# daily Published articles for daily. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Aaron Swartz Day - Why We're Pursuing Our Case Against Flux DevFeed: [Aaron Swartz Day - Why We're Pursuing Our Case Against Flux]() Original publisher: [Read original article]() Author: ladyada Published: 2026-11-08T21:00:20Z Content type: opinion Language: en Sources: [Adafruit Industries]() Topics: [Security]() Tags: [act](), [adafruit](), [adafruit-daily](), [amazing](), [arduino](), [chat](), [community](), [computer](), [cost](), [crime](), [daily](), [digikey](), [discord](), [discovery](), [don-t](), [eff](), [flux](), [question](), [report](), [security](), [security-concerns](), [security-research](), [share]() ### AI overview Adafruit says it is pursuing its case against Flux to bring the truth out through discovery and trial. On Aaron Swartz Day, it argues that threats of computer-crime prosecution, including under the CFAA, can silence people who question companies, report security concerns, or share findings. It says responsible security reporting should not be met with legal intimidation and frames its case as an effort to protect future researchers. The excerpt does not describe the underlying dispute or specific allegations. ### Source excerpt Why Adafruit is pursuing its case against Flux, and why the freedom to question companies, report security concerns, and share findings is worth defending. ## Apple Is Putting Employees On an AI Budget DevFeed: [Apple Is Putting Employees On an AI Budget]() Original publisher: [Read original article]() Author: BeauHD Published: 2026-11-05T21:00:00Z Content type: news Language: en Sources: [Slashdot]() Topics: [AI Chat](), [Claude](), [ChatGPT]() Tags: [ai](), [ai-cost](), [ai-spending](), [apple](), [bloomberg](), [budget](), [can](), [chatgpt](), [claude](), [coding](), [cost](), [costs](), [daily](), [device](), [employee](), [employees](), [engineers](), [gemini](), [limit](), [limits]() ### AI overview Apple is reportedly lowering some engineers' AI spending limits from $300 to $200 per day, while considering limits as low as $100; some teams are capped at $25. Employees are encouraged to use cheaper models or on-device AI. Engineers receive larger budgets because AI coding can consume many tokens, and Apple permits use of Claude, ChatGPT, Gemini, and other models. Bloomberg was told that few employees used the full $300 allowance, so savings may be small. Apple has also hired outside productivity consultants for some divisions; the cuts and reviews have raised worries about possible restructuring. ### Source excerpt According to Bloomberg, Apple is cutting employee AI spending limits to reduce costs, lowering some engineers from $300 to $200 per day and considering limits as low as $100, with some teams capped at $25. Employees are also being encouraged to use cheaper models or on-device AI. MacRumors reports: Engineers at Apple have the largest budgets because using AI for coding can consume a large number of tokens. Apple lets employees use Claude, ChatGPT, Gemini, and other models. Some Apple employees told Bloomberg that few were using their full $300 daily limit, so the savings may be small. Apple has also hired outside productivity consultants for some divisions. Combined with the AI cost cuts, the reviews have fueled worries about possible restructuring. Read more of this story at Slashdot. ## CARM: Conformal Adaptive Risk Masking for Reliable Scientific Super-Resolution DevFeed: [CARM: Conformal Adaptive Risk Masking for Reliable Scientific Super-Resolution]() Original publisher: [Read original article]() Author: Guangtao Zhang, Haonan Wu, Junxuan Gao, Zhengyang Liu, Zhaoxi Zeng, Zhihao Li, Jianguo Huang, Zhiqiang Liu Published: 2026-11-05T05:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [Machine learning](), [Image processing]() Tags: [abstract](), [adaptive](), [aggregates](), [aggregation](), [all](), [alpha](), [arxiv](), [balancing](), [based](), [but](), [can](), [changes](), [china](), [choice](), [control](), [cs-ai](), [cs-lg](), [daily](), [deep](), [masking](), [math-pr](), [risk](), [super-resolution]() ### AI overview CARM adapts spatial trust-mask thresholds for super-resolution as Earth-observation data shift over time. It combines threshold proposals from experts with different update rates using an asymmetric loss, balancing responsiveness with stability. Updates are causal and require feedback only after a fixed delay; the framework can use any SR model that provides a spatial uncertainty score. The authors prove that time-averaged mask-conditioned risk converges to at most α at an O(1/T) rate, without distributional assumptions. In a South China Sea chlorophyll-a stream at α=0.10, CARM lowered the empirical violation rate from 43.49% to 9.38% and moving-average exceedance frequency from 47.29% to 1.22%, while retaining a non-trivial trusted region. The abstract also reports consistent relative improvements across multiple SR backbones on ERA5 wind and OSTIA sea-surface temperature. ### Source excerpt arXiv:2611.06151v1 Announce Type: new Abstract: Deep learning-based super-resolution (SR) can reconstruct fine-scale Earth-observation fields from coarse observations, but reconstruction accuracy alone does not provide a calibrated rule for identifying reliable spatial regions. Conformal Mask Calibration (CMC), built on conformal risk control, converts a spatial uncertainty score into a trust mask and controls the expected worst-case local distortion under exchangeability. However, its fixed threshold can become miscalibrated in Earth-observation streams affected by seasonality and episodic events. We propose $\textit{CARM}$ (Conformal Adaptive Risk Masking), a statistical framework for adaptive spatial risk control under temporal distribution shift. CARM maintains experts with heterogeneous update rates and aggregates their threshold proposals online using an asymmetric surrogate loss, balancing rapid adaptation to distribution changes against stability during relatively stationary periods. All updates are causal and use only mature feedback available after a fixed delay, allowing CARM to operate with any SR model that provides a spatial uncertainty score. We prove that the time-averaged mask-conditioned risk converges to at most $\alpha$ at an $O(1/T)$ rate, without distributional assumptions and for any choice of aggregation weights across experts. On a daily South China Sea chlorophyll-$\textit{a}$ SR stream, CARM reduces the empirical violation rate from $43.49\%$ to $9.38\%$ and the moving-average exceedance frequency from $47.29\%$ to $1.22\%$ at $\alpha=0.10$, while retaining a non-trivial trusted region. Experiments on ERA5 wind and OSTIA sea-surface temperature further show consistent relative improvements across multiple SR backbones. ## An Open Book, On Whose Terms? Power Dynamics in Sensor-Derived, LLM-Generated Summaries Sharing Between Older Adults and Remote Families DevFeed: [An Open Book, On Whose Terms? Power Dynamics in Sensor-Derived, LLM-Generated Summaries Sharing Between Older Adults and Remote Families]() Original publisher: [Read original article]() Author: Jiachen Li, Reina Szeyi Chan, Shihan Fu, Zijian Wang, Akshat