AI Models Release Notes
Release notes for leading AI models, APIs and AI platforms
Products (16)
Latest AI Models Updates
- Jul 16, 2026
- Date parsed from source:Jul 16, 2026
- First seen by Releasebot:Jul 18, 2026
July 16, 2026
ChatGPT updates its desktop app with a clearer Chat and Work layout, unified Recents, Projects in the app, and cloud-synced Work conversations across web, mobile, and desktop for smoother continuity.
ChatGPT desktop app experience updates
We’ve updated the ChatGPT desktop app to make it easier to choose between Chat and Work, find your conversations and Projects, and continue Work across devices. These updates are now live for all plans on macOS and Windows.
macOS and Windows
- A clearer desktop layout: A global switcher lets you choose between ChatGPT and Codex. In ChatGPT, choose Chat for quick questions and conversational help, or Work to complete tasks end to end.
- Unified Recents: Chat and Work conversations now appear together in Recents, where you can sort, filter, and pin them.
- Projects in the desktop app: Your existing ChatGPT Projects now appear in the app. You can start a Chat conversation inside a Project or begin a Work thread using Project context.
- Continue Work across devices: Cloud Work conversations now sync across web, mobile, and desktop, so you can start on one surface and continue on another. Local conversations stay on your computer.
- Codex remains unchanged: As part of this update, Codex remains a separate view, and its workflows and history are unchanged. Quick Chats remain available for fast conversations.
- Jul 16, 2026
- Date parsed from source:Jul 16, 2026
- First seen by Releasebot:Jul 17, 2026
Cast yourself in AI video clips using your personal avatar with Gemini Omni in Vids
Gemini brings personal avatars to Google Vids with Gemini Omni, letting users create videos with a verified likeness directly in the app. The release adds secure avatar setup, easy selection in Vids, and admin controls, with launch limits by language, region, and age.
Users now have access to Gemini Omni directly within Google Vids. With Gemini Omni, you can create videos using your personal avatar to scale your presence without the studio time. Use a secure verification process to capture your likeness and then select it as a character in Omni generations within Vids.
- Secure verification: Verify and manage your personal avatar directly within your Google Account.
- Seamless integration: Easily add your personal avatar from the selector in Vids.
- Administrative oversight: Admins can manage or disable this feature through the Admin console.
A note on language and region availability
At launch, personal avatars are available in English only for users 18 years and older. They’re not available in the European Economic Area, Switzerland, or the United Kingdom. Learn more about personal avatar privacy settings.
Getting started
- Admins: This feature will be ON by default and can be disabled or enabled at the domain level. Visit the Help Center to learn more about managing personal avatars in Vids.
- End users: Visit the Help Center to learn more about using personal avatars in Vids.
Rollout pace
- Rapid Release domains: Gradual rollout (up to 15 days for feature visibility) starting on July 16, 2026
- Scheduled Release domains: Gradual rollout (up to 15 days for feature visibility) starting on August 5, 2026
Availability
- Business: Business Starter, Standard, and Plus
- Enterprise: Enterprise Starter, Standard, and Plus
- Education: Education Plus
- Consumer: Google AI Pro and Ultra
- Other Editions: Enterprise Essentials and Enterprise Essentials Plus; Nonprofits
- Education Add-ons: Google AI Pro for Education; Teaching and Learning
- Other Add-ons: AI Expanded Access*
*Users with AI Expanded Access add-on licenses have higher limits on usage of Omni in Vids.
Resources
- Google Docs Editors Help: Create, use & manage your personal avatar with Gemini in Google Vids
- Google Docs Editors Help: Use Omni in Google Vids
All of your release notes in one feed
Join Releasebot and get updates from OpenAI and hundreds of other software products.
- Jul 16, 2026
- Date parsed from source:Jul 16, 2026
- First seen by Releasebot:Jul 17, 2026
Generate higher quality AI video clips and edit any video with Gemini Omni in Vids
Gemini adds Omni directly in Google Vids, bringing higher-quality video generation and simple text-based video edits. Users can improve realism, text rendering, and physics or ask Gemini to change style, color grading, and even remove background noise.
