# A/B Testing Published articles for A/B Testing. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Pinterest Replaces Journey Clustering with 4B Qwen3 LLM, Reports Improved Engagement DevFeed: [Pinterest Replaces Journey Clustering with 4B Qwen3 LLM, Reports Improved Engagement]() Original publisher: [Read original article]() Author: Matt Foster Published: 2026-11-09T07:25:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [Large Language Model](), [clustering](), [LLM optimization](), [lora](), [vllm]() Tags: [a-b-testing](), [actions](), [ai](), [ai-ml-data-engineering](), [architecture](), [architecture-design](), [batching](), [behavior](), [cache](), [canada](), [challenge](), [clicks](), [clustering](), [continuous-batching](), [engagement](), [generative-ai](), [inference](), [journey](), [language-model](), [large-language-models](), [llm](), [machine-learning](), [ml-data-engineering](), [model](), [model-inference](), [news](), [pinterest](), [pinterest-journey](), [pipeline](), [qwen3]() ### AI overview Pinterest replaced its multi-stage keyword clustering pipeline for inferring user journeys with a fine-tuned 4B Qwen3 model that reads up to 150 actions across 360 days and generates ranked journey names. Search and save actions receive more weight than incidental clicks; the prompt also favors repeated, recent, specific activity, discourages unrelated merges, and permits no result when evidence is weak. Synthetic examples generated with frontier models supported fine-tuning, and Pinterest chose 4B after testing 0.6B-8B models. Serving uses NVIDIA Dynamo with vLLM on L40S GPUs; skipping users processed in the prior couple of days also improved throughput. In US and Canada A/B tests, Pinterest reports 1.1% higher email click-through and 1.3% higher push opens, plus statistically significant engagement and retention gains. These findings come from the supplied excerpt. ### Source excerpt Pinterest has moved its user-journey inference system from a multi-stage clustering pipeline to a fine-tuned 4-billion-parameter language model, reporting a 1.1% improvement in email click-through and 1.3% higher push opens. By Matt Foster ## What we learned as Webflow Campaigns' first customer DevFeed: [What we learned as Webflow Campaigns' first customer]() Original publisher: [Read original article]() Author: Webflow Team Published: 2026-11-05T00:00:00Z Content type: article Language: en Sources: [Webflow Blog]() Topics: [webflow](), [experience report](), [human review](), [Tool]() Tags: [2](), [3](), [a-b-testing](), [abm](), [about](), [account](), [ad-performance](), [added](), [ads](), [agents](), [ai](), [ai-agents](), [airops](), [all](), [customer](), [events](), [first](), [inside-webflow](), [more](), [search](), [webflow]() ### AI overview Webflow's first Campaigns users report that tailored landing pages helped them reduce page-building handoffs while keeping review and publishing control. For paid search, one template produced seven ad-group variations with distinct URLs and content; adding a page took about 30 minutes. A weekly report uses ad and warehouse data to suggest copy changes, which require approval before staging. For an event, Campaigns used Webflow's live site to establish a design system, and a page drafted from a brief generated 80 warm leads within minutes after a QR code was shown. For ABM, an n8n workflow gathers Salesforce, Gong, opportunity, product-use, and account data; Claude and Campaigns generate account-specific content, while selected sections vary within an approved design. Sales and marketing operations review drafts before publishing. The internal pilot began with 100 accounts. The team describes these as early experiments and says it is still learning how the pages perform. ### Source excerpt Across paid search, events, and ABM, fewer handoffs are giving us more control over what we launch and when. ## Automating feature-flag cleanup at Yazio: from a prompt to CI DevFeed: [Automating feature-flag cleanup at Yazio: from a prompt to CI]() Original publisher: [Read original article]() Author: Artem Zasypalov Published: 2026-11-04T08:29:23Z Content type: article Language: en Sources: [YAZIO]() Topics: [agentic workflows](), [CI/CD](), [Pull Request](), [jira]() Tags: [a-b-testing](), [ab-testing](), [agentic-workflow](), [agents](), [ai](), [ai-coding-agents](), [automation](), [autonomous](), [ci](), [cleanup](), [feature](), [flag](), [prompt](), [software-engineering](), [workflow-automation]() ### AI overview Yazio automated feature flag removal from a manually prepared engineering task to a GitHub CI workflow that handles cleanup through opening a PR, with human oversight retained for merging. A Product Manager's removal decision starts the process; the agent removes flags and prepares changes, while a review agent gives a preliminary review. Reliability changes let the workflow process multiple flags sequentially with a fresh agent context and separate commit for each. It reads the Jira decision alongside Growthbook results and skips a flag when they conflict. Failures, skipped flags, gate errors, and successful PR links are reported in Jira. Over almost a year, the workflow created 237 tasks across mobile squads. Median time from cleanup initiation to PR creation fell from 32 days before the local prompt, to 17 days with the local prompt or skill, and 5 days with CI. A successful CI run takes about 15 minutes and removes a median 243 lines across 7 files. The article says the latest reliability improvements had been running only about ten days, so their backlog effect was not yet clear. ### Source excerpt Photo by Franck V. on Unsplash Remember those days when we thought robots and AI were supposed to take care of tedious things nobody wanted to do? Like washing dishes, taking out the rubbish? My