# safely Published articles for safely. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How Grab Improved Mobile App Stability by Safely Handling Surge in AI-Generated Code Changes DevFeed: [How Grab Improved Mobile App Stability by Safely Handling Surge in AI-Generated Code Changes]() Original publisher: [Read original article]() Author: Sergio De Simone Published: 2026-11-07T11:00:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [mobile-app-performance](), [mobile devops](), [Static code analysis](), [qualitygates](), [lint](), [Firebase](), [Pull Request](), [Android](), [ai slop pull requests]() Tags: [accelerated](), [adoption](), [ai](), [ai-coding](), [ai-coding-assistants](), [ai-generated-code](), [ai-ml-data-engineering](), [android](), [app](), [app-stability](), [challenges](), [changes](), [code](), [coding](), [coding-assistants](), [contain](), [continuous-improvement](), [crashes](), [development](), [feature-toggle](), [grab](), [grab-ai-generated-code-safety](), [handling](), [ios](), [ml-data-engineering](), [mobile](), [mobile-app](), [news](), [runtime-analytics](), [safely](), [static-analysis](), [testing]() ### AI overview Grab manages the higher volume of changes associated with AI coding assistants through layered crash defenses across the release lifecycle. Before merge, Android Lint, SwiftLint, tests, QA, and Grab Early Access (GEA) aim to catch problems early; GEA builds are distributed to internal users and monitored for crashes. During release, staged rollouts limit initial exposure and can be paused if crash rates rise, while feature flags allow individual features to be activated gradually or disabled when predefined crash thresholds are exceeded. After release, Firebase Crashlytics and BigQuery support real-time anomaly detection; aggregated crash metadata and stack traces help teams trace incidents to specific PRs. Post-mortems feed findings back into lint rules and test coverage. The excerpt presents prevention as the primary defense and notes that shipped crashes can persist on devices where users do not automatically update. ### Source excerpt The growing adoption of AI coding assistants has accelerated development and increased the volume of code changes per release, bringing more opportunities for defects and crashes, says Grab. To address these challenges, they implemented a layered defense strategy to prevent, detect, and contain crashes throughout the development and release lifecycle. By Sergio De Simone ## How to Revert to a Previous Commit in Git (Safely) DevFeed: [How to Revert to a Previous Commit in Git (Safely)]() Original publisher: [Read original article]() Author: Marko Published: 2026-11-05T09:00:00Z Content type: tutorial Language: en Sources: [Marko Denic Blog]() Topics: [Git]() Tags: [branch](), [changes](), [checkout](), [commit](), [commits](), [delete](), [file](), [git](), [hash](), [history](), [how-to](), [lease](), [leave](), [local](), [look](), [merge](), [mistakes](), [recover](), [reset](), [safely](), [tutorial]() ### AI overview Choose the command based on whether the commits were pushed. For shared, pushed work, `git revert` adds a new commit that reverses changes while preserving history; use `--no-commit` to combine multiple reverts, and `-m 1` when reverting a merge to keep the branch it was merged into. For local commits, `git reset` moves the branch pointer: `--soft` keeps changes staged, `--mixed` (the default) leaves them unstaged, and `--hard` discards them, including uncommitted work. Check `git status` and stash work before a hard reset. Resetting pushed commits requires a force push that rewrites shared history; `--force-with-lease` checks for intervening commits, and the article recommends doing this only on your own branch. To inspect an old commit without changing the branch, use detached HEAD; restore a single file with `git restore --source`; recover a reset position using `git reflog`. The article says reflog can usually recover commits, but not uncommitted changes. ### Source excerpt Go back to a previous commit in Git: git revert for pushed work, git reset --soft, --mixed and --hard for local commits, restoring one file, reverting merges, and recovering from mistakes with reflog. ## How Figma manages sandboxes at scale DevFeed: [How Figma manages sandboxes at scale]() Original publisher: [Read original article]() Author: Pratik Agarwal; Jordan Rodgers Published: 2026-11-04T16:23:00Z Content type: article Language: en Sources: [Figma Blog]() Topics: [AI Sandbox](), [Platform Engineering](), [control-plane](), [Figma AI]() Tags: [ai](), [ai-products](), [api](), [authentication](), [automation](), [autoscaling](), [availability](), [capacity](), [cloud](), [code](), [compute](), [configuration](), [container](), [control-plane](), [figma](), [platform](), [safely](), [sandboxes](), [scale]() ### AI overview Nimbus is Figma's centralized platform for provisioning and operating sandboxes across products and providers. Product teams describe workload needs, resources, health checks, and security controls in a namespace; Nimbus selects a provider, applies policy, and manages lifecycle through GetOrCreate, Get, and Delete. A shared base image and sandboxd daemon support consistent execution and resource monitoring. For network security, explicitly trusted hosts may receive reads and writes; other destinations are limited to bodyless GET, HEAD, or OPTIONS requests. Credentials stay encrypted in the sandbox and are revealed only for requests to their intended destination. Nimbus enforces these rules through provider controls or a proxy sidecar, failing closed when enforcement or credential handling fails. Warm pools cut provisioning latency by 90% versus creating sandboxes on demand; combined with automated provider selection, reported provisioning availability rose from 99% to 99.95%. Nimbus dynamically sizes pools and shifts work using provider outcomes, while health checks and simulation help manage failures and