Users now have access to Gemini Omni directly within Google Vids. Omni provides higher quality video generation with significant improvements over previous models. Additionally, Omni’s world understanding unlocks simple video edits so you can ask Omni to tweak the video you have to get the video you need.
Generate clips with higher quality
Generate higher quality videos with improved text rendering, physics, and realism using Google’s latest Omni Flash model.
Edit videos by typing changes
For example, fix the color-grading, restyle the visuals in anime, or remove that New York siren in the background with a simple text instruction in Vids.
A note on language and region availability
At launch, editing non-AI videos with Omni is not available in the European Economic Area, Switzerland, United Kingdom, Texas, or Illinois.
Getting started
- Admins: This feature does not have an admin control.
- End users: Visit the Help Center to learn more about using Omni in Vids.
Rollout pace
- Rapid Release domains: Gradual rollout (up to 15 days for feature visibility) starting on July 16, 2026
- Scheduled Release domains: Gradual rollout (up to 15 days for feature visibility) starting on August 5, 2026
Availability
- Business: Business Starter, Standard, and Plus
- Enterprise: Enterprise Starter, Standard, and Plus
- Education: Education Plus
- Consumer: Google AI Pro and Ultra
- Other Editions: Enterprise Essentials and Enterprise Essentials Plus; Nonprofits
- Education Add-ons: Google AI Pro for Education; Teaching and Learning
- Other Add-ons: AI Expanded Access*
*Users with AI Expanded Access add-on licenses have higher limits on usage of Omni in Vids.
Resources
- Google Vids Editors Help: Use AI to generate video clips
- Jul 16, 2026
- Date parsed from source:Jul 16, 2026
- First seen by Releasebot:Jul 17, 2026
Automations in Grok
xAI introduces Automations in Grok, letting users set jobs to run on a schedule or when matching email arrives. Available on grok.com and iOS and Android, it supports templates, run history, reports back by email or app notification, and more.
Describe a job once and Grok runs it on a schedule or when an email arrives, then reports back.
Today, we're introducing Automations: jobs Grok runs on its own.
Describe the work once, choose when it runs, and Grok takes it from there, whether that's research done before you're awake or an important email flagged the moment it lands. Automations are available on grok.com and in the Grok app on iOS and Android.
Describe the job once
An automation's instructions read like any chat message: describe what you want, attach files for context, add connectors and skills, and pick a mode. Name it, save it, and from then on every run is a fresh request: same instructions, current data.
On a schedule, or on a trigger
Schedules run once, daily, weekdays, weekly, monthly, or yearly, at a time you choose in your timezone: a morning brief at 8:00 before the day starts, or a rent reminder on the 1st.
Email triggers watch your inbox instead. When an incoming email matches your filters (sender, recipient, or subject), the automation fires with that email as context, and Grok responds to the actual message. Instructions can also point at your tools directly: type @ to mention a connector, and Grok uses it on every run.
You can also start a run yourself with Run now, which is handy for testing an automation right after you build it.
Every run is a full conversation
When an automation fires, Grok opens a real conversation, does the work, and saves the result to its run history. Open any run to read the full thread, or pick up the conversation where Grok left off. You choose how each automation reports back: email, app notification, both, or neither if you'd rather check in yourself.
Start from chat, or a template
You can also create automations straight from chat: ask Grok to "check the news every morning and flag anything about pricing" and it sets one up for you. The Automations page has suggested templates to start from, and any automation can be paused, resumed, edited, or deleted at any time.
Get started
Create your first automation at grok.com/automations. Scheduled automations are available to everyone; email triggers are included with SuperGrok.
Original source - Jul 16, 2026
- Date parsed from source:Jul 16, 2026
- First seen by Releasebot:Jul 17, 2026
Kimi K3: Open Frontier Intelligence
Kimi introduces Kimi K3, its most capable open 3T-class model with native vision, a 1-million-token context window, and availability across Kimi.com, Kimi Work, Kimi Code, and the Kimi API. It also adds Widgets and Dashboard in Kimi Work for more visual, persistent workflows.
