---
title: "Latest reads"
description: "The engineering internet, summarised so you can actually read it."
---

# Latest reads
> The engineering internet, summarised so you can actually read it.

## Articles

### [NVIDIA Vera Storage Benchmarks: Faster Encryption, Compression, Integrity Checking, and Recovery for AI-Native Storage](https://yomu.fyi/post/nvidia-vera-storage-benchmarks-faster-encryption-compression-integrity.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Elizabeth Goodman
- Published: Aug 3, 2026
- Type: Benchmark

Agentic AI architectures require storage systems to secure, compress, and validate massive streams of persistent data and key-value cache entries across concurrent reasoning loops. These tasks run primarily on CPUs and frequently create processing bottlenecks that prevent modern networks and flash storage from operating at full capacity. NVIDIA evaluated the Vera CPU within the BlueField-4 STX Storage Processor using a purpose-built in-memory microbenchmark suite running standard libraries like OpenSSL, Zstandard, and LZ4. The results demonstrated that Vera outperformed x86 processors across core storage primitives including encryption, decompression, Reed-Solomon recovery, and CRC32C integrity checking. For a sequential multi-stage pipeline executing compression followed by encryption, Vera achieved up to 3.21x higher throughput than the comparison x86 CPU.


### [The New Monday Morning Report: How Generative AI can deliver the insights your executives need.](https://yomu.fyi/post/the-new-monday-morning-report-how-generative-ai-can-deliver-the-insigh.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Roberto Robles Nacif
- Published: Aug 3, 2026
- Type: Explainer

Retail and CPG joint planning meetings often spend time reconciling fragmented, stale, and inconsistent data instead of deciding how to adjust spend, inventory, or forecasts. The proposed Monday Morning Brief replaces the manually stitched weekly deck with an overnight agent-generated brief built from continuously refreshed point-of-sale, shipment, and inventory data alongside trade, promotion, forecast, and external signals. It uses Delta Sharing for a shared view, Genie Ontology for business context, Unity AI Gateway for permissions, guardrails, logging, rate limits, and human approval, and supports models across AWS, Azure, and Google Cloud. The agent scans item-store combinations, ranks material watchouts, answers cited natural-language follow-ups, and can draft actions such as purchase-order changes, while humans retain approval for consequential decisions. The proposed progression from report to ritual to intelligent decision system includes a 90-minute workshop, a day-30 pilot scope, and a first live Monday targeted for day 90.


### [Ingest semi-structured data faster and more efficiently with Variant - Now Generally Available](https://yomu.fyi/post/ingest-semi-structured-data-faster-and-more-efficiently-with-variant-n.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Jonathan Brito, Gene Pang, Harsh Motwani
- Published: Aug 3, 2026
- Type: Announcement

Databricks announces general availability of the Variant data type for ingesting semi-structured JSON, XML, and CSV while avoiding the usual choice between flexible storage and fast, schematized queries. Variant Shredding, also generally available, stores common fields as columns in Parquet files, while Predictive Optimization uses workload and query patterns to identify important fields, collect statistics, and improve file skipping. More than 5,000 teams write Variant, with users executing over 500 million Variant queries monthly across more than 160 TB of data; shredding delivers nearly four-times-faster reads than unshredded Variant and 30-times-faster reads than JSON strings. Auto Loader and Zerobus can ingest Variant into Delta or Iceberg, and future plans include Liquid Clustering by Variant fields, expanded SQL functions, and additional integrations.


### [ClickHouse launches ClickHouse Labs with Andy Pavlo as VP of Database Research](https://yomu.fyi/post/clickhouse-launches-clickhouse-labs-with-andy-pavlo-as-vp-of-database.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Aug 3, 2026
- Type: Announcement

ClickHouse announced the launch of ClickHouse Labs, a new research group led by Andy Pavlo, who joins as Vice President of Database Research. The group is intended to advance foundational work in database systems and share it openly with the broader community. Its research spans database and data architectures, query processing, systems performance, and infrastructure for both ClickHouse and PostgreSQL, with applications including real-time analytics, data warehousing, observability, and AI. Researchers will build and test ideas against demanding real-world workloads, collaborate with ClickHouse engineers, and publish findings in leading research venues so others can evaluate, extend, and build on them.


