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Alerts are more trustworthy and actionable when noise is reduced. See how we improved the verification step with context-aware LLM reasoning. The post Making secret scanning more trustworthy: Reducing false positives at scale appeared first on The GitHub Blog .

In December 2025, Anthropic acquired Bun , the JavaScript runtime written in Zig. In April 2026, the Bun team announced a 4× compile-time improvement on their fork of the Zig compiler — "parallel semantic analysis and multiple codegen units to the llvm backend" , in their phrasing. They also announced they would not be upstreaming the work, "as Zig has a strict ban on LLM-authored contributions." The framing landed badly with Zig observers, for two reasons. The first was that the framing made Zig's contribution policy the obstacle. The second, pointed out shortly afterwards by a Zig core contributor in the Ziggit thread, was that the patch had separate engineering reasons it would not have been merged regardless: "Parallel semantic analysis has been an explicitly planned feature of the Zig compiler for a long time" , with "implications not only for the compiler implementation, but for the Zig language itself" . The AI-ban explanation was, on a closer read, a tidy way of declining to litigate the engineering disagreement in public. Both readings are useful. They are also both downstream of the actual rationale, which is one of the most carefully argued OSS-governance documents to appear in 2026. What the policy actually says The relevant clauses, in the Zig code of conduct under the section heading Strict No LLM / No AI Policy , are three: No LLMs for issues. No LLMs for pull requests. No LLMs for comments on the bug tracker, including translation. English is encouraged, but not required. You are welcome to post in your native language and rely on others to have their own translation tools of choice to interpret your words. The translation clause is the surprising one. It is also the one that disambiguates the policy from a code-quality rule. A blanket ban on LLM-mediated communication, including translation, is not a heuristic about whether agentic tools produce good code. It is a stance about what the project's communication channels are for . Contributor poker Lor

Uber One, meet Waymo Premier. The robotaxi operator announced a new $29.99-a-month premium tier for riders who want a more elevated and exclusive autonomous experience. The invite-only membership service is aimed at Waymo customers who use the service most frequently, offering them a number of perks, including priority pickups, 10 percent cash back on every […]

Introduction Nowadays, AI agents are becoming increasingly powerful at assisting users in their daily web activities. However, we cannot yet allow them to act completely autonomously—there is still a risk of them clicking on the wrong elements, for instance. In theory, these agents are capable of performing impressive tasks, provided they are guided step-by-step through the interface. The challenge here is not a lack of intelligence in the model, nor a shortage of web APIs to expose data to the agent. The core issue lies in the fact that the agent must currently "guess" its way through applications that were designed exclusively for humans. This is precisely the problem that WebMCP is here to solve. It is important to note that these are not intended to replace standard APIs as access points for an application. Instead, they provide a structured way for a web application to "instruct" the AI agent used in the browser on how to navigate its interface. This results in: Fewer misplaced clicks. Less trial-and-error when interacting with the UI. When utilized to their full potential, WebMCPs could redefine the user experience in the coming years. What is WEBMCP? As you may have guessed, WebMCP is a browser-side "guide/standard" for exposing tools to an AI agent directly from an active web page. During Google I/O, this new feature was introduced as a way for web applications to describe how a page functions—and what actions can be performed—to various AI agents. As a result, agents can execute these described actions faster, more efficiently, and with greater precision. Unsurprisingly, the syntax for creating these descriptions relies on JavaScript functions. These functions take natural language descriptions as parameters, along with structured schemas directly exposed from the web page. This is exactly where the power of WebMCP lies. Today, while we have Playwright (designed for end-to-end testing of web applications) and Playwright MCP (which extends this model to LLMs

Alt Carbon said the agreement followed more than a year of scientific review and due diligence, with Microsoft requiring additional verification and data-sharing measures.

Three years ago, when the women's World Cup kicked off in Australia and New Zealand, my social feeds were in a strange place. Twitter had just transformed into X, newcomer Threads was seemingly ascendant, and places like Bluesky had yet to garner much momentum. It left me with an odd, and admittedly silly, dilemma: I […]

SpaceX alumni Andrew Redd is betting the ocean has vast amounts of untapped geothermal energy.

