Cloudflare has launched its Registrar API in beta, allowing domains to be searched, checked, and registered programmatically. The API is designed for AI agents, editors, deployment pipelines, and other automated workflows, closing the gap between developing an idea and securing its domain. Its three-step process uses cached search results, authoritative availability checks, and streamlined registration with account defaults.
## Built for Agents and Automation
- Supports domain registration directly from code editors, terminals, backend services, and deployment pipelines.
- Enables agents to:
- Generate domain name ideas
- Search candidate names
- Confirm availability and pricing
- Register a selected domain after user approval
- Registration responses can complete immediately or provide a status endpoint for polling.
- The API is available through Cloudflare’s existing API and MCP integration, so tools such as Cursor, Claude Code, and other MCP-compatible environments require no separate custom integration.
## Search, Check, and Register
- **Search:** Finds candidate domains, optionally across extensions, and returns registrability, tier, currency, registration cost, and renewal cost.
- **Check:** Queries the registry directly for current availability and pricing.
- **Register:** Completes the purchase using a minimal request containing the domain name.
Cloudflare recommends checking availability immediately before registration because search results are cached and popular names can become unavailable within seconds.
## Registration Defaults and Protection
- The only required registration field is the domain name.
- The account’s default registrant contact and payment method are used automatically when configured.
- Contact details can instead be supplied in the request.
- WHOIS privacy is enabled by default at no additional cost.
- Registrations include details such as active status, expiration date, auto-renewal, privacy, and domain lock state.
- Premium domains may be supported, but require explicit acknowledgement of their fees.
## Initial Availability
- The beta initially supports a curated selection of popular top-level domains.
- Cloudflare directs users to the Registrar API documentation for the current supported TLD list.
The API makes domain acquisition a natural part of automated development workflows: search a name, verify its live price and availability, obtain approval, and register it in seconds without leaving the development environment.
Image content moderation has evolved from simple rule-based filtering into an AI-powered decision system capable of handling visual context, text, and policy complexity. At large platforms, the challenge is not only accuracy but also latency, cost, scalability, and adaptability to changing policies. LY Corporation addresses these demands through optimized traditional ML models, a hybrid ML–multimodal LLM pipeline, and modular decision-making that combines OCR, visual analysis, and contextual reasoning.
## The Evolution of Content Moderation
- Early systems relied on keyword matching, rule-based filters, and predefined patterns.
- Machine learning enabled broader pattern recognition and detection of modified or less explicit violations.
- Modern systems combine:
- Deep learning for text and image classification
- Multimodal models for joint image–text understanding
- LLMs for context-sensitive judgments
- Separate prediction and policy layers for operational flexibility
- Despite these advances, image moderation remains difficult because images lack explicit structure and their meaning often depends on context.
## Why Image Moderation Is Difficult
- **Visual complexity:** Backgrounds, objects, people, colors, and composition interact in ways that simple object detection cannot fully interpret.
- **Context dependency:** Symbols, gestures, and imagery may have different meanings across cultures; embedded text can also determine whether an image is harmful.
- **Evasion and variation:** Memes, composites, partially obscured images, and AI-generated edits continually challenge existing detectors.
- **Scale requirements:** Platforms may receive millions or tens of millions of images daily, requiring high accuracy alongside low latency, reliability, and cost efficiency.
## LY Corporation’s Moderation API
- LY Corporation operates a monitoring platform designed to process large-scale traffic and enforce diverse content policies.
- Its image moderation API detects:
- Adult content
- Violent or graphic scenes
- Offensive or disturbing imagery
- Identity documents containing personal information
- Social media screenshots and other policy-sensitive images
- The system is designed to apply service-specific policies consistently while maintaining high throughput.
## Improving Accuracy, Speed, and Cost
### Traditional ML Model Optimization
- A PyTorch-based image classification model was selected with latency, cost, and throughput in mind.
- The model was converted to ONNX and optimized with FP16 precision.
- ONNX Runtime improved execution efficiency, while FP16 reduced memory usage and inference time.
- These changes increased throughput by up to **4.3 times**.
### Hybrid ML and Multimodal LLM Architecture
- The traditional classifier acts as a fast first-stage filter.
- Clear cases are resolved immediately by the image model.
- Ambiguous cases are sent to a multimodal LLM for deeper analysis.
- More than 90% of production data could be classified by the traditional model alone.
- Since multimodal LLM throughput was over 100 times lower than that of the traditional model, routing every image to the LLM would have significantly increased GPU usage and cost.
- The hybrid approach preserves high-quality reasoning where necessary while avoiding unnecessary LLM calls.
### vLLM-Based LLM Optimization
The team optimized multimodal LLM serving with vLLM, using characteristics such as repeated prompts, predictable token lengths, and prefill-heavy workloads.
