# function calling

Published articles for function calling.

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## Qwen выпустила Qwen3.8-Omni-Flash

DevFeed: [Qwen выпустила Qwen3.8-Omni-Flash](<https://devfeed.tech/articles/qwen-qwen3-8-omni-flash-42727.md>)

Original publisher: [Read original article](<https://habr.com/ru/companies/bothub/news/1083716/>)

Author: DashaPasha (BotHub)

Published: 2026-09-18T08:40:54Z

Content type: release

Language: ru

Sources: [Tagir Valeev](<https://devfeed.tech/sources/tagir-valeev.md>)

Topics: [qwen](<https://devfeed.tech/topics/qwen.md>), [qwen3](<https://devfeed.tech/topics/qwen3.md>), [API](<https://devfeed.tech/topics/api.md>), [function calling](<https://devfeed.tech/topics/function-calling.md>)

Tags: [alibaba](<https://devfeed.tech/tags/alibaba.md>), [api](<https://devfeed.tech/tags/api.md>), [function-calling](<https://devfeed.tech/tags/function-calling.md>), [qwen](<https://devfeed.tech/tags/qwen.md>), [qwen-3-8](<https://devfeed.tech/tags/qwen-3-8.md>), [qwen3](<https://devfeed.tech/tags/qwen3.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [responses](<https://devfeed.tech/tags/responses.md>), [tag-61cd5a476b1d](<https://devfeed.tech/tags/tag-61cd5a476b1d.md>), [tag-67847f6cdd99](<https://devfeed.tech/tags/tag-67847f6cdd99.md>), [tag-6e9d47cd6240](<https://devfeed.tech/tags/tag-6e9d47cd6240.md>), [tag-dc29f2a3d816](<https://devfeed.tech/tags/tag-dc29f2a3d816.md>)

### AI overview

Qwen introduced Qwen3.8-Omni-Flash, a multimodal model focused on long-video understanding, detailed reporting, content description, and more precise timestamps. It accepts text, images, audio, and video, supports function calling, web search, reasoning controls, and API access through Chat Completions and Responses.

### Source excerpt

Команда Qwen представила Qwen3.8-Omni-Flash -- новую мультимодальную модель. Главный акцент в новой версии сделали на понимании видео. Qwen3.8-Omni-Flash умеет анализировать длинные ролики, составлять по ним подробные отчёты и генерировать описания содержимого. В Qwen отдельно отмечают улучшение работы с временными метками: модель должна точнее связывать события с конкретными моментами видео. При этом текстовые возможности модели сохранили на уровне Qwen3.8-Flash. В API также доступны function calling и веб-поиск, а режим рассуждений включён по умолчанию и может настраиваться через reasoning_effort. Qwen3.8-Omni-Flash -- промежуточное звено для более сложных мультимодальных пайплайнов. Среди возможных сценариев разрабы называют монтаж видео, перевод, создание кинокомментариев, анализ и генерацию контента. Отдельный фокус релиза -- стоимость. По сравнению с предыдущей Qwen3.5-Omni-Flash расход токенов при работе с мультимодальными данными заметно снизился. В частности, Qwen заявляет о снижении стоимости обработки аудио примерно на 98%, а аудио-видео -- более чем на 93%. При этом заявленная производительность сопоставима с Gemini 3.8 Flash. Независимые данные также показывают снижение расхода токенов на одном из видеобенчмарков: с 145 736 до 79 117 токенов при росте результата OmniVideoBench с 63,4 до 67,8. Контекстное окно новой модели -- 1 млн токенов, максимальная длина генерируемого ответа -- 131 072 токена. Qwen3.8-Omni-Flash принимает текст, изображения, аудио и видео, а на выходе генерирует текст. Читать далее

## Building a Local, Multimodal AI Terminal Agent with Gemma 4

DevFeed: [Building a Local, Multimodal AI Terminal Agent with Gemma 4](<https://devfeed.tech/articles/building-a-local-multimodal-ai-terminal-agent-with-gemma-4-22852.md>)

Original publisher: [Read original article](<https://medium.com/google-developer-experts/building-a-local-multimodal-ai-terminal-agent-with-gemma-4-4fbaa50eb14b?source=rss----a67bd6fa7d58---4>)

Author: Arjun Prabhulal

Published: 2026-08-12T09:25:11Z

Content type: tutorial

Language: en

Sources: [Google Developer Experts - Medium](<https://devfeed.tech/sources/google-developer-experts-medium.md>)

Topics: [gemma4](<https://devfeed.tech/topics/gemma4.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [multimodal-ai](<https://devfeed.tech/topics/multimodal-ai.md>), [Command-line interface](<https://devfeed.tech/topics/cli.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Local AI](<https://devfeed.tech/topics/local-ai.md>), [Code](<https://devfeed.tech/topics/code.md>), [Google](<https://devfeed.tech/topics/google.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [code](<https://devfeed.tech/tags/code.md>), [function-calling](<https://devfeed.tech/tags/function-calling.md>), [gemma](<https://devfeed.tech/tags/gemma.md>), [gemma-4](<https://devfeed.tech/tags/gemma-4.md>), [google](<https://devfeed.tech/tags/google.md>), [google-cloud-platform](<https://devfeed.tech/tags/google-cloud-platform.md>), [local-ai](<https://devfeed.tech/tags/local-ai.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [ollama](<https://devfeed.tech/tags/ollama.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [terminal](<https://devfeed.tech/tags/terminal.md>)

### AI overview

A tutorial introduces Gemma 4 and builds a local multimodal terminal agent named gemma4-agent. It covers function calling, tool orchestration, text, image, and voice processing, plus Gemma 4's model variants and architecture.

### Source excerpt

Introduction Open-source LLM models have been improving rapidly with tool calling, extended context windows, and native vision and audio capabilities, all while delivering strong benchmark performance. Gemma 4, recently introduced by Google Deepmind brings all of these features together in sizes efficient enough to run locally. In this article, we'll look at the capabilities of Gemma 4 and build a multimodal (Text, Vision, Voice) CLI agent (gemma4-agent) with function-calling capabilities. By the end, you'll have an agent that can chat, write, execute code, analyze images, and process voice instructions to deliver highly grounded responses. What is Gemma 4 Model ? Gemma 4 is Google DeepMind's open model family, released in April 2026 under the Apache 2.0 license. Built from the same research and technology behind Gemini 3, Gemma 4 is designed for high-performance reasoning, coding, multimodal understanding, and local AI execution across different model sizes. Features of Gemma 4 Models Improved Tool calling : Native function calling and tool orchestration, letting agents act autonomously without bloating prompt instructions Thinking mode : Built-in step-by-step thinking mode via the <|think|> token for complex multi-turn logic Context Windows : Up to 256K tokens on the 12B and larger models (128K on the edge-sized E2B/E4B) for processing long document and tool outputs Extended Multimodality : Gemma 4 models can process text,voice and images simultaneously like extracting data from charts, analyzing screenshots , and reviewing UI mockups. Gemma 4 Model Variants & SpecificationsGemma 4 Architecture Gemma 4 comes in five model sizes built around four architectural variants, each making different trade-offs between performance, inference speed, compute, and memory. Gemma4 Unified 12B vs Effective Parameters Effective-parameter models (E2B and E4B) are dense transformer models optimized for edge and on-device deployment. The "E" stands for effective parameters use Per-La

