February 2026 Summaries

Coches.net, a leading mobility marketplace in Spain, has integrated Stream's chat solution to enhance its buyer-seller communication, crucial for facilitating high-intent transactions in real-time. Initially relying on legacy systems from Adevinta, coches.net faced challenges in scaling and maintaining these tools independently. The company needed a reliable, scalable chat infrastructure tightly integrated with its marketplace listings, supporting high message volumes and a 1:1 buyer-seller model. After evaluating several providers, Stream was chosen for its robust APIs, flexibility, and strong developer support. Stream's chat platform enables coches.net to manage the entire buyer-seller journey within the conversation, offering features like attachments, system messages, and pinned updates, while ensuring reliable performance even during peak activity. This integration allows coches.net to keep conversations on-platform, supporting transaction continuity and enhancing user experience. As coches.net continues to evolve, it is exploring additional features like live video and audio to further build buyer confidence and facilitate high-value transactions.

Cartesia Sonic 3, released in late 2025, revolutionizes text-to-speech technology for voice agents by offering sub-200 ms first-chunk latency, emotional expressiveness, multilingual support, and the ability to clone voices from brief audio samples. This new advancement facilitates the creation of voice agents that deliver realistic and natural-sounding conversations, integrating seamlessly with Vision Agents through a straightforward plugin. By streamlining the integration process, Cartesia Sonic 3 allows developers to concentrate on prompt engineering and agent logic without being bogged down by audio buffering issues. The tutorial provides a step-by-step guide to building an agent using Sonic 3, emphasizing its benefits such as instant customization of voice features and compatibility with various programming stacks, while also highlighting its superior human-like rhythm and quick speech onset, crucial for responsive user interactions.

Pipecat is an open-source Python framework designed for building real-time voice and multimodal conversational agents, providing developers with direct control over the orchestration of speech recognition, language models, and text-to-speech components into streaming pipelines. It emphasizes full visibility into the conversational loop, allowing for fine-tuned customization of agent behavior and low-latency responses, making it well-suited for teams prioritizing control over latency and agent logic. Unlike managed platform solutions, Pipecat requires teams to manage their own infrastructure, but offers flexibility in provider integration and customization, making it ideal for prototyping and custom deployments. However, the framework's complexity can be a drawback for those new to real-time systems, and it lacks built-in hosting or scaling features. Pipecat is free to use, though operational costs depend on infrastructure and third-party service usage, with alternatives like Vision Agents, LiveKit Agents, and Rasa Voice offering different levels of control and focus, such as multimodal interactions, real-time room integration, and structured dialogue management.

Stream's real-time communication APIs, including chat, video, activity feeds, and moderation, can significantly enhance user engagement and subsequently drive revenue across various app verticals. In transaction-based applications like marketplaces and gambling, increased user interaction correlates with more transactions and higher revenue. Subscription apps, such as dating and education platforms, benefit from enhanced retention rates when users actively engage in community features. Commission-based applications, including live shopping and delivery services, see increased sales and customer satisfaction through improved communication channels. Features like in-app chat and livestreaming not only boost user activity but also facilitate immediate transactions, thereby increasing conversion rates. Moderation ensures a safe environment, promoting trust and further engagement, which is crucial for sustained revenue generation. Across all sectors, from healthcare to gaming, these tools help maintain user interest, reduce operational costs, and capture more monetizable actions, ultimately leading to increased profitability.

ElevenLabs offers advanced text-to-speech (TTS) technology that provides highly realistic and emotionally nuanced voices with support for multiple languages, enhancing the human-like quality of AI agents. The integration of ElevenLabs with Vision Agents is straightforward, requiring minimal code adjustments, allowing developers to seamlessly incorporate TTS into their applications. This setup enables agents to perform tasks such as greeting new participants in calls or telling jokes, maintaining an engaging and dynamic interaction with users. The system supports various customization options, including voice and model adjustments, ensuring that the TTS fits seamlessly into existing workflows without disruption. The combination of open-source Vision Agents with ElevenLabs' proprietary TTS technology creates a robust platform for developing interactive and responsive AI solutions.

