搜索“Agent”

(pass@1 69.9% vs 68.5%) and solves more tasks four-for-four. Kimi K3 wins pass@2 and pass@4 and costs far less, so it is the stronger value pick for high volume or retry-tolerant agent work. In our run, Kimi K3 cost \$4.65 per rollout versus \$13.41 for Claude Fable 5 at its xhigh setting, roughly a third of the price. Measured per solved task, Kimi K3 returned 14.7 solves per \$100 against Fable's 5.3, about 2.8x the work per dollar. Yes. Kimi K3 is an open-weight model from Moonshot AI, so on

匹配字段正文

ected token budget, its reward is instead set to -1. This gives the model a strong incentive to stay within the budget. (The exact reward setup is different for non-verifiable and agentic tasks, and Kimi also uses learned reward models and pairwise comparisons.) To have Kimi K3 support the three reasoning effort modes, the training looked as follows. First, the researchers start by training a max-effort version (“specialist”) with a relatively generous budget. They then lower the budget to train

匹配字段正文

w go profile something. 🤗 Thanks to Noe Flandre for their reviews on the early draft of the post! The blog post was polished using an LLM. This in no way means that we have let an agent run in the background and let it generate the blog. Some of us in the team are non-english speakers and think LLMs (which are mostly trained in the English Language) can rectify silly grammar mistakes or rephrase sentences that sound less intimidating and cleaner. Hope this helps with the idea of "why should I re

匹配字段正文

ext • 10B • Updated Mar 2 • 9.28M • 2.08k xtuner/llava-llama-3-8b-v1 1 Image-Text-to-Text • 8B • Updated Apr 28, 2024 • 34 • 120 Spaces mentioned in this article 1 Running on Zero Agents Featured 37 PRX Pixel 📷 37 Public test Space for pixel-space PRX t2i Collections mentioned in this article 1 Qwen3-VL 782 More from this author PRX Part 3 — Training a Text-to-Image Model in 24h! 69 March 3, 2026 Training Design for Text-to-Image Models: Lessons from Ablations 77 February 3, 2026 Community Thank

匹配字段正文

was real, but reaching it required a caller to set up a state that was extraordinarily unlikely in real-world conditions, so the model logged it as unreachable and moved on. This agent kept going without human intervention and found several higher-impact issues. That discipline is the whole game. The value of the zlib harness came from automation plus a strict definition of what counted as a reportable finding . Without strong validity rules baked into the goal and a model truly capable of eval

匹配字段正文

Contents 1. TL;DR; 2. Why SGLang Is a Good Fit for Agent-Assisted Development; 3. From Prompt Engineering to SKILL: Protocols and Examples; 3.1 Current Skill Stack; 3.2 Recent Optimization and Workflow Examples; 4. Profiling, Review, and Loop Engineering; 4.1 Humanize/RLCR: Adding External Review to the Loop; 4.2 SGLang SOTA Performance Loop (Loop Engineering); 4.3 Codex Goal: A Lower-Cost Loop Implementation; 5. KDA-Based CUDA Kernel Optimization for SGLang Systems; 6. Practice Rules; 7. Acknow

匹配字段正文

are subsidized, by one audience estimate a factor of ten to forty. Angelos’s hope is that optimization, not subsidy, eventually closes that gap. The largest models, he said, have to be subsidized to make economic sense, but he expects smaller, Sonnet-class models to become good enough for nearly all everyday use, at margins that actually work. He already sees the shape of it in agent systems: route each request to the smallest model that can handle it, and reserve the expensive one for planning.

匹配字段正文

Many people reached out to me in the past asking about my local agent stack as well as how I set up my local agent stack. So, I thought it might be useful to put together a little tutorial on how to set up a local (coding) agent using open-source tools and open-weight LLMs. This article is a tutorial on setting up a production-ready coding agent with a fully local stack. We will use a locally served LLM together with a local coding harness that can read files, make edits, run commands, and verif

匹配字段正文

sponse comes back in under two seconds. It now runs in 20 countries, handles around 25,000 calls a month, and resolves a customer’s problem roughly eight times faster than a human agent. AIR, the latest layer, followed in Q2 2025 and pulls in transactional data: it can break down your spending, propose hotels inside a budget computed from your own history, or explain why a stock is moving. Across the arc from the old chatbot to today, the share of cases resolved without a human climbed from 17%

