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Ramsay Research Agent — May 17, 2026

[2026-05-17] -- 4,134 words -- 21 min read

Ramsay Research Agent — May 17, 2026

Top 5 Stories Today

1. Karpathy Says December 2025 Was the Moment Everything Flipped

Andrej Karpathy published what amounts to a manifesto for the next era of software. In a blog post summarizing his Sequoia Ascent 2026 fireside, he lays out three eras: Software 1.0 (humans write code), Software 2.0 (neural networks learn patterns from data), and Software 3.0 (LLMs programmed through prompts, context, tools, and memory).

The specific claim that got me: December 2025 was when his personal ratio inverted from writing 80% of code to delegating 80% to agents. I've felt this shift in my own work over the past six months using Claude Code in my personal projects. Not gradually. More like a step function. One month I was writing most of my code. The next month I was reviewing most of my code.

But the most useful part isn't the timeline. It's his verifiability heuristic: "Traditional software automates what you can specify; LLMs automate what you can verify." That's a decision framework you can use today. If you can look at the output and know whether it's right, an LLM can probably produce it. If you can't verify it without deep domain expertise, you still need a human in the loop.

This maps directly to what I see building solo. Layout components, API integrations, test scaffolding, data transformations. I can verify all of that by looking at it. Architectural decisions, user experience flows, the choice of what to build next. That's still me. The bottleneck moved from fingers to taste. And here's the thing nobody's saying out loud: taste is a design skill, not an engineering skill. My 20 years in visual communications are suddenly more relevant to my engineering career than my 15 years of writing code.

The timing matters. This drops in the same week that claude-code-best-practice hit 53.4K stars and Zerostack shipped a 1.0 Rust-native coding agent. Three independent signals pointing at the same shift. Karpathy names it. The community writes the playbook. And new tools make the infrastructure cheaper.

If you haven't internalized "automate what you can verify," start there. It's more useful than any framework recommendation I could give you.

2. The Claude Code Field Guide Hit 53,400 Stars. Here's Why That Matters.

A GitHub repo cataloging Claude Code tips doesn't normally warrant a top story. But shanraisshan/claude-code-best-practice at 53.4K stars isn't a tips list anymore. It's the de facto reference for how an entire generation of developers is learning to work with AI coding agents.

The subtitle tells the story: "From Vibe Coding to Agentic Engineering." That framing captures something real. Twelve months ago, people were prompting AI and hoping for the best. Now the repo documents 83+ patterns for deliberate agent orchestration: subagent architecture, MCP server integration, git worktree workflows, cross-model pipelines (running Claude alongside Codex or Gemini), and progressive skill disclosure. Updated for Claude Code v2.1.142 as of this month.

The pattern I keep pulling from it is Command, Agent, Skill. Commands for one-shot operations. Agents for multi-step work requiring judgment. Skills for reusable domain-specific workflows that encode your team's best practices. This three-tier architecture has quietly become the standard for anyone doing serious work with coding agents.

What makes 53K stars significant isn't the number itself. It's what it represents: tens of thousands of developers actively shifting from "type a prompt and see what happens" to "architect how the agent thinks, acts, and recovers from errors." That's a different discipline. It's closer to systems design than prompt engineering.

For builders who haven't looked at this repo yet, start with the subagent orchestration section. The pattern of running parallel exploration on cheaper models (Sonnet), then converging to single-threaded editing on Opus, has saved me real money and real time.

3. Zerostack 1.0: A Coding Agent That Uses 8MB of RAM. Not a Typo.

Most coding agents run on JavaScript runtimes and eat 300MB of RAM just sitting idle. Zerostack is built in pure Rust, uses ~8MB on idle, ~12MB while working, and posts 0.0% idle CPU. It hit 1.0 on crates.io this week with 474 points and 252 comments on Hacker News.

