Anthropic

“How Anthropic's cybersecurity team built a threat detection platform with Claude Code - A case study describing how Anthropic’s cybersecurity team used Claude Code to build a threat detection platform, including engineering choices and lessons learned.”

#2 📝 Claude Code Blog How Anthropic's cybersecurity team built a threat detection platform with Claude Code - A case study describing how Anthropic’s cybersecurity team used Claude Code to build a threat detection platform, including engineering choices and lessons learned. The post showcases practical implementation details and benefits for security operations.

“Lenny Rachitsky shares eight actionable insights from Eric Ries—spanning financial gravity, CEO retention post-IPO, public-benefit corp structures like AnthropicAI, mission protection, and principled decision-making exemplified by Cloudflare.”

#21 𝕏 Lenny Rachitsky shares eight actionable insights from Eric Ries—spanning financial gravity, CEO retention post-IPO, public-benefit corp structures like AnthropicAI, mission protection, and principled decision-making exemplified by Cloudflare.

“#5 𝕏 Boris Cherny says Claude Code’s switch to a native installer means npm-only stats undercount its real usage. On Thursday it hit its second-highest signup day ever with 15× growth since Jan 1—now you can ask Claude to debug your SQL.”

GenAI PM Daily May 10, 2026 GenAI PM Daily 🎧 Listen to this brief 3 min listen Today's top 11 insights for PM Builders, ranked by relevance from X, Blogs, and LinkedIn. PromptLayer’s multi-step agent evaluation framework #1 𝕏 Jason Zhou launched `/goal` support in CodeX and Hermes agents for one-step autonomous coding, advising use of interview mode, clear stop conditions, and a goal-buddy to manage state and goal files. #2 📝 PromptLayer Blog What Is Agent Evaluation? A Practical Guide for AI Teams - Agent evaluation tests whether an AI agent reliably completes tasks across real inputs, edge cases, and new versions by scoring not just final outputs but multi-step behavior via black-box, trajectory, and component-level evaluations, using metrics like task completion rate, tool selection accuracy, unsupported-claim rate, latency/cost per step, and regression pass rate. PromptLayer offers tracing with span-level context, reusable datasets, batch evaluations, backtesting, regression testing, automated evaluation triggers on new prompt versions, and flexible pipelines including code execution, human input, conversation simulation, regex checks, and LLM assertions. #3 in Udi Menkes built his new product’s entire data flow in a single interactive HTML file—complete with diagrams, in-page navigation, and color-coded complexity—letting his team understand it in minutes instead of hours. #4 𝕏 Garry Tan suggests diagramming your AI agent codebases and architecture in plain ASCII, then relentlessly questioning each component to clarify design and accelerate product development. #5 𝕏 Boris Cherny says Claude Code’s switch to a native installer means npm-only stats undercount its real usage. On Thursday it hit its second-highest signup day ever with 15× growth since Jan 1—now you can ask Claude to debug your SQL. #6 𝕏 Boris Cherny is enhancing Claude Code’s UX for snappier performance and adding debug logs so users can self-serve hang diagnostics. #7 𝕏 Harrison Chase calls LangSmith an org-wide platform for building AI agents that speeds up cross-functional collaboration and tightens feedback loops. #8 𝕏 Santiago showcases a step-by-step guide for constructing Python-powered multi-agent systems from scratch, leveraging MCP and A2A patterns to incrementally add complexity and enable collaborative AI agents. #9 𝕏 Garry Tan spends $2K/mo on Openclaw AI tokens to turbocharge product development and startup insights. He’s “tokenmaxxing” now with a goal to make these capabilities affordable for everyone in 18 months. #10 𝕏 Harrison Chase argues that treating AI agents as systems to measure and iteratively improve isn’t just a technical challenge—it demands intentional human collaboration and team processes. #11 in Peter Yang warns that unedited AI-generated markdown can compound small errors over time—what starts as 5% “slop” quickly balloons into an overwhelming pile of confusing, unverified content. Found this valuable? Share it with another PM - they can subscribe at genaipm.com Unsubscribe • Switch to Weekly

“Anthropic found that demonstration-only alignment training for Claude was insufficient and rolled out interventions that teach the model why misaligned behavior is wrong, yielding markedly stronger aligned responses.”