## AI, OAuth, And Other Platform APIs In The Core

DevFeed: [AI, OAuth, And Other Platform APIs In The Core](<https://devfeed.tech/articles/ai-oauth-and-other-platform-apis-in-the-core-19436.md>)

Original publisher: [Read original article](<https://www.codenameone.com/blog/platform-apis-in-the-core/>)

Author: Shai Almog

Published: 2026-05-31T00:00:00Z

Content type: article

Language: en

Sources: [CodeName One](<https://devfeed.tech/sources/codename-one.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [OAuth](<https://devfeed.tech/topics/oauth.md>), [OpenID connect (OIDC)](<https://devfeed.tech/topics/oidc.md>), [WebAuthn](<https://devfeed.tech/topics/webauthn.md>), [Passkeys](<https://devfeed.tech/topics/passkeys.md>), [Framework](<https://devfeed.tech/topics/framework.md>), [API](<https://devfeed.tech/topics/api.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Ollama](<https://devfeed.tech/topics/ollama.md>), [llama.cpp](<https://devfeed.tech/topics/llama-cpp.md>), [vllm](<https://devfeed.tech/topics/vllm.md>), [SQLite](<https://devfeed.tech/topics/sqlite.md>)

Tags: [agent-skill](<https://devfeed.tech/tags/agent-skill.md>), [ai](<https://devfeed.tech/tags/ai.md>), [api](<https://devfeed.tech/tags/api.md>), [authentication](<https://devfeed.tech/tags/authentication.md>), [function-calling](<https://devfeed.tech/tags/function-calling.md>), [llama-cpp](<https://devfeed.tech/tags/llama-cpp.md>), [llm](<https://devfeed.tech/tags/llm.md>), [oauth](<https://devfeed.tech/tags/oauth.md>), [oidc](<https://devfeed.tech/tags/oidc.md>), [ollama](<https://devfeed.tech/tags/ollama.md>), [sqlite](<https://devfeed.tech/tags/sqlite.md>), [streaming](<https://devfeed.tech/tags/streaming.md>), [vllm](<https://devfeed.tech/tags/vllm.md>)

### AI overview

This follow-up release article describes platform APIs moved into the framework core, including a first-class LLM client and ChatView, OAuth and OIDC authentication, WebAuthn passkeys, WiFi and connectivity APIs, and share-sheet callbacks. It also outlines streaming chat, tool calls, embeddings, image generation, and local-model support through Ollama-compatible endpoints.

### Source excerpt

Deeper AI integration in the framework core, modern authentication via OAuth / OIDC and WebAuthn passkeys driven from the system browser, and a few smaller additions alongside.

## Building a cooking assistant with Firebase AI Logic

DevFeed: [Building a cooking assistant with Firebase AI Logic](<https://devfeed.tech/articles/building-a-cooking-assistant-with-firebase-ai-logic-16664.md>)

Original publisher: [Read original article](<https://firebase.blog/posts/2026/05/building-cooking-assistant-ai-logic>)

Author: Marina Coelho

Published: 2026-05-29T00:00:00Z

Content type: tutorial

Language: en

Sources: [Firebase Blog](<https://devfeed.tech/sources/firebase-blog.md>)

Topics: [Firebase](<https://devfeed.tech/topics/firebase.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Android](<https://devfeed.tech/topics/android.md>), [SDKs](<https://devfeed.tech/topics/sdks.md>), [Mobile](<https://devfeed.tech/topics/mobile.md>), [WebSocket](<https://devfeed.tech/topics/websocket.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-logic](<https://devfeed.tech/tags/ai-logic.md>), [android](<https://devfeed.tech/tags/android.md>), [api](<https://devfeed.tech/tags/api.md>), [app](<https://devfeed.tech/tags/app.md>), [app-check](<https://devfeed.tech/tags/app-check.md>), [authentication](<https://devfeed.tech/tags/authentication.md>), [build](<https://devfeed.tech/tags/build.md>), [building](<https://devfeed.tech/tags/building.md>), [camera](<https://devfeed.tech/tags/camera.md>), [capture](<https://devfeed.tech/tags/capture.md>), [firebase](<https://devfeed.tech/tags/firebase.md>), [function-calling](<https://devfeed.tech/tags/function-calling.md>), [gemini](<https://devfeed.tech/tags/gemini.md>), [google](<https://devfeed.tech/tags/google.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [kotlin](<https://devfeed.tech/tags/kotlin.md>), [mobile](<https://devfeed.tech/tags/mobile.md>), [permission](<https://devfeed.tech/tags/permission.md>), [remote-config](<https://devfeed.tech/tags/remote-config.md>), [sdks](<https://devfeed.tech/tags/sdks.md>), [streaming](<https://devfeed.tech/tags/streaming.md>)

### AI overview

A tutorial on building a real-time cooking assistant for the Friendly Meals Android app with Firebase AI Logic, Gemini Live, bidirectional video streaming, and client-side function calling. It covers secure connections, authentication and App Check, camera and microphone permissions, and actions such as updating a grocery list.

### Source excerpt

News, tutorials, and updates from the Firebase team.

## Firebase AI Logic adds server prompt template support for chat and function calling

DevFeed: [Firebase AI Logic adds server prompt template support for chat and function calling](<https://devfeed.tech/articles/ship-production-ai-features-faster-with-firebase-ai-logic-16659.md>)

Original publisher: [Read original article](<https://firebase.blog/posts/2026/04/cloud-next-2026-ai-logic>)

Author: Miguel Ramos

Published: 2026-04-22T00:00:00Z

Content type: article

Language: en

Sources: [Firebase Blog](<https://devfeed.tech/sources/firebase-blog.md>)

Topics: [Firebase](<https://devfeed.tech/topics/firebase.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Mobile](<https://devfeed.tech/topics/mobile.md>), [Web](<https://devfeed.tech/topics/web.md>), [SDK](<https://devfeed.tech/topics/sdk.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-logic](<https://devfeed.tech/tags/ai-logic.md>), [android](<https://devfeed.tech/tags/android.md>), [app-check](<https://devfeed.tech/tags/app-check.md>), [chat](<https://devfeed.tech/tags/chat.md>), [cloud-next](<https://devfeed.tech/tags/cloud-next.md>), [firebase](<https://devfeed.tech/tags/firebase.md>), [firebase-ai-logic](<https://devfeed.tech/tags/firebase-ai-logic.md>), [firebase-console](<https://devfeed.tech/tags/firebase-console.md>), [flutter](<https://devfeed.tech/tags/flutter.md>), [function-calling](<https://devfeed.tech/tags/function-calling.md>), [gemini](<https://devfeed.tech/tags/gemini.md>), [hybrid-inference](<https://devfeed.tech/tags/hybrid-inference.md>), [kotlin](<https://devfeed.tech/tags/kotlin.md>), [prompt](<https://devfeed.tech/tags/prompt.md>), [sdk](<https://devfeed.tech/tags/sdk.md>), [security](<https://devfeed.tech/tags/security.md>), [server](<https://devfeed.tech/tags/server.md>), [web](<https://devfeed.tech/tags/web.md>), [web-app](<https://devfeed.tech/tags/web-app.md>)

### AI overview

This Firebase blog post describes Cloud Next '26 updates to Firebase AI Logic. Server prompt templates now support multi-turn chat and function calling, keeping system instructions, model configuration, tool definitions, and function schemas on Firebase's servers while clients reference the template ID.

### Source excerpt

What's new at Cloud Next '26

## Using Tools: A Meeting Scheduler

DevFeed: [Using Tools: A Meeting Scheduler](<https://devfeed.tech/articles/using-tools-a-meeting-scheduler-22284.md>)

Original publisher: [Read original article](<https://www.ardanlabs.com/blog/2026/03/using-tools-a-meeting-scheduler/>)

Published: 2026-03-16T00:00:00Z

Content type: tutorial

Language: en

Sources: [William Kennedy](<https://devfeed.tech/sources/william-kennedy.md>)

Topics: [Tool](<https://devfeed.tech/topics/tool.md>), [LLMs](<https://devfeed.tech/topics/llms.md>), [Code](<https://devfeed.tech/topics/code.md>)

Tags: [ardan-labs](<https://devfeed.tech/tags/ardan-labs.md>), [blog](<https://devfeed.tech/tags/blog.md>), [function-calling](<https://devfeed.tech/tags/function-calling.md>), [go](<https://devfeed.tech/tags/go.md>), [go-programming](<https://devfeed.tech/tags/go-programming.md>), [golang](<https://devfeed.tech/tags/golang.md>), [implementation](<https://devfeed.tech/tags/implementation.md>), [llms](<https://devfeed.tech/tags/llms.md>), [programming](<https://devfeed.tech/tags/programming.md>), [server](<https://devfeed.tech/tags/server.md>), [tools](<https://devfeed.tech/tags/tools.md>)

### AI overview

A tutorial on using LLM function calling to build a meeting-scheduling agent. It explains how tool calls work, covers the setup with the Kronk Model Server, and introduces the system prompt and tool definition.

### Source excerpt

Introduction LLMs are great, but they are trained on public data sets. In some cases, you need the LLM to use data that's not publicly available or that's frequently changing. There are several ways to make such data available to LLMs: Tool/function calls Retrieval-augmented generation (aka RAG) MCP In coding agents, you can also add skills. In this post we'll focus on function calling. How Does It Work? When interacting with an LLM, you can provide a description of available tools if the model supports tool calling. If the LLM reasons that the best answer is to use one of the tools, it will return a reply that contains a tool call with the parameters to use. Then you make the function call and return the answer back to the LLM.

## Building Gigaboy, An Autonomous Software Engineering Agent Orchestrator

DevFeed: [Building Gigaboy, An Autonomous Software Engineering Agent Orchestrator](<https://devfeed.tech/articles/building-gigaboy-an-autonomous-software-engineering-agent-orchestrator-39646.md>)

Original publisher: [Read original article](<https://www.gauravsarma.com/posts/2026-03-02_building-gigaboy-autonomous-software-engineering-agent>)

Published: 2026-03-02T00:00:00Z

Content type: tutorial

Language: en

Sources: [Gaurav Sarma's Blog](<https://devfeed.tech/sources/gaurav-sarma-s-blog.md>)

Topics: [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [Software Engineering](<https://devfeed.tech/topics/software-engineering.md>), [Linear](<https://devfeed.tech/topics/linear.md>), [GitHub](<https://devfeed.tech/topics/github.md>), [Go Language](<https://devfeed.tech/topics/go-language.md>), [pull-requests](<https://devfeed.tech/topics/pull-requests.md>), [PostgreSQL](<https://devfeed.tech/topics/postgresql.md>), [Redis](<https://devfeed.tech/topics/redis.md>), [function calling](<https://devfeed.tech/topics/function-calling.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [function-calling](<https://devfeed.tech/tags/function-calling.md>), [github](<https://devfeed.tech/tags/github.md>), [go](<https://devfeed.tech/tags/go.md>), [linear](<https://devfeed.tech/tags/linear.md>), [postgresql](<https://devfeed.tech/tags/postgresql.md>), [redis](<https://devfeed.tech/tags/redis.md>), [software-engineering](<https://devfeed.tech/tags/software-engineering.md>)

### AI overview

This article introduces Gigaboy, an autonomous software engineering orchestrator that watches Linear issues, takes tickets marked Todo, explores a repository, makes code changes, opens and iterates on pull requests, and can merge approved work. It explains the limitations of chat-based coding assistants and describes using Linear as the primary interface, with Redis and PostgreSQL supporting the system.

### Source excerpt

. [Building Gigaboy: An Autonomous Agent Orchestrator](building-an-ai-agent-orchestrator-cover...

## Gemini and Angular, Part II: Creating Generative UIs

DevFeed: [Gemini and Angular, Part II: Creating Generative UIs](<https://devfeed.tech/articles/gemini-and-angular-part-ii-creating-generative-uis-37466.md>)

Original publisher: [Read original article](<https://www.angularspace.com/gemini-and-angular-part-ii-creating-generative-uis/>)

Author: Armen Vardanyan

Published: 2026-01-22T13:48:08Z

Content type: tutorial

Language: en

Sources: [Daniel Glejzner](<https://devfeed.tech/sources/daniel-glejzner.md>)

Topics: [Angular](<https://devfeed.tech/topics/angular.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [function calling](<https://devfeed.tech/topics/function-calling.md>), [ui](<https://devfeed.tech/topics/ui.md>), [text-generation](<https://devfeed.tech/topics/text-generation.md>), [JSON](<https://devfeed.tech/topics/json.md>), [App](<https://devfeed.tech/topics/app.md>)

Tags: [angular](<https://devfeed.tech/tags/angular.md>), [article](<https://devfeed.tech/tags/article.md>), [code](<https://devfeed.tech/tags/code.md>), [function-calling](<https://devfeed.tech/tags/function-calling.md>), [gemini](<https://devfeed.tech/tags/gemini.md>), [generative](<https://devfeed.tech/tags/generative.md>), [generative-ui](<https://devfeed.tech/tags/generative-ui.md>), [json](<https://devfeed.tech/tags/json.md>), [llms](<https://devfeed.tech/tags/llms.md>), [model](<https://devfeed.tech/tags/model.md>), [schemas](<https://devfeed.tech/tags/schemas.md>), [structured](<https://devfeed.tech/tags/structured.md>), [text](<https://devfeed.tech/tags/text.md>), [text-generation](<https://devfeed.tech/tags/text-generation.md>)

### AI overview

This tutorial continues a series on LLMs and Gemini by exploring Generative UI in Angular. It describes dynamically rendering interactive Angular components from model responses, combining Gemini with Google's Nano Banana Pro image generation model, and using Angular signal forms. The article emphasizes minimizing boilerplate while balancing model capability, performance, and cost.

