From Artificial Intelligence to Machine Learning, we cover the shifts and trends in software and technology.
Agentic AI Agents are making real progress in building and integrating AI that acts within business and participate in workflows instead of just conversations.
An LLM research paper, titled “Artificial or Just Artful? explores the tension between pretraining objectives and alignment constraints in Large Language Models (LLMs). The researchers specifically investigated how models adapt their strategies when exposed to test cases from the BigCodeBench (Hard) dataset.
The Model Context Protocol provides a universal, standardized communication layer that eliminates the need for custom-coded integrations for each data source by allowing any AI model to seamlessly connect with any data source, MCP serves as the backbone for an agentic future where AI can orchestrate tasks across multiple systems like OpenSearch, databases, and APIs.
Google grounds AI agents in real enterprise data. Transforming AI agents from tools into indispensable, data-aware partners that can take concrete action directly within the development workflow.
Google Workspace is an investment in a central operating system for your business, one that can secure your data, automate unique processes, and turn feedback into fuel for growth. Moving beyond the surface-level functions of email and documents unlocks Workspace potential to fundamentally improve how your business operates.
In any organisation, fragmented workflows are a liability on your business. Google Workspace eliminates this by solving the fragmentation that plagues modern software productivity collaboration. With Gemini’s AI capabilities, Workspace becomes a powerful tool for unifying teams productivity.
RAGRecon, a system to improve Cyber Threat Intelligence through the integration of Large Language Models and Retrieval-Augmented Generation.
Network Solutions is rolling out a major 265% Black Friday sale on All their business tools and webservices.
DRAFT-RL is a evaluation framework fort LLMs designed to address critical limitations in LLM-based reasoning systems by integrating Chain-of-Draft (CoD) reasoning with multi-agent reinforcement learning.
The Language Model Council research suggests that the top spot on any given leaderboard might be an artifact of evaluation design rather than a reflection of superior, generalized capability.
Humanity’s Last Exam is a multi-modal case study designed to measure the capabilities of large language models.
Exoskeleton Reasoning is a process that inserts a directed validation scaffold into A language model’s workflow before it responds.
A new research paper from Humains-Junior language model reportedly matches the factual accuracy of GPT-4o on a specific public subset. According to the paper the Humains-Junior language model achieves this performance through an innovative method called “Exoskeleton Reasoning.”
A Small Language Model can be as Accurate as a Large Language Model with evaluation methods and frameworks. Methods like Exoskeleton Reasoning, Completeness, and Correctness, and using an LLM as a judge.
LLM-as-a-Judge is a critical tool for anyone building LLM and AI applications. It offers a consistent approach to evaluating large language models. It captures what truly matters: quality, safety, and accuracy.
Completeness and Corrective Guardrail Metric is an engineered solution by DeepRails. It is designed to measure how well an AI response addresses the entirety of a user’s question. Not only does this ensure it is not just accurate, but truly useful.
The biggest language model is not winning the journey to enterprise-grade AI. The real market value lies in building trust. A trust driven not just by APIs, but initially forged through deep evaluation of LLM software.
Understanding the skills in software needed whithn the job market positions you for success in a rapidly growing machine learning field.
The shortage of skilled professionals in AI/ML is not just a minor hurdle. It is a critical issue impacting growth, competitiveness, and the very future of many organizations.
AI/ML courses can enhance your skills, knowledge, and supercharge your roadmap for securing a position in AI.
The world is buzzing with AI and Machine Learning. If you’re not already part of the revolution, now’s the time to get in.
Python AI offers a wealth of opportunities in software development to explore and innovate.
Software developers and programmers possess unique skills that can unlock lucrative opportunities with digital assets.
Insider secrets for software developers that will help you build a profitable business and stop relying on traditional freelance techniques.
Coding isn’t just for tech geniuses anymore. In 2026, learning to program is more accessible than ever.
An LLM research paper, titled “Artificial or Just Artful? explores the tension between pretraining objectives and alignment constraints in Large Language Models (LLMs). The researchers specifically investigated how models adapt their strategies when exposed to test cases from the BigCodeBench (Hard) dataset.
RAGRecon, a system to improve Cyber Threat Intelligence through the integration of Large Language Models and Retrieval-Augmented Generation.
DRAFT-RL is a evaluation framework fort LLMs designed to address critical limitations in LLM-based reasoning systems by integrating Chain-of-Draft (CoD) reasoning with multi-agent reinforcement learning.
The Language Model Council research suggests that the top spot on any given leaderboard might be an artifact of evaluation design rather than a reflection of superior, generalized capability.
Humanity’s Last Exam is a multi-modal case study designed to measure the capabilities of large language models.
A new research paper from Humains-Junior language model reportedly matches the factual accuracy of GPT-4o on a specific public subset. According to the paper the Humains-Junior language model achieves this performance through an innovative method called “Exoskeleton Reasoning.”
