Business Software for Claude Code - Page 10

Claude for Small Business is an AI-powered solution designed to help small businesses automate tasks, streamline workflows, and improve productivity from day one. The platform integrates with widely used business tools like QuickBooks, PayPal, HubSpot, Slack, Canva, Google Workspace, Microsoft 365, and Docusign to centralize operations and simplify daily processes. Claude assists users with tasks such as payroll planning, invoice tracking, financial reconciliation, drafting reminder emails, and creating business forecasts while keeping users involved in important decisions. The platform is built with security in mind and emphasizes that customer data is not used for AI training purposes. Businesses can install the solution quickly without requiring complicated IT setup, making it accessible for growing teams and busy entrepreneurs. Claude also provides tutorials, AI fluency courses, and workflow guidance to help users maximize the benefits of automation and AI tools.

GuardionAI is an Agent and MCP Security Gateway that provides unified security for AI agents and Model Context Protocol tools operating on enterprise data. It sits in the execution path to discover, redact sensitive data, enforce protection, and give teams visibility into actions that traditional SIEM, DLP, and identity layers cannot see. Every agent action is inspected, enforced, and logged at the protocol level across AI agents, LLM apps, RAG systems, chatbots, coding agents, MCP servers, internal tools, databases, operating systems, and cloud environments. GuardionAI protects against critical AI threats such as prompt injection, system override, web attacks, MCP tool poisoning, malicious code execution, NSFW content, PII and credential exposure, confidential data leakage, off-topic drift, and unauthorized access, mapped to OWASP LLM Top 10 and agentic AI threat frameworks. Its gateway provides four layers of protection.

Puter.js AI allows developers to integrate artificial intelligence capabilities directly into their applications using models from various providers. It supports tasks such as chat, text-to-image, image-to-text, text-to-video, and text-to-speech conversion, making it possible to build AI-powered apps without managing a separate backend or setting up individual provider keys. Through the chat, developers can chat with AI models, analyze images and videos, and perform function calls using more than 500 models from OpenAI, Anthropic, Google, xAI, Mistral, OpenRouter, DeepSeek, and other providers. The chat API supports options such as model selection, streaming responses, tool calling, image input, video input, and structured interactions, with a default model available when no specific model is selected. Function calling lets AI models request data or perform actions by calling developer-defined functions, enabling applications to access real-time information and more.

SubQ 1.1 Small is a long-context AI model from Subquadratic designed to reason over complete enterprise artifacts such as codebases, document collections, contracts, and financial filings. It uses Subquadratic Sparse Attention, or SSA, to reduce the high compute costs normally associated with processing very large context windows. The model delivers near-perfect long-context retrieval across 1M, 2M, 6M, and 12M token tests while using far less attention compute than dense attention. SubQ 1.1 Small also maintains strong general reasoning, coding, knowledge, and agentic task performance across multiple benchmarks. Its capabilities make it useful for financial analysis, legal review, contract work, software engineering, due diligence, and other workflows where information is spread across large artifacts. SubQ is built for organizations that want to move beyond fragmented retrieval pipelines and enable direct reasoning over massive bodies of information.

UnoRouter is an OpenAI-compatible LLM gateway. One API key gives you 200+ models across providers (OpenAI, Anthropic, Google and more), drop-in for coding agents like Claude Code, Cline, Codex and Kilo Code. Point any OpenAI SDK at the base URL and switch models without changing code. UnoRouter also includes a built-in chat and character client (personas, lorebooks, SillyTavern card import) on the same key. Usage-based pricing with a free tier, live model and price data.

Constellation Gate AI is a drop-in defense layer for AI agents, built to sit between the agent and the model while screening every request for attacks and leaks. Gate acts as an inline gateway for coding agents and model APIs, protecting workflows without requiring major code changes. Users can point existing tools such as Claude Code, Cursor, OpenClaw, Codex, or OpenCode at Gate and inherit prompt-injection defense, secret scanning, PII redaction, token optimization, and a verifiable audit trail. The platform is designed around three real risks: prompt injection, credential and PII leakage, and hijacked tool calls. Instead of relying on the model to defend itself, Gate blocks attacks before they reach the model, redacts secrets before responses return, and stops attacker-controlled tool outputs before an agent acts on them. Gate accepts the same calls an agent already makes, forwards them to the model, scans every call and response in both directions.