Choube, Yingtian Shi, Rosemarie Santa Gonzalez, Thomas Ploetz, Agata Rozga, Xiang Zhi Tan, Elizabeth Mynatt, Varun Mishra Published: 2026-11-04T05:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [human computer interaction](), [User interface design](), [AI Chat]() Tags: [aging](), [ai](), [awareness](), [benefit](), [case-study](), [communication](), [complex](), [cs-ai](), [cs-hc](), [cs-ma](), [daily](), [data](), [deployment](), [design](), [engagement](), [experience](), [family](), [future](), [llm](), [power](), [remote](), [sensor](), [sharing]() ### AI overview A three-month case study with one older adult-remote family member dyad found that daily LLM-generated summaries of multimodal, non-intrusive sensing data fit into their routine, with engagement above 90%. Interviews with 12 remote family members and 2 older adults informed the system, and both parties helped refine it. The dyad's experience surfaced changing tensions around agency, reassurance, privacy, trust, and communication, influenced by sensor limitations and existing communication misalignments. The findings point to designing sharing systems around real-world sensing constraints and family care dynamics; the excerpt does not establish broader outcomes beyond this dyad. ### Source excerpt arXiv:2606.03876v3 Announce Type: replace-cross Abstract: As older adults (OAs) increasingly favor aging in place, remote family members (RFMs) need meaningful awareness of their loved ones without intrusive monitoring. Large language models can provide rich interpretations of non-intrusive sensing data, yet recent work has largely emphasized technical accuracy over how families use or benefit from this information. We conducted formative interviews with 12 RFMs and 2 OAs, followed by a three-month deployment case study with one OA-RFM dyad. Our system provided daily AI-generated summaries of multimodal sensing data, which we iteratively refined with both parties using a research-through-design approach. Although the summaries integrated well into the dyad's daily routine, sustaining over 90% engagement, their lived experience revealed evolving tensions around agency, reassurance, privacy, trust, and communication, shaped by sensor limitations and existing communication misalignments. Our findings inform the design of future AI-mediated sharing systems that account for real-world sensing constraints and complex family care dynamics. ## Where Does EEG Help Wearable Freezing of Gait Detection? DevFeed: [Where Does EEG Help Wearable Freezing of Gait Detection?]() Original publisher: [Read original article]() Author: Chuntian Chi, Lifang He, Huajie Shao, Gang Zhou Published: 2026-11-03T05:00:00Z Content type: article Language: en Sources: [arXiv: Machine Learning]() Topics: [Machine learning]() Tags: [abstract](), [all](), [analysis](), [arxiv](), [assemble](), [based](), [benefit](), [but](), [classification](), [compare](), [cross](), [cs-lg](), [daily](), [dataset](), [datasets](), [detection](), [eeg](), [external](), [help](), [helps](), [wearable]() ### AI overview In this abstract, EEG helps wearable freezing-of-gait detection in two ways: as an inference input, where it improves FoG F1 from 0.663 to 0.740 and gains in ranking performance are concentrated in the least-moving windows; and during joint EEG-IMU pretraining, which improves an IMU-only detector (F1 0.663 to 0.705 within the cohort, and +0.083 when trained on a small cohort and tested on another). The pretraining benefit from aligning EEG with IMU requires both Parkinson's disease and healthy participants in the pretraining corpus, while IMU detection itself is reported as robust to corpus choice. Cross-cohort detectors without annotated voluntary stops label nearly all such stops as freezing. The abstract also reports improved Parkinson's classification from walking EEG after joint pretraining (AUROC 0.750 versus 0.683). ### Source excerpt arXiv:2611.00497v1 Announce Type: new Abstract: Freezing of gait (FoG) detectors built on inertial measurement units (IMUs) are accurate while the legs move, but they become unreliable when the legs are nearly still, which is exactly when akinetic freezing, voluntary stopping, and standing must be told apart. Electroencephalography (EEG) captures motor-planning activity that leg sensors cannot, but EEG is impractical for daily use. We ask where EEG helps: as an input at inference time, or only during pretraining, so that the deployed detector still needs only IMUs. We assemble a pretraining corpus of synchronized EEG and IMU recordings (5 datasets, 184 people, 94 hours) and compare six settings that differ only in the modalities used for pretraining and as model input, each with tuned hyperparameters and five independent pretraining runs. On the BXHC FoG dataset (leave-one-patient-out, 10 patients with FoG), adding EEG as input raises FoG F1 from 0.663 to 0.740 (9 of 10 patients improve), and the gain in ranking performance is confined to the quartile of windows with the least leg movement (AUROC +0.063). Pretraining the IMU encoder jointly with EEG improves an IMU-only detector both within the cohort (FoG F1 0.663 to 0.705) and when it is trained on a small cohort and tested on another (FoG F1 +0.083, all 10 test patients improve). Repeating the study with PD-only and healthy-only pretraining corpora, each with its own hyperparameter search, shows that IMU-based detection is robust to the corpus, whereas the benefit of aligning EEG with IMU requires both. Cross-cohort analysis further shows that detectors trained without annotated voluntary stops classify nearly all stops as freezing. Finally, on PD versus non-PD classification in three external datasets, joint EEG+IMU pretraining improves PD detection from walking EEG over EEG-only pretraining (AUROC 0.750 vs. 0.683), while IMU-based detection does not depend on the pretraining. ## Build an AI Wellbeing App with Fireworks and Fitbit via Auth0 Token Vault. DevFeed: [Build an AI Wellbeing App with Fireworks and Fitbit via Auth0 Token Vault.]() Original publisher: [Read original article]() Author: Deepu K Sasidharan Published: 2026-11-02T19:25:00Z Content type: tutorial Language: en Sources: [Auth0 Blog]() Topics: [AI Chat](), [Auth0](), [function calling](), [Secure token management]() Tags: [ai](), [ai-sdk](), [app](), [auth0](), [build](), [daily](), [fitbit](), [next-js](), [sleep](), [token](), [vault](), [wellbeing]() ### AI overview Daily Wellbeing retrieves the signed-in user's Google Health readings through Auth0 Token Vault, builds a latest-completed-day report with seven-day personal averages, and streams Fireworks suggestions only after the user explicitly chooses to share the summary. The report preserves recorded local dates, distinguishes missing values from zero, counts available comparison days, and skips data streams without permission. Users can review readings without inference. Fireworks receives a normalized summary through a server-side tool; its prompt limits advice to general wellbeing and excludes diagnosis and treatment, though the source cautions that prompt rules do not guarantee suitable responses. Setup requires a personal Google account linked to a health profile, Google Cloud