与「SoftwareEngineering」相关的搜索结果

🧠Forum, Meta’s new standalone Groups app, was built by a small team that brought an AI agent into the development cycle as a participant — planning, building, testing, and verifying. Building Forum we moved from human-in-the-loop — engineer drives, AI assists inside each step — to human-on-the-loop, where the agent runs the iteration (plan, implement, build, test, correct, repeat) and the engineer sets the goal, watches the run, and owns the outcome. The key decision: success wasn’t measured in code volume. It was measured in changes that build, pass tests, and survive review. ✅ Reached core feature parity with Facebook Groups in eight weeks 📈 An autonomous stress-testing loop caught a memory issue during development, roughly halving memory use and page faults 🔗 Read how our engineers went AI-native to build Forum: #AI# #SoftwareEngineering# #DeveloperTools# #MobileEngineering# #Engineering#

🧠 Most AI bug-catching tools review your code the way a human reviewer would, only faster. What if the AI wrote a test that proved the bug instead? That’s Catching Just-in-Time Tests (JiTTests) — a novel approach where LLMs generate executable tests designed to fail, surfacing behavioral changes in a code change before it lands. After a year of research, it’s now part of Meta’s infrastructure. ✅ 589 engineers have engaged with it, reporting a 60% true-positive rate 📈 In the first half of 2026, it helped catch 74 privacy SEVs at the diff stage, lowering overall privacy SEV occurrence by 20% 🔗 Read how it works: 📄 Read our FSE 2026 paper, “Just-in-Time Catching Test Generation at Meta”: #AI# #SoftwareEngineering# #SoftwareTesting# #DeveloperTools# #Engineering#

Our strongest frontier model, Gemini 4 Carbon, is now available. Built for deeply complex, long-horizon workflows that push a frontier model’s intelligence, Carbon delivers frontier performance in agentic workflows across complex knowledge work, software engineering and web development, cybersecurity, and scientific reasoning. While Gemini 4 Argon remains the ideal model for the majority of software engineering and knowledge work, Carbon steps in for your hardest tasks that push a model to its limits — from complex, multi-step debugging to open-ended scientific research. Available starting today: — Consumers: use it in the @GeminiApp and AI Mode in Search for Google AI Ultra subscribers — Developers and Enterprises: build via the API in @GoogleAIStudio and @AndroidStudio, Google @Antigravity for Google AI Ultra subscribers, and Gemini for business — Scientists and Researchers: apply for a chance to get 6 months free via @Antigravity — Cybersecurity Defenders: apply to join the Fairwind Program for access to Carbon for cyber Watch how Carbon brings reasoning and coding together, turning avian research into an interactive 3D simulation of a songbird’s vocal organ that synthesizes the sounds of six bird species live.

Grok 4.7 takes the TOP 3 spots on VulcanBench Frontier v4, outperforming Fable 5.1, Opus 5.5, GPT-6 Astra, GPT-6.1 Sol and other leading frontier models • Grok 4.7 xHigh - 93.15 • Grok 4.7 High - 92.71 • Grok 4.7 Medium - 92.30 VulcanBench Frontier v4 tests whether AI models can handle difficult repository-level software engineering work rather than simply generate isolated code snippets The benchmark contains 23 behavioural-reconstruction tasks where models have to rebuild legacy software to match its actual behaviour, with functional correctness tested through hidden tests alongside security, lint/complexity and code quality And the task results are just as impressive: • Grok 4.7 xHigh - 23/23 passed • Grok 4.7 High - 22/23 passed • Grok 4.7 Medium - 21/23 passed Grok 4.7 took all #1#, #2# and #3# spots

Announcing Gemini 4 Argon, our new frontier model. Argon is built to sustain deep reasoning across complex, long-horizon workflows and delivers frontier performance in complex workflows across real-world software engineering, enterprise knowledge work like legal and finance, and cybersecurity defense. We’re also expanding the model’s output token limit to an industry-leading 1M tokens. Argon is currently rolling out to a set of trusted cyber defenders in the Fairwind Program, with broader availability as soon as possible.

Gemini 4 Argon is our next era of frontier intelligence. It shows significant improvements across benchmarks, setting a new state of the art for real-world long-horizon software engineering tasks.

Today we’re introducing Gemini 4 Argon. It delivers frontier performance in complex workflows across real-world software engineering, knowledge work, and cybersecurity defense with an industry-leading 1M token output limit.

Lots of discussion out there about our next model(!), so I wanted to give an early look as soon as possible. Introducing Gemini 4 Argon! It shows frontier performance in complex workflows, cyber defense and software engineering. Teams are using it extensively at Google, from coding to quantum computing, great feedback. Here’s a look at the benchmarks:

📢 GPT-6.1 Sol Is Now Live on An upgrade to GPT-6 Sol, GPT-6.1 Sol is built for complex software engineering, computer use, and professional workflows. OpenAI positions it as nearly matching GPT-6 Astra on agentic coding, computer use, and professional work, at about 1/5 of Astra’s standard input and output token prices. It supports a 1.05M-token context window, up to 128K output tokens, 5 reasoning levels, prompt caching, and Multi-agent delegation (Beta) via the Responses API. Now available on API and Web Chat. 👉 Try now: 🔗 Learn more: