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LLMs
71 posts about LLMs. Every summary links to the original.
Cloudflare AI Search: give your agents a search engine for your data
Cloudflare introduced developer experience improvements and a preview pricing model for Cloudflare AI Search. Previously, users had to manually connect separate Cloudflare primitives like Workers AI, AI Gateway, Vectorize, R2, and Browser Run to build a search solution. The platform now automates crawling, ingestion, embedding, and retrieval across structured and unstructured data sources. Developers can index sites without sitemaps using link discovery, expose public /search and /mcp endpoints across namespaces, bind search instances directly to Workers, or secure endpoints with Cloudflare Access. Cloudflare uses this system for developer documentation, its corporate blog, and the Cloudflare Dev Stack MCP server to supply coding agents with cited documentation.
Nelson DuarteThe next generation of MCP
Model Context Protocol previously required stateful connections between clients and remote servers, creating operational complexity around sticky sessions, message replay, and open streams. The newly released MCP 2026-07-28 specification transitions MCP to a fully stateless protocol alongside updated SDKs across TypeScript, Python, Go, and C#. The protocol eliminates mandatory handshakes and session identifiers from core request paths while enabling servers to run on request-scoped infrastructure like Cloudflare Workers. Interactive elicitation is restructured through Multi Round-Trip Requests, which allow servers to return input requirements without sustaining persistent connections. Standardized headers for method names and improved OAuth authorization mechanisms further enable standard web infrastructure to inspect and manage MCP traffic efficiently.
Matt CareyFrom ranking to recommended: get your site ready to thrive in the age of AI agents
Discoverability is shifting from traditional search engine rankings to recommendations generated by AI agents acting on behalf of users. To address this shift, Cloudflare integrated Agent Readiness diagnostics and an Answer Engine Optimization tool into its dashboard. The Diagnostics feature evaluates hostnames against machine-readable standards across tiers ranging from robots.txt and Markdown rendering to OAuth discovery and agent payment protocols. Simultaneously, the Answer Engine Optimization tool measures how frequently leading assistants like Claude and GPT cite, mention, and prominently position brands across industry categories. By evaluating prompt responses using Cloudflare AI Gateway and Workers AI alongside access logs, site owners can systematically track agent traffic, benchmark against competitors, and remediate crawl blockers.
Matthew ConroyBuilding an open Agentic Internet: readable, discoverable, callable, and payable
The modern web produces billions of bot requests that repeatedly fetch unchanged pages, highlighting an architecture fundamentally misaligned with automated visitors acting on behalf of paying users. Cloudflare outlines an open Agentic Internet structured around four foundational capabilities: readability, discoverability, callability, and payability. Identity and access rely on open specifications such as Web Bot Auth and Private Access Control Tokens, while Markdown for Agents and the Kitesurf browser reduce token overhead and context window bloat. For execution and monetization, WebMCP exposes structured tools directly through the browser, and protocols like x402 paired with Monetization Gateway enable per-fetch transactions. These open standards allow domain owners to define tool contracts, verify bot identities, and monetize agent traffic without relying on legacy advertising models.
Jack GalileeGithub ·
Turn one giant AI-generated pull request to a reviewable stack
Coding agents often consolidate full-stack features into monolithic pull requests spanning thousands of lines, resulting in delayed reviews and lower feedback quality. To resolve this bottleneck, engineers can decompose comprehensive features into ordered, single-concern layers using GitHub stacked pull requests and the gh-stack CLI extension. Distinct specialized agents handle individual branches—ranging from foundational data access modules up to client interfaces—while CI checks continuously evaluate each layer against the stack base. When reviewers request upstream modifications, running gh stack sync executes a cascading rebase across all dependent branches, preserving commit signatures and keeping the entire pull request chain mergeable.
Julia MuiruriGrab ·
How AI is transforming analytics at Grab
Grab is restructuring its analytics operations using a five-level AI autonomy ladder, transitioning analysts from manual artifact creation to problem framing and decision governance. The architecture leverages domain-specific systems like Spartan to process natural language queries through certified metric indexes and Scarlet to triage and repair failing data pipelines. To prevent agent hallucinations, ContextIQ manages context lifecycles by automatically updating metric definitions, SQL references, and golden-dataset test cases when instrumentation changes or failures occur. Furthermore, data teams use an internal portal called BriX to configure custom analytics surfaces and automated root-cause analysis commentaries using reusable Model Context Protocol connections. Autonomy scales mechanical query and validation tasks while keeping human oversight focused on canonical metric definitions and strategic sign-offs.
