The US Department of Justice is siding with X, as the social media platform owned by Elon Musk navigates a criminal investigation unfolding in France. As first reported by The Wall Street Journal, the Justice Department characterized the French probe as “an effort to entangle the United States in a politically charged criminal proceeding aimed at wrongfully regulating through prosecution the business activities of a social media platform.”

France launched its investigation into X in July, accusing the platform of manipulating its algorithm and “fraudulent data extraction.” Months later, French authorities raided X’s office in Paris and issued summonses to Musk and Linda Yaccarino, the former CEO of X, to appear for interviews on April 20 as part of the probe. According to WSJ, French officials are also investigating X for other charges, including disseminating CSAM and Holocaust denial. However, France’s latest move to ask the Department of Justice for assistance has been stonewalled.

“This investigation seeks to use the criminal legal system in France to regulate a public square for the free expression of ideas and opinions in a manner contrary to the First Amendment of the United States Constitution,” the DOJ wrote in letter, as seen by WSJ.

An xAI official told WSJ that it’s “grateful to the Justice Department for rejecting this effort by a prosecutor in Paris to compel our CEO and several employees to sit for interviews.” The company spokesperson also said there was “no wrongdoing” and that it was a “baseless investigation.”

The journey from a laboratory hypothesis to a pharmacy shelf is one of the most grueling marathons in modern industry, typically spanning 10 to 15 years and billions of dollars in investment.

Progress is often stymied not just by the inherent mysteries of biology, but by the “fragmented and difficult to scale” workflows that force researchers to manually pivot between the actual experimental design equipment, software, and databases.

But OpenAI is releasing a new specialized model GPT-Rosalind specifically to speed up this process and make it more efficient, easier, and ideally, more productive. Named after the pioneering chemist Rosalind Franklin, whose work was vital to the discovery of DNA’s structure (and was often overlooked for her male colleagues James Watson and Francis Crick), this new frontier reasoning model is purpose-built to act as a specialized intelligence layer for life sciences research.

By shifting AI’s role from a general-purpose assistant to a domain-specific “reasoning” partner, OpenAI is signaling a long-term commitment to biological and chemical discovery.

What GPT-Rosalind offers

GPT-Rosalind isn’t just about faster text generation; it is designed to synthesize evidence, generate biological hypotheses, and plan experiments—tasks that have traditionally required years of expert human synthesis.

At its core, GPT-Rosalind is the first in a new series of models optimized for scientific workflows. While previous iterations of GPT excelled at general language tasks, this model is fine-tuned for deeper understanding across genomics, protein engineering, and chemistry.

To validate its capabilities, OpenAI tested the model against several industry benchmarks. On BixBench, a metric for real-world bioinformatics and data analysis, GPT-Rosalind achieved leading performance among models with published scores.

In more granular testing via LABBench2, the model outperformed GPT-5.4 on six out of eleven tasks, with the most significant gains appearing in CloningQA—a task requiring the end-to-end design of reagents for molecular cloning protocols.

The model’s most striking performance signal came from a partnership with Dyno Therapeutics. In an evaluation using unpublished, “uncontaminated” RNA sequences, GPT-Rosalind was tasked with sequence-to-function prediction and generation.

When evaluated directly in the Codex environment, the model’s submissions ranked above the 95th percentile of human experts on prediction tasks and reached the 84th percentile for sequence generation.

This level of expertise suggests the model can serve as a high-level collaborator capable of identifying “expert-relevant patterns” that generalist models often overlook.

The new lab workflow

OpenAI is not just releasing a model; it is launching an ecosystem designed to integrate with the tools scientists already use. Central to this is a new Life Sciences research plugin for Codex, available on GitHub.

Scientific research is famously siloed. A single project might require a researcher to consult a protein structure database, search through 20 years of clinical literature, and then use a separate tool for sequence manipulation. The new plugin acts as an “orchestration layer,” providing a unified starting point for these multi-step questions.

• Skill Set: The package includes modular skills for biochemistry, human genetics, functional genomics, and clinical evidence.

• Connectivity: It connects models to over 50 public multi-omics databases and literature sources.

• Efficiency: This approach targets “long-horizon, tool-heavy scientific workflows,” allowing researchers to automate repeatable tasks like protein structure lookups and sequence searches.

Limited and gated access

Given the potential power of a model capable of redesigning biological structures, OpenAI is eschewing a broad “open-source” or general public release in favor of a Trusted Access program.

The model is launching as a research preview specifically for qualified Enterprise customers in the United States. This restricted deployment is built on three core principles: beneficial use, strong governance, and controlled access.

