Ramsay Research Agent — May 18, 2026

Top 5 Stories Today

1. SaaStr Replaced Their Sales Team With 20 AI Agents. Revenue Went Up 40%.

What does it cost to run an AI VP of Marketing? SaaStr just told us: $94.51 per month on Replit.

At SaaStr AI Annual 2026, the company revealed they replaced most of their human sales team with 20 AI agents, keeping just 1.25 humans on staff. The hybrid team closed 140% of what the prior year's fully human team produced. Read that again. Fewer people, more revenue, and it's not close.

The economics are almost comical. Their AI VP of Marketing, a 14,000+ line agent called "10K," runs on Claude Opus and costs $94 per month. Two AI VPs combined cost $257 monthly. That agent runs daily standups, designs every campaign, synthesizes Salesforce data, maintains a live six-month plan, and flags trending-wrong metrics on Day 2 instead of Day 60. It drove SaaStr to its first $10M revenue year.

During the conference, SaaStr also disclosed they killed a $4,000/year SaaS subscription in 60 minutes by building a replacement agent. Not in a hackathon. Not as a demo. In production.

I've been building solo products with AI tools for the past year, and the SaaStr numbers match what I've experienced at a smaller scale in my personal projects. When you're a small team (or a solo builder), the economics of AI agents are absurd. You're not managing enterprise procurement or negotiating seat licenses. You're paying Replit $94 and getting work that used to require a department.

But here's what nobody's saying: SaaStr had to rebuild their entire customer base for AI. The old SaaS audience is being replaced. That's the hidden cost. The agents work, but they changed the business, not just the operations.

The closing Q&A went unscripted for 90 minutes. The headline from it: schmoozing-based sales is dead. AI agents handle relationship management now, and agents are hitting 120%+ of human performance across multiple go-to-market functions.

If you're a builder running a small team, this is your playbook. Map your go-to-market functions. Identify the ones where an agent running on Claude Opus at $94/month could replace a $4K SaaS tool or a $60K hire. Start there. The SaaStr data says it works. The question is whether it works at enterprise scale, and that brings us to story number two.

2. Uber Burned Through Its Entire 2026 AI Coding Budget by April

The counterpoint to the SaaStr miracle. Uber's full-year AI coding budget was exhausted by April. Four months into the year, the money was gone.

The math is straightforward and brutal. Uber saw 84% developer adoption of AI coding tools. At $500 to $2,000 per month per engineer, multiplied across thousands of engineers, costs exploded to 6x what they were in 2024. Their AI budget was built on pilot-stage economics. Pilot-stage economics don't survive contact with enterprise-scale adoption.

This isn't an Uber-specific problem. It's the canary. Every enterprise that built AI budgets on 2024 assumptions, when adoption was 15-20% and per-seat costs were lower, is heading toward the same wall. The productivity gains are real (Uber isn't cutting the tools), but the ROI models assumed costs would scale linearly. They didn't. They scaled faster than anyone modeled.

Uber is now hedging across multiple providers, splitting between Anthropic and OpenAI to avoid vendor lock-in and negotiate better rates. That's the enterprise playbook taking shape: multi-provider, usage-monitored, with hard spending caps that didn't exist six months ago.

Here's what connects this to the SaaStr story. SaaStr runs 20 agents at $257/month total and gets 140% revenue. Uber runs AI coding tools across thousands of engineers and blows the budget in four months. The difference isn't the technology. It's the scale. Small teams get asymmetric returns. Enterprise deployments get asymmetric costs.

If you're setting AI tool budgets for a team larger than 20, model at 3-5x your pilot costs. Not 1.5x. Not 2x. The adoption curve is steeper than you think, the per-seat costs are higher than you've been quoted, and your engineers will use the tools more aggressively than your pilot group did. Every CFO I've talked to who budgeted conservatively has been surprised. None of them pleasantly.

The industry needs honest cost data, and Uber just provided it, involuntarily.

3. Codex CLI v0.130 Ships Remote Control, Computer Use, and a Browser Extension

Three features in one release. Each one would've been a headline on its own.

OpenAI shipped Codex CLI v0.130 with a codex remote-control entrypoint for headless, remotely controllable agent sessions. Computer use that lets Codex see, click, and type in macOS applications. And Codex for Chrome, a browser extension that runs agents across tabs on web applications. Over 4 million people use Codex weekly.

