CommPulse

I'm an indie dev and honestly exhausted. I've been stuck in a support loop with Google Cloud for nearly a full year over an acknowledged platform-side bug. Here is the short version of what's happening: The Bug: My GCP project was hit with an unexpected $8,367.11 charge surge on the Gemini 2.5 Flash image generation SKU. Product Lead Logan Kilpatrick publicly acknowledged this bug on the official forums and promised affected devs would be taken care of. The "90% Cap": Google Billing gave a 90% adjustment and declared that 90% is the "maximum allowable limit" for this incident—leaving $838.26 on my account for a bug on Google's infrastructure. The "No Logs" Excuse: When I demanded backend logs or proof showing why I owe this remaining balance, support literally replied: "they do not share external-facing documentation, internal case notes, or further detailed explanations regarding their final review process." 1 Year of Support Ping-Pong: Opened a billing ticket $\rightarrow$ immediately closed and bounced back. Tier 1 agents just rotate shifts, paste identical templates, and close tickets without consent. My account currently sits with a red banner warning and an active $838.26 balance for services I never consumed. Screenshots attached: 1.Billing Support confirming the charge was caused by the Gemini 2.5 Flash bug. https://preview.redd.it/x083tf255olh1.png?width=438&format=png&auto=webp&s=f57d82c545e94f41d28eee18802a0243731f2366 Support stating the 90% adjustment is final. https://preview.redd.it/eaq8leu55olh1.png?width=690&format=png&auto=webp&s=36fb40b01c9f27c07cbd0f1d920eacd011b0575d My billing console showing the pending $838.26 balance and account warning. https://preview.redd.it/ahyl62n65olh1.png?width=690&format=png&auto=webp&s=e9403517ca4b0c93b3fbcc102633f78f35cf7f4b Support's latest response admitting they cannot provide any logs or explanation. https://preview.redd.it/ec9uxzv95olh1.png?width=1481&format=png&auto=webp&s=a59b05554a4fd661828dc2d83d32d9a7178ad05a I've documented this on the Google AI Forum and am preparing consumer complaints in Japan (消費者庁 / 消費生活センター). Has anyone ever broken through this Tier 1 "final decision" wall without an Enterprise support plan? Any advice or visibility would be really appreciated. submitted by /u/Character-Candy1120 to r/googlecloud [link] [comments]

This is likely a bit of a niche question, but I am going a tad insane trying to figure out why my old script for purging emails via a compliance search is failing. Previously (around a year ago), this script would create a new compliance search in EXO, start it, poll for progress, and then export the results and ask if you wanted to purge the emails from the relevant mailboxes. Had something come up yesterday while I was unavailable, and my old script didn't work. Messing around with it this morning, I found that while I can create a new compliance search, I cannot preview the results OR export the results. It does however, seem like I can still purge emails. Only you know, I cannot preview the emails, or verify it actually found the right emails beyond hoping that the item count from the search makes sense. Not so cool. From what I found on Microsoft's own documentation pages, it looks like the preview, and export functions will only work for on-prem exchange now, and I really do not see any alternatives. All that being said, is this Microslop making things difficult for the sake of adding some new subscription in the coming months, or is there actually a way to do this and their documentation is woefully out of date? I have a feeling the answer is going to be a resounding "no" based on an hour or so of searching, but you never know 😉 submitted by /u/TheOnlyKirb to r/sysadmin [link] [comments]

Hi guys, I'm needing your guidance here. I work in an automation-focused role that gradually became much more technical than operational. A lot of my job is taking broken/manual processes, improving them, and building automations around them. In practice, that means dealing with business rules, integrations, permissions, data sources, testing, edge cases, etc. However, I still consider myself junior technically. My current challenge is that the company is becoming more restrictive about external/unapproved automation tools, so I’m trying to move everything into approved internal tools and infrastructure. The problem is that access is granted gradually, permissions change, and some features depend on other teams. Stakeholders often just see that “the automation isn’t ready,” while a lot of the delay is actually caused by access, security, infrastructure, or dependencies. I’ve started communicating blockers and development stages more clearly, but I’m curious about how this is normally handled in US/European companies. How much responsibility is usually placed on the developer when delivery is blocked by access or another team? And how do you manage expectations when something sounds simple from the business side but isn’t simple technically? submitted by /u/Kooky-Internet8806 to r/devops [link] [comments]

