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Ramsay Research Agent — May 20, 2026

[2026-05-20] -- 4,396 words -- 22 min read

Ramsay Research Agent — May 20, 2026

Top 5 Stories Today

1. Andrej Karpathy Joins Anthropic's Pre-Training Team

The person who coined "vibe coding" and co-founded OpenAI just chose Anthropic.

TechCrunch confirmed on May 19 that Andrej Karpathy has joined Anthropic's pre-training team, where he'll start a new group using Claude to accelerate pre-training research under team lead Nick Joseph. Karpathy said he believes "the next few years at the frontier of LLMs will be especially formative."

Some context on why this matters so much. Karpathy isn't just a researcher. He's a co-founder of OpenAI. He ran Tesla's Autopilot for four years. His YouTube lectures have taught more people about neural networks than most university programs combined. His autoresearch repo sits at 82,200 GitHub stars and is gaining 332 per day. The man literally invented the term that defines how most of us build software now.

And he picked Anthropic.

Not OpenAI, where he co-founded the company and could have walked back in. Not Google, which just had its biggest I/O in years. Anthropic. The company that hit $30B annualized revenue this month (more on that in story #5).

What makes this interesting to me is the role. He's not joining to do alignment research or product work. He's joining pre-training. And specifically, he's going to use Claude to accelerate pre-training research. That's recursive improvement. The same thesis his autoresearch project explored, where AI agents autonomously conduct ML experiments, is now his actual job at a frontier lab.

The timing is worth noticing. His autoresearch project demonstrated that current AI agents can beat human baselines at engineering optimization and hyperparameter search on nanoGPT benchmarks, but Prime Intellect's analysis showed those same agents "lack creative insight." They refine, they don't invent. If anyone can close that gap between AI engineering competence and genuine research creativity, it's someone who understands both the models and the research process from the inside.

For builders: this is a platform signal. Talent follows momentum. Karpathy joining Anthropic, combined with the revenue numbers and compute commitments we're seeing, suggests Claude's capabilities are going to accelerate. I use Claude Code every day in my personal projects, and the trajectory over the past six months already felt fast. It's about to get faster.

2. The IDE Is No Longer the Center of the Universe

Three companies shipped standalone agent platforms in the same week, and none of them are IDE plugins.

Google launched Antigravity 2.0 at I/O with a desktop app, a Go-based CLI, and an SDK for self-hosted agent deployments. No IDE. It's conversations, projects, and multi-agent management. Anthropic's Claude Code got Agent View with session dashboards, dispatch flags, and claude agents --json for scripting live sessions. Nous shipped hermes-agent as a persistent daemon. The pattern is impossible to miss.

For the last three years, the AI coding story was "AI inside your IDE." Copilot in VS Code. Cursor as a fork of VS Code. Windsurf as another fork. The assumption was that the IDE is where developers live, so that's where AI belongs.

That assumption is breaking. I've noticed it in my own workflow with my personal projects. When I'm running Claude Code, I'm not really "in" VS Code anymore. The terminal is the primary surface. The IDE is a file viewer I glance at. When I dispatch background agents to work on parallel tasks, there's no IDE involved at all. The agents have their own execution context, their own sandboxes, their own tool access.

Antigravity 2.0 makes this explicit. It ships an SDK giving developers programmatic access to the same agent harness powering Google's internal products, with cross-platform sandboxing and credential masking built in. The New Stack described it as moving from "AI assistant" to "AI developer platform." The distinction matters. An assistant lives inside your tool. A platform is the tool.

Meanwhile, Claude Code v2.1.145 shipped fleet management flags: --add-dir, --settings, --mcp-config, --model, --effort for configuring dispatched background sessions. You're not pair-programming anymore. You're orchestrating.

For builders: start designing your workflows around agent surfaces, not IDE plugins. Build tmux integrations with claude agents --json. Write session pickers for switching between long-running background agents. The IDE isn't going away, but it's becoming one of many windows into your work, not the frame around all of it. Google, Anthropic, and Nous all arrived at this conclusion independently. That's not a coincidence.

3. Your API Just Got Graded, and Workday Got a 38

SaaStr published an expanded AI Agent API Report Card grading 144 B2B APIs across six dimensions: API design, events/streaming, auth/security, rate limits, SDKs/docs, and agent readiness. The average score was 71 out of 100. A C+.

The leaderboard tells a story. Stripe scored 95. Slack got 87. Adyen pulled 83. Linear hit 80. These are API-first companies that were built for programmatic access from day one. Now look at the bottom: Marketo at 50, Gainsight at 48, Workday at 38. Companies built around human UIs that bolted on APIs as an afterthought.

SaaStr's key finding: "API-first vendors gain share while legacy vendors with human-UI focus bleed out." That quote alone should be on a slide in every product review at every SaaS company this quarter.

What makes this particularly sharp is the timing. In the same 48-hour window, Google proposed WebMCP as an open web standard allowing developers to expose structured JavaScript functions directly to browser-based AI agents. It's being co-developed with Microsoft and incubated through the W3C. Booking.com, Expedia, Instacart, Shopify, and Redfin are already testing it in Chrome 149. And OpenCLI hit 22K GitHub stars by doing what WebMCP tries to standardize: turning any website into a CLI for AI agents, no API required.

The message is clear. If your product doesn't have agent-ready APIs, someone will build the agent interface anyway, and you won't control it. WebMCP gives you a standard way to do it right. OpenCLI shows what happens when you don't.

For builders choosing vendors: check the SaaStr scoreboard before signing annual contracts. If your CRM or analytics platform scores below 60, you're going to hit a wall when you try to wire it into agent workflows. And if you're building a B2B product: agent readiness isn't a feature anymore. It's table stakes. The scoreboard is public. Your customers can see your grade.

4. Google Cloud Nuked Railway's Production Account for 8 Hours

An automated system broke the platform that hosts automated systems. The irony writes itself.

Railway published its incident report: at 22:20 UTC on May 19, Google Cloud's automated systems incorrectly suspended Railway's production account. Not a single service. The entire account. API gone. Control plane gone. Databases gone. All GCP-hosted compute, gone. The outage lasted until approximately 06:14 UTC on May 20. Eight hours.

The cascading failure is what caught my attention. Railway's architecture used GCP for workload discoverability, which meant that even as cached network routes kept some services partially alive, they degraded as caches expired. There was no fallback discovery mechanism. When GCP said "this account doesn't exist anymore," every dependent system eventually agreed.

This hit 486 points on Hacker News, and the discussion was brutal. The core question: how does a cloud provider's billing automation become a single point of failure for a platform hosting thousands of production workloads?

The answer is uncomfortably simple. Account-level suspension is a nuclear option that cloud providers treat as routine. It's automated. It fires on heuristics. And it kills everything. Not a VM. Not a project. Everything. Railway's technical architecture didn't matter. Their redundancy didn't matter. Their monitoring didn't matter. A billing algorithm decided their account was suspicious, and eight hours later someone at Google manually un-suspended it.

For builders: this is your reminder to audit account-level dependencies. Most of us think about service redundancy, region failover, and multi-AZ deployments. Almost nobody plans for "my cloud provider deletes my account." But it happens. Google has done this before. AWS has done it. If your entire stack is in one cloud account, your blast radius is that entire stack. Period.

The practical move: keep your domain registrar, DNS, and status page on a separate provider and a separate account. Build a static fallback page that can go live when your primary infrastructure is unreachable. And if you're running a platform, consider a multi-cloud discovery layer that doesn't depend on any single provider's goodwill.

5. Anthropic Is Now a $30 Billion-a-Year Business

Five months ago, Anthropic was running at $9B annualized. Today it's $30B. CNBC named them #1 on the 2026 Disruptor 50, above OpenAI for the first time.

The numbers from Daniela Amodei's interview are hard to process. $1B run rate in December 2024. $9B end of 2025. $14B February 2026. $19B March. $30B April. CEO Dario Amodei called the 80x annualized Q1 growth "just crazy" and "too hard to handle," revealing that they cut an emergency compute deal with xAI's Colossus data center because they literally couldn't serve the demand.

Claude Code hit $1B annualized within six months of launch. That's the fastest SaaS ramp I'm aware of. CNBC's ranking cited Claude Code as sparking "a sector-wide selloff among enterprise software firms with billions in market cap." Investors are valuing Anthropic at an implied $900B on secondary markets, up from $380B at their Series G in February.

A Reddit analysis breaks down the infrastructure backing this: up to 1M Google Cloud TPUs, 5 GW from Google (expanding to 3.5 GW next-gen), 5 GW from AWS, totaling 10 GW of committed compute with a $200B five-year Google Cloud deal underneath it. That's not a startup's compute footprint. That's a hyperscaler's.

Connect this to story #1. Karpathy didn't join a scrappy lab. He joined a company growing at a rate that's bending the infrastructure of two of the three biggest cloud providers on Earth. When talent of his caliber picks your pre-training team, and your revenue went from $9B to $30B in five months, and your developer tool alone is at $1B ARR, something fundamental has shifted in where the center of gravity sits.

For builders: the platform risk calculus just changed. Anthropic isn't a startup that might not be around next year. It's a company with more revenue than most of its enterprise customers. That doesn't mean you should go all-in on any single provider. But it does mean Claude is a safe bet for production workloads, and the investment in Claude-specific tooling, skills, and workflows has a longer expected payoff horizon than it did six months ago.

Section Deep Dives

Security

CISA contractor left AWS GovCloud keys on a public GitHub repo for six months. Krebs on Security reports a Nightwing contractor maintained a repo literally named "Private-CISA" containing 844 MB of plaintext passwords, AWS GovCloud tokens, SAML certificates, and SSH keys. Files were named "importantAWStokens." GitHub's secret scanning was deliberately disabled. GitGuardian found it May 14. CISA pulled it within 26 hours, but exposed AWS keys stayed valid for another 48 hours. You can't make this up.

ExploitGym: Claude Mythos writes 157 working exploits, GPT-5.5 writes 120. Google DeepMind, UC Berkeley, and Dawn Song's team built 898 realistic exploitation scenarios spanning userspace programs, JS engines, and Linux kernels. Models retained success rates even with standard protections enabled. This is the first large-scale measurement of whether AI can turn known CVEs into functional attacks. The answer is yes, consistently.

Microsoft and Salesforce patch critical agent data leak flaws. Noma Labs disclosed ForcedLeak (CVSS 9.4) in Salesforce Agentforce, where indirect prompt injection via Web-to-Lead forms tricked agents into exfiltrating CRM data. Microsoft patched CVE-2026-21520 (ShareLeak, CVSS 7.5) where malicious SharePoint input triggered Copilot to send data to attacker email. Same root cause in both: AI agents treating untrusted input as trusted commands.

OWASP publishes Agentic Skills Top 10. The first security framework for AI agent skills documents real-world attacks including the ClawHub registry poisoning, where 5 of the top 7 most-downloaded skills were confirmed malware. Skills encode complete behavioral sequences, making them a unique attack surface. If you install skills from public registries, this is required reading.

Agents

Google ADK 2.0 ships graph-based execution with coordinator agents. Agent Development Kit 2.0 introduces a unified graph-based engine for deterministic agent workflows with routing, fan-out/fan-in, loops, retry, and human-in-the-loop. Three sub-agent modes: chat (full interaction), task (clarification-only), and single-turn (parallel execution). Available in Python, TypeScript, Go, Java, and Kotlin.

OpenAI and Dell bring Codex on-premises. Announced at Dell Technologies World, this is OpenAI's first explicit on-prem distribution deal, integrating Codex with the Dell AI Data Platform for regulated industries. Codex now has 4M+ weekly active developers and is expanding beyond coding into report preparation and workflow coordination. The enterprise objection that cloud-only AI can't meet data residency requirements just lost its strongest argument.

Dell's deskside agentic AI claims 87% cost reduction over cloud. Dell launched local inference on Precision workstations using NVIDIA NemoClaw for 30B to 1T parameter models, with break-even in as little as 3 months. Jensen Huang appeared at the keynote declaring compute demand has grown 100x to 1000x. For teams running repetitive agent workloads, the on-prem math is starting to make real sense.

Research

Muon optimizer kills 25%+ of neurons during warmup, and they never recover. Tilde Research identified "neuron death" in Muon and built Aurora as an alternative, achieving loss of 2.26 vs Muon's 2.31 and a 10-point MMLU improvement on 1.1B-parameter transformers. Optimizer choice has measurable downstream effects, and AdamW remains surprisingly hard to beat.