An Open 3T-Class Model
Today, we are introducing Kimi K3 — our most capable model. Kimi K3 is a 2.8T-parameter model built on our Kimi Delta Attention and Attention Residuals, with native vision capabilities and a 1-million-token context window. It is the world's first open 3T-class model, designed for frontier intelligence across long-horizon coding, knowledge work, and reasoning.
While its overall performance still trails the most powerful proprietary models, Claude Fable 5 and GPT 5.6 Sol, Kimi K3 demonstrated frontier-level performance across our evaluation suite, consistently outperforming other tested models.
Kimi K3 is available today on Kimi.com, Kimi Work, Kimi Code, and the Kimi API. At launch, Kimi K3 will use max thinking effort by default, with low- and high-effort modes to be introduced in subsequent updates. We are currently working closely with inference partners and open-source maintainers to align technical details and ensure a reliable rollout across the ecosystem. The full model weights will be released by July 27, 2026. Further details on the architecture, training, and evaluations will be released alongside the Kimi K3 technical report.
An Open 3T-Class Model
Kimi K3 is the first open model to reach 2.8 trillion parameters. It marks the latest step in Kimi's sustained push at the scaling frontier: for nine of the past twelve months, Kimi models have set the upper bound of open-model sizes.
Kimi K3 is built on Kimi Delta Attention (KDA) and Attention Residuals (AttnRes), two architectural updates designed to improve how information flows across sequence length and model depth. We have also scaled up Mixture of Experts (MoE) sparsity, effectively activating 16 out of 896 experts when paired with a Stable LatentMoE framework. Together with refined training and data recipes, these structural changes yield an approximate 2.5× improvement in overall scaling efficiency compared to Kimi K2, allowing the model to convert compute into intelligence more effectively.
Coding
Kimi K3 has strong long-horizon coding performance. Operating with minimal human oversight, it can sustain long engineering sessions, navigate massive repositories, and orchestrate terminal tools.
Kimi K3 also excels in tasks blending software engineering with visual reasoning — it leverages screenshots and visuals to optimize game dev, frontend, and CAD.
The case studies below show how Kimi K3's coding capability translates into open-ended software creation and scientific research.
Kernel Optimization
We tested the models' capability to optimize GPU kernels. Each model works independently in an identical sandbox, with up to 24 hours to profile, rewrite, and benchmark four tasks spanning AttnRes, KDA, and a 512-head-dimension MLA kernel across NVIDIA H200 and GPGPU from an alternative vendor. Kimi K3 performed competitively with Fable 5 (with fallback) and substantially outperformed Opus 4.8, GPT 5.6 Sol, and GPT 5.5.
AttnRes Kernel Optimization
Given the FLA Triton implementation of AttnRes at its production shape (96 layers, model dim 8192, 8192 tokens), the task is to make the training-side operation as fast as possible without changing the numerics. Across 15 hours of iterations nonstop, K3 designed a novel two-phase kernel algorithm, fused kernels while preserving the same numerics, and cut forward+backward time from 283.6 ms to 114.4 ms. Notably, K3 and Fable 5 (w/ potential fallback) both achieved similar performance, with K3 optimizing faster per iteration.
Claude Fable 5 was evaluated by a third party, and its results may include fallback behavior. Across most models, some trajectories include small, acceptable precision shortcuts that remain within our numerical tolerance. GPGPU denotes general-purpose GPUs used for computation beyond graphics rendering.
In the late stages of Kimi K3 development, an early version of Kimi K3 handled the majority of the team's kernel optimization works.
