News and Tech Update

Microsoft has unveiled plans to incorporate Anthropic’s Claude Mythos Preview model and other AI models into its Security Development Lifecycle, embedding AI directly into the stages where code is written and tested. Rather than relying primarily on static analysis tools, Microsoft is adopting AI models capable of analyzing code dynamically and identifying complex vulnerabilities that might otherwise go undetected until later stages of development. Released on April 7, Anthropic’s Mythos model has already demonstrated a previously unmatched ability to uncover critical flaws across operating systems and widely used software. Anthropic claimed that the model’s ability to find security vulnerabilities is so advanced that it should not be released to the public. Microsoft gained access to the model through Anthropic’s Project Glasswing, a program that grants limited access to select tech firms for cybersecurity research. Within this framework, Microsoft is reporting measurable improve...

Most B2B applications collect incomplete data by design. A lead form captures a name and company. A recruiting tool surfaces a LinkedIn profile. An event registration system logs an email address and job title. The record enters your system and sits there, half-formed, waiting for someone to manually fill in the gaps before it can be acted on. This is an architectural problem, not a workflow problem, and solving it at the architecture layer is what separates applications that create operational leverage from ones that just digitize manual work. Understanding how to build contact enrichment into your application using professional data APIs changes  how you think about the data ingestion layer entirely. Rather than passing incomplete records downstream and hoping someone fills in the blanks, you enrich at the point of entry, automatically, before the record ever reaches a human. The Architecture Problem Behind Incomplete Lead Records The gap between the data a user submits and t...

A lot is going on at Anthropic. Access to the almost-fabled Mythos model remains restricted (despite some reports of unauthorized access), and nobody knows quite what is likely to happen or when in terms of its final rollout. Developers, meanwhile, are left with their own challenges; last week’s “upgrade” to Opus 4.7 has left some software engineers already longing for a return to 4.6 with its less literal instruction interpretation and its perhaps less cautious use of safeguards and controls. Then there’s the Claude quality conundrum in and of itself. Root of the Problem? Anthropic says it recognizes the fact that users are reporting that they are getting “worsened responses” over the past month. In answer to this, the organization confirms it has traced these reports to three separate changes that affected Claude Code, the Claude Agent SDK, and Claude Cowork. The Claude API and the inference layer were not impacted. All three issues have now been resolved as of April 20 (vers...

“AI-powered” has become the default label for every security tool on the market. But there’s a meaningful difference between a tool that uses AI to generate alerts after the fact and one that actively participates in development, preventing vulnerabilities as code is written. That difference is what separates reactive AI from agentic AI. And it matters more now than ever. What “Agentic” Actually Means in AppSec In the context of application security, agentic AI isn’t a buzzword. It describes a specific set of capabilities: the tool proactively surfaces security issues in real time, understands the context in which code is being written, and recommends fixes before insecure patterns reach the pipeline. The developer still makes the call. But instead of finding out about a vulnerability hours or days after committing it, they get guidance at the moment they can act on it most efficiently. Three qualities define the approach. Agentic AI is proactive, performing inline validation as...

Operations in DevOps is not just about keeping systems up anymore. Teams now have to support faster releases, manage cloud-native environments, improve security, and keep services reliable at scale. That is a big shift. Operations is no longer a back-office function. It plays a direct role in how fast and how safely the business can move. New technology has made this easier in some ways. Tools like Infrastructure as Code, observability platforms, and AIOps can reduce manual work and give teams better control. But they also add complexity. More tools do not automatically mean better operations. Many teams still deal with alert fatigue, messy handoffs, and too much operational noise. That is why modern operations need a different approach. The goal is not to add more processes. It is to build systems that are easier to run, easier to monitor, and easier to improve. In DevOps, good operations means less toil, better visibility and faster recovery when things go wrong. In this artic...

GitHub has suspended new sign-ups for several of its Copilot subscription tiers, a decision that follows a surge in demand driven by agentic coding workflows, which consume far more compute resources than earlier models of AI assistance. The company confirmed that new subscriptions for Copilot Pro, Pro+, and Student plans are paused, while existing users face tighter usage limits. Internally, the change is framed as a step to maintain service reliability. In practice, it signals that the original pricing model, built around predictable, lightweight usage, no longer aligns with how developers now use AI tools. “Cloud agent sessions running multi-step validation pipelines have materially raised per-interaction costs, and entitlement architecture is being reshaped accordingly,” Mitch Ashley, VP, Software Engineering, The Futurum Group, told Techstrong.it. “Enterprise teams evaluating Copilot Pro+ should treat this as an early signal of access control tightening across agentic develop...