搜索“RAG”

ng 100% utilization and 92% API gross margins, maxing out Opus 5.5 vs Fable 5.1 usage corresponds to -369% and 1% gross margins respectively. If we were to use a more realistic average utilization of 20%, then gross margins rise to 6% and 80% respectively. In other words, Anthropic would likely already have software like margins on their subscription plans if everyone only used Fable. Opus class models will continue to become cheaper to serve over time as Anthropic figures out how to make the mo

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s Python and Bash in a sandbox Anthropic manages, on the Messages API. The documented environment is a Linux x86 64 container with Python 3.11, 5 GiB of RAM, 5 GiB of workspace storage, and one CPU. Internet access is disabled and no outbound connections are permitted, so Claude works with the preinstalled libraries and can’t install a package during a run. Three tool versions exist, and every supported model accepts all three. code execution 20250825 supports Bash commands and file operations.

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e 5.1 share the same $10 input and $50 output list price. Sol and Argon list at one-fifth of that. Argon’s price is introductory and doubles later. Sources: OpenAI GPT-6.1 Sol coverage, Gemini 4 Argon coverage, GPT-6 Astra long-context pricing, Claude Fable 5.1 specs. Standard first-party list prices, short-context tier. The cached-input row matters most for agents. Agents resend system prompts, tool schemas and history on every step. Astra’s $1.00 cache read is 4x Fable 5.1’s and 10x Sol’s. Arg

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Qwen beat vLLM by 71%, at 10,307 output TPS vs 6,030 output TPS in vLLM. The second experiment: image segmentation with SAM 3.1 The results from this first experiment were so encouraging that I decided to try it out on another model, of a completely different type, using the newly expanded knowledge base generated from the VibeQwen experiment. This time I wanted an optimized inference server for SAM 3.1, a state of the art image segmentation model. In this case, I used Facebook’s reference serve

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What are Decision AI Models? Decision AI models are a new class of model that returns a decision, not a paragraph. You send text with typed questions. The model returns choices, scores or yes/no probabilities your code can branch on directly. The category went mainstream when TypeSafe AI launched Jev after 2 years in stealth. TypeSafe calls it a ‘System One model,’ after Daniel Kahneman’s fast, intuitive System 1 thinking. Within 3 weeks, Fastino Labs shipped 2 rival models, and open-source deve

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Qwen beat vLLM by 71%, at 10,307 output TPS vs 6,030 output TPS in vLLM. The second experiment: image segmentation with SAM 3.1 The results from this first experiment were so encouraging that I decided to try it out on another model, of a completely different type, using the newly expanded knowledge base generated from the VibeQwen experiment. This time I wanted an optimized inference server for SAM 3.1, a state of the art image segmentation model. In this case, I used Facebook’s reference serve

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ed experiments, project drift, and AI slop. Recently I had Codex run a multi-step finance task: read our company debit card spend data, calculate the trailing three-month spend average, add a buffer for taxes, and issue merchant-specific Ramp cards at that limit and then notify each cardholder in Slack. The cards came out correct, but the Slack messages didn’t. They read like polished corporate memos, opening with “Hey @person” and other phrases that I’d never use—even though Codex had read my S

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Teams that process documents at scale know the routine: files land in storage, someone notices, opens each one, decides what it needs, and routes it for review. Monitoring alerts queue up the same way, waiting for a person to act on them. The hours lost to manual triage are the operational problem ambient agents solve. Imagine a document lands in your Amazon Simple Storage Service (Amazon S3) bucket and within seconds a job appears on your Jobs page, ready to run (or already running if you confi

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y quirks. When migrating to GLM 4.7, a common mistake is reusing old prompts without adjusting them for its unique behavior, which can lead to suboptimal performance. To fully leverage GLM 4.7's strengths, it's essential to refine your prompts, architecture, and sampling parameters accordingly. Below are 10 rules to help you get the most from GLM 4.7. On Cerebras, GLM 4.7 supports up to 131K context length. However, like most other large models, the output quality for GLM4.7 is most accurate at

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generation. To get the most out of Spark, start fresh sessions often and keep your research context inside your repo. Treat the context window as working memory, not long-term storage. Keep durable state in files. Keep sessions focused. Reset when goals are complete. A useful mental model is: The best trick for managing context length: break larger tasks into small, bounded goals. Prompt each session to make incremental progress toward one goal, so that every session produces bug-free, well-doc

