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Summaries are not the original full articles. ## Gartner Report: The Attack Path is Closing Faster than Most Teams Can Patch DevFeed: [Gartner Report: The Attack Path is Closing Faster than Most Teams Can Patch]() Original publisher: [Read original article]() Author: Rapid7 Published: 2026-11-05T14:00:00Z Content type: article Language: en Sources: [Rapid7 Cybersecurity]() Topics: [Security](), [Exposure Management](), [exploit chaining](), [Frontier AI]() Tags: [about](), [ai](), [ai-assisted](), [also](), [artificial-intelligence](), [attack](), [backlog](), [behavior](), [business](), [but](), [can](), [change](), [combined](), [comes](), [connect](), [contain](), [cybersecurity-company](), [exposure-assessment-platform](), [exposure-management](), [faster](), [gartner](), [managed-detection-and-response](), [managed-security-solutions](), [patch](), [report](), [teams](), [vulnerability-management]() ### AI overview The excerpt argues that frontier AI can make it faster to turn separate weaknesses into executable attack paths, so security teams should judge progress by reduced enterprise risk and response speed rather than vulnerability counts alone. It cites measures such as attack path analysis coverage, patching and compensating control deployment cadence, mean time to remediate, and technology debt. Gartner recommends using attack path analysis to prioritize patches, virtual patches, and compensating controls in the context of critical services and business outcomes. The text says teams still need to identify affected systems, apply business context, choose and verify remediation, and maintain detection coverage. Rapid7 describes its capabilities as combining exposure visibility, threat intelligence, business context, and attack path analysis; the excerpt does not provide comparative product evaluations. ### Source excerpt Security teams have never lacked vulnerability data. The harder problem is deciding which issues create a credible path to business impact, then acting before that path can be used. As frontier AI makes it faster and less expensive to connect weaknesses, operational assumptions, and legitimate system behavior, that decision window is becoming even tighter. The Gartner® report, Mythos Reprioritizes Cybersecurity Outcomes and Investments, examines what this shift means for cybersecurity priorities and investment. Rapid7 is offering complimentary access to the research, which we believe can help security leaders reassess how they prioritize exposure and infrastructure teams reassess how they plan response and recovery. Why does AI change the vulnerability management equation? Gartner states, "Mythos collapses the distance between theoretical weaknesses and executable attack scenarios, forcing cybersecurity to optimize for outcomes, not activity." For leaders already managing more vulnerabilities than their teams can address, the pressure comes from the growing speed at which separate weaknesses can become a credible attack path. The change also extends beyond one model. Gartner states, "By 2027, Mythos will be overtaken by new frontier-AI-enabled models, signaling that this moment is not about Mythos, but a new era of AI-assisted vulnerabilities and functional exploits." At Rapid7, we believe this requires a more attacker-aware view of exposure. A large backlog can contain thousands of issues with very different implications for the organization. Leaders need to understand which exposures are reachable, how they could be combined, which business services they affect, and what action would reduce risk most effectively. What should security leaders measure instead? Vulnerability counts, backlog size, and generic remediation targets show how much work a team has completed. They provide less clarity on whether an attacker's options have narrowed or whether the organization ## Measuring Temporal Workflow health with OpenTelemetry and Parseable DevFeed: [Measuring Temporal Workflow health with OpenTelemetry and Parseable]() Original publisher: [Read original article]() Author: Praveen K B Published: 2026-11-05T00:00:00Z Content type: article Language: en Sources: [Temporal]() Topics: [OpenTelemetry](), [scheduling](), [workflow automation]() Tags: [alert](), [alerts](), [analyze](), [backlog](), [benchmark](), [community](), [correlation](), [custom](), [dashboard](), [dashboards](), [delay](), [health](), [onboarding](), [opentelemetry](), [queue](), [telemetry](), [temporal](), [time](), [worker](), [workflow]() ### AI overview The excerpt describes combining Temporal SDK metrics with Parseable lifecycle records and traces to explain Workflow duration beyond a simple Completed status. In a controlled Temporal Cloud onboarding run, verifyIdentity failed twice and succeeded on its third attempt; Parseable preserved each attempt and the Workflow's domain milestones. Metrics help locate Task Queue waiting and Worker slot pressure, while lifecycle records expose attempt status and duration, and traces show timing and parent-child relationships. The guidance pairs Schedule-to-Start latency with slot availability, host utilization, and poller behavior; it also tracks backlog age, retries, and Workflow Run duration. It recommends low-cardinality labels for dashboards and retaining execution IDs for targeted investigation. Backlog values are approximate, and the validation was not a production benchmark. ### Source excerpt A customer can complete onboarding and still be unhappy with the experience. A Workflow's Completed status tells you it finished, but not where it spent its time. Onboarding might have taken five minutes or 50 because an Activity waited for a Worker, an identity provider timed out, or a Task Queue received Tasks faster than Workers could process them. We built a simple onboarding Workflow that runs on Workers connected to Temporal Cloud and sent its telemetry to Parseable. ## AI Can Save Time If You Put in the Work Up Front DevFeed: [AI Can Save Time If You Put in the Work Up Front]() Original publisher: [Read original article]() Author: Maggie Criqui Published: 2026-11-03T13:00:18Z Content type: opinion Language: en Sources: [Atomic Object]() Topics: [AI-enhanced product development](), [User interface design]() Tags: [ai](), [ai-for-designers](), [ai-in-software-development](), [artificial-intelligence](), [backlog](), [canva](), [claude](), [client](), [communication](), [concepts](), [content](), [context](), [control](), [core](), [design](), [design-innovation](), [development](), [noise](), [processes](), [smart](), [software-development](), [workshop]() ### AI