The post When Employees Start Working For The Metrics Instead Of The Mission appeared on BitcoinEthereumNews.com.
When Employees Start Working For The Metric Instead Of The Mission getty I never loved taking statistics courses in school, and yet somehow that education influenced me more than I ever expected. I now find myself trying to quantify things I never used to think about. There is tremendous value in being able to predict performance, and many of the tools organizations use help leaders improve results. They rely on performance metrics to evaluate everything from customer satisfaction to productivity. Used well, those metrics help identify problems, reveal opportunities, and support better decisions. Used poorly, they can produce exactly the opposite of what leaders intended. The moment employees understand how their performance will be measured, many begin adjusting their behavior to improve the metric rather than the outcome the metric was designed to represent. I saw that of
GitHub continues to be a scintillating target for attackers because it sits in the middle of the software supply chain and gives threat actors three things they crave: source code, secrets, and automated pipelines to run amok in.
Datadog Security Research has been tracking what it calls a “sustained pattern” of GitHub API abuse over the past several months that seeks to map organizations and their members. While individually these requests are “unremarkable,” they become dangerous when they move across environments for weeks at a time, and, worse, progress to full-out cloning. The biggest challenge is that they blend into normal API usage patterns.
GitHub has been a goldmine for criminals looking to breach organizations because many development lifecycles are insecure, said David Shipley of Beauceron Security. Typically, threat actors are after API keys and cloud secrets.
“Now with everyone being pushed to do more, faster, with AI agents coding, the treasure trove of secrets is likely
The post Ethereum and Bitcoin face historic supply squeeze – THESE 2 metrics reveal what’s next appeared on BitcoinEthereumNews.com.
Despite months of market volatility, Ethereum and Bitcoin holders continue showing little interest in returning coins to exchanges. This does represent much more than decreased investor trading enthusiasm. Persistent withdrawals continued reducing the amount of liquid supply available on the market. As of press time, the total number of Bitcoins stored on exchanges was at an all-time low for any time period since 2017. At the same time, the total number of Ethereum [ETH] stored on exchanges was also at an all-time low for any time period since 2015. Source: Santiment Simultaneously, ongoing negative Netflows indicate that institutional and longer-term holders prefer to store their coins using self-custody models such as ETFs or corporate treasuries rather than storing them on exchanges. Therefore, this migration will remove additional coins from potential
Anthropic is bringing its Claude Cowork AI agent to web and mobile platforms, a move aimed at helping enterprise users monitor and manage long-running AI-driven tasks from anywhere as organizations increasingly adopt agents for operational and knowledge work.
The rollout, according to the company, is based on an analysis of 1.2 million anonymized and aggregated Claude Cowork sessions conducted between May 11 and May 31 that showed that business process and operations accounted for the largest share of the AI agent’s usage at 33.4%, followed by content creation and copywriting at 16.4%, the company wrote in a blog post.
Software development represented only 8.7% of sessions, ahead of DevOps and infrastructure at 7%, along with research and intelligence at 6.4%, and data analysis and business intelligence (5.8%).
Cowork’s expansion, which is currently in beta, will allow users to start and manage Claude Cowork sessions directly from the Claude interface on the web and mobile, while enabl
Developers looking to curb the cost of AI-powered coding tools have increasingly turned to the “Caveman” prompting style, which instructs coding assistants to communicate in blunt, telegraphic language and avoid conversational padding. The theory is simple: fewer words mean fewer tokens, translating into lower inference costs for organizations deploying AI agents at scale.
A new test from IDE maker JetBrains confirms that terse prompting styles such as the viral open-source Caveman project can reduce token usage without hurting coding performance. However, the company found that the savings were far smaller than supporters claim.
JetBrains used the Harbor open-source evaluation framework and tasks from SkillsBench for its test, and found that the Caveman technique reduced usage of output tokens by about 8.5%, far below its claimed 65%.
The IDE-maker ran paired benchmarks across 86 real-world software engineering tasks in Claude Code, comparing coding sessions that used the Caveman pro
With the rapid progress of AI capabilities and the move to agentic systems, organizations are expanding their use cases as the technology continues to grow. That constant evolution also introduces risk, leaving IT leaders to wonder which investments will prove valuable even six months into the future. Returning to the foundational elements of AI architecture—the…
As intelligent systems move into production environments and begin taking actions, organizations quickly discover that accountability becomes much harder. Unlike traditional enterprise software, these tools can produce unpredictable outcomes as they interact dynamically with data, APIs, and business workflows.
“When something goes wrong with AI, it is generally assigned to whoever was closest to the pain point,” says David DuChene, manager of data and AI pre-sales at SHI International, which works with enterprises on AI deployments and governance.
As these systems shift from advisor to actor within workflows, accountability becomes harder to enforce through policies alone. IT leaders must build it directly into the fabric of their operations through clear ownership, continuous observability, defined escalation paths, and infrastructure designed to make responsibility visible when things go wrong.
Here are six ways to make AI accountability enforceable in production.
1. Assign direct ow
jL's free agency could reshape team dynamics in esports, offering organizations a chance to acquire a proven Major MVP talent.
The post NAVI parts ways with Major MVP jL as contract expires, opening door for esports free agency moves appeared first on Crypto Briefing.
AWS's $1 billion investment in embedded AI engineers reflects a broader shift as enterprises focus less on choosing models and more on putting AI to work.