OAuth configuration, an Auth0 tenant with Token Vault and Connected Accounts, and a Fireworks key and compatible model. Google OAuth test-mode refresh tokens expire after seven days, requiring reconnection. ### Source excerpt Build Daily Wellbeing, a Next.js app that connects Fitbit through Auth0 Token Vault, compares your daily activity and sleep with your averages, and uses Fireworks to suggest what to try today. ## Your Mobile Carrier Stores Your Call Logs for Years DevFeed: [Your Mobile Carrier Stores Your Call Logs for Years]() Original publisher: [Read original article]() Author: Sponsored by Cape Published: 2026-11-02T15:01:11Z Content type: article Language: en Sources: [BleepingComputer]() Topics: [sensitive data](), [4g lte]() Tags: [2](), [2022](), [about](), [call](), [can](), [cape](), [carrier](), [cellular](), [computer-help](), [computer-security](), [computers](), [customers](), [cybersecurity](), [daily](), [data](), [infosec](), [linux](), [logs](), [mac](), [malware](), [malware-removal](), [metadata](), [mobile](), [privacy](), [records](), [relationships](), [retention](), [security](), [service](), [spyware](), [support](), [tech-support](), [technical-support](), [telecom](), [text](), [virus](), [virus-removal](), [windows]() ### AI overview Carrier call logs can expose relationships, locations, and routines even without call content or customer names. The excerpt says AT&T's 2024 breach exposed metadata from 2022 and 2023 affecting 109 million current and former customers. It describes how carriers retain signaling messages and call detail records for troubleshooting, billing, and roaming, while long retention and centralized storage create risks of breach, profiling, and disclosure. Cape says its own mobile core lets it control retention: it keeps SIP messages for 14 days, deletes internal call detail records after one day, retains daily usage totals for up to 60 days, and holds partner billing records for 30 days. It also rotates identifiers seen by tower operators. A Trail of Bits audit found no evidence that Cape violated two specified claims: timely deletion under its policy and no retention of individual customer call detail data beyond daily totals. The audit does not establish broader security or privacy claims. ### Source excerpt Mobile carriers can retain years of call and text metadata, exposing sensitive details about customers' relationships, locations, and daily routines if breached. Cape examines the privacy risks of long-term telecom data retention and how its mobile service reduces exposure by automatically deleting call records. [...] ## PALATE: Pretrained Autoregressive LATent Embeddings DevFeed: [PALATE: Pretrained Autoregressive LATent Embeddings]() Original publisher: [Read original article]() Author: Sizhang Zhao, Praveen Gondhi, Bin Li, Utsaw Kumar Published: 2026-10-30T04:00:00Z Content type: article Language: en Sources: [arXiv: Machine Learning]() Topics: [Embeddings](), [vector embeddings in AI](), [search-engines](), [hybrid-search]() Tags: [ads](), [arxiv](), [autoregressive](), [business](), [commerce](), [consumer](), [conversion](), [counts](), [cs-ir](), [cs-lg](), [daily](), [delivery](), [design](), [discovery](), [embedding](), [embeddings](), [engagement](), [eval](), [exploration](), [feature](), [food](), [generative]() ### AI overview PALATE creates a shared consumer embedding by training a GPT-style model to predict future engagements and using its final-token hidden state. A daily job refreshes this frozen vector, which rankers on browse and search surfaces consume as a dense feature. To adapt training to repurchase-heavy marketplaces, PALATE weights engagement with new businesses more heavily, pretrains on clicks before fine-tuning on conversions, and combines popular-business and shown-but-not-ordered negatives with random and in-batch negatives. Offline relative Eval-AUC gains were +0.59% for browse and +0.57% for search. Online, the embedding was one component of bundled ranker retrains; Discovery reported revenue +0.79% and conversion rate +1.17%, while Search reported revenue +0.94% and conversion rate +1.50%. ### Source excerpt arXiv:2610.39090v1 Announce Type: cross Abstract: Local-commerce and food-delivery marketplaces run large ads businesses: across hundreds of thousands of stores, merchants pay to be surfaced on browse and search surfaces, and a ranker decides which ads each consumer sees. Ranking well demands strong per-consumer personalization, yet a consumer's raw interaction history is sparse and high-cardinality. Our production ranker had personalized only through hand-engineered signals: aggregate engagement counts, categorical embeddings, and order-agnostic pooled history, with no learned per-consumer sequence representation. We learn one by training a GPT-style generative model of the consumer's engagement sequence to predict their next few engagements, and read its last-token hidden state as the consumer embedding. That single frozen vector becomes one shared personalization input for every ad surface: a daily job refreshes it, and each ranker consumes it as an ordinary dense feature. Offline it is the largest single-feature gain in our ranking-AUC ladder, a relative Eval-AUC gain of +0.59% on a browse-driven feed and +0.57% on a query-driven search surface. Online, it shipped as one component of bundled ranker retrains on both surfaces: Discovery (revenue +0.79%, conversion rate +1.17%) and Search (revenue +0.94%, conversion rate +1.50%). Three design choices adapt the generative objective to a repurchase-heavy marketplace, and are the core of our contribution: an exploration-weighted loss that up-weights businesses new to the consumer over ones already engaged; a two-stage curriculum: a click pretrain that keeps the representation broad and exploratory, then a conversion fine-tune that sharpens purchase intent; and a designed negative set that pairs a fixed popular-business pool and businesses shown but not ordered with random and in-batch negatives. ## Where You Inject Matters: The Trust Region Effect in LLM Agents DevFeed: [Where You Inject Matters: The Trust Region Effect in LLM Agents]() Original publisher: [Read original article]() Author: Chengquan Guo, Xiaodong Liu, Weiwei Yang, Mingqian Feng, Jialin Song, Qiyao Ma, Carolyn Buractaon, Christopher White, Bo Li, Jianfeng Gao Published: 2026-10-30T04:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence](), [arXiv: Cryptography and Security](), [arXiv: Machine Learning]() Topics: [LLM security](), [Large Language Model](), [trust]() Tags: [agents](), [attack](), [attacks](), [better](), [code](), [coding](), [cs-ai](), [cs-cr](), [cs-lg](), [daily](), [data](), [effect](), [gpt-5-4](), [indirect-prompt-injection](), [learning](), [llm](), [llm-agents](), [prompt-injection](), [readme](), [region](), [repository](), [tool-use](), [trust]() ### AI overview Across three testbeds for tool use, coding, and daily workflows, the study finds that an LLM agent's susceptibility to indirect prompt injection varies by where the instruction appears, even when its content is held fixed. The authors call this the "trust region effect": agents implicitly trust some environmental regions more than others. For GPT-5.4, an unsafe instruction in repository documentation such as a README or TODO file was followed 2.6 times more often on average than the same instruction in a code or data file. The authors also develop learning-based attackers that choose both injection content and placement, reporting that both affect attack strength. The supplied text is the abstract, so it does not provide further methodological details or results. ### Source excerpt arXiv:2610.38867v1 Announce Type: new Abstract: LLM agents are increasingly adopted in long-horizon tasks and interact with diverse external environments. Despite their growing capabilities, these agents remain vulnerable to real-world attacks such as indirect prompt injections, which manipulate agents through malicious instructions embedded in external environments. While existing risk assessment work has primarily focused on the injection content, the effect of where an injection appears has not been systematically characterized. Studying injection placement requires agents to operate over longer-horizon tasks and interact with diverse environmental surfaces. Thus, we build three agentic testbeds spanning tool use, coding, and daily workflow domains with diverse injection surfaces. Holding the injection content fixed, we find that agent susceptibility varies across injection placements. We refer to this phenomenon as the trust region effect: agents implicitly assign different levels of trust to different regions of their environments. For example, an agent powered by GPT-5.4 follows an unsafe injection instruction placed in a repository documentation file (i.e., a README or TODO file) 2.6 times more often on average than the same instruction placed in a code or data file. Building upon this observation, we develop learning-based attackers that generate injection content and select better injection placements. Our approach demonstrates that both where an injection is placed and what content it contains can affect the strength of indirect prompt injection attacks. To the best of our knowledge, this work provides the first systematic characterization for the effectiveness of different injection placements, spanning both coarse-grained environmental surfaces and fine-grained positions. The uneven trust that agents implicitly place across their environments provides important insights for future attack and defense design. ## CoraLive: Ultra-Low-Power Underwater Vision for Long-term Coral Reef Monitoring DevFeed: [CoraLive: Ultra-Low-Power Underwater Vision for Long-term Coral Reef Monitoring]() Original publisher: [Read original article]() Author: Sitong Shen, Chenshu Wu Published: 2026-10-30T04:00:00Z Content type: article Language: en Sources: [arXiv: Networking]() Topics: [recording](), [real-time face tracking](), [VENTUNO Q]() Tags: [abstract](), [adaptive](), [arxiv](), [based](), [battery](), [board](), [camera](), [co-design](), [content](), [continuous](), [coral](), [cs-ni](), [custom](), [daily](), [data](), [days](), [low-power](), [monitoring](), [power](), [reef](), [vision]() ### AI overview CoraLive targets long-term coral reef monitoring by shifting reconstruction and segmentation from an underwater camera to offline shore-side processing. Its dual-rate sensing pairs frequent low-resolution frames with sparse high-fidelity samples, while content-adaptive updates store compact task-specific data instead of raw video. In a custom STM32U575-based dual-camera prototype, this approach reduced daily storage by 4.51x and average power by 7.78x versus raw recording, extending reported battery life from 20.4 to 159.5 days while retaining high-accuracy segmentation and reconstruction. The abstract also reports validation in a real-world underwater deployment. ### Source excerpt arXiv:2610.39472v1 Announce Type: new Abstract: Effective coral reef protection and restoration demands continuous, long-term underwater monitoring. However, current solutions largely rely on fragmented, manual labor and costly infrastructure, or are severely limited by constrained battery lifespans. In this paper, we present CoraLive, an ultra-low-power underwater camera system for long-term, continuous coral reef monitoring. CoraLive presents a decoupled underwater-shore co-design that minimizes on-board computation and storage, offloading heavy reconstruction and segmentation tasks to offline shore-side processing. To achieve this, CoraLive introduces a dual-rate sensing scheme combining frequent low-resolution frames with sparse high-fidelity samples and a content-adaptive update mechanism that extracts and stores only compact, task-specific data, eliminating the need to save raw video frames. We implement CoraLive on a custom STM32U575-based dual-camera prototype with device-level computation and storage optimization. Compared with raw recording, CoraLive reduces daily storage by $4.51\times$ and average power by $7.78\times$, extending battery lifetime from 20.4 to 159.5~days while retaining high-accuracy segmentation and reconstruction. Further validated in a real-world underwater deployment, we envision CoraLive as a pioneering step for ubiquitous long-term coral reef underwater monitoring. ## As search traffic dries up, Reddit announces it's shifting to a logged-in experience DevFeed: [As search traffic dries up, Reddit announces it's shifting to a logged-in experience]() Original publisher: [Read original article]() Author: Sarah Perez Published: 2026-10-29T20:08:31Z Content type: news Language: en Sources: [TechCrunch | AI News & Artificial Intelligence]() Topics: [Reddit](), [search-engines](), [Google Search](), [AI-driven information retrieval]() Tags: [ads](), [advertising](), [ai](), [ai-data](), [ai-mode](), [ai-powered](), [ai-summaries](), [apps](), [behaviors](), [business](), [company](), [daily](), [data](), [deals](), [experience](), [media-entertainment](), [open](), [open-web](), [reddit](), [search](), [search-traffic](), [social](), [social-media](), [tc](), [web]() ### AI overview Reddit plans to move toward a primarily logged-in experience as Google search referrals fall, aiming to protect its data for AI licensing and improve ad targeting through user interest signals. In October, search referrals fell by more than 1 million per week on average; global daily users were flat quarter-over-quarter at 130.3 million, while U.S. daily users fell to 50.7 million. Revenue rose 53% year-over-year to $895 million, including $48 million in non-ad revenue such as AI licensing. Reddit says logged-in use may support personalization and ad revenue, but the change will be gradual: tests are planned in select markets outside the U.S. in Q4, with no worldwide rollout date. The company says logged-out front-page access and anonymous browsing will remain, including a rebuilt incognito mode. ### Source excerpt As AI transforms how people find information online, Reddit is rethinking its open-web roots to protect its audience and business. ## SymfonyCon Warsaw 2026: Symfony Mate: From Hallway Idea to Daily Driver DevFeed: [SymfonyCon Warsaw 2026: Symfony Mate: From Hallway Idea to Daily Driver]() Original publisher: [Read original article]() Author: Mélyne FREUZE Published: 