Loading…
Latest reads
Every post, summarised
Grounded summaries of engineering blogs, newest first. Each links to the original.
- 4,671 posts
- 20 companies
The Good Stuff
Paid picks we use ourselves. Links carry a referral code — a subscription through one earns us a commission at no extra cost to you.
Latest posts
Rollouts and Security Review
Cursor launched two bots for the last mile of shipping code: Rollouts and Security Review. Rollouts watches every change as it deploys and reports health per environment, with verdicts of verified healthy, regression detected, or inconclusive. It reads the diff and systems a pull request touches, writes a monitoring plan as a PR comment, and tracks deploy events per environment. Security Review reads every pull request in the context of the codebase and posts one review comment reporting exploitable bugs, including injection, authentication bypasses, and secrets. Both bots are available today on Teams and Enterprise plans, with usage credits for the next 10 days.
How to test that a dozen MongoDB drivers, in a dozen languages, conform to a dozen specs
MongoDB develops a dozen native client library drivers in a dozen languages, each idiomatic to its language but needing consistent API behavior. Instead of relying on English specs or Cucumber tests, engineers created a YAML-based domain-specific test language interpreted by runners in each driver. The Unified Test Format, built on this YAML language, now contains nearly 1,500 files testing conformance to specs covering retries, transactions, client-side encryption, and more. A natural experiment showed four of five drivers had lower nonconformance bug rates after adopting YAML tests; Node.js improved after fixing its test runner. The IEEE ISSRE 2026 accepted a paper on this approach, which has saved over 22,000 lines of test-runner code across seven drivers.
Conversations in the AI Era: Manchester Meetup Recap
Twilio and Ciptex hosted the Conversations in the AI Era meetup in Manchester on September 3rd, 2026, drawing developers, tech leads, and founders from the local scene. Darragh O’Connor demonstrated using a SIP trunk to connect callers directly into a livestream, emphasizing solving the problem with the simplest tool. Matthew Duggan of Ciptex presented an architecture where AI and human agents share conversation state and tools, treating handoff as a change of ownership rather than a recovery path. Paul D’Ambra of PostHog discussed how cheap code generation made reviewing code the bottleneck, leading them to build AI review agents that substantially dropped their revert rate. The next meetup is scheduled for November 4th, 2026.
Collected, stored, and useless: The lifecycle of most customer data
The post examines the typical lifecycle of customer data in companies, revealing that most data is captured but never effectively used. A survey of 660 business leaders found that 48% struggle to connect customer data across channels, making it the top obstacle over budget or headcount. Data is often captured in volume but siloed across systems, duplicated into separate records for the same customer, and reliant on stale overnight batch updates. This leads to AI agents working with outdated profiles, causing 71% of consumers to abandon conversations when the AI fails to recognize them. The post proposes shortening the path between capture and use through real-time data synchronization, exemplified by Twilio’s Conversations and Conversation Memory products.
Introducing Support for Local AI Models in the Antigravity SDK
The Antigravity SDK now supports local AI models, starting with Gemma 4 26B A4B via Google AI Edge’s LiteRT, enabling offline agentic assistance on local GPU and RAM with a recommended machine of 24GB VRAM or unified memory. Local execution eliminates API costs and rate limits while keeping code and requests on-device, offering privacy and offline resiliency. The SDK supports hybrid workflows where a cloud model like Gemini 3.8 Flash plans while local Gemma 4 instances execute, as in a code auditing task where 97.2% of tokens (3,322 of 3,417) ran locally. A built-in example generates a live-updating CLI resource monitor using psutil and rich from a single prompt, with the agent autonomously writing the script, creating requirements.txt, and testing the code entirely offline. The SDK also supports any OpenAI-compatible server like Ollama, LM Studio, or vLLM via LocalOpenAIAgentConfig without changing agent orchestration.
