Finance · SaaS
San Diego, California--(October 12, 2026) - Shareholder rights law firm Robbins LLP reminds investors that a class action was filed on behalf of all persons and entities who purchased or otherwise acquired Taboola.com Ltd. (NASDAQ: TBLA) securities between May 6, 2026 and August 4, 2026 (the "Class Period"). Taboola operates a platform that partners with websites, devices, and mobile apps to recommend editorial content and advertisements on the open web.The complaint alleges...
Oct 13 · 00:37·Newsfile
AI · SaaS
As usage, costs, and customer expectations change, AI companies are moving beyond traditional SaaS pricing. Leaders at Monetize 2026 shared how they’re combining subscriptions with usage-based billing, building spend controls into products, adapting packaging and value metrics, and making pricing readable for both human and AI buyers.
Oct 13 · 00:00·Stripe
AI · SaaS
AI coding tools have changed how fast software gets written, but not how fast it ships. As code generation accelerates, the bottleneck moves downstream into verify, review, and ship. Those stages still run on human time. While agents write fixes in minutes, delivery slows on constant wait times and manual handoffs. Reviewer signoffs, pipeline alerts, scan triage, and step-by-step prompting are examples of obstacles DevOps teams face in maintaining on-time delivery.
At GitLab Transcend, we announced new agent orchestration capabilities in GitLab Duo Agent Platform to eliminate that delay. Goal-driven flows take the outcome you define and run it end to end, through review, tests, security scans, and approvals from Duo CLI, headless mode, or Agentic Chat. GitLab for Slack starts work from the conversation where it was requested, like turning a thread into an issue or kicking off a flow to fix a failing pipeline. Custom Flows let you define your own multi-step sequences and start them from GitLab events like a mention, an assignment, or a pipeline. All three run under your identity, policy, and evidence chain.
How agentic orchestration speeds up delivery
Faster coding, process checklists, and single-chat coding agents all contribute to delays in software delivery. With agentic orchestration capabilities, you can accelerate each stage and reclaim time for your development team.
Fewer handoffs. You name the goal, and the flow keeps working across review, tests, scans, and approvals instead of stopping between each step.
Start work where the request already is. A Slack thread becomes an issue or a flow immediately from where you’re already discussing the work.
Automate the process your team already follows. Custom Flows let you define your own multi-step sequences and start them from a mention, an assignment, or a pipeline.
Stay in control. Flows run under the same identity, policy, and evidence chain as the rest of GitLab, so governance stays with the work. Foundational agents and flows already cover plan through analyze stages in the GitLab UI, your IDE, and Duo CLI, and you can enable more from the AI Catalog, bring in external agents, and connect tools through the MCP client and the MCP server.
Goal-driven flows: Set your objective
Goal-driven flows give agents the finish line instead of the next step. You describe the objective and what done looks like, and the flow keeps working until it gets there. Goal-driven flows automate handoffs, so review, tests, security scans, and approvals run as one connected sequence.
You start one with the /goal command, for example:
/goal Fix the checkout timeout. Continue until tests pass and CI is green.
A separate model checks the work against your goal at each step, and you can stop or redirect a run at any point. You can start a goal-driven flow in Duo CLI, in headless mode with duo run --goal, and in Agentic Chat.
GitLab for Slack: Start where the work gets discussed
GitLab for Slack lets you start work in GitLab without leaving Slack. Mention @GitLab in any channel or thread and describe what you need, such as "fix the failing pipeline," and a flow starts. Duo streams its progress back into the thread, so your team follows the work where the conversation is already happening. The flow runs under the same identity and policy as any other GitLab flow, so governance stays in GitLab. You can also use @GitLab to ask questions about your codebase or create an issue from the conversation.
Custom Flows: Turn your process into a flow
Custom Flows chain agents together to match the process your team already follows. You define a flow once, start it from GitLab events like a mention, an assignment, or a pipeline, and run it in CI/CD pipelines. A self-healing pipeline, for example, can analyze a failed test, generate a fix, commit it, and notify your team, with human-in-the-loop checkpoints at sensitive steps. Each flow runs under the same identity, policy, and evidence chain as every other flow, so governance stays in GitLab.