slow provisioning. The article says Nimbus launched internally in November ߞ ### Source excerpt Figma's AI products require tens of thousands of sandboxes to run untrusted code safely. That's why we built Nimbus: a platform that makes sandboxes fast, reliable, and easy for Figma product teams and operators to manage. ## A Practical Causal Framework for Autonomous Mobile Robots: From Simulation to Real-World Human Environments DevFeed: [A Practical Causal Framework for Autonomous Mobile Robots: From Simulation to Real-World Human Environments]() Original publisher: [Read original article]() Author: Luca Castri, Gloria Beraldo, Nicola Bellotto Published: 2026-11-04T05:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [causality](), [Physical AI]() Tags: [autonomous-mobile-robots](), [causal-inference](), [complex](), [cs-ai](), [cs-ro](), [framework](), [mobile-robot](), [parameter](), [rapid](), [real-world](), [safely](), [sim-to-real](), [simulation](), [structure](), [update]() ### AI overview The framework transfers causal relations learned in simulation to real-world mobile robot navigation while retaining the high-level causal structure. It adapts by updating causal model parameters from a limited set of new observations, reducing the need to collect extensive causal data in human-populated environments. The abstract reports efficient onboard operation and better performance than non-causal baselines in safely navigating complex, shared human environments; it provides no quantitative results or further implementation details. ### Source excerpt arXiv:2611.04027v1 Announce Type: cross Abstract: Causal inference has emerged as a powerful paradigm enabling robots to make safe, efficient decisions by reasoning about the ``why'' behind events. However, its real-world application is hindered by the difficulty of collecting causal data, especially in human-populated environments where training takes time, while users expect immediate operation. To this end, we tackle the challenge of transferring simulation-learnt causal relations to the real world with minimal effort. While retaining the invariant high-level causal structure, we empirically demonstrate the system adapts efficiently to real-world situations by updating only the causal model parameters with a limited set of new observations. Our results show that the parameter update enables rapid sim-to-real transfer. The framework runs efficiently onboard of a mobile robot and significantly outperforms a non-causal baselines navigating successfully and safely in complex human-shared scenarios. ## SSO just-in-time provisioning: How to match a login to an existing user safely DevFeed: [SSO just-in-time provisioning: How to match a login to an existing user safely]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-11-04T00:00:00Z Content type: article Language: en Sources: [WorkOS]() Topics: [Single sign-on (SSO)](), [OpenID connect (OIDC)](), [JavaScript]() Tags: [2026](), [api](), [app](), [attack](), [auth](), [authentication](), [automatically](), [blocks](), [branch](), [callback](), [condition](), [email](), [just](), [login](), [order](), [provisioning](), [safely](), [sso](), [store](), [user]() ### AI overview For a custom SSO callback, resolve an existing identity first using the persistent `idp_id` scoped to its connection; if none exists, link by email only when the connection's organization has verified the email domain or an authoritative identity provider asserts a verified address. Otherwise create a separate user or require inbox proof before linking. Store the identity separately from the user, key it by the `idp_id` and connection pair, and refresh mutable profile attributes on returning sign-ins. Scope email lookups deliberately because a global match may belong to another customer. WorkOS's domain check blocks nonmatching profiles by default, but the callback should still enforce its own linking condition. The excerpt says AuthKit follows a similar link-first, verified-email, then create order, with verification and guest-invitation behavior handled in its flow. ### Source excerpt Just-in-time provisioning is a resolution order in your SSO callback. Here is the order, the condition that makes the email match safe, and what to store. ## Benchmarking Claude Haiku, Sonnet, and Opus on 64 Broken Data Pipelines DevFeed: [Benchmarking Claude Haiku, Sonnet, and Opus on 64 Broken Data Pipelines]() Original publisher: [Read original article]() Author: Akshay Sharma Published: 2026-11-03T11:18:57Z Content type: news Language: en Sources: [HackerNoon]() Topics: [Data pipelines](), [data-engineering](), [Data Quality](), [Claude](), [data observability](), [AI Integration Strategies](), [CI/CD]() Tags: [agents](), [ai](), [ai-agent-evaluation](), [ai-agents](), [ai-benchmarking](), [batch](), [benchmark](), [benchmarking](), [broken-data-pipelines](), [claude](), [claude-haiku](), [claude-opus](), [claude-sonnet](), [data](), [data-engineering](), [data-pipeline-recovery](), [data-pipelines](), [haiku](), [haiku-vs-sonnet-vs-opus](), [incidents](), [opus](), [repair](), [safely]() ### AI overview In this synthetic benchmark of 64 DuckDB pipeline incidents, Sonnet and Opus tied when change notes were available, while Opus handled all 13 tested cases when notes were removed and Sonnet dropped two; Opus cost about four times as much. Haiku more often escalated fixable incidents and made two fixes that ran but produced incorrect data. All models escalated incidents with no safe fix, but sparse sample rows sometimes hid conflicting duplicate values, leading all three to silently choose a wrong amount. Ten data quality monitors did not catch the benchmark's corrupted fixes. The author recommends starting with Sonnet, using a human-review confidence floor (0.65 yielded zero harmful Sonnet fixes in this dataset while solving 47-49 of 52 fixable incidents), checking results against raw or trusted data, and improving source change notes. These findings come