Organizations requesting access must undergo a qualification and safety review to ensure they are conducting legitimate research with a clear public benefit.

Unlike general-use models, GPT-Rosalind was developed with heightened enterprise-grade security controls. For the end-user, this means:

• Restricted Access: Usage is limited to approved users within secure, well-managed environments.

• Governance: Participating organizations must maintain strict misuse-prevention controls and agree to specific life sciences research preview terms.

• Cost: During the preview phase, the model will not consume existing credits or tokens, allowing researchers to experiment without immediate budgetary constraints (subject to abuse guardrails).

Warm reception from initial industry partners

The announcement garnered significant buy-in from OpenAI parnters across the pharmaceutical and technology sectors.

Sean Bruich, SVP of AI and Data at Amgen, noted that the collaboration allows the company to apply advanced tools in ways that could “accelerate how we deliver medicines to patients”.The impact is also being felt in the specialized tech infrastructure that supports labs:

• NVIDIA: Kimberly Powell, VP of Healthcare and Life Sciences, described the convergence of domain reasoning and accelerated computing as a way to “compress years of traditional R&D into immediate, actionable scientific insights”.

• Moderna: CEO Stéphane Bancel highlighted the model’s ability to “reason across complex biological evidence” to help teams translate insights into experimental workflows.

• The Allen Institute: CTO Andy Hickl emphasized that GPT-Rosalind stands out for making manual steps—like finding and aligning data—more “consistent and repeatable in an agentic workflow”.

This builds on tangible results OpenAI has already seen in the field, such as its collaboration with Ginkgo Bioworks, where AI models helped achieve a 40% reduction in protein production costs.

What’s next for Rosalind and OpenAI in life sciences?

OpenAI’s mission with GPT-Rosalind is to narrow the gap between a “promising scientific idea” and the actual “evidence, experiments, and decisions” required for medical progress.

By partnering with institutions like Los Alamos National Laboratory to explore AI-guided catalyst design and biological structure modification, the company is positioning GPT-Rosalind as more than a tool—it is meant to be a “capable partner in discovery”.

As the life sciences field becomes increasingly data-dense, the move toward specialized “reasoning” models like Rosalind may become the standard for navigating the “vast search spaces” of biology and chemistry.

The new funding will be used to expand the Copenhagen-based company’s AI platform for banks and fintech companies, and accelerate adoption across financial institutions globally.

Danish financial compliance AI start-up Spektr has raised $20m in a Series A funding round led by New Enterprise Associates (NEA) with participation from existing investors including Northzone, Seedcamp and PSV Tech.

The new funding will be used to expand the Copenhagen-based company’s AI platform for banks and fintech companies, and accelerate adoption across financial institutions globally, according to the company.

According to Spektr, its specialised AI agents are designed to perform the work financial analysts typically do during compliance reviews – such as researching companies, interpreting information, verifying business activity and generating structured risk assessments – and instead of analysts spending hours gathering and interpreting data, the agents complete the work in minutes so compliance teams can review and approve the results.

“Compliance technology has mostly focused on workflow and data collection,” said Mikkel Skarnager, CEO and co-founder of Spektr.

“But the real bottleneck has always been the work itself – analysts researching companies, interpreting information and documenting decisions. Spektr automates those tasks with AI agents designed specifically for KYC and KYB compliance.”

Spektr was co-founded by Skarnager, CTO Ciprian Florescu, CRO Jan-Erik Wagner and CPO Jeremy Joly. Its live customers include Santander Leasing, Pleo, Mercuryo, Monta and Phantom.

“Financial institutions are under constant pressure to do more compliance work with fewer resources,” said Luke Pappas, partner at NEA.

“Spektr is tackling the most manual part of compliance operations in financial services. Their approach has the potential to redefine how compliance operations are run.”

Don’t miss out on the knowledge you need to succeed. Sign up for the Daily Brief, Silicon Republic’s digest of need-to-know sci-tech news.

Microsoft is warning that a recent Microsoft Edge browser update introduced a bug that breaks right-click paste in chats in the Microsoft Teams desktop client.

In an advisory published on April 14, Microsoft says users are reporting that they are unable to paste URLs, text, or images into Teams chats when using right-click context menus, with the “Paste” option greyed out.

To work around the bug, Microsoft says users can still copy and paste content using keyboard shortcuts: Ctrl + C and Ctrl + V on Windows, or Cmd + C and Cmd + V on macOS.

“Impacted users report that they are unable to copy and paste URLs, text, and images in Microsoft Teams desktop client chats, as the paste option appears greyed out when using the right-click dropdown menu method,” explains Microsoft.

