# recommendations Published articles for recommendations. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## pgAssistant 3.8.0 : continuous improvement loop for Postgres DevFeed: [pgAssistant 3.8.0 : continuous improvement loop for Postgres]() Original publisher: [Read original article]() Author: Pgassistant Dev Team Published: 2026-09-16T00:00:00Z Content type: release Language: en Sources: [PostgreSQL news]() Topics: [PostgreSQL](), [Monitoring](), [recommendations](), [configuration](), [real-time]() Tags: [configuration](), [measurements](), [monitoring](), [postgres](), [postgresql](), [real-time](), [recommendations](), [release]() ### AI overview pgAssistant 3.8.0 expands the PostgreSQL analysis and tuning tool into a continuous improvement platform. It adds historical workload and environment measurements, compares consecutive collections, tracks recommendations and configuration changes, and helps teams measure changes while distinguishing correlation from causation. ### Source excerpt With this release, pgAssistant is evolving beyond PostgreSQL analysis and tuning to become a continuous PostgreSQL improvement platform. The new positioning is built around a continuous improvement loop: Observe -> Diagnose -> Prioritize -> Plan -> Implement -> Collect again -> Measure pgAssistant already helped identify what should be improved and turn recommendations into a prioritized Executive Plan with clear DEV and OPS ownership. Combined with pgAssistant Collector, version 3.8.0 goes further by adding historical workload and environment measurements. The objective is to answer four essential questions: What should we improve? What did we decide to do? What did we actually change? What was the result? Workload Insights compares consecutive collections and highlights: new and no-longer-detected recommendations; changes to the PostgreSQL version and configuration; workload evolution by statement type; changes in execution time and call volume; the queries with the greatest impact on the overall workload. The ambition is to correlate the application of pgAssistant recommendations and the Executive Plan with observed performance changes. Correlation is not causation, and a recommendation that is no longer detected does not necessarily prove that it was implemented. pgAssistant keeps these distinctions explicit while bringing the relevant evidence together in one place. pgAssistant is not intended to replace real-time monitoring. Monitoring shows what is happening now; pgAssistant helps teams decide what to improve next, organize the remediation work, and measure what changed afterwards. From recommendations to action--and from action to measurable evidence. pgAssistant 3.8.0: https://github.com/beh74/pgassistant-community pgAssistant Collector: https://github.com/beh74/pgassistant-collector pgAssistant Grafana : https://github.com/beh74/pgassistant-grafana ## Running Agent Harnesses with Local Models DevFeed: [Running Agent Harnesses with Local Models]() Original publisher: [Read original article]() Author: Avi Chawla Published: 2026-09-15T21:59:31Z Content type: tutorial Language: en Sources: [Daily Dose of Data Science]() Topics: [Large Language Model](), [coding](), [Hardware](), [Open Source](), [Claude Code](), [codex]() Tags: [agent](), [coding](), [hardware](), [local](), [models](), [open-source](), [opentelemetry](), [profiling](), [recommendations](), [run-agent](), [video]() ### AI overview A video walkthrough explains how Magnitude profiles computer hardware, benchmarks local models, recommends practical candidates, and connects a selected model to coding agent harnesses such as Claude Code, Codex, OpenCode, and Pi. The article also presents a Dynatrace reference application for tracing LLM pipelines with OpenTelemetry. ### Source excerpt ...explained with a full video walkthrough. ## The Death of the Static UI: Building Context-Aware Mobile Apps in 2026 DevFeed: [The Death of the Static UI: Building Context-Aware Mobile Apps in 2026]() Original publisher: [Read original article]() Author: Rudraksh Shukla Published: 2026-09-14T17:02:27Z Content type: tutorial Language: en Sources: [Flutter Community - Medium]() Topics: [Mobile](), [ui](), [User experience (UX)](), [personalization](), [Flutter](), [real-time]() Tags: [agentic-ai](), [artificial-intelligence](), [dark-mode](), [flutter](), [machine-learning](), [mobile](), [mobile-development](), [personalization](), [real-time](), [recommendations](), [ui](), [ux]() ### AI overview This developer article argues that mobile interfaces are evolving from fixed layouts into context-aware surfaces that adapt navigation, touch targets, color, density, and surfaced actions using on-device signals. It discusses motion, location, time, usage history, and device or network state, with Flutter examples and references to patterns associated with Spotify and Netflix. ### Source excerpt Every app you've ever shipped made the same quiet assumption: the interface is a fixed thing. You design a screen, you lay out the widgets, and every user sees the same arrangement in the same order -- a 22-year-old on a commuter train at 8am and a 60-year-old at home on a Sunday get pixel-identical layouts. For thirty years that was simply what a UI was. That assumption is dying. In 2026 the leading mobile apps treat the interface as a live surface that reshapes itself in real time -- reordering navigation, resizing touch targets, shifting color and density, surfacing the one action you're most likely to want next -- driven by on-device signals about who you are, where you are, and what you're doing right now. The static screen is becoming the exception, not the default. Here's what's actually driving it, what it takes to build, and what it looks like in code -- with Flutter examples throughout. From static layout to living surface The old personalization playbook was recommendation, not adaptation. Netflix reordered a content row; Spotify built you a playlist. The chrome around those recommendations -- the navigation, the layout, the visual system -- stayed frozen. Context-aware UX pushes personalization down into the interface itself. Concretely, an adaptive UI reacts to signals