GPU Compiler Development
We further tested whether Kimi K3 could build a GPU programming system from scratch. Kimi K3 developed MiniTriton, a compact Triton-like compiler with its own tile-level IR layer over MLIR, optimization passes, and a PTX code-generation pipeline. Across supported roofline benchmarks, MiniTriton delivers performance on par with or better than Triton and torch.compile — beating Triton on certain workloads. Beyond microbenchmarks, MiniTriton sustains end-to-end nanoGPT training with stable convergence, the loss curve closely tracking the reference with only minor divergence — validating the full pipeline on a realistic workload. These results demonstrate that Kimi K3 can build a coherent end-to-end compiler — from DSL frontend and IR passes to PTX codegen and runtime — rather than isolated kernels; its from-scratch Tensor Core path already rivals Triton’s extensively optimized stack.
Game Dev and Digital Creation
Kimi K3 combines strong 3D reasoning, coding, and vision capabilities to turn concepts, images, and videos into fully playable interactive experiences. Kimi K3 achieves true "vision in the loop" by seamlessly iterating between code and live screenshots—instantly seeing and refining outputs.
Case 1: 3D Open World
Kimi K3 built a fully procedural browser-based 3D exploration game using Three.js WebGPU and GPU compute. It procedurally generated the environment, while using a 3D asset generation tool to create the rider and horse models, producing an expansive open world with forests, a log-cabin village, snowy mountains, and dynamic weather. External assets used: animated cowboy and horse models and terrain data.
Chip Design
As an early proof of concept, Kimi K3 designed a chip to serve a nano model built on its own architecture. In a single 48-hour autonomous run, K3 built, optimized, and verified the chip using open-source EDA tools on the Nangate 45nm library. Within 4 mm², the chip closes timing at 100 MHz and sustains over 8,700 tokens/s decode throughput in simulation, packing 1.46M standard cells, 0.277 MB of SRAM, and an INT4 MAC array with fused dequantization. A chip built by a model, for a model, reflects K3's long-horizon agentic capabilities.
Coding for Research
Kimi K3 bridges scientific literature and executable code, autonomously implementing, validating, and analyzing complex computational research workflows.
In one case, Kimi K3 completed in about two hours what would typically require one to two weeks of work by an experienced researcher. To reproduce the I–Love–Q universal relations in computational astrophysics, it reviewed and cross-validated 20+ papers, implemented the full numerical pipeline, evaluated 300+ equations of state, identified inconsistencies in published formulas, generated 3,000+ lines of Python code, and produced an interactive HTML dashboard for exploring the results.
Knowledge Work
Kimi K3 advances end-to-end knowledge work. Beyond public benchmarks, Kimi K3 (max) demonstrates consistent gains across our internal evaluations, which are derived from recurring patterns and challenges observed in real-world user-agent workflows. These consistent advantages across distinct production-oriented workflows reflect a broad improvement in Kimi K3's agentic knowledge work capabilities.
Research with Interactive Visualization
Below are a few examples of what Kimi K3 in Kimi Work can produce across financial consulting and scientific research:
Case 1: Interactive 42 years of AI ASIC industry research websiteAn interactive research report you can drill into: 42 years of the ASIC industry, created through 120+ rounds of recursive self-improvement. Kimi K3 transforms evidence into bespoke charts, animated diagrams, and interactive visual narratives. It pulled data via 2.8k+ web searches/fetches and 1.1k+ terminal data pulls, across 11k+ pages spanning 87 quarterly reports and 99 original PDFs.
Case 2: Fusion Industry ResearchA consulting-style industry report with interactive visualizations—including timelines, Funnel Chart, Range Bar Chart, Gantt Charts, and publication-quality slides.
Case 3: GWTC-5 Gravitational-wave AnalysisAn analysis of 391 gravitational-wave events using 20+ concurrent subagents, producing 7 scientific visualizations, 2 tables, and a literature synthesis from 10+ papers.
Kimi K3 is also particularly effective at producing infographic-style presentations, such as the fully editable heatmap and annual report shown below:
Widgets and Dashboard
In Kimi Work, we introduce two new features - Widgets and Dashboard - which make interactions with Kimi K3 more visual and persistent. Widgets let you generate interactive components directly within a chat, with connections to local data or external plugins for continuous updates. Dashboard brings the widgets you care about most into one persistent, personalized view organized around a topic, project, or goal.
