---
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

### [Securely indexing large codebases](https://yomu.fyi/post/securely-indexing-large-codebases.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Jeremy Stribling
- Published: Jan 27, 2026
- Type: Problem & solution

Cursor describes a secure way to index large codebases for semantic search, where naive processing can delay availability for hours. The system builds a Merkle tree of file and directory hashes, synchronizes only divergent branches, and caches embeddings by chunk content to reduce repeated work. For new users, a client-generated simhash finds a similar index within the same team or user's indexes, which is copied while background synchronization reconciles differences. Content proofs derived from the client's full Merkle tree filter search results so users see only files they can prove they possess. Reported time-to-first-query falls from 7.87 seconds to 525 milliseconds for the median repository, and from 4.03 hours to 21 seconds at the 99th percentile.


### [The safety of speed: How we ship code 180 times per day](https://yomu.fyi/post/the-safety-of-speed-how-we-ship-code-180-times-per-day.md)
- Company: [Intercom](https://yomu.fyi/company/intercom.md)
- Author: Danny Fallon,Ryan Sherlock
- Published: Jan 27, 2026
- Type: Explainer

Intercom describes how it averages 180 production ships per workday in January 2026 while targeting 99.8+% availability, with an average 12-minute merge-to-production cycle. The post focuses on its Ruby on Rails monolith, deployed to three data-hosting regions through independent pipelines, and frames small batches as a way to limit accumulated risk. An automated pipeline builds a deployable slug, runs parallel CI, verifies it in pre-production with boot, CI, and Datadog Synthetics gates, then performs a staggered rollout with process-level rolling restarts. Safety also depends on engineer ownership, feature flags, production testing on a single host, outcome-focused heartbeat metrics, and rapid automatic or manual rollbacks. Incident reviews then harden the system, supporting the conclusion that speed is used to keep changes small, feedback immediate, and recovery fast rather than to trade stability for velocity.


### [Alyah ⭐️: Toward Robust Evaluation of Emirati Dialect Capabilities in Arabic LLMs](https://yomu.fyi/post/alyah-toward-robust-evaluation-of-emirati-dialect-capabilities-in-arab.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Omar saif alkaabi, Ahmed Alzubaidi, Hamza Alobeidli, Shaikha Alsuwaidi, Mohammed Alyafeai, Leen AlQadi, Basma Boussaha, Hakim Hacid
- Published: Jan 27, 2026
- Type: Benchmark

Standard Arabic language model evaluations predominantly assess Modern Standard Arabic while overlooking regional spoken dialects that feature distinct syntax, vocabulary, and cultural grounding. To address this evaluation gap, researchers created Alyah, a benchmark comprising 1,173 manually curated multiple-choice questions collected from native Emirati speakers across seven cultural and linguistic categories. Distractor answer choices were synthetically generated using language models and manually reviewed for semantic plausibility. Evaluating base and instruction-tuned Arabic-native, adapted, and multilingual models revealed that instruction-tuned systems consistently outperformed base variants, with falcon-h1-arabic-7b-instruct achieving the top instruction accuracy at 82.18%. However, questions involving implicit meanings, rare expressions, and specific dialect phenomena remained difficult across all tested model families and sizes.


### [Unlocking Agentic RL Training for GPT-OSS: A Practical Retrospective](https://yomu.fyi/post/unlocking-agentic-rl-training-for-gpt-oss-a-practical-retrospective.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Jason Zhu, Hejian Sang, Arup De, Rohit Jain, Yanning Chen
- Published: Jan 27, 2026
- Type: Problem & solution

Engineers evaluated the GPT-OSS model for agentic reinforcement learning across multi-step tasks such as ReTool and GSM8K using the verl framework. Initial training runs suffered from exploding gradient norms, increasing KL divergence, and stagnating rewards. Investigation revealed that non-deterministic expert routing in the Mixture of Experts architecture caused log-probability mismatches between forward passes, which falsely triggered Proximal Policy Optimization clipping. The team resolved this by substituting detached current log-probabilities for old log-probabilities during on-policy updates. To address further training instabilities and memory bottlenecks, the implementation integrated an attention-sink-aware backward pass into FlashAttention v3 and applied sequence parallelism using all-to-all communication around attention layers.


