Loading…
Latest reads
Every post, summarised
Grounded summaries of engineering blogs, newest first. Each links to the original.
- 4,668 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
What is WAL backpressure, and why does ClickHouse Managed Postgres need it?
Postgres writes every change to the write-ahead log (WAL), and Managed Postgres uploads completed segments to object storage for point-in-time recovery; if uploads lag, retained WAL can fill the disk and cause a PANIC shutdown. ClickHouse Managed Postgres counters this with a data-plane systemd timer that checks pending segments every 15 seconds and uses cgroup v2 I/O controls to throttle client write bandwidth at 80%, 50%, or 20% of provisioned throughput as backlog thresholds rise. The drain path—including the archiver, checkpointer, and related processes—is placed in an immune cgroup, while client backends are throttled and reads remain uncapped. In a test with a 500 MB/s gp3 volume and an archive limited to 4 MB/s, the backlog reached 3,433 segments and 53 GiB before recovery. Once the limit was removed, the archiver cleared the queue, the throttle released automatically, and the disk stayed below 33% full.
amazon.science
34 Amazon Research Awards Build on Trainium recipients announced
Amazon announced 34 recipients from 30 universities for the Fall 2025 Build on Trainium: Responsible AI call for proposals. Part of a 110 million dollar credit initiative, the program funds academic research across new model architectures, machine learning libraries, optimizations, and distributed systems running on AWS Trainium hardware. The funded projects center on five key areas: AI safety and alignment, multilingual language models, representation engineering, sustainability with small language models, and deep learning for synthetic data generation. Awardees receive AWS Promotional Credits, access to more than 700 Amazon public datasets, assigned Amazon research contacts, and dedicated technical training resources. Highlighted efforts include parallelization strategies for trillion-parameter mixture-of-experts models scaling across 1,024 Trainium chips and inference optimization frameworks designed to enhance token efficiency.
Your OTel spans, our errors: A Sentry love story in one trace
Pointing a standalone OpenTelemetry SDK directly at Sentry ingests spans over standard OTLP, but the spans remain disconnected from Sentry error data. Because the separate SDKs share a process without sharing context, Sentry errors lack active OpenTelemetry trace and span IDs. Sentry introduced OtlpIntegration across backend SDKs including Python, Ruby, Node.js, Go, PHP, .NET, and Java to bridge this gap. The integration reads the active OpenTelemetry context, attaches trace and span identifiers to Sentry events, and configures ingestion. This decoupled model replaces the deeply integrated POTel strategy across most languages, except Java, removing mandatory OpenTelemetry dependencies while maintaining unified trace waterfalls.
Explorers, exploiters, and the myth of the 100x engineer
Engineering leadership often responds to rapid AI productivity gains by attempting to isolate and replicate the traits of individual outlier engineers. Vivek Raghunathan, SVP of engineering at Snowflake, frames this dynamic using reinforcement learning's explore-versus-exploit split, where roughly five percent of an organization fearlessly tests new AI tools while ninety-five percent prioritize execution on paved paths. Instead of treating these categories as fixed personality types or relying on external hiring, organizations succeed by viewing them as a continuum. Effective leadership establishes structured learning time, AI communities of practice, and direct mentorship to capture explorer discoveries and elevate the median engineer. Measuring progress requires tracking organizational movement along this capability scale rather than celebrating isolated high-performing individuals.
Postgres Changes gets AND filters, new operators, and column selection
Postgres Changes now supports filters composed across multiple columns, additional operators, and column selection for subscriptions over Postgres tables. It can receive insert, update, and delete events over WebSockets, while comma-separated conditions or the postgresChangesFilter builder combine conditions with AND. New operators include like, ilike, is, match, imatch, isdistinct, and negated forms, while selected payloads always include the table's primary key. Column selection is opt-in, requires @supabase/supabase-js 2.109.0 or newer, and columns must be selectable by the subscribing role; Row Level Security still applies. These changes can lower billable usage because rejected events avoid messages per subscriber and trimmed payloads send fewer egress bytes, although DELETE events carry only the primary key and OR composition is unsupported.