2026-10-29T13:30:00Z Content type: news Language: en Sources: [Symfony]() Topics: [Symfony](), [Tool]() Tags: [2025](), [2026](), [3](), [about](), [announcement](), [community](), [core](), [daily](), [driver](), [idea](), [international](), [let-s](), [people](), [php](), [poland](), [speaker](), [symfony](), [talk](), [up](), [warsaw]() ### AI overview Mate is presented as a way to give coding agents insight into a running Symfony application. In his SymfonyCon Warsaw 2026 talk, Johannes Wachter will explain its principles and demo it, including two changes since SymfonyLive Berlin: Mate replaced its MCP server with a native CLI after measuring how agents discover and use tools, and Agent Skills now ship with the packages they belong to so bundle builders can teach agents how to use them. The announcement says he will also discuss wrong turns; it does not detail the measurements or specific limitations. ### Source excerpt Let's meet up in Poland! SymfonyCon Warsaw 2026 brings the international PHP and Symfony community together on November 26-27. Come for the 3 talk tracks, stay for the people. 🎤 Speaker announcement Mark your calendars for Johannes Wachter, Core... ## Deepgram acquires Pipecat-maker Daily to bolster its voice AI infrastructure DevFeed: [Deepgram acquires Pipecat-maker Daily to bolster its voice AI infrastructure]() Original publisher: [Read original article]() Author: Ivan Mehta Published: 2026-10-29T13:00:00Z Content type: news Language: en Sources: [TechCrunch | AI News & Artificial Intelligence]() Topics: [AI Infrastructure](), [Open Source](), [Conversational AI](), [Artificial Intelligence](), [livekit]() Tags: [acquisition](), [acquisitions](), [agents](), [ai](), [daily](), [deepgram](), [framework](), [funding](), [gpt-4](), [infrastructure](), [investors](), [language-models](), [latency](), [open-source](), [pipecat](), [voice](), [voice-ai](), [voice-ai-infrastructure]() ### AI overview Deepgram is acquiring Daily, maintainer of the open-source Pipecat framework, in a deal with undisclosed terms; both companies say Pipecat will remain open source and vendor-neutral. Pipecat orchestrates real-time voice and video streams with components such as speech-to-text, language models, and text-to-speech, aiming to reduce latency. Deepgram says the acquisition will help developers combine models from different providers and explore real-time communication beyond voice. The companies also announced the Real-Time Context Standard, an open protocol defining audio-context metadata, instructions for language models, and guidance for how responses should sound. ### Source excerpt Deepgram said that it is committed to keep Pipecat open source. ## You can just ask AI to do things DevFeed: [You can just ask AI to do things]() Original publisher: [Read original article]() Published: 2026-10-29T00:00:00Z Content type: article Language: en Sources: [Sean Goedecke]() Topics: [agentic workflows](), [codex](), [Test-driven development]() Tags: [agents](), [ai](), [ai-models](), [astra](), [automate](), [change](), [claude](), [claude-code](), [codex](), [curl](), [daily](), [deploy](), [dev](), [feature-flags](), [gemini](), [github](), [logs](), [messages](), [metrics](), [model]() ### AI overview The author argues that people can get more from AI agents by proactively assigning them recurring work. Examples include starting a dev server and exercising its happy paths with curl, polling production metrics and logs around deploys or feature-flag changes, asking other models for code feedback, preparing a morning report from unread messages and relevant logs, improving the agent's own setup, and organizing stale sessions. These workflows can automate tedious or ongoing checks, though the author still writes their own messages, makes design decisions, reviews code, and chooses which suggestions to pursue. They note that access to logs and Slack data may raise trust concerns, and that they continue to read messages manually while evaluating the pre-work report." ### Source excerpt One of the most important skills in working with AI is agency: being willing to try new and ambitious things. This is true for what you build, but it's also true for how you build. AI models are improving so quickly that most of us are bottlenecked1 on noticing that AI agents can help automate particular tasks. Here are some examples of that from my daily work. Asking the agent to run my dev server. Agents are very good at fixing weird dev environment issues because they work fast, have infinite patience, and have a huge breadth of knowledge. I almost never run the bash commands to start my dev server anymore. Asking the agent to make requests to my dev server. Part of why I don't run my dev server manually is that I don't make requests to it manually either. Instead I'll say "okay, now start the dev server and run through the happy paths with curl". The relatively manual process of assembling a set of curl requests and validating the output can now be entirely automated2. Asking the agent to watch as I make a production change. Every time I do a deploy or flip feature flags, I ask an agent to poll metrics and logs before and after the change. Agents can ingest more logs than I can and are much more patient than me (for instance, I routinely ask for an hourly poll over the rest of the day, which I would not do for most changes on my own). Asking the agent to get a second opinion from other models. My daily driver is Claude Opus 5.5, which is a good model, but it's not strictly better than GPT-6-Astra or Gemini Argon3. After I've come up with a code change, I typically will say4 "please open subagents to get Astra and Argon's opinion". This is made easier by the fact that GitHub Copilot5 has all the top models built in, but I suspect it'd be straightforward enough to ask Claude Code to run a headless Codex session if you've got subscriptions to both. Asking the agent to do a pre-work pass through all my unread messages, logs, and so on. This is one of the most useful ## MiniMax Open-Sources MCode Stack: 15 Agent Skills Its Coding Team Uses Daily DevFeed: [MiniMax Open-Sources MCode Stack: 15 Agent Skills Its Coding Team Uses Daily]() Original publisher: [Read original article]() Author: Michal Sutter Published: 2026-10-28T17:20:29Z Content type: article Language: en Sources: [MarkTechPost]() Topics: [Agent Skills](), [Agent Harness](), [Claude Code]() Tags: [agent](), [agent-harnesses](), [agent-skills](), [agentic-ai](), [ai-infrastructure](), [ai-shorts](), [applications](), [artificial-intelligence](), [code](), [code-review](), [coding](), [commands](), [context](), [daily](), [editors-pick](), [for-devs](), [language-model](), [long-running](), [minimax](), [minimax-code](), [mit](), [new-releases](), [open](), [open-source](), [repo](), [review](), [skills](), [software-engineering](), [stack](), [staff](), [subagent](), [team](), [tech-news](), [technology]() ### AI overview MiniMax MCode Stack is an MIT-licensed collection of 17 Markdown skills, five checklists, and companion notes for workflows used by the MiniMax Code team; it contains no MiniMax Code source code. Its checks cover planning, context and tool budgets, long sessions