匹配字段正文

oom. That machine restores a saved browser VM, starts Chromium inside it, waits until Chromium is ready to be controlled, and returns a connection URL. That URL is what the user's agent connects to. Browser Use controls Chromium over a WebSocket using the Chrome DevTools Protocol, or CDP. CDP is the remote-control API for Chrome: click this button, type this text, read this page, take this screenshot. Three things made this take longer: restoring the VM's memory, launching Chromium, and keeping

匹配字段正文

about why we built the feature and how we use it ourselves today. Like a lot of people who build software, I felt the ground shift at the start of 2026. The models got better, the agents got better with them, and almost overnight the amount of code moving through my team went up. Between January and March our total issues created and PRs merged both climbed by 50%. That was exciting, and it was also a problem. With that much code moving, I felt the pull to “looks good!” my way through reviews ju

匹配字段正文

ng a high score, it bested the next-highest model, GPT 5.5, by 12 points. The result came at an exorbitant price: $580.87 in API cost for 100 tasks. BU Bench tests whether browser agents can complete real web tasks. It requires agents to handle multi-step navigation, search, information extraction, form filling, dynamic UI interactions, iframes, PDFs, downloaded files, and synthesis across live websites. Its tasks, code, and model scores are available here. Below is Claude Fable 5 compared to ot

匹配字段正文

type of skills are worth making? How do you structure a skill? When do you share them with others? WHAT ARE SKILLS? Skills are folders of instructions, scripts, and resources that agents can discover and use to do things more accurately and efficiently. This blog post assumes familiarity with skills basics; if you’re new, start with our Introduction to agent skills course on Skilljar. A common misconception we hear about skills is that they are “just markdown files.” They’re actually folders tha

匹配字段正文

fic patterns. The lesson was to keep profiling beyond the model. GPU time, queue depth, and model execution all looked normal; the latency spike lived in the Python runtime. Voice agents usually run as a cascade: ASR produces a transcript, an LLM generates the response, and TTS produces audio. ASR is the first stage in that path, so its latency and jitter set the earliest bound on user-visible response time. The optimizations above target different parts of that path. TensorRT multi-profile engi

匹配字段正文

y members of the Claude Code team use HTML instead of Markdown to produce richer, more readable, and easily shareable outputs. Markdown has become the dominant file format used by agents to communicate with humans. It’s simple, portable, has some rich text capability and is easy to edit. Claude has even gotten surprisingly good at using ASCII to make diagrams inside of Markdown files. But as agents have become more and more powerful, I’ve found that Markdown has become an increasingly restrictiv

匹配字段正文

up on a busy few weeks of open-weight LLM releases. The thing that stood out to me is how much newer architectures are focused on long-context efficiency. As reasoning models and agent workflows keep more tokens around (for longer), KV-cache size, memory traffic, and attention cost quickly become the main constraints, and LLM developers are adding a growing number of architecture tricks to reduce those costs. The main examples I want to look at are KV sharing and per-layer embeddings in Gemma 4

匹配字段正文

minal Bench 2.0: a smaller, sharper context produces less work for the model and a meaningfully smaller bill at the end of the run. What’s driving the token efficiency Most coding agents assemble context through grep and keyword search. While this approach has improved in quality over time, it remains inefficient: agents burn turns crawling files, reading large spans of code, and pulling in irrelevant matches just to find the few lines that actually matter. Every miss costs another round trip, a

匹配字段正文

ssion to save bandwidth during transfer and avoid bottlenecking the network during decompression. But we also try to keep image creation fast, especially to better support dynamic agent workloads. You can fast-forward through tens of seconds of application host-side startup with CPU memory snapshotting. Now, let’s consider our third step: start the application program on the host, ready it to service requests (tens of seconds) This encompasses all of the work that needs to be done to go from the

匹配字段正文

e gap to hardware efficiency. Together is continuing V4 kernel work across the prefill and decode paths. Developers should benchmark V4 in their actual regime. A 1M-context coding agent and a short-context chat assistant exercise different parts of the serving stack. V4 widens the gap between workload shapes. Long-context agents benefit most directly from the architecture. They read large amounts of cache during decode, so V4's compressed KV layout, larger tensor-parallel configurations, batchin

匹配字段正文

y shape their unit economics. Estimates put inference at 80-90% of the total lifetime cost of a production AI system , simply because it runs continuously across every user query, agent step, and API call. And while training is a bounded investment, inference scales with every new user and use case you ship. At NVIDIA GTC 2026, NVIDIA CEO Jensen Huang framed this shift plainly: “People pay for information, but people mostly pay for work. Agentic systems get work done.” That shift from AI as a no

匹配字段正文