team and I have been working to give AI a less physical version of that job: cleaning up after A/B tests. With more things being automated, we are expected to accomplish more as automatable work is often considered at least half done. There is truth to it. But it also means our context switching becomes the norm. A truly useful automation should preserve the focus of engineers as long as possible, meaning it should be able to act trustworthily without approval for as long as possible within the workflow. At the same time, critical stages should still be approved by a human. The definition of the critical stage might change over the next years with further advancements in AI and autonomous software development. Right now at Yazio, we consider merging a PR a critical stage that should be done under a human's oversight. We started experimenting with automated workflows that include AI coding agents over a year ago. Feature flag cleanup was one of the first ones for us to automate. Over several iterations, this effort has grown from a small 64-line prompt into an autonomous workflow, with an engineer only at the critical stage. I'd like to share our journey. Where cleanup starts Our A/B testing lifecycle has 3 core stages: Preparation, Running, and Cleanup. The Cleanup stage begins when the Product Manager chooses one of the 3 options after the test is stopped: 📦 Keep the flag if it still serves a purpose, for example, a feature config. 🔄 Update the code default if we want to roll out a variation as the new baseline while keeping the flag for another iteration. 🗑 Remove the flag if we decide to roll out one variation permanently. The workflow we designed covers the third case. The old process required me or someone else to pick up the A/B ticket, create an engineering task to r ## Learnings and Pitfalls from AI-Assisted A/B Experimentation at Scale DevFeed: [Learnings and Pitfalls from AI-Assisted A/B Experimentation at Scale]() Original publisher: [Read original article]() Author: Brett Daley, Ilya Gorbachev, Anubhav Natani, Nicholas Brown, Athena Dai, Rachel Song, Marcin Pekalski, Josip Domazet, Anchal Choubey, Daniel Ting Published: 2026-11-04T05:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [Artificial Intelligence](), [product-experimentation](), [Large Language Model](), [agentic workflows]() Tags: [a-b-testing](), [agent](), [agents](), [ai](), [ai-assisted](), [automate](), [autonomous](), [cs-ai](), [experimentation](), [experiments](), [large-language-model](), [learnings](), [llm](), [llms](), [pitfalls](), [scale]() ### AI overview The agent bounds LLM nondeterminism by letting the model select and sequence procedures while deterministic code computes every reported quantity. The authors report three lessons: withholding information improved answers; users relied more on routine retrieval than anticipated autonomous decisions; and the agent's eagerness to help could threaten statistical validity. The supplied text is an abstract, so it does not provide implementation details or evidence beyond these reported findings. ### Source excerpt arXiv:2611.04515v1 Announce Type: new Abstract: A/B testing underpins product decisions by providing trustworthy, reproducible quantification of proposed changes' effects. Growing large language model (LLM) capabilities have motivated efforts to automate experimentation workflows. Yet LLMs are nondeterministic and prone to hallucination, properties that conflict with the trust experimentation systems must provide. Experiments are also operationally messy: diagnosing anomalies, debugging configurations, and conducting bespoke follow-up analyses require flexible reasoning and manual effort. These tasks let agents accelerate experimentation without replacing its deterministic statistical foundations. We report lessons from building and operating such an agent. Our central finding is that this tension is resolved not by making the model deterministic, but by bounding where its nondeterminism can reach; the model selects and sequences procedures, while deterministic code computes every quantity it reports. Three consequences surprised us: withholding information from the model improved its answers, demand concentrated on mundane retrieval rather than the autonomous decisions we anticipated, and the agent's eagerness to help threatened statistical validity. ## How Ares Interactive runs live ops without waiting on client updates DevFeed: [How Ares Interactive runs live ops without waiting on client updates]() Original publisher: [Read original article]() Author: Adam Axler Published: 2026-11-04T00:00:00Z Content type: article Language: en Sources: [Unity]() Topics: [CI/CD](), [mobile devops](), [Continuous Delivery (CD)](), [firebase-remote-config](), [mobile-app-performance](), [Production Engineering]() Tags: [a-b-testing](), [assets](), [backend](), [balance](), [branch](), [build](), [build-times](), [builds](), [c-sharp](), [caching](), [calendar](), [call](), [ci](), [config](), [live](), [ops](), [release](), [remote](), [remote-config](), [ship]() ### AI overview Ares Interactive keeps many live changes independent of client releases, with different pipelines in its two games. Baseball Hits 26 uses a custom backend and remote configuration across more than 50 domains; validated, versioned changes reach servers within seconds, with staging, rollback, scheduling, segmentation, and A/B testing. Addressables deliver optional stadium bundles on demand, while a default stadium ships in the app. The Walking Dead: Aftermath carries forward proven live-service systems and keeps frequently changed content in platform-specific AssetBundles, separate from Addressables, which handle loading and memory. Bundles include compatibility metadata; configuration and balance can change remotely, while new art and