The remote-control feature is the quiet bombshell. It turns Codex into a scriptable, headless agent you can call from CI/CD pipelines, orchestration scripts, or other agents. No TUI required. Combined with the new thread automations (wake the same thread on a schedule while preserving context), this creates persistent, programmable coding agents that behave like services rather than interactive tools.

Computer use is the answer to a problem I've hit repeatedly: GUI-only bugs. When your agent can't reproduce a rendering issue because it can only read code, you're back to manual testing. Now Codex can operate macOS apps by seeing and clicking them. It's not perfect (I haven't tested edge cases), but the capability gap it fills is real.

The Chrome extension is arguably the most user-visible addition. Running an agent that can navigate web tabs, interact with web applications, and coordinate across browser contexts opens up testing and automation workflows that were previously duct-taped together with Playwright scripts and hope.

Separately, OpenAI pushed Codex into the ChatGPT mobile app with live thread state, file diffs, terminal output, and test results on iOS and Android. Remote SSH and Hooks hit GA across all plans including Free. Your phone becomes a live agent control panel.

For builders: try codex remote-control this week. If you're running any kind of automated development workflow, headless agent sessions are how you integrate AI coding into your pipeline without the TUI overhead.

4. Hermes Agent v0.14.0: Turn Your Subscriptions Into API Endpoints

You have a Claude Pro subscription. A ChatGPT Pro subscription. Maybe SuperGrok. What if you could use all of them as OpenAI-compatible API endpoints for any tool?

Nous Research released Hermes Agent v0.14.0, their largest release ever: 808 commits, 633 merged PRs, 215 community contributors. The headline feature is a local proxy that turns any OAuth-authenticated subscription into an OpenAI-compatible endpoint you can point Codex, Aider, Cline, or any other tool at.

This solves a real and annoying problem. I pay for multiple AI subscriptions in my personal projects. Each tool wants its own API key and config. The Hermes proxy sits locally, authenticates once per provider, and exposes a single endpoint that speaks the OpenAI protocol. Configure once, use everywhere.

The rest of the release is substantial too. xAI Grok joins as an OAuth provider with grok-4.3 and its 1M token context. LSP semantic diagnostics catch type errors before the agent's next turn instead of after it. Microsoft Teams gets end-to-end integration. The project has 140K+ GitHub stars and holds the #1 position on OpenRouter with 271 billion tokens processed.

The LSP integration is worth calling out specifically. When your agent writes code with a type error, the error surfaces immediately through the language server rather than waiting for a build step or test run. That's a tight feedback loop that prevents the "agent confidently writes broken code for 3 turns" failure mode I've seen with other tools.

If you're running multiple AI coding tools (and at this point, who isn't?), install Hermes Agent and set up the local proxy. It's the fastest path from "I have five subscriptions" to "they all talk to everything."

5. GitHub Hits 275 Million Commits Per Week. Infrastructure Is Cracking.

275 million weekly commits. Projected 14 billion for 2026. GitHub has shifted to an "Availability First" posture, which is corporate speak for "we're running out of headroom."

The platform built for human-speed collaboration is being stress-tested by machine-speed development. AI agents don't take lunch breaks. They don't context-switch. They commit code as fast as they can write it, and with tools like Codex's remote-control mode making headless agent sessions trivial, the volume is only going up.

GitHub has been dealing with escalating outages as autonomous agents hammer APIs and create merge conflicts at machine speed. Rate limiting is getting tighter. The fundamental assumption that version control could absorb development velocity at any scale is being tested in real time.

The Register reported that Git's sequential stop/go workflow model wasn't designed for this. The 180 million users across 630 million repositories create infrastructure pressure that concentrated AI agent activity amplifies. Proposed solutions range from local-first Git to global mirroring to agent-native version control tools like GitButler.

Here's what worries me more than the infrastructure. When agents commit faster than humans review, what does code quality mean? A separate Register piece called AI-generated code "pain waiting to happen," documenting how manager enthusiasm for AI coding tools has outpaced developers' ability to learn them. The maintenance time bomb is real. Code entering production faster than teams can review it will surface as incidents, not metrics.

If you're running automated agent workflows on GitHub, watch your rate limits. Consider whether your review process can actually keep pace with agent-speed commits. And start thinking about what version control looks like when the majority of commits aren't written by humans. We're not there yet. But 275 million a week says we're closer than most people think.

