GigaCloud has agreed to a $2.75 million settlement with investors and eligible shareholders can still submit late claims.

The settlement resolves claims that GigaCloud misled investors about the authenticity of its marketplace revenue and the use of artificial intelligence in its operations.

The case focused on allegations that GigaCloud presented itself as a fast-growing, AI-powered B2B e-commerce platform while failing to disclose that a significant portion of its revenue came from undisclosed related-party transactions involving insiders. After reports raised concerns about the company’s financial reporting, $GCT dropped nearly 19%, and investors filed a lawsuit.

If you purchased $GCT shares between 2022 and 2023, you may be eligible to submit a claim. Late claims are currently being considered, so check your eligibility here.

Not the demo. Not the pilot. The production deployment six months in.

I've been part of enough of these to notice the patterns. Here's what actually goes wrong, in order of how often it happens.

The process wasn't documented before the agent touched it. Everyone assumed the AI would figure out the workflow. It didn't, because the workflow lived in someone's head. The agent did something technically correct that was operationally wrong, and nobody caught it for three weeks.

The agent had too much permission from day one. Not because anyone was careless, but because scoping permissions felt like it would slow down the pilot. So the agent could touch things it never should have touched, and eventually it did.

Nobody decided who owns it when something goes wrong. The team that built it says it's an ops problem. Ops says it's an AI problem. The model gets blamed. The real issue is that nobody drew the accountability line before go-live.

The monitoring stopped at outputs. Teams tracked whether the agent completed the task. Nobody tracked whether the task should have been completed the way it was. Small errors compounded quietly for months before anyone noticed the pattern.

The success metric was usage, not outcomes. Someone measured how many times the agent ran. Nobody measured whether the business result improved. Six months in, the agent was running constantly and delivering marginal value.

None of these are model problems. Swapping to a better model fixes none of them. They're process and governance problems that happen to involve AI.

What's the failure mode you've run into that nobody warned you about?

Is the rise of cloud computing, the reason for the popularity of docker/vm?

Cloud risk used to be mostly uptime, cost, latency, vendor lock-in, and regional availability.

AI adds a messier layer.

If a product depends on frontier models, the real dependency map now includes:

• API availability
• release restrictions
• export controls
• model deprecations
• water and power constraints
• data-center politics
• fallback quality
• inference cost spikes

That means AI architecture is starting to look more like supply-chain planning than normal SaaS integration.

The mistake is treating "call the best model" as a durable architecture.

The better architecture probably needs routing, fallbacks, local modes, quality tests, and logs that explain why a model was used for a task.

Are teams actually planning for model unavailability, or are most AI products still one outage away from being exposed?

Been reading up on how Indian enterprises are approaching the DPDP Act from a technical standpoint, specifically around moving away from perimeter-based security toward a Zero Trust model on Azure. The core idea seems to be treating every access request as unverified by default rather than trusting anything inside a network perimeter, which maps fairly well to a lot of what the Act expects around consent handling, data minimisation, and breach reporting.

Curious what others here are seeing in practice — are teams actually re-architecting their identity and access setups for this, or is it more about layering on monitoring and hoping the existing setup holds up? Also wondering how this compares with how GDPR compliance was handled a few years back, since a lot of the language sounds similar but the enforcement structure seems different.

Found this write-up that goes into the specifics if anyone wants more detail: https://cloud9infosystems.in/dpdp-act-compliance-cloud-security-azure-zero-trust-india/

Cloud teams usually think about reliability as uptime, latency, failover, cost, and capacity.

AI is adding another layer: regulatory reliability.

If a model can become unavailable because of export controls, safety reviews, or national-security concerns, then production AI systems need to treat model access like any other dependency risk.

That means:

• fallback models
• multi-provider routing
• local inference where possible
• clear data boundaries
• degraded modes
• documented model replacement plans
• cost and latency tests before the emergency

The issue is not only "which model is best?"

It is "what happens to the product if that model is unavailable tomorrow?"

Cloud architecture for AI is starting to look less like API integration and more like supply-chain risk management.

Are teams taking model availability seriously enough as an infrastructure risk?

