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The Infotech Report

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.

09/21/2026

AI prompts can take many forms, and different structures can produce better results from a large language model. A recent overview highlights 20 approaches that teams can try, from concise instruction-based prompts to meta prompting, where the model writes the prompt itself.

The list also includes several practical styles that shape how the model responds:

• Few-shot or example-based prompting for formats like JSON or CSV
• Zero-shot prompting when examples are not needed
• Q&A, Socratic, role-playing, and fill-in-the-blank prompting
• Critique and revise, chain-of-thought, tree-of-thought, and skeleton-of-thought prompting
• Directional hints, prefetching prompts, emotional prompts, negative prompts, templates, and legally defensive prompts

The main theme is simple. Prompt writing is still an evolving art, and small changes in wording, structure, or tone can change the result. Which prompt style has worked best for your team?

09/19/2026

A zero-click flaw in popular AI coding agents could have allowed malicious code to run through compromised plugins, even without developer interaction. Researchers at AIR identified the issue, called Plugin4Shell, and reported it to the affected vendors.

The flaw affected OpenAI’s Codex, Anthropic’s Claude Code, Google’s Gemini CLI, and GitHub Copilot. It involved how the agents checked out plugin code from Git repositories and verified approved commits.

Some vendors have already released fixes. Anthropic addressed the issue in Claude Code version 2.1.179, and OpenAI addressed it in Codex version 0.146.0. Google has deprecated Gemini CLI and will not issue a fix, while GitHub has not yet released one.

Security teams are being urged to review vulnerable machines for unusual processes, network connections, plugin files, repository changes, suspicious Git activity, and abnormal credential use. How prepared are enterprise development teams for plugin-related risk?

09/18/2026

Python developers have several features to put to work, including dataclasses, editable installs, virtual environments, and a new free-threaded build. These tools can reduce boilerplate, simplify package development, organize dependencies, and improve parallelism.

Other Python updates also stand out:
- Pyrefly 1.3.0 adds better type inference and support for Django, SQLAlchemy, PyTorch, NumPy, and JAX.
- NumPy ufuncs continue to gain new capabilities and performance improvements.
- Python 3.15 lazy imports can speed up programs that do not use every import right away.
- Python documentation now includes time complexity details for built-in types and operations.

Which of these Python features is most useful in your own work?

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?

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