### Source excerpt

Let's continue our journey into LLMs and Gemini! In the previous article, we moved beyond simple text generation and learned: how to force the model to speak our language using structured outputs (JSON schemas) how to connect the model to our actual code and logic using function calling

## How Scout24 is building the next generation of real-estate search with AI

DevFeed: [How Scout24 is building the next generation of real-estate search with AI](<https://devfeed.tech/articles/how-scout24-is-building-the-next-generation-of-real-estate-search-with-ai-6646.md>)

Original publisher: [Read original article](<https://openai.com/index/scout24>)

Published: 2025-12-09T16:00:00Z

Content type: article

Language: en

Sources: [OpenAI News](<https://devfeed.tech/sources/openai-news.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [AI Chat](<https://devfeed.tech/topics/ai-chat.md>), [Generative AI](<https://devfeed.tech/topics/generative-ai.md>), [OpenAI](<https://devfeed.tech/topics/openai.md>), [Testing](<https://devfeed.tech/topics/testing.md>), [User Experience](<https://devfeed.tech/topics/user-experience.md>)

Tags: [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [evals](<https://devfeed.tech/tags/evals.md>), [function-calling](<https://devfeed.tech/tags/function-calling.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [gpt](<https://devfeed.tech/tags/gpt.md>), [trust](<https://devfeed.tech/tags/trust.md>), [user-experience](<https://devfeed.tech/tags/user-experience.md>)

### AI overview

Scout24 built HeyImmo, a GPT-5-powered conversational real-estate assistant that helps users search for property by asking clarifying questions, surfacing relevant listings, summarizing options, and adapting responses to individual preferences. The team used focused components and function calling, supported by custom evaluations, swarm testing, and an architecture designed for speed, reliability, safety, quality, and user experience.

### Source excerpt

Scout24 has created a GPT-5 powered conversational assistant that reimagines real-estate search, guiding users with clarifying questions, summaries, and tailored listing recommendations.

## Gemini and Angular, Part II: Structured Outputs and Tool calls

DevFeed: [Gemini and Angular, Part II: Structured Outputs and Tool calls](<https://devfeed.tech/articles/gemini-and-angular-part-ii-structured-outputs-and-tool-calls-37467.md>)

Original publisher: [Read original article](<https://www.angularspace.com/gemini-and-angular-part-ii-structured-outputs-and-tool-calls/>)

Author: Armen Vardanyan

Published: 2025-11-04T13:42:00Z

Content type: tutorial

Language: en

Sources: [Daniel Glejzner](<https://devfeed.tech/sources/daniel-glejzner.md>)

Topics: [Angular](<https://devfeed.tech/topics/angular.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [function calling](<https://devfeed.tech/topics/function-calling.md>), [Google AI](<https://devfeed.tech/topics/google-ai.md>), [SDKs](<https://devfeed.tech/topics/sdks.md>), [API](<https://devfeed.tech/topics/api.md>)

Tags: [angular](<https://devfeed.tech/tags/angular.md>), [api](<https://devfeed.tech/tags/api.md>), [articles](<https://devfeed.tech/tags/articles.md>), [configuration](<https://devfeed.tech/tags/configuration.md>), [function-calling](<https://devfeed.tech/tags/function-calling.md>), [gemini](<https://devfeed.tech/tags/gemini.md>), [generative-ui](<https://devfeed.tech/tags/generative-ui.md>), [sdk](<https://devfeed.tech/tags/sdk.md>), [tokens](<https://devfeed.tech/tags/tokens.md>)

### AI overview

This tutorial continues a series on using Gemini with Angular. It explains structured outputs and function calling (tool use), showing how these capabilities can support AI-powered UI and UX decisions and provide a foundation for generative UI. The article begins building an Angular writing assistant that compares two paragraph versions through the Gemini API.

### Source excerpt

Let's continue our journey into LLMs and Gemini! In the previous article, we learned how LLMs generate text, what are tokens, what configuration parameters like temperature, topP and so on mean how to create a Google Cloud project, get a Gemini API key, and use the SDK to

## Schema-Guided Reasoning: как научить языковые модели последовательно рассуждать

DevFeed: [Schema-Guided Reasoning: как научить языковые модели последовательно рассуждать](<https://devfeed.tech/articles/schema-guided-reasoning-24029.md>)

Original publisher: [Read original article](<https://habr.com/ru/companies/redmadrobot/articles/962236/>)

Author: redmadrobot (red\_mad\_robot)

Published: 2025-10-31T16:31:44Z

Content type: tutorial

Language: ru

Sources: [Redmadrobot EN](<https://devfeed.tech/sources/redmadrobot-en.md>), [Redmadrobot RU](<https://devfeed.tech/sources/redmadrobot-ru.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Structured-data](<https://devfeed.tech/topics/structured-data.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [JSON](<https://devfeed.tech/topics/json.md>), [Pydantic](<https://devfeed.tech/topics/pydantic.md>)

Tags: [function-calling](<https://devfeed.tech/tags/function-calling.md>), [json](<https://devfeed.tech/tags/json.md>), [llm](<https://devfeed.tech/tags/llm.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [schema](<https://devfeed.tech/tags/schema.md>), [scheme](<https://devfeed.tech/tags/scheme.md>), [sgr](<https://devfeed.tech/tags/sgr.md>), [structured-output](<https://devfeed.tech/tags/structured-output.md>), [tag-1cd610c0e518](<https://devfeed.tech/tags/tag-1cd610c0e518.md>), [tag-5335b6f99fba](<https://devfeed.tech/tags/tag-5335b6f99fba.md>), [tag-b0a411324cb6](<https://devfeed.tech/tags/tag-b0a411324cb6.md>)

### AI overview

This tutorial explains Schema-Guided Reasoning (SGR), an approach that guides large language models through predefined schemas. It describes how structured output, Pydantic models, JSON schemas, and constrained decoding can make model responses more consistent, transparent, and easier to test, while noting that SGR is not a universal solution.

### Source excerpt

LLM умеют многое: генерировать тексты, анализировать документы, писать код. Но на практике их работа часто непредсказуема -- сегодня модель даёт точный ответ, а завтра на тех же данных ошибается, пропускает ключевые шаги или придумывает факты. Для AI-инженеров это системная проблема. Возьмём автоматизацию документооборота: нужно классифицировать договоры, извлекать реквизиты, проверять стандарты. Но модель работает как лотерея -- результат не поддаётся логике или меняется при повторном запуске с одинаковыми данными. Как встроить такой результат в бизнес-процесс? Для решения этой задачи появился подход Schema-Guided Reasoning (SGR). Его активно продвигает Ринат Абдуллин в материалах по работе с LLM. Идея проста и эффективна: заставить модель мыслить не хаотично, а внутри заданной схемы. Это не панацея, но SGR серьёзно снижает количество ошибок, делает процесс прозрачнее, а также позволяет тестировать отдельные компоненты рассуждений. Читать далее

## Scaleway on Hugging Face Inference Providers 🔥

DevFeed: [Scaleway on Hugging Face Inference Providers 🔥](<https://devfeed.tech/articles/scaleway-on-hugging-face-inference-providers-7284.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/inference-providers-scaleway>)

Author: Guillaume Noale; Franck Pagny; Fred Bardolle; Guillaume Calmettes; Constance Morales; Célina Hanouti; Julien Chaumond; Simon Brandeis; Lucain Pouget

