Polymarket says it will fully reimburse customers who lost an estimated $3 million after hackers injected a malicious script into the platform's frontend following a breach at a third-party vendor. [...]
While European tourists marvel at ranch dressing, Buc-ee's, and other wonders of Middle America, would-be World Cup attendees from Africa, Asia, and the Middle East have experienced a different American pastime: exclusion. President Donald Trump's nakedly racist immigration policies have prevented scores of people from traveling to the United States for the event - even, […]
AI has really changed the game around software development. More people are leveraging AI than ever to contribute patches to projects they use. To me, this is a good thing as more folks will contribute patches rather than fork or not fix them. The main problem is that AI has made generating code fast but there has been very little improvement in maintaining code bases. In this post, we will highlight the ways the Kubernetes community is adapting to the world of AI assisted coding. The first step of this journey was to develop an AI policy. This seems mundane and bureaucratic but there were many PRs that derailed into discussions around AI usage. The AI policy helps steer the conversation around the project's stance on AI and provides a clear signal to contributors on how to use these tools responsibly. Kubernetes AI policy The Kubernetes project has established clear guidelines for AI-assisted contributions that balance innovation with accountability. These policies are designed to maintain code quality and ensure human oversight while acknowledging that AI tools can be valuable aids in the development process. Transparency first Contributors must disclose when AI tools have been used to assist with a pull request. A simple statement in the PR description such as "This PR was written in part with the assistance of generative AI" is sufficient. This transparency helps reviewers understand the context and apply appropriate scrutiny. Human accountability While AI tools can assist, the human contributor remains fully responsible for every change. The policy explicitly prohibits: Listing AI as a co-author on commits Using AI co-signing on commits Adding trailers like "assisted-by" or "co-developed" that attribute work to AI This isn't about diminishing AI's role as a tool—it's about maintaining clear accountability. If something breaks, there needs to be a human who understands why and can fix it. CLA enforcement for co-authors The CNCF provides a tool for verifying the contributor license agreements on each pull request. AI agents are not able to solve these contributor license agreements so one enforcement the project made is to enable the CLA check for co-authors. This provides a flag to reviewers that the PR is not ready to merge. Human engagement required Perhaps the most critical aspect of the policy: reviewers expect to engage with humans, not with AI. Contributors cannot rely on AI to respond to review comments. If you cannot personally explain changes that AI helped generate, your PR will be closed. This requirement ensures that knowledge transfer happens and that contributors genuinely understand the code they're submitting. Verification obligations Contributors must verify AI-generated changes through code review, testing, and personal understanding. It's not enough for the code to work—you need to know why it works and be able to maintain it. These policies reflect a mature approach to AI: embrace it as a tool, but never let it replace human judgment, understanding, or responsibility. Automated AI reviews There exist many tools to aid in reviewing code. AI pull request tools introduce governance challenges so one of the first tasks the community took on was to document the process for what is needed to bring in new AI tools. One of the major evaluation criteria for these tools is to find maintainers willing to test drive them in kubernetes-sigs repositories. Kueue, JobSet and Agent-Sandbox have been experimenting with these tools to provide more support for maintainers. Copilot One tool that many maintainers started using was GitHub Copilot. The CNCF provides access for maintainers so this ended up being the first tool many started using. It provides some good experience on tuning reviews but there were some growing pains with this tool. The biggest blocker for community adoption is relying on contributors to have a copilot license. Only maintainers were able to request copilot reviews and automated reviews of pull requests was out of reach for the community. One of the goals of AI review tools is to provide an automated review tool that maintainers don't need to request. This demonstrated the need for organization control rather than relying on contributors having access. CodeRabbit In mid 2026, the Kubernetes community has rolled out CodeRabbit to a few projects. As with copilot, some tuning has been required to provide better reviews but the overall feedback has been positive. There is a lot of configuration available for this tool and one of the most interesting uses of this tool comes from agent-sandbox. AI pull request tools can be a quality gate. Contributors can at least get a quick spot check review without waiting for a maintainer. Agent-sandbox has added a label on PRs to reflect that there is still a need to resolve some of the comments from AI tools. Next steps The reality is that leveraging AI in open source projects is an area of active exploration. The community could use your help in tuning reviews tools, evaluating tools or evaluating emerging technologies in the AI space. Some areas we are exploring more: The use of AI skills to reduce maintainer burnout. AI assisted triage of failing tests. Skills to aid the operational aspects of Kubernetes.
Incident Report: CVE-2026-LGTM Spectacular hypothetical incident report by Andrew Nesbitt. Day 2, 16:00 UTC --- Two AI review agents from competing vendors, both attached to a downstream pull request bumping foxhole-lz4 , enter a disagreement loop over whether the package is malicious. After 340 comments and $41,255 in inference spend, Finance revokes both API keys; one vendor's marketing team, cc'd on the cost anomaly alert, issues a press release citing "a 430% YoY increase in adversarial multi-agent security reasoning." The stock opens up 6%. Tags: security , ai , prompt-injection , generative-ai , llms , supply-chain , ai-security-research , andrew-nesbitt
I can understand why The Verge readers are flocking to the BirdBuddy deal we posted at the beginning of Prime Day. The BirdBuddy Pro with Solar Panels is a wholesome upgrade for your yard, but the full price of $299 is tough to stomach. After all, the BirdBuddy is essentially a video doorbell and solar […]
Threat actors are creating OpenAI tenants that impersonate legitimate companies and inviting employees to join them, in what appears to be a ploy to trick targets into submitting sensitive company information in chats and projects. [...]