Maanas PrabhakarNetflix ·
GenRec: Towards LLM-Native Recommendation at Netflix
Netflix developed GenRec, an LLM-backed recommendation ranker built by adapting an internal foundation model for large-scale personalization. Traditional recommendation stacks rely on thousands of hand-crafted features and specialized architectures that are costly to maintain across diverse content types. To replace manual feature pipelines, GenRec verbalizes user histories, metadata, and contexts into natural-language prompts and trains with multi-objective losses, including catalog-aware ranking and reward-weighted alignment. At inference time, the model executes in prefill-only mode on vLLM without decoding text. In large-scale online A/B testing against a mature production ranker, GenRec achieved statistically significant improvements in short-term and long-term metrics while using fewer labeled examples.
Netflix Technology BlogHow Salesforce Built an Agentic Engineering Enablement Strategy for Thousands of Software Engineers
Salesforce addressed the challenge of scaling agentic engineering across thousands of software developers by focusing on organizational learning rather than tooling. While individual engineers initially developed disparate, uncoordinated workflows, the Technology, People, Innovation, and Learning (TPIL) team unified these approaches into the Proficiency Level (PL) Framework. The framework maps developer growth across four mindset-driven stages: AI-Assisted, AI-Validating, AI-Orchestrating, and AI-Native. To facilitate progress across these stages, TPIL introduced global AI camps, weekly working sessions, and manager coaching guides. Rather than tracking course completions or telemetry scores, Salesforce measured transformation through behavioral indicators, such as shifts toward validating agent output, designing multi-agent workflows, and asking higher-level questions regarding human judgment.
Scott NybergGrab ·
Crowdsourced taxonomy verification: A feedback-driven framework for refining knowledge graph relationships via online search interactions
Maintaining accurate Knowledge Graphs in dynamic domains like e-commerce and food delivery is challenging because automated language models frequently hallucinate relationships while manual curation cannot scale. To validate structural taxonomy links continuously, a closed-loop verification framework operationalizes search interfaces by injecting unverified candidate edges as hypotheses into live user traffic. The system uses an exploration-exploitation strategy to place candidate relationships in lower-risk interface slots, tracking contextually anchored micro-interactions such as clicks, dwell times, and purchases. An offline verification engine aggregates these weighted interactions into normalized confidence scores, automatically promoting verified links to permanent graph edges and pruning refuted relationships.
Junpeng NiuAirbnb ·
Eval-driven development: Lessons from evaluating GenAI at scale
Airbnb outlines its framework for eval-driven development (EDD), treating GenAI evaluation as a continuous engineering discipline rather than an afterthought. The strategy combines programmatic checks, calibrated LLM-as-a-judge evaluators, and human review to detect subtle quality regressions. By inspecting intermediate agentic execution traces and real-world failure modes, teams ensure generative models remain reliable and aligned with product requirements.
Rohit GirmeBuilding Reliable Production AI with Durable Workflows
AI prototypes rely on simple prompt-response interactions, but production AI systems handling thousands of records must manage distributed execution issues like worker crashes, rate limits, and transient failures. Rather than treating long-running operations as single large jobs or rebuilding state from logs, systems require durable workflows that preserve execution history across failures. In Agentforce Grid, execution is organized into a hierarchy where column runs divide into recoverable batches and rows composed of discrete activities. Each batch checkpoints after every successful row, retrying up to ten times with exponential backoff and resuming strictly at incomplete work to prevent duplicated model calls. Exposing progress across these hierarchical boundaries ensures that both users and engineers can distinguish completed, failing, and retryable units.
Scott NybergGrab ·
Agent platform (Part 1): How we help Grab build and run AI agents at scale
Grab scaled its internal AI tooling by transforming the infrastructure behind a single technical support bot into a standardized agent framework named LLM-Kit. The original Go-based bot revealed critical bottlenecks, including the lack of automated evaluation, painful provider migrations, fragmented observability, and excessive production scaffolding overhead. To address these frictions, engineers developed a FastAPI template pre-wired with LangGraph ReAct agents, remote Model Context Protocol servers, and built-in ROUGE, BLEU, and LLM-as-judge evaluation suites. The framework centralizes model access through an OpenAI-compatible GrabGPT Gateway and unifies observability across services, outbound calls, and agent steps using OpenTelemetry. This extracted scaffolding reduced day-one production setup time from two weeks to roughly an hour while supporting hundreds of production agents.
Kendrick TanHow AI Rebuilt Salesforce’s Decades-Old Localization Pipeline
Salesforce needed to deliver major releases across 34 languages as product localization volume surged by more than 35% under fixed budgets and unchanged delivery windows. To address these scaling constraints, the engineering team replaced their legacy translation workflows with an LLM-powered orchestration pipeline integrating prompt engineering, context engineering, and multi-stage validation. The architecture transforms existing style guides, glossaries, and terminology databases into structured context, routing interface strings through specialized prompt stages—up to 85 stages for some languages—to enforce product context, grammar, and branding rules. Combined with an automated validation loop spanning AI editing, AI validation, and human review, this system reduced localization costs by 50 to 90% while accelerating turnaround times across enterprise releases.