Live selling platforms have revolutionized the eCommerce space by allowing brands and retailers to showcase products through interactive live streams, thus bypassing traditional marketing barriers like prime shelf space. These platforms integrate elements such as live video, audience interaction, in-stream product tagging, integrated checkout, and real-time inventory management, which together create a dynamic shopping experience that boosts consumer confidence and engagement. Prominent platforms like Amazon Live, Bambuser, Instagram Live, Whatnot, and TikTok Live Shopping each offer unique features, such as Amazon's algorithmic tiering, Instagram's co-hosting capabilities, and Whatnot's auction-style atmosphere, catering to diverse brands and consumer preferences. The global live commerce market is projected to reach $2.5 trillion by 2033, highlighting its rapid growth and the potential for integrating emerging technologies like AI to enhance user and seller experiences. As companies consider building or buying live selling components, they are encouraged to leverage existing infrastructure for cost-effectiveness while innovating unique features to set themselves apart in a competitive market.

Live streaming has evolved from simply broadcasting video to a more interactive and complex experience, integrating various technological components beyond just video transmission. Modern live streaming now includes features such as real-time shopping interactions, bidirectional communication, and business logic synchronization, all of which demand a sophisticated infrastructure with considerations for latency, scale, and interactivity. This infrastructure is composed of three planes: the media plane for video delivery, the control plane for managing connections and permissions, and the data plane for real-time events like chat and reactions. The architectural design of a streaming service must account for different latency requirements depending on the use case, such as passive viewing versus interactive or transactional live streams. Technologies like HLS and WebRTC are employed differently based on their tradeoffs between latency and scalability. Most systems must balance between these technologies to meet varying needs, often ending up with a hybrid model. Managed services like Stream's live streaming SDK help simplify the complexity by providing the necessary tools and APIs to build scalable, low-latency, interactive live streaming experiences.

Lokal, a company focused on building social and community apps, has achieved significant success by prioritizing speed and reuse in its product development strategy, leading to over 100 million downloads across its ecosystem. By opting for fast iteration and integrating proven real-time communication infrastructure from Stream, Lokal has been able to launch new products swiftly without reinventing the wheel, especially in voice and video calling. This approach has allowed Lokal to cater to Bharat users in India, who require simple, reliable apps in native languages, and to maintain consistent call quality across diverse devices and networks. Lokal's decision to treat communication as a shared platform capability rather than a feature to be rebuilt for each product has enabled them to maintain focus on differentiation and product logic. With Stream's robust infrastructure and moderation capabilities, Lokal has not only reduced technical risk but also ensured that its communication platform evolves alongside its rapidly scaling products. This collaborative relationship has been mutually beneficial, as Lokal's real-world usage informs Stream's product evolution, exemplifying the potential of a partnership driven by shared goals of speed, learning, and responsible scaling.

Real-time feeds have become essential for modern applications, offering features like instant messaging and activity streams that users expect. However, implementing these feeds can present challenges in scaling and reliability, especially when transitioning from development to production. The key to successful real-time infrastructure lies in understanding the trade-offs between the two main approaches: long polling and WebSockets. Long polling, which simulates server push using standard HTTP requests, can cause delays due to its request/response cycle, while WebSockets provide a persistent bidirectional communication channel with lower latency and overhead but require careful infrastructure management. Each approach has unique scalability, infrastructure, and user experience implications, with long polling being easier to deploy and debug but potentially slower and more resource-intensive, while WebSockets offer smoother real-time interactions but demand more operational complexity. Ultimately, the choice between these methods depends on specific use case requirements, such as update frequency and bidirectional communication needs, with many systems utilizing a combination of both to ensure optimal performance and reliability.

