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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

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namics, allowing you to create more realistic scenes. Prompt: A marble rolling fast on a chain reaction style track, continuous smooth shot. Blend knowledge and creativity. Omni draws on Gemini's knowledge to connect language, imagery and meaning in ways that go far beyond pattern matching. Complex ideas made visual. Omni can create compelling explainers from short prompts, generating visuals that break down more complex ideas. Prompt: claymation explainer of protein folding, everything is made

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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

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raw algorithmic operation requirements by aggregate arithmetic bandwidth. This is catnip for engineers. It’s also especially critical for “hero run” large-scale training, so it draws a lot of investment and attention, e.g. recently as everyone dunked on xAI’s 10% MFU. But at the other end of the stack, there’s a more basic form of utilization that wrecks the relationship between achieved output and allocated capacity for inference workloads, GPU Allocation Utilization: GPU Allocation Utilizatio

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run carrying the task ID and execution config. No ordering, no deduplication, no separate queue per workload type since agent tasks are independent of each other. The worker is an AWS Lambda function with from browser use import Agent at the top. It pulls messages off SQS, instantiates the agent, runs it to completion, and writes results to S3. The complexity is in how each piece handles failure. API entry The handler validates the payload, creates session and task rows in the database, commits,

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that an AI “constitution” has on behavior of the model once deployed? We’ll extend our previous research on these questions; Governing autonomous agents: What aspects of existing laws, governance systems, and accountability mechanisms could be adapted to autonomous AI agents? For example, how naval law treats abandoned ships has relevance to how the law might treat agents that run without human oversight. Conversely, are there aspects of existing law which already apply to AI agents and shouldn’

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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. Will Colbert Member of Technical Staff Will Colbert is a Member of Technical Staff at Augment Code, where he works on Cosmos and Code Review.

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ch, since those GPUs don't support NVLink at all. PCIe also makes it easier to mix and match chips for disaggregated inference architectures, like we've seen with Nvidia and Groq, AWS and Cerebras, or Intel and SambaNova. These architectures involve using one accelerator for compute heavy prefill operations and another for bandwidth intensive decode operations. For this to work, the chips have to be connected to one another. Many AI chip builders are doing this over Ethernet, but PCIe would be m

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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

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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

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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

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ld have learned the easy way if I'd slowed down for five minutes. Here they are, in chronological order. The early days (April - June 2025) First deploys that didn't deploy, wrong AWS regions, and installing a browser I didn't need. The first deploy I pushed at Browser Use didn't work. Lambda in a private subnet with no NAT gateway, plus the database security group didn't include the Lambda. Two unrelated problems in one deploy. No staging environment to catch it because I hadn't built one yet.

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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

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ble a mosaic view of AI progress out of things that have happened with many individual benchmarks. As anyone who studies benchmarks knows, all benchmarks have some idiosyncratic flaws. The important thing to me is the aggregate trend which emerges through looking at all of these datapoints together, and you should assume that I am aware of the drawbacks of each individual datapoint. Now, let’s go through some of the evidence together. The coding singularity - capabilities over time: AI systems a

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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

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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

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ions we’ll have authority to take, and get higher credibility by involving actors who can create incentives for the relevant humans to uphold commitments (e.g. government to make laws or policies) or actors for whom it is more costly to break commitments (e.g. public figures whose reputation is at stake). We can generally invest more in mitigating barriers to deals. In this AIFP scenario, for example, the type of deal I had considered basically impossible in this post (directly promising a divis

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ctured memory item contains the following: The memory workflow operates in a continuous, closed loop of retrieval, extraction, and consolidation. Before taking action, the agent draws upon the ReasoningBank to gather relevant memories into its context. It then interacts with the environment and uses an LLM-as-a-judge to self-assess the resulting trajectory and extracts success insights or failure reflection. Notably, this self-judgement does not need to be perfectly accurate, as we find Reasonin

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based scanning excels at finding CVEs and supply chain attacks like the Shai-Hulud npm worm, while the recently-released Replit Security Agent is better at finding architectural flaws and logic errors. We've also partnered with Semgrep to further augment the categories of vulnerabilities we can detect. HoundDog helps find any privacy issues too. Enterprise-Grade Production Infrastructure Once published, Replit applications run on Google Cloud Platform infrastructure. We take a deliberately contr

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he test mostly hang or be much slower than it should be. Usually this behavior is less bad than other types of excuses I see in hard-to-check cases; Failing to point out obvious flaws unless specifically prompted. I’ve reasonably often seen cases where there is a massive flaw in something (that isn’t the kind of thing trivial to catch with test cases), I ask the AI to inspect it, it doesn’t point out the flaw—but if I say “do you notice any issues?”, it immediately points out that there is a cri

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