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Updating the design of Notion pages
Notion revisited its page design after years of adding blocks with inconsistent spacing and related details. Rather than relying on baseline alignment, which is difficult across browsers and user-controlled block heights, the redesign uses standardized spacing to create rhythm while preserving flexibility. The team also changed paragraph spacing so readers can scan documents and see breaks more clearly, then addressed the resulting problem of lists feeling too loose or too tight. Its solution treats blocks above and below a block as adjacent and reduces padding when neighboring items belong to a list, including checklists, bulleted, toggle, numbered, and page lists. Notion says the system will roll out across all pages, including lists, headings, and mixed-content documents, over the coming weeks.
How ClickStack makes ClickHouse faster for observability
ClickHouse delivers high performance for telemetry data, but unoptimized queries on high-cardinality observability workloads can bypass pruning, inflate intermediate state, and exhaust cluster resources. ClickStack addresses this challenge by embedding engine-specific optimization best practices directly into how queries are generated, structured, and executed. For search workflows, the platform executes progressive queries across bounded time windows using optimize_read_in_order to return initial rows rapidly without scanning entire historical ranges. ClickStack also segments large chart aggregations into parallel bucket-aligned queries, automatically redirects map attribute filters to pre-materialized columns, and extends lazy materialization thresholds to minimize runtime input-output overhead. These query formulation strategies are being packaged into specialized analytical endpoints to power notebooks and external tools without requiring manual SQL tuning or exposing raw database interfaces.
Money Forward brings Cursor’s coding agents to product, design, and QA
Money Forward expanded Cursor coding agents beyond engineering to product, design, and quality assurance after developers began saving an estimated 15–20 hours per week. Adoption among engineers rose 30% within a week, and the company now reports more than 1,000 daily users; engineering examples include Rails optimization, Terraform deployments, and Vue-to-React migrations. For the company-wide rollout, its MEPAR department chose Cursor for minimal setup, visual browser capabilities, a unified workspace, model-agnostic asynchronous cloud agents, and context retrieval across complex codebases. QA uses MCP-connected Jira and Notion materials to generate test cases and Playwright scripts, reducing test-generation time by 70%, while product managers inspect repositories for requirements and designers prototype against live frontends and product analytics.
Introducing AI observability notebooks for Managed ClickStack in Private Preview
ClickHouse has launched AI Notebooks for Managed ClickStack in private preview to assist site reliability engineers during system investigations. Instead of isolating interactions in a standard chat window, the platform embeds Anthropic's Claude models directly into a cell-based notebook canvas. The system connects the model to internal ClickStack APIs, translating natural language questions into structured queries that leverage ClickHouse optimizations like materialized views and query chunking. Engineers can inspect reasoning, edit generated SQL queries, add manual visualizations, and branch investigations to evaluate multiple hypotheses independently. While currently utilizing a last-write-wins model for shared canvases, the roadmap includes true concurrent editing, customizable context, and external tool integration via an MCP server.
Enhance Your In-IDE Data Browsing Experience With MongoDB
MongoDB announced the general availability of an enhanced data browsing experience within the MongoDB for Visual Studio Code extension. The update introduces a paginated tree view that automatically detects human-readable fields like names or emails to create descriptive document titles instead of showing standard ObjectId hashes. An integrated action header inside the tree view enables developers to insert documents, refresh queries, sort by ObjectId, paginate results, and execute bulk deletes directly from the sidebar. Individual documents support hover-based contextual action menus for native deletion, copying, cloning, and editing. Furthermore, the extension now defaults to standard Shell syntax rather than Extended JSON across insert, clone, edit, and clipboard operations to maintain application code compatibility.
How we optimized Dash's relevance judge with DSPy
Dropbox Dash optimized its LLM-as-a-judge relevance scoring system using DSPy to migrate from expensive proprietary models to cheaper open-weight alternatives. By establishing automated feedback loops based on human agreement and strict JSON format validation, the team systematically generated robust prompts for new models. This reduced human-score disagreement by 45% and slashed model adaptation time from weeks to days while enabling 10x to 100x more data labeling.
State of Open Source on Hugging Face: Spring 2026
The post examines how open source AI on Hugging Face changed over the past year across competition, geography, technical trends, and emerging communities. It combines Hugging Face and community-contributed metrics to assess users, models, datasets, downloads, repositories, and specialized sub-ecosystems. In 2025, the platform reached 13 million users, more than 2 million public models, and over 500,000 public datasets, while the top 200 models represented 49.6% of downloads and China surpassed the U.S. in monthly and overall downloads. The analysis also describes rising participation by independent developers, increased Chinese releases, and active communities in robotics and science, framing open source AI as overlapping ecosystems rather than a uniform market.