code require a build. Spreadsheets feed designer-authored content, enabling some fixes in about 15 minutes. CI checks environment configuration and prevents uploads when required settings are missing. Both teams emphasize early device testing and designing for failures; Swift Games also advises proving the core loop with a small amount of content before expanding live-service features. ### Source excerpt How Baseball Hits 26 and The Walking Dead: Aftermath use remote config, versioned bundles, and CI to ship live ops changes without a client release. ## The 8 best CRO tools for every conversion stage DevFeed: [The 8 best CRO tools for every conversion stage]() Original publisher: [Read original article]() Author: Adam Lehman Published: 2026-10-30T00:00:00Z Content type: comparison Language: en Sources: [Webflow Blog]() Topics: [Tool](), [webflow](), [AI Chat](), [no-code website builders]() Tags: [8-best](), [a-b-testing](), [b2b](), [best](), [compare](), [conversion](), [lead](), [message](), [pipeline](), [research](), [stack](), [stage](), [strategy](), [testing](), [tools]() ### AI overview Choose a B2B CRO tool based on where buyers drop off, whether pages get enough traffic for testing, and your site and CRM setup. Contentsquare and Fullstory diagnose visitor behavior; Contentsquare adds heatmaps and form analytics on sales-quoted tiers, while Fullstory captures journeys across visits without manual event tagging. UserTesting records prospects explaining their reactions, and Wynter tests messaging with verified B2B buyers when traffic is too low for A/B tests, but its small samples offer directional feedback rather than proof of lift. VWO combines testing and on-page research, with separately priced modules; Webflow Optimize integrates testing into Webflow but lacks session replay and reserves CRM integrations for its top tier. For lead handoff, Chili Piper books meetings from form submissions and routes leads, while Qualified's AI agent can qualify visitors and book meetings with or without a form, including outside SDR hours. Both require ongoing setup or tuning. The article recommends matching the tool to the failing stage and notes that vendors provide no independent evidence that their AI features raise conversion rates. ### Source excerpt Compare 8 CRO tools for your B2B lead gen stack, including research, message testing, A/B testing, and pipeline handoff tools. ## Airbnb Cuts Recommendation Staleness from 2 Days to Seconds with Chronon DevFeed: [Airbnb Cuts Recommendation Staleness from 2 Days to Seconds with Chronon]() Original publisher: [Read original article]() Author: Leela Kumili Published: 2026-10-28T13:53:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [streaming data analytics](), [feature-store](), [personalization](), [data-engineering](), [Open Source]() Tags: [a-b-testing](), [ai](), [ai-ml-data-engineering](), [airbnb](), [airbnb-chronon-recommendation](), [allows](), [architecture](), [architecture-design](), [based](), [batch-processing](), [days](), [development](), [embedding](), [embeddings](), [event-driven-architecture](), [feature](), [machine-learning](), [ml-data-engineering](), [mlops](), [model](), [news](), [offline](), [online](), [platform](), [platforms](), [real-time](), [recommendation](), [sequence](), [streaming](), [testing](), [time](), [transformer-models](), [update](), [with]() ### AI overview Airbnb added Push Mode and Near Real-Time Model Transform to Chronon so guest activity can trigger streaming inference with its existing JourneyFormer sequence encoder. The system combines updated events with guest history, writes refreshed embeddings to the existing low-latency store, and leaves the ranking path largely unchanged. Typical updates arrive in 10-30 seconds, reducing embedding staleness from nearly two days to under a minute. Airbnb reports a 1.67% offline NDCG improvement and about a 0.33% increase in uncancelled bookings in online A/B testing. The excerpt says this approach trades additional writes and pipeline complexity for fresher predictions; it contrasts Chronon's integrated streaming and model transformation with Feast's documented cached-prediction architecture. The features were contributed to Chronon's public repository. ### Source excerpt Airbnb extended its Chronon feature platform with Push Mode and Near Real-Time Model Transform to update sequence-based recommendation embeddings within seconds. The architecture reduced embedding staleness from nearly two days to under a minute and improved offline NDCG by 1.67%, while online testing increased uncancelled bookings by approximately one-third of a percent. By Leela Kumili ## A Smart Instant Book Filter Model based on Dual-Objective Optimization DevFeed: [A Smart Instant Book Filter Model based on Dual-Objective Optimization]() Original publisher: [Read original article]() Author: Yi Hou, Li Fan, Huizhong Duan Published: 2026-10-28T04:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [Operations research and optimization](), [A/B Testing]() Tags: [a-b-testing](), [adoption](), [artificial-intelligence](), [booking](), [cs-ai](), [filter](), [instant](), [model](), [optimization](), [production](), [search](), [system]() ### AI overview The smart instant book filter aims to increase instant bookings while keeping overall booking rate from decreasing. The abstract says broadly promoting instant book listings could hurt bookings by surfacing low-quality listings, so the system applies the filter selectively to shift eligible request-to-book listings toward instant booking. In global-user A/B tests, it reportedly outperformed earlier human-expert-tuned models on both instant book rate and overall booking rate, and improved host instant book adoption. The system is described as deployed in production and serving millions of