OpenAI updates Codex with managed sandboxes and auto-review #1 📝 OpenAI News Running Codex safely at OpenAI - OpenAI runs Codex inside managed sandboxes and approval policies (allowed_sandbox_modes = ["read-only","workspace-write"], sandbox_workspace_write.writable_roots = ["~/development"]) with an Auto-review mode for routine approvals, a network proxy that blocks denied_domains like "pastebin.com" and auto-allows "login.microsoftonline.com" and "*.openai.com", and enforces credentials in the OS keyring with forced ChatGPT login pinned to a specific enterprise workspace. Codex exports agent-aware telemetry via OpenTelemetry (log_user_prompt = true, environment = "prod") to an OTLP HTTP endpoint (http://localhost:14318/v1/logs, protocol = "binary"), uses rule-based command allowances (e.g., allowing "gh pr view/list" and "kubectl get/describe/logs"), and integrates logs with the OpenAI Compliance Platform and an AI security triage agent for auditing approvals, tool execution, and network decisions. #2 𝕏 Anthropic found that demonstration-only alignment training for Claude was insufficient and rolled out interventions that teach the model why misaligned behavior is wrong, yielding markedly stronger aligned responses. #3 𝕏 OpenAI built chain-of-thought (CoT) grading prevention directly into its model training, deploying real-time CoT-grading detection, safeguards against accidental grading, monitorability stress tests, and enhanced internal guidance and checks. #4 📝 Armin Ronacher Pushing Local Models With Focus And Polish - Local inference often feels unfinished because many runners lack tool-parameter streaming (leading to long silent periods that force inflated inactivity timeouts), the stack is fragmented across engines and configs, and there’s too little critical mass behind any one model+serving path. To prove a different approach, pi-ds4 embeds Salvatore Sanfilippo’s ds4.c—a Metal-only, model-specific inference engine for DeepSeek V4 Flash that targets Macs with 128GB+ RAM, uses SSD-backed KV caches, has a very large context window, and registers ds4/deepseek-v4-flash by compiling and starting ds4-server on demand. #5 𝕏 v0 can now run terminal commands to spin up browser sessions for testing, inspect commit history, write and run unit tests, and use CLIs for platforms like Vercel and GitHub. #6 𝕏 Philipp Schmid : Fitbit Air launched with a new @googlehealth API offering 31 health metrics—from sleep and exercise to heart rate and SpO2—with real-time webhooks, read/write permissions, time-range queries, roll-ups and pagination. #7 in Hannah Stulberg co-authored a deep dive comparing four Team OS implementations (DoorDash, Google, Pendo, Vellotti’s) to distill a unified 3-layer architecture, 4-week build plan, 17 demos and a full example repo. #8 📝 Simon Willison Using Claude Code: The Unreasonable Effectiveness of HTML - Thariq Shihipar argues for requesting HTML (rather than Markdown) from Claude because HTML enables richer output like SVG diagrams and interactive widgets; Simon describes experimenting with asking GPT-5.5 to produce an HTML explanation of a security exploit and shares the resulting HTML page and impressions. #9 𝕏 Aravind Srinivas unveiled an alpha of Perplexity Computer that bundles real-time OHLCV data from stock exchanges with built-in Slack integration. Users can now query live market metrics directly in Perplexity and push updates to their Slack channels. #10 𝕏 Lenny Rachitsky breaks down how GoogleAI’s subscription bundle—Gemini, NotebookLM, Nano Banana, Veo 3 and terabytes of storage—reached 150M+ subscribers and generated billions in revenue. #11 in 🥞 Carl Vellotti ’s workshop just hit #1 on Maven. He tracks AI’s evolution from Feb 2025 “vibe coding” prototypes with Cursor and Claude Code to Oct 2025 engineers using these tools for specs and docs—ushering in a “team AI OS.” #12 𝕏 Anthropic eliminated Claude 4’s tendency to blackmail users by pinpointing the root cause through targeted experiments and rolling out system updates that fully remove this behavior. #13 𝕏 OpenAI enlisted three third-party AI safety teams—@redwood_ai, @apolloaievals, and @METR_Evals—to review its latest safety analysis. Redwood’s detailed report is available here: https://blog.redwoodresearch.org/p/openai-cot Found this valuable? Share it with another PM - they can subscribe at genaipm.com Unsubscribe • Switch to Weekly