It's "an exciting advance in efforts to restock the antibiotic arsenal."
Meta has to justify its AI spending to unlock the struggling stock.
Nvidia has dominated the AI chip market for years, but the era of total dependence might be ending. OpenAI just shared its plans to spice things up with Jalapeño, its custom inference chip built with Broadcom, joining Google, Apple, and SpaceX in a growing list of companies building their way out of single-supplier risk. The goal is less of a […]
Epoch AI's new MirrorCode benchmark tests whether AI models can recreate complete programs without access to the original code. Claude Opus 4.7 leads with a 56 percent solve rate, rebuilding a 16,000-line toolkit in just 14 hours. But every model tested still fails on the most complex tasks. The article An AI model programmed nonstop for 19 days on a single MirrorCode task that cost $2,600 to run appeared first on The Decoder .
We're beginning a limited preview of the GPT‑5.6 series: Sol, our flagship model; Terra, a balanced model for everyday work; and Luna, a fast and affordable model. Terra has competitive performance to GPT‑5.5 while being 2x cheaper and Luna brings strong capability at our lowest cost. [...] We believe in broad access, and we plan to make GPT‑5.6 Sol, Terra, and Luna generally available in the coming weeks. As part of our ongoing engagement with the U.S. government, we previewed our plans and the models’ capabilities ahead of today’s launch. At their request, we are starting with a limited preview for a small group of trusted partners whose participation has been shared with the government, before releasing more broadly. [...] GPT‑5.6 is priced per 1M tokens across three model sizes: Sol is $5 input / $30 output; Terra is $2.50 input / $15 output; and Luna is $1 input / $6 output. GPT‑5.6 also introduces more predictable prompt caching, including support for explicit cache breakpoints and a 30-minute minimum cache life. For GPT‑5.6 and later models, cache writes are billed at 1.25x the model’s uncached input rate, while cache reads continue to receive the 90% cached-input discount. — OpenAI , Previewing GPT‑5.6 Sol: a next-generation model Tags: gpt , generative-ai , ai-security-research , openai , llms , llm-release , llm-pricing
System card: https://deploymentsafety.openai.com/gpt-5-6-preview Comments URL: https://news.ycombinator.com/item?id=48689028 Points: 934 # Comments: 571
OpenAI said it previewed the new models' capabilities with the government ahead of the launch.
Less than 24 hours after news broke that OpenAI would stagger its next model release at the request of the Trump administration, that model, GPT-5.6, is here. On Friday, the company unveiled the limited preview of its new GPT 5.6 model suite: Sol, the flagship; Terra, a medium-tier model for "high-volume work"; and Luna, a […]
The best Stratechery content from the week of June 22, 2026, including a vibe coding adventure, Apple in Europe, and a midsummer mailbag.
Poor database performance is never just a database problem. In enterprise teams, it shows up as missed SLAs, delayed releases, frustrated development teams, and rising operational risk. The performance problem compounds further in business impact, often resulting in frustrated customers, retention and conversion risk, and lost revenue. The post The performance dividend: Optimizing PostgreSQL on Azure directly in Visual Studio Code appeared first on Microsoft Azure Blog .
Assa Abloy has laid off the majority of staff at Level Home, the smart lock company known for building smart tech into traditional-looking deadbolts, and is folding the business into Kwikset, according to a source familiar with the decision. The Verge obtained exclusive details from a person familiar with the restructuring who requested anonymity as […]
MAI-Code-1-Flash, Microsoft AI’s in-house coding model, is now generally available for GitHub Copilot Business and Copilot Enterprise, building on its recent expansion across Copilot surfaces. Purpose-built for coding and optimized… The post MAI-Code-1-Flash for Copilot Business and Copilot Enterprise appeared first on The GitHub Blog .
Join us on the livestream at 1 pm ET and ask questions about the aftermath of New Glenn.
AI models have progressed to the point where their capabilities have real political consequences. Dealing with those consequences will require collective action.
A Chinese-speaking advanced persistent threat (APT) actor has been linked to a new custom backdoor called TinyRCT as part of cyber attacks aimed at government entities and critical infrastructure in Southeast Asia. The activity, particularly aimed at state-owned enterprises in the energy and government sectors, has been attributed to a threat actor called CL-STA-1062, which Palo Alto Networks
If you’ve been curious about the emerging world of smart glasses, Prime Day is your chance to try some of the best models with a rare discount from Best Buy or directly from Xreal. The high-end Xreal One Pro is $549, $50 lower than the usual price, and the more entry-level Xreal 1S is $399, […]
The pivot into physical AI grows On Semi's addressable market by an additional $30 billion, the company said.
Arduino's Raspberry Pi-like microcomputer the Uno Q is joining many other devices in getting a price hike this week. The 2GB version of the Uno Q, which is powered by a Qualcomm Dragonwing QRB2210 processor, will increase in price from $44 to $59, while the 4GB model is getting a slightly steeper increase from $59 […]