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From resolutions to outcomes: Evolving how Fin delivers value
Fin is changing its pricing metric from “resolutions” to “outcomes” as its capabilities extend beyond fully autonomous support conversations. Historically, a resolution meant Fin solved an issue end to end without human involvement; the model gave teams a clear ROI comparison, and Fin’s average customer resolution rate now stands at 76% across more than 7,000 teams. Customers increasingly deploy Fin on complex, multi-step work involving external systems, policy constraints, and human oversight, where Fin may gather context and act before handing off for confirmation. An outcome counts when Fin successfully completes the action it was configured to perform, including a full resolution or a Procedure. The change keeps pricing simple while recognizing collaborative workflows, supporting compliance, and providing a metric that can scale as Fin takes on more roles beyond service.
5 ways to parse Dates and DateTimes in ClickHouse
ClickHouse provides several ways to convert dates and datetimes from Unix timestamps, packed numeric values, known-format strings, mixed inputs, and file data. The examples use fromUnixTimestamp and its millisecond and microsecond variants, YYYYMMDDToDate and YYYYMMDDhhmmssToDateTime, plus parseDateTime or parseDateTimeInJodaSyntax when the input format is known. For unknown or mixed formats, parseDateTimeBestEffort and its DateTime64 counterpart accept inputs such as ISO 8601 strings, dates, and timestamp strings; invalid values otherwise raise an exception, while OrNull returns NULL and OrZero returns January 1, 1970 at midnight. The post also shows direct ::DateTime casting, controlled by cast_string_to_date_time_mode: basic supports standard forms, whereas best_effort broadens parsing but still throws for completely invalid dates. An inline or session setting lets the same casting approach parse rows from a CSV file.
From Fragmented Logs to Full-Stack Visibility with SolarWinds Papertrail
Heroku applications combine dynos, databases, third-party APIs, and user interactions, making fragmented logs, metrics, and dashboards costly to correlate during production incidents. The expanded SolarWinds Papertrail add-on addresses this “swivel-chair” workflow by unifying real-time log management, metrics dashboards, and alerting in one interface. It supports live tailing and contextual search through the web, CLI, or API, while ingesting native Heroku metrics for dyno CPU, memory, load averages, application throughput, response times, error rates, and database performance. Custom thresholds based on minimum, maximum, average, or summary values can filter transient-noise alerts, with notifications sent to Slack, PagerDuty, and Microsoft Teams. The post’s incident example correlates a response-time spike with R14 “Out of Memory” errors from Dyno worker.1, identifying an oversized batch job; Librato and AppOptics were deprecated at the end of January 2026.
Observability and OpenTelemetry: Introducing MongoDB Atlas Log Integration
During database latency spikes or unexpected outages, operations teams frequently waste valuable time downloading compressed archives and manually inspecting log files to isolate root causes. MongoDB Atlas resolves this operational friction by introducing a direct log integration feature for dedicated M10+ clusters. The system routes mongod, mongos, and audit logs directly to third-party tools such as Datadog, Splunk, Amazon S3, Google Cloud Storage, and Azure Blob Storage. Furthermore, built-in support for the OpenTelemetry standard enables teams to pipe database logs into any OTel-compatible logging backend. This setup eliminates operational silos, streamlines security auditing, and correlates database query diagnostics with application traces and infrastructure metrics in real time.
What's new in ClickStack. February '26.
ClickStack introduced several platform updates and query performance enhancements across both open-source and Cloud environments for February 2026. ClickHouse Cloud OpenAPI now exposes ClickStack resources including dashboards, alerts, sources, and webhooks to support automated configuration-as-code workflows. In addition, ClickHouse 26.2 embeds the ClickStack UI directly into the database binary with a 4.1 MB footprint for local exploration and diagnostics. Visualizations now support native SQL mode for tables, allowing users to join disparate datasets like logs and traces across injected dynamic time variables. Finally, query execution speeds improve through streaming skip indexes, index pruning on disjunctions, and an expanded 100,000-row lazy materialization limit alongside new threshold alerting on number charts.
Enabling R8 optimization at scale with AI-assisted debugging
Grab enabled R8 optimization for its Android app after repeated attempts were blocked by debugging difficulty, organization-wide testing demands, and slow build feedback. The effort targeted a roughly 10-year-old codebase exceeding 9 million lines and combined AI-assisted debugging, a pragmatic testing strategy, and infrastructure investment. Its QA build was configured to match production R8 processing. Beyond existing shrinking, optimization added techniques such as method inlining, class merging, constant folding, dead-code elimination, and devirtualization. After about 10 weeks with one engineer coordinating across teams, the project reported around 25% lower ANR rates, a 16% reduction in reference-device download size, and nearly 27% faster startup; the startup result combined R8 with corrected Baseline and Startup Profiles.