overview The author's main finding is that AI saves design time when people define the direction and constraints first; open-ended prompts produced noisy presentations, overbuilt wireframes, and unwanted backlog stories. For reports, they outline slide goals and key points in Google Docs, then use AI to produce the presentation, finding HTML reports work better for them than Canva or Google Slides. For wireframes, they sketch first, then ask AI to digitize the chosen direction at low or high fidelity, generate variations, or critique designs; AI handles repetitive tasks such as iterating icon styles and creating flow error states. For tickets, they wait until the team has agreed on features and designs, and use a Claude skill refined with development-team feedback to omit implementation details and cap word count. The author stresses that teams must own the backlog and the work's direction, since redoing AI output or sorting through excess content erodes time and cost savings. ### Source excerpt At a recent AI for Design workshop at Duke's Design & Innovation Program, led by Smart Design, the facilitator said something about AI in software development that stuck with me: "Power without direction is noise." During the session, we talked about how AI is changing our processes and which parts of the work we hold [...] The post AI Can Save Time If You Put in the Work Up Front appeared first on Atomic Spin. ## Your AI coding agent now runs on a budget -- and it can cut you off mid-task DevFeed: [Your AI coding agent now runs on a budget -- and it can cut you off mid-task]() Original publisher: [Read original article]() Author: Amanda Caswell Published: 2026-11-02T19:03:38Z Content type: release Language: en Sources: [The New Stack]() Topics: [agentic-coding](), [AI Agent](), [software development triangle](), [it modernization]() Tags: [2026](), [about](), [admin](), [agent](), [ai](), [ai-agent](), [ai-agents](), [ai-coding](), [ai-coding-agent](), [backlog](), [budget](), [burn](), [can](), [coding](), [coding-agent](), [compute](), [days](), [finops](), [first](), [it](), [operations](), [oversight](), [stack]() ### AI overview Cognition's Devin Budgets lets enterprises allocate AI agent spending across business units, teams, and projects up to three levels deep, while central administrators retain visibility and limits. Separate controls cover cloud and desktop use. Engineers receive warnings and can request more Agent Compute Units (ACUs) in-product; budget owners may review requests manually or approve them automatically. An optional efficiency-based rule raises limits for engineers with strong Efficiency Scores, up to an administrator-set ceiling. The score estimates productive engineering hours relative to Devin consumption; it does not measure code quality or business outcomes. If a budget is exhausted, the session stops. Switching to lower-cost models after a soft limit is still in development, so warnings and budget increases are the current ways to avoid interruptions. The excerpt says customers assess value using measures such as merged pull requests, productive hours, DORA metrics, and business results. ### Source excerpt The days of pointing an AI agent at a backlog and letting it burn through compute without much oversight may The post Your AI coding agent now runs on a budget -- and it can cut you off mid-task appeared first on The New Stack. ## How to run coding agents in parallel without piling up PRs DevFeed: [How to run coding agents in parallel without piling up PRs]() Original publisher: [Read original article]() Author: Jacob Schmitt Published: 2026-10-31T03:45:00Z Content type: tutorial Language: en Sources: [CircleCI]() Topics: [agentic-coding](), [CI/CD](), [Integration testing](), [Multi Agent Systems](), [AI-assisted coding](), [dynamic test splitting](), [parallel workspaces](), [Documentation]() Tags: [agents](), [ai-development](), [backlog](), [branch](), [ci](), [circleci](), [circleci-cli](), [circleci-testsuite](), [claude](), [claude-code](), [cli](), [cloud](), [code](), [coding-agents](), [engineering-productivity](), [environment](), [how-to](), [multi-agent-coding-workflows](), [multiple-coding-agents](), [parallel](), [parallel-ai-coding](), [parallel-coding-agents](), [running-agents-in-parallel](), [split](), [tasks]() ### AI overview Parallel coding agents help delivery when tasks are independently reviewable and the team can merge their output. Define file ownership, completion checks, and shared interfaces before work starts; isolate branches and runtime resources such as ports, databases, and generated files. Validate early in isolated Chunk sidecars, then use CI on every branch. The article describes Smarter Testing for selecting affected tests, auto-canceling redundant workflows (never on `main`), and CircleCI CLI commands `circleci run watch` and `circleci run get --failure-report` to monitor runs and retrieve failures; CLI and hosted MCP servers expose pipeline feedback to agents. Review focused PRs as they arrive, then rebase or merge branches onto updated `main` and rerun CI, since separately passing branches can fail together. Limit new agents when PRs or revalidation queue up; the suggested starting point is two or three agents, expanding when PRs are reviewed and merged within about a day. ### Source excerpt Learn how to run coding agents in parallel: split tasks cleanly, isolate each agent's environment, validate every branch with CI, and keep PRs moving to main. ## Thirteen backlogs in a trenchcoat: Analysing Renovate's many requests DevFeed: [Thirteen backlogs in a trenchcoat: Analysing Renovate's many requests]() Original publisher: [Read original article]() Author: Jamie Tanna Published: 2026-10-30T17:45:56Z Content type: article Language: en Sources: [Jamie Tanna]() Topics: [renovate](), [open source maintenance](), [Open Source](), [request management](), [issue tracking](), [CI/CD]() Tags: [2019](), [agile](), [ai](), [backlog](), [contribution](), [contributions](), [cycle-time](), [dependency](), [deployment](), [extensions](), [features](), [lead](), [llm](), [mend](), [open-source](), [process](), [product](), [project](), [renovate](), [sources](), [story]() ### AI overview Renovate's large workload comprises 13 distinct backlogs, so its maintainers plan to use clearer categories to prioritize incoming work. They classified requests by areas such as ecosystem support, workflow fit, bugs, security, performance, documentation, and dependency updates, alongside cross-cutting labels for rough edges, audience, and customer