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developer-gets-4-years-for-activating-network-“kill-switch”-to-avenge-his-firing

Developer gets 4 years for activating network “kill switch” to avenge his firing

“The defendant breached his employer’s trust by using his access and technical knowledge to sabotage company networks, wreaking havoc and causing hundreds of thousands of dollars in losses for a U.S. company,” Galeotti said.

Developer loses fight to avoid prison time

After his conviction, Lu moved to schedule a new trial, asking the court to delay sentencing due to allegedly “surprise” evidence he wasn’t prepared to defend against during the initial trial.

The DOJ opposed the motion for the new trial and the delay in sentencing, arguing that “Lu cannot establish that the interests of justice warrant a new trial” and insisting that evidence introduced at trial was properly disclosed. They further claim that rebuttal evidence that Lu contested was “only introduced to refute Lu’s perjurious testimony and did not preclude Lu from pursuing the defenses he selected.”

In the end, the judge denied Lu’s motion for a new trial, rejecting Lu’s arguments, siding with the DOJ in July, and paving the way for this week’s sentencing. Giving up the fight for a new trial, Lu had asked for an 18-month sentence, arguing that a lighter sentence was appropriate since “the life Mr. Lu knew prior to his arrest is over, forever.”

“He is now a felon—a label that he will be forced to wear for the rest of his life. His once-promising career is over. As a result of his conduct, his family’s finances have been devastated,” Lu’s sentencing memo read.

According to the DOJ, Lu will serve “four years in prison and three years of supervised release for writing and deploying malicious code on his then-employer’s network.” The DOJ noted that in addition to sabotaging the network, Lu also worked to cover up his crimes, possibly hoping his technical savvy would help him evade consequences.

“However, the defendant’s technical savvy and subterfuge did not save him from the consequences of his actions,” Galeotti said. “The Criminal Division is committed to identifying and prosecuting those who attack US companies whether from within or without, to hold them responsible for their actions.”

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GitHub Copilot moves beyond OpenAI models to support Claude 3.5, Gemini

The large language model-based coding assistant GitHub Copilot will switch from using exclusively OpenAI’s GPT models to a multi-model approach over the coming weeks, GitHub CEO Thomas Dohmke announced in a post on GitHub’s blog.

First, Anthropic’s Claude 3.5 Sonnet will roll out to Copilot Chat’s web and VS Code interfaces over the next few weeks. Google’s Gemini 1.5 Pro will come a bit later.

Additionally, GitHub will soon add support for a wider range of OpenAI models, including GPT o1-preview and o1-mini, which are intended to be stronger at advanced reasoning than GPT-4, which Copilot has used until now. Developers will be able to switch between the models (even mid-conversation) to tailor the model to fit their needs—and organizations will be able to choose which models will be usable by team members.

The new approach makes sense for users, as certain models are better at certain languages or types of tasks.

“There is no one model to rule every scenario,” wrote Dohmke. “It is clear the next phase of AI code generation will not only be defined by multi-model functionality, but by multi-model choice.”

It starts with the web-based and VS Code Copilot Chat interfaces, but it won’t stop there. “From Copilot Workspace to multi-file editing to code review, security autofix, and the CLI, we will bring multi-model choice across many of GitHub Copilot’s surface areas and functions soon,” Dohmke wrote.

There are a handful of additional changes coming to GitHub Copilot, too, including extensions, the ability to manipulate multiple files at once from a chat with VS Code, and a preview of Xcode support.

GitHub Spark promises natural language app development

In addition to the Copilot changes, GitHub announced Spark, a natural language tool for developing apps. Non-coders will be able to use a series of natural language prompts to create simple apps, while coders will be able to tweak more precisely as they go. In either use case, you’ll be able to take a conversational approach, requesting changes and iterating as you go, and comparing different iterations.

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