Put agent orchestration to work
To achieve the benefits of agent orchestration, start with goal-driven flows. The documentation for Duo CLI slash commands walks you through /goal. Goal-driven flows are available on GitLab.com, GitLab Self-Managed, and GitLab Dedicated, on the Premium and Ultimate tiers, and require GitLab 19.3 or later and Duo CLI 9.17.0 or later.
Then extend that orchestration to your team's processes and channels with Custom Flows, which are generally available, and the MCP server and GitLab for Slack, which will be generally available later this month.
To design the foundation for your software factory, check out GitLab Duo Agent Platform. New to GitLab? Start a free trial of GitLab Duo Agent Platform. Already on Premium or Ultimate? Turn on GitLab Duo Agent Platform and use the GitLab Credits included with your subscription.
Oct 13 · 00:00·GitLab
AI · SaaS
Costs multiply with every task you hand to an agent. Each task consumes tokens and credits, and as agents spread across your teams and automations, consumption can grow faster than usage. If you run the platform, you own that growth, including which teams and workflows drive it and whether the usage ships work.
At GitLab Transcend, we introduced the governed software factory, our architecture for agentic software engineering, and new capabilities in GitLab Duo Agent Platform that bring that growth under control. GitLab-managed open-weight models matched frontier-model task completion at a fraction of the cost in GitLab's benchmark, so you can align your model to the task to improve your team’s cost efficiency. GitLab Orbit gives agents lifecycle context so they retry less. Duo Agent Platform Impact Analytics and credit caps show where credits go and put a limit on them.
Control costs in the governed software factory
A usage total tells you what you consumed, and leaves hard questions open: how much routine work sat on an expensive model, how many agent runs were wasted retries, and which users and teams burned through the credits. When the dashboard only shows tokens, platform and engineering leaders end up debating a number neither side can stand behind. If you can't attribute the usage, set a limit, and cut the waste, you can’t optimize or justify your spend.
Breaking that pattern starts with the architecture. The governed software factory is built in layers, and cost grows where those layers don't connect. Two of those layers matter most for cost. The first is agent orchestration, where open weight models put routine work on lower-cost models. The other is data and context, where Orbit, Duo Agent Platform Impact Analytics, and credit and usage controls give humans and agents the context to produce high-quality outcomes at the lowest possible cost.
You can see the problem in a shadow software factory, where each stage has its own tool, with no shared policy or context between them. Agents spend tokens searching for context across systems that were never built to integrate. A governed software factory gives agents shared context, shared policy, and one place to measure what they cost. When every AI credit counts, you can:
Spend fewer credits for the same work: Match models to the task at hand with open weight models, and ground agents in lifecycle context with GitLab Orbit so they retry less.
Trace spend to its source: See credits by user, team, task, and model in Duo Agent Platform Impact Analytics and the GitLab Credits dashboard so you can point to what drove the growth.
Set a ceiling before the next spike: Cap usage per user with credit caps, with overrides where needed, so agent loops stay inside the limits you set.
Match the model to the task
Agent cost starts with the price of each model call. GitLab Duo Agent Platform lets you pin a default per feature and control what your teams can use: your own model, self-hosted or from your provider, GitLab-managed frontier models, or GitLab-managed open-weight models, new in GitLab 19.4.
Three open-weight models are now hosted in GitLab Duo Agent Platform: GLM 5.3, Kimi K3, and MiniMax M3. In GitLab's internal benchmark with Duo agents, GLM 5.3 matched Claude Opus 5's task completion at approximately one-fifth the cost, Kimi K3 matched Claude Fable 5.1 at one-third the cost, and MiniMax M3 matched GPT-5.5 at one-eighth the cost. Model choice is an admin setting. In your top-level group's GitLab Duo settings, open Model Selection, select Manage Models, and pin a model to each feature. For example, pin Kimi K3 to Agentic Chat and MiniMax M3 to agents and flows, so routine work does not default to a frontier model. Group Owners also choose which models their teams can use.
Cut retries with the right context
Model choice sets the price of each call, and retries multiply the number of calls. Gartner found that a team of 40 developers spent 23 times more on AI because agents kept retrying without the right rules and context. Agents retry when they lack context. Across a monorepo or several repositories, an agent spends its first stretch crawling files to work out what connects to what, and every retry resends the full context window.