from one synthetic pipeline, one model family, and one run per case; the threshold was tuned on the same data. ### Source excerpt Can AI agents safely repair broken data pipelines? See how Claude Haiku, Sonnet, and Opus performed across 64 simulated data engineering incidents. ## A prospective randomized study of patient-centered AI for clinical visit preparation DevFeed: [A prospective randomized study of patient-centered AI for clinical visit preparation]() Original publisher: [Read original article]() Author: Xincheng Tan, Matthew Thompson, Xiang Ji, Angelica Willis, Will Vaughan, Roma Ruparel, Michael A. Sanchez, Sean Chang, Joseph Xu, Bhavna Daryani, Preeti Singh, Pinal Bavishi, Kejia Chen, Fan Zhang, Pr Published: 2026-11-02T05:00:00Z Content type: news Language: en Sources: [arXiv: Machine Learning]() Topics: [AI Chat](), [Large Language Model](), [Conversational AI](), [human computer interaction]() Tags: [agent](), [ai](), [ai-chatbots](), [alignment](), [arxiv](), [chatbot](), [conversational-ai](), [cs-cl](), [cs-hc](), [cs-lg](), [google-health](), [large-language-model](), [llm](), [questions](), [safely](), [safety](), [structured](), [study](), [workflow]() ### AI overview In a prospective randomized study in the Google Health app, a structured LLM visit-preparation agent outperformed a minimally prompted control chatbot built on the same LLM. Its workflow gathered clinical context, set expectations, provided relevant health information, prioritized questions for the clinician, and produced a summary. Among 802 U.S. adults, blinded clinician reviews found higher rates of comprehensive preparation (67.2% vs. 50.6%) and context alignment (98.9% vs. 90.4%); patient surveys also reported greater preparedness, confidence about what to ask, and satisfaction. Medical safety and appropriateness remained high across all study arms. Clinician and patient measures were not significantly correlated, suggesting they captured complementary outcomes. The findings concern preparation before visits; effects on in-visit communication and clinical outcomes remain for future study. ### Source excerpt arXiv:2610.41437v1 Announce Type: cross Abstract: Patients often arrive at brief, time-constrained clinical visits unsure of what to expect or ask, and millions of adults now turn to general-purpose AI chatbots to prepare beforehand. Yet whether conversational AI improves visit readiness remains unclear. We designed a large language model (LLM)-based visit preparation agent to guide patients before an appointment by gathering clinical context, setting visit expectations, providing relevant health information, building a prioritized list of questions for their clinician, and generating a summary. In a prospective consented randomized study in the Google Health app, 802 U.S. adults held 866 health-related conversations with the visit preparation agent (standard or concise variant) or a minimally prompted control chatbot built on the same LLM. The visit preparation agent significantly outperformed the control across blinded clinician evaluations and patient self-reports. In clinician reviews of completed sessions (n = 272), the agent achieved a 16.6% higher rate of comprehensive preparation (67.2% vs. 50.6%; P < 0.05) and an 8.5% higher rate of alignment with patients' health context (98.9% vs. 90.4%; P < 0.01), while maintaining high medical safety and appropriateness across all study arms. Patient post-surveys (n = 460) showed 16.8% higher visit preparedness (73.8% vs. 57.0%; P < 0.01), 14.3% higher confidence in knowing what to ask (81.4% vs. 67.1%; P < 0.01), and 12.0% higher overall satisfaction (74.0% vs. 62.0%; P < 0.05). Clinician and participant metrics were not significantly correlated, indicating that expert review and patient self-assessment capture complementary dimensions of patient-facing AI. These findings suggest that LLM chatbots can safely help patients prepare for clinical visits, with greater benefit when following a structured workflow, and motivate future studies on in-visit communication and clinical outcomes. ## Turo + New Relic: Turn AI code volume into production reliability DevFeed: [Turo + New Relic: Turn AI code volume into production reliability]() Original publisher: [Read original article]() Author: Greg Brown Published: 2026-10-29T09:13:57.740933Z Content type: article Language: en Sources: [New Relic]() Topics: [Production Engineering](), [Monitoring](), [AI-assisted coding](), [CI/CD](), [incident](), [Automation]() Tags: [agents](), [ai](), [ai-agents](), [ai-code](), [ai-monitoring](), [alerting](), [cloud](), [cloud-monitoring](), [code](), [context](), [developer-relations](), [incident](), [incident-response](), [kubernetes-monitoring](), [live](), [mcp](), [mcp-server](), [new-relic](), [production](), [releases](), [reliability](), [safely](), [scale](), [server](), [triage](), [uses](), [volume]() ### AI overview Turo moved from local AI coding assistants to cloud hosted agents connected to live production telemetry through the New Relic MCP server. It streams infrastructure and Kubernetes metrics, application logs, and deployment events into New Relic, allowing agents to query context in natural language and relate anomalies to recent changes without engineers writing NRQL queries. In a reported midnight incident, an agent queried telemetry, analyzed service connections and logs, identified the root cause, and produced an action ready diagnostic within 60 seconds, before the on call engineer logged in; the team says it mitigated the issue immediately. Turo also uses feature flag and deployment tracking and automated canary testing to address review and release bottlenecks as AI generated code volume grows. The excerpt describes a particular incident and does not establish broader performance results. ### Source excerpt See how Turo uses the New Relic MCP server to give AI agents live production context, slash incident triage to 60 seconds, and scale releases safely. ## The