---
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

### [Unwrap Innovation: How Sellers and Buyers Use AI to Brighten the Holidays](https://yomu.fyi/post/unwrap-innovation-how-sellers-and-buyers-use-ai-to-brighten-the-holida.md)
- Company: [Ebay](https://yomu.fyi/company/ebay.md)
- Author: eBay News Team
- Published: Nov 25, 2025
- Type: Announcement

eBay describes how its AI tools aim to reduce holiday-season friction for sellers and improve discovery and trust for buyers. More than 10M unique sellers have used its AI features, while sellers create more than half a million new listings daily using Generative AI. The simplified listing experience can start with an image, background enhancement adds festive professional-grade backgrounds, and AI Assistant for messaging drafts replies to common questions about item specifics or shipping. For shoppers, an AI shopping assistant pilot, LLM-powered fashion recommendations, and access to eBay’s inventory through third-party agents including OpenAI partnerships support personalized discovery. Trust features include Authenticity Guarantee expert verification for handbags, jewelry, and trading cards, plus Certilogo’s digital IDs and authentication for apparel, while eBay says these measures support safer participation in the circular economy.


### [Run for your money: Engineering a treadmill that prints store credit](https://yomu.fyi/post/run-for-your-money-engineering-a-treadmill-that-prints-store-credit.md)
- Company: [Shopify](https://yomu.fyi/company/shopify.md)
- Author: 2023-10-18
- Published: Nov 25, 2025
- Type: Problem & solution

For the 2025 NYC marathon weekend, Shopify and Endorphins Running built a gamified treadmill that awarded in-store credit for maintaining a self-selected pace. Participants had 30 seconds to reach their target, then earned credit for each second spent within a defined pace zone, with a personalized barcode receipt redeemable through a Shopify POS UI extension. The implementation used a curved, motorless TrueForm Trainer, FTMS messages, a Python relay, Socket.IO, a local Remix application, receipt printing, and a production-hosted POS integration. Interval timestamps were normalized because treadmill messages arrived irregularly, while a state machine coordinated warm-up, gameplay, operator controls, database updates, discounts, and printing. The resulting experience attracted new customers, with 63% of participants identified as new customers, and its patterns could support other sensor-driven in-store incentives.


### [Stablecoin payments for Stripe developers: zero crypto knowledge required](https://yomu.fyi/post/stablecoin-payments-for-stripe-developers-zero-crypto-knowledge-requir.md)
- Company: [Stripe](https://yomu.fyi/company/stripe.md)
- Author: Robert Love
- Published: Nov 25, 2025
- Type: Tutorial

The guide explains how Stripe developers can add USDC stablecoin payments without managing private keys, gas estimation, network selection, or wallet connection flows. Stripe supports USDC on Ethereum, Solana, Polygon, and Base, settles transactions as fiat in the merchant’s Stripe balance, and charges a 1.5% fee; the described feature is currently limited to US businesses. Integration can use payment\_method\_types: \['card', 'crypto'\], although Stripe recommends automatic\_payment\_methods for Dashboard-managed configuration, filtering, ordering, and testing. Stripe Checkout and Elements expose crypto without frontend changes, while confirmation, webhook handling, monitoring, and familiar failure processing remain aligned with existing payment flows. The guide also describes Sandbox testing without testnets or fake cryptocurrency and presents crypto payments as a way to offer global settlement and potentially lower processing costs.


### [Diffusers welcomes FLUX-2](https://yomu.fyi/post/diffusers-welcomes-flux-2.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: YiYi Xu, Daniel Gu, Sayak Paul, Alvaro Somoza, Dhruv Nair, Aritra Roy Gosthipaty, Linoy Tsaban, Apolinário from multimodal AI art
- Published: Nov 25, 2025
- Type: Tutorial

Diffusers introduces support for FLUX-2 models through the Flux2Pipeline and Flux2Transformer2DModel classes. Running the 4-bit quantized checkpoint diffusers/FLUX.2-dev-bnb-4bit requires loading Mistral3ForConditionalGeneration as the text encoder and the transformer in bfloat16 precision with CPU offloading enabled. During image generation with a prompt and 50 inference steps, the pipeline encodes text embeddings through the Mistral 3 model before executing diffusion operations. However, executing the pipeline on a GPU with 14.56 GiB capacity triggers a CUDA OutOfMemoryError during 4-bit dequantization inside bitsandbytes matrix multiplication operations. Additionally, the execution logs warn that Flax classes are deprecated in Diffusers and will be removed in version 1.0.0.