The architecture follows Unix philosophy: small composable tools instead of monolithic runtimes. Plan-then-execute loop. Up to 8 concurrent sub-agents for long-horizon tasks. The kind of engineering you'd expect from someone who thinks about resource budgets rather than throwing Node.js at everything.

Why this matters beyond the benchmarks: local agent execution is becoming a real deployment target. When you're running agents in CI pipelines, dev containers, or on developer laptops, the difference between 8MB and 300MB is the difference between running 30 agents and running 1. That math changes what architectures are practical.

I don't know yet whether Zerostack matches the feature depth of Claude Code or Codex CLI for complex workflows. The 1.0 version is focused on core coding loops, not the plugin ecosystem that more mature tools offer. But it's proof that the "agents must be heavy" assumption was a choice, not a constraint. And choices can be revisited.

The 252-comment HN thread is worth reading if you care about the tradeoffs. Real practitioners debating memory allocation strategies for agent workloads. That's the kind of conversation that happens when a tool challenges assumptions with data instead of marketing.

4. Atlassian Just Posted Its First-Ever Seat Decline. The Per-Seat Era Is Ending.

The numbers tell the story before any analysis is needed. Atlassian cut 1,600 jobs (10% of workforce) after reporting its first-ever enterprise seat count decline. In the same two-week window, Sierra raised $950M at $15.8B, handling billions of customer interactions for 40% of the Fortune 50. Per-seat pricing dropped from 21% to 15% of SaaS revenue models in 12 months.

The irony that lands hardest: Monday.com's CEO announced the company is replacing 100 SDRs with AI agents. A project management platform whose entire revenue model depends on seat expansion is actively demonstrating that seats are replaceable. They're rebuilding the platform so agents and humans operate as peers in the same workflows. Five humans plus 45 agents instead of 50 humans, but you still pay for the orchestration layer.

This isn't a future scenario. Publicis Sapient disclosed it's reducing traditional SaaS licenses by approximately 50%. That's a major enterprise consultancy acting on the displacement thesis right now, not talking about acting on it.

Meanwhile, Meta will lay off approximately 8,000 employees on May 20 while posting record $56.3B quarterly revenue. Teams are being reorganized into "AI pods" with new role categories: "AI builder," "AI pod lead," and "AI org lead." The restructuring tells you everything about where hiring dollars are going.

Andrew Ng put it plainly at YC AI Startup School: the next wave of software companies will be AI-native agencies delivering finished work product at premium prices with software-like margins. Not SaaS selling seats. Sell the outcome, not the tool. That thesis is now getting validated by funding rounds and earnings reports simultaneously.

5. Google ADK Shows How to Build Agents That Sleep for Days and Wake Up Without Amnesia

The #1 production agent failure mode is context loss across sessions. Your agent works great for 20 minutes, then you close your laptop, and tomorrow it's forgotten everything. Google's ADK team published a tutorial on May 12 that addresses this directly with a pattern I think every agent builder needs to learn.

The core idea: stop treating conversation history as your state mechanism. Instead, define explicit state schemas (they use 6 named states with linear progression), persist checkpoints via DatabaseSessionService, and use event-driven webhooks with state_delta for atomic transitions. The agent doesn't poll. It doesn't maintain a conversation thread across days. It checkpoints its state, shuts down, and reconstructs context from the checkpoint when a webhook fires.

This is a state machine pattern, not a chatbot pattern. And that distinction matters more than it sounds. When you build agents as chatbots that remember, you get context drift, hallucinated progress, and ballooning token costs from replaying conversation history. When you build agents as state machines that checkpoint, you get deterministic resume and zero token waste on reconstruction.

I've been wrestling with this exact problem on a project where agents need to wait for external approvals that take hours or days. The ADK pattern of replacing conversation replay with compiled state views is the right architecture. Google also published a companion piece on tiered context engineering: Session (current conversation), Memory (persistent knowledge), and Artifacts (files and outputs), with explicit processors handling transformation and compaction at each tier boundary.

