CIO
JANUARY 25, 2024
Software developers, no matter how skilled, face tasks they’re not very good at. Generative AI platforms like GitHub Copilot and ChatGPT have been trained on billions of lines of code for many programming languages and are surprisingly good at predicting what lines of code developers should use next.
CIO
DECEMBER 20, 2023
Generative AI can help businesses achieve faster development in two main areas: low/no-code application development and mainframe modernisation. Streamlined coding process : Generative AI provides real-time information on available functions, parameters, and usage examples as the coder types.
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CIO
NOVEMBER 14, 2023
Imposter syndrome—doubting your abilities to the point that you feel like a fraud—is an evergreen topic of conversation among software developers. For many devs, the explosion of GenAI and AI-powered coding tools makes feeling like an imposter more inevitable than ever. It’s just ‘learn Python,’ or ‘learn React,’” Hicks says.
CIO
OCTOBER 16, 2023
Earlier this year, I wrote about the importance of organizations reviewing their password management strategies. Caesars noted in a filing that an “unauthorized actor” had stolen data in a social engineering attack targeting an outsourced IT support vendor, according to an InfoSecurity report. Déjà vu can suck sometimes.
CIO
OCTOBER 25, 2023
Identity attacks use social engineering, prompt-bombing, bribing employees for 2FA codes, and session hijacking (among many techniques) to get privileged access. Plants decoys of internal apps like code repositories, customer databases, business applications, and objects like S3 buckets and AWS keys in your cloud tenants.
CIO
MARCH 12, 2024
Others include preparation for zero-day attacks, almost anything having to do with data stewardship, as well as IT training and social engineering audits. Yet without training, both IT and end-users are ill-equipped to move forward with new technologies that the company needs.
AWS Machine Learning - AI
MARCH 28, 2024
Large language models (LLMs) are generally trained on large publicly available datasets that are domain agnostic. For example, Meta’s Llama models are trained on datasets such as CommonCrawl , C4 , Wikipedia, and ArXiv. The resulting LLM outperforms LLMs trained on non-domain-specific datasets when tested on finance-specific tasks.
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