https://preview.redd.it/tj068f1g1cjh1.png?width=2107&format=png&auto=webp&s=35fb64e37f0fe01bc985df9e0407adee6f055cee https://preview.redd.it/7a3lvq2k1cjh1.png?width=1695&format=png&auto=webp&s=da48778e9b362485b942eb4f05a59f1b7256cb40 My usage this month was around IDR 170.000 but I was charged IDR 3 million. Why is that? The support bot said: Since your July usage ( Rp 863,006 ) was already paid and your August usage is only ( Rp 168,763 ), these charges of IDR 1,000,000 and IDR 2,000,000 processed today are highly likely due to one of the following reasons: Temporary Authorization Holds: When you update payment details, add a new card, or reach certain verification checkpoints, Google or your bank may place temporary authorization holds (often in round numbers like IDR 1,000,000 or 2,000,000) to verify your card's validity. These are not actual charges and will be automatically released back to your account by your bank within a few business days. Multiple Google Billing Accounts: If you use the same credit card for other Google services (such as Google Ads, Google Workspace, or a separate Google Cloud Billing account), these charges may have originated from those services. submitted by /u/rfajr to r/googlecloud [link] [comments]

We got the Azure notice about upgrading our Storage and Blob accounts from GPv1 to GPv2 ahead of the October 13th automatic migration deadline. The Azure portal makes it look incredibly easy with a simple "one-click" in-place upgrade button for the Storage Accounts which have been identified as GPv1 and Blob. Documentation says zero downtime and zero data loss. However, I know GPv2 flips the billing model (cheaper storage, much higher transaction costs). For anyone who has already gone through this migration: Did you just click the upgrade button and wing it? If so, did your bill spike unpredictably? Did you actually pull Azure Monitor metrics first? If you audited transaction volumes, what thresholds made you hesitate or re-architect a workload? Any hidden gotchas? Did you run into issues with default access tiers (Hot vs. Cool) or legacy ZRS replication during the flip? Since Microsoft is going to auto-upgrade us anyway in October, I want to know if it is worth digging into the transaction logs manually or if I am overthinking a routine upgrade. Appreciate any real-world horror stories or "it went fine" reassurances! submitted by /u/nomadicviking024 to r/AZURE [link] [comments]

submitted by /u/codingdecently to r/FinOps [link] [comments]

After working with Kubernetes in production, I've noticed that some of the most annoying incidents aren't caused by obvious failures. They're often caused by small configuration decisions that look perfectly reasonable during review. Things like: missing resource requests/limits incorrect probes overly permissive RBAC missing PodDisruptionBudgets unsafe container configuration incorrect readiness behaviour services without appropriate timeouts configuration drift between environments I'm curious what the DevOps community has actually encountered in production. What's one Kubernetes configuration mistake that caused you a real incident? I'd especially like to hear about the less obvious ones that aren't caught by the usual linters. submitted by /u/nerd3n to r/devops [link] [comments]