like these: Motion and activity -- accelerometer and gyroscope tell you the user is walking, driving, or still. A UI can enlarge touch targets and simplify layout when it detects movement, cutting mis-taps. Location and environment -- outdoors in bright light, boost contrast and switch to a high-legibility mode; on a known Wi-Fi network at home, load richer media. Time and calendar -- automatic dark mode at night, a leaving-for-a-meeting layout when the next calendar event is 15 minutes out. Usage history -- promote the three features this user actually touches, demote the ones they never open. A finance app foregrounds transfer for a power user and check balance for a casual one. Device and networ ## WSIS Forum 2026: From commitments to measurable implementation DevFeed: [WSIS Forum 2026: From commitments to measurable implementation]() Original publisher: [Read original article]() Author: Joyce Chen Published: 2026-09-06T23:14:00Z Content type: article Language: en Sources: [APNIC Blog]() Topics: [Development](), [ai-governance](), [SIEM, Security, Observability]() Tags: [2026](), [community](), [development](), [global](), [governance](), [igf](), [internet-governance](), [metrics](), [platform](), [policy](), [recommendations](), [wsis]() ### AI overview The WSIS Forum 2026 marked a shift from documenting digital-development commitments to demonstrating measurable implementation. The forum emphasized implementation roadmaps, evidence, targets, indicators, metrics, gap analysis, and policy recommendations aligned with the WSIS framework, the Sustainable Development Goals, and Global Digital Compact commitments. ### Source excerpt WSIS Forum 2026 marked the start of a new phase for global digital development, shifting the focus from commitments to measurable implementation. ## Instagram limits recommendations for unlabeled AI-generated profiles DevFeed: [Instagram limits recommendations for unlabeled AI-generated profiles]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-31T20:36:24Z Content type: news Language: en Sources: [WorkOS Blog]() Topics: [Instagram](), [Artificial Intelligence](), [Generative AI]() Tags: [ai](), [creators](), [generative-ai](), [instagram](), [product](), [profile](), [recommendations]() ### AI overview Instagram renamed its "AI creator" badge to "AI-generated profile" and now limits recommendations for detected AI-generated profiles that do not carry the label. Creators can add the label or appeal the decision; successful appeals restore recommendation eligibility. ### Source excerpt Instagram now limits the reach of AI profiles that skip its AI-generated label. Non-human disclosure just became an enforced account attribute with penalties. ## 10 Figma agent skills featured by Figma DevFeed: [10 Figma agent skills featured by Figma]() Original publisher: [Read original article]() Author: Miggi Cardona Published: 2026-08-13T16:00:00Z Content type: article Language: en Sources: [Figma Blog]() Topics: [Figma](), [Figma agent](), [Figma Community](), [Design system](), [recommendations]() Tags: [design-system](), [figma](), [figma-agent](), [figma-community](), [recommendations]() ### AI overview Figma explains how users can author skills for its agent directly in Figma, preview and edit them, and iterate on them through testing. The article highlights 10 skills from the Figma Community, including examples for generating radiographs and reviewing animation opportunities. ### Source excerpt Now you can author skills for our agent, directly in Figma, to put your point of view to work. Here are 10 of our favorites to get you started. ## Cloud Cost Optimization Strategy: Fix Your Approach DevFeed: [Cloud Cost Optimization Strategy: Fix Your Approach]() Original publisher: [Read original article]() Author: Kelsey Rosen Published: 2026-07-24T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [cloud cost management](), [finops](), [Optimization]() Tags: [cloud](), [cost](), [cost-optimization](), [dashboards](), [monitoring](), [recommendations]() ### AI overview This article argues that cloud cost optimization often fails when organizations rely on outdated, reactive practices and visibility tools alone. It describes how delayed cost feedback, weak accountability, and disconnected responsibilities across engineering, finance, and platform teams can allow cloud spending to grow, and presents modern FinOps and governance frameworks as a better approach. ### Source excerpt Your cloud cost optimization strategy may be failing. Learn why traditional approaches fall short and how to build a better framework. Explore now. | Blog ## Building a Transformer-Based Category Recommender at Thumbtack DevFeed: [Building a Transformer-Based Category Recommender at Thumbtack]() Original publisher: [Read original article]() Author: Andrew Morss Published: 2026-07-20T20:23:27Z Content type: article Language: en Sources: [Thumbtack Engineering - Medium]() Topics: [recommendation systems](), [Transformer](), [Machine learning](), [email](), [push-notification]() Tags: [catalog](), [data-science](), [email](), [engineering](), [machine-learning](), [model](), [push-notification](), [recommendations](), [recommender-systems](), [torch]() ### AI overview This engineering article describes Thumbtack's category recommender, which selects home-service categories for push and email notifications. It explains how the system addresses popularity bias through negative sampling and how Thumbtack's small category catalog and sparse user activity shape the recommendation problem. ### Source excerpt A look at compensating for popularity bias in recommender systems using negative sampling strategies By: Andrew Morss, Senior Applied Scientist Introduction A recommender system is a machine learning model that, given a user and a catalog of items, predicts which items that user is most likely to want. Recommenders set your YouTube playlist, determine what items Amazon suggests for you, push you songs on Spotify and customize your Steam store. If you're a homeowner, Thumbtack's recommender systems can suggest home projects for you such as house cleaning or lawn mowing. Thumbtack connects users with professionals spread across