Behind the Scenes: How Maintaining Cloud Native Buildpacks Powers Platforms Like Heroku
Maintaining the pack CLI and Cloud Native Buildpacks (CNBs) is presented as invisible infrastructure work supporting platforms including Heroku, Google Cloud, OpenShift, VMware Tanzu, and internal systems. Over the last 14 months, maintainers shipped 27 releases—11 for pack and 16 for buildpacks/github-actions—reviewed more than 65 community pull requests, and delivered Execution Environments and System Buildpacks. The work also covered Windows CI migration, a docker/docker to moby/moby client upgrade, FreeBSD binaries, multi-architecture publishing, and a Platform API 0.14 lifecycle-restorer fix. System Buildpacks address platform composition and defaults, while Execution Environments support consistent configurations across development, CI, and production. The post argues that company-funded maintainers and CNCF collaboration turn production requirements into shared infrastructure, with planned build observability, OCI image annotations, and Kaniko removal as further examples.
5 Career Myths From Women in Engineering
Three engineering leaders at Two Sigma—Angela Wang, Cecilia Ye, and Mae Santos—challenge conventional career progression models by examining five persistent industry myths. Diverse transitions across civil engineering, enterprise architecture, and customer-facing solutions architecture supplied foundational skills for modern roles in feature engineering, reliability engineering, and real-time trading optimizers. The leaders emphasize that deep domain expertise stems from direct proximity to an active system rather than cumulative years of tenure. Beyond core programming capabilities, proactive contributions such as upgrading operational standards and cultivating peer relationships drive career opportunities and leadership recognition. Adapting to emerging technologies and maintaining consistent execution allows engineers to build credibility across evolving technical environments.
Holotron-12B - High Throughput Computer Use Agent
Holotron-12B is a multimodal computer-use model released by H Company and available on Hugging Face. Post-trained from NVIDIA’s Nemotron-Nano-12B-v2-VL-BF16 on proprietary data, it targets agents that must perceive, decide, and act in interactive environments while handling long contexts with multiple images. Its hybrid State-Space Model and attention architecture stores a constant state per layer per generated sequence instead of attention’s per-token key and value activations, reducing memory demands for long-context inference. In a WebVoyager evaluation on one H100 with vLLM, it reached 8.9k tokens/s at concurrency 100 versus 5.1k for Holo2-8B, and its benchmark score rose from 35.1% to 80.5% after training on localization and navigation data.
How Socialpruf built a faster, more reliable data stack by replacing Neon with Postgres managed by ClickHouse
Socialpruf, a social media performance tracking platform ingesting hundreds of posts per second, experienced analytics latency, connection dropouts, and high network transfer costs while running Postgres on Neon alongside ClickHouse Cloud. To resolve these issues, the team migrated their transactional system of record to NVMe-backed Postgres managed by ClickHouse, colocating transactional and analytical workloads. Although standard Postgres logical replication failed on their 0.5 TB dataset, they achieved continuous synchronization using PeerDB before conducting a production cutover. The migration eliminated connection dropouts, reduced network transfer fees, and improved overall Postgres query performance by approximately 30 percent. To manage thousands of concurrent connections generated by ingestion workers, Socialpruf implemented ClickHouse's bundled, horizontally scalable PgBouncer instances.
Training Composer for longer horizons
Composer is trained for long-horizon coding tasks using self-summarization, allowing reinforcement-learning trajectories to exceed the model’s context window. When a fixed token trigger is reached, Composer receives a synthetic summarization query, uses scratch space to produce a condensed context, and resumes with the summary plus conversation state; training rewards every token in the chained rollout with the final outcome. Against a tuned prompt-based compaction baseline, self-summary reduced compaction-related error by 50% on CursorBench at both 80k and 40k triggers, while summaries averaged about 1,000 tokens instead of more than 5,000 and reused the KV cache. An early Composer checkpoint solved Terminal-Bench 2.0’s make-doom-for-mips after 170 turns, summarizing more than 100,000 tokens into roughly 1,000 tokens of retained information.
Making AI Write Android Code Our Way: A Practical Guide to Agent Skills
An Android team at Medium describes how it encoded its project conventions so AI assistants generate code aligned with Clean Architecture, Kotlin, Jetpack Compose, Hilt, Apollo GraphQL, and feature modules. An AGENTS.md file provides persistent context about module structure, dependency injection, state management, navigation, testing, and commands, while Markdown skills in .agents/skills/ provide ordered runbooks for specific tasks. Examples cover scaffolding screens and feature modules, migrating Material 2 to Material 3, enforcing ViewModel feature-flag handling, and adding deeplinks, analytics, translations, and tests. Detailed patterns include ViewModel-injecting and @VisibleForTesting composables, assisted injection, StateFlow, Result, MockK, Turbine, and Robolectric. The team iterates skills from review feedback, keeps them version-controlled, and plans file-aware rules plus commands for release diffs and reporting.