What happened On Aug 1, an IAM access key belonging to one of our accounts was compromised through a supply chain compromise — the key was exposed via a third party in our toolchain, not by us publishing it. This was a programmatic access key, so MFA (enforced on all human identities from day one) was never in the path. We caught it and reported it to AWS Support within 4 hours , well inside the 48-hour public-contract cancellation window. The case then sat unassigned for 14 days. As of today that's 20 days, still open, still no resolution. Where the bill is now Total USD 477,505.20 Contract creation (0 months) USD 4,800.00 Usage Fee | us-east-1 USD 472,705.20 47,270,520 Units @ USD 0.01 The part I don't understand We never activated the product. Specifically: - The Marketplace console still shows "Set up product" for this agreement - No License ID was ever issued - CloudTrail shows zero Marketplace events in our account after the initial compromise window on Aug 1 - We have never had credentials for, or logged into, anything on the seller's side Yet 47.2 million billable units were metered against us in us-east-1. As I understand the SaaS flow, usage on this kind of product is submitted by t he seller calling `BatchMeterUsage` against the entitlement's `CustomerIdentifier` — from the seller's own infrastructure, not from our account. If that's right, then nothing in our account was ever in the path, and no control we have could have stopped it. Revoking the key, deleting the attacker's IAM user, applying SCPs — none of it touches seller-side metering. The unit count has been static for several days now, so metering appears to have stopped, but the agreement status is the thing I can't get a straight answer on. Support so far Seller support (automated) told us Marketplace transactions are not their department and to contact AWS. AWS support has pointed at the seller for anything usage-related. The payer account has now escalated and asked AWS to investigate the agreement and stop metering. Still waiting. There is no phone line and no chat. The only channel is tickets, and they've been sitting for 20 days. Questions for people who actually know the internals For a SaaS contract-with-consumption product, is metering purely seller-side ? Is there any circumstance where usage gets attributed to a buyer account without the buyer ever completing registration? Does "Set up product" persisting in the console reliably mean the fulfillment/`ResolveCustomer` handshake never happened — or is that just stale console state that doesn't update? Is there any way for a buyer to see the registration record for their own entitlement? Anything in the Agreement APIs, CUR, or elsewhere that shows when/whether `ResolveCustomer` was called? The 48-hour window: AWS Customer Service can process a full refund on a public contract without seller involvement. Does that path survive a case sitting unassigned for 14 days, or is the window enforced strictly on wall-clock time regardless of AWS-side latency? Has anyone here had Marketplace usage fees (not just the contract fee) reversed after a credential compromise? Contract fee reversals I've seen written up. Usage fees at this scale, never. To be blunt about the stakes: we're a small company. We cannot pay $472k for consumption we did not generate, on a product we never activated, after cutting off access in under 4 hours. I'm trying to understand the metering mechanics, and whether there's a path here other than waiting on a ticket queue. submitted by /u/Round_Vegetable3764 to r/aws [link] [comments]

Azure weekly update #33 is here. This week Azure rolled out key networking and SQL platform improvements, new Copilot and healthcare agent offerings, and important service lifecycle updates including Azure VMware Solution retirement planning. Detailed list: * Launched | Generally Available: Summarized advertised gateway prefixes for route advertisement . This feature allows for advertising aggregated prefixes instead of every individual virtual network address space, which is great for large hub-and-spoke deployments. * Announcing: Azure Copilot introduces direct access to agents . Users can now select specific Azure Copilot agents like Troubleshooting or Deployment to move more quickly from questions to action. * Launched | Generally Available: Azure Databricks Lakebase in four additional regions . Lakebase is now available in North Central US, France Central, Germany West Central, and East Asia, expanding regional options for workloads. * Launched | Generally Available: Azure SQL updates for mid-August 2026 . This update brings enhancements to Azure SQL, including customizable keyboard shortcuts within Visual Studio Code. * In preview | Public Preview: SQL Formatter in MSSQL extension . The SQL Formatter is now in public preview, offering more customizable formatting options to help streamline development. * Launched | Generally Available: Azure SQL Database provisioning in MSSQL extension . You can now create and connect to a fully managed cloud database directly from your editor at no cost. * Launched | Generally Available: vCore Customization: Disable Multithreading and Configurable Constrained Cores . This new capability gives users greater control over virtual CPU configurations to optimize performance and reduce licensing costs. * Launched | Generally Available: BYON (Bring Your Own NIC) in Azure Site Recovery . Azure Site Recovery now supports attaching existing, pre-provisioned NICs in the target region for failover scenarios. * Retirement: Azure VMware Solution License-included service will be retired August 30, 2027 . Customers should be aware that the AVS license-included service will be retired on August 30, 2027, requiring transition planning. * Launched | Generally Available: Managed Instance on Azure App Service . Managed Instance is now available, allowing migration of web applications to Azure App Service with minimal configuration. * In preview | Public Preview: Ipv6 support in Azure Firewall . Azure Firewall now supports IPv6 in public preview, enabling dual-stack mode for both IPv4 and IPv6 traffic. * In preview | Public Preview: Zone redundancy for Azure SQL Managed Instance Next-gen General Purpose . Enhanced resilience is available via public preview zone redundancy for Azure SQL Managed Instance Next-gen General Purpose. * Launched | Generally Available: Dragon Copilot Physician Apps and Agents on Microsoft Marketplace . Dragon Copilot Physician Apps and Agents are now available for discovery and purchase through Microsoft Marketplace. * In preview | Public Preview: Azure Linux on WSL . Azure Linux on WSL is now available in Public Preview, allowing teams to use a consistent Linux foundation across development, testing, and production. submitted by /u/groovy-sky to r/AZURE [link] [comments]