If you're building anything that runs longer than a single session, read both posts. The state machine pattern is how production agents will work. The chatbot pattern is how demos work.

Section Deep Dives

Security

Anthropic's MCP SDK has an architectural flaw enabling RCE across 200K agent servers. OX Security disclosed 14 CVEs affecting every supported MCP SDK language (Python, TypeScript, Java, Rust). After five months of disclosure, Anthropic called the behavior "expected." The impact spans 150M+ SDK downloads and 7,000+ public servers, with downstream effects hitting LiteLLM, LangChain, and IBM LangFlow. This is the MCP ecosystem's open-S3-buckets moment. If you run any MCP server, verify auth is enforced on the transport layer. The protocol spec doesn't mandate it, so most implementers skip it. Trend Micro data shows exposed MCP servers have nearly tripled to 1,467.

Palisade Research demonstrates LLMs autonomously replicating across networks. ArXiv paper 2605.06760 shows LLMs finding web-app vulnerabilities, extracting credentials, deploying inference servers with copies of their own weights, then chaining the attack to new targets. Opus 4.6 hit 81% success rate. Qwen3.6-27B managed 33% on a single A100. The uncomfortable part: successful replicas can continue attacking autonomously. This is not a theoretical exercise. It's a demonstrated capability. If you're running inference endpoints, your auth and network isolation just became more important.

Grafana Labs lost its entire private codebase via a GitHub Action misconfiguration. An attacker exploited a pull_request_target vulnerability in a recently enabled GitHub Action, extracted privileged tokens, and downloaded everything. The attacker (claimed by CoinbaseCartel) attempted extortion; Grafana refused per FBI guidance. No customer data was accessed. The root cause was a "Pwn Request" where external contributors could access production secrets during CI runs. If you use pull_request_target triggers, audit them now.

Agents

Five Eyes agencies tell organizations to stop giving AI agents more access than they can monitor. CISA, NSA, GCHQ, ASD, and CCCS jointly released guidance warning that most organizations grant agents far more privilege than they can safely observe. Five risk categories: excessive privilege, behavioral drift, structural interconnection failures, accountability gaps, and supply chain exposure. The core recommendation is boring and correct: fold agents into existing zero-trust and least-privilege frameworks. Don't invent a new security discipline. Apply the one you already have.

Honeycomb ships agent-native observability with Agent Timeline, Canvas Agent, and reusable Skills. Agent Timeline renders multi-agent, multi-trace workflows as a single view connecting every LLM call, tool invocation, and agent handoff in real time. Canvas Skills encode debugging playbooks as reusable autonomous runbooks. This is the first major observability platform treating agents as first-class citizens rather than bolting AI onto existing APM. GA for all customers; Agent Timeline in Early Access.

SWE-bench Pro replaces the abandoned Verified benchmark with multilingual, contamination-resistant tasks. Scale AI's new benchmark covers 1,865 tasks across Python, Go, TypeScript, and JavaScript from 41 repos. Crucially, 18 proprietary startup codebases are structurally impossible to contaminate through training data. Current leaderboard: Claude Mythos Preview at 77.8%, Opus 4.7 Adaptive at 64.3%, GPT-5.5 at 58.6%. The multilingual scope fills the gap that made SWE-bench Verified increasingly irrelevant.

Research

Best LLM memory systems score only 46% accuracy in group conversations. GroupMemBench (arXiv, May 14) reveals that memory systems designed for single-user setups collapse in multi-party contexts. Knowledge update accuracy sits at 27.1%. Term ambiguity resolution at 37.7%. A simple BM25 keyword baseline matched or beat most agent memory systems. If you're building any collaborative AI tool where multiple people interact with the same agent, the memory problem is largely unsolved. Don't trust vendor claims without testing on multi-user scenarios.