Brutal self-assessment paints a picture of a Microsoft gaming division in crisis.

South Korean authorities issued the record-breaking fine following a data breach that affected over 30 million customers.

The general shape of the problem is that every public LLM benchmark is on a saturation clock that runs from the moment of its publication to the moment a model's training corpus has eaten it. The clock has been running, on the visible benchmarks of the last five years, for somewhere between twelve and thirty months before each one is no longer useful for differentiating frontier models. The benchmarks are not failing. They are doing exactly what they were designed to do, in the order they were designed to do it, and the field has been running through them faster than the people designing them anticipated. I want to put numbers on the saturation pattern, walk through what the contamination evidence actually says, and then sit with the question of what an honest benchmark would have to look like in 2026 — because the "private held-out eval" answer that the labs are converging on has economics that are worth examining carefully before any of us salute it as the solution. The saturation timeline, with numbers HumanEval (Chen et al., OpenAI, July 2021). 164 hand-written Python problems. The benchmark was published with Codex at 28.8% pass@1; the underlying GPT-3 base model scored 0%. GPT-4 (March 2023) hit 67% in the original Technical Report. By late 2024, OpenAI's o1-preview and o1-mini both reached 96.3% pass@1 ; Claude 3.5 Sonnet sat at 93.7%. The benchmark is saturated in the operational sense — the relative spread across the top ten models is around 10 percentage points, which is too small a gap to differentiate them on, and most of the new models arrive within a percentage point or two of the ceiling. The successor variants (HumanEval+ from EvalPlus, with augmented test cases) are the field's response. Lifespan from publication to operational saturation: about 36 months. MMLU (Hendrycks et al., September 2020). 57 subjects, ~14,000 multiple-choice questions, taken from publicly-available test prep and academic sources. The problem with MMLU is not that it's satura

The partnership will see TCS creating a business unit focused on deploying Anthropic's AI models to its customers.

Anthropic has apologized for stealthily throttling its new AI model, Claude Fable 5, with hidden guardrails that undermine both researchers and rivals using it to develop competing systems. The company says it is reversing course and will be more transparent about when the restrictions kick in, even if that means Fable refuses more queries. Fable […]

The surveillance company Leonardo wants more data : A surveillance company plans to add sensors to automatic license plate readers (ALPRs) that would mean the devices, as well as capture the license plate of passing vehicles, would also sweep up unique identifiers of mobile phones, wearables, and other Bluetooth-enabled devices in those cars, potentially letting law enforcement identify specific drivers or passengers. The technology, called SignalTrace, would turn ALPR cameras from devices focused on tracking cars to ones that can more readily track the location of particular people. ALPR cameras have become a commonly deployed technology all across the U.S.; SignalTrace would make some of those cameras capable of collecting much more data...

New research suggests that dozens of monetized YouTube channels are run by people and organizations that the US government has sanctioned for their ties to Tehran.

It’s the first estimate of how many Americans are sneaking onto Polymarket’s banned crypto-based platform.

This impressively light carbon commuter makes switching to an ebike easier than ever.

It might not feel all that different from older World Cups—for better or worse.

The games start June 11 and end with a grand finale in New Jersey on July 19. There are 104 of them. Here’s how to watch ’em all.

It’s a tale of two nuclear industries. In China, large reactors are coming together at a stunning pace. The country has nearly doubled its nuclear fleet since 2016, reaching nearly 60 gigawatts of total power capacity. The new facilities are nearly all gigawatt-scale pressurized-water reactors. Meanwhile, the US has built just two reactors in that…

In 2018, after nearly two decades working in Big Pharma, chemist Tim Cernak was ready to put his skills to a new use. For Merck, he’d developed precision therapies for cancer, HIV, and diabetes that could target disease while minimizing harm to healthy cells. But as a lifelong nature lover, he was increasingly concerned about…