---
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

### [Learn How to Lower Heroku Dyno Latency through Persistent Connections (Keep-alive)](https://yomu.fyi/post/learn-how-to-lower-heroku-dyno-latency-through-persistent-connections.md)
- Company: [Heroku](https://yomu.fyi/company/heroku.md)
- Author: Richard Schneeman
- Published: Nov 4, 2025
- Type: Tutorial

Repeatedly opening TCP connections between the Heroku Router and an application dyno can impose TCP slow start on every request, creating a latency penalty. The post explains how HTTP/1.0 tied each request to a new connection, while HTTP/1.1 keep-alive connections reuse an open connection and amortize setup overhead; unlike pipelining, they do not require requests to be prepared in advance. Heroku Router 2.0 uses keep-alive connections to dynos by default, avoiding repeated slow-start costs on repeat requests and exposing more available bandwidth and lower latency. Ruby applications that previously disabled keep-alives are directed to upgrade to Puma 7.1.0 or newer, then update the bundle, inspect settings, and deploy. Keep-alives still depend on both Puma and router configuration, and the post notes that the router also supports HTTP/2 features.


### [How we built a custom vision LLM to improve document processing at Grab](https://yomu.fyi/post/how-we-built-a-custom-vision-llm-to-improve-document-processing-at-gra.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Jia Chen
- Published: Nov 4, 2025
- Type: Problem & solution

Grab developed a specialized Vision LLM for extracting text and key information from Southeast Asian identity cards, driver’s licenses, and registration certificates, where varied languages and layouts challenged traditional OCR and existing models. It selected Qwen2-VL 2B for its efficient size, SEA language tokenizer coverage, and dynamic native-resolution image handling, while synthetic Common Crawl text images and Documint supplied reviewed training labels for detection, orientation, OCR, and KIE. LoRA fine-tuning improved Latin-script documents but struggled with Thai, Vietnamese, and dense unstructured layouts, leading to two-stage full-parameter training with synthetic SEA text images followed by task-specific document fine-tuning. The fully fine-tuned 2B model raised Thai accuracy by 70 percentage points and Vietnamese accuracy by 40 points over baseline, while a custom roughly 1B-parameter model combining Qwen2-VL 2B’s vision encoder with Qwen2.5 0.5B’s decoder stayed within a 3-point accuracy gap across most document types and achieved lower latency.


### [MongoDB Announces Leadership Transition](https://yomu.fyi/post/mongodb-announces-leadership-transition.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Author: MongoDB
- Published: Nov 3, 2025
- Type: Announcement

Dev Ittycheria announced his decision to retire as MongoDB President and Chief Executive Officer after eleven years of leading the company. Chirantan Desai will become the new Chief Executive Officer effective November 10, 2025. The transition follows a succession planning process initiated earlier this year when Ittycheria concluded he could not commit to an additional five-year term requested by the Board. Desai previously scaled ServiceNow organically from over $1 billion to more than $10 billion in revenue and served as President of Product and Engineering at Cloudflare. Ittycheria will remain on MongoDB's Board to support Desai through the transition into the company's next phase of growth.


### [Introducing Cursor for Enterprise](https://yomu.fyi/post/introducing-cursor-for-enterprise.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Cursor
- Published: Oct 31, 2025
- Type: Announcement

Cursor is introducing enterprise features intended to make its coding agents more capable, transparent, and secure for organizations already using the product. Hooks expose the agent loop to custom scripts that can log actions and tool calls, enforce compliance or command allowlists, scrub sensitive material, connect external systems, and trigger automations; they can be distributed through MDM or Cursor’s cloud option. Team Rules provide shared context and can recommend or require standards such as API schemas, conventions, and workflows, while upgraded analytics report activity, adoption, AI lines of code, directory-group filters, and exportable data with updates every two minutes. The release also adds an Audit Log covering 19 event types and Sandbox Mode, which restricts network and file access by default, with administrators controlling availability and team-wide Git and network access.