almost 500 different job categories. Job categories are a kind of occupational bucket that Thumbtack uses as a taxonomy to categorize local service professionals (pros). Pros have the opportunity to choose categories of jobs that they want to receive leads on; examples include things like "Electrical and Wiring Repair", "Local Moving" or "Snow Plowing". Thumbtack's Lifecycle marketing team uses a category recommender to choose which categories to surface to users in our push and email notifications. Choosing the right ones is critical. A well-chosen suggestion is how a customer starts a search for a new project, finds the right service professional, and ultimately gets work done. An example of a push notification and an in-email category recommendation. The Problem Space: Category Recommendation There are several features of category recommendations at Thumbtack that are fairly unique in the item recommender space. The number of categories that we have is relatively small (~500) compared to the number of items that are usually seen in recommender systems, which simplifies the problem considerably. Usually recommenders require a two stage approach where an initial, low compute, filtering stage reduces the number of items to some "candidate" list of items before a more complicated model ranks that smaller set of items. With a relatively small cat ## How Expedia Group Builds AI That Lasts at Scale DevFeed: [How Expedia Group Builds AI That Lasts at Scale]() Original publisher: [Read original article]() Author: Xavier Amatriain Published: 2026-07-14T11:01:01Z Content type: opinion Language: en Sources: [Expedia]() Topics: [Artificial Intelligence](), [AI Strategy](), [AI Development](), [Development](), [sdlc](), [Monitoring]() Tags: [agentic-ai](), [ai](), [data](), [evaluation](), [generative](), [governance](), [innovation](), [machine-learning](), [monitoring](), [recommendations](), [sdlc](), [software-engineering](), [tooling](), [use-cases]() ### AI overview Expedia Group describes a framework for building, deploying, and evolving AI systems that remain reliable and scalable over time. The article emphasizes principles covering business value, ownership, governance, evaluation, safe rollout, and monitoring, and describes Agentic Release tollgates that translate those principles into launch checks integrated with the SDLC. ### Source excerpt Expedia Group Technology -- InnovationA framework for how we build, deploy, and evolve AI systems for impact and scalePhoto by Florian Wehde on Unsplash There's an important distinction between Artificial Intelligence (AI) that just works today and AI that lasts at scale. Many companies optimize hard for the first one without ever asking whether they're building the second. Velocity without discipline and strategic direction is a liability, not an asset. The hardest part of building AI at scale isn't getting a model to work once. It's building systems that continue to work, scale beyond individual teams and use cases, and improve consistently over time. Today's AI systems do more than just predict and optimize. They converse, reason, and increasingly take action. An autonomous system making decisions on a traveler's behalf creates a very different set of expectations around reliability, governance, and accountability. As AI takes on more of those roles, the principles behind how these systems operate matter more than ever. At Expedia Group™, we have spent years applying AI and machine learning across the traveler journey from personalization, ranking, and recommendations, to fraud prevention, customer support, and, more recently, generative and agentic AI experiences. That depth of experience is what led us to develop a set of machine learning and AI principles to guide how we build, deploy, and evolve AI systems across the company. The goal is simple: make sure the systems we build create real business value, scale across the company, and operate safely. These principles define how we measure, design, govern, and operate the systems we use. From principles to practice Publishing principles is the easy part. The harder and more important work is turning them into operating mechanisms: recommendations, requirements, tooling, and release processes that teams actually use. At Expedia Group, we have started doing this through Agentic Release tollgates: a set of recommend ## How to Survive the Recommendation Engine Question DevFeed: [How to Survive the Recommendation Engine Question]() Original publisher: [Read original article]() Author: Raul Junco Published: 2026-07-11T11:35:16Z Content type: tutorial Language: en Sources: [System Design Classroom]() Topics: [recommendation systems](), [Architecture & Design]() Tags: [architecture](), [how-to](), [recommendations]() ### AI overview A tutorial on answering recommendation-engine system design questions by focusing on the surrounding system rather than only the matching algorithm. It emphasizes separating recommendation generation from recommendation serving and addressing behavior data, precomputation, latency, stale results, missing data, cold starts, and fallbacks. ### Source excerpt Most engineers focus on the matching algorithm. The real test is separating generation from serving. ## From Scoring to Spelling: Rebuilding Ads Retrieval at Instacart DevFeed: [From Scoring to Spelling: Rebuilding Ads Retrieval at Instacart]() Original publisher: [Read original article]() Author: Karuna Ahuja Published: 2026-06-02T18:50:19Z Content type: article Language: en Sources: [Instacart]() Topics: [Transformer](), [recommendations](), [systems](), [context](), [real-time]() Tags: [bert](), [context](), [generative](), [inference](), [machine-learning](), [model](), [real-time](), [recommendation-system](), [recommendations](), [retrieval]() ### AI overview Instacart describes rebuilding its ads retrieval system by moving from a product-scoring encoder to a generative model that predicts product IDs token by token. The system uses contextual shopping sessions and a BERT-like transformer to retrieve sponsored and organic recommendations. ### Source excerpt Key Contributors: Karuna Ahuja, Marko Avdalovic, Soroush Sobhkhiz, Shrikar Archak, Xiyu