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Worker Backpressure (Part 1)
Canva’s queue workers were designed to consume messages greedily, but that behavior could amplify dependency failures, waste scarce capacity, fill dead-letter queues, and create on-call work. Worker Backpressure adds a local feedback loop to the shared queue library: each processed message reports success or failure, a pluggable controller maintains a 0.0-to-1.0 backoff factor against a configured failure-rate set point, and permits scale concurrency at each poll. The mechanism requires no external coordinator or network calls and adds two arithmetic operations to the runtime path, while trading some throughput for failure containment. In two production incidents, it reduced worker pressure during errors and recovered automatically; one sustained-overload incident recorded a 2.76% average failure rate, 22 DLQ messages over 32.5 hours, and no paging, while rollout to all queue workers remained the immediate goal.
How to Use AI Agents to Prepare 3D Scenes for Simulation
Preparing a Blender scene for robotics simulation requires more than exporting attractive assets: the world needs semantic labels, simulation-aware materials, sensors, collision geometry, physics properties, and validation against a target profile. The described workflow uses Codex or Claude as an orchestration agent, NemoClaw to coordinate specialized Hermes subagents, and NVIDIA Omniverse Libraries to inspect, modify, render, and validate the scene. Blender MCP supplies a structured inventory, while OpenUSD becomes the shared scene contract; ovphysx handles physics authoring and checks, and ovrtx produces visual preflight views. Safe mechanical repairs can be automated, but ambiguous choices such as semantic labels or physical behavior are escalated to a human, with follow-up checks run after approved changes. The intended handoff is a simulation-ready USD world for NVIDIA Isaac Sim or NVIDIA Isaac Lab.
TensorRT Edge-LLM Completes the MLPerf Edge Agentic Benchmark 6.4x Faster on Jetson AGX Thor
Edge agentic workloads require repeated tool-calling turns, growing conversation histories, and valid function calls on constrained devices rather than single-prompt responses. On one NVIDIA Jetson AGX Thor Developer Kit, TensorRT Edge-LLM ran Qwen3.6-27B in SingleStream mode and completed the benchmark’s 1,007-turn performance workload in 24 minutes and 36 seconds, versus 2 hours and 37 minutes for the llama.cpp reference run. It reported 52.33 tokens per second, 247.12 ms median time to first token, and 87.94% BFCL overall accuracy. The implementation combines NVFP4 weights and activations, FP8 KV cache, reusable prompt prefixes with recurrent-state restoration, and tree-based multi-token prediction; roughly 96% of prompt tokens used hot cache, while tree-based MTP could achieve about 40% more decoding performance than linear MTP with three draft steps.
When scanners miss the attack: how Cloudflare Client-Side Security protects storefronts
Modern storefronts can appear healthy while malicious JavaScript diverts affiliate credit, hijacks searches or clicks, tampers with analytics, or loads further code. Cloudflare describes four operations spanning eight payloads that Page Shield ML detected in live traffic; humans verified the findings afterward, while seven payloads were absent from VirusTotal and none received a malicious verdict from URLScan. The detection system represents JavaScript as a graph with a graph neural network (GNN), sends fewer than 0.3% of analyzed scripts to a lightweight LLM on Workers AI, and uses a teacher cohort of frontier models for difficult investigations. This combination handles minification, renaming, some obfuscation, and behavior gated by device, time, geography, referrer, browser state, or cooldowns. The post concludes that continuous browser visibility and runtime context are needed because static, one-time scans can miss selectively executed attacks.
ClickHouse is now available on the dbt platform
The ClickHouse adapter for dbt v2, dbt’s Rust rewrite, entered public beta on September 16, 2026, while ClickHouse became a natively supported data warehouse on the dbt platform in private beta. The v2 adapter ships inside the open-source dbt binary, connects through an ADBC driver using Apache Arrow, and supports both open-source ClickHouse and ClickHouse Cloud. Existing models, tests, and profiles carry over, although some features remain incomplete and dbt plans to support the v1 and v2 adapters in parallel. The platform integration provides ClickHouse connections for Studio, environments, scheduled jobs, and Catalog, while the planned path to general availability includes parity items such as ON CLUSTER DDL and distributed materializations, followed by Fusion capabilities.