Had one of those fun discoveries recently: a nightly backup cron had been failing (or not running) for a stretch, and nobody knew. Host was fine, uptime checks were green, no ticket, nothing. Found it only when we actually needed a restore. Curious how other people handle the "job went quiet" case — not "the server is down", but "the scheduled thing didn't check in". What are you using in practice? - Healthchecks / Cronitor / Dead Man's Snitch / something else SaaS? - Self-hosted (Uptime Kuma push monitors, Prometheus + blackbox/heartbeat, custom scripts)? - Just mail on failure from the job itself (`MAILTO`, wrapper scripts, etc.)? - Or do you mostly not bother unless it's a critical path? Also interested in what actually matters day to day: - Grace periods vs exact schedules - Success-only heartbeat vs explicit fail signal - Email only vs Slack/Teams/PagerDuty - How many jobs you bother monitoring vs "we'll notice eventually" Not looking for a product pitch — just war stories and what you'd recommend to a small team that doesn't want another heavy observability stack for a handful of crons. submitted by /u/georgi_tsenov to r/sysadmin [link] [comments]

[ Removed by Reddit on account of violating the content policy . ] submitted by /u/astrodevops to r/FinOps [link] [comments]

I was wondering how many of you are still using legacy client/server apps, and come across challenges when it comes to remote/hybrid workers. We use an application that is rather old school. No web based access, full desktop client and server. It does not work well under any latency, so VPN's and anything similar are unworkable. The other challenge is that the application is heavily tied into MS Word, Outlook, etc. The application uses COM addins for Outlook to generate and send emails and allow interaction with the product. Similar happens with MS word. We've tried publishing the application as a "remote app" on platforms like Citrix, but the issue again is the required integrations with Outlook, Word, downloading of files like PDF's and other documents that are stored within the app. It essentially integrates heavily with other apps. So at the moment, we're using a full desktop experience for users over Citrix. I find this to be rather frustrating. Providing end users with new laptops with reasonable spec, for them to work remotely and use it like a thin client so they can connect to a Citrix desktop. It confuses users, it isn't particularly user friendly, and it just feels like a waste of local resources. Does anyone work in similar situations? Are there any solutions that could help with this? Or are we destined to forever use a full remote desktop solution to support our core, oldschool, clunky-ass product? submitted by /u/Thick-Incident-4178 to r/sysadmin [link] [comments]

So got an odd pickle I'm trying to solve. We have 5 Samsung A11+ devices. These devices need to allow any user to be able to use essentially the following Web browser, camera and probably the ability to browse photos when uploading them. Preferably that when the user closes the web browser it doesn't have any information like their password or session. Most of the apps they'd use would be through a portal where they'd log into for access. But I need to lock it down somehow, without using an MDM solution or spending a dime. So even using intune is out of the question. Really I just need to prevent users from using other apps or installing any apps. Preferably only changing to the android owner account when I need to make changes. I was going to set up a restricted user but for some reason following Samsung's instructions are either old or I guess unavailable in my region for some reason. Still slowly updating one of the devices to see if this actually changes. Guest mode seems useless since it's a fresh instance each time it's opened. EDIT: I might be able to use something that can link to a google account as well for this, but probably not able to link it to a business per-say submitted by /u/waddlesticks to r/sysadmin [link] [comments]