users daily; the excerpt provides no implementation details or numerical results. ### Source excerpt arXiv:2610.36287v1 Announce Type: new Abstract: Hospitality service in online marketplaces has become increasingly popular over the past few years, not only because it helps people easily rent short-term lodging but also because it empowers travelers to connect with locals and feel belonging. A typical pattern of these industries is a central platform mediating the economic interactions between guests and hosts, where a successful booking experience is the key to user satisfaction. However, one of the biggest challenges is the possible delay of response and chance of rejection when guests try to book a request-to-book listing. Previously, we enabled the instant book feature to allow hosts to instantly accept qualified guests and guests to confirm bookings immediately. Yet the symptom has not been alleviated given mixed request-to-book and instant book listing supply in search results. One possible solution is to promote all instant book listings in search results; however, booking rate will be damaged due to some improperly promoted low quality listings. In this paper, we present a novel smart instant book filter system that applies the instant book filter intelligently and shifts request-to-book to instant book without hurting booking rate. Compared with traditional two-sided markets research that targets finding strategies to maximize a single overall social welfare goal, our proposed system aims to maximize the instant book rate with the constraint that booking rate should not decrease, forming a dual-objective optimization problem. We show A/B testing results for our system exposed to global users, demonstrating that our model performs better than previous human-expert-tuned models in terms of both instant book rate and overall booking rate. We also achieved improved metrics such as the host instant book adoption rate. The proposed system has been deployed to production and is serving millions of users every day. ## MAREval: A Multi-Agent Framework for Evaluating Natural Language Recommendation Explanations DevFeed: [MAREval: A Multi-Agent Framework for Evaluating Natural Language Recommendation Explanations]() Original publisher: [Read original article]() Author: Reza Yousefi Maragheh, Jayesh Uddhav Kudase, Aysenur Inan, Ramin Giahi, Kai Zhao, Parth Parikh, Jianpeng Xu, Jason Cho, Evren Korpeoglu, Sushant Kumar Published: 2026-10-28T04:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [Multi Agent Systems](), [AI Agent](), [Large Language Model](), [ai alignment](), [Artificial Intelligence]() Tags: [a-b-testing](), [agent-framework](), [alignment](), [annotation](), [cs-ai](), [cs-ir](), [e-commerce](), [engagement](), [evaluation](), [evaluations](), [experiments](), [human-judgment](), [language-models](), [llms](), [monte-carlo](), [multi-agent](), [natural-language](), [online](), [quality](), [real-world](), [recommendation](), [recommender-systems]() ### AI overview MAREval evaluates natural-language recommendation explanations with a multi-agent process intended to improve consistency and agreement with human judgment. A planner uses Chain of Debate prompting to coordinate roles and set evaluation plans, a moderator mitigates prompt drift, and an arbitrator aggregates results across rounds; Monte Carlo sampling is also used to improve robustness. The supplied abstract reports stronger alignment with human judgments than single- and multi-agent baselines on a public benchmark and proprietary recommendation data, plus lower variability across repeated trials. It also reports meeting production thresholds in a large human-annotation gate and statistically significant engagement and revenue improvements in online A/B tests. The excerpt does not provide metrics or implementation details. ### Source excerpt arXiv:2610.36389v1 Announce Type: new Abstract: Evaluating natural language explanations in recommender systems is essential for fostering user trust, transparency, and engagement. However, existing evaluation approaches like human evaluations, while accurate, are resource-intensive and impractical at the scale required by modern recommendation platforms. Also, automated methods using single-agent LLMs suffer from prompt sensitivity and inconsistent outputs. To address these challenges, we propose MAREval, a structured multi-agent framework for evaluating recommendation explanations using large language models. MAREval orchestrates (i) a planner agent that uses a novel Chain of Debate (CoD) prompting strategy to coordinate agent roles and enforce logically consistent evaluation plans; (ii) a moderator agent that regulates discussions by mitigating prompt drift; and (iii) an arbitrator agent that aggregates outputs from multiple evaluation rounds and (iv) a Monte Carlo sampling method, improving robustness and alignment with human judgment. We conduct comprehensive evaluations on both public (TopicalChat) and proprietary recommendation datasets, demonstrating that MAREval outperforms state-of-the-art baselines. Comprehensive experiments on a public benchmark and a proprietary e-commerce dataset show that MAREval improves alignment with human judgments over strong singleand multi-agent baselines. Stability analyses indicate substantially lower variability across repeated trials. In a large human-annotation gate, MAREval meets production quality thresholds where prior evaluators fall short, and online A/B testing demonstrates statistically significant improvements in engagement and revenue metrics. These results establish MAREval as a scalable and reliable solution for human-aligned evaluation of recommendation explanations in real-world systems ## How Webflow's marketing team builds campaign pages in hours with Campaigns, Claude, and