Section Deep Dives

Security

Microsoft Semantic Kernel: Two CVSS 10.0 RCEs from prompt injection to sandbox escape. Microsoft's Security Blog disclosed CVE-2026-25592 (.NET) and CVE-2026-26030 (Python), both maximum severity. The .NET flaw abused an exposed DownloadFileAsync function with no path validation. The Python flaw used an f-string filter expression to traverse __class__.__bases__[0].__subclasses__() and achieve os.system-equivalent code execution. If you're running Semantic Kernel, patch to .NET SDK 1.71.0 or Python SDK 1.39.4 immediately. These aren't theoretical. Prompt injection escalating to arbitrary code execution in a Microsoft-maintained framework is exactly the attack chain security researchers have been warning about.

BitLocker backdoor 'YellowKey' gives unrestricted access with a USB drive and a reboot. Security researcher 'Nightmare-Eclipse' released YellowKey, a full BitLocker bypass for Windows 11. Plug in a USB, reboot to WinRE, enter a key combo, and the encrypted volume is wide open. The researcher accused Microsoft of intentionally embedding the backdoor, noting the triggering component exists only in the official WinRE image. This is the fifth zero-day from this researcher (rumored ex-Microsoft employee) this year. 571 points on Hacker News. If your threat model relies on BitLocker at rest, you need additional encryption layers now.

One million exposed AI services scanned: worst vulnerability class ever studied. Using certificate transparency logs, researchers identified over 2 million hosts with 1 million exposed AI services, many with zero authentication. Chat histories and agent interfaces were openly accessible. The rush to self-host LLM infrastructure is creating security debt faster than any previous technology adoption wave.

Agents

Sierra closes $950M at $15B valuation, now serving nearly half the Fortune 50. Sierra's round dwarfs every other agentic AI raise in 2026. Led by GV and Tiger Global with Benchmark and Sequoia participating, the company reports $150M ARR eight months after a $350M round. The sector pulled in $2.66B across 44 rounds through April, nearly 2.5x the same period last year. Customer service agents are where the money is going because ROI is easiest to measure.

A2A Protocol hits 150+ organizations and five production SDKs at one year. The Linux Foundation announced Agent-to-Agent protocol milestones: production SDKs in Python, JavaScript, Java, Go, and .NET. Tyson Foods and Gordon Food Service run collaborative A2A systems for real-time supply chain coordination. The protocol now supports signed agent cards with cryptographic domain verification. This is starting to look less like a spec and more like infrastructure.

Research

GPT-5.5 ran autonomously for 150+ hours improving protein folding models. A viral r/singularity post (579 upvotes) shared researcher Chris Hayduk's screenshot showing GPT-5.5 sustaining multi-day autonomous scientific research without human intervention. This extends the GPT-5/Ginkgo Bioworks protein synthesis work into long-horizon agentic territory. Whether the outputs are actually useful isn't clear from the screenshot. But the sustained execution without human prompting is the capability signal.

AI-mediated communication can steer collective opinion at scale. Researchers demonstrated that when LLMs mediate human-to-human communication (polishing LinkedIn posts, adding context on X), they systematically bias collective opinion formation, not just individual views. This is the first rigorous study of group-level opinion drift from AI intermediation. If you're building any system where AI sits between users, this paper should change how you think about the design.

Infrastructure & Architecture

SAP Autonomous Enterprise: 50+ domain agents, knowledge graph, seven model partners. At Sapphire 2026, SAP launched domain-specific Joule Assistants across finance, supply chain, HR, and CX, orchestrating 200+ specialized agents. Model partnerships span Anthropic, NVIDIA, Google, Microsoft, Mistral, Cohere, and n8n. This is the broadest multi-vendor agent infrastructure announced by an enterprise software company. The interesting move is the Knowledge Graph mapping business entities and processes. That's where defensibility lives.

AWS MCP Server hits general availability. AI agents now get IAM-guarded access to every AWS API through MCP. Sandboxed Python script execution handles multi-step workflows. No additional charge beyond the AWS resources consumed. For builders deploying agents in AWS environments, the gap between "agent writes code" and "agent operates infrastructure" just closed.

