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Infotech.Report is an extensive resource library for the IT industry, bringing you the latest market studies, research reports and product information.

Get access to whitepapers, webcasts, case studies on an array of topics for the IT industry including data, enterprise applications, infrastructure, management, network and communications, security, servers, software and web development and storage.

09/17/2026

AI-assisted test automation can turn a green dashboard into a false signal when a repair fixes the wrong target. A broken locator may be replaced with a nearby element, the test may pass again, and the dashboard may look healthy while the intended behavior is no longer being checked.

Three layers can report success at the same time, yet mean different things. The model may finish its task, the test harness may pass, and the production system may appear healthy, while none of them confirms that the same event actually occurred.

A proposed response is a cross-layer evidence record that connects model actions, test results, and runtime state. That record can include the original target, the proposed target, the evidence used for substitution, the confidence score, the resulting assertion, and human-review status.

What would make an AI testing tool trustworthy in your environment?

09/16/2026

Cockroach Labs has introduced Cockroach Continuum, a database fleet management platform designed to pool compute and storage across isolated CockroachDB databases. The goal is to help enterprises handle highly concurrent, unpredictable, and short-lived workloads without reserving dedicated infrastructure for every database.

Continuum uses database virtualization, Cockroach Plenum, and Cockroach Aegis to support shared capacity, separate compute from storage, and reduce manual DBA work across monitoring, troubleshooting, and tuning.

The potential benefit is lower idle capacity and a shift toward paying for consumption rather than peak capacity. Analysts also note that the actual savings will depend on how databases are provisioned and used.

For CIOs, the biggest question is straightforward: will shared capacity and lower idle compute translate into lower overall costs for their workloads? What matters most in your database planning, utilization, forecasting, or operational control?

09/15/2026

Microsoft is positioning its data platform around AI business value, with a focus on helping enterprises improve revenue, productivity, and decision speed.

CIOs and CDOs are being asked to think beyond architecture diagrams and focus on outcomes. The central question is how data, governance, and AI work together across ERP, CRM, operational systems, databases, warehouses, and analytics platforms.

Key themes include:

Data readiness, governance, interoperability, and operational ex*****on.

Different roles for Microsoft Fabric, Azure Databases, OneLake, Power BI, and governance.

Trusted context for copilots and agents, including quality, permissions, lineage, and security.

Microsoft also points to customer examples from KPMG Australia, BMW Group, Audi, and Levi Strauss & Co. as proof points tied to business results.

For enterprise leaders, the message is clear. Start with the business outcome, then map the data and context required. What would matter most in your organization: faster decisions, better governance, or simpler modernization?

09/14/2026

OpenCV has reached its fifth major version, offering image and video analysis tools for OCR, object detection, facial recognition, and other AI and machine learning applications.

The library is built for computer vision tasks, including real-time analysis of video or still images. It supports common use cases such as motion tracking, augmented reality, and gesture detection.

OpenCV also provides:

, Apache 2-licensed C++ libraries with support for Python, Java, JavaScript, and a WebAssembly interface
, a hardware acceleration layer that automatically selects the best option on the current system
, modules for video I/O, video analysis, barcode detection, pretrained models, datasets, and simple GUIs

Its DNN module can load third-party pretrained models from TensorFlow, PyTorch, or ONNX formats, while the OpenCV zoo offers models for tasks such as deblurring images, pose estimation, and object detection.

How do you see OpenCV fitting into AI and machine learning workflows?

09/12/2026

OpenAI has introduced a new managed Agents API in public beta, giving developers a single API call to design custom AI agents after specifying the task, model, tools, and environment.

The service packages orchestration, context management, and ex*****on infrastructure into one managed offering. OpenAI hosts and maintains the underlying harness and infrastructure, reducing the need for teams to assemble agent runtime, session management, tools, external data connections, and ex*****on environments on their own.

Developers can run agents in an OpenAI-managed sandbox, on their own infrastructure, or through supported sandbox providers including Blaxel, Cloudflare, Daytona, DigitalOcean, E2B, Modal, Oracle, Runloop, and Vercel.

