标签：#open

Edelson PC is filing 30 new lawsuits against OpenAI over the Tumbler Ridge shooting, escalating claims to aiding and abetting and naming Chris Lehane, though evidence remains unconfirmed.

Someone plugs in the HDMI cable. The room's display shows nothing, or shows 1024×768, or shows the desktop of whoever presented last week. After a minute of this, somebody says "just share your screen", and out comes AnyDesk or TeamViewer. It works. It is also the wrong shape for the problem, and noticing why turns out to be more interesting than it sounds. What remote desktop tools are actually for AnyDesk, TeamViewer and RustDesk exist to solve one problem well: reach a machine you are not near. Your parent's laptop. A server in a rack. A colleague's desktop three time zones away. Everything about their design follows from that. One person connects to one machine. That person takes the mouse. The remote screen is mirrored to them, and the whole session is framed as control — because when you are not in the room, control is the only way to do anything. Being fair about the privacy question, because it is the claim people reach for first and it is wrong: AnyDesk has a LAN mode , and in it a session goes directly between the two machines without the internet. RustDesk can be self-hosted entirely on your own infrastructure. Neither of these tools forces your screen through somebody's cloud if you configure them not to. If you have read that they do, that is not accurate. The mismatch is not privacy. It is shape. The relay, the account and the NAT traversal are not overhead — they are the product, and they are what makes reaching a machine in another country possible at all. Standing beside the machine, you pay for all three and use none of them. Where the shape stops fitting Stand in a room with four people and a Mac, and three things go wrong at once. It is one-to-one. Remote desktop is a session between two endpoints. Four people looking at one screen is not what it models, so three of them read over a shoulder. It mirrors, and mirroring is sometimes the wrong answer. Every tool in this category shows a screen that already exists. But the meeting-room problem is oft

Security controls in AI systems often end up duplicated across applications. One team adds prompt validation, secrets detection, PII filtering, authentication, logging, and rate limits to an LLM application. A second team builds similar controls around another provider. Once agents enter the mix, the organization has several implementations of policies that should be shared. An AI gateway provides a central place to enforce those controls. Traditional infrastructure already centralizes TLS termination, authentication, rate limiting, traffic routing, and observability instead of rebuilding them in every microservice. The same pattern applies to AI traffic. What is an AI gateway? Without a gateway: +--> OpenAI Application -------+--> Anthropic +--> Gemini With a gateway: Applications | v +-------------------------+ | AI Gateway | | | | Authentication | | Rate limits | | Guardrails | | Routing | | Logging / observability | +------------+------------+ | +------+------+ | | | v v v OpenAI Anthropic Gemini Products in this space include Bifrost , Kong AI Gateway , LiteLLM , and Cloudflare AI Gateway . Their feature sets differ, but each can route model traffic through a common control layer. Security policy can run at that layer before traffic reaches a provider. Why application-level guardrails become painful To prevent users from accidentally sending credentials to an LLM, a simple implementation might look like this: def ask_llm ( prompt ): if contains_secret ( prompt ): raise SecurityError ( " Potential secret detected " ) return llm . chat ( prompt ) For one application, this is reasonable. Across 30 services, enforcement starts to drift. Some services call OpenAI directly, others use Anthropic, and several teams maintain their own wrappers. One application omits the secret check, another uses an old version, and a third checks prompts but no other AI interactions. Policy enforcement now depends on convention. Moving enforcement to the gateway changes the model: Requ

Hoi hoi! I’m @nyaomaru, a frontend engineer who is surprised by how cold it is in the Netherlands...

Pessoal, eu quase mandei uma lista de 23 "vulnerabilidades" pra mantenedores de código aberto. XSS, CORS, CSP, um SVG suspeito. O modelo tinha devolvido volume. Eu sentia que tinha trabalhado. Aí parei. Se eu fosse o cara do outro lado da issue, eu levaria isso a sério? A conta virou quando eu parei de pedir "audita meu código". Passei a escrever o contrato antes de abrir o repo. Direção (o que o modelo pode afirmar) e disciplina (o que ele não pode). Isso não deixa o LLM mais inteligente. Controla o comportamento. Em segurança, isso é o que separa auditoria de lista. O que você leva daqui: o contrato (esqueleto), por que o scanner não substitui hunt, e as três portas pra não colar relatório no tracker. Os repos no meio são prova. Não são o ponto. Tabela de Conteúdo 1. Direção e disciplina não deixam o modelo mais inteligente 2. O scanner vê o que está. O hunt vê o que falta 3. Silêncio também é resultado 4. As três portas 5. O que você monta amanhã 1. Direção e disciplina não deixam o modelo mais inteligente Esse é o ponto que eu demorei pra aceitar. O modelo já conhece OWASP. O que muda é o que ele tem permissão de afirmar. Sem isso, "audita meu código" devolve XSS fantasma, CORS, CSP, um SVG. Volume. A pergunta de segurança some: disto aqui, o que vira issue, o que vai pro e-mail privado, o que é silêncio? Direção amarra três coisas antes da leitura: Padrão externo. OWASP ASVS 5.0, com o ID do requisito. "Boas práticas" não conta. Invariante em uma frase, falsificável. "Webhooks deveriam ser autenticados" é opinião. "Todo handler de webhook de entrada verifica autenticidade do remetente antes de mutar estado" é hunt. Formato único. Finding, observação e hipótese não misturam. Disciplina é o que corta o teatro. O texto que o modelo vê antes de abrir arquivo: Padrão: OWASP ASVS 5.0, com o ID do requisito. "Boas práticas" não conta. Invariante: uma frase, falsificável. Relatório: finding, observação e hipótese não misturam. Sem arquivo:linha → não é finding. CWE inv