Published: 2025-09-19T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [scaleway](<https://devfeed.tech/topics/scaleway.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [inference-providers](<https://devfeed.tech/topics/inference-providers.md>), [Serverless](<https://devfeed.tech/topics/serverless.md>), [gpt-oss](<https://devfeed.tech/topics/gpt-oss.md>), [SDKs](<https://devfeed.tech/topics/sdks.md>), [API keys](<https://devfeed.tech/topics/api-keys.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [Sovereign AI](<https://devfeed.tech/topics/sovereign-ai.md>)

Tags: [api-keys](<https://devfeed.tech/tags/api-keys.md>), [enterprise](<https://devfeed.tech/tags/enterprise.md>), [function-calling](<https://devfeed.tech/tags/function-calling.md>), [gemma](<https://devfeed.tech/tags/gemma.md>), [gpt-oss](<https://devfeed.tech/tags/gpt-oss.md>), [hub](<https://devfeed.tech/tags/hub.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [inference](<https://devfeed.tech/tags/inference.md>), [inference-providers](<https://devfeed.tech/tags/inference-providers.md>), [llms](<https://devfeed.tech/tags/llms.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [open](<https://devfeed.tech/tags/open.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [partnerships](<https://devfeed.tech/tags/partnerships.md>), [providers](<https://devfeed.tech/tags/providers.md>), [qwen3](<https://devfeed.tech/tags/qwen3.md>), [scaleway](<https://devfeed.tech/tags/scaleway.md>), [sdks](<https://devfeed.tech/tags/sdks.md>), [serverless](<https://devfeed.tech/tags/serverless.md>), [text-generation](<https://devfeed.tech/tags/text-generation.md>)

### AI overview

Hugging Face announces Scaleway as a supported Inference Provider on the Hugging Face Hub. The integration provides serverless access to open-weight and frontier AI models through model pages, client SDKs, and APIs, with European data centers, pay-per-token pricing, low latency, and production-oriented features.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Amazon Nova Models: Now Available on Heroku

DevFeed: [Amazon Nova Models: Now Available on Heroku](<https://devfeed.tech/articles/amazon-nova-models-now-available-on-heroku-26366.md>)

Original publisher: [Read original article](<https://www.heroku.com/blog/amazon-nova-models-now-available/>)

Author: Anush DSouza

Published: 2025-08-26T15:00:16Z

Content type: release

Language: en

Sources: [Heroku](<https://devfeed.tech/sources/heroku.md>)

Topics: [Heroku](<https://devfeed.tech/topics/heroku.md>), [Nova](<https://devfeed.tech/topics/nova.md>), [AI Models](<https://devfeed.tech/topics/ai-models.md>), [model-deployment](<https://devfeed.tech/topics/model-deployment.md>)

Tags: [ai-machine-learning](<https://devfeed.tech/tags/ai-machine-learning.md>), [amazon](<https://devfeed.tech/tags/amazon.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [function-calling](<https://devfeed.tech/tags/function-calling.md>), [heroku](<https://devfeed.tech/tags/heroku.md>), [heroku-ai](<https://devfeed.tech/tags/heroku-ai.md>), [inference](<https://devfeed.tech/tags/inference.md>), [managed-inference-and-agents](<https://devfeed.tech/tags/managed-inference-and-agents.md>), [models](<https://devfeed.tech/tags/models.md>), [nova](<https://devfeed.tech/tags/nova.md>), [product-features](<https://devfeed.tech/tags/product-features.md>), [release](<https://devfeed.tech/tags/release.md>)

### AI overview

Heroku announces that Amazon Nova models, including nova-pro and nova-lite, are available through Heroku Managed Inference and Agents. The integration initially supports text-to-text use, with multimodal support planned for a future release.

### Source excerpt

Building intelligent applications requires powerful, cost-effective AI. Today, we're simplifying that process by making Amazon's cutting-edge Nova models directly available via Heroku Managed Inference and Agents. Provisioning these models is as simple as attaching the add-on to your Heroku application, providing a direct, managed path for developers and businesses to leverage a new class of [...] The post Amazon Nova Models: Now Available on Heroku appeared first on Heroku.

## Heroku AI Expands Model Offering with OpenAI's gpt-oss-120b

DevFeed: [Heroku AI Expands Model Offering with OpenAI's gpt-oss-120b](<https://devfeed.tech/articles/heroku-ai-expands-model-offering-with-openai-s-gpt-oss-120b-26415.md>)

Original publisher: [Read original article](<https://www.heroku.com/blog/heroku-ai-openai-gpt-oss-120b-model-now-available/>)

Author: Anush DSouza

Published: 2025-08-20T15:00:14Z

Content type: release

Language: en

Sources: [Heroku](<https://devfeed.tech/sources/heroku.md>)

Topics: [Heroku AI](<https://devfeed.tech/topics/heroku-ai.md>), [gpt-oss](<https://devfeed.tech/topics/gpt-oss.md>), [OpenAI](<https://devfeed.tech/topics/openai.md>), [Heroku](<https://devfeed.tech/topics/heroku.md>), [API](<https://devfeed.tech/topics/api.md>), [Mixture of Experts (MoE)](<https://devfeed.tech/topics/mixture-of-experts-moe.md>), [SDKs](<https://devfeed.tech/topics/sdks.md>), [Open Source Models & Datasets](<https://devfeed.tech/topics/open-source-models-datasets.md>)

Tags: [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-infrastructure](<https://devfeed.tech/tags/ai-infrastructure.md>), [ai-machine-learning](<https://devfeed.tech/tags/ai-machine-learning.md>), [api](<https://devfeed.tech/tags/api.md>), [deploy](<https://devfeed.tech/tags/deploy.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [function-calling](<https://devfeed.tech/tags/function-calling.md>), [gpt-oss](<https://devfeed.tech/tags/gpt-oss.md>), [heroku](<https://devfeed.tech/tags/heroku.md>), [heroku-ai](<https://devfeed.tech/tags/heroku-ai.md>), [managed-inference-and-agents](<https://devfeed.tech/tags/managed-inference-and-agents.md>), [mixture-of-experts-moe](<https://devfeed.tech/tags/mixture-of-experts-moe.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [openai](<https://devfeed.tech/tags/openai.md>), [python](<https://devfeed.tech/tags/python.md>), [sdk](<https://devfeed.tech/tags/sdk.md>)

### AI overview

Heroku announces that OpenAI's open-weight gpt-oss-120b model is available through Heroku Managed Inference and Agents. Developers can access it through an OpenAI-compatible chat completions API and use it to build and deploy AI applications and agentic workflows.

### Source excerpt

Start building with OpenAI's new open-weight model, gpt-oss-120b, now available on Heroku Managed Inference and Agents. This gives developers a powerful, transparent, and flexible way to build and deploy AI applications on the platform they already trust. Access gpt-oss-120b with our OpenAI-compatible chat completions API, which you can drop into any OpenAI-compatible SDK or framework. [...] The post Heroku AI Expands Model Offering with OpenAI's gpt-oss-120b appeared first on Heroku.