Wang, Ji Chao Zhang, Hao Yan Introduction Every time a user opens Instacart, they see product recommendations: on the retailer home page, in search results, and alongside their cart. Many of these recommendations are sponsored products surfaced by a retrieval model that decides which products to show from a vast ads product catalog. A relevant ad helps users discover products they didn't know they needed; a less relevant one generates friction. Two years ago, we introduced Contextual Recommendations (CR), a BERT-based sequence model powering retrieval for both ads and organic recommendations across all major browse surfaces. In this post, we'll focus on our ads retrieval. We will detail how we rebuilt the system, by moving from an encoder that scores products to a generative model that spells them out, token by token. By doing so, we unlocked a new level of contextual matching -- ensuring brands appear exactly when users want them, while simultaneously opening up discovery of thousands of relevant products the previous system couldn't retrieve. Contextual Recommendations: A recap At its core, CR treats grocery shopping as a language modeling task, where atomic product IDs function as tokens and, the finite subset of the catalog it is trained on, acts as its 'vocabulary'. The model leverages the user's real-time session, which includes product views, item page visits, and cart additions, as a sequence of these product tokens. A BERT-like transformer is then trained on millions of authentic shopping sessions to predict the next token (i.e. singular product) in the sequence. This process allows the model to learn and capture complex purchasing patterns, such as the tendency for users who add pasta and olive oil to frequently add garlic next. This single retrieval layer replaced multiple ad-hoc systems and powers recommendation carousels across all major browse surfaces, serving both ## Recommendations for a Secure Agentic AI Setup Using Sandboxes and Worktrees DevFeed: [Recommendations for a Secure Agentic AI Setup Using Sandboxes and Worktrees]() Original publisher: [Read original article]() Published: 2026-05-20T05:22:00Z Content type: opinion Language: en Sources: [ipSpace.net blog]() Topics: [Artificial Intelligence](), [AI Development](), [GitHub](), [Development]() Tags: [agentic-ai](), [ai](), [article](), [github](), [recommendations](), [sandboxes](), [worth-reading]() ### AI overview This commentary recommends Mike McQuaid's article about using sandboxes and worktrees for a secure agentic AI setup, particularly when allowing AI agents to operate on a GitHub repository. ### Source excerpt Most of the hyperventilated AI "success stories" are as useful as the "ANSIBLE!!!" movement was a few years ago. It's thus always a pleasure to find someone with well-established software development chops who took the time to describe what works for them. One cannot argue with Mike McQuaid's credentials (at least if you happen to be using homebrew on MacOS, which you REALLY SHOULD), and his Sandboxes and Worktrees: My secure Agentic AI Setup in 2026 article is full of relevant recommendations in case you're brave enough to let AI agents loose on your GitHub repository. ## How Whatnot's Hourly ML Feature Pipeline Safely Handled a Failed Update DevFeed: [How Whatnot's Hourly ML Feature Pipeline Safely Handled a Failed Update]() Original publisher: [Read original article]() Author: Whatnot Engineering Published: 2026-05-07T16:01:01Z Content type: article Language: en Sources: [Whatnot Engineering]() Topics: [Machine learning](), [recommendation systems](), [datasets](), [data](), [real-time](), [Processes]() Tags: [ai](), [data](), [data-engineering](), [feature](), [feature-engineering](), [feature-store](), [machine-learning](), [ml](), [pipeline](), [ranking](), [real-time](), [recommendation-system](), [recommendations](), [run]() ### AI overview Whatnot describes how its hourly ML feature pipeline handled a bad pull request that caused a feature group to produce zero rows. The online feature store served last-known-good features for up to two days, anomaly monitors detected the row-count drop, and the team reverted the change before the freshness gap became significant. The post shares lessons about safe degradation and operating frequently updated recommendation features. ### Source excerpt David Kawashima, Miguel Fernandez-Montes Cuberta, Ford Bohrmann | Discovery A bad PR went out just as our hourly feature pipeline was about to run. The pipeline was responsible for recomputing fresh user behavior signals used in livestream ranking on the home feed. These features capture recent user intent: watches, bids, orders aggregated across users, sellers, and categories. A missed feature update means that the system stops responding to user signals immediately. This PR release caused one of our feature groups to suddenly produce zero rows ... but nothing broke! Even though a critical feature group for the model stopped updating, the online feature store continued serving the last-known-good features, buffered by a 2-day TTL and nothing broke explicitly. Our anomaly monitors went off immediately, flagging the drop in row counts. The team identified the bad change and reverted it before the freshness gap grew large enough to matter. No corrupted data reached production. The system absorbed the failure and degraded safely. This graceful degradation didn't happen by accident; it was the result of hardened processes from years of lived experience, seeing the way these systems can degrade in obvious or subtle ways. In this post, we share some of those lessons learned. The system: hourly feature pipeline Whatnot is a highly dynamic marketplace where buyers' preferences change at a moment's notice. A buyer might research a laptop, join a football-card break a few hours later, and buy chocolate before logging off for the night. Daily feature updates capture long-term preferences but miss intra-day shifts. To keep up with these rapidly changing buyer interests (in 2025) we moved from daily to hourly updates for features that power our ML feed ranking models. Fresher features are a critical component for capturing the live nature