Terraform gives you two layers of abstraction. CDK gives you four. That difference decides how much your own module has to justify itself, and what a refactor costs you a year later. The problem Teams moving between the two tools compare the syntax. HCL against TypeScript, terraform plan against cdk diff , state files against CloudFormation. The syntax is the smallest difference of the three. The larger one is how many layers sit between a line of your code and a resource in the account, and where that resource's identity comes from. In Terraform the identity is something you write. In CDK it is something the layer structure computes for you. That sounds academic until a pull request that changes no behaviour deletes a bucket. Terraform has two layers. CDK has four. In Terraform there is the provider resource, and there is the module you write around it. That is the entire ladder. When a guide says "write a module", it means: wrap raw resources, add defaults, validate inputs, expose outputs. CDK adds two rungs before you write anything. Layer What it is L1 ( CfnBucket ) Auto-generated from the CloudFormation resource spec. One construct, one resource, no defaults, raw property shapes. L2 ( Bucket ) Hand-written by AWS. Opinionated defaults, typed enums, helper methods. Can emit several resources. L3 (patterns) Several L2s wired together for a use case. Your wrapper Whatever your organisation adds on top. The consequence is easy to miss: an L2 already is a module in the Terraform sense. Curated, opinionated, typed inputs, sensible defaults, maintained by someone else. When you write your own construct around s3.Bucket , you are writing a module around a module. The layer count is not just conceptual, it shows up in the template. A plain L1 CfnBucket synthesises to exactly one resource. The L2 Bucket with enforceSSL: true and autoDeleteObjects: true synthesises to five: the bucket, a bucket policy, a custom resource, an IAM role and a Lambda function. One line of props, four extra resources, one of which executes code in the account. Neither number is wrong. They are different amounts of decision made on your behalf. Identity is where the layers bite In Terraform, a resource's identity is the address you wrote: aws_s3_bucket.bucket . It is in the file. You can grep for it. In CDK, identity is the CloudFormation logical ID, and CDK computes it by hashing the construct's path through those layers. You never write it. It does not appear anywhere in your source code. Here is the same bucket, bucketName: "demo" throughout, synthesised on aws-cdk-lib 2.189.1: Change to the code Logical ID baseline: new s3.Bucket(this, "Bucket", …) Bucket83908E77 added versioned , a lifecycle rule, a new output Bucket83908E77 renamed the TypeScript class Bucket83908E77 renamed the construct id to "Storage" Storage07F31EBC extracted the bucket into a SecureBucket construct BucketD7FEB781 added a grouping parent construct StorageBucket5CB7C8EA Properties are free. Class names, file names and variable names are free. What is not free is the id strings on the path from the stack down to the resource, and how many levels sit between them. The middle three rows are the interesting ones. Adding versioning and a lifecycle rule changes real infrastructure behaviour and the identity holds. Extracting a construct changes no behaviour at all and the identity moves. What CloudFormation does with that It matches resources on the logical ID alone. Not on the bucket name, not on the properties. So the extraction reads as one resource removed and a different one added: [-] AWS::S3::Bucket Bucket Bucket83908E77 destroy [+] AWS::S3::Bucket Bucket/Bucket BucketD7FEB781 cdk diff reports it accurately. The word destroy is right there. The difficulty is upstream of the diff: the pull request that produced it contains no bucket name, no property change and no resource. It contains a class extraction and two changed lines, which is normally the safest kind of change a reviewer sees. And the two logical IDs both begin with Bucket , differing only in eight hex characters. With the default removal policy the same diff reads orphan instead of destroy , which leaves the old bucket behind in the account, unmanaged and still billing. Which of the two you get depends on a removal policy set somewhere else in the file. The same trap has a name in Terraform Terraform has this problem too. Rename a resource inside a module and consumers get a destroy and create. The difference is that Terraform ships a repair tool: moved { from = aws_s3_bucket . bucket to = module . secure . aws_s3_bucket . bucket } That block lives inside the module. It travels with the version bump. A consumer who upgrades runs plan and reads "has moved to", followed by no changes. Once every consumer is upgraded, the author deletes the block in a later major. CDK's equivalent arrived later and works differently. The cdk refactor command, in preview and gated behind --unstable=refactor , compares your code against the deployed state, detects constructs that have been renamed or moved, and uses CloudFormation's refactoring API to preserve the resources while their logical IDs change. AWS names this exact case in the command's documentation: "Reorganize your construct hierarchy (like grouping AWS resources under a new L3 construct) while preserving the underlying cloud resources." That closes the gap, but not in the same place moved closes it. Terraform's block is written by the module author and travels inside the module, so a consumer repairs the