A beginner’s guide to Uber vouchers for riders
Uber connects riders with drivers through a mobile application, while Uber Vouchers lets transit agencies, universities, and colleges subsidize eligible rides. A voucher added to a rider’s account generally covers trip costs up to a stated amount; any excess is charged to the default payment method, while tips remain optional and are charged separately. The guide explains how to download the app, create an account with matching contact information, link payment, track and verify the arriving vehicle, complete a trip, rate the driver, and access in-app Help. It also covers requesting rides by text-capable phone through 833-USE-UBER (833-873-8237) from 4am to 10pm ET, service-animal access, lost items, and the in-app Safety Toolkit.
Scaling AI Agent Infrastructure with the MCP Stateless updates
The 2026-07-28 Model Context Protocol specification release candidate addresses production bottlenecks caused by the earlier session-oriented HTTP model, including session pinning, sticky routing, lost state, and Redis-backed infrastructure. It removes the initialize/initialized handshake and Mcp-Session-Id, carrying protocol version, client information, and capabilities in _meta on each request while exposing method and name through HTTP headers. The update also introduces ttlMs/cacheScope caching, Multi Round-Trip Requests for elicitations and task progress, issuer and resource-indicator verification, and full JSON Schema 2020-12 support. The post says the stateless core enables ordinary load balancing, serverless deployment, and transparent failover, while SDK beta releases for TypeScript, Python, Go, and C# support migration.
Sidekick's continual learning loop
Frontier models can accelerate an AI product launch, but their serving cost and latency can become impractical as usage grows, while their weights do not learn from production failures. Shopify’s proposed flywheel captures low-quality conversations, defines quality with a rubric and calibrated judges, improves the surrounding harness through autoresearch, and then converts repaired trajectories into supervised fine-tuning and GRPO training signal. A panel of frontier reasoning models critiques failures, an arbiter produces repair instructions, and unresolved cases go to expert annotators; gist compression then shortens the agent’s static prompt without measured judge-quality loss. In production, the GraphQL agent serves up to 2,000 requests per minute and reportedly achieves a 96% serving-cost reduction versus an estimated frontier-model cost, with lower latency and higher throughput. The approach is presented as a continual-learning system that turns production experience into model-weight updates.
Access On Demand pilot with RTD’s Access-a-Ride
RTD’s Access-on-Demand pilot gives eligible Access-a-Ride customers subsidized, curb-to-curb Uber trips that can be requested the same day instead of scheduled in advance. The program complements rather than replaces Access-a-Ride, offers up to 60 subsidized trips per month across participating providers, and operates throughout RTD’s service area during Access-a-Ride hours, except 1:30 a.m. to 3:30 a.m. Riders pay a $4.50 standard fare or $2.25 LiVE fare, while RTD subsidizes up to $20 per eligible trip; riders cover amounts beyond that limit. To use the benefit, customers need active certification, must complete RTD enrollment, and activate an Uber voucher linked to their enrolled account, which applies automatically to eligible trips.
How Mercado Libre rebuilt its observability platform on ClickHouse Cloud with 50x faster trace queries
Mercado Libre rebuilt its O11y events observability platform to answer granular business-level questions such as why a payment failed across a large microservice environment. The previous storage-layer design performed transformation, filtering, and aggregation at query time, causing queries to run longer than five minutes and making high-cardinality analysis expensive. The team moved storage to ClickHouse Cloud on AWS while retaining its existing ingestion path, and shifted heavy work into the database with materialized views, layered retention, and a dedicated lookup table for high-cardinality filtering. O11y events uses OpenTelemetry instrumentation with business-flow attributes and samples all traffic for critical applications. The resulting system reduced trace-query time to about four seconds, a 50x speedup, achieved up to 89% compression, and scaled from 7 million to 400 million spans per minute in ClickHouse.
