean, and there's context behind that decision. A separate Asymco piece by Horace Dediu noted that AI-driven memory demand has spiked so severely that Samsung is reportedly making more money on memory than Nvidia makes on GPUs — which helps explain why the Neo launched with 8GB rather than more.
The Linux gaming thread opened a philosophical wound that ThrowawayR2 framed as a Nietzsche riff: "He who fights with Windows should see to it that he himself does not become Windows. And when you gaze long into ntoskrnl, ntoskrnl also gazes into you." tetris11 asked a more cynical question: whether Microsoft even cares about the desktop gaming market anymore, too busy feeding on Azure cloud revenue. On BitLocker, the thread's texture was suspicion. iscoelho flagged a pattern of coordinated downplaying: "Why is it mainly brand new accounts?" defending Microsoft. stackghost called out 2 new accounts being "weirdly aggressive in their defense of BitLocker." felooboolooomba put the community's mood plainly: "When I see a bug that walks like a backdoor and swims like a backdoor and quacks like a backdoor I call that bug a backdoor." Nition traced it further back, noting TrueCrypt's sudden 2014 recommendation that users migrate to BitLocker and wondering whether that pattern was meaningful in retrospect. On the MacBook Neo, havaloc called it "a triumph" and headcanon reported their wife is running Claude Code and web development on it "with no noticeable lag" — concluding it "might cannibalize MacBook Air sales."
A blog post argues the US is winning the AI race not on papers published or engineer headcounts but on commercialization. The structural advantage is platform reach: AWS, Azure, and Google Cloud are the pipes through which models reach the world, while YouTube, GitHub, Google Drive, and Microsoft 365 are where the training data lives. DeepSeek matters not as commercial competition, the piece argues, but as China's bid to reduce dependence on Nvidia toward domestic chips like Huawei Ascend — supply chain autonomy, not profitable AI leadership. Europe has engineering talent but lacks the hyperscaler infrastructure to compete.
Anthropic made its commercial ambitions explicit today: Claude for Small Business is a new package embedding Claude inside QuickBooks, PayPal, HubSpot, Canva, DocuSign, Google Workspace, and Microsoft 365. It ships with 15 ready-to-run agentic workflows spanning finance, operations, sales, marketing, HR, and customer service — invoice chasing, payroll planning, month-end close, lead triage. The pitch: small businesses account for 44% of US GDP and employ nearly half the private-sector workforce, but their AI adoption has lagged because tools haven't been tailored to how they operate. The design philosophy keeps humans in the loop: Claude does the work; users approve before anything sends, posts, or pays.
Princeton's faculty voted — with a single dissenting vote — to end the university's 133-year-old unproctored exam tradition, effective July 1. The honor code was established in 1893 following a student petition to eliminate proctoring; AI has now reversed it. The policy cites AI tools on personal devices as making cheating "much harder for other students to observe" — directly undermining the peer-reporting model the system depended on. A 2025 senior survey found 29.9% of respondents admitted cheating during their time at Princeton; 44.6% knew of violations they chose not to report.
Meanwhile, Cisco announced it would cut fewer than 4,000 jobs — less than 5% of its workforce — in Q4. The notable detail: the announcement came in the same CEO email as Cisco's record Q3 revenue of $15.8 billion, up 12% year over year. Chuck Robbins framed the cuts as reinvestment toward silicon, optics, security, and employee AI adoption.
The US-wins-AI post drew 549 comments — the most of any story today — and skepticism ran high. thepasch drew the sharpest line: "If commercialization into rent-seeking SaaS is the endgame, the US is winning. If local LLMs and consumer hardware are the endgame, China is winning." diego_moita linked data on China's lead in physical AI and robotics. pj_mukh added a structural irony: the US is winning "primarily because of immigrant nerds, H1Bs and F1 bros who chose America and may not have this avenue in the future — potentially making this the last race USA wins."