### [How to build a Content Design Agent](https://yomu.fyi/post/how-to-build-a-content-design-agent.md)
- Company: [Intercom](https://yomu.fyi/company/intercom.md)
- Author: Russell Norris
- Published: Oct 31, 2025
- Type: Tutorial

An overloaded solo content designer built VERBI, an AI content design agent, to scale standardized work without replacing human judgment or filling products with generic copy. The agent runs in Glean, using a curated collection of Intercom’s style guide, tone of voice, product and monetization glossaries, messaging guides, research, and official websites as retrieval-augmented generation sources. The system prompt defines its role, use cases, tone, and component rules, while a Glean Indexing API connection makes Storybook’s Surge design-system guidance available as a live source. Additional instructions tell VERBI to challenge users, avoid guessing when sources lack an answer, and keep responses concise. After 700+ triggers, the reported benefits are consistent content rules, faster drafts and iteration, fewer repetitive questions and errors, and more time for higher-impact design review.


### [Heroku Connect: Faster, More Reliable Data Sync with Salesforce CDC](https://yomu.fyi/post/heroku-connect-faster-more-reliable-data-sync-with-salesforce-cdc.md)
- Company: [Heroku](https://yomu.fyi/company/heroku.md)
- Author: Vivek Viswanathan
- Published: Oct 30, 2025
- Type: Announcement

Heroku Connect customers needing current Salesforce data for applications such as custom quoting have faced stale records because legacy synchronization could lag, with Opportunity Line Items among unsupported objects for Accelerated Polling. Heroku is rolling out support for Salesforce Change Data Capture (CDC), allowing Accelerated Polling to listen for changes instead of relying only on the Salesforce Streaming API. That change adds more than 170 Salesforce standard objects to Accelerated Polling, including Opportunity Line Items, Pricebook2, Asset, and OpportunityLineItem, so critical data can synchronize more quickly. To enable it, a Salesforce administrator turns on CDC for selected objects, after which the corresponding Heroku Connect mappings expose the “Uses Accelerated Polling” option.


### [Headlines from Pioneer 2025: Fin 3, the vision for a unified Customer Agent, and what’s next for customer experience](https://yomu.fyi/post/headlines-from-pioneer-2025-fin-3-the-vision-for-a-unified-customer-ag.md)
- Company: [Intercom](https://yomu.fyi/company/intercom.md)
- Author: Declan Ivory
- Published: Oct 30, 2025
- Type: Announcement

Pioneer 2025 presented Intercom’s latest customer-service AI plans, centered on Fin 3 and a longer-term vision for a single Customer Agent covering the entire customer experience. Fin 3 adds Procedures for complex, policy-controlled resolutions, Simulations for pre-release testing, Slack and Discord deployment, improved Voice, and Insights features such as CX Score Reasons, Topic Trends, and AI-powered Suggestions. The proposed Customer Agent would combine specialized roles, user-defined goals, persistent memory, business knowledge, and interoperability across tools, systems, and channels. Customer examples described phased rollouts, rapid feedback, broad scaling, and support-team involvement, while Anthropic reported two specialists enabling nearly 50 full-time employees’ worth of coverage in its volume. The event framed AI as a force for converging products, roles, and organizational structures, with the Customer Agent future described as arriving quickly.


### [What’s new with Fin 3: The best AI Agent for complex queries across every channel](https://yomu.fyi/post/what-s-new-with-fin-3-the-best-ai-agent-for-complex-queries-across-eve.md)
- Company: [Intercom](https://yomu.fyi/company/intercom.md)
- Author: Declan Ivory
- Published: Oct 30, 2025
- Type: Announcement

Fin 3 is presented as an AI Agent for customer service focused on increasing automation rate by handling complex queries end-to-end across more channels. Procedures combine natural-language instructions with data connectors, conditional steps, code snippets, and approval checkpoints, allowing teams to encode standard operating procedures with varying levels of control. Simulations provide multi-turn testing for different customer segments and scenarios, while Fin Voice, Slack, and Discord expand deployment options. Insights upgrades add CX Score Reasons, Topic Trends, and AI-powered Suggestions that surface quality changes, topic shifts, and content improvements. The announcement says Fin 3’s broader goal is to increase the share of overall support workload it handles, beyond a resolution-rate metric that had reached 66% on average across more than 6,000 customers.