Tino Cuellar joins Anthropic as Chief Global Affairs Officer
Mariano-Florentino (Tino) Cuéllar will join Anthropic as its first Chief Global Affairs Officer, leading policy, strategic international engagement, and government relationships worldwide. He brings experience spanning law, technology, international security, and public institutions, including the presidency of Carnegie Endowment, a California Supreme Court justice role, Stanford leadership, and service on federal advisory boards. Cuéllar has also co-led California’s Frontier AI Working Group and organized artificial intelligence classes at Stanford Law School nearly a decade ago. He stepped down as a Trustee of Anthropic’s Long-Term Benefit Trust, a position he held since January 2026, to join the company; Anthropic says he will help engage governments, civil society, and communities on AI’s risks, opportunities, and democratic governance.
Turn one giant AI-generated pull request to a reviewable stack
Coding agents often consolidate full-stack features into monolithic pull requests spanning thousands of lines, resulting in delayed reviews and lower feedback quality. To resolve this bottleneck, engineers can decompose comprehensive features into ordered, single-concern layers using GitHub stacked pull requests and the gh-stack CLI extension. Distinct specialized agents handle individual branches—ranging from foundational data access modules up to client interfaces—while CI checks continuously evaluate each layer against the stack base. When reviewers request upstream modifications, running gh stack sync executes a cascading rebase across all dependent branches, preserving commit signatures and keeping the entire pull request chain mergeable.
Unity AI Gateway is Generally Available
Unity AI Gateway is generally available as a centralized way for enterprises to manage AI costs, security, governance, and model choice across agents and other AI assets. It positions Unity AI Gateway alongside Unity Catalog: Unity Catalog supplies identity, permissions, lineage, and auditing, while the gateway applies runtime guardrails and contextual policies across AI interactions. The gateway centralizes usage data in Unity Catalog, offering dashboards, Genie-powered analysis, granular cost attribution, budgets, hard spend caps, and a single-query API for frontier models from providers including Anthropic, OpenAI, Gemini, Kimi, and GLM. Smart Routing is in beta and dynamically selects models using quality, cost, performance, availability, and budget; the announcement says customers already use the gateway at scale, including more than one quadrillion tokens passing through it in the past year.
Beyond VLAs: How World Action Models Reshape Robot Manipulation
Robot policies built on vision-language-action models often struggle with physical generalization because vision-language backbones describe scenes instead of predicting physical dynamics. World action models address this limitation by building policies on video world models that jointly predict actions and future world states. NVIDIA Cosmos 3 provides an open foundation using a Mixture-of-Transformers architecture where an autoregressive transformer guides a diffusion transformer to synthesize continuous modalities including video and robot actions. Specializing Cosmos 3 on the DROID platform produced policies capable of simultaneously outputting action trajectories and predicted video observations while boosting RoboLab success from 28.1% to 36.8%. These models support flexible deployment tiers, from workstation serving on RTX PRO 6000 GPUs to real-time 15 Hz on-device execution on NVIDIA Jetson Thor embedded hardware.
The Software Supply Chain Is Under Siege. Devs Are Still the First Line of Defense
A research report by Omdia examines escalating software supply chain security risks driven by growing dependencies on third-party code, open-source software, and artificial intelligence. Over three-fourths of surveyed organizations experienced a software supply chain incident in the preceding year, with known vulnerabilities in third-party software representing the most common exploit vector. Surveyed respondents ranked AI technology as their top supply chain concern, alongside broader issues involving vulnerability remediation and code composition identification. While shifting security left is a high priority for 98% of organizations, 45% still lack confidence in the robustness of their supply chain defenses. Although secure container libraries and Software Bills of Materials provide measurable mitigation benefits, many organizations only generate inventories on a case-by-case basis.
Generate Trajectories, Reasoning Traces, and Auto-Labels with NVIDIA Alpamayo 2 Super
Autonomous vehicle development typically relies on disjoint models for trajectory generation, intent prediction, scene understanding, and data labeling. NVIDIA Alpamayo 2 Super addresses this fragmentation with an open 34-billion-parameter vision-language-action model that couples a 32-billion-parameter Cosmos 3 Super Reasoner with a 2-billion-parameter diffusion-based Action Expert. Processing surround-view video across up to seven cameras, the model simultaneously produces future vehicle trajectories, Chain-of-Causation reasoning traces, high-level meta-actions, and grounded scene question answers. In benchmark evaluations, the model achieved a 6.4-second minADE_6 of 0.911 meters on the Physical AI AV Dataset and ranked first on LingoQA with a score of 79.2. Released under the OpenMDW-1.1 permissive license, the architecture serves as an offline policy teacher, evaluation critic, or auto-labeling engine that can be distilled for NVIDIA DRIVE AGX Thor deployment.