AI is redrawing the database market
Emerging artificial intelligence workloads are fundamentally transforming data platform requirements across real-time analytics, data warehousing, and observability. Conversational analytics and autonomous agents replace periodic batch queries with rapid bursts of concurrent, low-latency requests, exposing the architectural limits of traditional cloud data warehouses and standalone transactional databases. To resolve these constraints, architectures are converging on engine-level integrations between Postgres for transactions and ClickHouse for analytical scale, while unifying operational observability and business event storage. ClickHouse also incorporates turnkey agent interfaces through its acquisition of LibreChat alongside dedicated execution tracing from tools like Langfuse. These combined capabilities ensure organizations can reliably support interactive agentic workflows without incurring severe latency penalties or compounding infrastructure costs.
Designing the new async-native ClickHouse Python client
The official ClickHouse Python client, clickhouse-connect, originally addressed asynchronous execution by wrapping its synchronous HTTP client in a thread pool executor. However, this executor model experienced thread exhaustion, GIL contention, and severe tail latency variance under heavy concurrent workloads. To resolve these scalability bottlenecks, the developers redesigned the client around a half-sync/half-async pattern utilizing aiohttp for async network I/O. A custom bounded queue bridges streaming socket reads on the event loop with synchronous, CPU-intensive binary parsing running inside background threads. Benchmarks demonstrate that this architecture stabilizes tail latencies, delivering an average P95 latency of 556 milliseconds compared to 869 milliseconds for the legacy executor client.
ClickHouse Release 26.2
ClickHouse version 26.2 introduces twenty-five new features, forty-three performance optimizations, and one hundred eighty-three bug fixes across the database engine. This release graduates both the text-index and the QBit vector embedding data type to production-ready status for search and retrieval workloads. For streaming ingestion pipelines where row arrival rates are low, the engine adds time-based block flushing configurations to write data parts before hitting default row or byte thresholds. The update also embeds the ClickStack observability user interface directly into the standard binary distribution, accessible through the local HTTP server port. Further enhancements include automatic min-max indexing for temporal columns in MergeTree tables, interactive time-based one-time password authentication in the client, and a high-performance prime number generator function.
Unordered mode for GCS ClickPipes is now available
ClickHouse Cloud has added unordered mode to its Google Cloud Storage (GCS) ClickPipes connector, enabling continuous data ingestion regardless of file naming or arrival sequence. Standard GCS ClickPipes rely on polling every 30 seconds and require files to arrive in strict lexicographical order, which causes out-of-order data and backfills to be ignored. Unordered mode replaces polling by consuming OBJECT_FINALIZE notifications from Google Cloud Pub/Sub whenever objects land in a bucket. State tracking is managed through a metadata store, and the connector automatically reprocesses batches upon failure while maintaining exactly-once insertion guarantees. Setup requires configuring a Pub/Sub topic and bucket notifications via tools like gcloud, followed by configuring the ClickPipe via the console or Terraform provider.
Securing our codebase with autonomous agents
After a fivefold increase in pull-request velocity over nine months, the team found static analysis and rigid code ownership insufficient for security at scale. It built a fleet of Cursor Automations powered by cloud agents, webhook and GitHub integrations, and a security MCP deployed as a just-in-time serverless Lambda function. The MCP provides persistent data, semantic deduplication using Gemini Flash 2.5, and consistently formatted reporting and follow-up actions, while Terraform standardizes review and deployment. Four templates cover PR review, segmented vulnerability hunting, dependency patching with reachability analysis and tests, and daily invariant-drift monitoring. Agentic Security Review has run on thousands of PRs in two months and prevented hundreds of issues from reaching production; the team says more automations are planned.
Transformation in action: What it takes to automate 81% of your customer service while improving CX
Intercom describes how its Customer Support team adopted an AI-first strategy after a 2022 review exposed rising demand, weak service-level commitments, and plans to broaden support access. The team began with high-volume informational queries, then expanded Fin through deeper documentation, internal procedures, customer-action capabilities, knowledge management, conversation design, skills-based routing, and organizational changes including a dedicated AI Support team. Fin now resolves over 81% of support volume and helped absorb a 300%+ demand increase without proportional headcount growth, while over 90% of customers receive improved first response performance, 24/7 coverage, and outbound phone support. The remaining human work has shifted toward consultative engagements, while Insights, Guidance, Tasks, and Procedures support ongoing optimization. Next, the team plans to use a four-stage Fin Flywheel—Train, Test, Deploy, and Analyze—and continue developing its Agent Blueprint.
AI doesn’t always generate perfect ClickHouse schemas (yet)
LLMs can generate syntactically valid ClickHouse schemas whose advanced choices are poorly matched to a workload. The post examines recurring pitfalls including query-driven partitioning, per-column codecs, premature projections, materialized-view sprawl, JSON configurations, and forced deduplication with OPTIMIZE TABLE ... FINAL. It explains that partitions primarily support data management, incremental materialized views add work to every insert, and ReplacingMergeTree deduplicates during background merges rather than at insertion. The recommended method is to begin with a simple MergeTree or ReplacingMergeTree, a well-chosen ORDER BY, default compression, and no optional structures, then measure production behavior before adding complexity or seeking human expertise for high-scale, interconnected designs.
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- AI Agents553
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