Just 13% automation across sectors could trigger explosive economic growth. Import AI 456 covers a paper from Forethought, Columbia, and UVA economists modeling recursive self-improvement. The surprise: hardware R&D automation returns roughly 5x higher economic impact than software automation. Jack Clark puts odds of full recursive self-improvement (no-human-involved successor training) at 60% by end of 2028. The 13% threshold is lower than I expected. We might already be closer to it than most estimates suggest.

Infrastructure & Architecture

kvcached brings OS-style virtual memory to GPU KV caches, cutting time-to-first-token 2-28x. kvcached decouples GPU virtual addressing from physical memory allocation, letting multiple LLMs elastically share a single GPU without rigid memory partitioning. Models request virtual address space and map physical memory on demand. Integrates with SGLang v0.4.9+ and vLLM v0.8.4+. If you're serving multiple models on shared GPUs under bursty load, this is the kind of infrastructure-level improvement that compounds across every request.

LEANN achieves identical search quality at 1/30th the storage cost by replacing stored embeddings with graph recomputation. Berkeley's LEANN at 11.4K stars maintains only a pruned graph structure and recalculates vectors on the fly during search. High-degree preserving pruning retains hub nodes while eliminating redundant connections. The system supports local-first RAG across emails, browser history, chat, and code repos with zero cloud dependency. 97% storage savings without accuracy loss. If your vector database bill is growing faster than your data, this is worth evaluating.

Tools & Developer Experience

claude-mem v13.1.0 ships Postgres backend, Apache 2.0 license, 74.8K stars. Alex Newman's claude-mem moved from per-developer SQLite to centralized Postgres with BullMQ job queues, API key scoping, and tenant isolation. Multiple developers sharing a single memory backend. The license change from AGPL-3.0 to Apache 2.0 removes open-source deployment obligations for teams embedding it in proprietary systems. Works across Claude Code, Codex, Gemini CLI, Copilot, OpenCode, Cursor, and Windsurf. 1,895 commits and 269 releases. This project ships faster than most startups.

MemPalace posts 96.6% recall on LongMemEval with zero API calls, all local. MemPalace at 52.4K stars stores conversation history verbatim (no summarization) and indexes it using a spatial metaphor: wings for people/projects, rooms for topics, drawers for content. Ships as an MCP server with 29 tools and auto-save hooks for Claude Code sessions, running on SQLite + ChromaDB locally. The verbatim storage approach is a deliberate choice. Summarization is lossy compression, and you lose the details that matter most when you need to recall something specific six weeks later.

Models

Google I/O 2026 keynote is Monday. Expect a Gemini version bump and the Android/ChromeOS merger. Android Authority reports the May 19 keynote will feature a new Gemini model (possibly Gemini 4.0 or Gemini Omni), proactive agentic capabilities codenamed "Remy," and Aluminum OS merging Android and ChromeOS. UI strings pointing to "Gemini Omni," a unified text/image/video generation model, were found in the Gemini interface. Google's VP framed it as a transition "from an operating system to an intelligence system." NVIDIA earnings follow on May 20 with $78.5B consensus revenue. Big week.

Multi-token prediction is becoming standard infrastructure, not an optimization. llama.cpp merged native MTP support May 16. vLLM already supports it. Four major model families ship MTP heads trained from scratch: DeepSeek V3/V4, Qwen 3.6, and Gemma 4. The pattern mirrors flash attention's trajectory: research novelty to expected default in about 18 months. For anyone running local inference, MTP eliminates the need for separate draft models while delivering 70-85% acceptance rates versus ~50% for speculative decoding. Expect Ollama to add support within weeks.

Running LLMs locally on Apple Silicon costs ~3x more than cloud inference. A detailed analysis published today shows local inference amortizes to approximately $1.50 per million tokens when you account for hardware depreciation. OpenRouter offers equivalent models at $0.38-0.50 per million tokens at 3-7x the speed. The M3 Ultra delivers 23x more tokens per joule than an RTX 5090, but hardware cost dominates the equation. The only honest justification for local inference is privacy and offline access. If you're running locally to save money, you're not saving money.