HQ is the shared AI context layer for teams, giving the whole team and every AI tool one workspace to work from, with knowledge, skills, and workflows compounding in one place, and any agent running on top. It works as an operating system for AI workers over Claude Code, Cursor, Codex, ChatGPT, and Claude chat through MCP, so every teammate and every agent can start from the same shared context instead of separate chat histories, scattered files, and siloed workflows. HQ turns one person’s best work into team infrastructure: any prompt or workflow can become a reusable /command, then /hq-sync ships it to the whole team so anyone can run it in one step. Knowledge that usually lives across decisions, docs, playbooks, policies, projects, code, and ideas accumulates in HQ as the team works, creating one source of truth that every agent can search, reuse, and build on. Agents can be deployed into email and Slack, acting on top of the team’s skills and knowledge with full context.

Ming-Flash Omni 2.0 is a full-modal large language model from Ant Group, built on a unified multimodal architecture with “modal unity + task unity” as its core design philosophy. As part of the Ming series, it is designed to achieve cross-modal understanding and generation across text, images, audio, and video, allowing one model to see, hear, speak, and draw instead of relying on multiple specialized models. Ming-Flash Omni 2.0 follows the evolution of Ming-Light Omni and Ming-Flash Omni Preview, moving from unified architecture validation and hundred-billion-parameter scaling to a Data Scaling strategy that achieves open-source SOTA performance on multiple benchmarks. The model integrates four core capability modules: image-text understanding, video analysis, speech synthesis, and image generation or editing. For image-text understanding, Ming introduces structured knowledge graphs for fine-grained visual perception.

Agentcard gives AI agents a safe way to pay for things online by issuing disposable virtual Visa cards built for agent workflows. Instead of sharing a real card in chat or making a human finish checkout, users can create single-use cards with fixed spend limits that self-destruct after one authorized payment. Agentcard is designed around control: a human approves every card and every charge, real card details are never shared with the agent, and users receive notifications when an agent tries to create a card or make a payment. It works with ChatGPT, Claude Desktop, Claude Code, OpenClaw, Cursor, and MCP-compatible agents through one-click integrations, an MCP server, CLI tools, REST API, Chrome Extension, and admin tools for companies. Agents can create cards, check balances, list transactions, close cards, and use cards to complete online purchases while the user stays in control.

LongCat-2.0 is a 1.6 trillion total-parameter Mixture-of-Experts language model built on AI ASIC superpods, with about 48 billion parameters activated per token and strong performance across coding and agentic tasks. It is a substantial step up from previous LongCat models, combining large-scale sparse architecture with dedicated post-training for real-world software engineering, tool use, long-context reasoning, and multi-step agent workflows. LongCat-2.0 is trained and deployed entirely on AI ASIC superpods, with pretraining spanning more than 35 trillion tokens and millions of accelerator-hours, demonstrating frontier-scale training on alternative hardware platforms. To strengthen long-horizon tasks, the model introduces LongCat Sparse Attention and is trained on hundreds of billions of tokens of 1M-context data, giving it native support for ultra-long context tasks and reliable long-document understanding.

Concentrate AI is the LLM gateway for fast-growing teams, one API for every major LLM provider, with routing, spend, logs, and controls in one place. It helps teams securely access, use, and manage AI through a single API, so every request can find the smarter, faster, cheaper model for the workflow or task. Teams can access 130+ models, benchmark speed, quality, and cost, and route each workload to the best fit without wiring separate provider APIs into every environment. Support bots, coding agents, internal tools, chat, and batch jobs do not need the same model or the same route, so Concentrate lets teams pick a model slug, limit allowed providers, sort by live latency, use fallbacks, and reroute traffic when a provider slows down, errors, or hits a rate limit. It also gives engineering, finance, security, and leadership a shared view of AI usage with request-level logs, models, provider, duration, token counts, spend, error rates, alerts, and exports.

condense.chat is an LLM input compression API and drop-in proxy that shrinks prompts, retrieved documents, tool outputs, and repeated agent context before they hit upstream models. Less context, same Claude Code; its harness intercepts an agent’s growing session history and passes it through compression models before it reaches the main model, helping long-running coding agents start each next turn with fewer tokens. Condense sits between an app and the upstream LLM provider, tracks the conversation as a content-addressed chain, and transparently compresses repeated context on the way upstream. Developers can point their SDK at the Condense provider route, add a Condense key, keep their existing provider key, and change nothing else. It supports Anthropic and OpenAI-compatible routes, plus pass-through behavior for other provider paths such as model lists and embeddings.