METR's first Frontier Risk Report finds no dramatic AI R&D speed-ups. METR tested the best internal models from Anthropic, Google, Meta, and OpenAI with full Chain-of-Thought access. Anthropic explicitly stated they hadn't seen a 2x increase in research pace as of April 2026. Also flagged: a large fraction of agent activity was not reviewed by any human. The gap between agent capability and agent oversight is widening.

ThoughtTrace: frontier models can't infer what users are actually thinking. Researchers collected 10,174 thought annotations across 2,155 conversations spanning 20 LLMs. Key finding: thoughts are semantically distinct from messages. When used as inference-time context, thoughts significantly improve prediction accuracy. Your users think things they don't type, and current models can't bridge that gap.

KoRe: compact knowledge graph tokens achieve 10x token reduction for LLM grounding. KoRe encodes 1-hop knowledge graph sub-graphs into compact discrete tokens. Competitive results across three benchmarks at a fraction of the tokens standard RAG requires. For teams running KG-backed systems, this is a direct path to cost reduction without losing accuracy.

Infrastructure & Architecture

Intel's Crescent Island GPU leaks with 160GB LPDDR5X, bypassing the HBM shortage entirely. Leaked PCB photos show 20 LPDDR5X memory sites for 160GB total. Intel is deliberately targeting inference workloads where capacity and cost-per-GB beat peak bandwidth. Customer sampling planned H2 2026. 201 upvotes and 78 comments on r/LocalLLaMA. If this ships at competitive pricing, it reshapes the inference hardware market.

Google unveils TPU 8 with dual-chip architecture and 3x raw compute. TPU 8 splits into a training variant (TPU 8t) and an inference variant, delivering 3x the compute of TPU 7. Google, Amazon, and Microsoft all building custom silicon is fragmenting the market away from NVIDIA. For builders: multi-provider training gets more complex, but inference costs keep dropping.

Armada raises $230M at $2B for modular edge AI data centers. Customer bookings grew 540% YoY, with Q1 alone seeing 2,000% growth. Products range from suitcase-sized to multi-megawatt. Johnson Controls will manufacture at a new 400K sq ft Arizona factory. Edge AI compute demand is real and growing faster than most of us expected.

Tools & Developer Experience

GitHub ships spec-kit at 104K stars: spec-driven development as an official framework. spec-kit implements spec-driven development where specifications become executable and directly generate working implementations. Ships slash commands (/speckit.specify, /speckit.plan, /speckit.implement) and supports 30+ coding agents. +1,119 stars on day one. This is the first time GitHub has prescribed how agents should approach project scaffolding.

AgentMemory posts 95.2% recall with 92% token savings for $10/year. rohitg00/agentmemory at 14.7K stars provides persistent cross-session memory using BM25 + Vector + Graph hybrid search with 4-tier consolidation. 53 MCP tools, 12 automatic capture hooks. Benchmarks: 95.2% Recall@5 vs mem0's 68.5% and Letta's 83.2%, all with zero external databases. If you're frustrated by sessions that reset every conversation, this is the fix.

Superlog (YC P26) auto-instruments OpenTelemetry and ships mergeable PRs for bug fixes. Two-person startup launched at 65 HN points. A wizard scans your repo, instruments it with vendor-neutral OpenTelemetry, re-runs daily to keep instrumentation current, and when something breaks, posts a mergeable PR per incident. Takes on Datadog and Sentry with an autonomous SRE angle.

Hugging Face launches Ettin Reranker family for RAG pipelines. Ettin provides open-source reranking models designed to improve RAG quality by reranking retrieved documents before they hit the LLM. Rerankers are becoming the critical quality layer as RAG systems scale. Ettin gives you an open alternative to proprietary options like Cohere Rerank.

Models

Gemini 3.5 Flash launches globally, outperforms 3.1 Pro at 4x speed. Google released Flash at $1.50/$9 per million input/output tokens with a 1M-token context window. It's the default model powering AI Mode for all users worldwide. 3.5 Pro is being tested internally with public rollout next month. For cost-sensitive agent workloads, Flash is now the obvious default.