GitLab Orbit maps your code, work items, merge requests, pipelines, deployments, and security findings into a single graph that stays current as your teams ship. Agents in GitLab Duo Agent Platform query it natively. External agents like Claude Code and Codex connect through Model Context Protocol (MCP), and custom tooling connects through the REST API. Every agent sees exactly what its user can see in GitLab.
With that context, an agent asks the graph and gets a precise answer faster and more efficiently. In GitLab testing, the same task in Claude Code needed up to 45x fewer retries with Orbit and, separately, used 4.5x fewer tokens to get the right context the first time. Orbit is in free beta on GitLab.com today and reaches general availability next month across all deployment options.
Manage what you can measure
Models and context lower the cost of each task. Impact data shows whether it worked. Duo Agent Platform Impact Analytics answers the two questions platform leaders ask: how much are we spending, and where is that spend concentrated? It breaks spend down by team, task, and model for the teams and date range you choose, and it shows how an open weight model stacks up against your current model mix, so you compare with data instead of guessing. The GitLab Credits dashboard adds detail below the team level, tracing usage to the user and session behind it.
Visibility pairs with limits. Without a limit, the only lever left is asking everyone to use less. Credit and usage controls set caps at the subscription, group, or user level, with alerts at 50%, 80%, and 100% of spend and an automatic pause once usage reaches the cap. A flat per-user cap, such as 1,000 credits per user/per month, applies to every team member, and an individual override, such as 500 additional credits, gives an engineer who is experimenting room to work without asking for more. Per-user caps apply once a user has consumed their included credits, and only the capped user loses access to credit-consuming features, until the next billing period or until an administrator raises the cap.
Duo Agent Platform Impact Analytics shows credit consumption alongside the work that reaches production, so you can see whether usage is turning into delivered work.
See Duo Agent Platform Impact Analytics in action in this demo from our Transcend global event:
Put agent cost control to work
Start your agent cost control journey with the model. GitLab-managed open-weight models are generally available. In Model Selection pin GLM 5.3, Kimi K3, or MiniMax M3 to a routine workload, such as a flow that runs on every merge request.
Next, cut retries. GitLab Orbit is in free beta on GitLab.com today. Turn it on for the group that runs the most agent sessions. Orbit reaches general availability next month across all deployment options.
Then, measure and set limits. Duo Agent Platform Impact Analytics is in early access, so you can see spend by team, task, and model. Credit and usage controls are generally available. Set a per-user cap for GitLab Credits with overrides for your heavy users. After a few weeks, compare usage by team and model against the previous period.
Design the foundation for your software factory with GitLab Duo Agent Platform. If you're new to GitLab, start a free trial of GitLab Duo Agent Platform. If you're on Premium or Ultimate, turn on GitLab Duo Agent Platform and use the GitLab Credits included with your subscription.
Oct 13 · 00:00·GitLab
AI · SaaS
AI agents are fundamentally changing the economics of software development. As the cost of producing code falls, the volume of changes moving through the software factory is accelerating beyond what infrastructure designed for human developers was built to handle.
GitLab anticipated this shift and began developing next-generation source code management (SCM) to support thousands of concurrent agents working against large repositories. In June, we announced next-gen SCM at GitLab Transcend, joined on stage by Nick G. from Anthropic's source control team. We've continued collaborating on the technical challenges of agent-scale development, and in August we shared more of our architecture and early performance results.
This week, Anthropic provided a compelling demonstration of why this work is so important. As daily merges to its monorepo grew from approximately 2,500 in May to more than 30,000 in September, its engineers built Scion, a specialized source control system designed for their environment.
Anthropic's experience reinforces the architectural direction we've been pursuing. Scion is purpose-built for enormous monorepos where nearly all clients are agents that are always online. Anthropic acknowledges that its approach isn't a general-purpose replacement for Git and requires its own code review, search, and CI infrastructure.
GitLab is building for the broader enterprise market: delivering agent-scale performance while preserving Git compatibility, infrastructure choice, and integration with the software development lifecycle.
"Scaling our monorepo for agentic development has changed how we think about Git server architecture. We see room to expand what Git servers can do for these workloads without departing from the overall Git model."
— Nick G., Source Control, Anthropic
Built on Git. Designed for agents.