of the Whatnot marketplace, where the inventory available to purchase changes in real time. Moving from daily to hourly updates was a meaningful ## Kubernetes for Agentic AI: Best Practices for Security and Observability DevFeed: [Kubernetes for Agentic AI: Best Practices for Security and Observability]() Original publisher: [Read original article]() Author: info@goteleport.com (Boris Kurktchiev, Jack Pitts) Published: 2026-04-01T00:00:00Z Content type: article Language: en Sources: [Teleport]() Topics: [Kubernetes](), [Security](), [observability](), [Containers](), [Availability](), [AI Platform]() Tags: [availability](), [container](), [kubernetes](), [kubernetes-security](), [least-privilege](), [monitoring](), [network-segmentation](), [observability](), [permissions](), [recommendations](), [security]() ### AI overview This article presents 18 Kubernetes best practices for securing and monitoring containerized agentic AI workloads. It emphasizes foundational controls such as least privilege, access control, network policies, image assurance, network segmentation, and continuous monitoring, while noting that many practices also apply to other containerized and serverless environments. ### Source excerpt Discover 18 Kubernetes security, observability, and availability best practices for container-based agentic workloads. ## How Whatnot's Taxonomy Keeps Pace with a Marketplace That Won't Sit Still DevFeed: [How Whatnot's Taxonomy Keeps Pace with a Marketplace That Won't Sit Still]() Original publisher: [Read original article]() Author: Whatnot Engineering Published: 2026-03-25T14:22:19Z Content type: article Language: en Sources: [Whatnot Engineering]() Topics: [recommendations](), [bug](), [real-time]() Tags: [2025](), [ai](), [bug](), [ecommerce](), [machine-learning](), [onboarding](), [platform](), [real-time](), [recommendation-system](), [recommendations](), [taxonomy]() ### AI overview Whatnot describes how it redesigned its marketplace taxonomy to change more rapidly across countries and discovery surfaces. The article explains why onboarding, browse, search, and recommendations need different taxonomy treatments, using supply-aware onboarding and the Labubu category as examples. ### Source excerpt Marcus Gee | Discovery The hottest trends don't happen over months, but start suddenly. At Whatnot, our best sellers update their strategies in real time to stay in step with the market. Connecting buyers and sellers in this fast-changing landscape is one of the most important problems Whatnot solves, but in 2025, it was a major challenge. In this post, we're going to walk through how we overhauled the taxonomy system to evolve more rapidly, so our team could help new communities grow. There is No Single Taxonomy Whatnot supports over 200 categories across seven countries, and that number increases weekly. Our taxonomy is a platform primitive that underpins nearly every discovery experience. It shapes onboarding (what categories new users see), browse (how they navigate), search (how results are ranked), and recommendations (what their feed looks like). A bug in taxonomy doesn't break one feature; it degrades the entire discovery surface. That makes every change high-stakes. Here's what we learned the hard way: maintaining a single taxonomy doesn't work. A single taxonomy can't optimally serve onboarding, browse, and search at the same time, because these surfaces have different goals. Let's look at two examples to understand why: When new users join Whatnot, they choose categories they're interested in. That choice shapes the first feed they see. If a user selects a niche category with limited supply, their initial experience may feel sparse or low quality. For this reason, onboarding taxonomies are country-specific and supply-aware. We fold categories into broader parents, reorder them, or exclude them based on local marketplace density. Categories play a critical role in connecting enthusiasts and sellers. When Labubu surged in 2025, it was subcategorized under Toys, and new users struggled to find relevant shows. When we elevated Labubu to the first page of onboarding (a similar treatment given to other big categories like Pokémon Cards), it reduced this frictio ## Working at the intersection of data and AI with Kenza Boulisfane DevFeed: [Working at the intersection of data and AI with Kenza Boulisfane]() Original publisher: [Read original article]() Author: Thumbtack People Team Published: 2026-03-05T19:31:06Z Content type: article Language: en Sources: [Thumbtack Engineering - Medium]() Topics: [Artificial Intelligence](), [data](), [recommendations](), [real-time]() Tags: [agent](), [ai](), [analytics](), [careers](), [data](), [real-time](), [recommendations](), [software-development](), [technology]() ### AI overview In a Q&A, Thumbtack software engineer Kenza Boulisfane discusses building an AI-powered Marketing Analytics Agent. The tool is intended to help people across the company ask questions about marketing performance, receive data-backed recommendations, and explore results in real time without requiring SQL expertise. She also describes the challenges of unifying fragmented, differently defined marketing data and translating it into clear insights. ### Source excerpt Kenza Boulisfane, Software Engineer at Thumbtack, works at the intersection of data, AI, and real business impact. In this Q&A, she shares how she's building an AI-powered Marketing Analytics Agent designed to make complex marketing data accessible to everyone. She also reflects on team culture, technical challenges, and why diverse perspectives make engineering stronger. What are you currently working on? I'm working on building a Marketing Analytics Agent. It's an AI-powered marketing expert that provides companywide support, regardless of technical background. The idea is simple: marketing data shouldn't require a SQL expert to interpret it. Whether someone is in Data Science, Growth, Brand, or Product, they should be able to ask questions like, "How did paid social perform last week?" or "Why did conversions dip in this channel?" and get a clear, reliable answer. Even better, they should receive thoughtful, data-backed