### [Andy Pavlo joins ClickHouse to establish ClickHouse Labs](https://yomu.fyi/post/andy-pavlo-joins-clickhouse-to-establish-clickhouse-labs.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Aug 3, 2026
- Type: Announcement

Andy Pavlo announces that he is joining ClickHouse to establish and lead ClickHouse Labs, an industry research team focused on database systems. He describes ClickHouse’s early use of C++ and vectorized query execution with SIMD in 2016, and says its development has remained relevant to his academic research. Labs will work directly with ClickHouse engineers, customers, collaborators, and industry partners rather than operating separately, while also collaborating with the PostgreSQL team on performance and reliability for its managed service. An immediate priority is validating and pushing an existing backlog of ideas and optimizations into production, followed by research into hardware, algorithms, data structures, execution strategies, and DBMS development for AI and agentic workloads. The stated aim is to combine scientific research with technology that matters to users.


### [Databricks Completes Acquisition of Panther: Accelerating the Security Lakehouse Era](https://yomu.fyi/post/databricks-completes-acquisition-of-panther-accelerating-the-security.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Andrew Krioukov, Jack Naglieri, Taylor Kain, Dave Herrald
- Published: Aug 3, 2026
- Type: Announcement

Databricks says it has completed its acquisition of Panther, an AI SOC platform, to accelerate its security lakehouse strategy. The combination pairs Lakewatch’s open, governed foundation for collecting, retaining, and analyzing petabyte-scale security telemetry with Panther’s operational SOC workflows and more than 100 out-of-the-box integrations. Panther adds detections-as-code, CI/CD-based authoring and deployment, and AI-native triage and investigation that correlate cloud, identity, SaaS, IT, and business data. The announcement presents the combined platform as a way to retain high-fidelity telemetry, preserve data ownership through open standards including OCSF, Spark, Unity Catalog, Delta, Parquet, and SQL, and automate investigations, detection refinement, and response workflows for modern security operations.


### [Scaling real-time AI agents with session-aware load balancing](https://yomu.fyi/post/scaling-real-time-ai-agents-with-session-aware-load-balancing.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Simerus Mahesh
- Published: Aug 3, 2026
- Type: Explainer

Real-time AI agents require infrastructure that manages continuous, bidirectional conversations rather than isolated requests, including interruptions that halt speech generation and update context without dropping the connection. The post argues that QPS and instantaneous CPU utilization are insufficient because they miss long-lived committed work and silent sessions that can become active simultaneously. It proposes application-level active-session tracking around the stream lifecycle, with cleanup in finally blocks and consistent snapshots, then combines session pressure with CPU and memory utilization in a hybrid routing model. The model normalizes session counts into a rate and uses target utilization, smoothed utilization, cost per session, and a safety scaler to estimate effective capacity and regulate new assignments. Validation should use long-lived, varied workloads and metrics such as session distribution, overloaded assignments, startup latency, dropped sessions, and counter behavior; high-concurrency trackers may require sharded or LongAdder-style aggregation.


### [Are Supabase docs agent-friendly? We didn't know, so we checked](https://yomu.fyi/post/are-supabase-docs-agent-friendly-we-didn-t-know-so-we-checked.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: miranda\_limonczenko,nik\_richers
- Published: Aug 1, 2026
- Type: Problem & solution

Supabase found that coding agents increasingly use its documentation to configure projects, creating risks when pages omit or bury critical instructions. To measure this, the team built an evaluation for the Row Level Security (RLS) guide, running an agent through realistic tasks and grading outcomes with checklist-based and agent-based judges. The initial runs found unauthenticated anon access with write privileges across tables and showed that test guidance was skipped when separated from the policy code. Supabase responded by adding procedural revocation steps, explicit policy snippets, and colocated test examples; the eval then passed the affected checks. The project concludes that agents and humans both benefit from selective-reading-aware structure, clear sections, explicit signposting, and recurring documentation evaluations, with the RLS scenario moving into the public supabase/evals benchmark.