Database Branching: A Developer's Guide to Git-Style Workflows
Database branching creates isolated database environments from a known parent state using copy-on-write, where branches share unchanged data and only store modifications. For a large parent database like one that is 40 GB, two branches may require just 5.6 MB of additional storage instead of 80 GB with traditional full copies. Developers can test schema migrations like adding a NOT NULL column to a table with millions of rows against realistic data and constraints, CI pipelines provision per-pull-request branches for isolated tests, and AI agents experiment across short-lived environments without risking the parent database. Safe operation relies on protecting parent branches from writes and deletions, restricting sensitive data in disposable branches, automating creation and cleanup, and keeping environments reproducible through version-controlled configuration. Databricks Lakebase provides database branching, enabling teams to adopt Git-style workflows for development, CI/CD, and AI agent use cases.
AI Agent Observability: Making Silent Production Failures Visible and Actionable
Agentforce Health Monitoring (AHM) addresses silent availability failures in production AI agents that traditional monitoring misses because agents can appear healthy even when unreachable. The team unified telemetry from approximately five or six locations across sessions, reasoning steps, tool calls, errors, and escalations into a coherent representation. End-to-end alerting latency was reduced from roughly 20 minutes to several minutes by converting Data 360 ingestion to streaming and simplifying query complexity. AHM provides 16+ built-in metrics including agent availability, error rate, response/escalation rate, engagement, and latency, with a drill-down interface connecting alerts to session context. The roadmap includes cost, TTFT/TTLT, toxicity, feedback metrics, RAG Quality Monitoring integration, and eventually a runtime insight agent for root-cause analysis and automated remediation.
The Genie One MCP is now Generally Available
Databricks has announced general availability of the Genie One Model Context Protocol (MCP) server, now a managed service within Unity Gateway with centralized governance, fine-grained policies, and audit logging across every invocation. The MCP provides a single governed interface for any AI agent to access structured and unstructured data from Genie One, grounded in governed business context from Genie Ontology, eliminating agent sprawl and inconsistent answers. It exposes tools for asking questions, getting query results, checking incremental progress, and steering responses, with an optional MCP App for real-time interactive visualizations and Genie Ontology citations. Connected agent clients automatically interpret domain semantics via Genie Ontology across relational data and document repositories without custom connectors. Use cases include enriching slide creation with richer data, bringing customer usage context to outreach agents, and integrating business definitions into developer workflows, as noted by GetYourGuide.
Genie One MCP: Give any AI Agent the Right Business Context
Business leaders often have access to plenty of data but still cannot get a reliable answer to a simple question like why net revenue declined 8% at their largest account last week. The answer may span sales, finance, promotions, inventory, and account data, with each source potentially correct in isolation yet different in definitions, detail, relationships, and authority. Without the right business context, general-purpose agents cannot reliably determine what metrics mean, which sources are authoritative, how data should connect, or what each user is allowed to see. Genie One MCP gives governed context to any MCP-compatible AI agent, connecting assistants like ChatGPT, Claude, Microsoft Copilot, and coding agents to Genie One. This lets them answer business questions using approved definitions, trusted data relationships, and permission-aware access controls, with traceable, permission-aware answers grounded in governed business context.
Enabling Private High-Performance Production AI Inference with NVIDIA Confidential Computing
NVIDIA Confidential Computing (CC) enables secure AI inference by using memory-encrypted confidential VMs, confidential GPUs, and encrypted NVLink. Running inference frameworks like TensorRT LLM in a CC environment introduces performance overhead because secure execution changes assumptions about memory movement, timing, and multi-GPU communication. To quantify this overhead, the post presents a controlled methodology comparing CC-on and CC-off performance on a workload with long input context, extended output generation, and low concurrency. With CC-aware adaptations—including pageable memory selection, asynchronous readback, GPU %globaltimer for autotuning, and CC-aware multi-GPU communication—TensorRT LLM retained 96.1–98.2% of baseline throughput on eight NVIDIA B200 GPUs while keeping per-token latency overhead below 5%. The post concludes that security configuration and inference optimization should be treated as a single deployment problem for organizations moving private inference into production.
