与「RELEASED」相关的搜索结果

NVIDIA released Auto Ontology, an open-source library that builds a semantic layer from your enterprise metadata and exposes it to agents via MCP. Vendor tests claim **90% faster ontology building**. The problem is familiar: agents that write SQL against your data use their own definitions, not the enterprise's. A "revenue" the agent calculates doesn't match the one in the financial report. Auto Ontology builds a semantic layer from existing metadata (schemas, tables, columns, relations), reconciles definitions across systems, and exposes it to agents via MCP, CLI, or API. Nine MCP tools are provided to query the catalog, validate a query, or retrieve certified definitions. What the account takes from this: the 90% gain comes from NVIDIA's own tests, not an independent measurement. The library automates construction, not governance. Business definitions, access rights, and auditing remain your responsibility. The gesture: clone the repository, point it at a test schema, and see what entities and relations it extracts. The README shows the complete flow in ten minutes.

🚀✨ GitHub Copilot App v1.4.2 released! 1 feature & enhancement in this release Bug fixes: • Fixed sessions failing to start or resume with 'Session plugin inputs changed during reload' error 🔧 when organizational policy conflicts with user settings on Computer Use #GitHubCopilotApp#

Just want to let everyone know I have been released and I am good,healthy and ready to go . I want to thank everyone in Abu Dhabi for your help in resolving this matter. I love visiting Abu Dhabi and am happy to be welcomed. I do hope that they release everyone - i would not press charges. I want peace and the best for all. Thanks everyone for your love and support🦾

Threat actors are weaponizing ARTEX, an open-source, large language model (LLM)-driven agentic platform to automate multiple phases of cyber intrusions at machine speed. Since at least August 2026, a variety of malicious cyber actors have used ARTEX to conduct campaigns targeting organizations globally across multiple sectors, including financial services, government, and technology. ARTEX significantly reduces the time required to chain attack techniques and enables threat actors with minimal technical expertise to conduct complex, multi-target operations simultaneously. Today, the FBI released a FLASH highlighting the tactics, techniques, and procedures and indicators of compromise associated with the ARTEX framework. Organizations should follow the mitigation steps outlined in the FLASH to improve their security posture in response to ARTEX and similar agentic SI frameworks:

OpenAI has yet to release GPT-6.2. There can only be three reasons: 1) They were surprised by how capable Fable 5.2 is and had to make GPT-6.2 better. 2) They found security vulnerabilities or other issues that need to be fixed. 3) Anthropic is going public next week, and OpenAI knows GPT-6.2 surpasses both Fable 5.2 and Opus 5.5. They’re trying to steal the spotlight ahead of their own planned IPO in 2027. I’m torn between 1 and 3, but I honestly don’t know. My gut tells me OpenAI was surprised by how good Fable 5.2 is. Either way, I expect GPT-6.2 to be released before November 19.

🚨 A hacker group announced at 3:00 AM: "We have locked all Starlink ground stations worldwide. Pay $500 million in Bitcoin, or the world goes offline!" 🚀 Elon Musk replied at 3:01 AM: "Check your firmware. While you were writing that ransom note, Neural-Auto-Patch released a micro-update that routed your entire operation through a dead-end satellite in deep orbit. Thanks for testing our hotfix."

Tempo released an AI agent spending limit framework: caps on amount, time window, and allowed payees — while the agent never holds funds. 2-3 hashtags max. #AI# #Payments# #Agents#

I have a major result to share. I’ve found conclusive forensic evidence that the top 4 Chinese AI labs—DeepSeek, Moonshot, MiniMax and Ant Group—have built their newest models on NVIDIA’s most advanced Blackwell chips, which are barred from export to China. This sounds insane, but I discovered that almost every AI model carries a hidden signature of the GPU that trained it. When a model is first set up, the starting values of its weights are laid out in a pattern that depends on the exact GPU. Each combination of GPU and seed number produces a unique pattern—a sort of fingerprint of the hardware used to create the model. Traces of that pattern survive training into the published model. Again, this evidence isn’t based on any insider information or leaking. It’s based on a forensic analysis of the model weights released by the companies themselves. I searched more than six trillion combinations of chip layout and seed number and matched them to open-weights models. In doing so, I found conclusive evidence that six Chinese AI models—including DeepSeek-V4-Pro, Kimi K3, and MiniMax-M3—were initialized on NVIDIA Blackwell systems. Four of the models match Grace Blackwell, the GB200 and GB300 parts in NVIDIA’s flagship liquid-cooled rack-scale systems. I then verified these results on real hardware. Running unmodified PyTorch with the seeds I’d recovered, a Blackwell B300 reproduced DeepSeek-V4-Flash’s untouched starting weights bit for bit, and a rented GB200 reproduced Kimi K3’s to a correlation of 0.999995. A Hopper H100 reproduced none of DeepSeek-V4-Flash’s, and changing the seed by one broke the match. (DeepSeek’s seed is 953049583 by the way.) The method also was verified to match 24 known open-weights models to the hardware that developers said they used. It is exceedingly unlikely that these results are a coincidence. Across six trillion combinations, the best accidental match should reach only about 8 sigma (i.e., eight standard deviations above what random chance would produce). The real matches range from 49 to 1,385 sigma, and even the weakest single test hits 20. These were data-center-scale training runs. With some analysis of release schedules and model details, I estimate that the largest, Kimi K3 and DeepSeek-V4-Pro, used 2,000 to 3,000 Blackwell GPUs each. Clusters of this size cost on the order of $150 to $200 million each to buy, and across these labs alone would add up to more than half a billion dollars of export-controlled GPUs, not counting experimental compute or otherwise. It’s quite likely that we see multi-billion dollar expenditure on export-controlled GPUs. I am highly confident in this result, and it has been independently verified by senior researchers at OpenAI and Anthropic. You can read the full report at There is much more I’ve identified about these models through other forms of checkpoint forensics like this, and will have more to share soon.

Rust 1.100.0 has been released! 🦀💯🌈🎈 This bigger-than-usual Rust release adds the never type (`!`), the Allocator api, DropGuard, Box::map, Vec::from_fn, and more! Cargo now warns for unused dependencies, rebuilds less often, and adds the min-publish-age setting!