标签：#open

TL;DR — I've been building the Social Resource Floor: an open blueprint for coordinating one person's access to basic survival resources — food, housing, energy, healthcare, and more — across many independent providers, so that reaching those resources is grounded in being human rather than in financial access. The first blueprint version is now complete: language-neutral schemas, prose specifications, a reference implementation, and a first adapter. This post is about the engineering choices behind it, and the reasons for each — how it stays a contract rather than a product, how it keeps personal data out of the coordination layer, why it binds to existing standards instead of inventing new ones, and how I check that the contracts are implementation-independent rather than just claiming they are. The problem the Floor is trying to help with Today, for most people, survival routes through financial access. To reach food, housing, energy, or healthcare you generally need money, and to hold or move money you need banking, employment, or purchasing power. Financial access has become the gate standing in front of the resources a person needs to stay alive. The goal of the Social Resource Floor is narrow and specific: to help make it so that financial status is not the condition that determines whether a person can reach the basic resources required to survive. It does not try to abolish money, banks, or markets — money stays a first-class resource and delivery method. It aims at one thing: a floor beneath which no person should fall, defined locally, reachable regardless of financial circumstances. That's the mission. Everything technical below exists to make that mission buildable by the institutions — governments, municipalities, NGOs, cooperatives, community providers — that would actually run it, without asking any of them to give up their own systems or hand over their data. Where the Floor sits The delivery systems for social protection already exist and are stron

OpenAI’s rogue agent hack was a watershed moment for AI safety and cybersecurity. It also sparked internal questions about the culture that led to it.

OpenAI is expanding ChatGPT beyond chat with ChatGPT Work , a cross-platform work environment that includes a desktop agent able to interact with local applications, files and browser content. The change matters because it moves ChatGPT closer to an operational role: not only explaining how to complete a task, but potentially clicking, typing, moving files and staying engaged with a project over time. In OpenAI's official announcement on ChatGPT Work , the company describes a unified experience across web, mobile and desktop. The desktop app combines Chat, Work and Codex, while its built-in browser and local computer capabilities are intended to support more contextual, end-to-end work. OpenAI's terminology centers on ChatGPT Work and Computer Use. "Computer History," the name used in the originating signal, is not the feature name used in the official announcement. The underlying shift is significant for developers and knowledge workers. A chat interface has traditionally depended on users copying information into a prompt, describing where files live, and manually carrying results into the next application. Desktop automation can reduce those handoffs, provided users grant the relevant access and organizations establish appropriate controls. From answers to work across a computer ChatGPT Work is positioned as an agentic layer for work that spans apps and files. OpenAI says the desktop agent can act locally in the background, including interacting with applications, files and browser content. It also highlights plugins, workflows and Scheduled Tasks as ways to connect tools and automate recurring actions across connected apps and local files. That does not mean every task should be delegated without review. The practical value depends on how clearly a workflow can be defined, the permissions it requires, and the consequences of an incorrect action. For example, moving or modifying local files is fundamentally different from drafting a response in a chat window. The

Another OpenAI executive is departing. Denise Dresser, who joined OpenAI as its chief revenue officer in December after serving as CEO of Slack, will be leaving in the "coming weeks" to "pursue other opportunities," she said in a team note posted to LinkedIn. Dali Rajic, president and COO of Wiz, will be taking over the […]

IBM plans to train and certify tens of thousands of consultants on OpenAI's technologies as part of this deal.

I have been an avid privacy advocate for quite some time now. It started with outright rejecting all "Big Brother" tech, and being hyper paranoid with every little detail, willing to sacrifice ease of use, in exchange for added privacy. However, as time went on, I slowly understood what is that I actually consider my "threat model" , and what exactly is my "sweet spot" between privacy and ease-of-use. I'm now back on multiple "Big Brother" tech, with some extra steps, to ensure I get the facilities they provide, while also being wary of my data. However, while I did make this compromise, I was very annoyed I had to make this compromise in the first place. In an ideal world, I would want the tech where everyone actually is, and is the standard for that particular domain, to have privacy features by default, and not be treated as a niche, or a luxury you have to go out of your way to avail. It was this annoyed version of myself, with my strong belief of privacy features and tools being the new norm, I started looking at everything with that lens. And that is how I got concerned about tracking in links and URLs. Try sharing any Instagram post, or YouTube video, by copying its URL, and you will see a bunch of garbage (garbage to you) in the link. Take for example this (fake) link: https://www.instagram.com/p/Cxyz123/?igshid=AbCdEf123456 These links contain something along the lines of utm_* (marketing attribution), or in this case, Ad-Click Identifiers, such as fbclid (Meta), gclid (Google), or igshid (Instagram). These pesky trackers help collect information regarding you, your device, and also help connect you across the internet, mapping your movement as you browse the web. The thing is, while there are good Samaritans who have built tools and websites to get rid of these trackers, and many privacy oriented browsers have introduced a "Copy Clean Link" option while copying the link from the browser, I believe there should be a tool which should not be restricted to a

