Search Results for "input-output model"

Qwen3-Coder is the latest and most powerful agentic code model developed by the Qwen team at Alibaba Cloud. Its flagship version, Qwen3-Coder-480B-A35B-Instruct, features a massive 480 billion-parameter Mixture-of-Experts architecture with 35 billion active parameters, delivering top-tier performance on coding and agentic tasks. This model sets new state-of-the-art benchmarks among open models for agentic coding, browser-use, and tool-use, matching performance comparable to leading models...

nanocode is a minimalist coding agent implementation designed as a compact alternative to Claude Code, packaged in a single Python file with no external dependencies and totaling around 250 lines of code. It implements a full agentic loop where the model can reason, decide when to use tools, execute those tools, and iterate until producing a final answer, making it useful for simple AI-assisted coding workflows. It includes a set of integrated tools such as read, write, edit, glob, grep, and bash that let the agent interact with the file system and shell commands directly from the terminal, and it keeps a conversation history with colored terminal output for readability. ...

Stop shipping unsafe AI output. Local-first verification, safety monitoring, and guardrails for any LLM 100% Local | No Telemetry | Privacy-First | 500 free calls/day Links Product Page: subodhkc.com/products/llmverify npm: npmjs.com/package/llmverify GitHub: github.com/subodhkc/llmverify-npm Python Package (PyPI): github.com/subodhkc/llmverify Author: Subodh KC — AI governance, compliance, and security leader Feature One-liner What it catches Verify output await verify(aiResponse) Hallucinations, harmful content, quality issues Block injections isInputSafe(userMessage) Prompt injection, jailbreaks, malicious input Redact PII redactPII(text) Emails, phones, SSNs, credit cards, API keys Fix broken JSON repairJSON(broken) Missing brackets, trailing commas, unquoted keys Monitor health monitorLLM(client) Latency spikes, token drift, behavioral changes Sentinel tests sentinel.quick(client, model) LLM regressions b