and commands, reviews, experiments, and hotfixes. For example, `independent-review` gives reviewers only the diff, requirement, and test results, while `subagent-split-check` recommends splitting only when context is the measured problem. The stack can be installed by copying individual skills or the full plugin, or through a marketplace; the catalog also works in Claude Code. The excerpt reports no benchmarks or evaluation results, so it does not establish effectiveness. ### Source excerpt MiniMax has open-sourced MCode Stack, an MIT-licensed repo of 17 SKILL.md skills and 5 checklists that its MiniMax Code team uses daily for code review, subagent splits, context budgets and long-running commands. The post MiniMax Open-Sources MCode Stack: 15 Agent Skills Its Coding Team Uses Daily appeared first on MarkTechPost. ## How an AI Bot Turned 30 School Emails a Week Into One Daily Digest DevFeed: [How an AI Bot Turned 30 School Emails a Week Into One Daily Digest]() Original publisher: [Read original article]() Author: Geoff Cox Published: 2026-10-28T07:44:13Z Content type: article Language: en Sources: [HackerNoon]() Topics: [Bot](), [AI Context Management](), [Pull Request](), [CI/CD]() Tags: [2](), [2026](), [3](), [about](), [account](), [agent](), [agents](), [ai](), [ai-agent](), [ai-agents](), [ai-companies](), [ai-labs](), [artificial-intelligence](), [automation](), [bot](), [can](), [daily](), [daily-digest](), [email](), [i](), [one](), [parenting](), [productivity](), [quiet](), [rules](), [school](), [software-engineering]() ### AI overview The bot consolidates school email and TalkingPoints messages into daily digests, with must-dos first, event times only when stated, duplicate notices removed, and calendar links for items not already listed. It sent 44 digests over the reported period, replacing 30-plus weekly emails and an app; the author estimates 30-45 minutes saved per week. Its key safeguards are read-only access to personal Gmail and the calendar, no school replies or calendar edits, and explicit warnings when a source cannot be checked. Failures such as silently skipping TalkingPoints and reporting a false sign-out led to checks on every run; a program also validates email formatting before sending. The author records bot rules and setup in a Git repository and describes a template where code evaluates sourced items and each run becomes a reviewed pull request. The school bot still needs manual text-code sign-in when TalkingPoints sessions expire. ### Source excerpt I built an AI bot that turns about 200 school emails into one daily digest: what it can read, what it must never do, and the quiet failures that became rules. ## LiveSearch: Questions as Executable Verifier Code for a Live Benchmark of Professional-Source Financial Search Agents DevFeed: [LiveSearch: Questions as Executable Verifier Code for a Live Benchmark of Professional-Source Financial Search Agents]() Original publisher: [Read original article]() Author: Sinuo Wang, Wentao Zhao, Jiacheng Lu, Rui Sun, Beidi Luan, Qiming Shi, Jing Li, Daxin Jiang, Zuo Bai Published: 2026-10-28T04:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [Benchmark](), [gaia](), [Finance](), [llm-reasoning]() Tags: [agent](), [agents](), [aggregate](), [analysis](), [annotation](), [automatic](), [benchmark](), [benchmarks](), [code](), [cs-ai](), [daily](), [data](), [declarative](), [evaluation](), [finance](), [financial](), [financial-data](), [live](), [questions](), [search](), [source]() ### AI overview LiveSearch keeps financial benchmark questions fixed while generating current gold answers by running typed programs against professional financial data at evaluation time. Daily re-execution is automated, and question construction uses a language model only to verbalize programs enumerated from real tool responses, not to choose answers. In general web search, six tool-calling models scored 70.9%-76.7%; 70% of served questions changed gold values over three days, and reusing an earlier snapshot failed on a majority within one day even with tolerance-aware matching. Per-question tracking revealed correctness shifts hidden by aggregate scores. ### Source excerpt arXiv:2610.37166v1 Announce Type: new Abstract: In finance, the answer that matters is usually the current one: prices, rates, and disclosures hold only as of a given day, so retrieving up-to-date facts is a core capability of a financial agent. Yet most financial benchmarks overlook timeliness. They fix each answer at annotation time, so the gold goes stale as markets move and, once leaked into training data, rewards recall rather than search and analysis. We introduce LiveSearch, a live benchmark that stores each question as executable verifier code: a typed program of calls to a professional financial data service, followed by a declarative extractor. The question is frozen, while its gold answer is determined by executing the program against professional financial data at evaluation time, and a daily job re-executes it with no human or language model in the loop. A memorized answer is thus likely outdated, and a model must reach the current one through tools and analysis. Construction is equally automatic: a language model only verbalizes programs enumerated from real tool responses and never chooses an answer. Under general web search, six contemporary tool-calling models reach 70.9% to 76.7% answer accuracy. Over three consecutive days, 70% of served questions change their gold value, and even under tolerance-aware matching, a literally reused earlier snapshot fails on a majority of questions within a single day. Per-question tracking uncovers dynamic shifts in correctness masked by aggregate accuracy. ## Causal DAG-Guided Reinforcement Learning for Long-Term Physical Activity Maintenance: Using Prior Data to Inform Future Deployment DevFeed: [Causal DAG-Guided Reinforcement Learning for Long-Term Physical Activity Maintenance: Using Prior Data to Inform Future Deployment]() Original publisher: [Read original article]() Author: Xueqing Liu, Young Hyun Cho, Bhanu Teja Gullapalli, Predrag Klasnja, Susan Murphy Published: 2026-10-28T04:00:00Z Content type: article Language: en Sources: [arXiv: Machine Learning]() Topics: [Reinforcement learning](), [Machine learning]() Tags: [abstract](), [arxiv](), [behavior](), [can](), [candidate](), [challenges](), [change](), [compare](), [cs-lg](), [dag](), [daily](), [data](), [deployment](), [design](), [designs](), [digital](), [future](), [learning](), [maintenance](), [reinforcement-learning](), [stat-ap](), [using]() ### AI overview The paper describes an online reinforcement learning system for a forthcoming trial that personalizes twice-daily physical activity suggestions to improve affective experiences associated with sustained activity. To address sparse participant-specific data, partially observed states and rewards, weekly outcomes despite frequent interventions, and possible intervention burden, the authors use an expert-specified causal DAG to guide state construction, estimation of hidden states, delayed credit assignment, and modeling beneficial and adverse intervention pathways. They also build a longitudinal simulator calibrated with