Video Editing
Kimi K3 excels at motion design, animation, and video editing because its native multimodal architecture understands text, images, and video within the same model.
In one example, K3 created a 3Blue1Brown-style motion-graphics explainer of its own architecture, translating technical ideas into animated diagrams and transitions.
In another, Kimi K3 edited its own teaser video from 56 source clips, handling clip selection, motion-matched cuts, frame-accurate beat synchronization, audio processing, and multiple rounds of revision. A high-density short video like this would typically take an experienced editor one to two working days, or a beginner three to five.
Architecture and Infrastructure
Kimi K3 is built on Kimi Delta Attention (KDA) and Attention Residuals (AttnRes). KDA provides an efficient foundation for scaling attention, while AttnRes selectively retrieves representations across depth rather than accumulating them uniformly. Together, they form the architectural backbone of a model designed to scale well beyond the trillion-parameter regime.
Kimi K3 uses Stable LatentMoE, effectively activating 16 of 896 experts. At this level of sparsity, routing and optimization become first-order challenges. Quantile Balancing derives expert allocation directly from router-score quantiles, eliminating heuristic updates and a sensitive balancing hyperparameter, while Per-Head Muon extends Muon by optimizing attention heads independently for more adaptive learning at scale. Sigmoid Tanh Unit (SiTU) and Gated MLA improve activation control and attention selectivity respectively. Together, these advances enable stable and efficient training at the 2.8-trillion-parameter scale.
Kimi K3 applies quantization-aware training from the SFT stage onward, using MXFP4 weights with MXFP8 activations for broad hardware compatibility. To prevent expert imbalance from degrading throughput at large expert-parallel scales, we introduce a fully balanced expert-parallel training method with static shapes and no host synchronization on the critical path. Since inference efficiency likewise benefits from larger high-bandwidth communication domains, we recommend deploying Kimi K3 on supernode configurations with 64 or more accelerators. Finally, as KDA poses new challenges for conventional prefix caching, we have contributed a corresponding implementation to the vLLM community, to be released alongside the model. KDA with prefill cache allows us to serve Kimi K3 at a highly competitive token price despite its scale and long context.
More technical details will be available in our coming report.
Availability
- Kimi K3 Agents: Download or update to the latest Kimi app from your mobile app store, available on iOS, Android, and HarmonyOS, or visit kimi.com.
- Work with Kimi K3: Download the latest Kimi Work desktop app, version 3.1.0 or later, available for Windows and Apple silicon Macs.
- Code with Kimi K3: Run Kimi Code in your terminal and select Kimi K3 using the /model command.
- Build with the Kimi API: Visit the Kimi API Platform and select kimi-k3. Pricing is $0.30/MTok for cache-hit input, $3.00/MTok for cache-miss input, and $15.00/MTok for output. Powered by Mooncake's disaggregated inference architecture, the official Kimi API achieves a cache hit rate above 90% in coding workloads.
- Bring Kimi to your organization: Kimi Enterprise provides enterprise-grade data privacy and member management, with complete separation between personal and organization accounts. Visit the pricing page and select “Get Kimi Enterprise” to subscribe for your team.
Limitations
- Sensitivity to thinking history. K3 was trained in the preserved thinking history mode. If the agent harness fails to pass back all the historical thinking content as required, or if an ongoing session with another model is switched over to K3, generation quality may become highly unstable. We recommend using a harness with verified compatibility, such as Kimi Code, and avoiding switching to K3 in the middle of a session.
- Excessive proactiveness. K3's training places particular emphasis on long-horizon, challenging tasks. As a result, when it encounters minor issues or ambiguous user intent during task execution, it may make unexpected decisions on the user's behalf. If your application requires the agent to operate within well-defined boundaries and refrain from excessive improvisation, please impose more explicit behavioral constraints on K3 in the system prompt or in AGENTS.md.