- **`enable_prefix_caching`:** Reuses KV-cache blocks for repeated system prompts and templates, reducing prefill computation.
- **`max_model_len`:** Limits the maximum input-plus-output length to avoid excessive KV-cache allocation.
- **`max_num_seqs`:** Controls concurrent requests, balancing throughput against per-request latency and resource contention.
- **`max_num_batched_tokens`:** Sets the token budget per scheduling step; larger values can improve throughput for prefill-heavy workloads.
- Regularly updating vLLM is recommended because new releases add improvements such as asynchronous scheduling, CUDA graph support, and broader quantization options.
## Moving Beyond Single-Model Policy Prediction
- Earlier end-to-end vision models directly predicted final policy categories from images.
- This worked for visually obvious violations, such as detecting smoking, but struggled with complex behaviors such as tobacco sales.
- Sales-related judgments may require combining:
- Product presence
- Prices
- Sales language
- Contact information
- Encouragement to purchase
- Directly learning every combination of national regulations, service policies, and exceptions created overly complex output classes.
- It also made the model harder to extend and maintain, while limiting the use of text embedded in images.
## Hybrid Decision-Making with OCR and Multimodal Reasoning
- The redesigned system separates visual and textual information rather than forcing one model to learn every policy combination.
- OCR extracts text from images when relevant.
- Extracted text helps identify policy-violating behavior or intent.
- Visual signals and textual evidence are then combined with a multimodal LLM.
- This allows the system to reason about context and intent beyond simple object detection, while making policy logic more modular and adaptable.
The practical recommendation is to avoid routing all traffic through expensive general-purpose models. Use fast specialized models for clear cases, reserve multimodal LLMs for ambiguity, optimize serving according to workload characteristics, and separate content understanding from policy decisions so the system can evolve as requirements change.
Bits AI uses large language models to generate first drafts of incident postmortems from Datadog Incident Management metadata and Slack discussions. The goal is to reduce documentation effort without replacing the human-led reflection that makes postmortems valuable. Datadog found that production-quality LLM features require extensive prompt experimentation, safeguards against hallucinations, careful model selection, and strong privacy and feedback mechanisms.
## Why LLMs Are Difficult to Use for Postmortems
- Postmortems must remain factual, consistent, and useful, unlike more creative LLM applications.
- LLMs can ignore instructions, produce inconsistent formatting, repeat information, or hallucinate plausible but false details.
- More than 100 hours were spent refining section structures and instructions so they worked across varied incidents.
- Building these systems requires a combination of software engineering, product management, data science, and technical writing.
## Balancing Structured and Unstructured Incident Data
- Structured Datadog data includes incident metadata, alerts, graphs, and manually entered fields such as customer impact.
- Slack discussions provide current, detailed, and unstructured context from the incident response process.
- Manually maintained fields are often more reliable but can become outdated, while LLMs can continually reassess live discussions.
- Combining both sources helped the model understand the full incident and prioritize information appropriately.
## Cost, Speed, and Quality Trade-offs
- Datadog compared models including GPT-3.5 and GPT-4.
- Results varied by as much as 50 times in cost and speed.
- GPT-4 generally produced more accurate output but was substantially slower and more expensive.
- Model choice therefore depended on the required balance between quality, latency, and operating cost.
## Preserving Human Control and Privacy
- AI-generated drafts were intended to support authors, not become final documents automatically.
- Keeping engineers involved preserves the discovery and learning process that postmortems are meant to encourage.
- Because incidents may contain sensitive information or secrets, Datadog scrubbed data before sending it to an LLM.
- The system was designed to ensure authors remained responsible for reviewing and improving the draft.
## Experimentation and Reliability Safeguards
- A custom API extracted and structured incident data from Datadog, Slack, and other sources.
- The framework enabled rapid testing of different datasets, models, input configurations, and output token limits.
- Datadog refined prompts and instructions to guide the model toward useful recommendations while retaining author agency.
- Lowering model temperature and tuning other parameters reduced irrelevant or fabricated content.
- A related feature generated concise incident summaries for responders joining Slack channels, providing a faster testing environment for LLM-based incident analysis.
## Evaluating Generated Drafts
- Datadog used both qualitative and quantitative evaluation methods.
- Authors of earlier postmortems compared their human-written documents with AI-generated drafts for the same incidents.
- Evaluation included criteria such as factual accuracy and conciseness, with the provided article ending before the full evaluation approach was described.
- Continuous feedback was used to refine prompts, models, and the overall drafting workflow.
Overall, reliable AI-assisted postmortems require more than selecting a capable model. The strongest approach combines carefully prepared incident data, privacy controls, model and prompt experimentation, systematic evaluation, and mandatory human review.
