ChatGPT Work의 브라우저 자동화, OpenAI 칩과 SpaceX 제조 인프라, WebMCP 에이전트 도구화, 로컬·보안형 실행 확장

ChatGPT

@ChatGPT

7시간 전

ChatGPT Work can now use its computer and browser to sign in to websites on web and mobile, without ChatGPT ever seeing your username or password. That means you can ask it to: • Set up utilities for a new apartment • Book a DMV or passport appointment • Check reimbursement costs through your insurance • Find and book an in-network doctor around your availability • Check when your car registration expires and prepare the renewal paperwork • Compare your rental insurance policy with an issue you’re emailing your landlord about • Find and save apartment listings that match your criteria • Restock something just by uploading a photo • Schedule a package pickup for a return • Cancel tickets for a rescheduled trip • Book a vet appointment • Submit reimbursement paperwork for medical treatments • Check resale sites for new drops and save things you might like • Find candidates with specific experience and draft outreach • Take invoices from your email and submit them to your accounting software • Draft replies to rental property inquiries • Fill out permit applications for your small business • Add action items to a vendor portal based on a recent client call • Analyze the latest ad campaign for your small business

Introducing ChatGPT Business Premium Seats The new $100 Premium seat is a game changer for small businesses and startups—giving lean teams better tools, faster workflows, and capabilities once reserved for big companies. A flexible plan that scales with your team’s ambition. https:// chatgpt.com/pricing/?type= team …

OpenAI Developers

@OpenAIDevs

9시간 전

WebMCP is an experimental open standard that lets web apps expose tools that agents can use directly on the page to complete tasks faster and more reliably. Curious what new experiences it enables? Take a look at our WebMCP demo showcase:

We’re adding support for WebMCP in the ChatGPT desktop app’s built-in browser and ChatGPT Sites. When you visit a compatible website, ChatGPT or Codex can automatically use it to complete your task. Update to the latest version of the ChatGPT desktop app, then just ask Codex to

Excited about our Jalapeno results today. An incredible achievement from the team, taking a new chip from concept all the way to very impressive performance on real workloads in the lab. Tomorrow’s fast will feel like today’s ultrafast. As I’ve mentioned before, we’re pushing to bring this to as many people as possible. We’ve seen a TON of demand for /ultrafast, made possible by our deep partnership with Cerebras and their unique hardware, which will push the frontiers of tomorrow's ultrafast even further. I’m very much looking forward for the collaboration to continue pushing the absolute limits of how fast we can run our most capable models on Cerebras and bring this to the most demanding customers.

Sam Altman

@sama

9시간 전

we made a chip and it is fast

DogeDesigner

@cb_doge

9시간 전

BREAKING: Elon Musk's TeraFab has officially advanced into the agreement phase in Texas. Texas has just published a fully signed tax agreement for SpaceX’s TeraFab, moving the massive chip and advanced-computing factory another major step closer to construction. A fully executed 34-page agreement between the Governor’s Office, Anderson-Shiro CISD and TeraFab AI was published today. The agreement: • Describes TeraFab as a next-generation, vertically integrated semiconductor manufacturing and advanced-computing facility • Lists a construction period from December 1, 2026, through December 31, 2028 • Establishes a 10-year tax-incentive period from 2029 through 2038 • Limits the qualifying property’s value for school district M&O taxes to zero during construction and 50% of market value during the incentive period • Requires a $12.98 million performance bond Another major step toward bringing TeraFab to life in Texas.

NVIDIA Vera Rubin NVL72 production racks are here. The compute tray is engineered for fast compute, assembly, and serviceability. Manufacturing is 100% automated, and every tray goes together in one minute. Congratulations to @Microsoft on the first operational Vera Rubin NVL72 racks, now rolling off @HonHai_Foxconn Ingrasys lines.