Traditional Git workflows require clients to download substantial repository data, even when an agent needs only a small portion. A single agent invocation can involve 5–10 GB of data transfer and more than 30 seconds of setup, just to answer one question. Multiply that overhead across thousands of agents and source control becomes a bottleneck.
Most enterprises can't move source control onto new infrastructure just to scale it. Next-gen SCM preserves the Git protocol while modernizing the architecture underneath. Stateless compute scales horizontally against shared object storage, while agent-native interfaces let agents request the files and history they need without repeatedly cloning entire repositories.
In early internal tests, workloads completed up to 50x faster, used up to 2x fewer tokens, and generated up to 1,000x less network traffic compared with conventional workflows. These are peak improvements under specific test conditions, and we're continuing to validate performance under sustained, concurrent load.
The same architecture is designed to run on GitLab.com and on S3-compatible storage that customers control, including in air-gapped data centers. Repository data stays within the customer's infrastructure, preserving data sovereignty and existing investments while delivering the scalability agentic development demands.
For a closer look at the architecture and what we're building, watch Mark and Kranthi's latest next-gen SCM update:
Source control is only the first bottleneck
Speeding up source control alone moves the bottleneck downstream. An agent that writes code in seconds creates little value if the resulting changes spend hours waiting for validation, security review, or deployment.
Traditional merge queues and CI workflows face growing pressure when agents generate changes faster than validation can complete. We're seeing this pressure across GitLab.com. Over the past year, customers created 40% more CI/CD pipelines, while code pushes increased 50%. Agent-scale CI will need to validate more changes with less compute per change while preserving evidence of what was tested and verified.
This is why next-gen SCM is part of GitLab's broader vision for a governed software factory. As agents accelerate software production, source control, context, orchestration, CI, security, and governance must evolve together.
We're designing next-gen SCM to let thousands of agents work in parallel in isolated sessions, where unsuccessful experiments can be discarded without disrupting shared code. Successful changes can flow into GitLab's existing merge requests, pipelines, security checks, and governance controls on their path to production.
GitLab Orbit is being developed to provide a context graph of the entire software lifecycle, while GitLab Duo Agent Platform orchestrates agent work across stages. We're also advancing capabilities to make agents accountable for their work, including attribution and authorization of agent actions, preserving the instructions and decisions behind code changes, and giving reviewers the context needed to focus human judgment where it matters.
The goal is to reduce the time and total cost of moving software from intent to production, even as the number of agents and changes grows dramatically.
We believe a governed software factory offers the best economics for agentic development: more software delivered at lower cost per change, with the security and control enterprises require.
Advancing Git through open collaboration
Git's open ecosystem has been foundational to modern software development, and we believe it will remain essential in the agentic era. GitLab has long been an active contributor to Git, and we'll continue working with the community to advance the standard that millions of developers and tools depend on.
Our next-gen SCM architecture builds on that foundation, combining Git compatibility with a redesigned backend and agent-native interfaces designed to deliver the performance and scalability these new workloads demand.
This approach preserves the investments customers have made in Git while allowing GitLab to innovate rapidly across the broader software lifecycle. Our differentiation is the governed software factory: bringing source control, context, orchestration, security, and governance together on one platform.
What's next
Next-gen SCM is in private beta, and we're working with customers to validate performance against their own repositories and workloads.
As agents dramatically increase software production, the entire software factory must operate at machine scale. GitLab is building that foundation.
Request early access to next-gen SCM and evaluate it against your own repositories.
This blog post contains "forward-looking statements" within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934. Although we believe that the expectations reflected in these statements are reasonable, they are subject to known and unknown risks, uncertainties, assumptions and other factors that may cause actual results or outcomes to differ materially. Further information on these risks and other factors is included under the caption "Risk Factors" in our filings with the SEC. We do not undertake any obligation to update or revise these statements after the date of this blog post, except as required by law.
Oct 13 · 00:00·GitLab
AI · SaaS
Fredericksburg, TX Monday Oct 12, 2026 @ 5:35 PM Central —
Episode 1911 of No Agenda, titled 'Surpriselets,' hosted by Adam Curry and John C. Dvorak, published Sunday, October 11, 2026, brings listeners another sweeping Sunday media deconstruction from the heart of the Texas Hill Country. With Dvorak sidelined, Curry is joined by returning guest co-host Sir Rob Dew of Infowars for a nearly three-hour tour through what the hosts dub the Trump administration's cascade of pre-midterm 'surpriselets,' little October surprises stacked like stocking stuffers heading into the 2026 vote.
