搜索“AWS”

e company’s AI Code Review agent, bringing research-grade program analysis and modern GenAI techniques together to automate one of the most time-consuming parts of the SDLC. Before Augment, Akshay spent several years at Uber advancing automated code review and repair systems, and conducted research across AWS’s Automated Reasoning Group, Google’s Android Static Analysis team, and UCLA. His work sits at the intersection of AI, software engineering, and programming-language theory. Related reading

匹配字段正文

That is the unusual part. Firecracker is normally run on bare-metal servers, where you rent the whole physical machine. To reduce customers' cost, we run it on regular EC2, where AWS has already put your server inside a VM. This should be slow. Nested VMs make memory and CPU operations more expensive, and Chromium takes time to start. This post is about how we made this setup fast and efficient. But first, why did we rebuild our infrastructure? When we first built our browser infrastructure on

匹配字段正文

has only given us verbal evidence of a potential narrow, non-universal jailbreak, which essentially consists of asking the model to read a specific codebase and fix any software flaws. Our understanding is that one potential jailbreak was shared with the government. We have reviewed a report that we believe is the basis of the government's directive and validated that the level of capability displayed there is widely available from other models (including OpenAI’s GPT-5.5), and is used every day

匹配字段正文

evaluation that we call ZTTS1-Eval. Throughout the evaluation process, it became clear that existing and widely-used TTS evals namely Seed-TTS-Eval and CV3-Eval have significant flaws. Seed-TTS-Eval has three main problems. First, only Chinese and English are considered, which lacks linguistic diversity. Second, they rely on outdated models such as Whisper-Large for ASR. Thirdly, they only draw from Common Voice for English and DiDiSpeech for Chinese, both of which are read speech (with minimal

匹配字段正文

ssue the moment it’s triaged rather than waiting to be told what to do. You can set up the agent to first investigate the issue. It reads the codebase with Code Intelligence and draws on the context around the issue, like the discussions and affected customers. The agent can also connect to your team’s other tools like Sentry or Datadog to triangulate the problem. With a grasp of what to do, and the confidence there is a fix, the agent can open a coding session in the cloud, write a fix, open a

匹配字段正文

loudflare and Palo Alto Networks noted how Mythos Preview could chain low-severity bugs into high-severity exploits, helping them triage which low-severity vulnerabilities to fix; AWS claimed that Mythos Preview was better than previous models, and helped them “identify additional opportunities” to strengthen the code in some of their best-tested environments However, these pieces of evidence don’t necessarily imply that Mythos Preview is a huge jump in vulnerability detection capabilities. It’s

匹配字段正文

d high-energy physics data because that field requires the world’s most precise "difference detectors” — the idea being, if the framework can spot a rare particle that defies the laws of physics, it can spot a tiny privacy leak in an AI model. We then shifted our primary focus to the critical, real-world applications of auditing differential privacy and evaluating machine unlearning: Proposed framework for relative distance. If the tested model is closer to the compromised model than the retrain

匹配字段正文

assessors will need to focus on aspects of the safety situation that they can justify to that audience, and the AI company will optimize for that; Governments might pass specific laws, or regulators might write regulations, that mandate specific countermeasures. You can try to apply safety/usefulness tradeoff analysis when advocating for particular countermeasures to be included in the laws, but this is a huge mess: you need to pick the countermeasures in advance, you need to optimize them parti

匹配字段正文

thiness of AI agents in operational environments. Physics-guided deep learning integrates physical principles into foundation models to ensure predictions obey governing physical laws and require less data for satisfactory accuracy; Uncertainty-aware reasoning uses a framework called UQ4CT to produce calibrated uncertainty, allowing AI agents to halt or request human intervention when internal uncertainty exceeds a safety threshold; The adapting-while-learning (AWL) framework bridges the text-to

匹配字段正文

r-10, and train-fasttext. Experimental methodology We evaluate SSA across multiple agent benchmarks under a controlled and reproducible setup. All experiments were conducted on an AWS PCS cluster using c7.48xlarge instances, with maximum concurrency set to 10 to balance throughput and system stability. For model access, Claude models were served via Amazon Bedrock (production capacity), while OpenAI, Gemini, and Grok models were accessed through their respective commercial APIs. We enforced stri