Tools & Developer Experience

AionUI hits 25.5K stars: single interface for 20+ CLI agents. AionUI auto-detects Claude Code, Codex, Gemini CLI, and 20+ other CLI agents, syncs MCP tools across all of them, and just shipped a Rust backend rewrite. That Rust move is notable. It decouples the server from Electron, enabling deployment on NAS boxes, Raspberry Pis, and remote servers with browser clients. All data stays local via SQLite.

GitHub Copilot ships CLI Agent with an "Ask Question" tool. The new Copilot CLI agent can pause and ask clarifying questions when it encounters ambiguity instead of guessing and building the wrong thing. Global .agent.md files now define custom agents available across all workspaces. The "ask instead of guess" pattern is exactly what's been missing from most coding agents.

Models

Meta 'Avocado' delayed to June after missing competitive bar. Internal testing showed Avocado landing between Gemini 2.5 and 3.0, which wasn't good enough to justify a release. The delay matters for the open-source ecosystem. Chinese labs (Kimi K2.6, DeepSeek V4) keep extending their leads in the open-weights tier while Meta takes longer to ship.

llama.cpp MTP gets 2x prompt processing speedup. PR #23198, merged by ggerganov, eliminates unnecessary logits copying during Multi-Token Prediction prompt decode. On RTX 5090 with Qwen 3.6 27B Q4_K, prompt processing roughly doubled. AMD MI50 saw 20% MTP throughput gain. If you run local models with MTP, pull latest llama.cpp.

Vibe Coding

Boris Cherny: 100% of production code is AI-generated since October 2025. Anthropic's Claude Code lead told Stephanie Zhan he hasn't written a line of code by hand since October 2025. This is the person building the tool saying the tool replaced his own coding. Make of that what you will. My take: it's true for greenfield projects with clear specs, and much less true for debugging production systems at 2 AM.

Google I/O opens tomorrow with goal-based agentic coding. Three sessions dedicated to telling the agent "raise test coverage to 80%" instead of specifying what to change. Gemini 4 is expected with up to 10M token context, potentially fitting million-line-plus codebases in a single API call. The goal-based framing is the right direction. I'm skeptical of the 10M context claim actually being useful in practice, but I'd love to be wrong.

Hot Projects & OSS

HyperFrames from HeyGen: HTML in, MP4 out, fully agent-addressable. 19.3K stars. Standard HTML compositions rendered to video via headless Chrome and FFmpeg. 50+ pre-built component blocks, a skills system teaching AI agents the framework's patterns, and deterministic rendering for CI pipelines. Apache 2.0, no per-render fees. If you're automating content pipelines, this is the missing piece between "AI writes copy" and "AI ships video."

Voicebox: open-source voice studio with MCP server for agent output. 26.6K stars. Tauri desktop app combining voice cloning, 7 TTS engines (including Qwen3-TTS), Whisper speech-to-text, and post-processing. The MCP server means any coding agent can generate speech output. Supports MLX on Apple Silicon. For builders adding voice to agent pipelines, this is the most complete local-first toolkit I've seen.

Supertonic: 31-language TTS on a Raspberry Pi with 99M parameters. Supertone shipped a model that runs on CPU, browser, mobile, and Pi. 44.1kHz quality, 10 expression tags, processes entire webpages into audio in under one second on CPU. At ~7x smaller than competing models, it trades peak quality for universal deployability.

SaaS Disruption

HubSpot drops agent pricing to $0.50 per resolved conversation. Down from $1.00, with the Prospecting Agent at $1 per qualified lead. A "resolution" requires no human escalation for 72 hours. HubSpot reports 65% resolution rate across 8,000+ activations. Intercom charges $0.99 per resolution. The race to the bottom in per-outcome pricing is accelerating, and it's going to compress margins across the entire support SaaS category.

Three major platforms launched agent hub architecture within three weeks. Notion Workers, ServiceNow Autonomous Workforce, and Google's Gemini Enterprise Agent Platform all shipped independently in May. The competitive battleground has shifted from "who has the best AI features" to "whose platform becomes the operating environment for autonomous agents." The winner becomes the control plane. The losers become features.

Policy & Governance

Anthropic briefing G20 finance authorities on Claude Mythos cyber vulnerabilities. At the Financial Stability Board's request, CEO Dario Amodei will present on vulnerabilities Mythos discovered, including nearly 300 in Firefox alone. Bank of England Governor Andrew Bailey requested the briefing. Amodei warned of a 6-to-12-month patching window before Chinese AI capabilities catch up. An AI company briefing G20 central banks on model capabilities is a first.

