Chat
Conversation, realtime voice, file and document interaction, research and task initiation, and authorized connected services.
COMING SOON
A Red Rocket flagship AI platform
LF is being built as an AI operating environment—not just another chatbot.
A modular, local-first environment designed to reason, research, create, automate, communicate, develop software and media, manage persistent work, and coordinate specialist tools from one system.
LF is under active development and final validation. It is not commercially available yet. Commercial availability is currently planned for later in 2026, with final timing dependent on development and certification.
What is LF?
Many kinds of AI work. One coordinated platform.
People increasingly use separate products for AI conversation, image generation, video, research, coding, voice, automation, project work, social production, and connected services. LF is being built to bring many of those capabilities into one environment where they can share Projects, assets, context, tools, and workload infrastructure.
LF is not intended to be a frontend wrapped around one permanent language model. Its architecture is modular: different modules can use different models, tools, workflows, and resource profiles according to the work being done. That also allows models and tools to remain interchangeable rather than permanently tying the platform to one AI vendor.
The goal is simple to understand even if the system underneath is sophisticated: give the user one place to do substantial AI-assisted work without manually stitching together every underlying tool.
Individual professionalsSmall businessesDevelopersCreatorsResearchersAdvanced AI users
Major LF capability areas
A broad working environment built from specialist modules.
These represent LF's current build scope. Development and validation status varies by capability, and this page does not imply that every area is finished, certified, or commercially available.
Image Lab
Image generation with multiple models and tools, model-aware settings, and Project integration.
Image Editor
AI-assisted object, style, and content editing designed for production-oriented image workflows.
Video Lab
AI video generation with model-specific controls and asset / Project integration.
Video Editor
A professional NLE-style environment with timeline editing, source/program monitoring, AI assistance, captions, audio, effects, and export.
URL Transcripts
Generate or extract supported transcripts and move them into other LF work for reuse, research, or production.
Music Lab
AI-assisted music and audio creation workflows inside the same broader environment.
Voice Lab
Voice creation, cloning, and higher-quality production voice workflows.
Talking Avatar
Generated speaking-avatar workflows for communication and content production.
Social Hub
Content creation and management, supported publishing workflows, platform-aware strategy, read-only X intelligence, and future advertising workflows.
App Coding
Software and Project development, repository work, coding assistance, builds, testing, and toolchain interaction.
Madden Lab / Legacy Lab
Lawful legacy-game analysis, structured binary and format research, reconstruction, and custom-tool workflows.
Game Development
Unreal, Unity, Blender, and related development-pipeline integration.
Linux Workspace
An integrated local Linux environment for development and technical workflows.
Library
Persistent assets, provenance, lineage, reusable resources, and relationships across Projects.
Research • Workflows • Training & Adaptation • LF Capability
Four connected professional workspaces for understanding, planning, expanding, and inspecting LF.
This module is designed as a four-quadrant landing workspace. The conversation is only one part of the experience; the surrounding workspace is intended to make complex work visible.
01
Research
A persistent analytical workspace intended to help the user understand a Project's current state and decide intelligent next steps. Conversation is deliberately only a small part of this workspace; the majority is intended for visual evidence, analysis, and Project outputs.
- graphs and tables
- evidence boards and source maps
- timelines and comparisons
- mockups and stat overlays
- scenario analysis and findings
- Project artifacts and visual research outputs
02
Workflows
A visual roadmap for a substantial Project from inception through completion.
- objective, phases, tasks, and dependencies
- milestones and checkpoints
- current position and completed work
- running, queued, blocked, or waiting work
- artifacts and decisions
- the remaining path to completion
03
Training & Adaptation
LF's capability-development workspace for deciding how a new idea should become a usable capability. Users can discuss an idea locally with reasoning dedicated specifically to training and adaptation before deciding which implementation path fits.
- training or datasets
- adapters / LoRAs
- model configuration and classifiers
- voice or style adaptation
- tools and workflows
- other compatible implementation approaches
Concept → research → workflow → adaptation → LF capability
04
LF Capability
The deeper inspection layer for understanding what LF actually has available and what state each capability is in.
- capabilities, modules, models, and tools
- versions, availability, and system health
- resource requirements and supported inputs / outputs
- limitations and implementation status
- validation or certification evidence
- licensing / commercial state where relevant
Preflight is intended as the concise operational-health view; LF Capability provides the deeper system inspection layer.
Projects & persistent work
Substantial work should not have to restart from scratch every time the chat closes.
LF is organized around persistent Projects rather than isolated prompts. A Project can retain the working context and artifacts needed to continue meaningful work over time.
Instructions / canonConversationsAssetsResearchWorkflowsGenerated mediaSource evidenceRevisionsModel / tool outputsDecisionsProvenanceLibrary relationships
Decision Fabric & intelligent orchestration
Coordinate substantial work without making the user manually manage every underlying tool.
LF includes an internal orchestration and resource-management layer designed to coordinate tasks across modules and machine resources at a high level.
The product-facing idea is straightforward: work can have priority, dependencies, progress, retries, and background execution while the system accounts for available resources and parallel work where appropriate.
PROJECT / USER INTENT
PriorityDependenciesProgress
GLOBAL TASK QUEUE + RESOURCE-AWARE COORDINATION
ModelsToolsModules
EXECUTE • RETRY • REQUEUE • REPORT
Realtime voice & assistant experience
Talk naturally while the work continues.
LF is intended to support streaming voice interaction that shares the same working context as text and ongoing tasks.
Natural conversationSpeak to the assistant without turning every interaction into a command syntax.