Databricks joins the Open Secure AI Alliance to advance AI safety and security
Databricks announces that it is a founding member of the Open Secure AI Alliance, formed with NVIDIA and more than 75 organizations to advance openly shared AI safety, AI security, and AI-enabled cyber defense research. The alliance’s scope extends beyond model weights to an open execution stack covering runtimes, guardrails, agent harnesses, risk frameworks, and governance. Databricks contributes Omnigent, an Apache 2.0 meta-harness supporting 13+ harnesses with contextual policies, spend caps, and sandbox isolation; DASF 3.0, which maps 97 risks across 13 components to 73 controls; DAGF; and BlackIce, a containerized toolkit bundling 14 red-teaming tools. Its Lakewatch Security Lakehouse applies the same openness to governed, agentic detection and response over portable data formats.
Fixed cadence to seconds: making ClickHouse Cloud autoscaling more reactive
ClickHouse Cloud’s vertical autoscaling recommender originally ran on a fixed timer, creating latency when workloads suddenly increased and services needed resources before the next scheduled pass. The redesign keeps the periodic sweep as a safety net while adding a reactive fast path, using controller-runtime as an event-processing layer with sources feeding a shared rate-limited work queue and reconciler. The queue provides deduplication, exponential backoff, bounded concurrency, leader election, and metrics, while ClickHouse stores near-real-time signals that a polling source reads; the excerpt notes polling sets a floor on reaction speed. The reactive path is rolling out cautiously in observe-only mode, with periodic reconciliation remaining the main driver, and the stated goal is to trigger scale-ups for critical events such as out-of-memory conditions in seconds rather than waiting for the next scheduled run.
Mixture-of-Kittens: our open-source MoE megakernel for NVL72s
Mixture-of-Kittens (MoK) is an open-source production mixture-of-experts (MoE) training megakernel designed for GB300 NVL72 systems, where the MoE layer had become a major training bottleneck and could consume more than half of end-to-end training time. It redesigns the layer around a single fully deterministic kernel that fuses communication and computation, minimizing CPU work and CPU-GPU synchronization while overlapping operations across the 72-GPU NVLink domain. The post describes target-specific scheduling, push-versus-pull communication choices, pipelined expert computation, ring token buffers, megakernel structure, determinism, and MXFP8 support for DeepSeek-V3-style layers. Across four model shapes, MoK is reported as up to 2.37x faster for MXFP8 forward and 1.78x for backward, while production training on 512 GPUs reached 1.41x tokens per second versus a DeepEP-based implementation. MoK is fully open-sourced and intended to remain modifiable for other platforms.
The 9 best tools for your early-stage startup tech stack in 2026
Early-stage startups face a crowded software market and limited budgets, making technology selection a question of leverage rather than completeness. The guide proposes three selection principles: choose tools that scale with usage, integrate with the existing stack, and solve a clearly defined problem. It then recommends Stripe for payments, Slack for collaboration, Granola for meeting notes, Fin and Intercom for customer service, Linear for planning, Datadog for observability, MongoDB for data, Rippling for workforce administration, and Apollo for outbound sales and CRM. The examples emphasize operational coverage, from Stripe’s subscriptions and MongoDB’s flexible schema to Fin’s stated ability to resolve up to 76% of support conversations. The Fin Startup Pack is presented as a way to reduce adoption costs through partner credits and discounts, including more than $500,000 in credits and specified offers for each recommended tool.
Top topics
- Architecture834
- Performance815
- LLMs659
- Machine Learning621
- Open Source563
- AI Agents553
- AI469
- Developer Experience448
Weekly digest
The best engineering reads, once a week.