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startup-projects

Show HN: Open-source customer-facing agents

Hi, Dmitri from Radioso here, an open-source platform to design AI agents for customers and visitors in any channel, in the style of Fin/Ada/Decagon. The repo is https://github.com/radioso-ai/radioso and our website is https://radioso.aiToday, your choices are:1. Buy a very expensive service for your vertical (usually starting with $$ five figures monthly) 2. Get a cheap off-the-shelf chatbot that mostly answers FAQs 3. Build your own, and then also build the inbox, human handoff, audit trail and integrations around itAt Radioso we wanted an open-source option that combines the best of all 3. You can integrate it with your own software via API/SDK or your CRM, or run it standalone, either self-hosted or in our cloud.The engine runs multi-step interactions and takes actions: calling your APIs, updating records, booking things. Maybe you’ve tried Parlant; Radioso is the alternative “with batteries included”: RAG, audit trail, customer service inbox, and integrations like Slack and email.Under the hood it’s plain TypeScript + PostgreSQL, works with OpenAI/Anthropic/other models, and is licensed under Apache 2.0 which includes all features (The only thing we keep under EE license is the multi-tenant layer we use to run our own cloud).We have existing customers running it for technical Q&A, course booking, online product recommendations and goods returns, among others, some reaching 92% of conversations automated. We’re currently working on voice support so we can offer a call center.Our ambition is to be “the WordPress of customer-facing AI” :), where people can contribute their favorite plugins and benefit from the ecosystem.Longer term, we think browsing the web will mostly mean talking to your own agent, and when you need a service or a purchase, your agent will talk to the vendor’s agent to get things done. For this, Radioso already publishes an A2A Agent Card and we’re working on the rest. Your Radioso services are also callable over MCP, for customers who already use it.Thanks for your attention. We’d especially love to hear which integrations you’d want, whether you’d self-host it, and what’s stopped you from deploying a customer-facing AI agent so far. Tell us what you think. Comments URL: https://news.ycombinator.com/item?id=50423820 Points: 1 # Comments: 0

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Hacker News Show HN
compute-cost

xAIはGrok Botを全面的にアップグレードし、トークン効率と知能性能の最適化に重点を置きました。xAIは傘下のGrok Botについて、基盤アーキテクチャと機能の新たな最適化サイクルを開始しました。コアメンバーのMatt Palmer氏と創業者イーロン・マスク氏はX上で発表し、トークン効率の向上とインテリジェントな対話行動の改善を重点としています。今回の最適化は、開発者や企業がGrok Botをベースに多数の主要なワークフローや自動化を構築する過程で明らかになった課題に対応し、効率と利用体験の向上を目的としています。関連するアップグレードはすでに全面的に展開されています。 さきほど 0

xAIチームはGrok Botの基盤アーキテクチャと機能の最適化を進めており、特にトークン効率と知的行動のパフォーマンス向上に注力しています。これにより、開発者の利用コストを削減し、複雑なタスク処理能力を改善することを目指しています。最適化がユーザーのニーズに合致するよう、チームは開発者からのフィードバックを公募しており、ユーザーの提案を今後のイテレーションに真摯に反映させることを約束しています。

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AIBase AI资讯
agent-frameworks

The Two-Ledger Model: Transaction-Grain Cost Accounting for Multi-Agent AI Systems Across Clouds, Models, and Systems of Record

arXiv:2611.06989v1 Announce Type: new Abstract: Enterprises run business transactions through multi-agent AI systems spanning clouds, large language models (LLMs), orchestration harnesses, and systems of record. Finance receives AI cost only at settlement grain, aggregated by SKU and charge period. The business question (what did this order, claim, or case cost in AI?) cannot be answered from billing data, because providers never see enterprise transaction boundaries. The FinOps Open Cost and Usage Specification (FOCUS) gives billing data one schema across providers but remains settlement-grain even as it adds AI token and requester attribution. We formalize this grain mismatch, derive seven requirements, and propose the Two-Ledger Model: a settlement ledger of FOCUS-conformant billing data joined to an attribution ledger of invocation-grain usage records built on OpenTelemetry GenAI telemetry. A layered correlation spine (trace context, A2A/MCP metadata, business-key correlation, probabilistic matching) assigns each record to its business transaction with recorded method and confidence; a versioned rate card prices usage in near real time; period reconciliation ties both ledgers to the invoice, and the unexplained residual identifies shadow AI. Costs are reported per order, claim, or outcome with agentic cost metrics. A simulation with known ground truth shows the spine sustaining cost coverage above 99% as trace propagation falls to zero, the reconciliation residual recovering planted unmetered spend within 0.24 percentage points while flagging provider billing errors, and mid-flight gating cutting p99 transaction cost by up to 61% at a 2-4% false-pause rate. We propose the attribution ledger as an invocation-grain FOCUS profile for standardization.

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arXiv cs.AI
video-generation

可灵 AI 宣布 Kling 4.0 Flash 全面开放，深度融入专业影视工作流人工智能视频生成领域迎重要进展。可灵AI宣布Kling4.0Flash面向全球用户全面开放。该版本主打高频创作与高性价比，支持官网和API调用，并打通多维接入生态，赋能创作者日常视频生产。在生态适配上，模型展现高开放性，推动多端接入与专业软件生态深度融合，助力视频生成应用普及。 刚刚 5.7K

可灵 AI 官方宣布 Kling4.0Flash 版本已面向全球用户全面开放，支持网页端、API、MCP、CLI 及 AI 插件接入，并深度集成至 Adobe Photoshop、Premiere Pro 和 After Effects 等专业软件，实现与传统后期制作工具的无缝衔接，显著提升创作者在高频内容创作与专业项目中的效率。

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AIBase AI资讯