The episode zeroes in on the news cycle's most provocative threads. Among the topics unpacked:
Defense Secretary Pete Hegseth's announcement that convicted Fort Hood shooter Nidal Hasan will face a public, potentially live-streamed firing squad on December 3rd
The bizarre FlyDubai axe attack by pilot Hammam al-Hammami and ex-CIA analyst Larry Johnson's theory tying it to Benjamin Netanyahu
Trump's surprise diesel deal with Vladimir Putin and the 2026 Nobel Peace Prize going to Navi Pillay
A suspected plague death at Russia's Anti-Plague Research Institute in Irkutsk
Walmart's electronic shelf labels and dynamic pricing
Curry and Dew press hard on the Hegseth announcement, noting Jake Tapper's point that the United States would join Afghanistan, Iran, North Korea, Somalia, and Yemen as the only nations conducting public executions. Dew offers a blunt reaction: 'I'm all for the execution. I don't know, the public execution, streaming it, you know, I'm kind of waffling. I definitely don't mind. I think it should have been done a long time ago.' They also replay Vice President JD Vance dodging a question about how the Pentagon plan squares with his Christian faith, and revisit the 1936 Owensboro, Kentucky hanging, America's last fully public execution.
The deconstruction then widens to Marco Rubio's sweeping speech in Athens defending Western civilization and sanctioning the International Criminal Court, Barack Obama's new midterm hope-and-change pitch for Democrats, and Mike Tyson's surprise endorsement of Trump at a Syracuse rally. Curry flags Bret Weinstein's warning on DarkHorse that any plague outbreak traceable to the Irkutsk facility could involve gain-of-function engineering or a psyop exploiting IgG4 immune suppression in mRNA-vaccinated populations. The pair also eulogize Chicago Bears legend Mike Ditka, dissect Kimberly Guilfoyle's ethics troubles as ambassador to Greece, and salute Slovak MEP Milan Mazurek's fiery European Parliament speech on mass deportations.
About No Agenda Show
No Agenda is a long-running, listener-supported podcast hosted by Adam Curry and John C. Dvorak that takes a skeptical, independent look at mainstream media, politics, culture, and the forces shaping the daily news cycle. Known for its sharp commentary, humor, and 'media deconstruction' approach, the show examines how stories are framed, repeated, amplified, or ignored. Episode 1911 'Surpriselets' is available now wherever podcasts are heard.
Oct 12 · 22:35·Newsworthy.ai
SaaS
CHICAGO, Oct. 12, 2026 -- CME Group, the world's leading derivatives marketplace, today announced it has completed initial performance testing for CME Globex in the cloud, reaching new milestones in the ongoing migration of its markets, including: Faster round-trip times for...
Oct 12 · 21:02·PR Newswire
Finance · SaaS
Shares of the combined company are expected to begin trading on Nasdaq under the ticker symbol “RNTZ” on Tuesday, October 13, 2026
Oct 12 · 20:45·GlobeNewswire
AI · SaaS
New 8-year data center services agreement for the A103 facility at the Cyberjaya campus brings secured AI Cloud data center facility to approximately 333.5MW, designed for next-generation NVIDIA Vera Rubin deployment
New 8-year data center services agreement for the A103 facility at the Cyberjaya campus brings secured AI Cloud data center facility to approximately 333.5MW, designed for next-generation NVIDIA Vera Rubin deployment
Oct 12 · 20:30·GlobeNewswire
Finance · SaaS
Combines real-time inventory tracking with PointClickCare® integration to reduce manual work and improve supply visibility NORTHFIELD, Ill., Oct. 12, 2026 -- Medline (Nasdaq: MDLN) today announced the launch of an enhanced version of TrackPoint™, its online inventory...
Oct 12 · 20:25·PR Newswire
SaaS · Healthcare
Mental Health Screening Month: How Tech Is Fixing Therapy’s First-Month Dropout Problem
Mon, 10/12/2026 - 15:35
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Every week, millions of people sit down with their mental health therapy provider, take a deep breath, and try to answer a deceptively simple question: “So, how was your week?”