break by upgrading and reading plan . cdk refactor is run by whoever owns the deployment, against deployed state, after the change has landed. For a construct published to other teams, the author can cause the break and cannot ship the fix. It also refuses to run on a mixed change. The command verifies that the application contains exactly the same set of resources as the deployed state, differing only in their location in the construct tree, and rejects the operation if it detects any resource additions, deletions or modifications. A pull request that extracts a construct and adjusts a property in the same commit is not refactorable by it. The older manual route is still there: reaching through the L2 to the L1 underneath and pinning the old value by hand. const cfn = bucket . node . defaultChild as s3 . CfnBucket ; cfn . overrideLogicalId ( " Bucket83908E77 " ); Three things separate that from moved . You have to know the hash, which means synthesising the old version or reading the deployed template. It cannot be removed later, because removing it changes the identity again. And it documents nothing: the code says SecureBucket/Bucket while claiming an identity from a shape that has not existed since the previous release. Why this compounds with the layer count The two findings are the same finding. Logical IDs are derived from the path through the layers, so every layer you add or remove is an identity change. Terraform's flatter ladder means fewer opportunities to move something by accident, and its repair is declarative, stable, and shipped by the author to the consumer. CDK has more rungs to move between, and its repair is a preview command run by the operator after the fact. CDK's extra rungs buy real things: the L2s carry AWS's own defaults, and the assertion tests that check them run in milliseconds with no cloud credentials, which is a tier most Terraform teams never reach. The rungs are also the mechanism by which a tidy-up deletes a database. The options Keep the construct tree flat. Resources sit directly in the stack. Fewer levels means fewer identity changes available. Composition happens by writing a new stack rather than restructuring an existing one. Cheap while nothing is deployed, and the decision is effectively frozen at the first deploy. Pin logical IDs by hand. overrideLogicalId or stack.renameLogicalId . Lets you restructure freely at the cost of a permanent hardcoded hash for every resource you move. Diff the synthesised template in CI. Synthesise the previous release and the current commit, extract the logical IDs from both, fail the build when an existing one disappears. Catches the class of change rather than repairing it, and it makes the invisible part visible in the pull request where the decision is actually made. Retain on delete for stateful resources. RemovalPolicy.RETAIN turns a deletion into an orphan. The resource survives, unmanaged, and the deploy may still fail if the physical name is taken. cdk refactor . Preview, behind --unstable=refactor . Detects moved or renamed constructs and calls CloudFormation's refactoring API to keep the resources while their logical IDs change. --dry-run prints the mapping without applying it, and an override file resolves cases where more than one mapping is valid. Costs you a preview dependency, and it rejects any change that is not purely a relocation. Where each one fits A flat tree fits work where nothing is deployed yet and the abstraction is not yet earned. The cost is that a shared construct extracted later is a migration, not a refactor. Before the first deploy that decision is free, and it stops being free permanently on the day something exists in the account. Hand-pinned logical IDs fit a small number of deliberate moves in an application stack you own end to end. They stop fitting in a shared construct library: the hashes accumulate across releases, and after the third one the tree's real identity lives in a pile of hex strings rather than in its shape. Template diffing in CI fits anyone publishing constructs that other teams consume by version. There, a refactor by the author is a destroy in someone else's account, and the author is the only person positioned to catch it. It fits less well on a single application stack with one reviewer who already reads every diff. cdk refactor fits a team that owns both the code and the deployment, can accept a preview command in the path to production, and is willing to split a restructuring commit from a behaviour commit so the command will accept it. It fits worst in the case that motivated this comparison: a construct library whose consumers are other teams. There the break travels with the version bump and the repair does not, so every consumer runs it separately in their own account, or does not run it at all. Retain on delete fits databases, state buckets and anything holding data, in every setup. What it does not do is prevent the identity change, so it pairs with one of the options above rather than replacing them. The layer question sits underneath all of this. If your organisation's wrapper adds real policy, naming guarantees and validated inputs that an L2 cannot express, the extra rung is doing work. If it forwards properties to s3.Bucket with a longer name, it is a layer of identity risk that buys nothing, and Terraform's guidance applies unchanged: a module wrapping one resource with pass-through variables is not abstraction. Want us to look for issues like this in your account? We offer a free AWS audit: upstood.com