interest. The analysis found strong user demand for deeper ecosystem integrations and workflow fit, more community bug-fix contributions, and rising supply-chain-security requests. Community PRs have grown, with self-reported figures suggesting much of the recent increase involved substantive AI use; review response times have also slowed as the small team handles more work. Going forward, the project expects to favor reproducible bugs, rough edges, and customer requests, while acknowledging that its prioritization approach is still being refined. ### Source excerpt 🤖 This post includes some LLM-derived content 🤖 In 2019, I saw a talk called Building and Growing an Agile Team by Tom Hoyland, which has probably been one of the most impactful talks I've attended in my career. In the talk, Tom talks about how he worked with a team to build them into a high-performing team, which, among other things, involved measuring things like "cycle time", improving the way the team's deployment process worked by doing it for every story, and by discovering the sources of work coming into the team. In the latter case, by understanding the sources of work the team was being given, the team could then have full visibility into their different backlogs, which had always been present, but maybe not clearly thought of as a backlog, and make better choices going forwards. This is something that I've internalised quite deeply over the years, and have used to build teams that enjoyed working together across several companies. Most recently, this talk has come up as I've been thinking about how the Renovate project has quite a large backlog of work, in part due to increased contribution over the last ~18 months due to AI. As the Project Lead, I've been thinking about how we can approach this better in the future, and decided that one step towards this is to map out the different "backlogs" that we have as a project. This exercise will help us understand more generally what sources of work we have to help us better prioritise the existing backlog alongside new requests that come in. A brief summary of the project For those that don't know, Renovate is the excellent dependency update tool that's always been Open Source and very much open to contributions + extensions. I've been an avid user for years, and prior to joining Mend to work on Renovate last year, I'd been waxing lyrical about Why I recommend Renovate over any other dependency update tools which holds true to today - or maybe even more so, given the features we've been shipping ever since. Reno ## Bedrock Data routes data exposure fixes to owners and verifies results DevFeed: [Bedrock Data routes data exposure fixes to owners and verifies results]() Original publisher: [Read original article]() Author: Industry News Published: 2026-10-30T12:39:09Z Content type: release Language: en Sources: [Help Net Security]() Topics: [Security](), [Exposure Management](), [jira]() Tags: [agents](), [ai](), [ai-adoption](), [ai-agents](), [ai-assisted](), [ai-risk](), [audit](), [audit-trail](), [automation](), [aws](), [backlog](), [bedrock-data](), [breach](), [data-exposure](), [industry-news](), [jira](), [remediation](), [security](), [servicenow](), [ticket](), [workflow]() ### AI overview Bedrock Data's generally available AI-assisted remediation feature assigns data exposure fixes to dataset owners through existing Jira or ServiceNow workflows, then checks whether each reported fix removed the exposure. It creates context-rich, configurable tickets, answers owner questions in the ticket, and tracks closure; if exposure remains, it reopens the ticket with a reason. Security teams can adjust routing rules in natural language, while the platform maintains an audit trail. For well-defined fixes, policies can apply changes directly--for example, revoking document sharing--and notify owners; owners may opt in to automatic remediation for recurring exposure types. The approach leaves owners responsible for fixes while automating routing and verification. The article says the feature is included with the Bedrock Data platform and requires no new tool or training. It presents this as a way to scale remediation beyond a centralized security team's capacity; the source provides vendor claims and survey/report statistics but no independent evaluation of outcomes. ### Source excerpt Bedrock Data has announced AI-assisted remediation, which federates the work of fixing data exposure to the data owners across the enterprise. Bedrock Data discovers who owns each exposed dataset, opens a ticket for that owner in the Jira or ServiceNow workflow the enterprise already runs and supplies the context needed to fix it. When the owner closes the ticket, Bedrock Data validates that the exposure is remediated and reopens the ticket if it is not. ... More -> The post Bedrock Data routes data exposure fixes to owners and verifies results appeared first on Help Net Security. ## Batch Inference Is Now Generally Available on Runpod DevFeed: [Batch Inference Is Now Generally Available on Runpod]() Original publisher: [Read original article]() Author: The Team at Runpod Published: 2026-10-28T16:04:37Z Content type: release Language: en Sources: [Runpod Blog.]() Topics: [model-serving](), [AI Infrastructure](), [GPU](), [Web APIs]() Tags: [2](), [3](), [account](), [all](), [api](), [api-key](), [application](), [authorization](), [away](), [b300](), [backlog](), [based](), [batch](), [existing](), [inference](), [queue](), [send](), [serverless](), [up](), [workers]() ### AI overview Runpod Batch Inference lets you submit up to 3 million requests a day to an existing queue-based Serverless endpoint, using dedicated workers so batch jobs do not take capacity from live `/run` traffic. It suits work that can wait: start times are not guaranteed, requests run in no set order, and multi-hour latency is possible. Create a batch through the console or API, then finalize it to start processing; draft batches do not run. Check request counts and statuses, inspect per-request errors, and page through results. A batch can hold up to 5,000 requests, and an endpoint can run up to 10 active batches, subject to account limits. Billing is based on compute time per request, with discounts of up to 30% depending on GPU type (20% on H100, H200, B200, and B300). Since the service provides no pre-finalization cost estimate, the excerpt recommends trying 100 requests and extrapolating. ### Source excerpt Batch Inference is now generally available on Runpod Serverless. Send up to 3 million requests a day to an existing queue-based endpoint, on dedicated