This summer, the White House rolled out the Gold Eagle Initiative , billed as an “unprecedented” effort to coordinate cybersecurity vulnerability…
Category: InfoWorld
Google aims to make BigQuery spending on AI functions more predictable with token quotas
Google has made daily token quotas generally available for BigQuery’s generative AI functions to give enterprises a way to limit AI consumption by project…
When an agent acts, who is accountable?
For most of the history of enterprise software, accountability has been relatively easy to understand. A person logs in, a person has a set of…
Building Fabric Apps with Microsoft’s Rayfin
Using vibe coding to scratch an itch has many of the same issues as using low-code and no-code development tools. That’s not surprising. Putting together…
Your AI agent needs more than memory. It needs reliable state
Most teams building AI agents are pouring effort into better memory, longer context windows, conversation summaries, retrieval systems that pull up…
Bank of America spent millions on anti-money-laundering tech, but not enough to avoid a $1.2B fine
Bank of America has had to spend big on IT in response to an AI security audit and findings that its anti-money-laundering (AML) controls were lacking.…
The $690 billion cloud spending spree
Every quarter, I look at the capital expenditure numbers coming out of the major cloud providers, and every quarter, they manage to surprise me. This…
Pop the champagne, Python 3.15 is here!
The best Python yet isn’t just faster than before. It’s also smarter at telling you what’s wrong, better at letting you profile applications, and loaded…
Cyberattack AIs must be trained only on air-gapped systems, hundreds of AI researchers say
Current security procedures for training AI models are inadequate, hundreds of AI researchers have warned governments and the AI community. They are…
TypeSafe’s Jev ‘even more susceptible to attack’ than LLMs, says security vendor
Although the Jev model rolled out last month by TypeSafe claims to have significant advantages over traditional LLMs, a security report published…
Google positions Gemini 4 Argon for long-horizon agentic work
Google has launched Gemini 4 Argon, a new flagship AI model aimed at complex enterprise and software-development workloads, pairing claims of stronger…
5 agentic AI trade-offs to consider before production
Companies across industries are moving quickly to build agentic AI systems. But getting an agent to work in a prototype is very different from getting one…
ServiceNow makes it easier to build AI agents, gives them verifiable identities
ServiceNow is giving its AI Control Tower new features to make AI agents more governable — and a new name, Super Intelligence Control Tower (SICT), to…
Seven ways to encourage critical thinking in AI users
Employees are increasingly using large language models (LLMs) and AI agents to get work done. Many are using agents to complete tasks, and others are…
What building an Android weather app taught me about designing for reliability
I spent 13 years building Android apps, and for most of them, the backend was someone else’s problem. Building Weather Pal, my own forecast app, broke…
It’s time to rewrite all that old code
The first substantial software project I ever undertook (at least it seemed substantial at the time) was porting the famous text-based Star Trek game from…
The value of software is approaching $0
Imagine a vendor comes to you and explains how they can dramatically de-risk your infrastructure. They have a new software product or SaaS that detects…
Google aims to make BigQuery spending on AI functions more predictable with token quotas
Google has made daily token quotas generally available for BigQuery’s generative AI functions to give enterprises a way to limit AI consumption by project…
When an agent acts, who is accountable?
For most of the history of enterprise software, accountability has been relatively easy to understand. A person logs in, a person has a set of…
TechCrunch Disrupt: What’s next for AI and software development
First AI gave us code completion, predicting the lines of code, functions, and boilerplate we needed based on what we’d already typed. Then came code…