new cost-based join optimizer with reordering support for non-inner joins DevFeed: [The new cost-based join optimizer with reordering support for non-inner joins]() Original publisher: [Read original article]() Author: Fisnik Kastrati Published: 2026-10-28T14:12:53Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [JOIN](), [Test-driven development]() Tags: [000](), [17](), [2](), [2013](), [3](), [about](), [actions](), [algorithm](), [algorithms](), [all](), [based](), [clickhouse](), [cost](), [exhaustive](), [join](), [plan](), [safely](), [semi](), [support](), [with]() ### AI overview ClickHouse's DPsub cost-based join optimizer can reorder inner, left/right/full outer, semi, and anti joins while preserving query results. A conflict detector uses join types, predicate references, and NULL behavior to reject invalid regroupings and admit valid ones; the article reports exhaustive validation with 2.8 million core plans, no invalid or missing plans, plus zero mismatches across 1,200 fuzzed queries. DPsub searches exhaustively up to 12 tables, though the default limit is 10; larger joins fall back to the greedy optimizer. The optimizer becomes the default in ClickHouse 26.10, with settings to select the algorithm and disable conflict detection. ### Source excerpt ClickHouse's new exhaustive cost-based join optimizer safely reorders inner, outer, semi, and anti joins to find the cheapest valid plan. ## The web's having a rough week. Here's how to browse safely DevFeed: [The web's having a rough week. Here's how to browse safely]() Original publisher: [Read original article]() Author: Pieter Arntz Published: 2026-10-27T12:22:37Z Content type: article Language: en Sources: [Malwarebytes]() Topics: [Web](), [frontend security](), [Risk](), [Google Chrome]() Tags: [7-4](), [attacks](), [authentication](), [automatic](), [browser](), [browser-extensions](), [bugs](), [certificates](), [compromised-websites](), [fake](), [hacked](), [heres](), [how-to](), [news](), [node-js](), [php](), [risk](), [rogue](), [safely](), [sites](), [the-web](), [tls-certificates](), [updates](), [websites]() ### AI overview A familiar, genuine website can be compromised, so judge unexpected requests by whether they make sense for what you came to do. Don't run commands, install software, or change device settings just to pass a verification check; independently check update or account prompts through your browser's settings or an official app or support channel. Keep your operating system, browser, and apps updated, leave browser phishing and malware protection enabled, and use real-time anti-malware protection, unique passwords, and multifactor authentication. HTTPS protects the connection but does not establish that page content is safe. Browser warnings and security tools add protection, but cannot certify every unflagged page. The article cites attacks using hacked WordPress sites and fake verification pages, alongside separate PHP, Node.js Brotli, and certificate issues; it says most browsers blocked the compromised certificates, while other software and devices may still need updates. ### Source excerpt PHP and Node.js flaws, hacked sites, fake updates, and rogue certificates can put you at risk even on trusted websites. Here's how to stay safe. ## BARC-Bench: Benign Asks, Risky Consequences DevFeed: [BARC-Bench: Benign Asks, Risky Consequences]() Original publisher: [Read original article]() Author: Kangkang Li, Kelvin Luu, Lily Ma, Tu Trinh, Ying Liu, Mohamed Elfeki Published: 2026-10-26T04:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [AI Agent](), [ai alignment](), [harvestbench](), [Frontier AI]() Tags: [agents](), [bench](), [benchmark](), [calendar](), [context](), [cs-ai](), [delegation](), [discussions](), [employee](), [environments](), [frontier-model](), [instructions](), [models](), [operational-risk](), [records](), [results](), [risk](), [safely](), [safety](), [schedule](), [share]() ### AI overview BARC-Bench tests whether agents can complete benign requests while respecting safety boundaries implied by workspace records, context, and social norms. Its 110 tasks span nine simulated workspaces; for example, scheduling off-boarding discussions should not expose an employee's resignation through a shared calendar invitation. Across four model configurations and five attempts per task, only 9.1% to 72.7% of tasks were completed safely on every attempt. Even the strongest configuration violated a safety boundary on 27.3% of tasks, showing that successful workflow completion does not ensure consistent safety. ### Source excerpt arXiv:2610.32024v1 Announce Type: new Abstract: Task instructions from users for agents are inherently underspecified, especially regarding safety boundaries--creating behavioral risks. To study these risks, we introduce BARC-Bench, a collection of 110 tasks across nine simulated workspace environments where benign requests leave openings for unsafe actions. In this benchmark, we evaluate agent behavior against safety boundaries established by workspace records, implicit environmental context, and unstated social norms. For example, an agent asked to schedule off-boarding discussions must arrange the meeting without prematurely revealing an employee's resignation through a shared calendar invitation. Evaluating four frontier model configurations across five attempts per task reveals that models are not consistently safe: the share of tasks completed safely on all five attempts ranges from 9.1% to 72.7%, with even the strongest configuration producing a safety violation on 27.3% of tasks. These results highlight an asymmetric operational risk: while models frequently complete complex workflows, their inability to consistently respect unstated boundaries presents a substantial barrier to safe autonomous delegation. ## Open-Source Models Are Ready for Enterprise...Here's How to Deploy Them Safely DevFeed: [Open-Source Models Are Ready for Enterprise...Here's How to Deploy Them