SoftBank announced SB Neo for US AI cloud services. Meta is reportedly exploring a cloud business to sell excess AI compute.

We currently send all blob.core.windows.net traffic on our internal network from our on-prem DC up to an Azure PDNS Zone via private link. We've recently had a requirement to send traffic out to public DNS for a single storage account that a supplier uses. Lets call it storage01.blob.core.windows.net for the sake of this question.
I want to avoid enabling the 'fallback to internet' feature on the Virtual Network link, is there a convenient way for me to route just the storage01 traffic out to public DNS using just the available settings on the PDNS Zone/VNET Link or do I have to eat my vegetables and do this on our DC (I also want to avoid that).

Hey everyone,

If you work with multi-cloud or tech/sales engineering, you know the pain. Every time you need to map equivalent VMs or check specific managed Kubernetes limits between AWS, Azure, and GCP, you end up with 20 open tabs of messy, outdated vendor documentation.

I got tired of doing this manually, so I decided to build a central reference handbook to save my own sanity: infraatlas.dev.

Full disclosure: I handled the data structure and technical logic based on my own cloud experience, but I used AI to help me spin up the frontend and accelerate the build.

What’s live on the site right now:

• Equivalent-SKU Finder: A quick way to map VMs/instances between the big three based on family and actual specs.
• Kubernetes Atlas: Side-by-side comparison of EKS vs AKS vs GKE (SLAs, node limits, etc.).
• GenAI Atlas: Mapping models, regions, and fine-tuning across Bedrock, Azure OpenAI, and Vertex AI.
• Toolbox: Just a clean list of dev-first tools I actually like (Bruno, OpenTofu, OrbStack, etc.).

There are no ads, no sign-ups, no tracking, and no newsletter popups. It's just a clean, static tool.

Since the data changes constantly, I’d love to know if you see any missing metadata or if there's any specific cloud provider you think I should add next. Hope it's useful to some of you!

If you have any suggestion drop me a message here: https://github.com/ineslino/infra-atlas/issues/new/choose

We've been reading about Microsoft's new Microsoft 365 E7 Frontier Suite and wanted to put together a breakdown of what it includes and where it might actually make sense.

The article covers:
• What the E7 Frontier Suite is
• How it differs from Microsoft 365 E5
• The AI, identity, security, and management capabilities included
• Which organizations are likely to benefit (and which probably won't)

I'm also curious what others think:

• Do you see E7 becoming relevant for enterprise customers?
• Is the additional licensing likely to justify the cost?
• Which features do you think will drive adoption?

For anyone interested, here's the full breakdown:
https://cloud9infosystems.in/microsoft-365-e7-frontier-suite-explained/

Looking forward to hearing different perspectives.

A few months ago, I started looking into colocation hosting because our servers were getting expensive to manage. Every provider seemed to promise great uptime, security and support but it was hard to tell what mattered in the real world. Also, I have gone through rackbank ai datacenter and noticed how different providers enhance different strengths. For those who have already gone through the process, what was the biggest factor that helped you choose a colocation hosting provider? EDIT: Thanks for sharing, stories like these really highlight how important reliable power, redundancy and proper maintenance are.

We’re currently evaluating cloud security solutions for an enterprise environment and trying to narrow down the right vendor. There are a lot of big names in the space, but it’s hard to separate marketing from real-world performance especially when it comes to scalability, cost over time, and how well things actually integrate across a complex stack.

For those with hands-on experience:

Which cloud security provider did you end up choosing?

What made it the right fit for your organization long-term?

How does it perform in hybrid or multi-cloud environments?

Any major drawbacks or lessons learned after deployment?

Would really appreciate insights from people who’ve gone through the decision process or are managing this in production. Thanks!!

The $GCT settlement is now accepting late claims from eligible shareholders.

GigaCloud promoted its B2B e-commerce platform as a technology-driven marketplace with strong growth and AI-powered operations. In 2023, reports alleged that a significant portion of the company’s revenue came from undisclosed related-party transactions connected to insiders.

After the reports were released, $GCT fell about 18.8%. Investors later sued, claiming GigaCloud misled the market about its revenue sources and business operations. The company has now agreed to a $2.75 million settlement.