Introduction to the AI-Driven SaaS Revolution The world of Software as a Service (SaaS) has undergone a significant transformation in recent years, thanks to the integration of Artificial Intelligence (AI) . This revolutionary technology has disrupted the traditional SaaS model, enabling businesses to offer more personalized , efficient , and cost-effective solutions to their customers. In this article, we will explore the impact of AI on the SaaS industry, highlighting real-life examples of startups that have successfully leveraged AI to drive innovation and growth. The Rise of AI-Driven SaaS Startups The SaaS market has experienced rapid growth over the past decade, with the global market size projected to reach $436.9 billion by 2027. This growth has created a fertile ground for startups to innovate and disrupt traditional industries. AI-driven SaaS startups have been at the forefront of this revolution, using machine learning algorithms and natural language processing to develop intelligent software solutions . For instance, startups like Zendesk and Freshdesk have leveraged AI to offer automated customer support and personalized customer experiences . Key characteristics of AI-driven SaaS startups: Data-driven decision making : Using data analytics and machine learning to inform product development and business decisions. Automated workflows : Leveraging AI to automate repetitive tasks and streamline business processes. Personalized user experiences : Using AI to offer tailored solutions and recommendations to customers. Benefits of AI-driven SaaS startups: Increased efficiency : Automating tasks and streamlining processes to reduce costs and improve productivity. Enhanced customer experiences : Offering personalized solutions and support to improve customer satisfaction and retention. Competitive advantage : Leveraging AI to innovate and differentiate from traditional SaaS providers. Real-Life Examples of AI-Driven SaaS Startups Let's take a closer look at some successful AI-driven SaaS startups that have disrupted traditional industries. For example, HubSpot has leveraged AI to offer predictive lead scoring and personalized marketing automation . This has enabled businesses to optimize their marketing campaigns and improve conversion rates . Another example is Calendly , which has used AI to offer automated scheduling and meeting coordination . This has simplified the scheduling process and reduced no-show rates . Other notable examples : Slack : Using AI to offer automated chatbots and personalized communication experiences . Trello : Leveraging AI to offer predictive project management and automated task assignments . Hootsuite : Using AI to offer social media analytics and automated social media management . Common traits among these startups: Focus on customer experience : Using AI to offer personalized solutions and support. Emphasis on automation : Leveraging AI to automate repetitive tasks and streamline business processes. Data-driven decision making : Using data analytics and machine learning to inform product development and business decisions. The Role of AI in SaaS Customer Support AI has revolutionized the way SaaS companies approach customer support . Traditional customer support models often rely on human representatives to resolve customer issues. However, this approach can be time-consuming and costly . AI-driven SaaS startups have leveraged chatbots and virtual assistants to offer automated customer support . For instance, Freshdesk has used AI to offer predictive ticket routing and automated ticket resolution . This has reduced response times and improved customer satisfaction . Benefits of AI-driven customer support: Faster response times : Using AI to offer instant support and resolve issues quickly. Improved accuracy : Leveraging AI to provide accurate and personalized solutions. Cost savings : Reducing the need for human representatives and minimizing support costs. Best practices for implementing AI-driven customer support: Start with simple use cases : Begin with basic support queries and gradually move to more complex issues. Train your AI model : Use high-quality data to train your AI model and ensure accuracy. Monitor and evaluate : Continuously monitor and evaluate your AI-driven customer support to identify areas for improvement. The Future of AI-Driven SaaS As AI technology continues to evolve and improve , we can expect to see even more innovative applications in the SaaS industry. For instance, Harish APC ( https://www.harishapc.com ) is exploring the use of AI in cybersecurity and data analytics . This has the potential to revolutionize the way businesses approach data security and make data-driven decisions . Another area of focus is explainable AI , which aims to provide transparent and interpretable AI models . This will enable businesses to trust and understand AI-driven decisions, leading to widespread adoption . Key trends to watch in the future: Increased adoption of AI : More businesses will leverage AI to drive innovation and growth. Advances in machine learning : Improvements in machine learning algorithms will enable more accurate and efficient AI models. Growing importance of data quality : High-quality data will become increasingly important for training and evaluating AI models. Challenges and limitations : Data quality and availability : Access to high-quality data is essential for training and evaluating AI models. Explainability and transparency : Ensuring that AI models are transparent and interpretable is crucial for building trust. Regulatory compliance : Ensuring that AI-driven SaaS solutions comply with regulatory requirements is essential. Conclusion The integration of AI in SaaS has disrupted the traditional software industry , enabling businesses to offer more personalized , efficient , and cost-effective solutions. As we move forward, it's essential to stay up-to-date with the latest trends and developments in AI-driven SaaS. By visiting websites like https://www.harishapc.com , you can stay informed about the latest advancements in AI and learn from industry experts . Additionally, you can explore the resources available on https://www.harishapc.com to deepen your understanding of AI-driven SaaS and stay ahead of the curve . Final thoughts : Embracing AI-driven SaaS : Leveraging AI to drive innovation and growth is essential for businesses to stay competitive. Focusing on customer experience : Using AI to offer personalized solutions and support is crucial for improving customer satisfaction and retention. Staying informed and adapted : Continuously monitoring and evaluating the latest trends and developments in AI-driven SaaS is essential for long-term success. Connect https://www.harishapc.com https://www.harishapc.com/blog https://www.linkedin.com/in/harisha-p-c-207584b2/ https://github.com/reach-Harishapc