Webflow MCP DevFeed: [How Webflow's marketing team builds campaign pages in hours with Campaigns, Claude, and Webflow MCP]() Original publisher: [Read original article]() Author: Natalie Martell Published: 2026-10-27T00:00:00Z Content type: article Language: en Sources: [Webflow Blog]() Topics: [webflow](), [Claude](), [anthropic]() Tags: [2](), [3](), [a-b-testing](), [abm](), [about](), [account](), [act](), [actions](), [brand](), [builds](), [campaign](), [claude](), [experience](), [inside-webflow](), [marketing](), [mcp](), [pages](), [search](), [team](), [webflow](), [with]() ### AI overview Webflow's Campaigns workflow lets marketers build on-brand campaign pages with fewer handoffs, while the brand team focuses on larger launches. In paid search, a marketer used Claude and Webflow's MCP server to build a reusable template with seven keyword-specific variations in a four-hour session; adding another page took about 30 minutes. A proposed optimization loop uses ad performance to recommend copy changes and stage them for review, with nothing published without approval. For an event, the team adjusted and published a lead page the morning of a session, generating 80 form fills within minutes. For ABM, n8n gathers account context, Claude and Campaigns AI create tailored copy, and the MCP server fills an approved template; reps review pages, while Marketing Ops reviews and publishes during a 100-account beta. Paid search results are still coming in, and the ABM beta is just starting. ### Source excerpt Creating an on-brand experience for every audience, from paid search to ABM. ## Best practices for scaling A/B testing with experiment protocols DevFeed: [Best practices for scaling A/B testing with experiment protocols]() Original publisher: [Read original article]() Author: Eddie Cai; Lukas Goetz-Weiss Published: 2026-10-26T00:00:00Z Content type: tutorial Language: en Sources: [Datadog]() Topics: [A/B Testing](), [experiments](), [product-experimentation]() Tags: [a-b-testing](), [analysis](), [best-practices](), [experiment](), [metrics](), [protocols](), [scaling](), [teams]() ### AI overview Datadog Experiments protocols turn an approved experiment methodology into reusable defaults for metrics, randomization, exposure, duration, analysis, and notifications. Published protocols prefill new experiment drafts; copied settings remain editable, and later protocol changes affect only new experiments. Define a primary outcome and secondary guardrails before launch, set acceptable thresholds, and choose stable subject identifiers and consistent assignment inputs across services. Staged exposure can limit risk, while duration should span a full business cycle. Review diagnostics before interpreting results: traffic imbalance is a blocker, mixed assignments are excluded from analysis, and dimensional or segment warnings can reveal localized problems. Agree on the analysis plan in advance: sequential frequentist analysis supports repeated checks, fixed-sample analysis requires a preset sample or duration, and Bayesian analysis needs its own decision criteria. CUPED can reduce variance when representative pre-experiment data is informative, but it does not correct biased randomization. Use separate protocols when recurring designs or decision processes differ. ### Source excerpt Learn how to standardize A/B testing at scale with experiment protocols. Define metrics, randomization, exposure, and analysis, then reuse them across teams. ## The 7 best Unbounce alternatives for paid-ads teams DevFeed: [The 7 best Unbounce alternatives for paid-ads teams]() Original publisher: [Read original article]() Author: Adam Lehman Published: 2026-10-24T00:00:00Z Content type: comparison Language: en Sources: [Webflow Blog]() Topics: [no-code website builders]() Tags: [7-best](), [a-b-testing](), [ads](), [alternatives](), [best](), [compare](), [reviews](), [rules](), [strategy](), [teams](), [testing](), [traffic]() ### AI overview The best Unbounce alternative depends on traffic billing, testing needs, and whether campaign pages must share the main site's design. Leadpages and HubSpot do not meter landing-page visits; Webflow and Framer use bandwidth limits with a warning period before an upgrade or charge. Swipe Pages publishes an overage rate, Landingi does not, and Instapage locks accounts when visitor caps are exceeded. Testing also differs: Swipe Pages, Leadpages, Landingi, and Instapage include testing on specified plans; Webflow, HubSpot, and Framer charge for it through add-ons or higher-tier products. Webflow and Framer build pages from the brand site's components, while Webflow Campaigns can publish standalone pages but does not include testing there. Instapage offers server-side tests and ad-to-page mapping; Swipe Pages targets mobile traffic; Landingi supports client subaccounts; HubSpot ties pages and ad audiences to CRM records. The article notes that Unbounce pages generally must be rebuilt when migrating, with redirects, scripts, and integrations planned separately. ### Source excerpt Compare seven Unbounce alternatives for paid-ads teams. See how they compare on traffic overage rules, A/B testing plans, and user reviews. ## The 5 best Instapage alternatives for personalized landing pages DevFeed: [The 5 best Instapage alternatives for personalized landing pages]() Original publisher: [Read original article]() Author: Adam Lehman Published: 2026-10-24T00:00:00Z Content type: comparison Language: en Sources: [Webflow Blog]() Topics: [Website](), [hubspot](), [Dynamic AI user experience](), [Design system]() Tags: [a-b-testing](), [alternatives](), [compare](), [landing-pages](), [marketing](), [pricing](), [reviews](), [strategy]() ### AI overview The alternatives differ in how they personalize pages. Webflow uses the main site's components