data from a prior micro-randomized trial to compare candidate designs under realistic evolution, missingness, and participant heterogeneity before deployment. ### Source excerpt arXiv:2610.36019v1 Announce Type: cross Abstract: Digital health interventions can provide scalable, personalized support for physical activity (PA), yet sustaining PA over the long-term remains difficult. Because affective experiences with PA are important for activity sustainment, we developed an online reinforcement learning (RL) system for the forthcoming ADAPT factorial trial that personalizes twice-daily activity suggestions aimed at improving affective associations with PA. This setting poses several challenges: participant-specific data are scarce relative to the candidate state space, important states and rewards are partially observed, interventions are frequent while the primary outcome is measured weekly, and activity suggestions can both improve PA-related experiences and increase intervention burden. We address these challenges using an expert-specified causal directed acyclic graph as an integrated RL design tool, guiding state construction, estimation of partially observed states, delayed credit assignment, and representation of beneficial and adverse intervention pathways. We further construct a longitudinal simulator calibrated from a prior micro-randomized trial to compare candidate RL designs under realistic state evolution, missingness, and participant heterogeneity before deployment. Together, this work illustrates how causal domain knowledge and prior trial data can make long-horizon, participant-specific RL more practical for digital health interventions targeting sustained behavior change. ## LM-DSB: A Lightweight Multi-agent System for Automated Daily Safety Bulletin Generation in Construction DevFeed: [LM-DSB: A Lightweight Multi-agent System for Automated Daily Safety Bulletin Generation in Construction]() Original publisher: [Read original article]() Author: Xianxiang Zhao, Advik Mehta, Falak Sethi, Brian Gue, Qipei Mei, Lingzi Wu Published: 2026-10-28T04:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [Multi Agent Systems](), [hierarchical agent systems](), [Retrieval Augmented Generation (RAG)](), [Fine-tuning](), [Large Language Model]() Tags: [adoption](), [agent](), [agents](), [alignment](), [architecture](), [automate](), [automated](), [bleu](), [coherence](), [communications](), [complete](), [compliance](), [computers](), [cs-ai](), [daily](), [generation](), [lightweight](), [multi-agent](), [multi-agent-system](), [safety](), [system]() ### AI overview LM-DSB automates construction daily safety bulletins using a locally deployable Llama 3-8B model, planner, researcher and writer agents, domain fine-tuning, and retrieval from 37 HSE documents. In comparative evaluation, the multi-agent RAG configuration produced more complete bulletins than single-agent RAG (length ratio 1.01 versus 0.39) and scored TF-IDF 0.70, BLEU 0.39, and ROUGE-1 0.66. Forty experts rated practical acceptability from 3.50 to 4.20 out of 5, with regulatory compliance at 4.20 and factual accuracy at 4.10. The evidence is limited to feasibility assessment: the authors identify knowledge-base scope and language constraints, and report no field deployment validation. ### Source excerpt arXiv:2610.36433v1 Announce Type: new Abstract: Construction safety management still struggles to produce daily safety bulletins (DSBs), structured daily communications that identify site-specific hazards, convey safety protocols, and set preventive measures for workers. Manual DSB creation takes 2-3 hours daily and yields inconsistent quality, with 25% of crews never conducting pretask planning (Olson et al., 2016), diverting safety personnel from field supervision. This study proposes the Lightweight Multi-Agent DSB System (LM-DSB), which integrates retrieval-augmented generation (RAG) with a locally deployable Large Language Model (LLM) to automate DSB generation on consumer-grade personal computers. LM-DSB coordinates specialized planner, researcher, and writer agents in a hierarchical architecture, enhanced by domain-specific fine-tuning on construction safety documentation and RAG grounding in 37 health, safety, and environment (HSE) documents. A two-phase validation assessed feasibility. Phase 1 comparative evaluation showed the multi-agent RAG system (Llama 3-8B) outperformed all alternative configurations, with strong lexical alignment and coherence (TF-IDF: 0.70, BLEU: 0.39, ROUGE-1: 0.66) and near-complete document generation (length ratio: 1.01 vs. 0.39 for single-agent RAG). Phase 2 expert evaluation (N=40) confirmed practical acceptability (mean scores 3.50-4.20 on a 5-point scale), particularly in regulatory compliance (4.20) and factual accuracy (4.10). The findings show that architectural design enables lightweight models to handle safety-critical documentation on consumer-grade hardware, advancing technology democratization in construction safety management. Limitations include knowledge base scope, language constraints, and lack of field deployment validation; future work should address multimodal integration and workflow adoption. ## From Experience to Evidence: How Growers in Ningxia Use SenseCAP Sensors to Help Prevent Cherry Tomato Cracking DevFeed: [From Experience to Evidence: How Growers in Ningxia Use SenseCAP Sensors to Help Prevent Cherry Tomato Cracking]() Original publisher: [Read original article]() Author: violet Published: 2026-10-27T22:49:33Z Content type: article Language: en Sources: [Seeed Studio]() Topics: [IoT predictive analytics](), [Physical AI](), [seeed studio]() Tags: [2](), [2026](), [3](), [4g](), [about](), [ai](), [climate](), [cracking](), [daily](), [deploy](), [experience](), [feature](), [first](), [greenhouse](), [help](), [iiot](), [know-how](), [microclimate](), [news](), [practical](), [precision-farming](), [reports](), [resilient-communities](), [risks](), [sdg-cases](), [seeed-studio](), [sensecap](), [sensor](), [sensors](), [smart-agriculture](), [studio](), [teams](), [tech](), [tomato-growing](), [weather-sensor](), [weather-station]() ### AI overview Across two growing areas in Ningxia, 18 SenseCAP devices collected 25,152 environmental records over 40 days. OpenClaw analyzed readings from greenhouses and outdoor weather, including air and soil conditions, light, rainfall, and wind, then sent daily risk reports to the team's WeChat group. The reports helped growers compare cracking rates, flag prolonged high humidity before picking, and reassess irrigation and ventilation using soil readings alongside atmospheric conditions. The team adjusted picking and sorting plans and revised irrigation and ventilation strategies; recommendations included opening vents before 10 a.m. and checking fruit shoulders. The excerpt reports a 0.3% cracking rate in a greenhouse with upper vents versus 6.3% in one without, and links extended high humidity to increased cracking the following day. These are findings from one 40-day project; the account does not establish broader causal effects. ### Source excerpt From a real-world deployment in Ningxia, see how 18 SenseCAP sensors and daily AI reports helped growing