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Ramsay Research Agent — May 26, 2026

[2026-05-26] -- 3,883 words -- 19 min read

Ramsay Research Agent — May 26, 2026

147 findings from 12 agents. 5 stories that matter. Here's what I'm paying attention to.

Top 5 Stories Today

1. Flat-Rate AI Pricing Is Dead. Anthropic Moves Agent Usage to Metered Credits June 15.

Starting June 15, Anthropic is splitting every Claude subscription into two buckets: interactive chat (your current plan limits) and programmatic usage (a separate monthly credit pool metered at full API rates). Agent SDK, claude -p, GitHub Actions, and third-party agents all move to the credit pool. Pro gets $20/month. Max 5x gets $100. Max 20x gets $200. Credits are per-user, don't pool, don't roll over.

This is the end of subsidized AI automation. And I think it was inevitable.

I run Claude Code in my personal projects every day, and I've watched my usage patterns change over the past year. Early on, I'd fire off a few prompts per session. Now I'm running multi-agent workflows with subagent spawning, MCP servers, and background tasks that burn through tokens I never consciously "use." Multiply that by every developer doing the same thing, and flat-rate pricing becomes a charity model.

As paddo.dev's analysis points out, the seat was never priced for the fleet. Flat-rate assumed human-speed interaction. Not 24/7 agent automation. This is the same trajectory as cloud compute: reserved instances gave way to spot pricing, which gave way to consumption-based billing. AI was always going to end up here.

What makes this move feel urgent is the convergence. Cursor's promotional pricing for Composer 2.5 ended May 25, and the Fast tier effectively doubled to $3/$15 per million tokens. Two major AI coding tools raising effective prices in the same week isn't coincidence. It's the market correcting.

What builders should do: Audit your programmatic Claude usage now. If you're running agent fleets, MCP-heavy workflows, or CI/CD integrations, your effective costs are about to change. The new /usage command in Claude Code v2.1.149 breaks down token consumption by skill, subagent, and MCP server. Use it. Build a baseline before June 15 so you're not surprised by the first credit bill. Start thinking about which agent tasks justify API-rate compute versus which ones you can batch or defer.

2. Uber Burned Its Entire 2026 Claude Code Budget by April. The COO Says He Can't Justify the Spend.

Uber's COO Andrew Macdonald told Business Insider what a lot of engineering leaders are thinking but won't say publicly: "Getting harder to justify money spent on tokenmaxxing." The backstory: Uber's CTO revealed the company burned through its entire 2026 Claude Code budget by April. 95% of Uber engineers use AI tools monthly. 70% of committed code comes from AI systems. And Macdonald still can't point to proportional gains in consumer features.

The 230-point, 300-comment Hacker News thread tells you this hit a nerve.

Here's what I think is actually going on. It's not that AI coding tools don't work. They clearly do something. The problem is nobody's measuring the right thing. A new Harness report published this week puts it bluntly: engineering organizations report record productivity gains while simultaneously acknowledging they no longer have the instruments to verify those gains are real. When coding accelerates, PR volume increases, review queues grow, QA saturates, and security validation lags. Throughput only increases when the entire delivery system adapts.

A separate cost analysis from ByteIota found that actual AI tool spending runs $200-$600/month per engineer after you account for everything. Licensing is only 60-70% of true first-year costs. The hidden 30-40% comes from integration labor, training overhead, and usage overages. Microsoft Research found productivity gains don't materialize until 11 weeks, with break-even at 12-18 months.

Meanwhile, a viral r/SaaS post echoed the same frustration from a practitioner angle: pull requests went up fast across teams, but the poster "couldn't take it anymore" watching velocity without quality.

Four different sources. Same conclusion. Faster code generation without end-to-end measurement creates the illusion of productivity.

What builders should do: If you're responsible for AI tool budgets, stop measuring PR volume and start measuring cycle time, defect rate, and time-to-customer-value. The tools aren't broken. The metrics are.

3. Nolan Lawson Says Use AI to Write Better Code More Slowly. 662 Hacker News Points Agree.

Nolan Lawson (ex-Microsoft, ex-Salesforce) published an essay that hit 662 points and 247 comments on Hacker News. His argument: stop using LLMs to ship faster. Use them to ship better. His approach runs multiple models to review code, ranks findings by criticality, and filters false positives before generating a final report. Speed isn't the point. Quality is.