Shopify’s journey to faster breadth-first GraphQL execution
Shopify describes how high-cardinality, deeply nested GraphQL queries exposed execution overhead beyond data loading, with field resolvers assembling responses consuming much of request time. Its GraphQL Cardinal engine replaces conventional depth-first traversal with breadth-first execution, resolving each selected field once across an aggregated set of objects and reducing repeated field-level work. For large list queries, Shopify reports up to 15x faster execution, 90% less memory use, and reductions of many seconds in P50 latency. The migration included a tuned interpreter, cheaper tracer behavior, shadow verification, benchmarks, and ongoing conversion of tens of thousands of legacy resolvers, while future work includes asynchronous patterns and lower-level C bindings.
How we compare model quality in Cursor
Cursor describes a hybrid online-offline process for comparing coding-agent quality as developer tasks become longer, more complex, and multi-file. Its offline CursorBench suite uses tasks derived from real Cursor sessions, paired with ground-truth solutions through Cursor Blame, and evaluates correctness alongside code quality, efficiency, and interaction behavior; the post focuses on correctness. Tasks are intentionally short and underspecified, span larger codebases and multi-workspace environments, and are scored with agentic graders, while correctness is plotted against median completion tokens to show the compute/latency trade-off. Controlled live-traffic experiments supplement the suite, catching regressions that offline grading misses, and the post reports that CursorBench separates frontier models more clearly and its rankings track developer experience more closely than public benchmarks.
Announcing chDB 4: write Pandas, run ClickHouse, now on Hex
chDB 4 introduces Data Store, a native component that allows users to write Pandas-like code that executes on the ClickHouse OLAP SQL engine. Standard Pandas workflows suffer from eager execution, excessive memory materialization, and lack of automatic query optimizations. To solve this, Data Store implements a four-layer architecture that lazily records operations as a chain of LazyOp objects and compiles them into optimized ClickHouse SQL plans upon evaluation triggers. When encountering operations unsupported by ClickHouse, the QueryPlanner splits the pipeline into segments, routing tasks between ClickHouse and Pandas while using Python's memoryview to minimize data copying overhead. This release also integrates natively into Hex notebooks.
Over 30 new plugins join the Cursor Marketplace
Cursor is adding more than 30 plugins to its Marketplace, including integrations from Atlassian, Datadog, GitLab, Glean, Hugging Face, monday.com, and PlanetScale. The plugins extend Cursor across infrastructure and productivity workflows: users can query observability data, manage GitLab repositories and pipelines, work with PlanetScale schemas and queries, handle Atlassian issues and backlogs, search company knowledge in Glean, and manage monday.com boards and tasks. Each plugin bundles capabilities such as MCPs with skills that instruct an agent how to use them, and those tools can support manually started or automatically invoked cloud agents. Cursor also supports community-built plugins, while Teams and Enterprise admins can create private team marketplaces. Automations can run agents on schedules or events with custom instructions.
Fin Product Updates: February recap
February updates for Fin address constraints in complex automation, voice quality, Shopify deployment, and helpdesk operations. Procedures can be drafted from existing source material, edited in natural language with hooks for live data, business logic, and deterministic code, then tested step by step in simulations using conversation data and defined criteria. Voice changes add pronunciation rules, 11 new voices, and automatic background-noise reduction, while Shopify setup connects a store, installs Messenger without code, and deploys Fin in under two minutes. Helpdesk additions include 11 call metrics and automatic public-holiday office hours; the post says 19 improvements shipped in 2026, and concludes that all featured updates are live and available now.
GitTrends: A Google Trends style view of the GitHub ecosystem
GitHub produces billions of events across issues, pull requests, and comments, but turning this vast stream into a real-time trends analyzer presents significant technical challenges. GitTrends is an open-source demo application designed to search and compare technology keywords across nearly ten billion GitHub events. Built to showcase the full-text search capabilities recently released in ClickHouse, the application indexes raw text without pre-computed answers or intermediate data transformation. ClickHouse executes full-text searches and aggregations within a single engine in one pass, eliminating cross-system joins and data movement. The demo includes live comparisons toggling between full-text search, bloom filters, and full table scans to illustrate query performance at scale.
Announcing General Availability of ClickHouse Full-text Search
ClickHouse has reached general availability for its native Full-text Search feature, designed to accelerate token-based text filtering across large datasets. The capability relies on deterministic inverted indexes rather than probabilistic Bloom filter skip indexes, mapping individual tokens directly to row numbers. It supports plain strings, string arrays, and map keys or values while allowing configurable pre-processing and tokenization using standard SQL expressions. The system does not implement relevance scoring or store positional data for ranking, focusing instead on rapid filtering paired with large-scale analytical aggregations. Testing shows significant reductions in scanned granules alongside substantial latency improvements, though text indexes require more storage than Bloom filters.
Sydney becomes Anthropic's fourth APAC office
Anthropic plans to open an office in Sydney in the coming weeks, making it the company’s fourth Asia-Pacific office alongside Tokyo, Bengaluru, and Seoul. The expansion responds to strong demand in Australia and New Zealand and is intended to support their distinct AI ecosystems, with initial attention on enterprise, startup, and research customers. Anthropic says it will hire locally, deepen engagement with Australian institutions, pursue partnerships in national-interest and priority sectors, and explore additional compute capacity through third-party infrastructure already in place, partly to address data-residency requirements. Australia and New Zealand rank fourth and eighth globally in Claude.ai usage relative to population, while reported usage is especially strong for computer and coding tasks, educational instruction, and research.