### [Continuous batching from first principles](https://yomu.fyi/post/continuous-batching-from-first-principles.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Rémi Ouazan Reboul, Arthur Zucker, Luc Georges
- Published: Nov 25, 2025
- Type: Explainer

Large language model serving requires running expensive next-token generation across multiple concurrent user requests. Traditional batching approaches introduce severe padding inefficiencies when mixing variable-length prompts and different generation phases, especially under static shape constraints like CUDA graphs. Continuous batching resolves these inefficiencies by combining key-value caching, chunked prefill, ragged batching, and dynamic request scheduling. Ragged batching eliminates the traditional batch axis by concatenating token sequences into a single tensor and using boolean attention masks to isolate independent sequences. By dynamically removing completed prompts and packing decoding tokens alongside chunked prefill tokens up to a hardware memory budget, serving systems maintain high hardware utilization and throughput.


### [Building Deep Research: How we Achieved State of the Art](https://yomu.fyi/post/building-deep-research-how-we-achieved-state-of-the-art.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Michael Griff, Dean Sacoransky, Noah Nefsky
- Published: Nov 24, 2025
- Type: Problem & solution

Building production AI research agents presents challenges around context window pollution, escalating token costs, and architectural brittleness across model updates. To resolve these issues, Tavily rebuilt its deep research system around simplified orchestration, compact tooling, and active context curation. The agent mimics human research workflows by distilling tool outputs into concise reflections for ongoing reasoning, withholding raw retrieved web data until the final deliverable stage. Compared to traditional ReAct propagation architectures where token consumption scales quadratically, this reflection-based approach achieves linear token growth. As a result, the system reduced token consumption by 66% compared to Open Deep Research while reaching state-of-the-art performance on DeepResearch Bench.


### [OVHcloud on Hugging Face Inference Providers 🔥](https://yomu.fyi/post/ovhcloud-on-hugging-face-inference-providers.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Gilles Closset, Fabien Ric, Elias Tourneux
- Published: Nov 24, 2025
- Type: Announcement

OVHcloud is now integrated as a supported Inference Provider on the Hugging Face Hub, expanding serverless inference options across model pages and client SDKs. The integration enables access to open-weight models, including gpt-oss, Qwen3, DeepSeek R1, and Llama, using European infrastructure with pay-per-token pricing starting at €0.04 per million tokens. Developers can connect via Python and JavaScript SDKs using either direct custom API keys or automatic routing through Hugging Face tokens. The service supports structured outputs, function calling, multimodal workflows, and embedding models while providing sub-200ms first-token response times. Calls routed through Hugging Face pass through standard provider pricing without markups, and PRO tier subscribers receive monthly inference credits.


### [Unlocking Peak Performance on Qualcomm NPU with LiteRT](https://yomu.fyi/post/unlocking-peak-performance-on-qualcomm-npu-with-litert.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Lu Wang, Weiyi Wang, Andrew Zhang
- Published: Nov 24, 2025
- Type: Announcement

Google introduces the LiteRT Qualcomm AI Engine Direct (QNN) Accelerator, developed with Qualcomm to replace the previous TFLite QNN delegate and simplify NPU deployment across supported Qualcomm SoCs. It integrates vendor compilers and runtimes behind a unified workflow, supports ahead-of-time and on-device compilation, and can fall back to GPU or CPU when needed. Across 72 canonical vision, audio, and NLP models, the accelerator supports 90 LiteRT ops, enables full NPU delegation for 64, and reports up to 100x CPU and 10x GPU speedups. On Snapdragon 8 Elite Gen 5, FastVLM-0.5B uses int8 weights, int16 activations, and specialized transformer kernels to reach 0.12 second time-to-first-token, over 11,000 prefill tokens per second, and over 100 decode tokens per second.


