Loading…
Latest reads
Every post, summarised
Grounded summaries of engineering blogs, newest first. Each links to the original.
- 4,675 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
Databricks Completes Acquisition of Panther: Accelerating the Security Lakehouse Era
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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.
Using Uber: your guide to Pace's RAP Program
Pace’s Ride Access Program (RAP) is a supplementary service for ADA paratransit riders in its six-county region, using transportation network companies such as Uber to expand transportation options through ride vouchers. RAP riders can use subsidies for up to eight Uber trips daily, pay a $3.25 co-pay, and receive up to $26.75 per trip, while covering costs above the $30 total limit; trips must start and end within the Pace ADA service area. The guide explains enrollment-related account setup, including matching the Uber account’s contact details to the RAP profile, verifying identity, and recognizing the in-app voucher through a strikethrough market price and subsidized initial charge. It also documents a no-smartphone booking option at 833-USE-UBER, support channels, optional tipping, service-animal access, lost-item recovery, and in-app safety tools.
Using Uber: your guide to NJ TRANSIT’s Access Link Riders’ Choice Pilot 2.0
NJ TRANSIT’s Access Link Riders’ Choice (ALRC) Pilot 2.0 gives eligible Access Link customers another option for eligible trips through participating transportation network companies, including Uber. Access Link subsidizes up to 10 Uber trips per month at up to $20 per trip, provided each ride begins and ends within an eligible service area and occurs between 6 a.m. and 9 p.m., seven days a week. After enrollment, riders match their Uber account information to their ALRC profile, accept the voucher from Uber, verify it in the Uber Wallet, and confirm its application before requesting a ride. The guide explains app and phone booking, voucher opt-out, payment, safety checks, support, tipping, service animals, lost items, and incident reporting.
Introducing Supabase Evals
Supabase has open sourced supabase/evals, a benchmark and framework for testing how AI agents build using Supabase. It runs coding agents including Claude Code, Codex, and OpenCode against real tasks such as schema creation, Edge Function debugging, and RLS policy repair, scoring behavior with deterministic checks and LLM-as-a-judge. The framework executes scenarios in hosted-like and local containerized environments through the actual MCP server and CLI, with separate breadth-focused benchmark and depth-focused regression suites, plus a retry after failure. Initial findings show many agents pass broad tasks without skills, while skills improve some scores and documentation use; agents still often choose hand-written migrations, miss newer libraries, and use documentation inconsistently across harnesses.
Autoscaling endpoints for LLM inference
Dedicated LLM inference deployments face a trade-off between paying for idle GPUs and suffering sharp p95 latency when replicas hit batching limits. The autoscaling loop converts observed load into desired replicas with ceil(N × observed/target), then applies scale-up and scale-down windows, replica bounds, and GPU placement. It presents inflight_requests as a leading, engine-agnostic default, while TTFT and end-to-end latency target responsiveness but trail demand, and GPU or token utilization prioritize efficiency with less latency headroom. Cold starts can take several minutes because replicas require GPU placement, weight downloads, VRAM loading, and warm-up, so policies should scale early; streaming is required for TTFT, decoding speed, and throughput-per-replica metrics, whereas end-to-end latency supports both streaming and non-streaming workloads.
Enable on-demand expertise with Agent Skills in Genkit Go
Agent Skills in Genkit Go address the prompt-context problem caused by loading every procedure, guide, and document into an agent’s persistent context. The tutorial defines skills as discoverable knowledge packs organized around a SKILL.md file, whose frontmatter is exposed first and whose instructions, references, assets, and scripts are loaded through progressive disclosure when needed. It integrates the Skills middleware with Genkit’s wrapper architecture, scans SkillPaths for metadata, matches descriptions, and invokes the use_skill tool before injecting the selected skill into active context; Go examples use ai.WithUse during Generate or GenerateText. Recipe and image-restoration examples show skills activating for matching requests, while the post notes that the restoration skill may require several attempts because model behavior is non-deterministic.
Top topics
- Architecture834
- Performance816
- LLMs660
- Machine Learning622
- Open Source563
- AI Agents553
- AI470
- Developer Experience448
Weekly digest
The best engineering reads, once a week.