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Introducing Batchwork ⚡️ Unified batch API for AI providers. Process LLM requests in bulk with a single call for lower costs. Processing, uploading, polling, and result parsing handled for you. → 7 providers → JSONL handled → ▲ AI SDK support → Server + Next.js helpers

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Hayden Bleasel

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78,899 görüntüleme • 2 ay önce •via X (Twitter)

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Anthropic won't like this open-source repo. It is going to cost LLM providers a lot of money. Every CI run of an AI app today sends real requests to providers like OpenAI or Anthropic. Like any other LLM call, this too gets billed at actual API rates. So for teams with high commit volumes, this accumulates into a meaningful chunk of API spend. One common hack devs use is that instead of invoking the LLM API, the test calls a fake local server that speaks the same API and returns a dummy response. The catch is that the dummy response is a copy of what the provider returned on the day it was saved, and providers keep adding fields and changing types. So the tests keep passing against a schema that's no longer valid, while the real integration breaks in production. A smart approach is now actually implemented in CopilotKit🪁's recently open-sourced aimock project. Every day, the repo's own CI sends a handful of requests to the real API and the same requests to the fake server, then compares both against the official client library's type definitions. Those are the only real API calls in the whole setup, and they run on the repo's own keys, not in anyone else's CI. A single team can push hundreds of commits a day, and thousands of teams are already doing that with coding agents. All of those runs stay offline, because one repo checks against the real API on everyone's behalf. When a check fails, a coding agent updates aimock's built-in response schema, the full test suite has to pass, and a patch version ships to npm. By simply upgrading the package, the corrected schema gets reflected in every project using it. The capability is not just limited to a single provider. The same server works for Claude, OpenAI, Gemini, Bedrock, Azure, Ollama, plus MCP tools, A2A agents, AG-UI event streams, vector DBs like Pinecone and Qdrant, and search, speech, image, and video endpoints. Here's the repo: (don't forget to star it ⭐) That said, mocking your API calls is one thing. AI engineers should also know how to test agents properly in the first place, which several teams still skip. I wrote a full walkthrough on that, covering build, testing, evals, tracing, and deployment. Read it below.

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Introducing LiveKit Inference — a new cloud service that gives you access to the most popular voice AI models with just your LiveKit API key. We manage rate limits for you, report on usage, and consolidate billing. All LiveKit Cloud plans now include free monthly inference credits. A single string update allows you to call models from: AssemblyAI Deepgram Google DeepMind Inworld AI OpenAI Rime

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most AI APIs either cost too much or lock the best models behind expensive plans. then i came across Agnes AI. it’s one of the few platforms giving developers access to text, image, and video AI models through a single API. their ecosystem gives you direct access to top-tier models like - Agnes-2.0-Flash for text - Agnes-Image-2.0 for image - Agnes-Video-V2.0 for video Instead of managing different providers for different tasks, everything works from one place. what stood out to me: > one API for text, image, and video generation > OpenAI-compatible API, so integration is straightforward > built for speed and scalability > free access for developers to start building > useful for everything from chatbots to image generation and content automation if you’re building AI products, it’s worth taking a look while it’s still flying under the radar. sometimes the biggest advantage is finding great tools before everyone else does. check out Agnes AI here, get free access for developers : and if you’re building with it, join the developer community here: Agnes AI #AgnesAI #AgnesAPI #BuildWithAgnes #MultimodalAI #AIforDevs #DeveloperAPI #AIParity

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🎉 Introducing: wallet․fund() You can now fund any wallet from fiat via a single API call using the CDP SDK! wallet․fund() consolidates the legacy multi-step fiat onramping process into a single programmatic flow for users. With wallet․fund(): fiat in your bank → crypto in your wallet Without wallet․fund(): fiat in your bank → fiat on an exchange → swap for crypto on exchange → transfer to your wallet Watch Lakshay’s demo below to see two ways to fund a wallet: → A quote-based flow, where you preview the transaction before executing → A direct flow, where you skip the quote and fund your wallet immediately

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This is insane Just built a fully working AI Room Designer in minutes, no coding, no API keys, just chatting with OnSpace AI. I use Nano Banana Pro for generation and handles UI, logic, and integration automatically. Not a demo. A real app: • No API setup required – Just told OnSpace to use Nano Banana Pro for content generation and it worked instantly • Full app, not a prototype – Complete UI, logic, and AI integration handled automatically • Actually works – Nails my tone every single time The whole thing came together through conversation. No design work. No wrestling with code. Just described the app and OnSpace made it real. This is what building with AI should feel like. Check my app link in the thread and try it yourself

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New open source project: Easel, a macOS workspace for AI-assisted product design and frontend iteration powered by the Codex SDK. Think of it as "Claude Design for Codex." It brings together a local project library, design-system setup, Codex chat, and a live web preview with a point-and-click inspector. Set up your design system however you like. Import one from Figma , bring your own, or have the agent generate it as a design.md. Then use it to build prototypes and slide decks. Codex-first, with Claude and other AI providers and local models planned for future releases. OpenAI Developers

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