### [Configuring Stripe using Terraform and AI agents](https://yomu.fyi/post/configuring-stripe-using-terraform-and-ai-agents.md)
- Company: [Stripe](https://yomu.fyi/company/stripe.md)
- Author: Michael Selander
- Published: Jan 27, 2026
- Type: Tutorial

The post addresses the difficulty of using AI agents to configure Stripe resources when one-off API calls leave no durable source of truth for state, changes, or environment differences. It recommends using agents to author Terraform configuration rather than operating Stripe directly, so products, prices, and webhook endpoints become explicit, reviewable code. The example defines a Standard Plan with $20 monthly and $200 annual prices, selected webhook events, and outputs for both price IDs, then applies it with terraform plan and terraform apply. Terraform provides declarative, repeatable configuration supported by Git history, pull-request review, and environment-specific workspaces. Separate sandbox and livemode workspaces, paired with matching STRIPE\_API\_KEY values, are presented as a way to reduce accidental cross-environment changes.


### [GIC, Sequoia, Index purchase Notion shares in private tender offer](https://yomu.fyi/post/gic-sequoia-index-purchase-notion-shares-in-private-tender-offer.md)
- Company: [Notion](https://yomu.fyi/company/notion.md)
- Author: Rama Katkar
- Published: Jan 26, 2026
- Type: Announcement

Notion held a private tender offer in which GIC, Sequoia Capital, and Index Ventures purchased about $270 million of shares directly from current and former employees at an $11 billion valuation. GIC is a new investor, while Sequoia and Index are returning investors; Notion says GIC’s long-term outlook and experience supporting growth in APAC and globally aligned with its future plans, including its Singapore office and regional presence in Tokyo, Seoul, and Sydney. The company says its growth accelerated in 2025 alongside AI adoption: more than 50% of ARR came from AI-enabled customers at the end of last year, and that percentage more than doubled over the last year. To broaden participation, Notion removed the one-year vesting cliff on options for current employees, providing financial liquidity to more Notinos and their families.


### [Dropbox uses Cursor to index over 550,000 files and build an AI-native SDLC](https://yomu.fyi/post/dropbox-uses-cursor-to-index-over-550-000-files-and-build-an-ai-native.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Cursor
- Published: Jan 26, 2026
- Type: Explainer

Dropbox adopted Cursor to increase software-development velocity across a demanding environment: its own data centers handle more than 300,000 requests per second, while thousands of engineers maintain a monorepo exceeding 550,000 files. Engineers first experimented organically in 2024, then AI champions, easier sign-up, and a company-wide hackathon helped broaden adoption; CTO Ali Dasdan’s two-hour “smart finder” experience reinforced executive support. Cursor indexed the monorepo by scanning non-ignored files, splitting code into structured chunks, and generating embeddings for a semantic index that supports code generation and editing. More than 90 percent of engineers now use AI tools weekly, with Cursor appearing across coding, review, testing, documentation, and migrations. Dropbox reports that PR throughput and cycle time have moved into the upper tier of industry benchmarks, while describing an ongoing redesign of its software-development lifecycle.


### [SkyPilot at Shopify: Multi-cloud GPUs without the pain](https://yomu.fyi/post/skypilot-at-shopify-multi-cloud-gpus-without-the-pain.md)
- Company: [Shopify](https://yomu.fyi/company/shopify.md)
- Author: 2023-10-18
- Published: Jan 26, 2026
- Type: Explainer

Shopify uses SkyPilot to run machine-learning training workloads across GPU clusters on Nebius and GCP, addressing scarce hardware, fragmented capacity, and provider-specific interfaces. Persistent Kubernetes clusters remain managed by Shopify, while SkyPilot launches jobs and a company plugin routes requests, validates labels, injects configuration, and applies organizational policies. H200 workloads go to Nebius for InfiniBand-equipped distributed training, while L4 and CPU-only jobs go to GCP; labels also connect jobs to cost ownership, Kueue quotas, priority classes, and development-environment rules. The plugin automatically configures InfiniBand access, shared caches, and temporary storage, reducing manual setup and speeding repeated workloads. The resulting YAML-based interface preserves direct resource control while hiding cloud-selection decisions, giving teams common workflows as providers, prices, and GPU availability change.