Safely]() Original publisher: [Read original article]() Author: Aphanarc Published: 2026-10-26T03:14:01Z Content type: article Language: en Sources: [HackerNoon]() Topics: [Open Source Models & Datasets](), [LLM security]() Tags: [access-controls](), [ai-security](), [cadence](), [data-transmission](), [deepseek-security](), [deploy](), [deployment](), [enterprise](), [enterprise-ai](), [enterprise-ai-security](), [good-company](), [hosting](), [key](), [models](), [open](), [open-source](), [open-source-ai](), [provenance](), [safely](), [secure-ai-deployment](), [self-hosting](), [sovereign-ai]() ### AI overview The article argues that safe enterprise use of open models depends on where and how they are deployed. It distinguishes bans on the consumer DeepSeek app running on Chinese-controlled servers from self-hosting an open-weight model inside an enterprise network, which the article says can prevent prompts from leaving that network and reduce risks from opaque logging and retention. It cites reports of more than 80% lower inference costs when routine work goes to cheaper models and harder tasks to premium ones, while warning that poor infrastructure can erase savings through egress, redundant compute, and compliance costs. Self-hosting still leaves risks such as a compromised environment; organizations should assess model provenance, patch cadence, and access controls. The article also describes Aphanarc's approach of distributing compute, model execution, and data handling to avoid a single point holding the full picture, but provides no independent evidence of its effectiveness. ### Source excerpt The key to safe deployment lies in self-hosting open models, controlling data transmission, and ensuring model provenance, patch cadence, and access controls. ## Python's with Statement: Manage External Resources Safely DevFeed: [Python's with Statement: Manage External Resources Safely]() Original publisher: [Read original article]() Author: Leodanis Pozo Ramos Published: 2026-10-25T14:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [Python](), [Learning](), [Pytest](), [Boilerplate]() Tags: [python](), [resource-management](), [resources](), [safely](), [with]() ### AI overview Python's `with` statement calls a context manager's `.__enter__()` on entry and `.__exit__()` on exit, so cleanup runs even when the block raises an exception. This makes common setup and teardown--such as opening and closing files or acquiring and releasing locks--more concise and less error-prone than repeating `try` ... `finally`. The article contrasts the approaches: `try` ... `finally` works with any object but is more verbose; `with` requires an object that implements the context manager protocol. It also covers multiple managers, standard-library and pytest examples, and custom class-based or `contextlib.contextmanager` implementations. For asynchronous code, `async with` uses awaitable `.__aenter__()` and `.__aexit__()` methods; the tutorial demonstrates this with `aiohttp`. ### Source excerpt Understand Python's with statement and context managers to streamline the setup and teardown phases in resource management. Start writing safer code today! ## Agentic AI Foundation (AAIF) announces four new projects DevFeed: [Agentic AI Foundation (AAIF) announces four new projects]() Original publisher: [Read original article]() Author: David Rubinstein Published: 2026-10-22T19:19:54Z Content type: news Language: en Sources: [SD Times]() Topics: [Agentic AI](), [linux foundation](), [hugging face]() Tags: [agent-skills](), [agentic](), [agentic-ai](), [agentic-ai-foundation](), [agentic-stack](), [agents](), [ai](), [announcement](), [applications](), [contributed](), [foundation](), [latest-news](), [layers](), [major](), [models](), [opensandbox](), [post](), [projects](), [providers](), [run](), [safely](), [skills](), [stack]() ### AI overview The Agentic AI Foundation announced four projects contributed by Anthropic, OpenAI, Hugging Face, and Alibaba, bringing its hosted project total to 10 in its first year. Agent Skills addresses how agents gain capabilities; Open Responses focuses on how applications communicate with models across providers; OpenEnv covers agent training and evaluation; and OpenSandbox addresses where agents can run safely. The projects join existing foundation efforts including A2A, MCP, AGENTS.md, and goose. The announcement describes a neutral governance model intended to support shared standards, protocols, and infrastructure. ### Source excerpt The Agentic AI Foundation today announced four new projects contributed by major AI players that address layers of the agentic stack. The layers being addressed are how agents gain new skills, how applications talk to models across providers, how agents are trained and evaluated, and where agents run safely, the AAIF wrote in its announcement.... ... continue reading The post Agentic AI Foundation (AAIF) announces four new projects appeared first on SD Times. ## Introducing Our New Surveillance Self-Defense Guide: Online Privacy for Parents and Youth DevFeed: [Introducing Our New Surveillance Self-Defense Guide: Online Privacy for Parents and Youth]() Original publisher: [Read original article]() Author: Thorin Klosowski Published: 2026-10-21T23:07:38Z Content type: release Language: en Sources: [Deeplinks]() Topics: [online privacy](), [Security & Privacy]() Tags: [guide](), [online-privacy](), [privacy](), [risks](), [safely](), [security](), [security-education](), [software](), [solutions](), [threat-modeling](), [threats](), [tool](), [young-people]() ### AI overview EFF's Online Privacy for Parents and Youth guide uses security planning, or threat modeling, to help families identify the privacy risks and concerns that matter to a young person and decide how to mitigate them. It presents no quick technical fix: young people's needs change over time, and parents should spend time learning how the internet works, what harms may arise, and how to navigate