workers. ## Martingale-Based Resource Partitioning Bounds for URLLC and eMBB Network Slicing in 5G DevFeed: [Martingale-Based Resource Partitioning Bounds for URLLC and eMBB Network Slicing in 5G]() Original publisher: [Read original article]() Author: Hanbing Li, Yantong Wang Published: 2026-10-28T04:00:00Z Content type: article Language: en Sources: [arXiv: Networking]() Topics: [5G](), [Algorithms]() Tags: [5g](), [abstract](), [algorithm](), [applying](), [arxiv](), [backlog](), [based](), [block](), [broadband](), [capacity](), [coexistence](), [communications](), [complexity](), [constraint](), [cs-ni](), [delay](), [effective](), [effectively](), [network](), [partitioning](), [resource]() ### AI overview The proposed martingale based allocation framework bounds URLLC delay violations and eMBB backlog violations using exponential martingales built from arrival and service moment generating functions, together with Doob's maximal inequality. A bisection algorithm selects martingale parameters, then a deterministic search finds minimum resource block requirements. In the reported numerical results, the bounds upper bound Monte Carlo results; at a target violation probability of 10⁻⁶ per slice, the framework uses 44 RBs within a 50 RB capacity, compared with 64 RBs for the conventional effective capacity baseline, a reported reduction of about 31.2%. These are abstract level results. ### Source excerpt arXiv:2610.36602v1 Announce Type: new Abstract: Network slicing is a key enabler for the coexistence of ultra-reliable low-latency communications (URLLC) and enhanced mobile broadband (eMBB) in 5G networks. However, allocating limited radio resources to heterogeneous slices while satisfying stringent quality-of-service (QoS) requirements remains challenging, particularly in the ultra-reliable regime where conventional effective-capacity-based approaches may lead to excessive resource allocation. To address this issue, this paper proposes a martingale-based resource block (RB) allocation framework for coexisting URLLC and eMBB slices. By constructing exponential martingale processes using the moment generating functions (MGFs) of the arrival and service processes and applying Doob's maximal inequality, explicit non-asymptotic upper bounds are derived for the URLLC delay violation probability and the eMBB backlog violation probability. Based on the resulting theoretical stability conditions, a low-complexity bisection algorithm is developed to determine the optimal martingale parameters, followed by a deterministic search algorithm to obtain the minimum RB requirements for both slices. Numerical results show that the analytical bounds effectively upper-bound the Monte Carlo simulation results. At a target violation probability of $10^{-6}$ for each slice, the proposed framework requires 44 RBs in total, satisfying the system capacity constraint of 50 RBs. Compared with the conventional effective-capacity baseline requiring 64 RBs, the proposed framework reduces total RB consumption by approximately 31.2% relative to the conventional effective-capacity baseline. ## 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 ## From prompt to PR: how Careem's AI writes and validates dbt models before a human ever sees them DevFeed: [From prompt to PR: how Careem's AI writes and validates dbt models before a human ever sees them]() Original publisher: [Read original article]() Author: Daniel Poppy Published: 2026-10-26T14:04:00Z Content type: news Language: en Sources: [dbt Labs]() Topics: [Artificial Intelligence](), [Pull Request](), [Software Testing](), [human review]() Tags: [agent](), [agent-architecture](), [ai](), [ai-agent](), [ai-generated-code](), [analytics-engineering](), [automated](), [backlog](), [code](), [code-generator](), [column](), [context](), [context-layer](), [daily](), [data](), [dbt](), [learn](), [models](), [pr](), [prompt](), [reviews]() ### AI overview Careem's dbt agent is built around end-to-end validation: code generation alone was not enough to give reviewers confidence. Before writing a model, it uses metadata and metric definitions to resolve terms, asks analysts to specify grain, metrics, and filters, and routes governed metrics to a golden layer while one-off requests go to a transient layer with a 30-day lifespan. Each change runs in an isolated development sandbox, where the agent compiles, tests, and materializes the model, fixes failures, and checks data movement and storage conventions before opening a pull request for human review. In the described demo, it corrected a type mismatch and found a required column in another source table. Careem reports delivery time falling from two to three weeks to hours, about 90% of new models using the framework, and no sev0 or sev1 incidents since its introduction. ### Source excerpt Careem's AI agent writes and validates dbt models end to end before a human ever reviews the PR. ## ProjectDiscovery adds Neo Triage to confirm which vulnerabilities are exploitable DevFeed: [ProjectDiscovery adds Neo Triage to confirm which vulnerabilities are exploitable]() Original publisher: [Read original article]() Author: Duncan Riley Published: 2026-10-26T14:00:17Z Content type: news Language: en Sources: [SiliconANGLE]() Topics: [alert triage](), [code security](), [ai code review]() Tags: [ai](), [ai-assisted](), [ai-code-review](), [artificial-intelligence](), [backlog](), [bug-bounty](), [bug-hunting](), [crv](), [cybersecurity](), [cybersecurity-company](), [exploited](), [github](), [hackerone](), [neo](), [neo-security](), [neo-triage](), [news](), [nuclei](), [open-source](), [orca-security](), [penetration-testing](), [platform](), [projectdiscovery](), [qualys](), [rishi-sharma](), [scanners](), [security](), [security-testing](), [the-latest](), [triage](), [vulnerabilities](), [vulnerability-management](), [vulnerability-triage]() ### AI overview Neo Triage aims to reduce manual vulnerability triage by trying to reproduce each reported finding against the live asset from an isolated sandbox designed not to cause damage. It returns verdicts such as valid, duplicate, or spam, provides the test traffic as evidence, and bases severity on demonstrated impact while also considering asset exposure and reachable data or systems. Findings from sources including HackerOne, GitHub advisories, security mailboxes, Qualys, and Orca Security go to one board; other tools can connect through an API. For confirmed issues, Neo suggests fixes, drafts researcher replies, and retests after changes ship. ProjectDiscovery estimates human review of 100,000 findings at 