## Open R1: Update #4

DevFeed: [Open R1: Update #4](<https://devfeed.tech/articles/open-r1-update-4-7425.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/open-r1/update-4>)

Author: Leandro von Werra; Vaibhav Srivastav; Daniel Vila; Yacine Jernite

Published: 2025-03-26T18:47:29Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [deepseek](<https://devfeed.tech/topics/deepseek.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [Code](<https://devfeed.tech/topics/code.md>), [coding](<https://devfeed.tech/topics/coding.md>), [math](<https://devfeed.tech/topics/math.md>), [post-training](<https://devfeed.tech/topics/post-training.md>), [Front end](<https://devfeed.tech/topics/frontend.md>)

Tags: [benchmark](<https://devfeed.tech/tags/benchmark.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [code](<https://devfeed.tech/tags/code.md>), [coding](<https://devfeed.tech/tags/coding.md>), [deepseek](<https://devfeed.tech/tags/deepseek.md>), [function-calling](<https://devfeed.tech/tags/function-calling.md>), [math](<https://devfeed.tech/tags/math.md>), [post-training](<https://devfeed.tech/tags/post-training.md>), [release](<https://devfeed.tech/tags/release.md>), [update](<https://devfeed.tech/tags/update.md>)

### AI overview

The article reports on a new DeepSeek model that updates DeepSeek-V3, retains its architecture, and adopts an MIT license. It describes improvements in instruction following, coding, mathematics, front-end web development, writing, search, and function calling, supported by stronger benchmark results.

### Source excerpt

This week, a new model from DeepSeek silently landed on the Hub. It's an updated version of DeepSeek-V3, the base model underlying the R1 reasoning model. There isn't much information shared yet on this new model, but we do know a few things! The model has the same architecture as the original DeepSeek-V3 and now also comes with an MIT license, while the previous V3 model had a custom model license.

## How to Write an Agent

DevFeed: [How to Write an Agent](<https://devfeed.tech/articles/how-to-write-an-agent-41271.md>)

Original publisher: [Read original article](<https://www.evilsocket.net/2025/03/13/How-To-Write-An-Agent/>)

Author: Simone Margaritelli

Published: 2025-03-13T01:36:08Z

Content type: tutorial

Language: en

Sources: [evilsocket](<https://devfeed.tech/sources/evilsocket.md>)

Topics: [AI Development](<https://devfeed.tech/topics/ai-development.md>), [function calling](<https://devfeed.tech/topics/function-calling.md>), [LLMs](<https://devfeed.tech/topics/llms.md>)

Tags: [adk](<https://devfeed.tech/tags/adk.md>), [agent](<https://devfeed.tech/tags/agent.md>), [agent-development-kit](<https://devfeed.tech/tags/agent-development-kit.md>), [agent-evals](<https://devfeed.tech/tags/agent-evals.md>), [ai](<https://devfeed.tech/tags/ai.md>), [autonomous-agents](<https://devfeed.tech/tags/autonomous-agents.md>), [blog](<https://devfeed.tech/tags/blog.md>), [cybersecurity](<https://devfeed.tech/tags/cybersecurity.md>), [evals](<https://devfeed.tech/tags/evals.md>), [evaluations](<https://devfeed.tech/tags/evaluations.md>), [function-calling](<https://devfeed.tech/tags/function-calling.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [howto](<https://devfeed.tech/tags/howto.md>), [llm](<https://devfeed.tech/tags/llm.md>), [nerve](<https://devfeed.tech/tags/nerve.md>), [nerve-adk](<https://devfeed.tech/tags/nerve-adk.md>), [project-release](<https://devfeed.tech/tags/project-release.md>), [tool-use](<https://devfeed.tech/tags/tool-use.md>)

### AI overview

This tutorial explains how software agents use models to select tools in a loop, and how function calling lets language models invoke tools. It introduces Nerve as a project intended to simplify implementing an agent and discusses executing tool calls and returning their outputs to the model.

### Source excerpt

Hello friends. This blog post was supposed to be the second part of this re

## Building Intelligent Agentic Applications with Amazon Bedrock and Nova

DevFeed: [Building Intelligent Agentic Applications with Amazon Bedrock and Nova](<https://devfeed.tech/articles/building-intelligent-agentic-applications-with-amazon-bedrock-and-nova-18003.md>)

Original publisher: [Read original article](<https://blog.guilleojeda.com/building-intelligent-agentic-applications-with-amazon-bedrock-and-nova>)

Author: Guillermo Ojeda

Published: 2025-03-07T20:44:58Z

Content type: tutorial

Language: en

Sources: [Guille Ojeda](<https://devfeed.tech/sources/guille-ojeda.md>)

Topics: [Amazon Bedrock](<https://devfeed.tech/topics/amazon-bedrock.md>), [Nova](<https://devfeed.tech/topics/nova.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Amazon Web Services](<https://devfeed.tech/topics/aws.md>), [foundation-models](<https://devfeed.tech/topics/foundation-models.md>), [Architecture & Design](<https://devfeed.tech/topics/architecture-design.md>)

Tags: [agentic-ai](<https://devfeed.tech/tags/agentic-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [amazon-bedrock](<https://devfeed.tech/tags/amazon-bedrock.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [aws](<https://devfeed.tech/tags/aws.md>), [function-calling](<https://devfeed.tech/tags/function-calling.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [nova](<https://devfeed.tech/tags/nova.md>)

### AI overview

This article explains agentic AI architectures and how Amazon Bedrock and the Amazon Nova model family support them. It describes agents as systems that break complex tasks into steps, call external tools, and use results to guide subsequent actions. It also introduces Bedrock's managed infrastructure and orchestration, Nova's function-calling, context-window, and multimodal capabilities, and considerations for building agentic systems at scale.

### Source excerpt

What Are Agentic AI Architectures? I won't waste your time with a long, fluffy introduction about how AI is changing the world. Let's get straight to the point: agentic AI architectures are fundamentally different from the prompt-response pattern you...

## internal AI: A Genkit-Based Internal AI Chat Released as Open Source!

DevFeed: [internal AI: A Genkit-Based Internal AI Chat Released as Open Source!](<https://devfeed.tech/articles/internal-ai-a-genkit-based-internal-ai-chat-released-as-open-source-23891.md>)

Original publisher: [Read original article](<https://medium.com/firebase-developers/internal-ai-a-genkit-based-internal-ai-chat-released-as-open-source-37795896a106?source=rss----8e8b7dc6774d---4>)

Author: tanabee

Published: 2025-02-17T17:14:41Z

Content type: article

Language: en

Sources: [Firebase Developers - Medium](<https://devfeed.tech/sources/firebase-developers-medium.md>)

Topics: [Genkit](<https://devfeed.tech/topics/genkit.md>), [AI Chat](<https://devfeed.tech/topics/ai-chat.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Firebase](<https://devfeed.tech/topics/firebase.md>), [Retrieval-Augmented Generation](<https://devfeed.tech/topics/retrieval-augmented-generation.md>), [Generative AI](<https://devfeed.tech/topics/generative-ai.md>)

Tags: [emulator](<https://devfeed.tech/tags/emulator.md>), [firebase](<https://devfeed.tech/tags/firebase.md>), [firestore](<https://devfeed.tech/tags/firestore.md>), [function-calling](<https://devfeed.tech/tags/function-calling.md>), [gemini](<https://devfeed.tech/tags/gemini.md>), [genkit](<https://devfeed.tech/tags/genkit.md>), [google-cloud-platform](<https://devfeed.tech/tags/google-cloud-platform.md>), [llm](<https://devfeed.tech/tags/llm.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [rag](<https://devfeed.tech/tags/rag.md>), [vertex-ai](<https://devfeed.tech/tags/vertex-ai.md>)

### AI overview

This article introduces internal AI, an open-source Genkit-based internal chat implementation. It describes its usage-based pricing, customizable RAG and function-calling support, Firebase deployment, monitoring, domain restrictions, and local testing with the Firebase Emulator.