“To bypass impact, we recommended that users attempt to copy the intended URLs, text, and images using Ctrl + C and paste using Ctrl + V for Windows, and corresponding Cmd + C and Cmd + V for Mac.”

Microsoft says the bug is caused by a recent browser update that introduced a code regression in Microsoft Edge, which Microsoft Teams uses for certain functionality.

Admins on Reddit and the Microsoft forums report that the problem is affecting users in corporate environments as well as individual users.

“I have multiple users on version 26072.519.4556.7438 experiencing this issue, including myself. Cannot right-click Paste, but CTRL+V and paste as text are allowed,” an admin posted to the Microsoft Forums.

Other users said that reinstalling Teams or clearing the cache did not fix the problem.

Microsoft says it identified the cause and is rolling out a fix in stages while monitoring telemetry to confirm that systems are recovering.

As of the latest update on April 16, Microsoft has not provided an exact timeline for when the fix will be fully rolled out.

AI chained four zero-days into one exploit that bypassed both renderer and OS sandboxes. A wave of new exploits is coming.

At the Autonomous Validation Summit (May 12 & 14), see how autonomous, context-rich validation finds what’s exploitable, proves controls hold, and closes the remediation loop.

Claim Your Spot

Sometimes the best finds at AXPONA 2026 aren’t planned. I walked into Chesky Audio’s room chasing Schiit Audio gear in Room 709; there was plenty of it, including the Yggdrasil Singular DAC, Loki Max, Kara, and a pair of Tyr monoblocks driving the new Chesky LC2 loudspeakers, but no one from Schiit to talk shop. So I stayed put, listened, and let the room tell its own story.

That story changed fast when the pricing banner came into focus: $1,995. Not each. Per pair. In a show full of six-figure loudspeakers, the Chesky LC2 doesn’t just feel affordable; it feels like a direct challenge to how high-end audio defines itself.

And that’s where this gets more interesting. If high-end audio wants a future, it needs more designers like Lucca Chesky. He comes from a family name that carries real weight in the music world, but he’s not coasting on it. He’s studying engineering at Carnegie Mellon University, and it shows in how he approaches both design and people.

There’s no gatekeeping here, no “you don’t belong in this room” energy. The LC1 and now the LC2 are priced where actual listeners can engage, and he speaks about them in a way that makes you feel like you’re part of the conversation and not being lectured from behind a stack of gear you can’t afford.

The kid gets it. And judging by what I heard in that room, he’s not just talking a good game.

Admittedly, a $1,995 price tag only matters if the speakers can actually deliver. The original Chesky LC1 set a high bar, earning multiple “Best of Show” nods from the eCoustics team at previous events; something Chesky made no effort to hide with the awards laid out on the table. So yes, I was a bit late to the party.

Better late than never.

I stayed for several tracks to get a clearer sense of what the team had already heard in the Chesky LC1, and what that might mean for the new Chesky LC2. It didn’t take long to recognize a familiar foundation, but with more scale and a bit more weight behind it, suggesting this isn’t a departure so much as a more developed version of the same idea.

An Affordable Speaker With Real Ambition

Much like the original Chesky LC1, the Chesky LC2 sticks to a compact two way monitor format. It pairs a dual chamber aperiodic 1 inch tweeter with a roughly 6.5-inch mid bass driver, both modified in house rather than pulled off a shelf. The familiar passive radiator approach is still here as well, now using larger 8-inch radiators on either side to extend low frequency output without relying on a traditional port.

Where things diverge, and where Chesky is clearly doing its own thing, is the cabinet. The front baffle is a 5/8-inch thick slab of machined aluminum, and the rest of the enclosure is 3D printed around that structure. It is an unusual approach, but the result is a cabinet that feels both rigid and relatively lightweight for its size. Each speaker measures roughly 13 x 9 x 13 inches and comes in just under 30 pounds.

It is also worth noting that these are not outsourced, mass produced boxes. Chesky Audio assembles, finishes, and tests the speakers in New Jersey before they ship. In a category where “designed here, built somewhere else” is the norm, these are actually made in the United States, and that still matters.

Lucca Chesky is also quick to point out that the drivers are not an afterthought. The mid-bass unit uses a cast-basket high-definition design more commonly found in higher-priced speakers, and the tweeter follows that same philosophy. He stops short of naming suppliers, but the implication is clear this is not generic OEM hardware.

The crossover is designed in house, although Chesky remains somewhat tight-lipped on specifics. Instead of locking into a fixed number, the crossover point is described as falling somewhere in the 3 to 5 kHz range. On paper, the speaker is rated at 86 dB sensitivity with a 4 ohm impedance that does not dip below 3.1 ohms across a stated 40 Hz to 20 kHz frequency range.