teams monitor greenhouse conditions, identify cherry tomato cracking risks earlier, and turn environmental observations into practical growing decisions. The post From Experience to Evidence: How Growers in Ningxia Use SenseCAP Sensors to Help Prevent Cherry Tomato Cracking appeared first on Latest News from Seeed Studio. ## Pavee OS: Ikey Doherty's New Daily-Driver Desktop Distro Being Built Atop Barney DevFeed: [Pavee OS: Ikey Doherty's New Daily-Driver Desktop Distro Being Built Atop Barney]() Original publisher: [Read original article]() Author: Michael Larabel Published: 2026-10-27T13:11:30Z Content type: release Language: en Sources: [Phoronix]() Topics: [Fedora]() Tags: [daily](), [desktop](), [desktop-linux](), [distro](), [linux](), [linux-benchmarking](), [linux-hardware-benchmarks](), [linux-hardware-reviews](), [linux-how-to](), [linux-performance](), [linux-server-benchmarks](), [open-source-graphics](), [os](), [phoronix](), [phoronix-test-suite](), [ubuntu-benchmarks](), [ubuntu-hardware]() ### AI overview Pavee OS is Ikey Doherty's planned opinionated daily-driver desktop, built on Barney, bny-base and other planned layers. Doherty describes the goal as separating reusable distribution infrastructure from individual distro design choices: Pavee OS will demonstrate the tooling while keeping his own preferences in a distinct project. The excerpt says development progress can be followed in its GitHub repository; it provides no technical details about implementation or current functionality. ### Source excerpt Back in September Ikey Doherty of Solus Linux and Serpent OS notoriety as well as being a former Intel Clear Linux developer announced Barney as his planned showcase for how to "properly" build a modern Linux distribution in 2026+. Today he's provided an update on this Barney distro tooling effort as well as announcing Pavee OS as his planned daily-driver desktop distribution built atop it... ## SIDN Publishes RSSAC002 Traffic Metrics for .nl DevFeed: [SIDN Publishes RSSAC002 Traffic Metrics for .nl]() Original publisher: [Read original article]() Author: Giovane Moura Published: 2026-10-27T08:41:57Z Content type: article Language: en Sources: [RIPE Labs]() Topics: [Domain Name System (DNS)](), [Internet Traffic](), [DNSSEC]() Tags: [article](), [automate](), [cctld](), [community](), [comparisons](), [daily](), [data](), [data-warehouse](), [datasets](), [dns](), [dnssec](), [files](), [format](), [guest-post](), [metrics](), [open](), [operators](), [root](), [server](), [sharing](), [tools](), [traffic]() ### AI overview SIDN Labs publishes daily aggregated DNS traffic metrics for the .nl authoritative name servers in RSSAC002 format, enabling direct comparison with root-server data. The files, graphs, metric definitions and methodology are available on its website and GitHub. Metrics include query and response volumes, response codes and unique sources; the article says .nl handles about as much traffic on a typical day as D-Root and 50% more than B-Root. Researchers and operators can use the data to study trends, protocol adoption, nonexistent-domain queries and unusual traffic changes; tools built for root-server RSSAC002 data can process it without changes. The open-source pipeline uses ENTRADA and a SIDN fork of a tool originally developed by TU Delft students. Files contain daily aggregates, with no IP addresses, individual queries or looked-up domain names. Publication is currently in batches; SIDN plans continuous daily releases. ### Source excerpt SIDN Labs has released open DNS traffic data for .nl in the same format used by root server operators. In this guest post, they explain what they're sharing and how it opens up new comparisons across the DNS. ## RIPE 93 Daily Meeting Blog DevFeed: [RIPE 93 Daily Meeting Blog]() Original publisher: [Read original article]() Author: RIPE NCC Communications Published: 2026-10-26T20:51:23Z Content type: news Language: en Sources: [RIPE Labs]() Topics: [Internet Trends](), [Maintainability](), [Tool]() Tags: [ai](), [ai-agent-applications](), [application](), [applications](), [asics](), [bgp](), [blog](), [bottlenecks](), [cern](), [collectors](), [community](), [daily](), [demo](), [dns](), [docker](), [events](), [recordings](), [ripe](), [slides]() ### AI overview RIPE 93's first day in Sofia covered practical IPv6 testing, routing measurements, and operational questions. NIC.br demonstrated v6bench, an open-source Docker-based tool for testing application behavior in IPv4-only, dual-stack, and IPv6-only environments, including NAT64. Research on CERN data paths found high packet loss on UK corridors and called for NOCs and NRENs to share incident records. The RIPE NCC's RISwhois gained its first test suite and can now update data hourly rather than every eight hours; its collectors aim to show BGP topology while keeping archive size manageable. Researchers also reported unexpected TTL rewrites that can obscure traceroute paths, with causes still uncertain. Discussions of Layer 3 Internet exchanges were cautious, raising concerns that they could shift operational complexity and control away from participants. The excerpt also summarizes IETF topics including AI, post-quantum cryptography, IPv6, DNS, and space networking. ### Source excerpt RIPE 93 has kicked off in wonderful ancient Sofia. As always, you can view the slides, you can watch the recordings, but if you want to get to the bottom of what's really going on, read this daily meeting blog! ## Nine Months of Daily Calls: How I Took a Customer From 20% to 100% Customer Satisfaction Index DevFeed: [Nine Months of Daily Calls: How I Took a Customer From 20% to 100% Customer Satisfaction Index]() Original publisher: [Read original article]() Author: SnehaPatil Published: 2026-10-26T19:31:14Z Content type: article Language: en Sources: [HackerNoon]() Topics: [Support](), [etl]() Tags: [admin](), [automation](), [backlog](), [backup](), [breaches](), [call](), [cron](), [csi](), [customer](), [customer-experience](), [customer-satisfaction](), [customer-satisfaction-index](), [daily](), [dashboard](), [dashboards](), [delivery-lead](), [email](), [etl](), [informatica](), [leadership](), [service-level-agreement](), [support](), [team](), [team-management](), [team-productivity]() ### AI overview A 13-person Informatica administration team cleared a 300+ request backlog and brought reported SLA breaches from 90% to zero over nine months, while customer satisfaction rose from 20% to 100%. The lead paired one of four experienced admins with a junior on each shift, added buddy reviews and shift handover emails, held weekly technical training, assigned a dedicated request dispatcher, and tracked work on a dashboard shared live with the customer. After difficulties obtaining transition materials from the outgoing vendor, the lead escalated to secure key documents. The team later automated password resets, removing a reported 10-15 manual requests per month at about 20 minutes each. CSI was measured quarterly through a customer survey. ### Source excerpt How I turned a failing Informatica Admin project around - Fixing SLA, clearing 300+ backlog, and rebuilding high-performing support team [Next page]()