AI Changed How Spotify Builds. What We Learned (and Fixed) About Quality at Higher Velocity
Spotify examines how AI-accelerated development and broader industry compute demand challenged quality across content processing, fleet updates, regional failover, and the mobile app. At its scale—777 million monthly active users, about 100 million concurrent clients, 11–12 million backend requests per second, and nearly 3,000 production services—small control gaps can become visible to listeners and creators. A June 24 processing incident combined competing batch work, higher per-episode compute, and a scheduler bug that cut throughput about 10%, leading to hour-long publication delays; Spotify responded with end-to-end monitoring, scheduler fixes, prioritization, and more capacity. Incident reviews found no material direct contribution from AI-authored code, but change volume outpaced some verification controls, prompting stronger review, testing, rollout, observability, rollback, quality signals, and failover practices. Spotify reports no corresponding rise in rework rate, while continuing to watch increasing PR size and complexity.
How Data 360 Builds Trusted Context: The Enduring Layer for Enterprise AI
Enterprise AI needs a way to assemble the smallest sufficient set of current, relevant, and authorized evidence for each agent turn instead of sending an entire data estate to a model. Salesforce presents Data 360 as a shared Trusted Context runtime spanning Salesforce, external platforms such as Databricks and Snowflake, operational systems, documents, knowledge bases, and live signals. Its progressive pipeline connects data through ingestion and Zero Copy, then harmonizes, unifies, models, retrieves, reconciles, governs, and compiles evidence into a token-fit Context Pack with citations and action contracts. The Agent Context Engine also returns typed interaction traces, promoting only approved facts into durable memory and validated outcomes into learnings. Q2 FY27 production usage is reported at 104.3 trillion connected records, including 81.5 trillion Zero Copy rows, while proposed benchmarking covers correctness, authorization, provenance, freshness, latency, cost, and memory continuity.
"Regex for Rows": Simplifying Pattern Detection in SQL with MATCH_RECOGNIZE
SQL's unordered-set model makes it difficult to detect ordered event sequences such as consecutive login failures followed by a success, especially when timing matters. MATCH_RECOGNIZE, now available in Databricks compute including Lakehouse Real-Time, expresses row sequences through regex-like states while supporting time anchoring and row-relative checks. The post shows its use for credential-stuffing anomalies, V-shaped stock reversals, checkout abandonment, and equipment-failure signals: FIRST can anchor a failure window, the end-of-partition marker $ can represent inactivity, and PREV/NEXT can compare sensor readings. By replacing chains of common table expressions, gaps-and-islands scaffolding, self-joins, and window-function logic, the clause is presented as easier to validate, maintain, and update for event-sequence analytics.
Translating CUDA Tile Operations from Python to Rust Using Agentic AI
cuTile Rust (cutile-rs) extends Rust’s ownership model to tile-based GPU kernels, and this post describes an agentic workflow for translating cuTile Python and Triton-TileIR implementations. Because all three front ends target CUDA Tile IR and share the tileiras compiler, the workflow compares reference and translated IR while making Python’s implicit specializations explicit through Rust const generics, fixed dtype symbols, and FFI checks. A bounded multi-agent pipeline covers analysis, device kernels, host and FFI code, correctness, and benchmarking, with machine-checkable verdicts controlling progress. It ported all 24 public TileGym operators, roughly 40 kernels, and reported 99.5% average cuTile Python performance across 347 paired configurations on NVIDIA DGX B200. The skill ships with TileGym and uses acceptance checks requiring correctness, backend execution, and a geomean speedup of at least 0.95.
Logpoints Walkthrough
Logpoints in IntelliJ IDEA provide a way to inspect a running Java process without suspending it, making them useful when println logging or regular breakpoints would be disruptive. The walkthrough uses a Dockerized gRPC client and server whose incorrect tenant discount is traced by attaching to the server and adding expressions at runtime, without rebuilding or redeploying. Progressive logging shows the tenant value as JetBrains while the expected value is jetbrains, and the correct-discount block is never entered; the supplied fix uses equalsIgnoreCase and returns 2,000 basis points. The article also demonstrates testing changes through logpoint side effects, extending gRPC deadlines selectively, logging in dependencies, and using the ij-debugger AI agent skill. IntelliJ IDEA 2026.2 adds faster placement and instrumentation that removes debugger-introduced overhead, although heavy expressions still execute in the VM.