Topology-Aware Workload Scheduling with NVIDIA Topograph
AI factories are power-limited systems where GPU workload placement is a key optimization; poor placement fragments topology domains and forces traffic across shared links, reducing throughput and raising job costs. NVIDIA Topograph discovers cluster topology from cloud APIs or on-premises fabric systems, normalizes it into a common model, and publishes it in formats each workload manager expects, such as Kubernetes node labels, Slurm topology configuration, or Slinky ConfigMaps. It uses five components: API Server, Node Observer, Node Data Broker, Provider, and Engine to keep the topology view current as the cluster changes. The post covers deploying Topograph with Helm on Kubernetes, as a system package on Slurm, and as a Helm chart on Slinky, with support for cloud providers including Google Cloud, Lambda, Nebius, Nscale, and OCI. Topograph works alongside Dynamic Resource Allocation and KAI Scheduler to enable topology-aware gang scheduling, improving AI factory efficiency and tokens per watt.
How Fountain rebuilt its data plane on ClickHouse Cloud to power Cue, the Frontline Superintelligence
Fountain rebuilt its data plane on ClickHouse Cloud to power Cue, its agentic AI platform for frontline hiring, moving off a multi-vendor BigQuery and S3/Iceberg batch stack. The migration cut analytics latency from three hours to under two minutes, with most queries returning in under a second, at roughly 66% lower cost. Data from 13 Postgres production databases and four MongoDB databases streams into ClickHouse Cloud via ClickPipes managed CDC, landing as Iceberg and Parquet in S3 before reaching BigQuery and ClickHouse. The new architecture uses two symmetrical pipelines—one for Postgres, one for MongoDB—each running a short path: source, ClickPipes, materialized views, and Cue. The system now carries 8.48 billion CDC rows replicated continuously by ClickPipes and 10.3 billion analytics rows across 15 deployments, served by over 2,000 incremental materialized views.
We just shipped support for the ugliest part of HTTP: Vary
The Vary response header, often called the ugliest part of HTTP, is now supported in Cache Rules on every Cloudflare plan. Vary tells caches which request fields may affect a response, but not which differences actually matter, leading to cache fragmentation or incorrect responses. For example, a server might deliver different image formats to different browsers, and a cache ignoring Vary risks serving the wrong bytes. The origin still names the request headers that may affect a response, but Cloudflare lets users decide how to handle each one. This support addresses the challenge of balancing correct caching with cache efficiency.
Code Quality Q&A With the JetBrains Qodana Team
The Qodana team answers frequently searched questions about code quality, distinguishing technical debt from poor code quality and offering practices to reduce cognitive complexity in nested loops. They explain that code coverage measures how much production code is executed by unit tests, while mutation testing evaluates test quality by checking whether tests detect deliberate code changes. Unit tests provide the fastest feedback on code changes by catching regressions close to their introduction and making refactoring safer. To track improvement, teams should monitor static analysis trends such as new issues and technical debt, and also look for fewer Sev 1 issues in production and faster mean time to repair. False positives in automated scanning can be reduced by giving the tool more repository context, such as excluding inspections in qodana.yaml or silencing them with inline comments.
ClickHouse appoints Mike Scarpelli, former Snowflake and ServiceNow CFO, to Board of Directors
ClickHouse appointed Michael Scarpelli, former CFO of Snowflake and ServiceNow, to its Board of Directors as an independent director and chair of the Audit Committee. Scarpelli previously guided three companies through initial public offerings and said ClickHouse is redefining the category for real-time and agentic workloads. The company has surpassed $350 million in annualized revenue, up from roughly $200 million at the end of 2025, and achieved a $15 billion valuation in its Series D round, with over 4,000 customers including DoorDash, Meta, Tesla, and Visa. ClickHouse now employs nearly 800 people across 27 countries, plans to reach 1,000 by year-end, and derives more than half its revenue from outside the United States as it scales its enterprise motion. The appointment is part of a deliberate build-out of finance and governance capabilities, including the 2025 hire of CFO Jimmy Sexton, who also came from Snowflake.