### [Unlock the Secrets of a Custom Sign-In Page with Tailwind and JavaScript](https://yomu.fyi/post/unlock-the-secrets-of-a-custom-sign-in-page-with-tailwind-and-javascri.md)
- Company: [Okta](https://yomu.fyi/company/okta.md)
- Author: Alisa Duncan
- Published: Nov 24, 2025
- Type: Tutorial

This tutorial shows how to customize an Okta-hosted sign-in page for a fictional to-do application while retaining the Okta Identity Engine and Sign-In Widget (SIW). It starts with a custom domain and brand configuration, then uses the page code editor, SIW design tokens, custom text, Tailwind CSS via CDN, and external JavaScript behavior to create a responsive layout, branded footer, and terms-and-conditions modal. The modal uses Alpine.js transitions and a MutationObserver watching the authentication container so it appears after SIW elements render; Google Fonts also require CSP allowlist entries for googleapis.com and gstatic.com. The post notes that browser manipulation can bypass the modal, recommends accessibility testing after customization, and describes connecting the brand to an Okta React sample through an OIDC custom issuer URL.


### [SpellVault’s evolution: Beyond LLM apps, towards the agentic future](https://yomu.fyi/post/spellvault-s-evolution-beyond-llm-apps-towards-the-agentic-future.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Felix Haryanto Lie
- Published: Nov 21, 2025
- Type: Explainer

SpellVault began as Grab’s no-code platform for building LLM apps, using Retrieval-Augmented Generation (RAG) and plugins to connect applications with knowledge sources and external systems. Users could ground responses in Wikis, Google Docs, text, and PDFs, invoke custom HTTP endpoints, and access apps through the web, Slack, or APIs. The platform later added citations, Python execution for mathematical problems, scheduled actions, and drag-and-drop Workflows combining LLM calls, code, and Knowledge Vault lookups, while its executor moved toward graphs with nodes, edges, and states. SpellVault now exposes apps and RAG through MCP, allowing external agents and compatible clients to invoke apps and search, retrieve, or add Knowledge Vault information; TinyMCP adds MCP capabilities to FastAPI as a router.


### [20x Faster TRL Fine-tuning with RapidFire AI](https://yomu.fyi/post/20x-faster-trl-fine-tuning-with-rapidfire-ai.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Kamran Bigdely, Arun Kumar, Quentin Gallouédec
- Published: Nov 21, 2025
- Type: Announcement

Hugging Face TRL now officially integrates with RapidFire AI to accelerate LLM fine-tuning and post-training experimentation through concurrent configuration evaluation. Instead of executing training configurations sequentially, the platform shards datasets into discrete chunks and cycles multiple model configurations across GPUs at chunk boundaries. The runtime handles automatic checkpointing via a shared-memory-based adapter and model spilling mechanism to maintain consistent training state. Experimenters can monitor live metrics on an MLflow-based dashboard and use Interactive Control Ops to stop underperforming runs or clone and warm-start promising configurations mid-flight. Benchmark results on NVIDIA A100 GPUs demonstrate experimentation speedups ranging from 15x to 20x over sequential runs when training TinyLlama-1.1B and Llama-3.2-1B models.


### [Open ASR Leaderboard: Trends and Insights with New Multilingual & Long-Form Tracks](https://yomu.fyi/post/open-asr-leaderboard-trends-and-insights-with-new-multilingual-long-fo.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Eric Bezzam, Steven Zheng, Eustache Le Bihan, Vaibhav Srivastav
- Published: Nov 21, 2025
- Type: Benchmark

The Open ASR Leaderboard expanded its evaluation framework by introducing dedicated multilingual and long-form transcription tracks across eleven datasets. Benchmarking more than sixty open- and closed-source models from eighteen organizations revealed clear operational trade-offs between accuracy and inference speed. Systems combining Conformer encoders with large language model decoders achieved the lowest word error rates in short-form English speech recognition. In contrast, connectionist temporal classification and token-and-duration transducer decoders yielded tenfold to hundredfold improvements in throughput with only slight accuracy penalties. While closed-source commercial systems maintained performance leads in long-form audio transcription, specialized single-language models consistently outperformed generalized systems supporting hundreds of languages.