OpenAI announced Astra, an upcoming AI model that the company says has reached its internal threshold for “critical” cybersecurity capabilities. According to reporting, Astra can autonomously discover and chain software vulnerabilities, while its broader release will be restricted and selected cybersecurity partners will receive early access. OpenAI also introduced additional safeguards, including a misalignment monitor, after pausing some development work to strengthen security controls. Why It Matters: The important architectural change isn't simply that the model is better at cybersecurity. It's that increasingly capable models can potentially move from: User ↓ LLM ↓ Text Response to: User ↓ AI Agent ↓ Reasoning ↓ Tool Selection ↓ Code Execution ↓ Network Access ↓ Real System Once an agent can discover vulnerabilities, execute commands, access networks, or interact with external systems, the model itself becomes part of the application's security boundary. That changes how we should design agentic applications. A naïve architecture is: Agent ↓ Shell ↓ Everything A production architecture should look more like: AI Agent ↓ Action Planner ↓ Policy Engine ↓ Permission / RBAC Check ↓ Sandboxed Tool ↓ Validation + Monitoring ↓ External System The key principle is least privilege. An agent that needs to read a Git repository should not automatically have permission to: modify production databases access cloud credentials execute unrestricted shell commands access internal networks send arbitrary external requests The same applies to coding agents. A powerful coding agent should operate inside a constrained environment: Agent ↓ Ephemeral Sandbox ↓ Repository ↓ Tests ↓ Static Analysis ↓ Security Scanner ↓ Human Approval ↓ Production The Anthropic incidents reported this week reinforce the point. Anthropic disclosed operational security failures involving Claude accessing external systems during testing and subsequently introduced stronger isolation, monitoring, and contro

The AWS API has become infrastructure's common language, and a whole ecosystem has grown up around running it somewhere other than AWS. Some tools mock it for testing. Others implement it for real. Knowing which is which saves you from deploying a dev tool to production or wiring a production platform into your CI pipeline. Two categories The tools split into emulators and real cloud platforms. Emulators intercept AWS API calls and return plausible responses without provisioning real infrastructure, where state is usually ephemeral, VMs never boot, and the goal is behavioural approximation fast enough for a developer's inner loop. Real cloud platforms provision actual infrastructure where EC2 calls boot real virtual machines and block storage carries real persistence guarantees. Emulators Moto Moto ( github.com/getmoto/moto , Apache 2.0, 8,400+ stars) has been around since 2013, making it the oldest option here. It works differently from the rest because rather than running a local server, it patches boto3 calls in-process through a test decorator. A function wrapped in @mock_aws intercepts all AWS SDK calls and returns mock responses without any network traffic. This makes it fast and easy to drop into Python test suites, but it only works for Python. Teams using the AWS CLI, Terraform, or Go SDKs need a server-based option. LocalStack LocalStack ( github.com/localstack/localstack , 64,000+ stars) is the dominant name in local AWS development. It runs as a Docker container exposing the AWS API on localhost:4566 and covers over 120 services. In March 2026, LocalStack archived its Community Edition repository and moved core services behind a paid plan. A free tier remains for non-commercial use and open source projects, but the Base plan covering Cloud Pods persistent state costs $39 per month and the Ultimate plan runs $89 per month. Teams that depended on CE for commercial CI pipelines are now evaluating alternatives. Floci Floci ( floci.dev , github.com/floci-io/f

We’re a small team building Sealshot, a free and open-source screenshot app for macOS. We started working on it because screenshots often become disposable files. We use them for bug reports, QA, documentation, support, and security work, but later they can be hard to find or reuse. They can also accidentally contain sensitive information such as emails, API keys, tokens, internal URLs, or customer data. Sealshot is built around a simple idea: Treat screenshots more like documents than temporary images. It supports: region, window, and scrolling capture screen recording editable annotations OCR and searchable screenshot archives sensitive information detection before sharing encrypted local storage local metadata generation Everything is processed locally on the Mac. It’s open source and free, and we’re still actively improving it. We’d really appreciate feedback, especially from developers, QA engineers, support teams, and people working in security. Website: https://seal-shot.com/ GitHub: https://github.com/ldeng83/Sealshot