Live shopping events, characterized by features such as live video streams, interactive chats, and immediate purchasing options, require a robust infrastructure to prevent technical failures that can disrupt the user experience. The architecture of a successful live shopping system involves three independent yet interconnected planes: the video plane for streaming, the real-time plane for interactions like chat and reactions, and the commerce plane for transactions. Each plane should function independently to ensure that the overall experience remains stable even if one component fails. Low-Latency HLS (LL-HLS) is recommended for most live shopping use cases due to its reliability and scalability, while WebRTC can be used for scenarios requiring sub-second latency. Synchronization issues between video and real-time interactions can be mitigated by timestamping events and adjusting displays based on each viewer's video latency. Handling large volumes of interactions, such as chat messages and reactions, involves server-side aggregation, client-side buffer management, and server-side rate limiting to prevent crashes. Furthermore, backend support with strict timeouts, circuit breakers, and smart autoscaling is crucial to prevent client-side crashes caused by overloaded systems. Overall, the key to a seamless live shopping experience lies in isolating the functional planes, managing resources prudently, and implementing robust backend strategies to handle high traffic efficiently.

Content moderation, particularly in text form, poses significant challenges due to users' ability to circumvent filters through creative means such as leetspeak or indirect phrasing. Keyword filtering, a technique that scans for specific words or patterns, helps identify and manage harmful content across user-generated platforms like social media, chat rooms, and forums. However, this method has limitations, including evasion by users, lack of context understanding, and the need for constant updates to stay relevant with evolving language. Resources like premade keyword lists, public repositories, and research from trust and safety organizations help moderators quickly implement and maintain keyword filters. To enhance effectiveness, combining keyword filtering with large language models (LLMs) provides deeper insights into context and intent, balancing the need for fast automated responses with nuanced human judgment. Best practices in moderation include tailoring keyword lists to specific platform risks, localizing for language and culture, and integrating user reports and moderation reviews to refine systems over time.

Integrating Vision AI into applications is primarily an engineering challenge rather than just choosing the right model, as constructing robust infrastructure is key to moving from a prototype to a production system. The decision of whether to run AI models on the client (edge) or server involves weighing factors like latency, privacy, bandwidth, and operational costs, often resulting in a hybrid approach where initial processing occurs on-device with more complex tasks handled by the server. Efficient processing requires optimizing frame sampling rates, typically much lower than 30 FPS, to manage costs and computational resources effectively. Mitigating issues like bounding box drift involves synchronizing inference results with video frames using metadata and possibly employing predictive tracking methods. To prevent bottlenecks, frames should be extracted efficiently using a three-stage pipeline that minimizes delays. Cost considerations for managed cloud APIs like AWS Rekognition highlight the importance of sampling strategies to avoid expensive per-image billing, especially at high frame rates. Handling false positives involves using AI outputs as evidence rather than final judgments, implementing a graduated response to violations, and setting calibrated thresholds to reduce the risk of erroneous bans. Establishing audit trails and monitoring false positives in production are critical for maintaining trust and improving system reliability over time.

Kimi K2.5 from Moonshot AI is an advanced open-source multimodal AI capable of instantaneously interpreting visual data from everyday objects or code shared via a webcam or screen, reasoning through it, and explaining it in natural language. It leverages a vast 1T-parameter MoE model with 256k context and native vision understanding to deliver seamless video, vision, and voice interactions through its integration with Vision Agents and an OpenAI-compatible API. The system allows for real-time voice and vision analysis, enabling users to receive visual descriptions and coding assistance during live interactions. This setup is achieved with a straightforward pipeline involving technologies such as ElevenLabs for text-to-speech, Deepgram for speech-to-text, and Smart-Turn for turn detection, all orchestrated through a WebRTC framework. The process is detailed in a demo that illustrates how to build a similar AI agent in under five minutes, offering a user-friendly interface for natural, low-latency conversations and coding help.

