---
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

### [Access On Demand pilot with RTD’s Access-a-Ride](https://yomu.fyi/post/access-on-demand-pilot-with-rtd-s-access-a-ride.md)
- Company: [Uber](https://yomu.fyi/company/uber.md)
- Published: Aug 4, 2026
- Type: Announcement

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](https://yomu.fyi/post/how-mercado-libre-rebuilt-its-observability-platform-on-clickhouse-clo.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Aug 4, 2026
- Type: Problem & solution

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](https://yomu.fyi/post/tino-cuellar-joins-anthropic-as-chief-global-affairs-officer.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Aug 4, 2026
- Type: Announcement

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](https://yomu.fyi/post/turn-one-giant-ai-generated-pull-request-to-a-reviewable-stack.md)
- Company: [Github](https://yomu.fyi/company/github.md)
- Author: Julia Muiruri
- Published: Aug 4, 2026
- Type: Problem & solution

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](https://yomu.fyi/post/unity-ai-gateway-is-generally-available.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: David Nasi, Stefania Leone, Kevin Stumpf, Kasey Uhlenhuth, Kelly Albano
- Published: Aug 4, 2026
- Type: Announcement

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](https://yomu.fyi/post/beyond-vlas-how-world-action-models-reshape-robot-manipulation.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Michelle Horton
- Published: Aug 4, 2026
- Type: Explainer

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](https://yomu.fyi/post/the-software-supply-chain-is-under-siege-devs-are-still-the-first-line.md)
- Company: [Docker](https://yomu.fyi/company/docker.md)
- Author: Jin Kim
- Published: Aug 4, 2026
- Type: Explainer

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](https://yomu.fyi/post/generate-trajectories-reasoning-traces-and-auto-labels-with-nvidia-alp.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Elizabeth Goodman
- Published: Aug 4, 2026
- Type: Announcement

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](https://yomu.fyi/post/databricks-joins-the-open-secure-ai-alliance-to-advance-ai-safety-and.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Katie Cummiskey, Maria Pere-Perez, Arun Pamulapati, Nishith Sinha
- Published: Aug 4, 2026
- Type: Announcement

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](https://yomu.fyi/post/fixed-cadence-to-seconds-making-clickhouse-cloud-autoscaling-more-reac.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Aug 4, 2026
- Type: Problem & solution

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](https://yomu.fyi/post/mixture-of-kittens-our-open-source-moe-megakernel-for-nvl72s.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Stuart Sul, Nash Brown, Henry Wildermuth, William Lin & Federico Cassano
- Published: Aug 4, 2026
- Type: Problem & solution

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](https://yomu.fyi/post/the-9-best-tools-for-your-early-stage-startup-tech-stack-in-2026.md)
- Company: [Intercom](https://yomu.fyi/company/intercom.md)
- Author: Alan McGlinchey
- Published: Aug 4, 2026
- Type: Explainer

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.


### [Bringing serverless functions closer to the speed of wire](https://yomu.fyi/post/bringing-serverless-functions-closer-to-the-speed-of-wire.md)
- Company: [Modal](https://yomu.fyi/company/modal.md)
- Published: Aug 4, 2026
- Type: Announcement

Modal’s legacy I/O plane routed all function inputs and outputs through us-east servers, adding up to ~200ms of network latency for calls from other regions. The new geographically distributed I/O plane, available in four or more regions, reduces end-to-end function call latencies by roughly 80ms at p50. The redesign involved moving non-critical work to background async processing, minimizing shared storage interactions on the hot path, and rewriting the server layer in Go for better concurrency. The team evaluated Valkey but chose Redis 7.1 because engine CPU usage spiked aggressively on Redis 7.2 onwards during input queue load testing. Users can further optimize by selecting the nearest routing\_region, keeping input and output payloads under 2MiB to avoid blob storage uploads, and batching small inputs to reduce network hops.


### [Model routing with Google Cloud API Gateway](https://yomu.fyi/post/model-routing-with-google-cloud-api-gateway.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Mak Ahmad, Sanjay Pujare
- Published: Aug 4, 2026
- Type: Tutorial

Google Cloud API Gateway adds model routing in Public Preview for AI applications that need to direct traffic among models without hardcoded endpoints or open-source proxies. The serverless ingress layer accepts OpenAI-compatible requests and uses routing rules in an OpenAPI 3.x specification, where x-google-api-management defines backends and routers map virtual model names to targets such as Gemini, Claude, or OpenAI OSS-GPT. After deployment, clients call a stable endpoint; the Gateway transcodes the request to the backend’s native schema, adds an Agent Platform authentication token, and forwards it, separating client authentication from provider credentials. The guide also notes that every backend in one router must share a host, and shows standalone routing alongside possible Agent Gateway integration for security governance.


### [Migrating a Large Flow Monorepo to TypeScript](https://yomu.fyi/post/migrating-a-large-flow-monorepo-to-typescript.md)
- Company: [Yelp](https://yomu.fyi/company/yelp.md)
- Author: Shawn Walton, Software Engineer
- Published: Aug 4, 2026
- Type: Problem & solution

Yelp migrated a 1.4-million-line JavaScript monorepo from Flow to TypeScript after community support and package ecosystem tooling shifted toward TypeScript. To maintain continuous type safety without code freezes, engineers converted the codebase package by package starting at the deepest internal dependencies. The toolchain used flowts for code conversion and flowgen to turn generated TypeScript declaration files back into Flow-compatible headers for downstream consumers. Webcore dogfooded the tooling across 90 packages, automated tracking through internal metrics, and assisted feature teams with bottleneck conversions. Completed in February 2026, the migration increased overall type coverage from 83.15% to 96.44% and unlocked TypeScript-only tooling.