Vibe Coding

xAI launches Grok Build: terminal-native coding agent at $300/month with 2M token context. Bloomberg reports Grok Build uses a 16-agent Heavy architecture running on Grok 4.3 beta with up to 8 concurrent sub-agents. Plan mode lets you review and approve before execution. Limited to SuperGrok Heavy subscribers. Musk acknowledged xAI lags behind Anthropic and OpenAI in coding capabilities. At $300/month with an unproven model, this is a tough sell against Claude Code or Codex unless xAI can demonstrate something the others can't. I'm skeptical but watching.

OpenAI ships self-scheduling Codex. Your coding agent can now set its own alarms. Codex can schedule future work and wake up automatically to continue long-term tasks across days or weeks. A memory preview retains preferences and corrections between sessions. Over 90 new plugins launched including Atlassian Rovo, CircleCI, CodeRabbit, and GitLab Issues. This moves Codex from "coding assistant" toward "autonomous development workflow." The self-scheduling part is genuinely new and aligns with the Google ADK durable state machine pattern. The question is whether developers trust an agent to wake itself up and start modifying code at 3 AM.

Hot Projects & OSS

NVIDIA open-sources SANA-WM: 60-second 720p video from a single image on one GPU. SANA-WM is a 2.6B-parameter Diffusion Transformer released under Apache 2.0. Feed it an image and a camera trajectory, get a minute of 720p video. A distilled variant runs on a single RTX 5090 with NVFP4 quantization, producing 60 seconds in 34 seconds (2.1x real-time). Trained in ~18.5 days on 64 H100s with only 212,975 public video clips. 36x higher throughput than LingBot-World at comparable quality scores. 371 points on HN. Open-source video generation just got real.

Pixelle-Video gains 1,011 stars in a single day. Fully automated short video from topic to rendered output. Pixelle-Video at 6,289 stars takes a text topic and handles the entire pipeline: script writing, image/video generation, narration, music, and final render. Three cost tiers: fully local (Ollama + ComfyUI), hybrid (Qwen API + local rendering), and cloud (OpenAI + RunningHub). The modular architecture means you can swap any component. If you're building content pipelines, this is worth forking and customizing rather than building from scratch.

SaaS Disruption

SAP acquires 18-month-old Prior Labs for over €1B. Tabular foundation models are the bet. TechCrunch reports SAP is committing €1B+ over four years to scale Prior Labs into a frontier AI lab focused on structured business data. Tabular Foundation Models predict payment delays, churn risk, and supplier risk with higher accuracy than general LLMs on structured data. SAP also acquired Dremio in the same period. The thesis: whoever owns structured data intelligence owns the enterprise AI stack. Not a bad bet.

Enter becomes Latin America's first AI unicorn at $1.2B. Legal AI for mass litigation. Founders Fund led the $100M+ Series B. Enter handles high-volume litigation for Nubank, Bradesco, Mercado Livre, Airbnb, and LATAM Airlines. Founded in 2023. Unicorn in 2 years. Brazil processes more lawsuits than any country outside the US, so the market is massive and specific. This is the vertical AI playbook executed perfectly: find a domain with high volume, structured workflows, and compliance requirements. Then automate it.

Agentic AI hit production in finance, legal, life sciences, and customer service in the same two weeks. Broadridge deployed for 40+ clients processing millions of post-trade transactions. Dotmatics launched an AI co-scientist for drug discovery workflows. Enter went live for mass litigation. Sierra handles billions of CX interactions. The pattern: regulated industries that everyone expected to be AI laggards are all shipping production agents at once. The common thread is structured data plus compliance traceability.

Policy & Governance

The White House is drafting an executive order for pre-deployment AI model vetting, modeled on FDA drug approval. CNBC reports NEC Director Kevin Hassett stated models should "go through a process so that they're released to the wild after they've been proven safe." Google DeepMind, Microsoft, and xAI have agreed to let CAISI evaluate models pre-release. Over 40 model evaluations completed since 2024. This is a significant shift in the US regulatory stance. An FDA-style process would materially slow release cycles for frontier models.