50,000 hours versus roughly 8,300 hours reviewing Neo's verdicts; these are company estimates, not independent results. Neo Triage is included in all Neo tiers, including free. ### Source excerpt Open-source cybersecurity company ProjectDiscovery Inc. today added Neo Triage to its Neo security testing platform to confirm which reported vulnerabilities can actually be exploited before teams start fixing them. The new capability goes after the triage backlog, which ProjectDiscovery blames on artificial intelligence. Scanners, AI code review and AI-assisted bug hunting now turn up findings [...] The post ProjectDiscovery adds Neo Triage to confirm which vulnerabilities are exploitable appeared first on SiliconANGLE. ## Initiative: A Self-Hosted Workspace for Groups DevFeed: [Initiative: A Self-Hosted Workspace for Groups]() Original publisher: [Read original article]() Author: Jeremy Published: 2026-10-26T11:56:26Z Content type: opinion Language: en Sources: [Noted]() Topics: [Self-hosted](), [To-Do](), [Homelab]() Tags: [api](), [arm64](), [automation](), [backlog](), [billing](), [calendar](), [communities](), [community](), [compose](), [container](), [core](), [dashboards](), [database](), [deployment](), [docker](), [groups](), [initiative](), [projects](), [self-hosted](), [self-hosted-task-management-to-do-lists](), [server]() ### AI overview Initiative is a self-hosted workspace that groups projects and collaborative documents around communities, with member permissions that can be scoped to projects or files. It offers task boards with table and calendar views, real-time documents, whiteboards, spreadsheets, plug-ins, and dashboards. Official deployment uses Docker Compose with PostgreSQL; the image supports AMD64 and ARM64, defaults to port 8173, and requires an application address and secret key. The first registered account becomes server owner. Initiative is pre-1.0, and its API may change between minor releases; developers warn that development-image database migrations may complicate upgrades. The core is AGPL-3.0, while automation and billing components for the hosted service are proprietary. The excerpt also notes that the extension registry is maintained outside the self-hosted server. ### Source excerpt Excerpt: Initiative gives communities a shared workspace for organizing projects and working together, all hosted on their own server. ## Bounding Queue Waits for Fair Scheduling of Bursty HPC Workloads via Hierarchical Algorithms DevFeed: [Bounding Queue Waits for Fair Scheduling of Bursty HPC Workloads via Hierarchical Algorithms]() Original publisher: [Read original article]() Author: Karaen Senthilkumar, Suman Raj, Maxime Gonthier, Kyle Chard, Donpaul Stephens, Ian Foster Published: 2026-10-26T04:00:00Z Content type: article Language: en Sources: [arXiv: Distributed Computing]() Topics: [scheduling](), [High-Performance Computing](), [queueing theory](), [Algorithms]() Tags: [2](), [abstract](), [algorithms](), [arxiv](), [automated](), [backlog](), [based](), [best](), [can](), [clusters](), [cost](), [created](), [cs-dc](), [either](), [ensuring](), [existing](), [fairness](), [first](), [hpc](), [queue](), [scheduling]() ### AI overview HyDRA combines per-job deadlines with waiting-time ranking to address fairness and bound queue waits in shared HPC clusters. On arrival, it sets deadlines using job size, project and user priorities, backlog, and each project's and user's distance from their entitled share. It favors jobs nearing deadlines and permits backfilling only when deadline and fairness limits remain satisfied. In evaluations using workloads derived from a year of ALCF Polaris traces, HyDRA achieved twice the project-level Jain's fairness of the best baseline, started 47% of jobs by their deadlines versus at most 36% for baselines, and reduced 95th-percentile bounded slowdown by 4.2 times relative to the next-best baseline, at a moderate utilization cost. ### Source excerpt arXiv:2610.32027v1 Announce Type: new Abstract: HPC clusters are shared among many projects, each holding an allocation of node-hours, and ensuring fairness is becoming harder. Foundation models workflows and automated pipelines have made job submissions bursty and concentrated among a few heavy users, so occasional users absorb queueing delays created by frequent ones, with no indication of how long their jobs will wait. Existing schedulers either forget past usage, protect only the job at the head of the queue, or assume that running jobs can be preempted. We propose HyDRA, a Hybrid Deadline and Ranking-Aware scheduler that enforces fairness among users while bounding the queue wait of every job. HyDRA assigns each job a deadline on arrival based on its size, the priorities of its project and user, the current backlog, and how far its project and user are from their entitled share. It ranks waiting jobs by a priority score that grows with waiting time, serves jobs close to their deadlines first, and backfills a job only when no deadline, or fairness limit is violated. We evaluate HyDRA on workloads derived from a year of ALCF Polaris job traces, against conventional schedulers and variants inspired by PCS, Themis, and the PBS job-sort formula used at ALCF. HyDRA achieves $2\times$ the project-level Jain's fairness of the best baseline, starts 47\% of jobs by their deadlines compared with at most 36\% for the baselines even for largest workloads, and lowers the 95th-\%ile bounded slowdown by 4.2$\times$ relative to the next best baseline, at a moderate cost in utilization. ## Hunting Replication Lag: A Queue-by-Queue Method for Diagnosing Oracle GoldenGate DevFeed: [Hunting Replication Lag: A Queue-by-Queue Method for Diagnosing Oracle GoldenGate]() Original publisher: [Read original article]() Author: Zalak Shrof Published: 2026-10-23T20:53:46Z Content type: article Language: en Sources: [HackerNoon]() Topics: [Oracle Database](), [queueing theory](), [Parallelism]() Tags: [apply](), [backlog](), [benchmark](), [capacity](), [capture](), [checkpoint](), [commit](), [constraint](), [data-replication](), [database](), [distributed-systems](), [lag](), [oracle](), [oracle-database](), [oracle-goldengate](), [oraclegoldengate](), [parallelism](), [queue](), [replicat](), [replication](), [replication-lag]() ### AI overview GoldenGate lag is the buildup of work in a pipeline of queues, so adding Replicat appliers helps only when apply capacity is the constraint. The article recommends measuring each hop and using target heartbeat tables for end-to-end lag; process LAG reports the age of the record being processed and can look