and CMS, with Optimize targeting campaigns and visitor behavior; account and CRM targeting require its sales-led Enterprise add-on. Unbounce matches copy to paid-search terms with Dynamic Text Replacement and offers AI routing on its $249 Optimize plan. HubSpot applies CRM list and lifecycle rules to tracked contacts, while URL and ad-source rules can serve first-time visitors. Leadpages targets anonymous visitors by company and fit, with CRM personalization on Optimize. Landingi supports keyword insertion, reusable sections, and spreadsheet-driven bulk page creation; programmatic pages start on its $299 Scale plan. The article frames the choice around visitor data, page volume and brand consistency, and security requirements, with each tool's capabilities constrained by its plan. ### Source excerpt Compare the best Instapage alternatives for marketing teams that personalize landing pages by ad and audience, with pros, cons, user reviews, and pricing. ## Launching a trillion-parameter model at Sakana AI DevFeed: [Launching a trillion-parameter model at Sakana AI]() Original publisher: [Read original article]() Author: Peri Langlois; Saatwik Nagpal Published: 2026-10-23T00:00:00Z Content type: article Language: en Sources: [Modal]() Topics: [LLM optimization](), [model-deployment](), [quantization](), [NVFP4](), [Post-training optimization](), [Mixture of Experts (MoE)](), [Large Language Model](), [LLM evaluation / benchmarking](), [function calling]() Tags: [a-b-testing](), [acceptance-rate](), [ai](), [api](), [blackwell](), [challenge](), [inference](), [language](), [memory](), [mixture-of-experts-moe](), [model](), [parameter](), [post-training](), [precision](), [quality](), [quantization](), [sakana](), [tool](), [tool-calling](), [training]() ### AI overview Modal and Sakana AI launched Sakana Namazu, a roughly 1T-parameter Japanese business model, using a serving stack tuned for quality, speed, and elastic capacity. They quantized routed experts to NVFP4 while keeping attention, routing, and shared experts in BF16, and calibrated on Japanese, domain-representative conversations; the text reports Japanese instruction following matched Sakana's production checkpoint within benchmark noise. A custom speculative draft model trained on raw production token IDs improved token acceptance and reduced decode latency; a later DFlash2 drafter trained on about 1M real requests raised average accepted draft length from 3.51 to 5.01 and lowered p99 latency from 68 to 52 seconds in a held-out replay. Further work removed CPU-GPU synchronization stalls, accelerated expert kernels, cached request setup, fixed a rare cross-GPU verification disagreement, and enabled speculative JSON-schema generation. A KV-aware router reduced queueing and tail latency. Modal's on-demand GPU pool let the API scale for launch without a long-term capacity reservation. ### Source excerpt Deploying a \~1-trillion parameter Mixture-of-Experts (MoE) model for API inference is a classic systems challenge. At full FP16 or BF16 precision, the memory footprint requires very large clusters. Standard post-training quantization (PTQ) recipes shrink that footprint but can degrade model quality, especially for non-English models, where the usual quantization calibration datasets don't capture language register or tool-calling behavior. ## What is an email blast? (how to do it right) DevFeed: [What is an email blast? (how to do it right)]() Original publisher: [Read original article]() Author: Jesse Sumrak Published: 2026-10-19T00:00:00Z Content type: tutorial Language: en Sources: [Twilio](), [Twilio Blog]() Topics: [SendGrid](), [email]() Tags: [3](), [a-b-testing](), [about](), [advanced](), [all](), [allows](), [also](), [announcements](), [api](), [email](), [examples](), [how-to](), [industry-insights](), [large](), [learn](), [list](), [plus](), [right](), [send](), [sendgrid](), [tips](), [what-is]() ### AI overview An email blast is one message sent to a large list at once, usually with little segmentation. The source recommends using an email service provider, a permission-based list, audience segments, and tailored content so large sends stay relevant and support deliverability. It also advises testing subject lines and content, including a clear call to action, and monitoring sender reputation. For sends above 5,000 emails a day, it says Gmail and Yahoo bulk-sender rules require SPF, DKIM, a DMARC policy, visible and one-click unsubscribe with requests honored within two days, and a spam complaint rate under 0.3%. SendGrid disallows purchased lists and offers segmentation and automation tools; the excerpt gives no comparative assessment of other providers. ### Source excerpt An email blast sends one email to a large list at once. Learn how to send one that reaches the inbox, plus tips and examples. ## How Grupo Boticário uses AI to strengthen human connections DevFeed: [How Grupo Boticário uses AI to strengthen human connections]() Original publisher: [Read original article]() Published: 2026-10-16T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [AI for customer service automation](), [AI Agent](), [Artificial Intelligence](), [AI Chat](), [human centric design]() Tags: [000](), [2025](), [2026](), [a-b-testing](), [about](), [accessible](), [act](), [adoption](), [agent](), [agent-adoption](), [agent-orchestration](), [ai](), [operational](), [pharmacy](), [platform](), [resellers](), [sales](), [take](), [up](), [uses]() ### AI overview Grupo Boticário uses a proprietary agent platform built with OpenAI foundation models and company business rules to support resellers and franchisees across its complex, multichannel business. The reseller journey has more than 30 agents and combines generative and predictive AI: AI initially receives and qualifies 90% of direct-sales