- Despite being a highly competitive model overall, K3 nonetheless exhibits a noticeable gap in user experience compared with Claude Fable 5 and GPT 5.6 Sol.
- Jul 16, 2026
- Date parsed from source:Jul 16, 2026
- First seen by Releasebot:Jul 16, 2026
- Jul 15, 2026
- Date parsed from source:Jul 15, 2026
- First seen by Releasebot:Jul 16, 2026
Grok Build is Now Open Source
xAI opensources Grok Build, its coding agent and TUI, and makes the source available on GitHub. The release highlights a fully local-first setup, with the harness now easier to inspect, extend, and run from your own config.
Explore the harness behind our coding agent and TUI.
We're open-sourcing Grok Build, SpaceXAI's coding agent and TUI. The source is now available on GitHub.
Publishing the code is the most direct way to build toward a robust and reliable harness. You can read the source to see exactly how it works, from context assembly to tool-call dispatch.
Open-sourcing also makes the harness easier to explore and extend: if you're working with skills, plugins, hooks, MCP servers, or subagents, the source is the definitive reference for how each is loaded and invoked.
Finally, Grok Build can now run fully local-first: compile it yourself, point it at your own local inference, and drive everything from your config.toml.
About the codebase
About the codebase
The published source includes:
- The agent loop: how context is assembled, how model responses are parsed, and how tool calls are dispatched
- The tools: how the agent reads, edits, and searches code, and how it runs commands
- The terminal UI: rendering, input handling, plan review, and the inline diff viewer
- The extension system: skills, plugins, hooks, MCP servers, and subagents
Explore the source on GitHub.
View on GitHub Browse the source on GitHub.
Get Grok Build Install with one command and run it in your terminal.
Original source - Jul 15, 2026
- Date parsed from source:Jul 15, 2026
- First seen by Releasebot:Jul 15, 2026
- Modified by Releasebot:Jul 17, 2026
July 15, 2026
ChatGPT increases custom instructions to 5,000 characters for Plus, Enterprise, Business, and Education users.
Increased custom instructions limit
We’re increasing the character limit for custom instructions in ChatGPT. Plus, Enterprise, Business, and Education users can now save up to 5,000 characters, up from 1,500, giving them more room to customize ChatGPT’s response style and behavior.
Original source - Jul 14, 2026
- Date parsed from source:Jul 14, 2026
- First seen by Releasebot:Jul 15, 2026
July 14, 2026
Claude adds self-serve HIPAA configuration for Enterprise and API orgs with BAA review, guide download, and one-step enablement.
HIPAA configuration for your Claude organizations is now self-serve
You can now manage HIPAA readiness for your Claude organizations yourself. This applies to both Claude Enterprise and the Claude Platform (API). In each product, an eligible admin can review the Business Associate Agreement (BAA), download the implementation guide, and enable the HIPAA configuration in a single flow. For more information, refer to HIPAA-ready Enterprise plans and HIPAA readiness for Claude API.
Original source - Jul 14, 2026
- Date parsed from source:Jul 14, 2026
- First seen by Releasebot:Jul 15, 2026
Empowering India’s next generation of innovators with ATL Saathi
Gemini launches ATL Saathi, a Gemini-powered web app piloting in 100 Indian schools to give Tinkering Lab educators a 24/7 planning and training assistant. It supports micro-learning, project ideas, multilingual use, and curriculum-aligned guidance for teachers.
Atal Tinkering Labs (ATL) is bringing access to new technology—such as 3D printing, IoT, and robotics—for over 1.1 crore students across India. The initiative is now leveraging AI to scale high-quality mentorship, shifting the focus from access to providing physical lab infrastructure to driving meaningful outcomes like accelerated innovation and enhanced learning metrics.
At the AI Impact Summit in February 2026, Google DeepMind announced that it will help incorporate robotics and coding into local curricula, integrate Gemini thoughtfully into teacher workflows, and build a safely guardrailed AI assistant for students grounded in national curriculum standards that can act as an educational partner. I am happy to share that today we are launching a live pilot of ATL Saathi, a Gemini-powered web application that provides every Tinkering Lab educator with a 24/7 planning and training assistant, transforming ATLs into AI-Augmented Discovery Labs.