recommendations. Instead of waiting on reports or digging through multiple tools, teams can explore performance in real time, ask follow-up questions, and move faster with confidence. I love that my work sits at the intersection of data, AI, and real business impact. How would you describe the culture of your team? I'd describe my team culture as genuinely supportive. People are always willing to jump in, answer questions, share context, or sanity-check an idea, regardless of how busy they are. We're in it together, and that makes tackling ambitious projects feel a lot less daunting. At the same time, we don't take ourselves too seriously. Some of my favorite moments have been at offsites when we play games and you suddenly see everyone's competitive side come out. It's funny how the same people who are thoughtfully debating technical implementation during the day are fully locked in trying to win a Mafia/Werewolf round. What are some of the unique technical challenges you get to solve? One of the biggest challenges is turning fragmented marketi ## Our Early Journey to Transform Instacart's Discovery Recommendations with LLMs DevFeed: [Our Early Journey to Transform Instacart's Discovery Recommendations with LLMs]() Original publisher: [Read original article]() Author: Moein Hasani Published: 2026-02-26T18:55:35Z Content type: article Language: en Sources: [Instacart]() Topics: [recommendation systems](), [LLMs](), [Generative AI](), [personalization](), [User Experience](), [Artificial Intelligence]() Tags: [ai](), [discovery](), [generative-ai](), [instacart](), [large-language-models](), [llm](), [llms](), [machine-learning](), [personalization](), [recommendation-systems](), [recommendations](), [recommender-systems](), [systems](), [user-experience]() ### AI overview Instacart describes its early effort to use large language models in the Shopping Hub, an app surface for personalized product discovery. The article covers an AI-native platform for content generation, evaluation, and retrieval, and reports that generative models show promise for improving recommendations at scale. ### Source excerpt Key Contributors: Moein Hasani, Hamidreza Shahidi, Trace Levinson, Guanghua Shu Introduction At Instacart, we are laser-focused on improving the user experience by making shopping feel easy, engaging, and personalized. Our discovery surfaces play a central role in bringing this to life. Alongside explicit Search intents, discovery is our opportunity to meet customers' implicit needs, presenting them with the most relevant and inspiring content we have to offer. The main discovery surface within the Instacart app, referred to here as the "Shopping Hub", is one of the most critical in this regard. This is the surface a customer lands on within the Instacart app after selecting their desired retailer, guiding them along their entire journey. What users see here shapes not just what they buy, but how intuitive and enjoyable their experience feels. Given its importance, our team runs dozens of Shopping Hub experiments per year, constantly evaluating new ways to enrich the discovery experience. Historically, these experiments have been constrained by static content libraries feeding our recommendation systems. With the rapid advancement of generative AI, a critical opportunity began to emerge: rather than incrementally improving a swath of legacy systems, could we leverage LLMs to rethink how content shows up for a user from the ground up? Which new primitives could we build to uplevel quality, personalization, and cohesion across the page? This blog post walks through our early journey to answer these questions. By investing in a new AI-native platform for content generation, evaluation, and retrieval, we have found generative models to show real promise in improving recommendations at scale. Below, we highlight the approach we took in developing this platform, a few key learnings so far, and where we're most bullish moving forward. Limitations of Traditional Recommendation Engines Our Shopping Hub page is constructed from multiple subcomponents called placements. Each p ## Beyond Prompt Engineering: How We Used Supervised Fine-Tuning for Travel Recommendations DevFeed: [Beyond Prompt Engineering: How We Used Supervised Fine-Tuning for Travel Recommendations]() Original publisher: [Read original article]() Author: Amit Meitin Published: 2026-01-29T10:53:21Z Content type: article Language: en Sources: [Booking.com Data Science]() Topics: [Fine-tuning](), [recommendation systems](), [Large Language Model](), [real-time](), [AI Development]() Tags: [ai](), [blog-posts](), [engineering](), [fine-tuning](), [inference](), [language-models](), [machine-learning](), [models](), [prompt](), [recommendations]() ### AI overview Booking.com describes how it used supervised fine-tuning to improve travel recommendations for its AI Trip Planner. The approach combines large language models' understanding of unstructured traveler requests with structured behavioral data from searches, clicks, and bookings, while the article reports superior recommendation metrics and 3x faster inference. ### Source excerpt How fine-tuning delivered superior recommendation metrics while achieving 3x faster inference Every traveler is unique, and so is every trip. At Booking.com, we're always looking for new ways to make trip planning feel less like a chore and more like an adventure. That's why we set out to build an AI Trip Planner that doesn't just answer questions, but actually understands what travelers want, expressed in ways they are most comfortable with. But here's the challenge: travelers today are expressing their needs in ways that are more unstructured and nuanced than ever before. Instead of ticking boxes or picking from drop-down menus, people describe their dream vacations in their own words. Sometimes these requests are vague, sometimes very specific, but always personal. Traditional machine learning models, which thrive on structured data and clear signals, can struggle to keep up with this new level of expressiveness. At the same time, we at Booking.com have years of valuable, structured data from searches, clicks, and bookings. This