Our reserved instances are up for renewal in about 8 weeks and the easy path is to just renew the same mix we had last year. Before I push back I want to actually have data behind it. What should I be pulling before that conversation? Usage trends over the last 12 months obviously but what else tends to get missed when teams auto-renew RIs without reviewing them first? submitted by /u/isaywhatisee [link] [comments]

cPanel has patched a flaw that let an authenticated hosting customer execute SQL in the database's root context, crossing the privilege boundary between a cPanel account and the server's administrative database identity. It shipped in a targeted security release that closes two other routes past account boundaries. The database bug is tracked as CVE-2026-58048 (CVSS 4.0 score: 9.4) and affects all supported versions of cPanel & WHM, along with WP Squared. Reaching it requires a valid cPanel account and access to the MySQL/MariaDB feature. From there, the vendor says the account holder could execute arbitrary database commands with full administrative privileges. Depending on the operating system and database engine configuration, “this may extend to operating-system-level compromise.” cPanel patched CVE-2026-58048 in these builds: 11.110.0.137 11.118.0.71 11.126.0.78 11.134.0.48 11.136.0.32 138.1.6 for WP Squared Servers that cannot update immediately can ..... https://thehackernews.com/2026/08/new-cpanel-critical-flaw-could-let.html

I've been doing a lot of vibe coding with Claude Code and Codex, and one thing keeps happening I ask for one small change, then later realize Al changed my code in places I never expected. By the time I notice, I can't remember exactly what changed or when. Is anyone using something besides Git to track Al changes or keep an Al coding activity log, or is this just one of those vibe coding problems we all live with? submitted by /u/pacifio [link] [comments]

Trying to streamline Defender onboarding. About to enable this toggle in Intune. What's the real impact, risk, and any visible changes for end users after it applies? submitted by /u/aPieceOfMindShit [link] [comments]

Hi everyone, I'm a Network & Security Engineer with ~7 years of experience working with Fortinet (FortiGate, FortiAnalyzer, FortiAuthenticator), Cisco, VPNs, HA, Linux, VMware, Hyper-V, and enterprise infrastructure. I want to specialize in AI applied to Cybersecurity (SOC, network security, automation, LLMs, AI agents, etc.), not become a data scientist. If you were in my position today: What learning roadmap would you follow? Which platforms are actually worth paying for (Coursera, TryHackMe, HTB, SANS, Microsoft Learn, etc.)? What's a reasonable monthly learning budget? Which certifications provide the best ROI? What projects would make my resume stand out? I'd love to hear what worked for you and what you'd avoid. submitted by /u/CartoonistAdvanced52 [link] [comments]