That combination suggests an easy enough load for most modern amplifiers, whether it is a vintage Kenwood receiver, a newer NAD integrated, or even a well-sorted ST-70 style tube amp build. But if our experience with the Chesky LC1 taught us anything, it is that specs do not tell the whole story. The LC1 benefited from more power than you might expect, and giving it better amplification paid off.

Until we get the Chesky LC2 in for a full review, it is too early to say how closely it follows that pattern.

Chesky LC2 in a Real Room at AXPONA 2026

Sound wise, the Chesky LC2 delivers clean mid-bass with solid detail and impact for a speaker of this size, but sub-bass is limited. That is not a surprise given the form factor. In a nearfield setup such as a desktop or small studio, there is enough low end to get by without a subwoofer, but in a larger room, adding one would make sense.

The midrange is where things come into better focus. There is a clear emphasis on clarity and balance, which aligns with what you would expect from anything carrying the Chesky name. Vocals come through naturally without sounding nasal or forced, and strings have enough presence to avoid sounding thin. That is not always a given with compact speakers, where cabinet limitations can work against natural timbre. The construction here likely plays a role, but that is something that needs more controlled listening to fully evaluate.

The top end had good energy and dynamic presence, but this is where the limitations of the show environment start to creep in. Between room noise and less than ideal setup conditions, it would be premature to draw firm conclusions without spending more time with the speakers in a more controlled space.

The Bottom Line

I can see several use cases for the Chesky LC2. Those looking for unpowered monitors for nearfield use will find them easy to live with as a standalone pair, and they also make sense in smaller rooms where space is limited. For larger spaces or mixed use systems that pull double duty for music and home theater, Chesky offers two, three, and five speaker packages that can be built out as needed.

Adding a subwoofer would round things out in those scenarios. Models like the REL Tzero or SVS 3000 Micro R|Evolution come to mind as good matches, offering tight, controlled low end without taking over the room or the budget.

With that kind of setup, the LC2 starts to make a lot of sense for multi purpose spaces where flexibility matters just as much as performance.

Where to buy: $1,995/pair at Chesky Audio

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Proof-of-concept exploit code has been published for a critical remote code execution flaw in protobuf.js, a widely used JavaScript implementation of Google’s Protocol Buffers.

The tool is highly popular in the Node Package Manager (npm) registry, with an average of nearly 50 million weekly downloads. It is used for inter-service communication, in real-time applications, and for efficient storage of structured data in databases and cloud environments.

In a report on Friday, application security company Endor Labs says that the remote code execution vulnerability (RCE) in protobuf.js is caused by unsafe dynamic code generation.

The security issue has not received an official CVE number and is currently being tracked as GHSA-xq3m-2v4x-88gg, the identifier assigned by GitHub.

Endor Labs explains that the library builds JavaScript functions from protobuf schemas by concatenating strings and executing them via the Function() constructor, but it fails to validate schema-derived identifiers, such as message names.

This lets an attacker supply a malicious schema that injects arbitrary code into the generated function, which is then executed when the application processes a message using that schema.

This opens the path to RCE on servers or applications that load attacker-influenced schemas, granting access to environment variables, credentials, databases, and internal systems, and even allowing lateral movement within the infrastructure.

The attack could also affect developer machines if those load and decode untrusted schemas locally.

The flaw impacts protobuf.js versions 8.0.0/7.5.4 and lower. Endor Labs recommends upgrading to 8.0.1 and 7.5.5, which address the issue.

The patch sanitizes type names by stripping non-alphanumeric characters, preventing the attacker from closing the synthetic function. However, Endor comments that a longer-term fix would be to stop round-tripping attacker-reachable identifiers through Function at all.

Endor Labs is warning that “exploitation is straightforward,” and that the minimal proof-of-concept (PoC) included in the security advisory reflects this. However, no active exploitation in the wild has been observed to date.

The vulnerability was reported by Endor Labs researcher and security bug bounty hunter Cristian Staicu on March 2, and the protobuf.js maintainers released a patch on  GitHub on March 11. Fixes to the npm packages were made available on April 4 for the 8.x branch and on April 15 for the 7.x branch.

Apart from upgrading to patched versions, Endor Labs also recommends that system administrators audit transitive dependencies, treat schema-loading as untrusted input, and prefer precompiled/static schemas in production.

AI chained four zero-days into one exploit that bypassed both renderer and OS sandboxes. A wave of new exploits is coming.

At the Autonomous Validation Summit (May 12 & 14), see how autonomous, context-rich validation finds what’s exploitable, proves controls hold, and closes the remediation loop.

Claim Your Spot