OpenWorker -- an open source agent that doesn't just chat but completes tasks on your laptop -- just released a new version with many features for security workflows. After our initial release, many users found it especially useful for cybersecurity. Attackers are already using AI; OpenWorker is committed to giving defenders the same leverage. Running an agent requires both (i) A model and (ii) A harness (the software around the model). Because the OpenWorker harness is fully open source, security teams can audit it to make sure we haven't built any backdoors that exfiltrate your code and data to some company or even a foreign adversary. OpenWorker now comes with built-in cybersecurity agents for (i) Scanning your code for vulnerabilities. (ii) Scanning dependencies for supply chain injections. (iii) Checking your cloud security configuration for attack surfaces. This enables developers to do much more security work before deployment (part of what's called the "shift left" movement). You choose the model: you can run open weight models fully locally so sensitive code never leaves your machine. This helps with legitimate security work (like reproducing a known exploit to defend against it) that can trigger refusals in leading closed models. Or use your ChatGPT subscription, or stealth preview models like Ox Alpha, or any model via API key. Thanks also to all the open source contributors! Join work with @rohitcprasad so please follow him too to get more frequent updates. Try it out: https:// openworker.com Code: https:// github.com/andrewyng/open worker …

OpenAI Developers

@OpenAIDevs

7시간 전

Rounding out the winners is Sentinel by Malik Bashaar Javaid, which takes second place in Developer Tools. It checks MCP servers for security risks with static analysis, GPT review, and tests in a Docker sandbox. https:// devpost.com/software/senti nel-way5bd …

Aravind Srinivas

@AravSrinivas

9시간 전

Big thanks to @nvidia for working together with us and supporting this research on DGX Spark as well as enabling an open ecosystem around cost-effective open-weight models, inference frameworks, and hardware with unified memory.

OpenCode

@opencode

9시간 전

Grok 4.6 is now available on OpenCode Go you get one hundred 69 requests every 5 hours

I just built my own multi-agent GTM research assistant! (it finds the reason to reach out before it writes a single message) Cold outreach usually fails on timing, not on wording. By the time you find out that a target company raised a round or hired a new data leader, the window has closed and your message reads like every other cold email in the inbox. So the research has to happen before the writing, and it has to run across the whole target list at once. Today, we're building a system where you drop in target company names and three agents handle the rest. Here's how it works: ↳ Agent 1 searches recent news and pulls trigger events for each company ↳ Agent 2 finds people at those companies and enriches their career background ↳ Agent 3 joins the two and writes a ready-to-send message per contact ↳ Results come back ranked by how strong the trigger is ↳ The whole pipeline runs inside a Streamlit UI The order matters more than the agent count. The writer agent runs last and receives the trigger event and the contact's background as its input, so it never starts from a blank company name. If the news agent finds nothing recent for a company, there is nothing to write from, and that company drops down the ranking instead of producing a generic message. Tech stack: ↳ Seltz as the data layer, running the news scope and the people scope over the same target list ↳ CrewAI to orchestrate the three agents in sequence ↳ Streamlit to host the interface Here's why I picked this stack: Seltz maintains its own web index instead of wrapping a search engine, so the news scope and the people scope return structured results you can join on the company name. That join is the whole system. A trigger event with no contact attached is not actionable, and a contact with no trigger gives you nothing to say. Get started here: https:// seltz.ai CrewAI keeps the handoffs explicit. Each agent receives the previous agent's output as context instead of starting its own search from scratch, so the message writer already knows both what happened at the company and who it is writing to. Streamlit keeps the target list, the pipeline run, and the final ranked output in one place, which makes the intermediate agent output easy to inspect when a message comes out wrong. Find all the code and everything you need to run this app in the studio: https:// lightning.ai/dailydoseofdat ascience/templates/build-a-multi-agent-gtm-workflow … I also wrote a comprehensive article that covers this entire idea and how to replicate this for your own use case in more detail. The article is quoted below.

“Speculative Programmatic Tool Calling” Code agents waste time waiting for the LLM to finish generating code before tool calls can start. So this paper speculatively executes predictable tool calls from partial generations, while a shadow REPL tracks dependencies safely. This provides ~1.2x lower wall-clock time without changing the model or agent logic. https:// alphaxiv.org/abs/2608.spec- ptc …

Boris Cherny

@bcherny

8시간 전

A small improvement: memory is now simpler and more powerful. Enjoy!