### [How we prepare Shopify for BFCM](https://yomu.fyi/post/how-we-prepare-shopify-for-bfcm.md)
- Company: [Shopify](https://yomu.fyi/company/shopify.md)
- Author: 2023-10-18
- Published: Nov 20, 2025
- Type: Explainer

Shopify’s BFCM readiness program addresses the challenge of operating critical commerce infrastructure at traffic levels informed by 2024 records, while preparing APIs with no prior holiday-traffic history. Starting in March, teams combined capacity planning on Google Cloud, an infrastructure roadmap, risk assessments, chaos-engineering Game Days, and a Resiliency Matrix covering failure modes, recovery objectives, runbooks, and on-call coverage. Genghis load tests and Toxiproxy network faults ran across three GCP regions, while five April–October scale tests exercised browsing, checkout, analytics, webhooks, regional failover, and cascading failures; testing reached 146 million requests per minute and a p99 target of 200 million. The work exposed and addressed Kafka partition, memory, timeout, routing, queueing, and failover issues, while the operational plan adds monitoring, 24/7 incident coverage, merchant communications, live tuning, and a post-BFCM review.


### [Introducing AnyLanguageModel: One API for Local and Remote LLMs on Apple Platforms](https://yomu.fyi/post/introducing-anylanguagemodel-one-api-for-local-and-remote-llms-on-appl.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Mattt
- Published: Nov 20, 2025
- Type: Announcement

Apple developers frequently face integration friction when trying to support a mix of local and cloud language models across disparate APIs. To resolve this fragmentation, AnyLanguageModel introduces a unified Swift package that acts as a drop-in replacement for Apple's Foundation Models framework. The package standardizes interactions across local backends such as Core ML, MLX, and llama.cpp alongside cloud providers like Anthropic and OpenAI. To avoid dependency bloat from multi-backend support, the library employs Swift 6.1 package traits so projects only import the specific runtime engines they require. Furthermore, the library extends beyond current Foundation Models limitations by adding prompt image support for vision-language models.


### [Intercom is now a Shopify Plus Technology Partner](https://yomu.fyi/post/intercom-is-now-a-shopify-plus-technology-partner.md)
- Company: [Intercom](https://yomu.fyi/company/intercom.md)
- Author: Gayathri Padmanabhan
- Published: Nov 19, 2025
- Type: Announcement

Intercom announces that it is now a certified Shopify Plus Partner on the Technology Track. The program recognizes technology providers whose product quality, service, performance, privacy, and support meet advanced requirements for Shopify Plus merchants. Intercom says its integration is vetted and optimized, and describes recent additions including Data Connector templates for Fin, multi-store support, inbox order actions, EU workspace support, and updated data mapping and custom fields. The announcement also lists planned expanded Fin Tasks, Model Context Protocol support, and smarter product search powered by Shopify data, positioning these capabilities as ways to resolve ecommerce support requests faster and personalize conversations at scale as merchants prepare for peak season and rising customer expectations.


### [First Block with Jesse Zhang, Co-founder and CEO of Decagon](https://yomu.fyi/post/first-block-with-jesse-zhang-co-founder-and-ceo-of-decagon.md)
- Company: [Notion](https://yomu.fyi/company/notion.md)
- Author: Anastasia Crew
- Published: Nov 19, 2025
- Type: Explainer

The episode profiles Jesse Zhang, co-founder and CEO of Decagon, whose AI agents support customer service and reportedly achieve 70–80% deflection rates and 3x improvements in customer satisfaction. In its first 18 months, Decagon prioritized short-term execution and customer deployment, then shifted toward longer-term thinking; the source says this helped it move from stealth to unicorn status in a year. Zhang also describes intentional customer discovery, Agent Operating Procedures (AOPs), enterprise trust, and the need to pair technical excellence with commercial thinking. He credits senior generalists from his network with establishing an early team foundation that supported growth from 12 to 200 people in 18 months, while cautioning against applying generic advice instead of judging personal strengths and timing.