### [How AI is transforming analytics at Grab](https://yomu.fyi/post/how-ai-is-transforming-analytics-at-grab.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Maanas Prabhakar
- Published: Aug 1, 2026
- Type: Explainer

Grab describes analytics as central to business decisions and presents AI agents as a way to democratise access to trustworthy answers. Over two years, increased model capability has enabled agents to prepare data, write and run queries, analyse opportunities, design experiments, interpret results, and draft commentary in minutes rather than a week. The proposed ladder ranges from AI-assisted work through human-planned and agent-planned workflows to end-to-end autonomy, while people retain accountability for framing, definitions, causal interpretation, decisions, and stakeholder relationships. Grab describes Spartan, which routes natural-language Slack requests across more than 50 skills and 120 analysis frameworks, plus Scarlet for pipeline failure triage and recovery. The programme also maintains agent context through ContextIQ and reports lower analyst intervention, a roughly 33% reduction in cycle times, and greater self-serve resolution in its support channel.


### [Kimi K3: the complete developer guide](https://yomu.fyi/post/kimi-k3-the-complete-developer-guide.md)
- Company: [Together AI](https://yomu.fyi/company/together-ai.md)
- Published: Aug 1, 2026
- Type: Tutorial

Kimi K3 is presented as Moonshot AI’s 2.8-trillion-parameter open-weights model for long-horizon coding, end-to-end knowledge work, and deep reasoning, served through Together AI’s OpenAI-compatible API. Its architecture combines Kimi Delta Attention (KDA), Attention Residuals, and Stable LatentMoE, which activates 16 of 896 experts per token—roughly 2%—and supports a 1M-token context. The implementation guide shows Python calls for configurable reasoning effort, reasoning-free instant mode, streaming, multi-image vision, strict JSON-schema output, and function tools, while noting automatic prefix caching. It also covers production-oriented details such as generous max\_tokens for reasoning-heavy structured output, a 100 MB request-body limit for vision, tool-call history handling, and deployment from US infrastructure.


### [Backstage with Lakebase, part 3](https://yomu.fyi/post/backstage-with-lakebase-part-3.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Cameron Casher, Shanil Anushka Fernando, Kevin Hartman
- Published: Jul 31, 2026
- Type: Explainer

This third part describes joining Backstage’s live operational Postgres catalog with analytical billing data so teams can query resource ownership and cloud spend together. Lakebase isolates compute per workload, allowing portal traffic and FinOps queries to use the same underlying storage; benchmarked catalog queries ran at 55–65 ms end-to-end and searches at two to four ms. Because Lakehouse Federation’s Postgres connector accepts static credentials while Lakebase applications use OAuth JWTs, the proof-of-concept creates a separate SCRAM-SHA-256 Postgres role for federation. This enables a single query to join Backstage resource names with Unity Catalog’s system.billing.usage without data movement, while branch billing remains visible per branch and endpoint. The operational considerations include password rotation, read-only grants, branch TTLs, and scale-to-zero endpoints that stop billing when idle.


### [Co-Designing AI Model Attention for Fast, Interactive Long-Context Inference](https://yomu.fyi/post/co-designing-ai-model-attention-for-fast-interactive-long-context-infe.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Tanya Lenz
- Published: Jul 31, 2026
- Type: Explainer

As context lengths increase in long-context and agentic workloads, attention increasingly dominates GPU inference runtime. Dense attention performance depends on architectural parameters like group size, head dimension, and sequence length, alongside GPU parallelization strategies. Prefill remains compute-bound and scales quadratically with input sequence length, showing minimal runtime sensitivity to group size variations. Conversely, decode generation is memory-bound and scales linearly with KV cache length, where doubling group size roughly halves decode execution time. To maximize throughput and interactivity on NVIDIA hardware, model architects should push group sizes higher, select head dimensions of 128 or 256, reduce effective KV state, and keep tensor parallelism less than or equal to the number of KV heads.