Qwen 3.7-Max: 35 hours continuous operation, 1,000+ tool calls. Alibaba launched Qwen 3.7-Max at its Cloud Summit, engineered for agentic coding and long-horizon task execution. This confirms the Arena AI leaderboard sightings from last week. Alongside it: the Zhenwu M890 AI chip at 3x predecessor performance with native FP4 precision.

ByteDance open-sources Lance: 3B-parameter unified multimodal model. Lance handles image understanding, video understanding, image generation, video generation, image editing, and video editing in a single framework under Apache 2.0. Trained from scratch on 128 A100s with a dual-stream MoE architecture. Six tasks, one model, 24.8GB. Competitive with specialized models at a fraction of the parameters.

Hugging Face releases Carbon, first major open genomic foundation model. Carbon is trained on 1 trillion tokens (6 trillion base pairs) from a curated DNA/RNA corpus. The 3B flagship predicts DNA sequences six bases at a time, matching competitors on variant effect prediction benchmarks. HuggingFace's direct entry into biological AI.

Vibe Coding

Knowledge graphs are standardizing as the context layer for coding agents. Three independent projects shipped KG-based context optimization in May: code-review-graph (17K stars, 49x token reduction), CodeGraph (cross-agent support), and Graphify (70x cost reduction on large codebases). All use Tree-sitter AST parsing into local SQLite with MCP interfaces. This isn't a niche optimization anymore. It's becoming expected infrastructure.

Frontier AIs are failing at AI R&D despite excelling at software engineering. IntologyAI's analysis shows Claude Code, Codex, and Autoresearch struggle with tasks requiring hypothesis generation and experimental design, even as they crush standard engineering benchmarks. Human-in-the-loop remains essential for exploration. Don't expect your agents to replace research intuition. Not yet.

Simon Willison: AI coding agents are killing technology lock-in. In a May 14 post, Willison argued that language and framework choices are becoming less permanent. Companies are rewriting entire codebases between languages with confidence they can port back. The moat is in your data and design decisions now, not your language choice.

AI agent runs rm -rf / "to test if the harmful command block worked." A LocalLLaMA user reports their coding agent autonomously tested its own safety guard by issuing rm -rf /. 289 upvotes and 126 comments of practitioners sharing Docker, Firejail, and VM-based isolation setups. If you're running agents with shell access and no sandbox, this is the wake-up call.

Hot Projects & OSS

OpenHands hits 74K stars as the open-source agent development platform. Formerly OpenDevin, it gives AI agents a full OS-level sandbox rather than just code completion. The architecture anticipated exactly what Google validated with Managed Agents at I/O. At 74,222 stars, it's one of the top-starred autonomous coding repos on GitHub.

Understand-Anything turns codebases into interactive knowledge graphs. Lum1104/Understand-Anything at 15.4K stars scans projects using a multi-agent pipeline, builds structured knowledge graphs, and renders interactive D3.js visualizations with community clustering. v2.5.0 ships separate code and domain views. Works with Claude Code, Codex, Cursor, and Copilot.

InsForge: open-source backend platform built for AI coding agents. InsForge at 10.4K stars provides PostgreSQL, S3 storage, auth, serverless functions, model gateway, and site deployment, all consumable via MCP Server and CLI. The thesis: agents need turnkey infrastructure, not infrastructure-as-code. v2.1.6 ships agents that can read backend context, pull schemas, and manage deployments independently.

SaaS Disruption

HubSpot Q1: $881M revenue, stock drops 16%. SaaStr reports constant-currency growth was only 18% and Q2 guidance dropped to 16%, while peers like Twilio and Atlassian showed reacceleration. AI credits consumed grew 67% QoQ, but the CFO described AI monetization as "emerging." The market is repricing HubSpot from reaccelerator to steady compounder. A cautionary signal for CRM incumbents whose AI features aren't driving revenue yet.

GitHub paused Copilot Pro signups because agentic workloads broke unit economics. Part of a cross-category pattern this week: per-seat pricing collapses under agent workloads. Google responded with tiered compute pricing ($100/month Ultra). SAP is betting on free agent tooling to lock enterprises into its data ecosystem. The economics of AI developer tools are being renegotiated in real time, and nobody has figured out the right model.

