Large Language Models (LLMs) are increasingly being deployed as the reasoning engines for agentic AI systems, yet they exhibit a critical flaw: a rigid adherence to explicit rules that leads to decisions misaligned with human common sense and intent. This "rule-rigidity" is a significant barrier to building trustworthy autonomous agents. While prior work has shown that supervised fine-tuning (SFT) with human explanations can mitigate this issue, SFT is computationally expensive and inaccessible to many practitioners. To address this gap, we introduce the Rule-Intent Distinction (RID) Framework, a novel, low-compute meta-prompting technique designed to elicit human-aligned exception handling in LLMs in a zero-shot manner. The RID framework provides the model with a structured cognitive schema for deconstructing tasks, classifying rules, weighing conflicting outcomes, and justifying its final decision. We evaluated the RID framework against baseline and Chain-of-Thought (CoT) prompting on a custom benchmark of 20 scenarios requiring nuanced judgment across diverse domains. Our human-verified results demonstrate that the RID framework significantly improves performance, achieving a 95% Human Alignment Score (HAS), compared to 80% for the baseline and 75% for CoT. Furthermore, it consistently produces higher-quality, intent-driven reasoning. This work presents a practical, accessible, and effective method for steering LLMs from literal instruction-following to liberal, goal-oriented reasoning, paving the way for more reliable and pragmatic AI agents.

Fire safety practices are important to reduce the extent of destruction caused by fire. While smoke alarms help save lives, firefighters struggle with the increasing number of false alarms. This paper presents a precise and efficient Weighted ensemble model for decreasing false alarms. It estimates the density, computes weights according to the high and low-density regions, forwards the high region weights to KNN and low region weights to XGBoost and combines the predictions. The proposed model is effective at reducing response time, increasing fire safety, and minimizing the damage that fires cause. A specifically designed dataset for smoke detection is utilized to test the proposed model. In addition, a variety of ML models, such as Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Nai:ve Bayes (NB), K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (ADAB), have also been utilized. To maximize the use of the smoke detection dataset, all the algorithms utilize the SMOTE re-sampling technique. After evaluating the assessment criteria, this paper presents a concise summary of the comprehensive findings obtained by comparing the outcomes of all models.

Scott Petry, co-founder and CEO of Authentic8, maker of Silo, a platform for secure and controlled access to the web. Security misconfiguration and broken authentication. It plays out time and again: A bad person invents a way to attack a computer or a network. A good person discovers the attack and figures out how to detect future attacks. More good people build on that work and learn how to block them.

It began with a pulled muscle. Each day after school, as the sun sank dusky purple over the hills of my hometown, I'd run with my track teammates. Even on our easy days, I'd bound ahead, leaving them behind. It wasn't that I thought myself better than them--it's that when I ran fast, and focused on nothing but the cold air burning my lungs and my feet pounding, my normally anxious thoughts turned to white noise. I limped a little, and then tried running again: sharp, hot pain radiated down my thigh. Panic flooded me, as I imagined weeks without running: weeks without a predictable break from my own thoughts, weeks immersed in adolescent loneliness.

One way to build out a smart home is to buy lots of components--sensors, smart bulbs, security cameras, speakers, and whatnot--and connect them all to a hub that helps them communicate with each other and with you, via your smartphone. But let's be real: That can involve spending a lot of money and investing a lot of time. If your wants and needs are simpler, just a few relatively inexpensive products will deliver most of the conveniences a high-end smart home can deliver, and on a much more modest budget. And if you make sure those smart home products are compatible with each other, you'll build a solid foundation that you can expand over time. The key is knowing which smart home products don't depend on a smart home hub to operate.

We do not need a soothsayer to realize how Artificial Intelligence (AI) has transformed our lives. From using machine learning for drug discovery to facial unlock ID using facial recognition, its' application is everywhere. While AI may not say what the next reading on a dice (or magic 8) ball can be, it surely can predict the probability of getting 6 in the next roll of dice. The predictive aspect of AI has become more refined and accurate with time, thanks to deep learning and data analytics. However, the question is, can Artificial Intelligence do more than just prediction like forecasting or detection of a trend?

An emerging form of AI known as neuromorphic computing has been used to recognize scents emitted by explosives, chemical weapons, and narcotics. Researchers from Intel and Cornell University made the breakthrough by equipping Intel's neuromorphic test chip Loihi with neural algorithms that mimic what happens in your brain when you smell something. This enabled the system to recognize the smell of each hazardous chemical from just a single sample. The study could pave the way to a vast range of applications of neuromorphic computing, which mimics the brain's basic mechanics to make machine learning more efficient. Intel believes the "electronic nose systems" could be used by airport security to detect weapons and explosives, by police and border control to find narcotics, by robots to monitor gases pimped out into the atmosphere, and by the makers of smoke detectors to improve their products.

Artificial intelligence (AI), digital signal processing (DSP), control and I/O are delivered in a single device, the xcoer.ai, Single device prices for the xcore.ai It can be used by electronics manufacturers to integrate high-performance processing and intelligence economically into products. According to XMOS, xcore.ai is a new generation of embedded platform. It has fast processing and neural network capabilities to enable data to be processed locally and actions taken on-device within nanoseconds. It can interpret data without communication with the cloud and delivers the performance of an applications processor with the ease-of-use of a microcontroller, enabling embedded software engineers to deploy every different class of processing workload on a single multi-core crossover processor, says XMOS.

A home full of smart gadgets is something that everyone wants. But the smart home appliances should settle with the ecosystem too. Every gadget in this sector has a different smartphone app. For these apps, it gets easy for a person to control the device from a remote place. Least of the gadgets do not have any smartphone app.

When I reviewed First Alert's Onelink Safe & Sound smoke alarm in mid-2018, I found it to be a powerful entry in the smart smoke detector market. Its inclusion of Bluetooth and an Amazon Echo-compatible smart speaker set it apart from every smoke and carbon-monoxide detector on the market. But its $199 price tag also made it far and away the most expensive device of its type on the market--and that price hasn't budged since its release. Enter the Onelink Smoke Carbon Monoxide Alarm, which lowers the cost of the original product by stripping out its most compelling features: The smart speaker and Bluetooth. Like other products in this category, the Onelink Smoke Carbon Monoxide Alarm is designed to extend the capabilities of a smoke detector by linking it with your smartphone. It still functions by firing off a (rather loud) local siren whenever smoke or CO are detected, but it also (supposedly) sends push alerts to your mobile device, a feature that is most helpful for times when you aren't at home but still want the peace of mind that it's not on fire.