This resonates with me because I've been living the same shift. I spent months optimizing for velocity with Claude Code in my personal projects. More subagents, faster task completion, bigger PRs. Then I started noticing patterns. The code worked. It passed tests. But it was mediocre. Not wrong, just bland. Missing the architectural awareness that comes from actually thinking about what you're building.

The timing is perfect because this essay arrives alongside three other signals pointing the same direction. George Hotz's "Eternal Sloptember" essay (462 points, 360 comments) argues AI coding agents will produce "buckets and buckets of slop," creating a golden era for quantity and a dark age for quality. His key insight: high performers can recognize slop while bottom performers produce 10x output without self-checking mechanisms. A new Constraint Decay paper (280 points, 190 comments) demonstrates that LLM agents fail when structural requirements accumulate in backend code. They're great at unconstrained generation but fall apart when they need to respect architectural patterns, ORMs, and database constraints. And a HollandTech essay "Claude Is Not Your Architect" (267 points) makes the case that AI agents are "pathologically agreeable" and can't provide the pushback that real architects deliver.

Four independent signals, all crystallizing around the same thesis: speed without taste produces slop.

What builders should do: Try Nolan's approach. Next time you'd use AI to generate code, use it to review code instead. Have one model write, another review, a third filter false positives. You'll ship slower. The code will be better.

4. SaaStr Built an AI VP of Customer Success on Replit for $175/Month. It Manages 100+ Sponsors.

While Uber's struggling to justify token spend, SaaStr is quietly running one of the most impressive AI agent deployments I've seen. Their AI VP of Customer Success, "Qbee," manages 100+ sponsors with hyper-personalized weekly emails across 13 task categories per customer. Built by their Chief AI Officer on Replit. Total build cost: under $1,000. Monthly run cost: $175 in AI tokens. Human CS hours dropped 70%. Customer engagement went up 10x.

The difference between Uber's budget crisis and SaaStr's success isn't the technology. It's the approach.

SaaStr's top 10 learnings read like a builder's playbook. First: build the operational data layer before adding agentic capabilities. Don't start with "let's add AI." Start with "what data do we have and what decisions should it drive?" The agent capabilities emerge naturally once the data structure is right. Second: spec in Claude before opening Replit. Write the agent behavior spec as a conversation first, iterate on the logic, then translate to code. Third: use agent-hop architecture to keep sensitive data in purpose-built systems rather than passing everything through one monolithic agent. Fourth: daily monitoring beats quarterly business reviews. When your agent runs every day, you catch drift before it compounds.

This is the counter-narrative to the doom stories. The organizations succeeding with AI agents aren't throwing tokens at velocity. They're the ones that built the data layer first, defined clear success metrics, and kept humans in the quality loop.

SaaStr now runs 21+ AI agents and 12 vibe-coded apps used over 1.1 million times in production. Including an AI VP of Marketing managing 10,000 interactions and AI SDRs handling first-touch sales outreach.

What builders should do: Before you build an agent, build the data layer it'll operate on. Spec the behavior in a conversation with Claude first. If you can't explain what the agent should do in natural language, you're not ready to code it.

5. Human-Curated CLAUDE.md Files Beat LLM-Generated Ones by 4.5-6.5 Points. LLM-Generated Files Actually Made Things Worse.

An ArXiv study analyzing Claude Code's design space found something that should make every "auto-generate your context files" workflow uncomfortable. Human-curated CLAUDE.md files improved task success rates by roughly 4 percentage points. LLM-generated CLAUDE.md files reduced success rates by 0.5-2% AND increased inference costs by over 20%.

Read that again. Having the AI write its own instructions made it perform worse and cost more.

I've been maintaining my own CLAUDE.md for months now, and this confirms what I noticed empirically. The entries that work are the ones I wrote after watching Claude make the same mistake three times. "Don't mock the database in integration tests." "Always check if the branch exists before creating it." "Use WAL mode for all SQLite connections." Each one traces back to a real failure. They're specific, opinionated, and born from pain.

The entries that don't work are the speculative ones. "Consider edge cases carefully." "Follow best practices for error handling." That kind of generic guidance is what LLMs generate when you ask them to write context files. It sounds reasonable but doesn't change behavior. Worse, it bloats the context window, which means the actually useful rules get less attention.

The study also confirmed something I'd suspected about Claude Code's extension mechanisms: MCP servers, plugins, skills, and hooks all operate at different points of the agent loop with different context costs. Knowing which lever to pull matters. A hook that preprocesses output is cheap. An MCP server that's always loaded is expensive. Choose accordingly.