data captures what travelers actually do: the trips they plan and the vacation they go on. The big question for us became how to combine the best of both worlds -- How can we harness the power of Large Language Models (LLMs) to understand and respond to unstructured requests, and use the wealth of behavioral data that has driven our recommendations for years? This post is about how we tackled that question and what we learned along the way. What is the AI Trip Planner? The AI Trip Planner is Booking.com's conversational assistant that helps travelers find destinations, accommodations and other trip components through natural, real-time chat. Instead of searching with filters, users describe what they want in their own words. The planner suggests options, whether it is inspiration or specific places to stay. Integrated into the app, it makes planning and discovering seamless and is now available in multiple countries and languages. Bridging ## Closing the loop between user questions and documentation DevFeed: [Closing the loop between user questions and documentation]() Original publisher: [Read original article]() Author: Han Wang Published: 2026-01-07T00:00:00Z Content type: release Language: en Sources: [Mintlify Blog]() Topics: [Documentation](), [recommendations](), [pull-requests](), [dashboards](), [Artificial Intelligence]() Tags: [agents](), [ai](), [analytics](), [announcements](), [documentation](), [pull-requests](), [recommendations](), [updates]() ### AI overview Mintlify describes agent suggestions, a feature that analyzes questions users ask its documentation assistant to identify documentation gaps and recommend updates. The feature expands an existing pull-request-based system with conversation-derived signals. ### Source excerpt Learn how Mintlify now surfaces documentation updates not only from pull requests but also from real user conversations, giving teams a clearer and more complete signal of what needs to change ## AI progress and recommendations DevFeed: [AI progress and recommendations]() Original publisher: [Read original article]() Published: 2025-11-06T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [AI Strategy](), [Software Engineering]() Tags: [2026](), [ai](), [chatbots](), [company](), [cost](), [discovery](), [future](), [recommendations](), [research](), [safety]() ### AI overview The article describes rapid advances in AI, including systems that can outperform top human performance in difficult intellectual competitions and increasingly support software engineering and knowledge discovery. It discusses falling costs, expected capability gains through 2026 and beyond, societal adaptation, safety, and the possibility of a better future. ### Source excerpt AI is advancing fast. We have the chance to shape its progress--toward discovery, safety, and a better future for everyone. ## Offline Evaluation of Open-Source Embedding Models for Model Selection DevFeed: [Offline Evaluation of Open-Source Embedding Models for Model Selection]() Original publisher: [Read original article]() Author: Tiago Cabo Published: 2025-10-07T15:11:01Z Content type: article Language: en Sources: [OLX]() Topics: [Embeddings](), [Benchmark](), [Open Source](), [recommendation systems](), [hugging face](), [AI Models]() Tags: [ai](), [classification](), [embedding](), [embeddings](), [evaluation](), [machine-learning](), [open-source](), [recommendations](), [search]() ### AI overview This article explains how OLX evaluated open-source embedding models against its existing internal model. It describes embeddings, the MTEB benchmark, and practical constraints such as memory, latency, modality, and multilingual support when selecting a model for deployment. ### Source excerpt The Importance of Offline Evaluation to Guide Model ChoiceIntroduction Recent advancements in open-source AI models make it challenging to justify the development of custom models, given the high quality of existing options. This also applies to embedding models, which are available in impressive quality. At OLX, we utilize a model called Item2vec to generate similar item recommendations. For more details, please refer to our blog post. Item2Vec: Neural Item Embeddings to enhance recommendations In this work, we developed an embedding model that not only improved recommendations but was also used by other teams across OLX, such as the search team. For an in-depth look at this application, see our post on Hybrid Search, where we tried to balance the benefits of semantic and lexical search. Hybrid Search -- Where Keywords Meet Vectors, Enabling Classifieds Discovery This article discusses our evaluation of open-source embedding models compared to our existing internal model. Open Source Embeddings An embedding model converts inputs, such as text or images, into vectors. These vectors are then used to compute similarities, often cosine distance, where closer vectors are deemed semantically related. Cosine similarity Many architectures types for embedding models support this, including word2vec, GloVe, RNN, LSTM, and transformers. This leads to hundreds of publicly available models, making selection a challenge. Common benchmarks play a crucial role in this process. After exploring various options, we chose the MTEB: Massive Text Embedding Benchmark. We selected this benchmark due to its widespread community adoption and extensive features, such as: 56 datasets across 8 tasks Support for up to 112 different languages Easy extensibility via the repo MTEB HuggingfaceBenchmark MTEB Hugging Face provides a comprehensive leaderboard showcasing top-performing models across tasks (retrieval, classification, etc), reflecting advancements in machine learning and AI. The higher, t ## Engineering stories behind the Medium Daily Digest Algorithm: Part 1 DevFeed: [Engineering stories behind the Medium Daily Digest Algorithm: Part 1]() Original publisher: [Read original article]() Author: Raphael Montaud Published: 2025-08-26T11:31:37Z Content type: article Language: en Sources: [Medium]() Topics: [recommendation systems](), [Machine learning](), [Algorithms]() Tags: [algorithm](), [engineering](), [machine-learning](), [model](), [recommendation-system](), [recommendations](), [software-engineering](), [technical]() ### AI overview This engineering article begins a four-part series about improving Medium's Daily Digest recommendation algorithm. It describes a mismatch between recommendations in the Digest and the homepage feed, despite both using the same algorithm, model, and features, and introduces an investigation into differences in filtering. ### Source excerpt How we made our email story recommendations better In this Part 1, you'll understand how we improved one of the main ways our users are exposed to our product and how that led to a massive 7% increase on the average reading time for the digest users. Intro: This is a 4-part series breaking down improvements to the algorithm behind the Medium's Daily Digest over the past year. When we started this work, the Digest was suboptimal -- and since it's a huge distribution surface, reaching millions of readers every day, we started working on incremental improvements.By the end of these projects, the digest was 10% more likely to convert users to paying members, less expensive to run, more flexible and easier to maintain and it's now providing higher quality recommendations for all our users, including our "power readers".This is told through the lens of our engineering team tackling a series of challenges one by one. Medium has a small team but we operate on a big scale. We're working our way through some technical debt and at the same time, striving to provide the best experience for our readers. This is the source of many interesting challenges.I hope this series helps you understand how the recommendations algorithm work and can help others who are facing similar technical challenges.Some Concepts Here's a little cheat sheet with some concepts you may need to follow along with this story You may need this to understand the rest of this postThe Discovery A little while back, Leigh, our Machine Learning Engineer and model training guru, started noticing something weird. The recommended stories in his digest were consistently not great matches for his reading profile. At the same time, the recommended stories in his homepage feed (what we call the "For You" feed) were consistently very well targeted. This was a bit puzzling and unexpected. Those two recommendation surfaces rely on exactly the same algorithm. We source stories the same way, and we rank them using the same mo ## Fix issues faster with Recommended Remediations DevFeed: [Fix issues faster with Recommended Remediations]() Original publisher: [Read original article]() Author: Gavin Cahill Published: 2025-08-22T00:00:00Z Content type: release Language: en Sources: [Gremlin Blog]() Topics: [Chaos Engineering](), [Testing]() Tags: [chaos-engineering](), [features](), [gremlin](), [recommendations](), [uptime]() ### AI overview Gremlin's Recommended Remediation analyzes fault-injection test results to identify likely failure causes and suggest ways to address reliability issues. It builds on Experiment Analysis by combining test data, metrics, health checks, and key events, then applies reliability expertise to produce tailored recommendations. ### Source excerpt Recommended Remediation speeds up teams with tailored suggestions to help you address reliability risks before they cause failures. ## Scaling recommendations service at OLX DevFeed: [Scaling recommendations service at OLX]() Original publisher: [Read original article]() Author: Jordi Esteve Sorribas Published: 2025-07-08T15:03:23Z Content type: article Language: en Sources: [OLX]() Topics: [FastAPI](), [recommendation systems](), [Latency](), [Back end](), [Python](), [distributed-systems](), [async]() Tags: [async](), [backend](), [concurrency](), [distributed-systems](), [fastapi](), [latency](), [machine-learning](), [optimization](), [python](), [recommendation-systems](), [recommendations](), [scalability]() ### AI overview This article describes how OLX scaled a Python FastAPI recommendations service to handle tens of thousands of requests per second with p99 latency below 10 ms. It discusses the service's ownership, data sources, and the use of asynchronous non-blocking I/O. ### Source excerpt Optimizing FastAPI at Scale: Lessons from OLX's Recommendation PlatformPhoto by Rosy KoIn distributed systems, there is a motto that says 'you are as slow as your slowest tasks'. In Python, thanks to the notorious Global Interpreter Lock (GIL), this issue is amplified: 'your slowest task will make every other task slower'. In this article, I'll walk you through the optimizations we made to scale a FastAPI service that now handles tens of thousands of requests per second, achieving a p99 latency under 10ms.Introduction OLX is a global online marketplace that enables users to buy and sell goods and services, primarily through classified ads. We have a clear vision: to create leading marketplace ecosystems enabled by tech, powered by trust, and loved by customers. Every month, we engage 45 million app users and support over 73 million active listings. To help users seamlessly navigate this vast inventory, we've integrated recommendation systems across multiple touchpoints in all our platforms. These recommendations are powered by the recommendations platform, which is responsible for delivering personalized suggestions across various contexts. Most, if not all, of these are served through a dedicated recommendations service. Over the past few months, we've built and scaled this system within the data team, successfully shifting the ownership from a shared backend service to a service fully owned by the team to gain greater autonomy and flexibility. The team decided to build it with Python, as it is the go-to language for the data and machine learning team and is the most widely used language within both the team and the broader domain. While Python allows for rapid development and prototyping, working at scale has surfaced several challenges and trade-offs. It hasn't been an easy journey, but it's one that's taught us a lot and significantly matured our infrastructure and processes. To Async or Not Async The service consumes data from various sources: ScyllaDB, DynamoD [Next page]()