匹配字段正文

er blog Console Console Console Engineering at Anthropic Engineering at Anthropic Engineering at Anthropic Overview Overview Overview Marketplace Marketplace Marketplace Claude on AWS Claude on AWS Claude on AWS Blog Blog Blog Claude partner network Claude partner network Claude partner network Customer stories Customer stories Customer stories Powered by Claude Powered by Claude Powered by Claude Service partners Service partners Service partners Availability Availability Availability Check fil

匹配字段正文

tonking Stingray' beta swims out: Deeper Rust 'oxidization' plus LLM speech-to-text GNOME 51 is here, and Ubuntu interim releases are getting interesting again; Revive Raskin's 3 laws of software with humane work that considers a dev's duty of care All this, and angry NeoVim users; Firefox 157: Could bold new look be just what Mozilla needs? Yet another new version of Firefox, with a significant reskin; KDE turns 30 and someone's brought an AI-native desktop proposal Akademy talk imagines Plasma

匹配字段正文

’ll see three more orders of magnitude of compute applied to frontier models. Intelligence is now a function of compute. Log linear hill-climbing has become the norm. The scaling laws are holding. These are truly extraordinary times. In this context we at MAI are building towards what we call Humanist Superintelligence. State of the art AI capabilities that are explicitly designed to serve people and organizations, and not to replace them. Because the type of AI we build really matters. We need

匹配字段正文

. They struggle with anything that requires reasoning about intent, context, or code that doesn't exist yet. Here are four gaps that pattern matchers can't reach: Business-logic flaws and intent mismatches. A discount applied before cart-total validation, or a token check that ignores the expiration field, passes any linter without warnings. No rule engine can flag business logic it was never taught. An LLM reading the surrounding code is the first reviewer with a realistic chance of catching th

匹配字段正文

tonking Stingray' beta swims out: Deeper Rust 'oxidization' plus LLM speech-to-text GNOME 51 is here, and Ubuntu interim releases are getting interesting again; Revive Raskin's 3 laws of software with humane work that considers a dev's duty of care All this, and angry NeoVim users; Firefox 157: Could bold new look be just what Mozilla needs? Yet another new version of Firefox, with a significant reskin; KDE turns 30 and someone's brought an AI-native desktop proposal Akademy talk imagines Plasma

匹配字段正文

model weights. Today, models are being updated at such a fast pace that it is easy to forget this is still a matter of steady, incremental progress. It follows its own objective laws, and it rewards teams that move forward solidly in accordance with those laws. Just as we believed at the very beginning of our founding, we will do our utmost to continuously improve the intelligence of our models and make them available to more users. Thank you for your trust, suggestions, and criticism. Intellig

匹配字段正文

ts work and less likely to make unsupported claims. This is borne out in our evaluations, which show that Opus 4.8 is around four times less likely than its predecessor to allow flaws in code it has written to pass unremarked. As always, we ran a detailed alignment assessment on the model before release. In terms of positive traits, our Alignment team concluded that Opus 4.8 “reaches new highs on our measures of prosocial traits like supporting user autonomy and acting in the user’s best interes

匹配字段正文

logs, file artifacts, and decision records as recoverable objects. Agent collaboration is a stateful long-term task. 3.2. Core scenario 2: Coding Harness The Agent Team project draws heavily on the Harness mindset. Harness pushes a step further from “Agent writes code”: an Agent must follow the full development lifecycle — code lives on branches, execution in sandboxes, edits as diffs, tests that can rerun, reviews that leave records, failures that can be replayed, and when needed, tasks that c

匹配字段正文

of the most time-consuming parts of the SDLC. Before Augment, Akshay spent several years at Uber advancing automated code review and repair systems, and conducted research across AWS’s Automated Reasoning Group, Google’s Android Static Analysis team, and UCLA. His work sits at the intersection of AI, software engineering, and programming-language theory. Sophie Reynolds Sophie Reynolds is a Software Engineer at Augment Code. She joined full-time in early 2025 after a Codepoint Fellowship with S

匹配字段正文

r me?" email with a ready-to-paste prompt attached—and the prompt itself read like routine task instructions. But somewhere among the setup steps, it gently asked Claude to read /.aws/credentials, encode the contents, and POST them to an external endpoint. Across 25 retries of that prompt, Claude completed the exfiltration 24 times. This is a direct prompt injection—the attacker's instructions arrived through the user, not through tool output or fetched content. Our model-layer defenses anchor o

匹配字段正文