#APM

[op814kitty]

Just walked past apm monaco and there's no video of Charles playing

What have they done to my boy ?

[itsfirmulaone]

[makeagif]

charles_leclerc: Pit stop in Dubai mall at @apmmonaco to make sure we look good for the las race of the season 😘

[kneipe]

leipzig 2024

[dutch-trainworld]

Swiss State Railways (SBB-CFF) Apm, a series of 12 Panorama Cars.

[apassionateman]

YOU... Are Number 1

Don't ever forget that!

[lattesqueeze]

apm monaco newsletter - 29 November 2023

[karlstad]

zonke | ny73 | apm

[madison-tourmaline]

One of my all time favorite pieces of library music: the Killing Ground by Richard Harvey

[thefashionforwardfiles]

Brussels,BE|Forest national arena|April 16 2025

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Central Cee performs wearing a APM Monaco 2024-2025 home jersey

[memeticsolutions01]

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[jcmarchi]

Digma's preemptive observability engine cuts code issues, streamlines AI - AI News

New Post has been published on https://thedigitalinsider.com/digmas-preemptive-observability-engine-cuts-code-issues-streamlines-ai-ai-news/

Digma's preemptive observability engine cuts code issues, streamlines AI - AI News

Digma, a company offering products designed to act on pre-production observability data, has announced the launch of its preemptive observability analysis (POA) engine. The engine is designed to check, identify, and provide ‘fix’ suggestions, helping to balance systems and reduce issues found in codebases as their complexity increases.

The application of preemptive observability in pre-production may be more important as AI code generators become more common , the company claims. For instance, a 2023 Stanford University study revealed that developers using AI coding assistants were more likely to introduce bugs to their code. Despite this, major companies like Google are increasing their reliance on AI-generated code, with over 25% of the company’s new code being AI-created.

Nir Shafrir, CEO and Co-founder of Digma, commented on the growing resources that are being dedicated to ensuring systems perform well, saying, “We’re seeing a lot of effort invested in assuring optimal system performance, but many issues are still being discovered in complex code bases late in production.”

“Beyond this, scaling has often remained a rough estimation in organisations anticipating growth, and many are hitting barriers in technology growth that arise precisely during periods of significant organisational expansion. It means that engineering teams may spend between 20-40% of their time addressing issues discovered late in production environments, with some organisations spending up to 50% of engineering resources on fixing production problems.”

Preemptive observability is expected to become a key factor helping companies gain competitive advantage. It has several potential benefits for AI-generated code, including speed increases and improvements to the reliability of human-written code. According to Digma, preemptive observability helps ensure manually written code is more trustworthy, and reduces risk in the final product.

As well as tackling bugs introduced by AI code generation, Digma’s preemptive observability analysis engine has been designed to combat common, long-established issues companies may have experienced with human-made code, which may result in service level agreement (SLA) violations and performance issues. For high transactional establishments, like retail, fintech, and e-commerce, this technology could become valuable.

Digma’s algorithm has been designed to use pattern matching and anomaly detection techniques to analyse data and find specific behaviours or issues. It is capable of predicting what an application’s response times and resource usage should be, identifying possible issues before they can cause any noticeable damage. Digma specifically detects the part of the code that is causing an issue by analysing tracing data.

Preemptive observability analysis prevents problems rather than dealing with the aftermath of the issues. Teams can monitor holistically, and address potential issues in areas that are frequently ignored once in production.

Roni Dover, CTO and Co-founder of Digma, highlighted what differentiates Digma’s preemptive observability analysis engine from others: “By understanding runtime behaviour and suggesting fixes for performance issues, scaling problems, and team conflicts, we’re helping enterprises prevent problems and reduce risks proactively rather than putting out fires in production.”

Application performance monitoring (APM) tools are used to identify service issues, monitor production statuses, and highlight SLA errors. APMs are practical for sending alerts when services fail or slow during production. But unlike preemptive observability, APMs are limited in non-production settings, and can’t provide analysis of problems’ sources.

By identifying performance and scaling issues early on in the production process, even when data volumes are low, preemptive observability helps prevent major problems and reduce cloud costs.

Digma recently completed a successful $6 million seed funding round, indicating a growing confidence in the technology.

Image source: “Till Bechtolsheimer’s – Alfa Romeo Giulia Sprint GT No.40 – 2013 Donington Historic Festival” by Motorsport in Pictures is licensed under CC BY-NC-SA 2.0.

See also: Microsoft and OpenAI probe alleged data theft by DeepSeek

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

[makeagif]

charles_leclerc: Pit stop in Dubai mall at @apmmonaco to make sure we look good for the las race of the season 😘

[team-ombrulla]

Asset Performance Management: Future Trends Shaping the Industry

The landscape of Asset Performance Management (APM) is rapidly evolving, driven by advancements in technology, sustainability goals, and enhanced data analytics. As businesses aim to optimize their assets for greater efficiency and reduced costs, several trends are emerging as key drivers of change in APM.

1. AI and IoT Integration

The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) is transforming how businesses monitor and manage assets. AI-powered algorithms analyze real-time data from IoT sensors to predict failures, optimize maintenance schedules, and provide actionable insights. This shift from reactive to proactive maintenance enhances operational efficiency and reduces downtime, providing a competitive edge in asset management.

2. Focus on Sustainability

As businesses face mounting pressure to meet sustainability targets, APM will play a pivotal role in driving green initiatives. Through predictive maintenance and optimized asset usage, companies can reduce waste, conserve energy, and lower emissions. This not only supports environmental goals but also aligns with corporate social responsibility efforts, improving brand reputation and regulatory compliance.

3. Advancements in Technology

With continuous advancements in technology, APM systems are becoming more sophisticated and capable of handling larger volumes of data. Cloud-based solutions, edge computing, and blockchain are enhancing the way data is stored, processed, and secured. These technologies improve real-time decision-making, speed up response times, and increase the accuracy of asset performance predictions.

4. Enhanced Data Analytics

The future of APM will see the evolution of data analytics. With the growing use of big data, machine learning, and AI, businesses will have access to deeper insights into asset performance. This allows for better forecasting, risk assessment, and performance optimization. The ability to analyze large sets of data in real-time will empower businesses to make more informed, data-driven decisions that drive cost savings and operational excellence.

5. Personalization and Customization

As APM solutions become more advanced, there will be an increasing focus on personalization and customization. Businesses will be able to tailor APM systems to their specific needs, adjusting parameters, dashboards, and insights to match operational goals. This flexibility will allow organizations to maximize the value of APM by focusing on the most relevant data and insights, improving decision-making across the board.

Conclusion

The future of Asset Performance Management is being shaped by innovations in AI, IoT, sustainability, and advanced data analytics. As businesses integrate these trends into their operations, they will not only enhance asset performance but also drive greater efficiency, reduce costs, and support sustainability initiatives. Embracing these trends will enable organizations to stay competitive in a rapidly evolving industry and ensure long-term success in managing their assets.

[anugerahprimamotor]

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[apassionateman]

ALL emotions are a choice, yet majority of people seem to think they are a physiological "reaction" to environmental situations.

In my opinion men are generally thought of as stoic and can come actoss as emotionless by some maybe even many, I'm sure some might say we are a bit like moving granite. Yet men DO have very deep emotions over a great deal of topics and things... we simply "choose" what and to whom we open up and show.

I love my emotions, yet I do keep them in check most days.

#JC