A new contribution to Indian Education with Atal Innovation Mission
We believe behind every good student is a great teacher. That’s why for over 20 years, Google has been dedicated to supporting the education ecosystem by introducing technology into teaching and learning through a teacher-led approach. With foundational platforms like Google for Education and Google Classroom, we build products tailored to the needs of schools, keeping the teacher in the lead. To further support the empowerment of educators, our new Google Educator AI Series ensures teachers are equipped with both the tools and the digital skills required for today's classrooms. We see Gemini as a great tool to enable our partners to create unique, augmented learning experiences.
We have been working closely with the Atal Innovation Mission, which is part of NITI Aayog, the Government of India’s flagship initiative to promote a culture of innovation and entrepreneurship in the country, to ensure that the tool is grounded in teacher’s needs, accurately reflects ATL’s educational principles and pedagogy to "Cultivate one Million children in India as Neoteric Innovators", and creates genuine value for the ATL educators.
Atal Innovation Mission has always believed that the journey towards a Viksit Bharat @2047 will be powered by the imagination, creativity, and problem-solving abilities of our young innovators. At the heart of this movement are our educators, the mentors who ignite curiosity and transform ideas into impact. ATL Saathi represents the power of AI as an enabler, empowering ATL teachers with accessible knowledge, simplified resources, and contextual support so that they can focus on what truly matters , nurturing the next generation of innovators and nation builders. Through such collaborative efforts, we continue to strengthen India’s innovation ecosystem from the grassroots and take forward the Hon’ble Prime Minister’s vision of making innovation a people’s movement.
Here are some of the key features in the ATL Saathi:
1. Streamlined onboarding and content curation
All ATL training modules and curriculum materials are organised and maintained within NotebookLM, ensuring educators always have access to the most up-to-date content. The application provides summarized modules, AI-generated infographics, video overviews, and interactive quizzes for 12 core modules from the ATL prescribed curriculum. This micro-learning approach replaces lengthy videos, allowing teachers to quickly familiarize themselves with complex topics.
2. Advanced project generation (Push & Pull mentorship)
For 10 core modules, teachers now have access to an advanced project generation interface. This feature elegantly supports both "push" and "pull" teaching mechanisms:
Generate project ideas (Push): Teachers can instantly create distinct, grade-appropriate project suggestions aligned with the curriculum to inspire student curiosity.
Detailed experiments (Pull): When students bring their own problem statements, the AI provides educators with step-by-step assembly instructions, wiring diagrams, and necessary safety precautions to bring those ideas to life safely.
3. Multilingual accessibility
Understanding the linguistic diversity of India's classrooms, educators can interact with the assistant in their preferred language. The assistant responds and generates materials in that same language, starting with 8 languages and the flexibility to support more.
Leveraging Gemini as the underlying intelligence
Powering the ATL Saathi, the Gemini model provides the underlying intelligence to transform tinkering labs into AI-augmented discovery environments. Its ability to create concise instructional materials, including AI infographics, video overviews, and interactive quizzes for core curriculum modules, helps teachers easily navigate complex training materials and seamlessly adopt micro-learning content.
Our latest Gemini 3.5 Flash model instantly generates grade-appropriate, curriculum-aligned project ideas to spark student curiosity and provides educators with step-by-step assembly instructions, wiring diagrams, and safety precautions for unique problem statements brought forward by students.
Looking ahead to the future
We are rolling out ATL Saathi to an initial cohort of 100 pilot schools across the country. By adopting AI-assisted tools and micro-learning formats, we hope for teachers to report significant reductions in their administrative load, higher efficiency, and an increased readiness and comfort level to help students tinker and innovate.
By shifting the burden of administrative overhead and curriculum translation onto AI, we are freeing our educators to do what they do best: mentor, inspire, and guide. Together, let's tinker and build the future.
Original source