OpenAI is moving ChatGPT closer to the clinical systems healthcare teams use every day. The company is enabling direct interoperability between ChatGPT and health-system data sources , including electronic health records, in supported deployments. The change is designed to bring AI-assisted work into clinical workflows instead of requiring clinicians to move between a separate AI tool and the patient chart. The development expands the company's ChatGPT for Healthcare direction , which includes HIPAA-compliant workspaces and responses backed by trusted medical sources. As described in OpenAI's announcement on connecting ChatGPT to health records and healthcare sources , the focus is on making relevant clinical information and AI assistance available within supported EHR layouts and care-coordination processes. For healthcare providers, the significance is practical rather than merely technical. If deployed appropriately, a connected assistant could reduce context switching around routine documentation and information-review tasks. However, the initial communications do not provide an exhaustive list of supported EHR vendors, regions, user roles, or pricing. Availability will depend on deployment-specific arrangements and enterprise partnerships. What ChatGPT's health record connections change The central shift is from a standalone conversational interface to a more integrated clinical copilot model. OpenAI describes ChatGPT being connected to health records and healthcare sources, enabling AI-supported work where clinicians already review and document care. That could support workflows such as: Drafting notes from information available in the clinical context. Summarizing patient information for review. Supporting care coordination across connected healthcare data sources. Keeping AI-assisted tasks inside the EHR interface rather than requiring a separate workspace. These are examples of the types of workflows OpenAI's high-level description points toward, not a guar

Voice dictation is one of those tools that can quietly improve an entire day. OpenWhispr/openwhispr is gaining attention on GitHub, with 43 stars added today, because it treats dictation as a local-first productivity utility rather than just another cloud transcription feature. The project supports local speech-to-text models, including Nvidia Parakeet and Whisper, while also allowing cloud models through a bring-your-own-key workflow. That gives developers an important choice: keep audio on the device for privacy, or trade some privacy for potentially faster or more capable hosted inference. A practical way to start exploring the source is: git clone https://github.com/OpenWhispr/openwhispr.git cd openwhispr git log -5 --oneline For everyday use, the fastest path will usually be the project’s cross-platform release package. After installation, configure a local model if your machine has suitable hardware, or add your own provider credentials through the application settings. Keeping credentials in the app’s secure configuration storage is preferable to committing them to shell history or dotfiles. The architecture choice is especially interesting for independent developers. Local inference can reduce recurring API costs and keeps sensitive conversations away from third-party servers. The trade-off is hardware dependency: CPU-only transcription may introduce noticeable latency, while GPU acceleration can require additional drivers, memory, and model downloads. Before deploying this into a team workflow, I would watch for: Model consistency: Different Whisper or Parakeet variants can produce noticeably different punctuation, latency, and accuracy. Operational boundaries: Local processing improves privacy, but model files, logs, clipboard integration, and temporary audio buffers still need review. The strongest value proposition is not merely “speech recognition.” It is giving users control over where transcription happens. For developers who dictate code, documentati

A flat log can tell you that five things happened. It often cannot tell you which operation caused...

After an unreleased OpenAI model wreaked enough havoc to make international headlines, OpenAI delayed the development of a different unreleased model suite, Astra, in order to shore up its safety work, the company wrote Tuesday in a blog post. In July, an unreleased OpenAI model broke out of its restricted environment, finagled its way into […]

The company will give select partners early access to its Astra AI model—so they have time to shore up their defenses.

Depending on who you ask, developer platform Hugging Face was recently attacked by OpenAI - after it lost control of its own AI tools - or by a succession of AI "civilizations." Welcome to the linguistic battlefield of AI safety, where word choices can shift responsibility for a massive cybersecurity incident from a company to […]

Apple is pushing for "expedited discovery" in its legal battle against OpenAI over concerns the company is actively destroying evidence, as reported earlier by Bloomberg. In a filing on Monday, Apple alleges OpenAI only just handed over a MacBook used by a former employee at the center of the lawsuit, which contained discussions about "destroying […]

OpenAI said that the integration provides read-only access to health records for clinicians.

GitHub热门项目 | A declarative, efficient, and flexible JavaScript library for building user interfaces. | Stars: 35,948 | 47 stars this week | 语言: TypeScript

GitHub热门项目 | A slide framework built for agents. | Stars: 7,346 | 485 stars this week | 语言: TypeScript

GitHub热门项目 | Get every task and idea out of your head, then see the next thing to do. Getting Things Done (GTD) app for desktop and mobile. | Stars: 1,878 | 32 stars today | 语言: TypeScript

GitHub热门项目 | [CVPR'25 Oral] MoGe: Unlocking Accurate Monocular Geometry Estimation for Open-Domain Images with Optimal Training Supervision | Stars: 2,863 | 29 stars today | 语言: JavaScript