### [First Block with Gerry Giacomán Colyer, Co-founder and CEO of Clara](https://yomu.fyi/post/first-block-with-gerry-giacoma-n-colyer-co-founder-and-ceo-of-clara.md)
- Company: [Notion](https://yomu.fyi/company/notion.md)
- Author: Anastasia Crew
- Published: Jan 21, 2026
- Type: Explainer

First Block features Gerry Giacomán Colyer, Clara’s co-founder and CEO, discussing the company’s founding, rapid growth, and expansion of AI agents for finance operations across Latin America. Clara’s bank partner shut down only months after launch, prompting the team to secure direct issuer licenses with Visa and MasterCard in two weeks and build more robust infrastructure from the outset. The episode connects that resilience to product-led growth: a self-serve onboarding flow supported more than 20,000 customers across three countries, from pre-revenue startups to the Mexican Stock Exchange. The company is now applying its transaction data across Latin American businesses to a financial analyst agent that automates policy enforcement and approval workflows. It also covers the founding lesson from a previous company’s financial-tooling difficulties, early milestones, scaling decisions, and a roadmap across markets, alongside questions about efficiency, resilient infrastructure, and distinctive data advantages.


### [AI in Investment Management: 2026 Outlook (Part II)](https://yomu.fyi/post/ai-in-investment-management-2026-outlook-part-ii.md)
- Company: [Two Sigma](https://yomu.fyi/company/two-sigma.md)
- Author: Emily Majewski
- Published: Jan 21, 2026
- Type: Explainer

Two Sigma leadership outlines the evolving role of artificial intelligence across quantitative investing workflows heading into 2026. The firm is embedding frontier large language models into internal systems, incident management, and feature generation pipelines to accelerate research tasks that previously took months into days. Technical focus across the broader field is shifting from raw parameter scaling toward efficiency optimizations, multimodal unified representations, and mechanistic interpretability circuits. In forecasting pipelines, rapid automated hypothesis generation introduces severe risks of overfitting and compromised backtesting, particularly when pre-trained models already contain historical regime knowledge prior to their cutoff dates. Consequently, engineering success requires strong institutional research discipline, production monitoring, and skepticism alongside the adoption of automated agentic tooling.


### [Salesforce accelerates velocity by over 30% and ships higher-quality code with Cursor](https://yomu.fyi/post/salesforce-accelerates-velocity-by-over-30-and-ships-higher-quality-co.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Cursor
- Published: Jan 21, 2026
- Type: Problem & solution

Salesforce has made Cursor available to thousands of engineers working across a codebase built over more than 25 years, and reports more than 30% higher velocity alongside gains in code quality. Junior engineers used it to understand existing code and contribute more effectively, while senior engineers began with tedious tasks before expanding to higher-value work as trust grew. Adoption spread from small groups that demonstrated impact, and AI eventually reached every part of the software development lifecycle rather than code writing alone. Salesforce tracks cycle time, quality measured by bug count, and throughput; it reports double-digit improvements across all three, an 85% reduction in one team’s legacy code coverage time, and far more unit tests generated with Cursor.


### [AssetOpsBench: Bridging the Gap Between AI Agent Benchmarks and Industrial Reality](https://yomu.fyi/post/assetopsbench-bridging-the-gap-between-ai-agent-benchmarks-and-industr.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Dhaval Patel, James Rayfield, Saumya Ahuja, Chathurangi Shyalika, Shuxin Lin, Zhou, Ayhan Sebin
- Published: Jan 21, 2026
- Type: Benchmark

AssetOpsBench is an evaluation framework designed to benchmark agentic AI systems within industrial Asset Lifecycle Management operations, such as chillers and air handling units. The benchmark incorporates 2.3 million sensor telemetry points, 4,200 work orders, 53 structured failure modes, and over 140 curated scenarios spanning four agent roles. Systems are assessed across six qualitative criteria, including task completion, retrieval accuracy, result verification, sequence correctness, clarity and justification, and hallucination rate. Evaluation traces are analyzed using a trajectory-level diagnostic pipeline called TrajFM, which extracts and clusters failure patterns. In community tests covering over 300 agents, no evaluated model family reached the 85-point readiness threshold.