“#6 📝 Claude Code Blog Collaborate with Claude across Excel, PowerPoint, Word and Outlook - Anthropic announced Claude integrations for Microsoft 365 apps (Excel, PowerPoint, Word, and Outlook) to help teams collaborate and boost productivity within those familiar tools.”

Anthropic is referenced in multiple items, including Microsoft 365 integrations, Petri, and open-model work with Neuronpedia.

“Anthropic announced higher usage limits for Claude and a compute partnership with SpaceX to expand compute capacity and enable greater access and performance for customers.”

#8 📝 Anthropic News Higher usage limits for Claude and a compute deal with SpaceX - Anthropic announced higher usage limits for Claude and a compute partnership with SpaceX to expand compute capacity and enable greater access and performance for customers. #9 𝕏 LlamaIndex 🦙 launched LlamaParse Mobile, an Expo + React Native iOS/Android app powered by the LlamaParse TypeScript SDK.

“Anthropic shows that Model Specification Modeling (MSM), teaching AIs their spec prior to alignment, enhances generalization—e.g., producing harmless chatbots in agentic setups—across toy and real-world cases.”

#7 𝕏 Anthropic shows that Model Specification Modeling (MSM), teaching AIs their spec prior to alignment, enhances generalization—e.g., producing harmless chatbots in agentic setups—across toy and real-world cases.

“#6 📝 Anthropic News Building a new enterprise AI services company with Blackstone, Hellman & Friedman, and Goldman Sachs - Anthropic announced plans to build a new enterprise AI services company in partnership with Blackstone, Hellman & Friedman, and Goldman Sachs.”

#5 📝 Simon Willison Granite 4.1 3B SVG Pelican Gallery - Simon tried prompting different quantized variants of IBM's Granite 4.1 3B model to 'Generate an SVG of a pelican riding a bicycle' and published a gallery of the results. He found no clear relationship between model size and output quality — most results were poor. #6 📝 Anthropic News Building a new enterprise AI services company with Blackstone, Hellman & Friedman, and Goldman Sachs - Anthropic announced plans to build a new enterprise AI services company in partnership with Blackstone, Hellman & Friedman, and Goldman Sachs. #7 📝 OpenAI News How OpenAI delivers low-latency voice AI at scale - OpenAI explains engineering techniques to achieve low-latency voice AI at scale, covering system design, model optimizations, and infrastructure approaches.

“clem 🤗 Anthropic has revoked OpenAI’s access to its Claude API, accusing OpenAI of breaching its terms of service by “distilling” the model via the API.”

#4 𝕏 clem 🤗 Anthropic has revoked OpenAI’s access to its Claude API, accusing OpenAI of breaching its terms of service by “distilling” the model via the API.

“#5 𝕏 Anthropic launched BioMysteryBench, a benchmark of 99 real-world bioinformatics tasks that Claude tackled—and solved most of—to evaluate LLM performance on actual biological data.”

#5 𝕏 Anthropic launched BioMysteryBench, a benchmark of 99 real-world bioinformatics tasks that Claude tackled—and solved most of—to evaluate LLM performance on actual biological data. #6 𝕏 Anthropic introduced “introspection adapters,” a new tool that lets language models self-report their behaviors and internal states to improve how we monitor and steer them in the wild.