Analysts said the API could reduce engineering work and speed production deployments, while also raising concerns about lock-in, pricing leverage, and data privacy governance. How do you see enterprises weighing convenience against control?

09/11/2026

Cloud capacity is moving into a new middle market, as negotiated bulk deals become more visible and more formalized. Large enterprises now have another path for AI workloads, especially when they need maximum GPU capacity for model training or inference.

The choice is becoming clearer:
• Hyperscalers offer metered services, governance, and integration.
• Bulk capacity deals can deliver lower costs, but often with minimal metering, limited monitoring, and more self-managed operations.

Some analysts cite pricing gaps ranging from 10 to 100 times between bulk capacity and public cloud offerings. That gap is pushing organizations to rethink procurement, architecture, and workload placement.

Many enterprises are expected to keep steady-state and security-sensitive workloads on hyperscaler platforms while shifting cost-intensive training runs and elastic inference to bulk-capacity providers.

How should enterprises balance cost, control, and operational complexity in this new market?

09/10/2026

Databricks has introduced Adaptive Instructed-Retriever, a new retrieval model designed to improve enterprise AI search by adding extra steps for complex queries and stopping early on simpler ones.

The model is built to balance answer quality, latency, and cost. It combines parallel retrieval with sequential, multi-step search when additional evidence is needed.

Databricks says the model was trained with synthetic enterprise retrieval environments and online reinforcement learning. The company also said internal evaluations showed it matched or exceeded the retrieval quality of Claude Sonnet 5, GPT-5.6 Luna, and DeepSeek-V4-Flash while completing requests in 5.8 seconds.

The model can produce different checkpoints with different quality-latency trade-offs, giving enterprises more flexibility across workloads.

Analysts noted that the approach could help reduce unnecessary search, make costs more predictable, and cut some orchestration work for developers. At the same time, teams still need to test settings, manage data quality, and verify results.

How should enterprises balance retrieval quality with cost and latency?

09/09/2026

A developer with a long history in BASIC, Pascal, Delphi, and TypeScript says a side project quickly made him a Go developer, too.

He was asked to build a cross-platform application for a company using Go and Wails Version 3. With Claude helping him learn the basics, he says he understood the architecture in about an hour and had a basic application running soon after.

The setup uses Go for the back end and React for the front end, all in a single binary. Claude also managed the Git repository and created a CI/CD pipeline that built binaries for Mac, Linux, and Windows in one step.

He says he went from being basically ignorant of Go to having a moderately sophisticated application in a day, even though he still does not feel very strong in the language. Could AI change how quickly developers pick up new languages?

09/08/2026

AI is being framed as a threat to SaaS, but the opposite case is gaining attention. The argument is simple. Cheaper code does not solve the real problems software users face, and it does not remove the need for support, stability, or product knowledge.

Shopify’s Sidekick offers a clear example. In December, Shopify introduced custom app generation, allowing a merchant to describe a tool and have Sidekick write the code using Shopify’s interface components and Admin API. Shopify says merchants created almost 4,000 custom apps in the first three weeks.

That kind of customization can make SaaS products more useful and more difficult to leave. It can also help customers shape software around their own work without replacing the platforms they already rely on.

AI may change what customers expect from SaaS, but it does not make SaaS irrelevant. What kind of customization would matter most in your own software tools?

09/07/2026

AI-first development is becoming a central strategy for software teams that want to build faster and design for AI from the start. Organizations are integrating artificial intelligence across the full software development life cycle, with developers acting as architects who oversee AI agents.

Key takeaways include:

AI-first teams can complete work faster and surface defects earlier.
Designing for AI from day one can reduce rework and improve documentation.
Many teams will need new roles, training, testing methods, and human review checkpoints.

The shift also moves developers toward agentic workflows, where the focus is less on typing code and more on defining goals, architecture, data flow, and outcomes. Leaders say starting small and tracking results can help teams learn what works.

How prepared is your organization for AI-first development?

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