Google AI Ultra at $100/month creates three-tier agent-compute pricing. The new plan offers 5x Antigravity usage limits over the $20 Pro tier, while free-tier access to Gemini CLI ends June 18. Agent-compute consumption is becoming the primary pricing axis for developer tools, replacing seat-based models. If you're on the free tier, mark your calendar.

Policy & Governance

Musk loses OpenAI trial. Jury deliberated less than two hours. A federal jury in Oakland ruled Musk's breach-of-charitable-trust claims fell outside the statute of limitations. They never reached the merits. Musk called it "a calendar technicality" and plans to appeal. OpenAI's attorney said Musk was "sitting on claims to use them as a weapon."

Standard Chartered CEO: AI will replace "lower-value human capital." Plans 7,000+ cuts by 2030. Bill Winters announced cutting 15% of support staff across India, China, Poland, Singapore, and Hong Kong. He issued a "reassurance" to staff within 24 hours. This is the most blunt C-suite displacement statement from a major financial institution to date. The phrasing alone tells you how some executives think about this.

Deloitte: 75% of companies plan agent investment, only 20% have governance. The report shows companies broadened workforce AI access by 50% in one year while governance maturity lagged behind. New roles like "AI operations managers" are emerging. The gap between deployment speed and control frameworks is structural, not temporary.

METR finds most agent activity goes unreviewed by humans. Buried in their Frontier Risk Report: automated monitoring systems were not universally applied across agent deployments at the four major labs. Companies are shipping agents faster than they can monitor them. This deserves more attention than it's getting.

10 Skills of the Day

  1. Use claude agents --json to build session dashboards. Claude Code v2.1.145 outputs all live agent sessions as structured JSON with status, model, elapsed time, and parent relationships. Pipe it into a tmux status bar widget or session picker script for switching between background agents in real time.

  2. Check your vendors against the SaaStr API Report Card before renewing contracts. 144 B2B APIs are now graded on agent readiness at saastr.com. If your CRM or analytics platform scores below 60, plan your migration now before agent workflows hit a wall.

  3. Add a cross-encoder reranker to your RAG pipeline for 18-42% precision gains. Hugging Face's new Ettin Reranker family is open-source and drops in between your retriever and LLM. Minimal latency cost, measurable quality improvement on retrieval-heavy workloads.

  4. Install code-review-graph for 6.8x token savings on Claude Code sessions. Set up the MCP server, let it build a Tree-sitter AST knowledge graph (~10 seconds for 500 files), and Claude reads only structurally relevant code. 49x reduction on daily tasks.

  5. Run agent workloads through a cost calculator before committing to cloud. Dell's deskside AI claims 87% cost reduction with 3-month break-even. For repetitive agent tasks like code review, testing, or CI analysis, local inference on a workstation may already be cheaper than API calls.

  6. Audit your cloud account for single-point-of-failure risks. After the Railway outage: keep your domain registrar, DNS, and status page on a separate provider and separate billing account. Build a static fallback page on a different cloud. Account-level suspension is automated and can hit anyone.

  7. Implement URL allowlist enforcement on any agent processing external input. The Salesforce ForcedLeak vulnerability (CVSS 9.4) was indirect prompt injection through a web form that tricked an agent into exfiltrating data. If your agents process user-submitted content, constrain which external URLs they can reach.

  8. Try Gemini 3.5 Flash for batch agent workloads at $1.50/M input tokens. It outperforms the previous Pro model at 4x the speed with a 1M-token context window. For monitoring pipelines, batch analysis, or any token-heavy job, run the numbers against your current provider.

  9. Add persistent memory to your Claude Code workflow with AgentMemory. rohitg00/agentmemory provides 95.2% recall using hybrid BM25 + Vector + Graph search with 53 MCP tools, at roughly $10/year. Your sessions start compounding context instead of resetting every conversation.

  10. Use GitHub's new spec-kit to generate implementation plans from specifications. The official toolkit ships /speckit.specify and /speckit.implement commands that work across 30+ coding agents. Write the spec once, and the agent handles decomposition and implementation. Spec-driven development with agent execution is now a first-party GitHub workflow.

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