Scott NybergDropbox ·
How our universal content processing platform Riviera evolved for AI and beyond
Dropbox developed Riviera, a centralized content processing platform that evolved from an internal file preview generator into a shared infrastructure supporting over 300 file formats. By decomposing complex conversion tasks into modular, reusable transformations, the system now powers search indexing, media transcoding, and AI context extraction across Dropbox products.
Ilya Yakovlev,Andrew Cheung,Binoy DashNetflix ·
In-House LLM Serving at Netflix
Netflix established an in-house serving platform to run large language model inference directly inside existing production environments alongside traditional machine learning models. Built upon NVIDIA Triton Inference Server and vLLM, the unified architecture handles member-scale routing, candidate generation, feature fetching, and model execution over gRPC and OpenAI-compatible HTTP endpoints. The team shifted their primary engine from TensorRT-LLM to vLLM to support custom architectures, simpler debugging, and non-trivial constraint logic. Addressing production obstacles required patching Triton's frontend for guided decoding, pinning dependent library versions, rewriting logits processors in C++ for vLLM V1, and handling state machine resets during engine preemptions. The resulting platform unifies deployment flows while preserving operational stability across evolving model schemas.
Netflix Technology BlogGithub ·
The cost of saying yes has changed
Generating initial code patches has become significantly cheaper with AI agents, shifting the primary expense of small feature requests from writing code to debating scope in meetings. Teams can use agent-generated patches as diagnostic probes rather than final deliverables, turning abstract scope debates into concrete artifacts that reveal true system touchpoints and risks. However, low generation costs do not translate to low ownership costs, as changes touching areas like authorization, compliance, or public contracts still demand significant human review. Constrained attempts allow engineers to price uncertainty quickly and shift scope discipline from pre-implementation speculation to evidence-based code review.
Dalia AbuadasMeta ·
Exploring Hierarchical Interest Representation For Meta Ads Deep Funnel Optimization
Meta developed Hierarchical Interest Representation as an upstream representation layer to optimize deep funnel ad ranking across its platforms. The architecture organizes interactions among users, advertisers, and products into a heterogeneous, time-decayed graph and projects sparse raw edges into multi-level super-graphs of latent interest primitives. To encode these structures, a transformer-based hierarchical encoder incorporates multimodal product semantics processed via language models alongside topology-aware attention biases. Computation scales across billions of entities by pairing deep hash ID embeddings with FlexAttention to evaluate graph biases on the fly without materializing full matrices. Self-supervised cross-view distillation trains student representations against broader teacher graph views to yield universal embeddings and interest tokens.
By Yuhui Ouyang, Di Wang, Sreedal Menon, Jie TianAirbnb ·
From weeks to a day: how we made LLM evaluation fast enough to iterate on
Airbnb built a four-layer LLM infrastructure framework to reduce iteration and evaluation turnaround from weeks to a single day. By using per-sample caching for generated references and judge scores, they established a deterministic evaluation foundation that separates genuine model drift from measurement noise. This setup enables rapid, bounded model hotfixes using micro LoRA adapters and comprehensive end-to-end validation across component boundaries.
Baharak SaberidokhtGithub ·
Better tools made Copilot code review worse. Here’s how we actually improved it.
When migrating GitHub Copilot code review from custom navigation tools to shared Unix-style CLI utilities—grep, glob, and view—benchmarks revealed higher review costs and fewer caught issues. Rather than an issue with the underlying tools, trace analysis showed the agent used general-purpose coding assistant instructions that triggered expansive repository browsing loops. In response, the team rewrote the tool guidance to enforce a review-specific workflow anchored to pull request diffs. The updated instructions direct the agent to narrow candidate call sites and files using batched discovery before reading minimal line ranges with view. This workflow tuning reduced average review costs by approximately 20% in production while maintaining review quality.
Napalys KliciusGithub ·
Automating cross-repo documentation with GitHub Agentic Workflows
Maintaining documentation across separate repositories often leads to severe lag because technical writers must reverse-engineer shipped features weeks after release. To address this in the Aspire project, the team implemented an automated pipeline using GitHub Agentic Workflows to bridge the product and documentation repositories. When product pull requests merge, a bash step maps milestones to docs release branches before an LLM agent evaluates the diff, drafts documentation updates, and emits structured pull request intents. A dedicated safe-outputs handler materializes these drafts via a scoped GitHub App and assigns the original code reviewers to verify accuracy. Across 396 product pull requests, the system generated 82 documentation pull requests that all merged with a median turnaround time of 44.8 hours.
David Pine