Activity feeds are crucial features in many social platforms, such as X's home timeline, Facebook's news feed, and LinkedIn's updates, and they must balance high write and read volumes. To achieve this, feed systems generally separate event capture from feed serving, using three layers: immutable activity events, a serving view, and hydration. An important architectural decision is whether to use a "push" (Fan-Out-On-Write) or "pull" (Fan-Out-On-Read) strategy, or a hybrid of both, to manage the balance between write amplification and read latency. Performance issues, such as slower feeds with deeper scrolling, often stem from inefficient pagination strategies, which can be improved with cursor-based pagination. Aggregating activities, such as grouping notifications, presents challenges in data modeling and concurrency, which can be addressed by either read-time or pipeline aggregation strategies. While "real-time" feed updates can be achieved using technologies like Server-Sent Events or WebSockets, most feeds benefit from a balance between immediacy and user control. Graph databases are generally unsuitable for storing feed timelines, which are better managed with key-value or wide-column stores, but they are effective for managing social graph data, such as follower relationships.

Amazon Interactive Video Service (IVS) is a managed live streaming service optimized for low-latency, one-to-many broadcasts similar to Twitch. It simplifies the process of embedding live streaming into apps by handling ingest, transcoding, distribution, and playback, and is tightly integrated with AWS services. While IVS provides a fast and reliable way to deliver large-scale broadcasts with minimal setup, it is less suited for applications requiring real-time interaction, multi-publisher streams, or extensive media control, leading some teams to explore alternatives. Competitors like Stream Video, Mux, Agora, Vonage Video API, ZEGOCLOUD, Ant Media, Cloudflare Stream, Akamai, and Wowza offer varying degrees of flexibility, interactivity, and deployment models, catering to different needs such as interactive video features, real-time engagement, and protocol customization. The choice between IVS and its alternatives largely depends on the specific architectural and operational requirements of the project, including the need for real-time interaction, control over media workflows, and whether the infrastructure resides within AWS.

This tutorial guides users through building a simple Instagram-style "For You" feed using React Native Expo, which personalizes content recommendations based on user interests and content popularity. The project consists of two main screens: Home, which displays personal posts, likes, and comments, and For You, which presents curated content of interest. Utilizing the Stream's Feed SDK v3, the final application supports real-time timeline feeds, comments, reactions, and more. It requires a free Stream account and API credentials for setup and is compatible with both iOS and Android platforms, requiring Xcode and Android Studio for testing on respective simulators or devices. The tutorial includes instructions for cloning the project, installing dependencies, and running the application, as well as configuring the Stream Activity Feed API and building the Activity Feed UIs. Additionally, it covers creating and displaying activities, posting new ones, and integrating features like follow/unfollow, media attachments, comments, and reactions to enhance user engagement. The guide encourages further exploration by integrating content moderation and push notifications for a more robust application experience.

ElevenLabs has unveiled Scribe v2 Realtime, a speech-to-text model known for its impressively low latency of approximately 150 milliseconds and support for over 90 languages, boasting the lowest Word Error Rate in several benchmarks. This model is tailored for applications such as live meetings, note-taking, and conversational AI, where real-time accuracy is crucial. Scribe v2 Realtime can transcribe both user speech and agent responses in real-time, providing seamless conversations without noticeable lag. The model's setup involves a tech stack that includes ElevenLabs' solutions for STT and TTS, along with the Gemini LLM and Vision Agents framework, requiring API keys from ElevenLabs, Google AI Studio, and Stream. The open-source Vision Agents framework facilitates easy integration of Scribe v2 Realtime for applications needing precise live captioning and understanding, making it ideal for voice AI applications.

Text-to-speech (TTS) technology has advanced from producing robotic, synthetic outputs to generating human-like speech that is nearly indistinguishable from real voices. This evolution has made TTS a critical interface between software and humans, particularly in applications where audio is preferable to reading, such as voice assistants and AI agents. Modern TTS systems are structured as two-stage pipelines comprising a frontend that converts text into a linguistic representation and a backend that generates audio from this representation. These systems are now integral to real-time and batch voice applications due to their ability to handle low latency and high-quality speech output. With the integration of large language models (LLMs), TTS infrastructure has evolved to support real-time conversation and voice interfaces. Developers must consider architectural choices, trade-offs in quality versus latency, and deployment options—cloud, self-hosted, or edge—when building TTS systems. As TTS continues to improve, challenges around privacy, consent, and potential misuse of voice cloning become increasingly important, necessitating careful consideration and implementation of safeguards.