leads, handing them to a human when they intend to place an order; support agents handle operational questions such as payment slips, catalogs, and app use. Since November 2025, agents have covered all conversations generated in appchat and WhatsApp Enterprise, resolving up to 80% of operational requests autonomously. The company frames automation as improving service and freeing sales staff to advise resellers, who in turn can focus on their own customers. Sales, service, and support teams manage prompts and run ongoing iterations and A/B tests; dedicated agents validate response content and effectiveness. The excerpt presents this frontline involvement as a way to improve service and create new career paths. ### Source excerpt Almost 50 years after starting in a compounding pharmacy in Paraná, the group operates a proprietary agent platform that autonomously resolves up to 80% of resellers' operational requests--enabling the sales force to take on an advisory role. ## Instagram Modernized its UI with an AI-Native Architecture, Cutting Token Costs by 33% DevFeed: [Instagram Modernized its UI with an AI-Native Architecture, Cutting Token Costs by 33%]() Original publisher: [Read original article]() Author: Sergio De Simone Published: 2026-10-15T16:00:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [Jetpack Compose](), [Vibe coding](), [React]() Tags: [a-b-testing](), [ai-assisted](), [ai-coding-agents](), [android](), [bugs](), [development](), [facebook](), [feature-flags](), [instagram-compose-migration](), [jetpack-compose](), [migration](), [mobile](), [news](), [performance]() ### AI overview Instagram Direct migrated its Android UI from Android Views to Jetpack Compose, shaping the codebase so AI coding agents can work with established patterns and architectural boundaries. The engineers report 50% less UI code, 33% lower token costs per agent session, 35% less agent execution time, and 32% fewer engineer-agent exchanges. They evaluated performance using production metrics and A/B tests as screens moved to Compose. ### Source excerpt Instagram Direct migrated its Android UI from the legacy Android View system to Jetpack Compose, creating an AI-native UI codebase that is easier for AI coding agents to understand, modify, and maintain. According to Instagram engineers, the migration reduced UI code volume by 50% and token costs per AI agent session by 33%. By Sergio De Simone ## The 7 best Optimizely alternatives for enterprise marketing teams DevFeed: [The 7 best Optimizely alternatives for enterprise marketing teams]() Original publisher: [Read original article]() Author: Adam Lehman Published: 2026-10-09T00:00:00Z Content type: comparison Language: en Sources: [Webflow Blog]() Topics: [webflow]() Tags: [7-best](), [a-b-testing](), [adobe](), [alternatives](), [best](), [cms](), [compare](), [enterprise](), [marketing](), [strategy](), [teams]() ### AI overview The best Optimizely replacement depends on which modules your team uses and who will build the front end. Webflow suits website-focused teams that want marketers to design and publish pages, with testing and analytics available as add-ons; it lacks a CDP, DAM, and cross-channel personalization. Adobe Experience Manager and Sitecore consolidate broader DXP capabilities, but require sales-led pricing and specialist implementation; Adobe fits teams already using its stack, while Sitecore bundles customer data and personalization. Acquia fits Drupal portfolios needing regulated hosting, including documented FedRAMP authorizations, and requires Drupal expertise. The headless options--Contentstack, Contentful, and Storyblok--expect developers to build the front end. Contentstack offers approval workflows on a self-service plan; Contentful's visual builder and personalization require Enterprise; Storyblok includes a visual editor on its plans, but separate spaces and stronger uptime guarantees may require custom pricing. Before choosing, review current Optimizely modules, budget constraints, and front-end ownership; the article recommends phased migration, testing imports on sample content, ### Source excerpt Compare the best Optimizely CMS alternatives for enterprise marketing teams. ## User Experience (UX) Surveys: The Ultimate Guide DevFeed: [User Experience (UX) Surveys: The Ultimate Guide]() Original publisher: [Read original article]() Author: Mads Soegaard Published: 2026-10-06T16:00:00Z Content type: tutorial Language: en Sources: [UX Daily - User Experience Daily]() Topics: [User Experience](), [Business, Industry, Usability, User Experience, User Research, Web Strategy](), [product analytics](), [UI/UX Design](), [inclusive design]() Tags: [a-b-testing](), [accessibility](), [best-practices](), [surveys](), [usability](), [user-experience]() ### AI overview This guide explains how UX surveys can help evaluate product features, uncover user pain points, measure satisfaction and loyalty, and inform product improvements. It covers survey types, question design, accessibility, privacy, piloting, analysis, and sharing findings. It also provides sample questions and discusses survey templates. ### Source excerpt Imagine you're a business owner who wants to know what's working and what's not on your website--and, oh yes, where you need improvements. Sure, there are a bunch of research methods you can try--like user interviews, usability tests, and A/B testing, but read on and see how a user experience (UX) survey helps gather valuable insights in and pinpoint the areas you'll need to work on to get a site--or any other digital product or service for that matter--in tip-top shape!See how UX surveys can offer actionable insights, presenting qualitative data that informs decisions. [[video:925]]What are UX Surveys?UX surveys--or user experience surveys--help you gather information