I’ve been building an open-source project called TraceMotive. It started from a problem I kept running into with AI agents: When an agent run fails, the place where the error appears isn’t always where the execution first started going wrong. That makes debugging agent workflows harder than it looks. So I built TraceMotive, a local-first tracing and debugging tool for AI agent execution. What TraceMotive does The current v0.1 includes: Python SDK canonical traces and spans a local Collector backed by SQLite a React UI for inspecting agent runs optional OpenAI Agents SDK integration TraceMotive is local-first, and content capture is disabled by default. I’m intentionally keeping the first version small. I’m not trying to add replay, automatic root-cause analysis, cloud sync, or support for every agent framework yet. Why? I’d rather get real feedback before adding a lot of features. Right now I want people who actually build AI agents to try it and tell me: where setup is confusing what breaks what information is missing from traces what feels awkward in the API The longer-term direction is: “The causal debugger for AI agents.” Eventually, I want TraceMotive to help identify where an agent execution first started going in the wrong direction, instead of only showing where the final error appeared. But first, I want to make the basic observation and debugging layer solid. Try it PyPI: pip install tracemotive GitHub: https://github.com/doraemonfv-glitch/tracemotive If you build AI agents, I’d really appreciate you trying it for a few minutes and telling me what you run into. Even small feedback is useful.

OpenAI is expanding its inference infrastructure through a multi-year partnership with Cerebras, aiming to support faster responses for real-time AI workloads. The centerpiece is GPT-5.6 Sol Ultrafast , a Cerebras-backed deployment that OpenAI says can reach up to 750 tokens per second during a limited preview. For enterprises, the development is less about a minor model setting and more about whether frontier-model intelligence can be used in workflows where latency materially affects the experience or business process. OpenAI's official Cerebras partnership announcement confirms plans for 750 megawatts of ultra-low-latency AI inference capacity for OpenAI customers. The capacity is scheduled to come online in multiple tranches through 2028, making the agreement a long-term infrastructure expansion rather than a one-off model launch. What the OpenAI and Cerebras partnership changes OpenAI is adding Cerebras wafer-scale compute to its inference stack. The stated objective is to provide faster responses and enable real-time AI experiences across customer workloads. Cerebras has separately identified GPT-5.6 Sol as the model used for the Ultrafast deployment, positioning the offering around high-speed access to OpenAI's flagship GPT-5.6 family model. The relevant distinction is between building a more capable model and serving an existing frontier model with a lower-latency compute path. OpenAI's announcement is focused on the latter. Cerebras hardware is being deployed to accelerate inference, the stage at which a trained model processes prompts and generates responses for users or applications. That focus matters for enterprise systems where delay can compound across a workflow. A faster model response can improve the feel of interactive tools, but it can also shorten multi-step agentic processes , reduce waiting in human review loops, and make real-time assistance more practical. The announcements do not specify which individual business applications will receive a

GitHub: https://github.com/kaifcodec/user-scanner.git If you've spent any time mapping digital footprints or doing threat intelligence, you know the drill: run Holehe for email registration checks, jump over to Sherlock or Maigret for usernames, and manually piece together the findings. While Holehe set the benchmark for password recovery endpoint checks, modern targets use complex handles, and web anti-bot defenses have gotten aggressive. Lately, I've integrated user-scanner into my workflow—a high-concurrency Python CLI engine that merges email enumeration, username profiling, and automated cross-pivoting into a single execution stream. Here is a breakdown of how it holds up against legacy OSINT tools and why it’s worth adding to your toolkit. Tool Matrix: user-scanner vs. Traditional Registration Checkers Feature / Metric Holehe Sherlock / Maigret user-scanner Input Flexibility Email Only Username Only Dual Engine (380+ Vectors) Vector Split ~120 Email Sites Web Form Scrapers 155+ Email & 225+ Username Modules Target Pivoting Manual Manual Automated Recursive Cross-Scanning Infostealer Intel None None Built-In Hudson Rock API ( --hudson ) Networking Core Basic Async Standard Requests httpx + curl_cffi (TLS Impersonation) Output Options Text / JSON Text / CSV PDF (with Avatar Scrapes), JSON, CSV Package Support Pip Pip Pip, Virtualenv, Nix ( nix run ) Standout Technical Features 1. Automated Cross-Scanning & Pivot Chains ( --cross-scan ) The biggest time-saver is the pivot pipeline. Standard tools tell you whether a target exists on a platform and stop there. user-scanner parses profile metadata returned during a run—looking for linked accounts, published bios, handles, and public emails—and automatically launches follow-up scans across secondary modules. -e → Username Pivoting: Mines handles and linked profiles returned from an email lookup. -u → Email Pivoting: Harvests public email addresses listed on social profile pages. Configurable Chain Depth: Dial in how