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han multiplying listed rates by estimated token counts; Rerun the comparison when a candidate model or its price changes What a leaderboard rank does not tell you A leaderboard averages a model’s results across tasks that have nothing to do with yours. The model that ranks first for coding is not necessarily first at your structured-data extraction task, and a mid-tier model that looks unremarkable on a reasoning benchmark may answer your FAQ traffic accurately enough for your bar. Agent tasks a

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t the studio’s own back catalog. If the screen objects, the agent calls back to the composition agent for a replacement and re-screens. Delivered as a zip file on Amazon Simple Storage Service (Amazon S3) The workflow: a producer starts a track. The composition agent writes a brief and renders real audio on the instance’s GPU. The delivery agent opens that file, measures it, applies a chain it derived from those measurements, and measures again to prove the result landed on target. The complianc

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ing to get a sense of the type of problems these models face. I found I was surprised by the scope and ambition of a lot of these tasks. They are much more complicated than the average task I’d face. For example, some of the tasks included: Hardware (retro-console-soc): build an 8-bit game console in Verilog that fits a small FPGA and renders a test ROM; Science (takens-embedding-lean): formally prove Takens’ embedding theorem in Lean 4; ML (mp-checkpoint-consolidation): merge 16 shards of a mix

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size of the conversation, the session took many turns, and the conversation is worth reading to find where the loop repeated For a baseline, the Claude Code costs docs give an average across enterprise deployments of about $13 per developer per active day, and under $30 per active day for 90% of users. A session that costs well above your own normal level is worth reviewing. KEEP IN MIND Use medium effort for well-scoped daily work; Give the model a way to check its work, and start changes that

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may have heard of. Today, we finally go through the full breadth and depth of our testing, more thorough in ClusterMAX 3.0 than ever before. This includes compute, networking, storage, orchestration, UI, monitoring, support, and just about everything that you could think to check on a GPU cloud. We’ll explain who’s been cutting deals, whose engineers have been hard at work, and whose clusters you should negotiate to come with a bottle of Aspirin. Without further ado, voilà the ClusterMAX 3.0 po

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paths there’s no instruction counting under Chromium, but there’s a ladder of other deterministic counts: React commits per interaction, function call counts from V8’s precise coverage, layout and style-recalc counts, DOM mutations. Which do you want first?; SSam12:49 PMLet’s explore valgrind + Ir + --predictable in one thread and each of the browser/react benches in new threads. Ping me in all of them. You know what we want. Let’s go Eleven minutes later, five threads were running, each focused

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l factors, not a few tens of percent. Counterintuitively, it is fairly easy to produce a speculator that predicts four, eight, or even more of the next tokens in the output, on average, especially for coding agent workloads. Roughly, there are two reasons this is the case: the target model sets speculators up for success and the majority of tokens do not use the full intelligence of the target model. Speculators can re-use the work of the target model. First, the target language model has alread

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ports to any OTLP backend, and Pipecat Cloud adds logs with Datadog integration and stored transcripts. What isn't included is the assembled view: the backend, retention, audio storage, and the correlation between media events and model events are yours to build, which usually means one more dashboard someone maintains. Who helps when you need it? The tiers look alike until the top. Both run community → email → enterprise: LiveKit's forum and email support on Ship and above; Pipecat's Discord an

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the intelligence, security, reliability, and performance, and they want it shared so that a fix in one place fixes it in all of them. Most of the products listed in the previous paragraph initially implemented their own agent loop, but have since replaced it with the GitHub Copilot SDK, which is the entry point to the Copilot agent runtime. Doing so enables them to focus on their core business value and leave the details to the runtime. That’s all the more important given the pace of the industr

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rcentage of tokens exist. DeepSeek improved the KV Cache utilization on DeepSeek-V4.1-Flash compared to previous generation DeepSeek V4, only consuming ¼ the HBM, and ⅛ the SSD Storage. Figure 2: KV Cache on DeepSeek-V4.1-Flash This new KV Cache optimization unlocks better economics for your coding agents. From the DeepSWE run, we had the following economics: 99.6% of input tokens on this run were cache hits, and cache-hit charges are 60% of the bill. Most people assume your agent economics resu

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