them. The excerpt also argues that age-gating laws can compromise everyone's privacy and restrict young people's access to information and supportive communities, with particular harm to vulnerable and marginalized youth. ### Source excerpt The internet is a vast and confusing place no matter your age. Trying to help youth engage with it safely and privately is a complicated topic that touches on many different skillsets and ideas. But just because it's complicated doesn't mean it's impossible. We've launched our Online Privacy for Parents and Youth Surveillance Self-Defense guide in hopes to provide a framework for finding solutions that work for you. EFF rolls out this crucial resource as state and local governments consider ill-advised laws that cut young people off from essential information and experiences. This particularly harms vulnerable youth and marginalized groups who often find safety in supportive online communities they can't access offline. Such laws also entail age gating, which compromises the privacy of all internet users and stifles young users' ability to learn, grow, and express themselves. Young people have unique needs and those needs change over time. Trying to protect from everything all the time is an exhausting process, but the security community has long had a solution for this: security planning (also called threat modeling). The idea is to provide a series of questions designed to help you focus on what's truly important. When it comes to online privacy, this is as applicable for youth and parents as it is for anyone else. We used a security plan as our foundation for this guide because deciding what is and isn't important is the first step we should all take when assessing our privacy risks. But this isn't a place for quick fixes, and every young person has their unique needs and considerations. There is no single tool or technology fix that can substitute for spending time learning, and then teaching a young person, how the internet works, how to use it, what sorts of harms exist, and how to navigate those harms. Solutions are rarely about the tools or software you use. It's about understanding the threats and concerns you have, and then determining what steps you can t ## Test Lightwell dependency upgrades locally DevFeed: [Test Lightwell dependency upgrades locally]() Original publisher: [Read original article]() Author: Barry Mosakowski, Mark Parzygnat Published: 2026-10-21T13:31:40Z Content type: tutorial Language: en Sources: [Red Hat](), [Red Hat Developer]() Topics: [ibm red hat project lightwell](), [Dependency management](), [software composition analysis](), [code security](), [Developer Tools](), [redhat]() Tags: [ci](), [ci-cd](), [dependencies](), [dependency](), [developer](), [developer-tools](), [developers](), [enterprise](), [enterprise-software](), [environments](), [feedback](), [lightwell](), [loops](), [middleware](), [patching](), [red-hat](), [remote](), [safely](), [security](), [software](), [test](), [upgrades]() ### AI overview LightwellDeveloperDemo shows how to test dependency upgrades in a self-contained local workflow: Podman runs JFrog Artifactory, while Red Hat's dependency analytics plug-in scans manifests and surfaces CVE severity, safe versions, and inline fixes. For transitive vulnerabilities, it identifies the parent package to update or override. The setup is described as isolated and runnable offline, avoiding shared staging infrastructure; dependency analytics can use hosted or customer-deployed Trusted Profile Analyzer instances. The post presents Lightwell as a source of signed backported fixes for software versions already in production. To get started, clone the repository and follow its README for Podman prerequisites and build steps. The supplied text does not include the referenced architecture figure. ### Source excerpt Upgrading dependencies and patching vulnerabilities in enterprise software is rarely straightforward. Between remote staging environments, slow CI/CD feedback loops, and complex middleware configurations, developers waste hours just trying to test dependency upgrades safely. The post Test Lightwell dependency upgrades locally appeared first on Red Hat Developer. ## A CLI for Claude Code and Cursor to Call Third-Party APIs DevFeed: [A CLI for Claude Code and Cursor to Call Third-Party APIs]() Original publisher: [Read original article]() Author: Swytchcode Team Published: 2026-10-20T15:07:31.919507Z Content type: tutorial Language: en Sources: [Swytchcode]() Topics: [Command-line interface](), [Postman Passport](), [cursor](), [Claude]() Tags: [agent](), [api-key](), [apis](), [cli](), [code](), [coding-agents](), [engineering](), [safely]() ### AI overview This tutorial explains how to use the Swytchcode `swy` CLI to let coding agents call third-party APIs. It describes schema validation, explicit method allowlists, credential storage outside the project, policies, safe retries, and audit logs, and compares CLI use with MCP and SDK options. ### Source excerpt Coding agents are good at running shell commands and bad at inventing API parameters. A CLI between the agent and the API fixes both: the agent names the operation, the command attaches the credential, checks the arguments, applies your rules and retries safely. ## How to Review and Share HubSpot Breeze Assistant Artifacts Safely DevFeed: [How to Review and Share HubSpot Breeze Assistant Artifacts Safely]() Original publisher: [Read original article]() Author: Pratik Thakker Published: 2026-10-19T15:25:24Z Content type: tutorial Language: en Sources: [HackerNoon]() Topics: [hubspot](), [human review](), [human-in-the-loop UX](), [Documentation]() Tags: [ai](), [ai-content-version-control](), [ai-context-governance](), [ai-document-approval](), [ai-security](), [application](), [data](), [document-review](), [documentation](), [enterprise-ai](), [hubspot](), [hubspot-ai-artifacts](), [knowledge-management](), [review](), [revops](), [safely](), [version]() ### AI