### [Using Triggers On Schemaless, Uber Engineering's Datastore Using MySQL](https://yomu.fyi/post/using-triggers-on-schemaless-uber-engineering-s-datastore-using-mysql.md)
- Company: [Uber](https://yomu.fyi/company/uber.md)
- Published: Jul 31, 2026
- Type: Explainer

Schemaless triggers provides a scalable, fault-tolerant, and lossless way to listen for changes in Schemaless, Uber Engineering’s datastore using MySQL, and drives workflows including trip billing, analytics-warehouse ingestion, and cross-datacenter replication. Schemaless stores immutable cells and all their versions, allowing each shard to function as a partitioned change log with monotonically increasing added IDs while retaining random-access APIs. The framework tails shards through get\_cells\_for\_shard, assigns them to worker processes under a leader, persists offsets in shared storage, and invokes column-specific callbacks such as bill\_rider; handlers must be idempotent because retries can occur. Failed workers’ shards are reassigned, repeatedly failing cells can move to a separate queue, processing continues during leader or shared-storage outages although redistribution pauses, and replicas insulate triggers from database-node failures.


### [The Architecture of Schemaless, Uber Engineering's Trip Datastore Using MySQL](https://yomu.fyi/post/the-architecture-of-schemaless-uber-engineering-s-trip-datastore-using.md)
- Company: [Uber](https://yomu.fyi/company/uber.md)
- Published: Jul 31, 2026
- Type: Explainer

Schemaless is Uber's scalable, fault-tolerant datastore whose immutable cells are identified by row key, column name, and ref key, while JSON content remains schemaless and supports eventually consistent secondary indexes. Its worker nodes accept HTTP requests, route them to storage nodes, and aggregate results, while storage data is partitioned into typically 4096 shards, each replicated across one master and two minions in multiple data centers. Writes go to the master and replicate asynchronously, with idempotent retries, circuit breakers, failover, and configurable reads from masters or minions. To reduce loss during master failure, buffered writes persist each request to a primary and secondary cluster, though buffered data may not be immediately readable. Each shard maps to a MySQL database, where an auto-increment added\_id primary key supports linear insertion and trigger processing, and MessagePack with ZLib compresses cell JSON blobs.


### [Kafka App? There’s a Skill for That](https://yomu.fyi/post/kafka-app-there-s-a-skill-for-that.md)
- Company: [Etsy](https://yomu.fyi/company/etsy.md)
- Author: Isobel Scott
- Published: Jul 31, 2026
- Type: Problem & solution

Etsy uses Kafka streams to provide fresh features and embeddings for machine learning models, which infer shopper intent and listing meaning from visits and listing data. Previously, engineers manually assembled Java and YAML applications, selected schemas and base classes, and handled testing and local execution. Etsy introduced conversational Kafka Skills that use language models to interpret requests, pass structured inputs into versioned scaffolding and validation tools, run applications locally, and open pull requests. Telemetry records Skill invocation and pull-request completion through protobuf messages in Kafka and BigQuery, while least-privilege ACLs, repository permissions, and deployment controls constrain agent actions. Initial adoption was low because developers did not know the Skills existed, so Etsy added vocabulary-specific and repository-level entry points; it now has Skills for seven Kafka workflows and is focusing on discoverability.


### [Modeling Device Capabilities for Analytics](https://yomu.fyi/post/modeling-device-capabilities-for-analytics.md)
- Company: [Netflix](https://yomu.fyi/company/netflix.md)
- Author: Netflix Technology Blog
- Published: Jul 31, 2026
- Type: Problem & solution

Netflix supports diverse features, such as 4K streaming, immersive audio, and cloud gaming, across varied hardware platforms. Because hardware constraints like RAM, CPU cores, and display capabilities limit feature support on certain models, understanding device capabilities is crucial for managing user experiences. To address this challenge, Netflix built a comprehensive device capability data model integrated with internal feature flags. The architecture employs a cumulative table to capture the latest state of device capabilities alongside a histogram table tracking 28-day active device counts grouped by model and software version. These datasets power analytical products that evaluate feature reach and inform rollout decisions for capabilities like Netflix Spatial Audio and 4K Ultra HD.