about users' feelings,... ## Turning Customer Identity into A Growth Engine DevFeed: [Turning Customer Identity into A Growth Engine]() Original publisher: [Read original article]() Author: Bradford Peirce Published: 2026-10-06T00:00:00Z Content type: article Language: en Sources: [Auth0 Blog]() Topics: [identity and access management](), [Auth0](), [feature flags](), [MFA]() Tags: [a-b-testing](), [auth0](), [client](), [customer-identity](), [identity]() ### AI overview Auth0 presents the Identity Conversion Suite, combining Anonymous Sessions and Experiment Center to help teams preserve visitor context and test authentication flows. The article describes its use of anonymous user sessions, A/B testing, and gradual rollouts to balance conversion goals with security across retail, travel, and financial services. ### Source excerpt Unveiling the Auth0 Identity Conversion Suite. ## Building Lead Capture Forms That Convert: A Developer's Guide DevFeed: [Building Lead Capture Forms That Convert: A Developer's Guide]() Original publisher: [Read original article]() Author: SitePoint Team Published: 2026-10-02T20:43:55Z Content type: tutorial Language: en Sources: [SitePoint]() Topics: [Forms](), [Test-driven development]() Tags: [a-b-testing](), [accessibility](), [developer](), [forms](), [guide]() ### AI overview A practical guide to building lead capture forms that are easier to complete and more resilient. It covers native HTML5 validation, when to validate fields, accessible error messages, preserving partial entries across steps or interruptions, handling failed submissions, and deciding whether to build a custom form or use a no-code builder. ### Source excerpt null Continue reading Building Lead Capture Forms That Convert: A Developer's Guide on SitePoint. ## How to Build a Model Router in the Harness DevFeed: [How to Build a Model Router in the Harness]() Original publisher: [Read original article]() Author: LangChain Accounts Published: 2026-10-02T13:31:57Z Content type: tutorial Language: en Sources: [LangChain Blog]() Topics: [Agent Harness](), [AI Context Management](), [LLM observability](), [ai observability](), [AI Integration Strategies]() Tags: [a-b-testing](), [agent](), [agent-harness](), [build](), [context-engineering](), [cost]() ### AI overview LangChain describes building a model router within Open SWE's agent harness. It analyzes task traces, selects among model tiers, and measures outcomes with evaluations and A/B tests. In a test across 973 threads, routing reduced median thread cost by 64% compared with always using GPT-6 Astra, with no measurable change in quality. The post also discusses limitations and possible improvements, including routing subagents and rerouting when tasks change. ### Source excerpt How we built a model router into Open SWE's harness that cut median cost per coding task by 64% with no measurable drop in quality, and how to build your own. ## How to Create a Video Monetization Strategy in 5 Steps (2026) DevFeed: [How to Create a Video Monetization Strategy in 5 Steps (2026)]() Original publisher: [Read original article]() Author: Jon Whitehead Published: 2026-10-02T08:45:59Z Content type: tutorial Language: en Sources: [DaCast]() Topics: [API Monetization](), [API Business Models]() Tags: [a-b-testing](), [analytics](), [build](), [monetization](), [revenue](), [strategy](), [streaming](), [streaming-video](), [the-video-experts-blog](), [video]() ### AI overview The article outlines a five-step approach to monetizing video: define goals and pricing, choose subscription, pay-per-view, ad-supported, or hybrid models, select a platform with payment and analytics features, refine pricing and retention, then launch and monitor results. It also discusses FAST distribution, advertising, compliance, and revenue metrics. ### Source excerpt By Dacast Editorial Team | Reviewed by Jon Whitehead, COO at Dacast | Updated October 2026. A solid video monetization strategy presents a real opportunity for today's broadcasters. Organizations of all sizes are using live streaming, video on demand, and OTT technologies to distribute content quickly and effectively while generating revenue at the same time. [...] The post How to Create a Video Monetization Strategy in 5 Steps (2026) appeared first on Dacast. ## Uplifting conversion across the acquisition funnel with personalization using contextual bandits on AWS DevFeed: [Uplifting conversion across the acquisition funnel with personalization using contextual bandits on AWS]() Original publisher: [Read original article]() Author: Chidi Prince John Published: 2026-10-01T16:51:04Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [Artificial Intelligence](), [thompson sampling](), [MLOps](), [Amazon Bedrock](), [Amazon DynamoDB]() Tags: [a-b-testing](), [amazon-bedrock](), [amazon-dynamodb](), [amazon-sagemaker](), [amazon-sagemaker-ai](), [bandits](), [customer-solutions](), [funnel]() ### AI overview Amazon Payments describes using a multi-objective contextual bandit on Amazon SageMaker AI to personalize content across an acquisition funnel. In a seven-week A/B test, one customer population saw a high single-digit relative lift in final-funnel conversion, while another saw no improvement; the authors identify content quality as a constraint for the latter. The article explains the bandit approach, funnel optimization, AWS batch architecture, evaluation, and operational lessons. ### Source excerpt Generative AI makes it cheap to produce personalized content at scale, but which variation do you show each customer? Amazon Payments used a multi-objective contextual bandit on Amazon SageMaker AI to personalize an acquisition funnel, achieving a high single-digit conversion lift for one audience, and learning why content, not the model, was the constraint. [Next page]()