Data liberation: Apache Kafka's native cluster mirroring
KIP-1279 introduces native cluster mirroring, embedding cross-cluster replication directly into the Kafka broker. This eliminates external tooling, offset translation tables, and recompression, as destination brokers fetch committed records using the standard fetch protocol and append them byte for byte. The approach preserves offsets, compression, and consumer group state, simplifying failover to stopping the mirror and redirecting clients. The architecture uses mirror fetcher threads inside the broker, extending AbstractFetcherThread, and leverages existing client quota mechanisms. Migration involves creating a new KRaft cluster, setting up a mirror, and starting mirroring, with future work including synchronous mirroring and tiered storage integration.
Extend OpenShift Lightspeed with Red Hat Satellite, MCP, and BYOK
The article demonstrates extending Red Hat OpenShift Lightspeed by integrating the Red Hat Satellite MCP server and bring your own knowledge (BYOK). The Satellite MCP server exposes live operational data, such as kernel versions, CVEs, and errata, through the Model Context Protocol. BYOK enriches the assistant with private enterprise knowledge, enabling retrieval-augmented generation (RAG) to combine internal documentation with infrastructure data. The integration requires installing the OpenShift Lightspeed operator and deploying the Satellite MCP server in the openshift-lightspeed project. Authentication is handled by storing the header value in a Secret, and the MCP server expects clients to include appropriate Accept headers for the text/event-stream media type.
Introducing Worker Previews: isolated preview environments for every change your agent makes
Worker Previews gives every Git branch its own production-like environment with an isolated URL, configuration, state, and observability. Developers deploy a Preview using npx wrangler preview, which creates a separate Worker version with its own variables, secrets, and bindings, distinct from production, on a stable URL that updates with each push. Durable Objects and Containers automatically receive new namespaces per Preview, preventing state contamination across branches during migrations or schema changes. Each Preview supports full observability—logs, errors, metrics, and traces—scoped to that branch, enabling agents to autonomously test, catch failures, push fixes, and verify before production. The feature enables an Agent Development Lifecycle where every change is atomic, independently deployable, and revisable before merging to production.
Accelerating a ROS 2 Node with an AI Agent and NVIDIA Isaac ROS
GPU acceleration alone does not guarantee fast ROS 2 graphs because messages may still be serialized or copied through CPU memory. NVIDIA contributed the rosidl::Buffer abstraction and CUDA buffer backend to ROS 2 Lyrical, enabling zero-copy transport of GPU-resident payloads between co-located nodes when they share the same host, CUDA device, Linux user, and a supported RMW implementation, with automatic CPU fallback otherwise. An AI coding agent using the migrate-node-to-rosidl-buffer skill can audit an existing CUDA-accelerated node by tracing data movement, running a copy-boundary audit, and implementing a minimal interface-preserving patch. The tutorial demonstrates this on the Depth Anything 3 TensorRT node, updating subscription options and message storage to use CUDA buffer backend without changing the sensor_msgs/msg/Image message definition. The resulting accelerated workload can be deployed on NVIDIA Jetson AGX Thor, preserving standard ROS 2 messages while avoiding unnecessary serialization and host copies.
JetBrains Air: Building a System of Products for Agentic Software Development
JetBrains Air is a system of products for agentic software development, built on the premise that no single model, agent, or service fits every developer, team, or task. It connects products for individual work, team coordination, organizational control, context, and process automation, while remaining open to tools and agents developers choose, including those JetBrains does not build. This openness lets developers select the agent, model, or service suited to a task without forcing the organization to give up context, visibility, or control. The Post notes that every new model, agent, or service an organization adds reduces visibility into its own development work. Because intelligence does not belong to one model, the benefit can extend across supported agents and services.
Top topics
- Architecture834
- Performance816
- LLMs660
- Machine Learning622
- Open Source563
- AI Agents553
- AI470
- Developer Experience448
Weekly digest
The best engineering reads, once a week.