### [Preparing for the Customer Agent future](https://yomu.fyi/post/preparing-for-the-customer-agent-future.md)
- Company: [Intercom](https://yomu.fyi/company/intercom.md)
- Author: Bobby Stapleton
- Published: Oct 30, 2025
- Type: Announcement

At Pioneer 2025, Eoghan McCabe presented a plan for Fin to become a unified Customer Agent spanning the entire customer experience, from first contact through purchase, support, success, and upsell. The proposal responds to the risk that separate AI Agents for sales, marketing, success, and service will use conflicting priorities, disconnected information, and inconsistent interactions. Fin is intended to evolve from a task-based system into an agentic system with fluid roles, user-defined goals, persistent customer memory, comprehensive business knowledge, and interoperability across tools, systems, and channels. The post argues that customer service leaders are positioned to guide this wider AI rollout because they have experience, data, and operational insight from early AI adoption. Early activity includes WHOOP using Fin for sales conversations, where it is resolving 84%, while new capabilities are expected to roll out over the coming months.


### [Cloud Agents](https://yomu.fyi/post/cloud-agents.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Cursor
- Published: Oct 30, 2025
- Type: Announcement

Cursor’s Cloud Agents let developers run multiple agents remotely without keeping a laptop connected to the internet. Agents can be launched and managed from the Cursor editor, cursor.com/agents, Slack, Linear, and GitHub, supporting background bug fixes, small todos, and larger feature work. For difficult bugs, the team commonly has multiple models attempt the same problem and selects the best result; for complex features, a locally developed plan can be sent to a cloud agent for implementation, allowing work to continue on another task. Cursor also revamped its GPT-5 Codex agent harness for longer time horizons in the cloud, while plan mode supports sending implementation plans remotely. The workflow is available today in Cursor editor and Cursor Web, as well as the listed integrations.


### [Aligning to What? Rethinking Agent Generalization in MiniMax M2](https://yomu.fyi/post/aligning-to-what-rethinking-agent-generalization-in-minimax-m2.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: MiniMax
- Published: Oct 30, 2025
- Type: Problem & solution

MiniMax M2 addresses the performance gap between agent benchmark scores and real-world deployment across diverse scaffolding frameworks and environments. While initial alignment efforts relied on scaling tool variety, models continued to fail when subjected to slight variations in prompts, scaffolding, and tool responses. To resolve this instability, the team introduced interleaved thinking, which enables models to execute internal reasoning at arbitrary steps throughout long-horizon tasks to adapt dynamically to external tool outputs. Additionally, developers constructed a data pipeline designed for full-trajectory generalization by introducing perturbations across system prompts, user objectives, environments, and tool feedback. In internal evaluations, MiniMax M2 maintained robust tool-calling and instruction-following performance when tested against unfamiliar, cold-start agent scaffolding frameworks.


### [Machine-learning predictive autoscaling for Flink](https://yomu.fyi/post/machine-learning-predictive-autoscaling-for-flink.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Minh Nhat Nguyen
- Published: Oct 30, 2025
- Type: Problem & solution

Grab’s Flink platform saw application count grow 2.5 times, making cost-effective, self-service CPU provisioning difficult for users who often relied on trial and error and over-provisioned resources. Reactive autoscaling with Adaptive Scheduler, Kubernetes HPA, and metrics such as CPU usage or consumer latency caused restart spikes, scaling spirals, fluctuations, and additional constraints when connectors such as Kafka reached fixed partition parallelism. Predictive Resource Advisor instead uses vertical scaling and predictive, deterministic CPU decisions, automatically collecting data, training models, and applying configuration changes. The source says applications using it experienced fewer autoscaling-related disruptions, adapted to workload growth and surges, and averaged more than 35% cloud infrastructure cost savings. It also reports simpler default-configuration deployment, while planned work includes memory tuning, improved burst handling, and more efficient model training.