### [Don’t stop early: Case-folding source code at memory speed](https://yomu.fyi/post/don-t-stop-early-case-folding-source-code-at-memory-speed.md)
- Company: [Github](https://yomu.fyi/company/github.md)
- Author: Alexander Neubeck
- Published: Jul 31, 2026
- Type: Problem & solution

GitHub's code search engine, Blackbird, must case-fold over 480TB of source code across 180 million repositories during indexing and query matching. To accelerate this operation on source code that is overwhelmingly ASCII, the engineering team replaced early-exit branching with an unconditional branch-free loop. The implementation tests uppercase ASCII ranges using wrapping arithmetic, modifies bits in place, and detects non-ASCII bytes with an accumulator register tested only after the loop completes. Eliminating data-dependent exits allowed LLVM to generate SIMD instructions and achieve throughput exceeding 45 GiB/s on an Apple M4 processor. The optimized implementation was released as the open-source Rust crate casefold.


### [NVIDIA Video Codec SDK 13.1: Zero-Copy Transcode, AV1 B-Frames, and Frame-Accurate Seek](https://yomu.fyi/post/nvidia-video-codec-sdk-13-1-zero-copy-transcode-av1-b-frames-and-frame.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Elizabeth Goodman
- Published: Jul 31, 2026
- Type: Announcement

Modern video pipelines require higher efficiency and support for complex workloads across streaming, collaboration, and artificial intelligence media tools. NVIDIA released Video Codec SDK 13.1 to expand GPU-accelerated encoding, decoding, and transcoding capabilities. The release introduces Hierarchical Reference Mode for AV1 with up to 31 B-frames, combines UHQ tuning with iterative encoding, and extracts per-macroblock decode statistics for H.264 and HEVC without CPU overhead. It also implements frame-accurate, GOP-aware seeking through the NvVideoDecoder class and adds stereoscopic MV-HEVC decode metadata. To simplify setup across different environments, the update delivers an official multi-stage Docker container pinned to Ubuntu 22.04 LTS, CUDA 12.3.2, and Vulkan SDK.


### [Best Delivery App to Work with in 2026 | Uber Uber](https://yomu.fyi/post/best-delivery-app-to-work-with-in-2026-uber-uber.md)
- Company: [Uber](https://yomu.fyi/company/uber.md)
- Published: Jul 31, 2026
- Type: Explainer

Choosing a delivery platform in 2026 depends on hourly earning potential, payout speed, vehicle requirements, scheduling flexibility, and extras beyond base fares. The guide compares six US apps—Uber Eats, DoorDash, Instacart, Amazon Flex, Grubhub, and Walmart Spark—across restaurant, convenience, grocery, retail, and package work. It describes Uber Eats features including per-delivery fares, 100% tip retention, Tip Guarantee, Uber Pro Card access to earnings after each delivery, promotions, vehicle flexibility, and scheduling options. Competitors use different models: Instacart shoppers pick groceries, Amazon Flex pays guaranteed block fares, and some services impose market-specific conditions. The guide cautions that earnings are market-dependent, figures may not be directly comparable, and net income must account for fuel, maintenance, phone data, and self-employment taxes.


### [Dispatches from O'Reilly: The best risk mitigation strategy in data? A single source of truth](https://yomu.fyi/post/dispatches-from-o-reilly-the-best-risk-mitigation-strategy-in-data-a-s.md)
- Company: [Stack Overflow](https://yomu.fyi/company/stack-overflow.md)
- Author: Jeremy Arendt
- Published: Jul 31, 2026
- Type: Explainer

Data organizations face severe operational risks from inconsistent metrics, sprawling access controls, and incomplete change management across disparate BI tools and warehouses. Traditional remediation relies on centralized BI gatekeepers and fragmented governance frameworks, which introduces costly bottlenecks and fails to scale. Implementing a semantic layer mitigates these risks by consolidating metric definitions, business logic, and calculations into a single governed location. Centralizing access shrinks the governance surface area and allows metric updates to propagate automatically to downstream consumers, including Tableau, Power BI, Excel, and AI tools. Structured metadata captures context directly alongside models, enabling reliable self-service and federated hub-and-spoke delivery while keeping definitions auditable under version control.


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