### [Docker lazy loading at Grab: Accelerating container startup times](https://yomu.fyi/post/docker-lazy-loading-at-grab-accelerating-container-startup-times.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Huong Vuong
- Published: Jan 21, 2026
- Type: Problem & solution

Large Docker images for Airflow and Spark Connect caused minute-scale downloads, delaying cold starts and weakening autoscaling responsiveness. The post describes testing eStargz and Seekable OCI (SOCI) with containerd remote snapshotters, which expose registry-backed layers through a virtual lowerdir and fetch file content on demand. In production, SOCI delivered 30–40% faster startup times for Airflow and Spark Connect, while benchmark results showed SOCI matching standard images’ 5.0-second Airflow application startup; eStargz took 25.0 seconds. Configuration tuning increased download concurrency, unpack concurrency, and chunk size, reducing fresh-node image download time from 60 seconds to 24, a 60% improvement. Grab chose SOCI because Bottlerocket supports it natively, while noting that lazy loading redistributes rather than removes download work and requires workload-specific tuning.


### [Claude's constitution](https://yomu.fyi/post/claude-s-constitution.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Jan 21, 2026
- Type: Explainer

Claude’s constitution describes how explicit principles can guide a language model’s behavior instead of relying on values implicitly determined through large-scale human feedback. The approach uses AI feedback in two training phases: the model first critiques and revises responses using the principles, then reinforcement learning selects the more harmless output using AI-generated evaluations. The post says Constitutional AI produced a Pareto improvement in tests, with the model responding more appropriately to adversarial inputs while remaining helpful and not evasive; harmlessness results came without human harmlessness data. The constitution draws on the UN Declaration of Human Rights, safety practices, other AI-lab principles, global platform guidelines, and attempts to include non-Western perspectives, while remaining an iterative, adjustable document.


### [Take User Provisioning to the Next Level with Entitlements](https://yomu.fyi/post/take-user-provisioning-to-the-next-level-with-entitlements.md)
- Company: [Okta](https://yomu.fyi/company/okta.md)
- Author: Alisa Duncan
- Published: Jan 21, 2026
- Type: Tutorial

This workshop extends a React and Express.js Todo application’s SCIM-based user provisioning to include entitlements such as roles, licensing, and permissions for enterprise B2B SaaS use. Starting from completed SCIM workshop code, it adds a /Roles endpoint, database-backed roles, SCIM schemas and resource types, and TypeScript implementations that conform to SCIM responses and user resources. The walkthrough connects the API to an Okta SCIM application, discovers schemas and characteristics, and demonstrates synchronizing existing entitlements and assigning a custom is\_tall characteristic to a user. It also discusses multi-tenant designs, where organization-specific roles can be addressed through an API key in the Authorization header, while recommending OAuth for production.


### [One Year Since the “DeepSeek Moment”](https://yomu.fyi/post/one-year-since-the-deepseek-moment.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Adina Yakefu, Irene Solaiman
- Published: Jan 20, 2026
- Type: Explainer

The January 2025 release of DeepSeek's R1 model transformed China's artificial intelligence sector from a closed ecosystem into an organic open-source ecosystem. By releasing under the MIT license and sharing reasoning paths and post-training methods, DeepSeek turned complex reasoning into a reusable, downloadable engineering asset suitable for distillation in compute-constrained environments. Chinese technology organizations subsequently shifted from comparing standalone benchmark scores to developing ecosystem interfaces, application scenarios, and system-level infrastructure. Major players rapidly expanded their open footprints, driving downloads of newly created Chinese models on Hugging Face past those of any other nation. This momentum catalyzed global open-source adoption across international markets while prompting Western organizations to develop competing open-weight alternatives or build directly upon open Chinese foundations.