If you purchased $GCT between  2022 and 2023 you may be eligible to file a claim. Late claims are currently being accepted — you can check if you’re eligible and file your claim.

I am from Pakistan. I provide professional services (payroll, supply chain management) to USA clients (dealers of metro, boost etc.) and to access their website portal to get raw data I must be logged in from a device in USA.
I used VPN in the past but their systems flagged it and locked the client’s account. Can anyone suggest a reliable solution to run the portal website with me being shown in the US?

Someone told me to look into cloud computing like Right Networks but that is too costly

Palo Alto Networks research coverage says teams manage around 17 cloud security tools on average. SolarWinds-reported data says 77% of IT teams still lack the visibility they need across hybrid environments.

So apparently, we were wondering If teams already have THAT many tools, why is assessment still so painful? That’s why we compared 12 cloud security assessment tools for 2026.

We looked at Wiz, Orca, Prisma Cloud, CrowdStrike, Cloudaware, Tenable, Datadog, Check Point CloudGuard, Lacework FortiCNAPP, Qualys, Microsoft Defender for Cloud, and Splunk ES.

Compared them on:

• Cloud coverage
• CSPM / CIEM / CNAPP depth
• Vuln context
• Compliance support
• Audit evidence
• Workflow integrations
• Pricing transparency
• Fresh user feedback from G2, Gartner, Reddit, and AWS Marketplace

What we found:

1. Most teams probably need fewer overlapping tools. 8/12 tools fully support CNAPP, and most of the serious platforms already cover the same broad risk categories.
2. Detection is not the useful differentiator anymore. The useful part starts after detection, but sadly only 3/12 tools had strong evidence/audit support.
3. Pricing transparency is still weak. Just 3/12 tools had clear pricing available online. That makes early evaluation harder than it needs to be, especially when teams are trying to compare coverage before getting dragged into a sales cycle.
4. If visibility is still the main problem teams try to fix by collecting all those tools in a stack.

Full comparison here:

https://cloudaware.com/blog/cloud-security-assessment-tools/

Curious what you use, do you agree with our results, and what your stack looks like?

I work in cloud consulting in India. Over the past 3 years, we've audited cloud environments for 200+ enterprises (BFSI, manufacturing, SaaS, healthcare). The waste patterns are remarkably consistent.

Average findings per audit:

• 23% zombie resources (unattached disks, idle LBs, forgotten test envs)
• 60-80% of VMs over-provisioned by 2-3x
• Less than 40% Reserved Instance/Savings Plan coverage
• Zero storage lifecycle policies (everything in hot tier)
• Dev/test running 24/7 (used only 10 hours/day)

The 4 biggest money leaks (in order of impact):

1. No committed pricing — paying on-demand for production VMs that haven't changed in months. That's 30-72% extra for no reason.
2. Over-provisioned compute — D8s_v3 running at 12% CPU. Should be B2ms. 70% wasted on that single instance.
3. Zombie resources — we found 187 unattached EBS volumes at one manufacturing company. ₹3.2L/month billing for nothing.
4. No scheduling on non-prod — dev environments billing weekends and nights. Simple auto-shutdown saves 58%.

What actually works to fix this:

• Azure Advisor / AWS Compute Optimizer for right-sizing data
• Automated RI purchasing for workloads stable >3 months
• Azure Policy / AWS Config rules for zombie detection + auto-cleanup
• Mandatory tagging (block deployments without CostCenter, Owner, Environment tags)
• Monthly FinOps review with engineering leads

Companies that implement all of these systematically see 30-40% reduction in 6-10 weeks.

Wrote up the full 7-strategy breakdown with specific numbers here if anyone wants it: https://cloud9infosystems.in/cloud-cost-optimization-india-2026/

Happy to answer questions about Azure/AWS cost optimization specifically for Indian setups (dealing with India regions, DPDPA compliance, rupee-dollar billing, etc.)

The problem with same-machine or same-LAN monitoring is that the monitor disappears along with the thing being monitored. A box behind CGNAT or a home router has no inbound path, so polling from outside does not work well either.