### [Apriel-H1: The Surprising Key to Distilling Efficient Reasoning Models](https://yomu.fyi/post/apriel-h1-the-surprising-key-to-distilling-efficient-reasoning-models.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Torsten Scholak, Oleksiy Ostapenko, Raymond Li, Luke Kumar, Joel Lamy-Poirier
- Published: Nov 19, 2025
- Type: Problem & solution

Retrofitting efficiency into existing large language models often fails when naive distillation disrupts complex multi-step reasoning mechanisms. The Apriel-H1 project addresses this challenge by converting a 15B full-attention reasoning model into a Mamba hybrid using targeted distillation on high-quality SFT reasoning traces rather than diffuse pretraining data. Built on the open-source Fast-LLM framework, the process applies reverse KL divergence alongside a staged layer replacement strategy combining Leave-One-Out analysis and dynamic loss evaluations. Across fifty total blocks, replacing thirty attention layers with Mamba mixers yielded the flagship Apriel-H1-15b-Thinker-SFT checkpoint. This configuration achieved a 2.1x throughput increase with minimal benchmark degradation after 76.8B training tokens.


### [2026 customer service planning series: Vol. 01](https://yomu.fyi/post/2026-customer-service-planning-series-vol-01.md)
- Company: [Intercom](https://yomu.fyi/company/intercom.md)
- Author: Declan Ivory
- Published: Nov 18, 2025
- Type: Explainer

Part one of a five-part 2026 customer service planning series argues that support organizations must be redesigned around AI rather than adding AI to existing tools, processes, and org charts. It frames AI Agents as end-to-end resolution engines and infrastructure that can serve as the first and last touchpoint, manage workflows, orchestrate handoffs, and take actions. The proposed mindset shift includes planning for changing work distribution, treating support as a product function, measuring resolution, impact, and system reliability, and assigning clear ownership for agent performance and continuous feedback. The post also says teams should give AI increasingly complex workflows while people improve the system, creating compounding returns. It concludes that adaptability will be essential as AI evolves, and previews a future edition on designing the resulting support organization.


### [Secure Authentication with a Push Notification in Your iOS Device](https://yomu.fyi/post/secure-authentication-with-a-push-notification-in-your-ios-device.md)
- Company: [Okta](https://yomu.fyi/company/okta.md)
- Author: Darko Spasovski
- Published: Nov 18, 2025
- Type: Tutorial

The tutorial shows how to build a native iOS sign-in flow using Okta DirectAuth and push notification MFA. It starts with an Okta Integrator Free Plan org, configures an OIDC Native Application and enables Okta Verify push notifications, while noting additional authorization policies may be required for a custom authorization server. In Xcode, the app uses Swift, SwiftUI, Swift Package Manager, and the Okta mobile Swift packages OktaDirectAuth and AuthFoundation to handle credentials, tokens, and MFA challenges without browser redirects. An AuthService coordinates authentication state, user and token information, sign-out, access-token refresh, and session restoration from stored credentials. The completed flow is described as phishing-resistant and able to maintain an active session directly within a SwiftUI app.


### [Customer Experience: The 2026 Marketer's Guide](https://yomu.fyi/post/customer-experience-the-2026-marketer-s-guide.md)
- Company: [Brandwatch](https://yomu.fyi/company/brandwatch.md)
- Author: Gemma Joyce
- Published: Nov 17, 2025
- Type: Tutorial

Customer experience (CX) spans every interaction across the journey, from advertising and purchase through support, product use, and advocacy, rather than referring only to customer service, UX, or customer success. The guide argues that AI has raised expectations for fast, personalized responses, while privacy-first marketing increases the importance of consented zero-party and first-party data, voice-of-customer programs, and social listening. Its framework maps awareness, consideration, purchase, post-purchase, and loyalty or advocacy; combines social, survey, support, and review data; prioritizes fixes with an impact-versus-effort matrix; and establishes workflows, response SLAs, and ongoing measurement. It recommends metrics including NPS, CSAT, CES, sentiment, and churn rate, alongside competitive benchmarking and continuous iteration. Examples describe using Brandwatch's Data Upload API and Social Panels to identify product issues, customer questions, and improvement opportunities before broader launches or operational changes.


[Newer posts](https://yomu.fyi/posts/page/164.md) · [Older posts](https://yomu.fyi/posts/page/166.md)