overview This tutorial proposes treating consequential HubSpot Breeze Assistant briefings as releases with a named owner and a review process. Its five-part release card covers source evidence, audience, version, access, and retirement. It also explains that AI input settings do not approve generated outputs for an audience, and recommends checking the recipient-facing view and matching review effort to the consequences of sharing. ### Source excerpt Learn how to review, approve, version, and safely share HubSpot Breeze Assistant artifacts using a five-part release framework. ## Stop tuning your models and fix your data DevFeed: [Stop tuning your models and fix your data]() Original publisher: [Read original article]() Author: Charlotte Daniels Published: 2026-09-17T09:00:00Z Content type: opinion Language: en Sources: [InfoWorld]() Topics: [AI Agent](), [Data Infrastructure](), [Large Language Model](), [AI Strategy](), [Security](), [prompt injection](), [Slack]() Tags: [agent](), [agentic-ai](), [agents](), [ai](), [artificial-intelligence](), [artificial-intelligence-data-and-information-security-data-management-generative-ai-security-so](), [data-and-information-security](), [data-infrastructure](), [data-management](), [generative-ai](), [safely](), [security](), [software-development](), [strategy]() ### AI overview The article argues that reliable enterprise AI agents depend more on a strong data foundation than on increasingly tuned language models. It emphasizes clean, documented data infrastructure and recommends prioritizing security, quality, and observability. ### Source excerpt Walk into any boardroom or technology conference today, and the conversation is entirely consumed by the promise of agentic AI. We are told that autonomous AI agents will soon handle our corporate analytics, democratizing data so that anyone, from an intern to the CEO, can type a natural-language question into Slack and get an instant, data-backed answer. Sounds great, right? Not so fast. If you simply drop a cutting-edge large language model (LLM) into the average corporate data warehouse, it doesn't become a brilliant data analyst. It becomes a confident idiot. The reality is that an AI agent cannot fix bad data, missing joins, or undocumented columns. So if your underlying data infrastructure is a chaotic web of isolated silos and ambiguous schemas, your agent will simply deliver wrong answers faster to everyone in your organization, and probably way too confidently (which is by design). When we built a conversational data agent at Runpod to let our teams query infrastructure metrics directly in Slack, our biggest takeaway wasn't about the model. It was about the architecture underneath it. Improving the model produced small, incremental gains. Improving the data foundation fundamentally changed the quality and usefulness of the agent's responses. If we wanted anyone at the company to be able to ask questions like "How many GPUs were run through maintenance today?" or even "How many GPUs were taken offline in the last hour?" and get an accurate, actionable answer, the lesson was clear: Our data foundation is our AI strategy. Many enterprise leaders are blowing massive budgets trying to fine-tune better models or build complex custom prompting layers. But they're solving the wrong problem. Just like building a house, building an AI agent requires a solid foundation. To make an agent reliable, you must start with the data foundation. To safely move from experimental AI to reliable enterprise execution, technology leaders must focus on three key pillars of the data ## How We Built Automated Capacity Testing for Kafka Consumers DevFeed: [How We Built Automated Capacity Testing for Kafka Consumers]() Original publisher: [Read original article]() Author: Kaan Karakaya Published: 2026-09-14T09:46:34Z Content type: article Language: en Sources: [Booking.com Development]() Topics: [Apache Kafka](), [Test automation](), [health check](), [Distributed Systems & Parallel Computing](), [Software Testing](), [traffic splitting]() Tags: [automated](), [capacity](), [consumer](), [failure](), [health](), [health-checks](), [http](), [kafka](), [limit](), [load](), [load-balancer](), [parallelism](), [partition](), [partitions](), [platform](), [ratio](), [reliability](), [resources](), [safely](), [site-reliability-engineer](), [sre](), [testing]() ### AI overview The system measures Kafka consumer capacity by temporarily shifting partition assignments to concentrate more work on one target instance, reproducing the load redistribution of a failure without stopping a healthy consumer. A scheduler requests successive load ratios; a Kafka API converts them to feasible partition counts, and a client library uses a priority-aware assignor to trigger rebalances. After each assignment settles, required liveness, resource, lag, throughput, and error checks determine whether to continue. The last healthy assignment is recorded, then the original strategy is restored; a time-limited lease also resets assignments if the controller fails. Tests run one target at a time because rebalances affect the whole group. Capacity resolution is limited by partition count, and health signals may need observation time. The approach supports repeatable capacity evidence and N-1 resilience checks, while requiring coordinated rollout of the custom assignor. ### Source excerpt Photo by GuerrillaBuzz on Unsplash Kafka makes it easy to distribute work across consumer instances. It is much harder to prove, safely and repeatedly, how those instances behave when the distribution changes and one of them has to carry more than its usual share. For teams that run Kafka at scale, this is a practical reliability question: how much load can a consumer instance actually handle? We had automated capacity testing for HTTP services, but Kafka consumers were still tested with manual drills. Those drills could tell us something, but they were disruptive, difficult to