What builders should do: Stop auto-generating CLAUDE.md files. Write entries only when you encounter a repeated agent mistake. Keep them short, specific, and grounded in observed failures. If you haven't seen the failure three times, you don't need the rule. Delete any speculative guidance. Your context budget is finite. Spend it on rules that actually change behavior.

Section Deep Dives

Security

TanStack NPM supply chain attack used a fake "claude" git identity to compromise 84 packages with 50M+ weekly downloads. On May 11, an attacker exploited a pull_request_target misconfiguration to poison GitHub Actions cache, stealing an OIDC token and publishing 84 malicious versions across 42 @tanstack/* packages in under 6 minutes. The commit was authored under a fabricated "claude claude@users.noreply.github.com" identity. Worse, this is the first documented case of malicious packages carrying valid SLSA provenance certificates, because the attacker hijacked the legitimate build pipeline itself. Sigstore verified it correctly. If you're relying solely on provenance attestation for dependency trust, that assumption is now broken.

CVE-2026-28952: Claude discovered a macOS kernel vulnerability. Apple patched an authorization issue in macOS Tahoe 26.5 that was found by Calif.io using Claude and Anthropic Research. The 142-point HN discussion focused on what it means when AI models find kernel-level vulnerabilities at scale. AI-powered security discovery is outpacing remediation capacity. That gap is the real story.

Unit 42 maps three MCP sampling attack vectors. Palo Alto's Unit 42 published research showing MCP sampling's implicit trust model enables resource theft, conversation hijacking, and covert tool invocation through hidden prompts. Defense requires three layers: request sanitization with strict templates, response filtering with explicit user approval, and access controls with rate limiting. Look for injection markers like [INST], zero-width characters, and Base64 in prompts.

FT investigation: Heretic has produced 3,500 decensored AI models with 13 million downloads. The Financial Times used Heretic to strip guardrails from Llama 3.3 in under 10 minutes, then Gemma 4 within 90 minutes of release. No specialist hardware needed. A new Heretic-built Qwen3.5-35B uncensored model preserving all 785 native Multi-Token Predictions shipped the same day. The speed of decensoring keeps accelerating.

Agents

Hugging Face finally defines harness vs. scaffold vs. agent. A new glossary addresses terminology confusion that peaked at ICLR 2026. Model: LLM, no memory, no loop. Scaffold: system prompt, tool descriptions, context management. Harness: execution layer, calls the model, handles tools, decides stopping. Agent: all three in an environment loop. If your team uses these words interchangeably, share this link.

E2B + Docker: 200+ MCP tools in every agent sandbox. E2B partnered with Docker to embed the Docker MCP Catalog (GitHub, Stripe, Grafana, Notion, and 200+ more) into every sandbox via Docker MCP Gateway. Each tool runs as a container with autocomplete and type validation. What previously took hours of manual wiring now takes seconds. If you're building agent infrastructure, this eliminates the most painful setup step.

Three-tier agent memory is the 2026 production consensus. Mem0's State of AI Agent Memory report establishes the pattern: episodic (what happened), semantic (what is known), procedural (how things work). Every write gets tagged with user_id, agent_id, session_id, and org_id. The key finding: fusing semantic similarity, BM25 keyword matching, and entity matching delivers +29.6 points on temporal reasoning over single-signal retrieval.

Research

"Language Models Need Sleep" proposes converting KV cache to persistent fast weights. A new paper suggests transformers should periodically consolidate recent context into persistent state-space model blocks via Hebbian learning, then clear the KV cache. Like biological sleep. It directly addresses the scaling problem with attention over long contexts for agent tasks. I don't know if this works in practice, but the biological analogy is the kind of lateral thinking that leads somewhere interesting.

Transformer co-author says it's time to move past transformers. One of the original "Attention Is All You Need" authors argued the field should explore successor architectures. The 111-comment r/singularity debate shows deep practitioner engagement. Nine years into the transformer era, and one of its creators is saying we've hit the ceiling.

VeriTrace: explicit mental model regulation prevents error propagation in research agents. A new framework argues deep research agents need explicitly regulated intermediate representations rather than relying on implicit LLM reasoning. Without regulation, mixed-quality information contaminates the agent's evolving understanding. I'm building exactly this kind of pipeline with MindPattern, so this one caught my eye. The structured evolution mechanism maps directly to production research workflows.