### [Improving performance by prefetching product pages from Etsy Search](https://yomu.fyi/post/improving-performance-by-prefetching-product-pages-from-etsy-search.md)
- Company: [Etsy](https://yomu.fyi/company/etsy.md)
- Author: David Weinzimmer
- Published: Oct 29, 2025
- Type: Problem & solution

Etsy’s search and web performance teams tested the Speculation Rules API (SRA) to reduce loading time for product listing pages reached from desktop organic search. They added a speculationrules configuration that prefetches eligible listing HTML after a 200-millisecond hover, using URL and data-attribute selectors with moderate eagerness; unlike link prefetch, SRA uses both memory and HTTP caches. The experiment required attention to a two-page memory limit, five-minute retention, non-cacheable listing HTML, video hover behavior, cookies, new-tab targets, and analytics events triggered during prefetches. Results showed 23.6% faster 75th-percentile TTFB, with improvements of 20.7% in FCP, 21.1% in LCP, 20.4% in DOMContentLoaded, and 10.6% in Page Load, while roughly one in 14 prefetches led to navigation.


### [First Block with Fabian Hedin, Co-founder and CTO of Lovable](https://yomu.fyi/post/first-block-with-fabian-hedin-co-founder-and-cto-of-lovable.md)
- Company: [Notion](https://yomu.fyi/company/notion.md)
- Author: Anastasia Crew
- Published: Oct 29, 2025
- Type: Explainer

First Block features Fabian Hedin, co-founder and CTO of Lovable, an AI-powered software development platform that lets non-technical people build production-ready applications through natural language. The conversation covers Lovable’s launch in November 2024 and its reported growth from $1M to $100M ARR in eight months, alongside Hedin’s path from running Minecraft servers to founding the company. Hedin argues that teams should build around what foundational models can do now instead of designing elaborate agent systems for anticipated capabilities. He also says Lovable launched later than it should have because of its waitlist and uses its own product extensively for internal tools, including office CRM and operations software. The episode frames early user feedback and obsessive dogfooding as practical ways to improve an AI product.


### [On the Shifting Global Compute Landscape](https://yomu.fyi/post/on-the-shifting-global-compute-landscape.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Tiezhen WANG, Irene Solaiman
- Published: Oct 29, 2025
- Type: Explainer

United States export controls on advanced artificial intelligence hardware have catalyzed rapid expansion across China's domestic silicon and open-weight model ecosystem. Faced with restricted access to high-end NVIDIA GPUs, Chinese developers accelerated deployments on domestic accelerators, including Huawei Ascend, Cambricon, and Baidu Kunlun. Hardware scarcity spurred architectural and algorithmic innovations in compute efficiency, notably DeepSeek's Multi-head Latent Attention and Group Relative Policy Optimization, alongside substantial post-training cost reductions. Organizations such as Baidu and Ant Group now train foundation models directly on domestic hardware, fostering non-CUDA software stacks and lowering inference costs globally. Consequently, the global artificial intelligence infrastructure is shifting from an exclusively American-focused paradigm toward a dual-ecosystem landscape powered by domestic chips and open-weight architectures.


### [Building our next R&D hub in Berlin](https://yomu.fyi/post/building-our-next-r-d-hub-in-berlin.md)
- Company: [Intercom](https://yomu.fyi/company/intercom.md)
- Author: Darragh Curran
- Published: Oct 29, 2025
- Type: Announcement

Intercom will open its next R&D hub in Berlin on January 19, 2026, to support investment in its AI customer service platform and Fin, its AI Agent. It has already welcomed its first Berlin hires and plans to hire 100 people over the following year across engineering, AI, data science, product, and design. Berlin was chosen for its technical talent, creative culture, AI-focused startups and research labs, and proximity to the existing R&D hubs in Dublin and London. Fin is described as the primary contributor to this growth, with more than 7,000 businesses having transformed customer service with it and Fin months away from passing $100 million in ARR. The new office at Zimmerstraße 78 will host an R&D recruitment event on February 26, 2026, while an AI builders day is planned for February.