### Source excerpt

internal AI: An open-source Genkit-based internal AI chat implementation I have released internal AI, an AI chat solution you can easily and cost-effectively run internally, under an open-source license! Recently, Firebase Genkit reached version 1.0 and became stable for production use. With Genkit, you can quickly build applications that leverage generative AI. However, Genkit itself does not include a user interface. As a use case for an internal AI chat, I decided to make internal AI available as open source. Because internal AI uses a pay-as-you-go model instead of charging per user, you can optimize costs by only paying for the amount of usage. Moreover, thanks to Genkit's extensibility, you can combine RAG (Retrieval-Augmented Generation) and Function Calling to tailor AI features to your organization's needs. This opens the door to more practical AI applications, such as specialized internal information retrieval or automated workflow processes! GitHub - tanabee/internal-ai internal AI -- ChatKey Features Open-source project, allowing customization to fit your organization's needs Abstracted interface with generative AI via Genkit Deployable on Firebase Firebase AI Monitoring for monitoring requests to generative AI Restricting Usage to Specific Domains Usage-based pricing, independent of the number of users Open-source project, allowing customization to fit your organization's needs internal AI is provided as a boilerplate, which you can freely fork and customize according to your organization's requirements. Genkit can be extended in many ways, such as using RAG for accessing internal data or utilizing Google Search for grounding. To keep things simple for initial adoption, this example just communicates with Gemini 2.0. const ai = genkit({ // ... model: gemini20Flash001, }) export const chatFlow = ai.defineFlow( // ... async (messages) => { const response = await ai.generate({ messages }) return response.messages }, ) internal-ai/functions/src/genkit/chatFlow.t

## Benchmarking Language Model Performance on 5th Gen Xeon at GCP

DevFeed: [Benchmarking Language Model Performance on 5th Gen Xeon at GCP](<https://devfeed.tech/articles/benchmarking-language-model-performance-on-5th-gen-xeon-at-gcp-7290.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/intel-gcp-c4>)

Author: Matrix Yao; Ke Ding; Ilyas Moutawwakil

Published: 2024-12-17T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [benchmarking](<https://devfeed.tech/topics/benchmarking.md>), [LLM evaluation / benchmarking](<https://devfeed.tech/topics/llm-evaluation-benchmarking.md>), [cpu](<https://devfeed.tech/topics/cpu.md>), [Google Cloud Platform (GCP)](<https://devfeed.tech/topics/google-cloud.md>), [intel](<https://devfeed.tech/topics/intel.md>), [text-generation](<https://devfeed.tech/topics/text-generation.md>), [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [llama3](<https://devfeed.tech/topics/llama3.md>)

Tags: [agentic-ai](<https://devfeed.tech/tags/agentic-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [architectures](<https://devfeed.tech/tags/architectures.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [backend](<https://devfeed.tech/tags/backend.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [benchmarking](<https://devfeed.tech/tags/benchmarking.md>), [compute](<https://devfeed.tech/tags/compute.md>), [cpu](<https://devfeed.tech/tags/cpu.md>), [embedding](<https://devfeed.tech/tags/embedding.md>), [function-calling](<https://devfeed.tech/tags/function-calling.md>), [gcp](<https://devfeed.tech/tags/gcp.md>), [google](<https://devfeed.tech/tags/google.md>), [google-cloud](<https://devfeed.tech/tags/google-cloud.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [intel](<https://devfeed.tech/tags/intel.md>), [llama3](<https://devfeed.tech/tags/llama3.md>), [llm](<https://devfeed.tech/tags/llm.md>), [performance](<https://devfeed.tech/tags/performance.md>), [text-generation](<https://devfeed.tech/tags/text-generation.md>)

### AI overview

This article benchmarks language-model performance on Google Cloud Compute Engine C4 and N2 instances powered by different generations of Intel Xeon processors. It compares text embedding and text generation for agentic AI workloads, focusing on the benefits of Intel Advanced Matrix Extensions (AMX) and CPU-based hosting of systems using small language models such as Meta's 1B and 3B Llama 3.2 models.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Releasing Outlines-core 0.1.0: structured generation in Rust and Python

DevFeed: [Releasing Outlines-core 0.1.0: structured generation in Rust and Python](<https://devfeed.tech/articles/releasing-outlines-core-0-1-0-structured-generation-in-rust-and-python-7432.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/outlines-core>)

Author: bwillard; David Holtz; Erik Kaunismäki; Kaustubh Chaudhari; Remi Louf; Umut Şahin; Will Kurt

Published: 2024-10-22T00:00:00Z

Content type: release

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [Rust](<https://devfeed.tech/topics/rust.md>), [Python](<https://devfeed.tech/topics/python.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>)

Tags: [chain-of-thought](<https://devfeed.tech/tags/chain-of-thought.md>), [format](<https://devfeed.tech/tags/format.md>), [function-calling](<https://devfeed.tech/tags/function-calling.md>), [generation](<https://devfeed.tech/tags/generation.md>), [llm](<https://devfeed.tech/tags/llm.md>), [llms](<https://devfeed.tech/tags/llms.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [python](<https://devfeed.tech/tags/python.md>), [rust](<https://devfeed.tech/tags/rust.md>), [structured-generation](<https://devfeed.tech/tags/structured-generation.md>), [synthetic-data](<https://devfeed.tech/tags/synthetic-data.md>)

### AI overview

The article announces Outlines-core 0.1.0, a lightweight Rust core with Python bindings for structured generation. It describes performance, portability, and integration improvements, and explains how constrained generation makes LLM outputs conform to formats such as JSON, Pydantic models, regular expressions, and context-free grammars.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Vertex AI in Firebase is now Generally Available

DevFeed: [Vertex AI in Firebase is now Generally Available](<https://devfeed.tech/articles/vertex-ai-in-firebase-is-now-generally-available-16566.md>)

Original publisher: [Read original article](<https://firebase.blog/posts/2024/10/vertex-ai-in-firebase-ga>)

Author: Miguel Ramos

Published: 2024-10-21T00:00:00Z

Content type: release

Language: en

Sources: [Firebase Blog](<https://devfeed.tech/sources/firebase-blog.md>)