Unfortunately, Sprites Now Speak MCP
Sprites are disposable cloud computers with durable filesystems, near-zero idle cost, and support for running coding agents and agent-managed workloads. The post announces an MCP interface, allowing Claude Desktop and other MCP-compatible agent tools to authenticate to a Fly.io organization and create or manage Sprites. It argues that MCP is aesthetically and operationally inferior to command-line tools and discoverable APIs because loading tool descriptions consumes context and can overemphasize capabilities an agent does not need. For agents that can run shell commands, the recommended pattern is a short skill description backed by progressive CLI or API discovery; MCP sessions remain a fallback for agents without command execution. Session guardrails can restrict the organization, cap Sprite creation, and apply name prefixes, while the broader design favors real filesystems and networks over stateless sandboxes.
Introducing Storage Buckets on the Hugging Face Hub
Hugging Face introduces Storage Buckets as a mutable, S3-like object storage layer on the Hub for intermediate machine-learning artifacts such as checkpoints, optimizer states, processed shards, logs, and traces. Unlike versioned Models and Datasets repositories, Buckets support frequent writes, overwrites, directory synchronization, and cleanup while retaining standard Hub permissions and public or private visibility. Built on Xet, they chunk and deduplicate content across files, reducing repeated bytes, bandwidth, transfer time, and, for Enterprise customers, billed storage. The CLI and Python APIs support creation, syncing, dry runs, plans, inspection, individual file operations, and programmatic workflows; HfFileSystem and fsspec also allow compatible libraries to read and write through hf:// paths. Pre-warming can place hot data near AWS or GCP compute, while roadmap work aims to connect Buckets directly with versioned repos.
Keep the Tokens Flowing: Lessons from 16 Open-Source RL Libraries
At-scale reinforcement-learning training is constrained by long, variable data-generation workloads that leave training GPUs idle, especially for reasoning and agentic models. The surveyed architecture separates inference and training onto distinct GPU pools, connects them through a rollout buffer, and synchronizes weights asynchronously so both stages can proceed concurrently. Sixteen open-source libraries are compared across orchestration, buffering, weight synchronization, staleness, partial rollouts, LoRA, and distributed backends; Ray appears in 8 of 16 orchestration implementations, while NCCL broadcast is the default weight-transfer method. The proposed TRL async trainer favors a lightweight bounded queue with per-token model_version metadata, packed NCCL transfers, and experimental support for prefix-resume or abort-and-retry handling of rollouts interrupted by weight updates.
How Critical Manufacturing uses ClickHouse to bring real-time intelligence to the factory floor
Critical Manufacturing faced performance and scaling bottlenecks while processing millions of factory floor events using a legacy Microsoft SQL Server analytics stack. Increasing data volumes caused historical queries to take hours, straining real-time dashboards and escalating operational maintenance. The engineering team replaced this architecture by migrating analytics workloads to ClickHouse connected directly to streaming Kafka topics. They implemented an ELT pattern alongside ReplacingMergeTree engines to deduplicate late-arriving sensor records and utilized TTL policies for automated lifecycle management. The resulting platform delivers sub-second analytical queries across historical and real-time operational data, expands into OpenTelemetry observability storage, and maintains embeddings for retrieval-augmented generation workloads.
Financial Benchmarks
Ramp describes how it benchmarks large language models (LLMs) used in financial products against day-to-day tasks such as invoice extraction, financial-statement OCR, policy review, accounting autocoding, compliance detection, and fund routing. These benchmarks combine task-specific metrics with cost, latency, reasoning effort, human decisions, historical context, and ground-truth datasets where available. For contextual invoice OCR, perfect extraction requires every field to match the user's final bill, while financial-statement OCR uses a 1% relative-error threshold against transcriptions from over 500 real P&L documents. Results show application-specific trade-offs: Gemini 3 Flash is described as a cost-efficient leader for several visual and financial tasks, while Claude models lead some high-accuracy or low-miss-rate settings, and behavior can differ within one provider. The framework emphasizes Pareto trade-offs and continuous testing, with plans to expand from benchmarking current performance to hill climbing on capabilities.
What Replo learned optimizing 100+ billion events in ClickHouse
Replo built an analytics pipeline on ClickHouse to serve more than 4,000 Shopify merchants processing 3,000 to 5,000 events per second. The initial single-table architecture recalculated session metrics on the fly, leading to poor query efficiency as volume scaled. To speed up dashboard queries, the team implemented a mark-and-unmark pattern with SummingMergeTree and refreshable materialized views, but real-time deduplication and fractional attribution caused runaway query backlog loops. Replo resolved the issue by limiting real-time recomputations to a specialized table holding only the last 40 minutes of purchase-related events. This architecture allowed processing over 100 billion records while keeping query times fast and ingestion lag to approximately one minute.
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