### [Composer: Building a fast frontier model with RL](https://yomu.fyi/post/composer-building-a-fast-frontier-model-with-rl.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Cursor
- Published: Oct 29, 2025
- Type: Announcement

Composer is a mixture-of-experts agent model for software engineering that targets interactive use, with reported frontier coding results and generation speed four times faster than similar models on the authors’ benchmarks. It is trained with reinforcement learning on real-world software engineering challenges in large codebases, using production search and editing tools, terminal commands, and codebase-wide semantic search. Training responses may be code edits, plans, or informative answers, while the process rewards efficient tool use, parallelism, minimal unnecessary responses, and evidence-based claims. Evaluation uses Cursor Bench, built from real agent requests and curated optimal solutions, measuring correctness alongside adherence to existing abstractions and software engineering practices. The training stack combines asynchronous reinforcement learning with PyTorch and Ray, MXFP8 MoE kernels, expert parallelism, hybrid sharded data parallelism, and sandboxed cloud environments scaled to hundreds of thousands concurrently.


### [Introducing Cursor 2.0 and Composer](https://yomu.fyi/post/introducing-cursor-2-0-and-composer.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Cursor
- Published: Oct 29, 2025
- Type: Announcement

Cursor 2.0 introduces Composer, described as the product’s first coding model, alongside an interface designed for running multiple agents in parallel. Composer is built for low-latency agentic coding, completes most turns in under 30 seconds, and was trained with tools including codebase-wide semantic search to improve work in large codebases. The new interface centers on agents and outcomes rather than files, while still allowing users to open files in a redesigned layout or return to the classic IDE; parallel runs are isolated through git worktrees or remote machines. The release also targets review and testing bottlenecks by simplifying change inspection and adding a native browser tool that lets Cursor test its work and iterate, and Cursor 2.0 is now available.


### [Building a Healthcare Robot from Simulation to Deployment with NVIDIA Isaac](https://yomu.fyi/post/building-a-healthcare-robot-from-simulation-to-deployment-with-nvidia.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Steven Palma, Andres Diaz-Pinto
- Published: Oct 29, 2025
- Type: Tutorial

Simulation in healthcare robotics has historically been slow, siloed, and difficult to translate into real-world physical systems. The NVIDIA Isaac for Healthcare v0.4 release introduces an end-to-end SO-ARM starter workflow to streamline simulation, training, and deployment for embodied surgical assistants. The architecture relies on a Sim2Real mixed training approach that fine-tunes the GR00T N1.5 foundation model on combined real-world and synthetic datasets using LeRobot. Synthetic data generated in simulation constitutes over 93 percent of the policy training data to bridge physical data collection constraints. Real-time inference on the SO-ARM101 precision manipulator utilizes RTI DDS communication, dual-camera vision, and TensorRT model conversion.


### [How to Build a Healthcare Robot from Simulation to Deployment with NVIDIA Isaac for Healthcare](https://yomu.fyi/post/how-to-build-a-healthcare-robot-from-simulation-to-deployment-with-nvi.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Asawaree
- Published: Oct 28, 2025
- Type: Tutorial

Simulation in healthcare robotics has historically been slow, siloed, and difficult to translate to physical hardware. NVIDIA introduced the Isaac for Healthcare v0.4 developer framework to streamline this lifecycle through an end-to-end SO-ARM starter workflow. Developers collect mixed real-world and synthetic datasets using LeRobot and SO-101 teleoperation, with synthetic generation accounting for over 93 percent of training data. The pipeline post-trains GR00T N1.5 models using dual-camera vision inputs, evaluates policies in Isaac Lab, and deploys them to physical hardware with RTI DDS communication. This integrated approach allows surgical assistant robots to interpret natural language instructions and execute physical manipulation tasks reliably.


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