Dali Rajic will take over as OpenAI's top salesperson.

X is expanding the open source code behind its 'For You' feed and launching new transparency tools that show users when its ranking systems have affected their accounts or posts.

See how the open source projects in Session 4 of the GitHub Secure Open Source Fund combined AI-assisted workflows, maintainer expertise, GitHub security tools, expert guidance, and funding to improve project security. The post What 50 open source projects taught us about security in the AI era appeared first on The GitHub Blog .

If you've been watching the open text-to-video space, MiniMax-H3 is one of the more interesting drops of the year. It generates short cinematic clips with a synced soundtrack from a text prompt, and you can drive it end-to-end without ever touching a GPU yourself. The easiest way to explain what that actually looks like is to point at the results people have been posting. My feed has been full of H3 recreations of famous TV moments — Breaking Bad lab scenes, Friends coffee-shop bits, mockumentary moments from The Office . // Detect dark theme var iframe = document.getElementById('tweet-2084562933162602866-755'); if (document.body.className.includes('dark-theme')) { iframe.src = "https://platform.twitter.com/embed/Tweet.html?id=2084562933162602866&theme=dark" } In this post I'll cover: What MiniMax-H3 actually is How you can run it yourself What is MiniMax-H3? MiniMax-H3 is a text-to-video model that produces short clips at cinematic resolutions. Two things make it stand out compared to earlier open video models: Sound comes out of the same model. Most open text-to-video pipelines output silent frames and you bolt on a separate audio model afterwards. H3 emits a soundtrack aligned with the visual content in one pass. // Detect dark theme var iframe = document.getElementById('tweet-2084353489061499021-723'); if (document.body.className.includes('dark-theme')) { iframe.src = "https://platform.twitter.com/embed/Tweet.html?id=2084353489061499021&theme=dark" } Keyframe conditioning. You can pass an optional first frame and/or last frame image and the model will interpolate a motion path between them. This turns it from a pure "vibe generator" into something you can actually direct. // Detect dark theme var iframe = document.getElementById('tweet-2084378446122319973-582'); if (document.body.className.includes('dark-theme')) { iframe.src = "https://platform.twitter.com/embed/Tweet.html?id=2084378446122319973&theme=dark" } The knobs are the ones you'd expect: Prompt — free f

GitHub热门项目 | Self-hosted, semantically-connected personal knowledge base | Stars: 1,785 | 44 stars today | 语言: Rust

GitHub热门项目 | 缠中说禅技术分析工具；缠论；股票；期货；Quant；量化交易 | Stars: 5,804 | 61 stars today | 语言: Rust

GitHub热门项目 | Full-stack web application template with FastAPI, React, SQLModel, PostgreSQL, Vite, Tailwind CSS, shadcn/ui, FastAPI Cloud, and Docker Compose. | Stars: 44,827 | 90 stars today | 语言: TypeScript

GitHub热门项目 | Desktop app to generate 3D models from images using local AI — runs entirely on your GPU | Stars: 5,165 | 70 stars today | 语言: TypeScript

GitHub热门项目 | Open-source All in One AI agent workspace. Run any agent — Claude Code, Codex — across your tools (100+ integrations + MCP), apps, browser, and files, with shared memory. Built-in models or BYOK. | Stars: 6,191 | 258 stars today | 语言: TypeScript

GitHub热门项目 | Virtual Machine for the Web | Stars: 17,219 | 14 stars today | 语言: JavaScript

GitHub热门项目 | An Industrial-Level Controllable and Efficient Zero-Shot Text-To-Speech System | Stars: 22,800 | 137 stars today | 语言: Python