Rider and ReSharper 2026.2.2 Are Out!
Version 2026.2.2 of ReSharper, Rider, and .NET tools is available through the products, Toolbox App, or website. Rider adds an AI Agent Setup widget to the bottom-right status bar, where users can review supported MCP tools, skills, and hooks, see which are enabled, and open the relevant settings. Its hooks now give agents narrower inspection results for newly added code, while the Reformat file hook reports IDE formatting changes as a diff; dotCover also adds TUnit coverage, requiring JetBrains.dotCover.Framework and Microsoft.Testing.Platform 2.3.0 or later. The release fixes a macOS backend-resume freeze, removes dependence on Unity editor executable names for attachment, and enables MCP file operations across attached solution folders; ReSharper also receives TUnit coverage support.
Why your Kafka topic ignores retention.ms (and how to fix it)
A Kafka topic configured with retention.ms=43200000 (12 hours) retained messages for four days because the active log segment never rolled. Kafka retains data by deleting whole closed segments, not individual records, and an active segment remains writable and cannot be deleted. Segment rolling occurs when segment.bytes or segment.ms is reached; in this case, traffic was too modest to fill the 1-GiB segment and only four days had passed, short of the seven-day default. Once a segment closes, retention.ms is evaluated against its newest record, followed by the retention check interval and file.delete.delay.ms before physical deletion. The proposed fix is to set segment.ms to a fraction of the retention window (one hour for the 12-hour example), while tuning segment.bytes as needed; a smaller rolling cadence makes effective retention more predictable.
Constraining AI agents with Red Hat AI: Containment, identity, and governance
AI agents running on laptops or clusters may inherit broad filesystem, network, and credential access, making faulty permissions or prompt injection potential security risks. The guide uses Red Hat OpenShift to build layered controls around an OpenClaw agent: namespace isolation, default-deny network policy, ResourceQuota, and optional Kata Containers contain workloads and limit escape or resource abuse. SPIFFE and SPIRE issue rotating SVIDs based on workload attributes such as namespace and ServiceAccount, allowing services to verify an agent without shared secrets. Governance adds OPA Gatekeeper admission policies, an MCP Gateway with risk-tiered tool authorization, NeMo Guardrails for prompt and completion filtering, and MLflow traces for auditability. The recommended rollout starts with namespace isolation and quotas, then adds sandboxing, identity, and the more involved governance controls.
Behind the Scenes: How the OpenTelemetry Plugin Maps Your Microservices in Real-Time
Outdated architecture diagrams and static code analysis can miss how services are connected at runtime, so the OpenTelemetry plugin builds a Service Map from live telemetry. With a lightweight local OpenTelemetry backend, the plugin supplies standard OTel environment variables, receives application spans, and continuously updates an internal architecture model that the IDE renders on demand. Its stream-processing algorithm classifies spans through semantic attributes, identifies services, matches HTTP CLIENT and SERVER spans across propagated trace context, and infers database or messaging dependencies while retaining unmatched spans in memory. Because spans can arrive out of order and traces have no definitive finish signal, the map is incrementally reconstructed rather than built from complete traces; the result is a language- and vendor-agnostic view for OpenTelemetry-instrumented applications across several JetBrains IDEs.
How Real-Time Rewards Drive 2.5x Higher Customer Spend
Many retail loyalty programs use batch processing, delaying rewards by up to 72 hours or 10 business days, eroding customer trust. Nearly 75% of shoppers prefer instant rewards, 82% of Gen Z and Millennials expect real-time responsiveness, and real-time credit can increase basket size by 36% and spending by 2.5 times. Relational databases struggle with atomic writes across multiple tables, forcing batch cycles; layering microservices on top only distributes fragmented data without solving latency. MongoDB’s document model solves this by storing the complete loyalty profile in a single document for atomic writes; Triggers and Change Streams push updated balances instantly and allow promotional rule changes without redeployment. This architecture turns the post-purchase moment into immediate reinforcement, reduces support inquiries, and drives engagement, as seen with Starbucks Rewards contributing nearly 60% of total revenue.