### [Removing the Security Barrier to Agentforce Adoption](https://yomu.fyi/post/removing-the-security-barrier-to-agentforce-adoption.md)
- Company: [Salesforce](https://yomu.fyi/company/salesforce.md)
- Author: Scott Nyberg
- Published: Aug 3, 2026
- Type: Problem & solution

Enterprise AI adoption across regulated industries stalls when internal governance mandates private, dedicated network paths for sensitive data. To eliminate the need for manual VPN tunnels and complex proxy layers, Salesforce re-engineered Private Connect into a multi-cloud networking platform connecting customer environments to Hyperforce services. The Private Connect v2.0 architecture utilizes private links, transit gateways, and direct endpoint routing alongside an automated control plane for rapid Hyperforce regional rollouts. In addition to supporting cross-substrate interconnects for Azure and decoupling endpoint management, the platform incorporates a generic connector framework supporting tools like Snowflake, Databricks, and Kafka. Today, the system provisions secure connectivity in under 30 minutes while processing roughly 120 TB of data and 683 million requests monthly across 15 AWS regions.


### [GEM Training: How Meta Doubled the Efficiency of Its LLM-Scale Ads Foundation Model](https://yomu.fyi/post/gem-training-how-meta-doubled-the-efficiency-of-its-llm-scale-ads-foun.md)
- Company: [Meta](https://yomu.fyi/company/meta.md)
- Published: Aug 3, 2026
- Type: Explainer

Meta’s Generative Ads Recommendation Model (GEM), a hybrid foundation model for ads recommendations on Instagram and Facebook, trains across several thousand latest-generation GPUs and combines trillions of sparse embedding parameters with billions of dense parameters. The post frames its engineering problem around recommendation-specific jagged and asymmetric sequences, memory-bound operations, numerical sensitivity, communication overhead, activation pressure, and load skew that make conventional LLM training techniques insufficient. Its co-design approach combines custom kernels such as Jagged Flash Attention (JFA), BlockAttention, and Generalized Dot-Product Attention (GDPA), mixed ultra-low-precision training including MXFP8 attention and MLP, topology-aware 5D parallelism, SM-free collectives, and memory and load-balancing techniques. The reported result is a doubling of end-to-end training efficiency to 20–25% Model FLOPs Utilization (MFU) while total training FLOPs increased fourfold over 12 months; Base Batch Shuffling (BBS) alone delivered a reported 4% efficiency gain, with 4% QPS improvement and 4% peak memory reduction.


### [Introducing Delayed Ride Guarantee](https://yomu.fyi/post/introducing-delayed-ride-guarantee.md)
- Company: [Uber](https://yomu.fyi/company/uber.md)
- Published: Aug 3, 2026
- Type: Announcement

Delayed Ride Guarantee is launching in selected cities to increase a driver’s fare when a ride takes longer than the estimate shown in the offer card. After pickup, a ride that runs at least five minutes beyond the original estimate receives an automatic adjustment for driving time from pickup to drop-off, shown on the trip receipt. Wait-time fees, tips, and trip-change rules remain separate: rider-added stops are recalculated using time and distance rates, while unnecessary long detours do not qualify. The policy is intended to protect drivers from delays outside their control, but it excludes New York City, Washington state, Oregon state, and the listed products, including Hourly, Intercity, Connect, Rentals, High Capacity Vehicles, specified Taxi rides, and Matched Share Rides.


### [Introducing Claude for Nonprofits](https://yomu.fyi/post/introducing-claude-for-nonprofits.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Aug 3, 2026
- Type: Announcement

Claude for Nonprofits launches with GivingTuesday to help nonprofit organizations address limited resources while fitting AI into existing workflows and meeting community privacy expectations. The program offers discounts of up to 75% on Team and Enterprise plans, including Claude Opus 4.6, Sonnet 4.5, and Haiku 4.5, with plan capabilities and intended organization sizes described in the announcement. It also adds open-source connectors for Benevity, Blackbaud, and Candid, linking Claude to nonprofit and fundraising data and searches. A free, no-background course, AI Fluency for Nonprofits, offers instruction on grant writing, program evaluation, donor engagement, and efficiency. Pilot partnerships with more than 60 grantee organizations and customer examples are being used to learn how nonprofits can apply Claude to impact analysis, fundraising, documentation, and operational work.


### [How to Run Isolated Tenant Kubernetes Clusters on Shared GPU Infrastructure](https://yomu.fyi/post/how-to-run-isolated-tenant-kubernetes-clusters-on-shared-gpu-infrastru.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Tanya Lenz
- Published: Aug 3, 2026
- Type: Tutorial

Organizations sharing a single Kubernetes cluster across multiple teams frequently encounter conflicting custom resource definitions, overlapping role-based access control, and difficulties budgeting GPU capacity. To address these operational conflicts without provisioning separate hardware, administrators can deploy vCluster alongside KAI Scheduler on a shared host cluster. vCluster supplies each team with a dedicated, virtualized control plane featuring its own API server and permissions, while KAI Scheduler dynamically time-slices GPU resources through hierarchical queues. In a demonstration using an NVIDIA L40S GPU on MicroK8s, three separate tenant clusters successfully scheduled workloads with fractional allocations of 0.33 GPU each. The combination achieves tenant autonomy and visibility isolation while consolidating AI workloads on shared physical infrastructure.


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