TrayToken is a privacy-focused engineering intelligence platform that helps organizations understand and improve how software teams use Claude Code. A desktop tray app for macOS, Windows, and Linux analyzes selected local Claude Code sessions and turns them into prompt-quality scores, activity timelines, adoption trends, token and tool-usage metrics, and actionable coaching. Role-based web dashboards give administrators organization-wide visibility, team leads team-level insights, and engineers personal analytics. TrayToken supports project-level opt-in, CSV exports, alerts for declining scores or inactivity, and side-by-side comparisons. Source code, file contents, Claude responses, and tool outputs remain on the device and are not transmitted.

Laguna XS 2.1 is an upgraded open weight agentic coding model designed for long-horizon work on a local machine. It uses a 33-billion-parameter Mixture-of-Experts architecture with 3 billion activated parameters per token, retaining the same efficient architecture as Laguna XS.2 while improving multilingual software engineering and terminal-style task performance. The model is built to support coding agents that inspect repositories, reason through complex changes, use tools, execute commands, and continue working across extended tasks. It is served with a 256K context window, giving agents room to work with large codebases, lengthy histories, and multi-step workflows. Laguna XS 2.1 is supported by vLLM, SGLang, NVIDIA TensorRT-LLM, Hugging Face Transformers, and Ollama, with native llama.cpp support planned. It is available in BF16, FP8, INT4, and NVFP4 checkpoints, allowing developers to choose between maximum fidelity and configurations suited to tighter VRAM or compute budgets.

Laguna S 2.1 is an open weight agentic coding model designed to pursue longer-horizon work and make effective use of reasoning. It uses a 118-billion-parameter Mixture-of-Experts architecture with 8 billion active parameters per token and supports a context window of up to one million tokens in both thinking and no-thinking modes. Its compact active size makes it suitable for complex work on local machines while remaining competitive with models many times larger on terminal, software-engineering, codebase-question-answering, and tool-use benchmarks. Laguna S 2.1 is built to keep working through difficult tasks with greater persistence, verification, and willingness to backtrack instead of declaring success too early. In demonstrated runs, it built and validated a browser rendering engine from an empty folder, optimized an agent harness for faster execution and substantially lower memory allocation, and completed extended mathematical research using the tools in its environment.

Claude Mythos Preview is a highly advanced AI model developed with strong capabilities in cybersecurity, particularly in identifying and exploiting software vulnerabilities. It demonstrates the ability to autonomously discover zero-day vulnerabilities across major operating systems, browsers, and critical software systems. The model can also generate complex exploit chains, including privilege escalation and remote code execution attacks. Its capabilities extend beyond vulnerability detection to reverse engineering and exploit development in both open-source and closed-source environments. Mythos Preview operates through agentic workflows, enabling it to analyze codebases, test hypotheses, and validate exploits independently. These abilities represent a significant leap compared to previous models, which struggled with exploit generation. Overall, Claude Mythos Preview highlights a new era where AI can both strengthen and challenge global cybersecurity practices.

C is a programming language created in 1972 which remains very important and widely used today. C is a general-purpose, imperative, procedural language. The C language can be used to develop a wide variety of different software and applications including operating systems, software applications, code compilers, databases, and more.

Claude Conway is an AI-driven platform designed to help businesses build, deploy, and manage intelligent AI agents with greater control and flexibility. It enables users to automate workflows, handle complex tasks, and integrate AI seamlessly into existing systems. The platform focuses on combining reasoning, execution, and tool usage to create highly capable autonomous agents. With support for multi-step decision-making, Claude Conway helps businesses streamline operations and improve efficiency. It allows integration with various tools, APIs, and data sources for enhanced functionality. The system is built to support both technical and non-technical users with scalable deployment options. Overall, Claude Conway empowers organizations to leverage AI for smarter automation and innovation.

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