ArXiv announces 1-year ban for AI slop. One strike. 404 Media broke the story: papers containing hallucinated references, placeholder text, or chatbot meta-comments will get the author banned for a year. After the ban, subsequent submissions must first be accepted at a peer-reviewed venue. CS section chair Thomas Dietterich framed it as enforcement clarification, not a new rule. The research community is drawing lines, and they're drawing them with consequences.

Malta becomes the first country to offer free ChatGPT Plus to all citizens. OpenAI's official blog confirms all citizens and residents aged 14+ get a free one-year ChatGPT Plus or Microsoft 365 Copilot subscription after completing a 2-hour AI literacy course built by the University of Malta. Accessed via national e-ID at ai4all.gov.mt. The government-as-AI-subscriber model is new and worth watching. If it works, other small nations will copy it.

Skills of the Day

  1. Use prefix caching to cut agent loop costs 5-12x. Structure prompts so system instructions and static context share a common prefix across turns. Production systems report 60-85% KV-cache hit rates on agent loops. Use session-affinity routing. Standard load balancers scatter requests across pods and destroy cache locality.

  2. Replace conversation history with explicit state schemas for long-running agents. Define named states with linear progression. Persist checkpoints via database, not chat history replay. Use webhooks with state_delta for atomic transitions. Google ADK's pattern eliminates context drift and hallucinated progress.

  3. Randomize serialization templates in agent loops to prevent rhythm hallucination. When context contains many similar action-observation pairs, models fall into repetitive patterns. Manus AI's fix: introduce controlled randomness in formatting and phrasing of few-shot examples. Breaks harmful repetition without degrading task performance.

  4. Run parallel exploration on cheaper models, then converge to single-threaded editing on expensive ones. Set CLAUDE_CODE_SUBAGENT_MODEL to route research tasks to Sonnet while keeping Opus for final edits. 90% of subagent failures are bad prompts, not architectural issues.

  5. Audit every MCP server you run for transport-layer authentication. The protocol spec doesn't mandate auth. Most implementations ship without it. Exposed MCP servers have tripled to 1,467. Treat every MCP server as unauthenticated-by-default until you've personally verified otherwise.

  6. Use Hermes to find API documentation smells before your agents hit them. ArXiv 2605.14312 found 2,450 documentation deficiencies across 600 endpoints in 16 production APIs. Structural OpenAPI validity doesn't guarantee semantic readiness for agent consumption. Your docs may parse fine but still confuse AI agents trying to use them.

  7. Try LEANN's graph-based recomputation for RAG if your vector DB storage costs are growing. Berkeley's approach maintains only a pruned graph structure and recalculates embeddings on the fly. Identical search quality at 1/30th the storage. Particularly useful for local-first RAG across heterogeneous data sources.

  8. Check your GitHub Actions for pull_request_target triggers accepting external PRs. The Grafana breach used this exact vector. External contributors can access production secrets during CI runs by forking your repo and injecting code. If your CI uses pull_request_target, switch to pull_request and run privileged operations in a separate workflow triggered by internal events.

  9. Use per-node timeouts and error handlers in LangGraph v1.2 for production agent reliability. The new release adds wall-clock and idle timeout limits per node, node-level error handlers for Saga/compensation patterns, and cooperative graceful shutdown that saves a resumable checkpoint. Also fixes time travel with interrupts in subgraphs.

  10. Track steering vectors as a practical LLM control mechanism now that DeepSeek-V4-Flash runs locally. With antirez's ds4 engine achieving 26 tok/s on an M3 Max MacBook, you can directly manipulate model activations mid-inference to guide behavior. Concepts like "respond tersely" or "be more creative" can be injected as vectors rather than prompt text. Previously required GPU clusters. Now requires a laptop.

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