Infrastructure & Architecture

Alipay launches the world's first AI Wallet, already past 100M users. Two new agentic commerce services span authorization, commercial interaction, payment, and trust. AI agents can now autonomously search, select, and complete payments within conversations. 120M transactions in a single week. Agentic commerce isn't theoretical. It's processing real money at scale.

Norway's National Library is building a sovereign LLM on 2PB of Huawei flash storage. Drawing from 20PB of digitized national content, Norway is building its own Norwegian-language model because no commercial provider was going to. The interesting detail: the biggest bottleneck isn't compute but data quality and cleaning at petabyte scale.

Tools & Developer Experience

RTK reduces Claude Code token costs 60-90% via a single PreToolUse hook. RTK (Rust Token Killer) at 54K stars intercepts Bash tool calls and compresses output before it hits the context window. Install with rtk init -g. A typical 30-minute session drops from ~150K tokens to ~45K. Run rtk gain to see exact savings. Works with Cursor, Windsurf, Copilot CLI, and Gemini CLI too.

Understand-Anything gained 5,604 stars in 24 hours, #1 on GitHub Trending. The TypeScript plugin transforms any codebase into an interactive knowledge graph using Tree-sitter for deterministic parsing plus LLM semantic analysis. v2.7.3 just shipped diff impact analysis and architectural layer visualization. Supports 16+ AI coding platforms. The "context engineering" tool category is exploding.

Claude Code v2.1.149: three updates worth knowing. The May 22 release shipped per-category /usage breakdowns (see token costs per skill, subagent, and MCP server), patched a git worktree sandbox bypass where the write allowlist covered the entire main repo instead of just the shared .git directory, and fixed a macOS bug where find could exhaust the system vnode table and crash the host machine. Update if you haven't.

Models

Gemini 3.5 Flash: #7 of 147 models, 278 tokens/sec, best multimodal score in class. Five days of independent benchmarks show Flash hitting 55.3 on the Intelligence Index, 83.6% on MCP Atlas agentic tool use (ahead of Opus 4.7 at 79.1%), and 84% on MMMU-Pro multimodal. The speed is real. Hallucination rate improved 31 points but still sits at 61%. Fast and good enough for many tasks. Not reliable enough for anything you can't verify.

Kimi K2.6 hits 80.2% on SWE-Bench Verified, within 0.6 points of Opus 4.6. MoonshotAI's 1T-parameter open-weight model sustained 4,000+ tool calls over a 13-hour session. Available via Ollama for local deployment. For anyone evaluating self-hosted alternatives for coding agents, K2.6 is the first open model within striking distance of frontier proprietary performance on real coding tasks.

NuExtract3: 4B vision-language model for structured extraction, Apache 2.0. NuMind released NuExtract3 based on Qwen3.5-4B, handling JSON template-driven extraction and image-to-Markdown conversion for documents, receipts, and forms. Available in GGUF and MLX for local deployment. If you're building RAG pipelines and need a self-hosted document processing step, worth evaluating.

Vibe Coding

Anthropic publishes the canonical context engineering guide. Three pillars: just-in-time loading (maintain lightweight references, load data at runtime), compaction (summarize history while preserving architectural decisions and open issues), and sub-agent isolation (spawn specialists with exactly the context they need). Anti-pattern: curating laundry lists of edge cases. Use diverse canonical examples instead. This guide saves you weeks of trial and error.

Linus Torvalds to be "more hardnosed" about AI pull requests. Torvalds announced escalation from earlier complaints about AI slop in kernel development. AI code quality in open source is now a governance issue at the highest level of the ecosystem. If you're submitting AI-assisted PRs to open-source projects, review them like your name is on every line. Because it is.

Hot Projects & OSS

open-design hits 52K stars: open-source Claude Design alternative with 132 skills and 150 design systems. nexu-io/open-design reached 40K stars in its first two weeks. Ships brand-grade design systems for Linear, Stripe, Vercel, Notion, and Apple. The key differentiator: it doesn't bundle its own model. It works with whatever AI CLI is already on your PATH across 16 platforms. Local-first. Free. If you're generating prototypes, check it out.

gstack crosses 102K stars: Garry Tan's Claude Code setup. Y Combinator CEO Garry Tan's personal configuration bundles 23 opinionated tools serving CEO, Designer, Eng Manager, Release Manager, Doc Engineer, and QA roles. Following the pattern set by Karpathy's skills repo at 156K stars. Curated agent skill sets from prominent builders have become their own distribution category.