Musk v. OpenAI jury begins deliberating Monday. A nine-person Oakland jury takes up the most consequential AI corporate governance trial in history. The threshold question: whether Musk's 2024 filing came too late, six years after his last board involvement. If it survives standing, the jury weighs breach of charitable trust and unjust enrichment, with Musk seeking to unwind the PBC conversion with Microsoft's 27% stake and disgorge up to $134 billion. Most scholars predict he fails the standing test.

CAISI completes pre-deployment AI evaluation agreements with all five frontier labs. Google DeepMind, Microsoft, and xAI joined OpenAI and Anthropic in NIST's review program. 40+ evaluations completed covering cybersecurity, biosecurity, and chemical weapons risks. Pre-deployment government review is now the baseline expectation for every major US model release. Whether you think this is sufficient or theater depends on your priors, but the norm is set.

Skills of the Day

1. Use Codex CLI's remote-control entrypoint to integrate AI coding into CI/CD pipelines. The headless mode accepts programmatic input and returns structured output without the TUI. This turns a coding agent into an automatable service you can call from shell scripts, GitHub Actions, or other agents.

2. Set up Hermes Agent's local proxy to unify your AI subscriptions. Configure it once with your Claude Pro, ChatGPT Pro, or SuperGrok OAuth tokens and get an OpenAI-compatible endpoint any tool can hit. Eliminates managing separate API keys and configs per tool.

3. Deploy Pipelock 2.3.0 as an egress firewall between your agents and the network. The 20MB Go binary runs an 11-layer scanner covering SSRF, credential patterns, path traversal, and domain blocking. Your agent holds secrets but no network access. Pipelock holds network access but no secrets.

4. Use Cloudflare MCP Code Mode to compress agent tool access by 60-80%. Instead of sending every tool's full schema to the LLM each turn, Code Mode reduces toolsets to two meta-commands: search and execute. Significant context window savings when running dozens of MCP servers.

5. Build structured agent loops with small local models (Qwen 3.6 32B, Gemma 4 9B). Limit scope per step, use a strict plan/act/observe/refine cycle, and keep tools small. For classification, extraction, and routing tasks, local models match cloud performance at zero per-call cost.

6. Lock project invariants in a constitution document with GitHub Spec Kit. Spec Kit captures testing conventions, CLI-first requirements, and design system standards in one file that every SDD phase references automatically. Works across 30+ agents, not just one vendor.

7. Model enterprise AI coding budgets at 3-5x pilot costs, not 1.5x. Uber's budget exhaustion data shows 84% adoption at $500-$2K/month/engineer scales non-linearly. If your pilot had 15% adoption, assume production will hit 60-80% within six months.

8. Use JetBrains TeamCity 2026.1's built-in MCP endpoint to let agents debug build failures. The endpoint exposes build log retrieval, REST GET, and build trigger (forced to personal runs). Point your coding agent at it and it can investigate, fix, and retrigger failed builds from the terminal.

9. Add paper.json companion files to research papers you publish. The convention gives each claim a citable ID, makes scope boundaries explicit, and includes machine-executable figure reproduction commands. If you're building RAG pipelines over academic literature, request paper.json from sources.

10. Use HyperFrames to close the gap between AI-generated copy and rendered video. Write HTML compositions, render to MP4 via headless Chrome and FFmpeg, and teach agents the framework patterns through the built-in skills system. No React, no proprietary DSL. Apache 2.0 licensed, zero per-render cost.

How This Newsletter Learns From You

This newsletter has been shaped by 14 pieces of feedback so far. Every reply you send adjusts what I research next.

Your current preferences (from your feedback):

• More builder tools (weight: +3.0)
• More vibe coding (weight: +2.0)
• More agent security (weight: +2.0)
• More strategy (weight: +2.0)
• More skills (weight: +2.0)
• Less valuations and funding (weight: -3.0)
• Less market news (weight: -3.0)
• Less security (weight: -3.0)

Want to change these? Just reply with what you want more or less of.

Quick feedback template (copy, paste, change the numbers):

More: [topic] [topic]
Less: [topic] [topic]
Overall: X/10

Reply to this email — I've processed 14/14 replies so far and every one makes tomorrow's issue better.