For many, answering that question feels like trying to reconstruct a faded memory. The intense anxiety felt while sitting in traffic on Tuesday or the sudden wave of overwhelm on Thursday evening has lost its sharp edges by Monday morning. Clients often spend the first 15 minutes of a 50-minute appointment trying to recap past events rather than diving into meaningful healing work.
This disconnect between weekly visits is a major contributor to a quiet crisis in mental healthcare: one in four clients discontinue psychotherapy, most after a single session.
At the same time, the market has been flooded with thousands of standalone consumer wellness apps, with research showing that roughly 89% - 92% of mental health applications are abandoned by clients. The reason is simple: self-guided algorithms can’t replace the trust, empathy and safety of a human therapist.
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Why Do Standalone Apps Fail Where Clinician-Guided Tools Succeed?
Unlike standalone apps that ask users to navigate their mental health journeys alone, next-generation platforms are designed to operate directly within existing clinical practices. By keeping the mental healthcare provider at the center of the care journey, technology transforms from a passive self-help tool into an active bridge between sessions.
Exemplifying this shift is ReliefAI Health, a digital health venture created through LG NOVA (the North American Innovation Center for LG Electronics), exemplifies this shift. ReliefAI is built as a digital cognitive behavioral therapy (CBT) platform, for providers and clients.
The mobile app enables clients to engage with their care plan daily and capture their mental state throughout their week. This gives providers the ability to understand what is going on with their clients at those crucial times to be better able to assess and support their clients through the therapy process.
Rather than relying on paper notebooks or delayed memory, clients can use voice-journaling features while emotional experiences are still raw, work with interactive CBT exercises, complete clinical assessments and capture real-time mood patterns on their own schedule.
How Does Between-Session Tracking Turn Passive Time Into Active Care?
When technology integrates seamlessly into a clinic’s electronic health records system, the impact on care continuity is profound.
"We see a crucial opportunity for technology to transform mental healthcare by strengthening the vital relationship between therapists and their clients," says the head of LG NOVA Sokwoo Rhee, Executive Vice President of Innovation at LG Electronics. "ReliefAI answers that need."
Instead of starting an appointment with a blank slate, clinicians can review synthesized, objective data beforehand—observing mood trends, completed assessments and key themes from voice entries. Mental health providers begin the session already informed, bypassing routine recaps to focus immediately on deeper therapeutic progress.
Rhee says this continuous model provides several distinct advantages for both clients and providers:
Keeping clients connected to their therapeutic goals throughout the week can significantly reduce early dropout rates and help individuals stay committed through the critical first month.
Tracking real-time insights that allow providers to tailor treatment plans dynamically and show clients tangible evidence of their growth over time.
Remote therapeutic monitoring frameworks give practices structured clinical documentation and continuous engagement capabilities.
What Does a Human-Centered Future for Mental Healthcare Look Like?
As National Depression & Mental Health Screening Month highlights the growing demand for accessible, effective care, the narrative around digital health is evolving. The future of mental healthcare does not belong to standalone algorithms or automated substitutes for human connection, according to Rhee.
Instead, the true breakthrough lies in next-generation platforms giving practitioners the data and visibility they need to do their best work, he says, adding: “By turning the days between sessions into an active, supported part of the healing process, clinician-guided technology can help assure that more people stay in care, build resilience and experience lasting well-being.”
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Oct 12 · 18:03·3BL Media
SaaS
WASHINGTON, D.C, Oct. 12, 2026 -- The Crowdfunding Professional Association (CfPA) today released the CfPA Private Market Reporting Standard (PMRS) v1.0, a voluntary framework that defines how private market platform's structure and export investment, payment and performance data in a consistent, spreadsheet-readable format.
The effort was led by Paul Lovejoy, CfPA’s Investor Committee Chair, with contributions from the Investor and Intermediary committees. They built the standard to address a practical challenge: investors holding investments across multiple platforms must often reconcile different data formats to track their portfolios. That fragmentation limits what investors, advisers, custodians and the industry as a whole can see. The standard gives platforms a common reporting layer: shared field definitions, consistent formatting and explicit treatment of missing or inapplicable data.
Oct 12 · 18:02·GlobeNewswire