healthy while newer data is delayed upstream. Its model illustrates how large-transaction barriers, hot-row dependencies, unusable target indexes, or network throttling can limit applier gains. A missing target index can leave scans in place even when KEYCOLS identifies rows, and per-key ordering caps throughput on hot rows. After a bottleneck clears, backlog shifts downstream, which then determines drain time; in active-active replication, the lag also widens the conflict window. The model is illustrative, omits applier contention and other behaviors, and overstates the benefit of splitting transactions by assigning pieces unique keys. ### Source excerpt When replication falls behind, the reflex is to raise Replicat parallelism, which works only when apply is the binding constraint. ## The Same Old Bullshit DevFeed: [The Same Old Bullshit]() Original publisher: [Read original article]() Author: David Pereira Published: 2026-10-22T12:29:32Z Content type: opinion Language: en Sources: [Untrapping Product Teams]() Topics: [feature bloat](), [human review](), [To-Do]() Tags: [adapting](), [agentic-ai](), [agents](), [ai](), [alignment](), [amazon](), [analytics](), [archive](), [audacious](), [automatically](), [backlog](), [data](), [delete](), [digital](), [discuss](), [easy](), [factory](), [feature](), [features](), [ideas]() ### AI overview The article argues that stale backlog items can distract teams and, when connected to agentic AI, lead to implementing work that no longer fits current goals. It recommends setting direction first--where you want to go, where you are, and what comes next--then clearing work that does not support it. Suggested routines: filter for items untouched for 90 days, review and remove or archive them every two weeks, and check backlog alignment with the main goal every six weeks. The author also proposes deleting an entire backlog as a bold option, especially for startups, while acknowledging it may feel threatening and is less realistic for enterprises. Start with one neglected item and share the results. ### Source excerpt We struggle to renounce old ideas, even when they don't serve us any longer. ## Turn Your Vulnerability Backlog Into Pull Requests With Agentic Remediation DevFeed: [Turn Your Vulnerability Backlog Into Pull Requests With Agentic Remediation]() Original publisher: [Read original article]() Author: Brendan Hann; Ryan McMorrow Published: 2026-10-22T04:00:00Z Content type: release Language: en Sources: [Snyk]() Topics: [cve remediation](), [Dependency management](), [Agentic AI](), [Open Source]() Tags: [agentic](), [agentic-appsec](), [ai](), [application-security](), [appsec](), [automated](), [backlog](), [blog](), [dependency](), [developer](), [devops](), [devsecops](), [enablement](), [interest](), [open-source-security](), [preview](), [pull-requests](), [remediation](), [scm](), [security](), [snyk-open-source](), [snyk-platform](), [teams](), [vulnerability](), [vulnerability-insights]() ### AI overview Evo Remediation Agent is in Open Preview for Snyk Open Source vulnerabilities, letting AppSec teams define recurring campaigns across selected repositories. It uses Snyk findings to plan dependency updates, applies them in a hosted sandbox, and checks builds, available tests, and rescans before opening pull requests. Developers retain review and merge control; campaigns can be restricted to fixes assessed as non-breaking. In a 307-task VulnBump benchmark, the CLI-based agent passed all four checks--dependency resolution, vulnerability removal on rescan, build success, and preservation of previously passing tests--on 90.2% of tasks, compared with 77.5% for Claude Code with Snyk findings and 59.6% for Claude Code alone; all used the same model. Campaigns require Workspaces, SCM integration, and Tenant preview access. Open Preview has a daily fix allowance, and hosted sandboxes use public package registries. ### Source excerpt The Evo Remediation Agent is now in Open Preview, bringing automated dependency resolution to Evo Agentic AppSec. AppSec teams can now run ongoing remediation ## Koala: Non-disruptive Reconfiguration for Long-running Stateful Dataflows DevFeed: [Koala: Non-disruptive Reconfiguration for Long-running Stateful Dataflows]() Original publisher: [Read original article]() Author: Lei Huang, Sakshi Sharma, Chenxu Yu, Zikun Wang, Yuanli Wang, Zichen Zhu, John Liagouris, Vasiliki Kalavri Published: 2026-10-22T04:00:00Z Content type: article Language: en Sources: [arXiv: Distributed Computing]() Topics: [streaming data analytics](), [Stateful]() Tags: [abstract](), [all](), [applications](), [architectures](), [art](), [arxiv](), [backlog](), [benchmarks](), [but](), [change](), [changes](), [choice](), [core](), [cs-dc](), [dataflow](), [design](), [designs](), [disruption](), [long-running](), [running](), [stateful]() ### AI overview Koala is a reconfiguration protocol for long-running stateful dataflows that aims to combine the steady-state performance of embedded state with disruption-free adaptability. Instead of migrating all state during reconfiguration, it moves state lazily and at fine granularity, using lightweight ownership transfers to decouple event routing from state location. In evaluations on real-world and synthetic benchmarks, Koala consumed backlog up to 23x faster and transferred 8.7x less state on average than DRRS. The supplied text is an abstract, so it does not provide further implementation details or evaluation conditions. ### Source excerpt arXiv:2610.28939v1 Announce Type: new Abstract: Stateful dataflow systems must decide where state lives relative to the tasks that process it. For long-running applications that require frequent reconfiguration due to changes in resource allocation, partitioning, or task placement, this design choice presents a core trade-off between adaptability and performance. Co-locating state with computation ensures low latency at steady state but makes reconfiguration disruptive, as any change forces state migration. On the other hand, keeping state in an external store makes reconfiguration seamless, but incurs overhead on every access. We present Koala, a reconfiguration protocol for distributed dataflow systems that retains the operational performance of embedded-state architectures while matching the disruption-free adaptability of external-state designs. Unlike prior protocols which relocate all state regardless of whether it will soon be accessed, Koala migrates state in a fine-grained, lazy manner. To achieve that, it employs a lightweight ownership transfer mechanism that safely decouples