### [Differential Transformer V2](https://yomu.fyi/post/differential-transformer-v2.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Li Dong
- Published: Jan 20, 2026
- Type: Explainer

Differential Transformer V2 introduces an architectural refinement to differential attention that pairs and subtracts query heads within the same grouped-query attention group. The architecture doubles query heads while preserving key-value head counts, which avoids increasing key-value cache demands and removes the need for custom kernels. DIFF V2 replaces the per-head RMSNorm of its predecessor with a per-token, per-head projected lambda passed through a sigmoid function. This formulation expands the context root-mean-square range to between zero and root two, preventing massive gradient magnifications, mitigating attention sinks, and stabilizing large learning rate pretraining. Ablations confirm that sharing keys and values across subtracted heads and constraining lambda scaling are necessary to prevent training instability and elevated loss.


### [Introducing xaa.dev: A Playground for Cross App Access](https://yomu.fyi/post/introducing-xaa-dev-a-playground-for-cross-app-access.md)
- Company: [Okta](https://yomu.fyi/company/okta.md)
- Author: Sohail Pathan
- Published: Jan 20, 2026
- Type: Announcement

xaa.dev is a free, beta playground for exploring Cross App Access (XAA) end to end without local setup, infrastructure provisioning, or signup. It addresses the difficulty of testing XAA, which otherwise requires configuring an Identity Provider, resource authorization server, resource API, and requesting application before a token exchange can be observed. The site pre-configures these roles and guides users through authentication, token exchange, access token request, and resource access while exposing requests, responses, and cryptographic claims. Developers can also connect their own apps, including MCP clients, and test both OIDC and SAML identity assertions; it is currently in beta and requires no account.


### [Introducing Waypoint-1: Real-time interactive video diffusion from Overworld](https://yomu.fyi/post/introducing-waypoint-1-real-time-interactive-video-diffusion-from-over.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Andrew Lapp, Louis Castricato, Scott Fox, Shahbuland Matiana, David Rossi
- Published: Jan 20, 2026
- Type: Announcement

Overworld introduced Waypoint-1, a real-time interactive video diffusion model controllable through text prompts, mouse movements, and keyboard inputs. The architecture uses a frame-causal rectified flow transformer trained on compressed latent representations across 10,000 hours of video game footage paired with control signals. Pre-training uses diffusion forcing with causal attention, while post-training incorporates self forcing via DMD to mitigate autoregressive rollout error accumulation, enable one-pass classifier-free guidance, and support few-step denoising. To achieve low latency on consumer hardware, Overworld developed WorldEngine, a pure Python streaming inference library. Utilizing AdaLN feature caching, static rolling key-value caching with Flex Attention, fused matrix multiplications, and PyTorch compilation, the 2.3B parameter Waypoint-1-Small model reaches up to 60 frames per second on an RTX 5090 GPU.


### [Okta Developer Connect Recap](https://yomu.fyi/post/okta-developer-connect-recap.md)
- Company: [Okta](https://yomu.fyi/company/okta.md)
- Author: Akanksha Bhasin
- Published: Jan 19, 2026
- Type: Announcement

Okta Developer Connect Bengaluru was Okta’s first flagship Developer Connect event in India, bringing developers, architects, engineering managers, IAM practitioners, and technology leaders together to discuss identity as systems scale and AI agents work alongside humans. Sessions framed identity as foundational infrastructure spanning users, applications, APIs, services, and AI agents, while connecting core standards and practices to platform use cases including the Okta MCP Server, Cross App Access, Okta Integration Network, Okta Workflows, lifecycle management, and Verifiable Digital Credentials. Interactive discussions, audience Q&A, quizzes, surveys, and a research panel complemented talks and enabled exchanges with Okta engineers, product leaders, and advocates. The Bengaluru event begins an ongoing series intended to support practical identity education and open technical dialogue about enterprise-ready systems, identity standards, and AI-driven access and security assumptions.


[Newer posts](https://yomu.fyi/posts/page/159.md) · [Older posts](https://yomu.fyi/posts/page/161.md)