reproduce, and risky precisely when the system was close to its limit. We wanted a controlled way to answer three questions: What is the maximum sustainable throughput of a consumer instance? If an instance or failure domain disappears, can the remaining consumers absorb the reassigned work? Are we overprovisioning resources because we do not know the real limit? The result was an extension to our capacity-testing platform that turns Kafka partition assignment into a safe, measurable load-control mechanism. Why HTTP capacity testing did not translate Our existing platform was designed for request-response services behind a load balancer. A scheduled test selects one instance, routes an increasing share of traffic to it, runs health checks after each step, and records the highest ratio the instance can sustain. After the test, traffic returns to its normal distribution and the result is reported to the service owner. Kafka has no equivalent traffic knob. Consumers pull records, and the unit of parallelism is the partition. Within a consumer group, each partition is owned by one consumer at a time. If a topic has 12 partitions and four equally loaded instances, each instance owns about three. When one instance disappears, a rebalance gives the survivors more partitions -- and the extra work arrives as a step change, not as a smooth increase from a load balancer. The key translation: for an HTTP ## Building Secure Income Verification Workflows in Web Applications DevFeed: [Building Secure Income Verification Workflows in Web Applications]() Original publisher: [Read original article]() Author: Saifullah Adenwalla Published: 2026-09-10T09:51:15Z Content type: tutorial Language: en Sources: [SitePoint]() Topics: [Web app](), [sensitive data](), [Access Control](), [state-management](), [business logic](), [audit](), [file](), [Parsing]() Tags: [access-control](), [applications](), [building](), [business-logic](), [data](), [developers](), [review](), [safely](), [sensitive-data](), [state-management](), [structure](), [web](), [web-applications](), [workflows]() ### AI overview This tutorial explains how to design secure income-verification workflows in web applications. It treats the feature as a structured business process involving file uploads, validation, secure storage, review, approval or rejection, and auditing, rather than as simple PDF storage. ### Source excerpt null Continue reading Building Secure Income Verification Workflows in Web Applications on SitePoint. ## Safely Evolving Production SQL Schemas Without Downtime DevFeed: [Safely Evolving Production SQL Schemas Without Downtime]() Original publisher: [Read original article]() Author: Jerry Nixon Published: 2026-09-03T19:09:04Z Content type: tutorial Language: en Sources: [Azure SQL Dev Corner]() Topics: [Structured-data](), [migration](), [Database](), [SQL](), [integrity](), [Deployment]() Tags: [azure-sql](), [database](), [database-change-management](), [deploy](), [downtime](), [migration](), [production](), [safely](), [schema](), [strategy](), [t-sql]() ### AI overview A phased approach to changing a production SQL schema while applications remain online. The article describes expanding the schema, supporting old and new application versions, and sequencing database and application deployments. ### Source excerpt Schema changes are inevitable, but downtime doesn't have to be. Learn a phased approach to safely evolve a production SQL schema while applications remain online. The post Advocating for Uptime: The 6 Phases of Change appeared first on Azure SQL Dev Corner. ## Workflow Versioning for Reliable Automation and Maintenance DevFeed: [Workflow Versioning for Reliable Automation and Maintenance]() Original publisher: [Read original article]() Author: n8n team Published: 2026-08-27T16:28:34Z Content type: article Language: en Sources: [n8n]() Topics: [versioning](), [workflow automation](), [Git](), [configuration](), [JSON](), [Security]() Tags: [automation](), [environments](), [git](), [guides](), [itops](), [rollback](), [safely](), [secrets](), [versioning](), [workflow]() ### AI overview This article explains how n8n workflow versioning records workflow definitions as saved snapshots, helping teams track changes, identify regressions and concurrent overwrites, and restore working states. It also compares n8n's Git-based approach with Temporal workflow versioning. ### Source excerpt Learn how workflow versioning works with Git-based source control. Track changes, manage environments, and roll back safely in production. ## How to accelerate agentic adoption DevFeed: [How to accelerate agentic adoption]() Original publisher: [Read original article]() Author: Simon Sear Published: 2026-08-06T08:37:00Z Content type: article Language: en Sources: [Scott Logic]() Topics: [Artificial Intelligence](), [Software Engineering](), [Automation]() Tags: [adoption](), [agentic-ai](), [agents](), [ai](), [ai-adoption-spectrum](), [apps](), [artificial-intelligence](), [automation](), [business-process](), [claude](), [delivery](), [engineering](), [safely](), [software-engineering]() ### AI overview This article presents a framework for accelerating agentic AI adoption by grouping initiatives into four categories: employee productivity tools, business transformation and workflow automation, AI features embedded in existing applications, and AI-enabled software engineering. It emphasizes clear direction, safe deployment, secure data access, education, and organizational change. ### Source excerpt Agentic AI is impacting every part of an organisation, from customer journeys to back office, supply chains and software engineering. Everything is changing at an accelerating speed, all at the same time, everywhere, all at once. In response, organisations don't want drawn-out analysis engagements with consultants or long strategy projects; they want action. They want to quickly and safely deploy agents to production and deliver value fast. [Next page]()