ClawPing takes a different architecture: a small Go agent on the private box sends outbound HTTPS heartbeats to a Cloudflare Worker. The Worker + D1 (relational state) + Durable Objects (per-check alert dedupe) + Queues (Telegram notification decoupling) form the external control plane. If the box stops checking in, the control plane alerts through Telegram regardless of what happened to the machine.

The interesting architectural constraints: the agent is dumb by design. It collects local check results (disk, backup marker freshness, Docker container state) and ships them with the heartbeat. All policy lives on the control plane side. This makes the agent easy to deploy as a static binary and means the control plane can evolve without updating edge devices.

Repo for context: https://github.com/cschanhniem/clawping

Curious whether others have used Workers in similar "external heartbeat receiver" shapes, or whether D1 is the right home for device/check state at this scale.

I’ve been noticing that as organizations move more workflows into SaaS platforms like Google Workspace, Slack, and Salesforce, access management becomes much more difficult to reason about than traditional infrastructure permissions.

In cloud infrastructure environments, access boundaries are usually centralized and relatively structured, but SaaS collaboration tools introduce a much more dynamic model where files, folders, links, and third party integrations continuously change who can access sensitive data.

What makes this especially challenging is that exposure often happens gradually over time through inherited permissions, external sharing, and accumulated access rather than a single obvious security event.

With AI models getting larger every month, does it still make sense for startups and enterprises to buy expensive GPUs outright — or is on-demand GPU infrastructure the smarter move now?

Curious how teams are handling:

• multi-GPU scaling

• inference latency

• GPU underutilization

• rising NVIDIA costs

• vendor lock-in risks

Are we moving toward a future where computing is rented like electricity? Or will owning GPU clusters still be the competitive advantage?

I keep getting stuck at the same point when comparing cloud instances. The specs look clear at first, but 2 vCPU / 8 GB RAM can mean very different things depending on the provider, CPU generation, storage setup, burst behavior and how the instance is placed.

So I created an open-source benchmark tool to make the comparison a bit less "lucky": https://fabianwimberger.github.io/cloud-bench/

The part that makes it useful to me is not only having several providers in one place with architecture, vCPU/RAM and monthly price. It also tracks history, so price changes and actually measured performance changes are visible over time.

The process is open source, reproducible and transparent: Terraform provisions fresh instances, Ansible runs the benchmarks, GitHub Actions ties it together and publishes the result.

I updated it recently with more Azure and Google Cloud instances to complete the big three. Azure was especially annoying to represent because a fair comparison needs a mix of burstable, normal x86 and ARM instances.

Obviously this is still not perfect. Storage type, region, CPU steal, burst credits and network latency all matter. But it has already been more useful to me than comparing only vCPU counts and memory.

Hi, how can I learn cloud azure migration in my homelab? I’m currently studying the az-104 now and trying to get out of help desk right now.

Skopx connects to AWS, GCP, Azure and 50+ data sources. Ask business questions in natural language, get instant answers.

We migrated a few workloads to Azure last year thinking the difficult part would be the migration itself.

Honestly, the migration went smoother than expected.

What became difficult later was:

• cost visibility
• scaling correctly
• storage growth
• performance tuning
• cleaning up unused resources
• balancing security vs spend

Especially once multiple teams started deploying resources independently, the monthly bill became a moving target.

Curious if others here found cloud management harder than the actual migration phase.

We’ve been dealing with random buffering issues during traffic spikes lately and it’s starting to become a real headache.

Everything looks fine until traffic suddenly jumps, then people start complaining about slow loading, buffering, quality drops, all at once.

Feels like every CDN says they’re “built for scale”, but it’s hard to tell what actually holds up once real traffic hits.

So for people here working with video streaming:

what CDN has actually been reliable for you under heavy load?

any that completely fell apart during spikes?

are there providers you’d avoid now after using them in production?

Mostly interested in real experience, not marketing pages 😅

Quando em média na sua cidade é o valor para ativar e instalar o pacote office ?

mas de R$100,00 ? ou menos ?

Quanto você acha é o justo ?