Marketplaces rely on mutual trust between buyers and sellers, with content moderation becoming an essential element to maintain this trust and ensure safety as platforms scale. Effective moderation, which includes AI-driven and human review processes, is crucial for preventing scams, harassment, and fraudulent activities that can damage a marketplace's reputation and growth. As user-generated content (UGC) increases, moderation must cover various surfaces such as chat, listings, images, and reviews, and adapt to different regions and categories. Real-time moderation and behavior-based rules help detect and prevent abuse quickly, while consistent guidelines and moderation tools like APIs offer scalable solutions for marketplaces to protect their community and drive long-term growth. A well-implemented moderation system not only prevents harm but also fosters a healthy environment that attracts quality participants, ultimately enhancing liquidity and sustaining marketplace growth.

Artificial intelligence is increasingly moving from centralized systems to edge devices, enabling a wide range of applications, such as fitness coaching, accessibility aids, and real-time translation. This transition requires speech workflows optimized for the edge, involving components like speech-to-text (STT) and text-to-speech (TTS) that can function with minimal cloud dependency. A hybrid approach is often used, combining cloud-based solutions like Deepgram for STT with local or cloud-based Fish Speech for TTS, ensuring responsiveness and reliability even with intermittent connectivity. The architecture supports real-time streaming, smart formatting, and emotion-controlled voice synthesis, allowing for applications that are both intuitive and adaptable. This edge-optimized framework, exemplified by a coaching assistant, demonstrates how AI can be leveraged for continuous listening and interaction, offering immediate feedback and enhancing user engagement. As AI continues to integrate into various devices, the focus shifts to developing innovative applications that are robust and efficient in varying connectivity conditions.

Stream has achieved a significant milestone in its activity feed infrastructure by successfully benchmarking over 37 million operations with a 10% write and 90% read workload distribution across a dataset comprising 100 million users, 500 million activities, and 200 million follow relationships. Tested at varying request rates, Stream demonstrated stable performance with ~11-15ms p50 read latency and sub-32ms p50 write latency, maintaining a 100% success rate across scenarios, even with up to 4.5 million peak concurrent users. Stream's architecture supports real personalization by enabling dynamic blending and ranking of multiple activity sources, allowing teams to experiment and evolve feed logic without the need to overhaul backend systems. This infrastructure is designed to handle the complexities of scaling personalized feeds, which often become challenging as user numbers grow, leading to potential issues with latency and feed freshness. Stream provides scalable, reliable performance, backed by transparent benchmarks, making it a robust choice for teams managing social platforms, marketplaces, and media apps, ensuring confidence in handling traffic spikes and maintaining user engagement.

A2UI (Agent-to-UI) is a protocol developed by Google to facilitate standardized communication between AI agents and user interfaces, making AI-driven experiences more interactive, portable, and UI-independent. It allows AI systems to offer more than just text responses by enabling users to interact with buttons and forms across devices. The protocol, still in development, is integrated into Stream's Chat SDK, allowing for the creation of interactive demos like a restaurant finder that lets users make mock reservations. The current specification, A2UI v0.8, uses a JSONL-based protocol for progressive rendering and compatibility with large language models (LLMs), detailing user interfaces as abstract messages for incremental updates. A shift toward a prompt-first design is seen in the draft v0.9, embedding JSON schemas in the model's system prompt for context-aware UI message generation. The text explains integrating A2UI on both server and iOS sides, converting interactions into model prompts and structured responses, and rendering them using GenUI, maintaining the chat model while allowing for rich interaction. This integration highlights the potential for creating interactive, AI-enhanced interfaces without altering the fundamental chat structure.