event routing from state location. We implement Koala in a new streaming dataflow system and evaluate it on real-world and synthetic benchmarks. When compared with the state-of-the-art protocol DRRS, Koala consumes backlog up to 23x faster and transfers 8.7x less state on average during reconfiguration. ## SAP delivers record quarterly revenue as its autonomous enterprise strategy picks up speed DevFeed: [SAP delivers record quarterly revenue as its autonomous enterprise strategy picks up speed]() Original publisher: [Read original article]() Author: Mike Wheatley Published: 2026-10-22T01:01:08Z Content type: news Language: en Sources: [SiliconANGLE]() Topics: [Agentic AI](), [Use Cases for Every Cloud Service Model]() Tags: [agent](), [ai](), [ai-agent](), [ai-models](), [applications](), [autonomous](), [autonomous-enterprise](), [backlog](), [cash-flow](), [christian-klein](), [cloud](), [cloud-backlog](), [cloud-revenue](), [earnings](), [enterprise-strategy](), [financial-results](), [news](), [outlook](), [q3](), [revenue](), [sap](), [sap-joule](), [software](), [the-latest](), [third-quarter]() ### AI overview SAP reported quarterly sales of EUR 10.27 billion, above analysts' EUR 10.10 billion estimate, with cloud revenue up 25% to EUR 6.67 billion and its cloud backlog up 26% to EUR 24.11 billion. It kept its full-year operating profit outlook at EUR 11.8-EUR 12.2 billion and expects fiscal 2026 subscription revenue near the upper end of its EUR 25.8-EUR 26.2 billion forecast, while warning that the economic outlook is gloomy and the fourth quarter will be critical. CEO Christian Klein linked results to SAP's autonomous enterprise strategy. Its Joule AI agent connects to more than 70 AI models and acts as a conversational layer alongside SAP applications; Klein said AI drove cloud adoption during the quarter and hundreds of customers subscribed to access Joule. ### Source excerpt Europe's biggest software company SAP SE delivered record-breaking quarterly revenue after surpassing the EUR 10 billion ($11.2 billion) barrier for the first time earlier today, while delivering a 26% increase in its current cloud backlog and lifting its revenue outlook for the full year. The Germany-based firm reported earnings before certain costs such as stock compensation [...] The post SAP delivers record quarterly revenue as its autonomous enterprise strategy picks up speed appeared first on SiliconANGLE. ## Remediation in the era of AI: Fix what matters first DevFeed: [Remediation in the era of AI: Fix what matters first]() Original publisher: [Read original article]() Author: Anna Boulware Published: 2026-10-21T18:45:47Z Content type: article Language: en Sources: [Computerworld]() Topics: [Exposure Management](), [cve remediation](), [known exploitable vulnerabilities](), [CVE fatigue]() Tags: [actively-exploited](), [ai](), [ai-assisted](), [applications](), [artificial-intelligence](), [artificial-intelligence-risk-management](), [backlog](), [business](), [cloud](), [configuration](), [critical](), [customer](), [cve](), [development](), [discovery](), [edition-us](), [exploit](), [gateway](), [internet](), [it](), [language-en](), [lateral](), [library](), [remediation](), [risk-management](), [storytype-brandpost]() ### AI overview Prioritize reducing reachable, consequential exposures rather than chasing vulnerability counts. Combine active exploitation, attacker access paths, business impact, sensitive data, and compensating controls: the excerpt contrasts a critical flaw on an internal development server with a publicly exploitable high-rated flaw on a customer-facing server, and favors the latter. Replace periodic scan-and-ticket cycles with continuous monitoring of disclosures, configuration changes, and newly exposed assets, then verify fixes and retain audit-ready evidence. Automate routine endpoint updates and established configuration corrections with staged rollouts; use human judgment and scheduled windows for higher-impact production or network changes. Measure outcomes such as time to remediate actively exploited flaws and trends in internet-facing exposures, rather than patches or tickets closed. The stated goal is deliberate, continuous reduction of meaningful risk, not zero vulnerabilities. ### Source excerpt Every IT and security team knows the feeling of opening a vulnerability report and seeing a number that only goes up. Thousands of findings. Hundreds marked critical. A backlog that grows faster than any team can close it. For years, the default response has been to work harder: more scans, more tickets, more overtime. But the volume of vulnerabilities, accelerated by AI-assisted discovery and a sprawling mix of endpoints, cloud services, and applications, has outpaced that approach. Shift from vulnerability counts to exposure reduction -> The question has shifted from, "How do we fix everything?" To, "What should we fix first, and why?" Vulnerabilities are not the same as exposures A vulnerability is a weakness in software or configuration. An exposure is a weakness that can realistically be reached and exploited in your environment, with consequences that matter. That difference is where remediation prioritization begins. A vulnerable library on a decommissioned server with no network access is technically a finding. It is not a meaningful exposure. A misconfigured remote access gateway facing the internet may never appear on a CVE list, yet it can represent serious risk. Focusing on exposure reduction, rather than raw vulnerability counts, helps teams spend limited time where it changes outcomes. Risk-based prioritization works best when it combines technical and business factors. Is the weakness being actively exploited? Can attackers reach the affected system from the internet or through common lateral paths? What business service does the system support, and what would disruption cost? Does it store or process sensitive data? Are compensating controls already reducing the risk? Consider two findings: a critical-rated flaw on an internal development server with no sensitive data, versus a high-rated flaw on a customer-facing web server with exploit code circulating publicly. On severity alone, the first wins. On actual risk, the second is the clear priority, as ## Why Enterprise AI Agents Still Fail in Production DevFeed: [Why Enterprise AI Agents Still Fail in Production]() Original publisher: [Read original article]() Author: Swytchcode Team Published: 2026-10-21T09:53:12.592027Z Content type: article Language: en Sources: [Swytchcode]() Topics: [Production