Migrating from closed to open source models, Together
The post presents a five-stage playbook for moving production workloads from closed-source models to open-source models: discover, evaluate, adapt, decide, and take the system to production. It argues that managed services, model-agnostic harnesses, gateways, and tooling can reduce migration complexity, potentially making the effort weeks to months rather than months to years. Discovery combines workload definition, relevant benchmark filtering, cost-per-task and runtime analysis, playground testing, and narrowing candidates to two or three models. Evaluation separates accuracy from performance and recommends replaying real traffic against explicit goals for cost, speed, and quality, while adaptation may involve prompts, inference settings, context, and the surrounding harness. The excerpt frames migration as iterative rather than assuming a model passes unchanged.
Welcoming Roberto Braga, our new Growth Marketer
Resend welcomes Roberto Braga as its new Growth Marketer, adding a leader with more than 10 years of experience building and scaling technology businesses. He previously co-founded and served as CMO of YC-backed BX Blue, led growth through its acquisition by PicPay, ran organic growth at PicPay with SEO and ASO, and most recently was Reddit’s Country Growth Lead in Brazil. At Reddit, he helped double the platform’s DAU through content and partnership-led growth strategies; at Resend, he plans to create the Growth discipline and systematize the factors driving user acquisition and retention. Braga describes experimentation, channel mixing, email, and user conversations as central to his work, and advises growth marketers to focus on user pains rather than the newest tools or frameworks.
I got frustrated with AWS, so we built our own sandbox
At a healthcare startup, an agent workflow built with LangGraph was constrained by context length and by the limited tools exposed to it for data analysis. Changing one node gave the agent access to Amazon Bedrock AgentCore Code Interpreter, allowing it to use pandas and other available data-science libraries in a sandbox. It could inspect files and outputs, write and run code, and adjust its analysis iteratively while keeping working data and intermediate artifacts in the workspace rather than sending every row through the conversation. Although the experiment demonstrated the usefulness of this controlled autonomy, regional availability forced the author to use a US region from Malaysia, motivating BoxCompute, an infrastructure project intended to provide agent workspaces with control over execution, resources, output collection, and cleanup.
What's new in ClickStack - Aug ’26
The August ClickStack update covers five releases, v2.34 through v2.38, with dashboards as the main focus. Dashboard variables are generally available, letting filters supply selected values to raw SQL, builder fields, Lucene, and PromQL queries, while chained filters can narrow one another. Charts add formulas for combining series, a metrics explorer previews emitted metrics, and release markers align service versions with events such as latency spikes. The update also introduces the TimeSeries engine and PromQL support in private preview for Managed ClickStack and experimentally in open source, alongside a beta LLM observability dashboard, alerting improvements, OIDC collector authentication, and MCP server changes, including up to ten notification targets, alert names and tags, evaluation history through the API, and richer webhook payloads.
On-Demand Masked Sessions with Twilio Proxy, Voice and Serverless
This tutorial presents a Just-in-Time (JIT) masked session workflow for securely connecting callers when phone numbers cannot be pre-associated because of inventory constraints. The design routes an inbound call to a reserved Twilio number through Twilio Voice, uses an IVR to collect a six-digit tracking code, and dynamically creates a private Twilio Proxy session. It uses five serverless endpoints plus helper functions; because the Out-of-Session Callback lacks keypad digits, a “2-Bounce” flow uses a redirect and a temporary Twilio Sync Document keyed by res- plus CallSid to carry the resolved destination, with a short TTL such as 900 seconds. On the second bounce, the destination is retrieved and deleted, and Proxy receives instructions to auto-create the session and bridge the caller. The result is an on-demand masked connection across standard cellular connections that avoids static phone-number preallocation and preserves the number pool.
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