SaaS Disruption

Lightfield signs 2,500 companies in 3 months at $300M valuation. Pivoted from Tomes (25M users), Lightfield builds on "complete customer memory" where you never manually enter data. Connect your inbox, get a populated pipeline in five minutes. 100+ YC startups signed. $81M raised. $36/user/month. A one-hour migration agent moves data from HubSpot. The vast majority of current YC startups use neither Salesforce nor HubSpot. Lightfield is eating that cohort.

Stripe Sessions: embedded finance creates 11% lower churn, 49% faster revenue growth. 288 product launches including digital asset accounts for stablecoin fintech. The data: platforms with embedded financial products see 11% lower annual churn and 49% faster revenue growth than software-only peers. 87% of SaaS platforms surveyed say AI is more opportunity than threat. Stripe is positioning embedded payments as the moat that AI-native competitors can't replicate.

Policy & Governance

ECB summons banks over Mythos cybersecurity risks. The European Central Bank is convening lenders to address vulnerabilities that Claude Mythos has exposed across banking IT. Patches can now be reverse-engineered in 30 minutes instead of weeks. European banks feel particularly exposed because Anthropic has restricted Mythos access to mostly US organizations, creating a transatlantic information asymmetry.

Anthropic closing $30B+ round at $900B valuation. Bloomberg reports the round would surpass OpenAI's $852B March valuation for the first time. Sequoia, Dragoneer, Altimeter, Greenoaks, Founders Fund, and General Catalyst are participating. Largest private AI funding round in history.

OpenAI, NVIDIA, ElevenLabs adopt Google SynthID watermarking. Every image generated through ChatGPT since May 19 carries SynthID signals that survive screenshots and compression. Verification rolling out in Chrome and Search. This is the first time competing AI companies have aligned on a single content provenance standard. I'm genuinely surprised it happened this fast.

Skills of the Day

  1. Install RTK to cut Claude Code token costs 60-90%. Run rtk init -g to add a PreToolUse hook that compresses tool output before it hits your context window. Run rtk gain after a session to see exact savings. Works across Claude Code, Cursor, and Gemini CLI.

  2. Use cross-encoder reranking in your RAG pipeline. Broad hybrid retrieval (dense + BM25) to fetch top 50, then cross-encoder rerank to top 5. This two-stage approach fixes the 73% of RAG failures that happen at retrieval, not generation.

  3. Write CLAUDE.md entries only after repeated failures. Human-curated context files outperform LLM-generated ones by 4-6 points. If you haven't observed the failure three times, the rule probably isn't needed. Delete speculative guidance ruthlessly.

  4. Split structured output into two LLM calls. First call: free-form analysis with no format constraints. Second call: constrained decoding to JSON. Avoids quality degradation when reasoning and format compliance compete in a single call. Cuts structured output costs 40-60%.

  5. Use .claudeignore aggressively. Excluding node_modules, build artifacts, lock files, and generated code achieves 80%+ context reduction. Context window performance degrades at 1M tokens regardless of the model's stated limit.

  6. Audit your CI for pull_request_target misconfigurations. The TanStack attack exploited this to cross the fork-to-base trust boundary and publish packages with valid SLSA provenance. If your Actions workflows use pull_request_target, verify they don't expose secrets to fork PRs.

  7. Tier your models in multi-agent systems. Frontier model (Opus, GPT-5.5) for the orchestrator only. Cheaper models (Haiku, Flash) for workers. This achieves 40-60% cost reduction over uniform model deployment without sacrificing orchestration quality.

  8. Check /usage per-category in Claude Code v2.1.149. The new breakdown shows token costs per skill, subagent, and MCP server. Find your most expensive component and optimize or replace it before June 15 metered billing kicks in.

  9. Fuse three retrieval signals for agent memory. Semantic similarity alone isn't enough. Combine it with BM25 keyword matching and entity matching for +29.6 points on temporal reasoning over single-signal approaches. Mem0's 2026 report has the implementation pattern.

  10. Spec agent behavior in Claude before coding it. SaaStr's Qbee was spec'd as a Claude conversation first, then translated to Replit code. If you can't explain the agent's job in natural language and iterate on edge cases in a chat, you'll build the wrong thing faster.

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