Topics: [Firebase](<https://devfeed.tech/topics/firebase.md>), [Google AI](<https://devfeed.tech/topics/google-ai.md>), [SDKs](<https://devfeed.tech/topics/sdks.md>), [API](<https://devfeed.tech/topics/api.md>), [JSON](<https://devfeed.tech/topics/json.md>), [Mobile](<https://devfeed.tech/topics/mobile.md>), [multimodal](<https://devfeed.tech/topics/multimodal.md>), [Web](<https://devfeed.tech/topics/web.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-logic](<https://devfeed.tech/tags/ai-logic.md>), [android](<https://devfeed.tech/tags/android.md>), [api](<https://devfeed.tech/tags/api.md>), [app-check](<https://devfeed.tech/tags/app-check.md>), [cloud-storage](<https://devfeed.tech/tags/cloud-storage.md>), [firebase](<https://devfeed.tech/tags/firebase.md>), [flutter](<https://devfeed.tech/tags/flutter.md>), [function-calling](<https://devfeed.tech/tags/function-calling.md>), [gemini](<https://devfeed.tech/tags/gemini.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [ios](<https://devfeed.tech/tags/ios.md>), [javascript](<https://devfeed.tech/tags/javascript.md>), [json](<https://devfeed.tech/tags/json.md>), [launch](<https://devfeed.tech/tags/launch.md>), [mobile](<https://devfeed.tech/tags/mobile.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [remote-config](<https://devfeed.tech/tags/remote-config.md>), [sdks](<https://devfeed.tech/tags/sdks.md>), [structured-output](<https://devfeed.tech/tags/structured-output.md>), [vertex-ai](<https://devfeed.tech/tags/vertex-ai.md>), [web](<https://devfeed.tech/tags/web.md>)

### AI overview

Firebase announces the general availability of Vertex AI in Firebase, enabling mobile and web apps to access Gemini models through Firebase client SDKs. The release includes support for multimodal prompting, system instructions, function calling, and structured JSON output with schemas.

### Source excerpt

Securely add generative AI features to your app using Firebase client SDKs

## Un Ministral, des Ministraux

DevFeed: [Un Ministral, des Ministraux](<https://devfeed.tech/articles/un-ministral-des-ministraux-7038.md>)

Original publisher: [Read original article](<https://mistral.ai/news/ministraux/>)

Published: 2024-10-16T02:00:00Z

Content type: release

Language: en

Sources: [Mistral AI Blog](<https://devfeed.tech/sources/mistral-ai-blog.md>)

Topics: [Language models](<https://devfeed.tech/topics/language-models.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Low Latency](<https://devfeed.tech/topics/low-latency.md>), [vllm](<https://devfeed.tech/topics/vllm.md>), [llama](<https://devfeed.tech/topics/llama.md>), [gemma](<https://devfeed.tech/topics/gemma.md>)

Tags: [agentic](<https://devfeed.tech/tags/agentic.md>), [agentic-workflows](<https://devfeed.tech/tags/agentic-workflows.md>), [ai](<https://devfeed.tech/tags/ai.md>), [comparison](<https://devfeed.tech/tags/comparison.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [function-calling](<https://devfeed.tech/tags/function-calling.md>), [inference](<https://devfeed.tech/tags/inference.md>), [language-models](<https://devfeed.tech/tags/language-models.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [mistral](<https://devfeed.tech/tags/mistral.md>), [models](<https://devfeed.tech/tags/models.md>), [vllm](<https://devfeed.tech/tags/vllm.md>)

### AI overview

Mistral introduces Ministral 3B and Ministral 8B, compact language models designed for on-device and edge use cases. The models support up to 128k context, with Ministral 8B adding interleaved sliding-window attention for faster, more memory-efficient inference. They target privacy-first, compute-efficient, low-latency applications such as translation, smart assistants, analytics, autonomous robotics, and agentic workflows.

### Source excerpt

The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models.

## Mistral NeMo

DevFeed: [Mistral NeMo](<https://devfeed.tech/articles/mistral-nemo-7068.md>)

Original publisher: [Read original article](<https://mistral.ai/news/mistral-nemo/>)

Published: 2024-07-18T08:00:00Z

Content type: release

Language: en

Sources: [Mistral AI Blog](<https://devfeed.tech/sources/mistral-ai-blog.md>)

Topics: [NeMo](<https://devfeed.tech/topics/nemo.md>), [AI Models](<https://devfeed.tech/topics/ai-models.md>), [long-context](<https://devfeed.tech/topics/long-context.md>), [Compression](<https://devfeed.tech/topics/compression.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>)

Tags: [compression](<https://devfeed.tech/tags/compression.md>), [context-window](<https://devfeed.tech/tags/context-window.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [function-calling](<https://devfeed.tech/tags/function-calling.md>), [models](<https://devfeed.tech/tags/models.md>), [nemo](<https://devfeed.tech/tags/nemo.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [tokens](<https://devfeed.tech/tags/tokens.md>)

### AI overview

Mistral NeMo is a 12B multilingual model developed with NVIDIA, offering a 128k-token context window, strong reasoning and coding performance, function calling, and Apache 2.0 licensing. The release highlights its efficient Tekken tokenizer, FP8 inference, instruction fine-tuning, and comparisons with Gemma 2 9B and Llama 3 8B.

### Source excerpt

The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models.

## Giving our AI superpowers with OpenAI Tools

DevFeed: [Giving our AI superpowers with OpenAI Tools](<https://devfeed.tech/articles/giving-our-ai-superpowers-with-openai-tools-40844.md>)

Original publisher: [Read original article](<https://mutto.fyi/posts/2024/06/ai-superpowers-tools/>)

Published: 2024-06-03T00:00:00Z

Content type: tutorial

Language: en

Sources: [Mutt0-ds Notes](<https://devfeed.tech/sources/mutt0-ds-notes.md>)

Topics: [OpenAI](<https://devfeed.tech/topics/openai.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [function calling](<https://devfeed.tech/topics/function-calling.md>), [Azure OpenAI](<https://devfeed.tech/topics/azure-openai.md>), [Databases](<https://devfeed.tech/topics/databases.md>), [API](<https://devfeed.tech/topics/api.md>), [SQL](<https://devfeed.tech/topics/sql.md>), [LangChain](<https://devfeed.tech/topics/langchain.md>), [Retrieval Augmented Generation (RAG)](<https://devfeed.tech/topics/retrieval-augmented-generation-rag.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-tools](<https://devfeed.tech/tags/ai-tools.md>), [api](<https://devfeed.tech/tags/api.md>), [azure-openai](<https://devfeed.tech/tags/azure-openai.md>), [function-calling](<https://devfeed.tech/tags/function-calling.md>), [langchain](<https://devfeed.tech/tags/langchain.md>), [openai](<https://devfeed.tech/tags/openai.md>), [retrieval-augmented-generation-rag](<https://devfeed.tech/tags/retrieval-augmented-generation-rag.md>)

### AI overview

This article explains how the author used OpenAI Tools, also called Function Calling, with Azure OpenAI to build an AI Analyst connected to a complex order database. It contrasts this approach with earlier GPT-3.5, LangChain, SQL, and DAX-based methods, and distinguishes tool calling from Retrieval Augmented Generation.

### Source excerpt

In recent months, I have been experimenting with AI tools to leverage new, powerful technologies and create value. Like you, I am inundated...