Engineering](), [site-reliability-engineering](), [staging](), [Monitoring]() Tags: [agent](), [agents](), [ai](), [ai-agent](), [ai-agents](), [api](), [apis](), [arguments](), [audit](), [auth](), [backlog](), [code](), [component](), [composition](), [enterprise](), [enterprise-ai](), [fail](), [production](), [staging](), [test]() ### AI overview Enterprise agents can pass component tests and still fail in production because tests miss problems that arise when API calls, credentials, policies, retries, and traffic interact. The excerpt describes failures such as schema drift, secrets rotating mid-run, cross-tenant credential mix-ups, and shared rate limits starving an agent's backlog. It argues that these are usually stitching-layer problems rather than model-quality problems. Its proposed approach is to treat that layer as infrastructure, applying credential brokering, allowlisting, schema validation, policy checks, safe retries, response validation, and auditing consistently across frameworks. A custom-code stitching layer that cannot be clearly audited is presented as a warning sign. ### Source excerpt Every component passes its own test and the composition still breaks. Why the stage between "it works in staging" and "it holds up in production" keeps failing. ## DX's Justin Reock on the AI ROI that isn't showing up in the data DevFeed: [DX's Justin Reock on the AI ROI that isn't showing up in the data]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-10-20T23:37:57Z Content type: news Language: en Sources: [WorkOS]() Topics: [agentic-coding](), [Artificial Intelligence](), [Instrumentation]() Tags: [2026](), [adoption](), [agentic](), [agentic-coding](), [agents](), [agile](), [ai](), [ai-generated-code](), [ai-roi](), [backlog](), [benchmark](), [coding](), [coding-tools](), [cto](), [data](), [dx](), [pr](), [roi](), [throughput](), [tools]() ### AI overview DX Deputy CTO Justin Reock reports that PR throughput has risen steadily but remains below 2x its level a year ago, while AI spend at larger organizations is up about 50x; DX sees no clear measurable ROI yet, and its innovation ratio has stayed roughly flat. The excerpt links higher shares of AI-generated code reaching production with longer review times: organizations shipping 80% AI-generated code take twice as long for first review as those shipping 40%. Reock argues that constraints shift from code creation to review, rework, and earlier product-development stages. He recommends tracking where work queues form, giving engineers time and safe pilots to learn tools, and communicating transparently about augmentation. Zapier is offered as an example, with reported faster onboarding and higher engineer contribution, though the excerpt notes it provides no corresponding downside data. ### Source excerpt DX Deputy CTO Justin Reock on why PR throughput keeps climbing, AI spend is up 50x, and the data still shows no clear return on agentic coding tools yet. ## There's only so much code: the rise in vulnerability numbers DevFeed: [There's only so much code: the rise in vulnerability numbers]() Original publisher: [Read original article]() Author: Kri Dontje Published: 2026-10-20T15:31:22Z Content type: article Language: en Sources: [Cisco Talos Blog]() Topics: [code security](), [CVE fatigue](), [known exploitable vulnerabilities](), [Exploit](), [exploit chaining](), [Static code analysis](), [CVE-2021-44228](), [snyk-security-intel]() Tags: [ai](), [ai-assisted](), [ai-evaluation](), [ai-models](), [anomaly-detection](), [attack](), [attacks](), [automation](), [backlog](), [behavioral-detection](), [blocking](), [bottlenecks](), [bug](), [code](), [microsoft](), [microsoft-patch-tuesday](), [on-the-radar](), [patch](), [patch-tuesday](), [vulnerabilities](), [vulnerability]() ### AI overview AI-assisted scanning is surfacing vulnerabilities in the backlog of older, widely used code, driving a sharp rise in advisories and straining open-source maintainers and the traditional 90-day disclosure window. The increase has not translated proportionally into known exploitation: the excerpt reports 105% year-over-year growth in published CVEs versus 37% growth in known exploited vulnerabilities, and three in-the-wild exploits among September's 975 Microsoft advisories. The article says disclosure does not equal weaponization, while cautioning that AI may help attackers analyze patches faster. It recommends prioritizing flaws that offer viable paths into an organization's environment, upgrading and using interim mitigations such as LiveProtect, adding behavioral anomaly detection and defense in depth, and evaluating new code with current AI models. The author expects discovery rates may eventually level off as legacy code is remediated, but presents this as a possibility, not a certainty. ### Source excerpt The September Microsoft Patch Tuesday release included 975 vulnerability advisories. This month, 1,415 vulnerabilities were just dropped. Compared to a pre-June average of 100 a month, that increase is staggering. Where did they come from? And what does the future look like? ## How to Start an AI Food Slime Channel for TikTok, Reels, and YouTube Shorts DevFeed: [How to Start an AI Food Slime Channel for TikTok, Reels, and YouTube Shorts]() Original publisher: [Read original article]() Author: Naive Published: 2026-10-15T00:00:00Z Content type: tutorial Language: en Sources: [Naïve]() Topics: [Loop Engineering](), [scheduling]() Tags: [account](), [agent](), [agents](), [ai](), [ai-agents](), [backlog](), [budget](), [cadence](), [channel](), [company](), [context](), [control](), [cost](), [food](), [guide](), [reels](), [sound](), [team](), [tiktok](), [video]() ### AI overview The guide describes a ready-made naïve template for producing AI-generated food-to-slime ASMR shorts without filming or making slime. On a user-set schedule, agents select one food, generate a continuous press-and-knead transformation with wet sound, and draft a caption. Each video waits in a review queue; the user can edit, approve, or hold it, and approval is required before posting to TikTok, Reels, or Shorts. The template uses reference frames for a consistent look, stops generating when the queue is already ahead, and enforces per-agent daily and per-task spending caps by pricing generations before they run. Setup begins with a naïve account, deploying the template, and choosing a posting cadence. ### Source excerpt A step-by-step guide to running an AI food slime channel: an agent team generates one real-looking food kneaded into glossy slime, with ASMR sound, and queues each video for you to approve. [Next page]()