Claude for Small Business split sharply between adopters and skeptics. sergiotapia was blunt: "In no world would I give Claude or any AI agent direct write access to financial operations like payouts/settlements." devmor said they'd start job hunting "quickly" if their employer used Claude for payroll. arjie offered the pragmatic middle: they use Claude Code with read-only bank tokens to automate invoice categorization, leaving hard cases for human review — precisely the human-in-the-loop model Anthropic claims to be following. On Cisco, 0xbadcafebee called the combined announcement "fucked up on a new level — it used to be you were supposed to at least pretend you were forced into a layoff." 0x0000000 delivered the taxonomy drily: "Revenue flat? Layoffs. Revenue down? Layoffs. Revenue grows less than guidance? Layoffs. Record revenue exceeding guidance? Believe it or not, layoffs." On Princeton, former TA rosstex noted that while students genuinely took pride in the honor system — signing each other's names on tests — the bureaucratic machinery for handling even minor accusations was so punishing that it created its own injustices long before AI arrived.
Laurent Le Brun's inside account of IDE (integrated development environment — the software developers use to write code) history at Google traces how the company went from total editor fragmentation — Jeff Dean in 2011 explicitly called agreeing on a common editor "a recipe for unhappiness" — to near-universal adoption of Cider-V, a VSCode-based web IDE. The key driver: traditional IDEs assume source code, build metadata, indexing, and analysis all happen locally; at Google scale that assumption collapses. Cider began around 2013 as a lightweight way for technical writers to edit Markdown files without dealing with version control, and grew into something engineers actually preferred. The piece hints at the next shift: Antigravity, Google's AI coding tool, is being pushed into Cider-V by default — with AI features turned back on by system updates even when engineers disable them.
Tom MacWright's "Emacsification" essay argues that AI coding assistants have unlocked a renaissance of personal software — programs built for an audience of one. The title riffs on Emacs, the legendary editor-cum-operating-system that embodied the original vision of software infinitely customizable to the individual. MacWright built a macOS Markdown viewer better than anything on the App Store in roughly 30 minutes of interactive Claude sessions. The point isn't the quality of the engineering — it's that the friction cost of solving your own specific, weird problem has collapsed to near zero.
In a story with the opposite lesson: Muneeb and Sohaib Akhter, twins with prior 2015 convictions for computer fraud, were hired by a Washington DC firm serving 45 federal government clients. When fired via Microsoft Teams at 4:50 PM, Sohaib immediately found his VPN revoked — but Muneeb's account had been overlooked in the credential sweep. By 4:56 PM, Muneeb was issuing DROP DATABASE commands against government systems. He ultimately wiped 96 databases including a Department of Homeland Security system. Before the firing, he had harvested 5,400 plaintext passwords from his employer's network and written Python scripts to test them against hotel and airline loyalty systems, booking travel with victims' miles.
The Google IDE thread surfaced a pointed insight from compiler-guy: having all engineers on one IDE gives Google telemetry no other company has — feature usage patterns, AI adoption rates, and the ability to force-enable tools across thousands of engineers overnight. jason1cho noted the irony: "Cider was initially celebrated for opening faster than traditional IDEs — now users forget they chose it for being lightweight." The Emacsification thread was warm but grounded. empath75 loved the frame: "Content creation for an audience of one is really the revolutionary change happening because of AI — disposable apps, disposable books, made for a single person, used once or a handful of times." tuo-lei applied the reality check: "I've made maybe 20 personal LLM tools this year. 3 survived past the first week — not because the rest weren't useful, just wasn't willing to debug them when something broke." kettlez tried the Markdown viewer and it crashed on large files: "Making a small proof of concept is easy, but performance and reliability are still hard." On the database deletion, kaikai asked the question nobody could answer: "How did someone previously convicted of hacking get access to so many production government databases?" scottlamb worried companies will take the wrong lesson: using this story to justify making firings as abrupt and dehumanizing as possible.
All three threads were, in their way, about the same thing MacWright's essay touched on: building things for the pleasure of it, outside commercial logic.