Interruption / barge-inInterrupt naturally rather than waiting for a monologue to finish.
Task awarenessThe assistant can reference work status, modules, queues, and ongoing progress.
Shared contextVoice and text are intended to participate in the same working environment.
Persona experiencesDifferent cleared Persona experiences can change assistant identity, voice, visual environment, and interaction style without changing LF's underlying capabilities.
Connected services
Bring authorized external work into the same environment.
LF is being designed to work with supported external services after the user explicitly authorizes the relevant account or capability. Integration does not mean unrestricted access.
GmailMultiple authorized accounts where supported
Google DriveFiles and connected work
Google DocsDocument workflows
Microsoft OneDriveAuthorized cloud files
GitHubRepositories and development work
Connected functionality depends on account authorization, provider support, permissions, network access, and the capabilities implemented for each service.
What substantial LF work could look like
Concrete workflows, not vague “AI for everything” claims.
These examples illustrate the kinds of multi-stage work LF is being designed to coordinate. They are examples of intended use, not claims that every workflow is already certified for commercial release.
Business
- Research a market and preserve the evidence.
- Organize findings into a usable operating view.
- Build a workflow from objective through execution.
- Create supporting marketing or business materials.
- Work with authorized communications and connected services.
- Analyze operational information without losing Project context.
Content creation
- Research a topic and preserve source material.
- Write and revise a script.
- Generate imagery, narration, music, or video assets.
- Edit the final production inside an integrated workflow.
- Prepare platform-specific social content from the same Project.
Development
- Plan an application or technical Project.
- Research libraries, frameworks, and implementation options.
- Write, test, and debug code.
- Work with authorized repositories and toolchains.
- Package a completed product or internal tool.
Research
- Gather multiple sources and preserve evidence.
- Map competing claims or conflicting information.
- Create tables, comparisons, timelines, and visual findings.
- Maintain a long-running research Project over time.
- Use the current state of the evidence to guide the next question.
AI capability development
- Identify a technique or capability worth exploring.
- Research how it could fit LF.
- Construct a roadmap and implementation workflow.
- Train, adapt, configure, or build the capability.
- Register and validate the completed capability inside LF.
- Prepare it for later packaging or sharing when that ecosystem exists.
Evidence & demonstrations
The page is built to grow with proof.
Screenshots, workflows, case studies, benchmark context, media outputs, before/after demonstrations, and certification evidence can be added here as comprehensive validation and practical use continue.
EVIDENCE LIBRARY
Demonstrations and certified use cases will be added as LF completes final validation.
No fabricated performance statistics. No fake testimonials.
What's next
Upcoming LF development.
Everything in this section is roadmap material. Scope, sequence, and timing may evolve as the platform is developed and validated.
Screen Vision — Phase 1
Watch a selected screen or window and discuss what the user is seeing through text or streaming voice for websites, software, gaming, troubleshooting, creative tools, and technical work.
Smart Phone
Phone-oriented communication and business-assistant workflows designed to extend LF into natural communications work.
Cloud API Takeover
Optional cloud escalation for especially difficult reasoning tasks while LF remains the orchestrating environment.
Social Hub — Meta Ads
Guided advertising workflows for helping create, launch, analyze, and optimize Meta campaigns.
LF Skills / Capability Ecosystem
Package, import, export, and eventually share trained or adapted LF skills and capability packs.
Environmental / Spatial Interaction
Exploration of webcam vision, gesture interaction, projected workspace concepts, and other spatial or environmental interfaces.
Long-term LF ecosystem
Capabilities that can eventually move with the community.
LF's longer-term ecosystem vision includes portable capabilities. Users may eventually be able to train or adapt LF, package compatible skills or capability packs, export and import them, share compatible packages with the LF community, and download official Red Rocket / LF capability packs.
This ecosystem is not available today. It is a longer-term direction intended to make LF extensible without permanently tying every capability to one model vendor or one installation.
Local-first / privacy differentiation
Core workloads should not have to leave the user's computer by default when local execution is practical.
LF is designed around user-owned computing, user-controlled data, and local model / tool execution where it makes sense, while still allowing explicitly authorized external integrations or future cloud escalation when they add meaningful value.
This is a design direction, not an absolute privacy guarantee. The actual data path depends on the specific module, connected service, user authorization, and workflow involved.
LOCALModels • tools • files • Projects • workflows
USER CONTROLApprovals • connected accounts • routing • privacy choices
OPTIONAL EXTERNALAuthorized services • cloud capability when justified
EARLY ACCESS LIST
Know when LF becomes available
Join the LF waitlist.
LF is not commercially available yet. Join the list to be notified when LF launches and to receive the generous early-adopter launch discount planned for early waitlist members.
No purchase obligation.
Commercial availability is currently targeted for later in 2026.
Exact pricing and early-adopter discount terms will be announced later.
FAQ
Early LF questions.
Commercial details are intentionally limited until development and final validation are complete.
Is LF available now?
No. LF is currently under active development and final validation and is not yet commercially available.
When will LF launch?
Commercial availability is currently targeted for later in 2026. Final timing depends on development and certification, so no specific launch date is being guaranteed yet.
Will LF require the cloud?
LF is designed local-first. Some authorized external integrations require internet access, and optional future cloud escalation is planned for work where remote capability adds meaningful value.
Can I join the waitlist now?
Yes. The LF early-access list is open on this page.
Will early adopters receive a discount?
Early waitlist members are planned to receive a generous early-adopter launch discount. Exact pricing and discount terms will be announced later.
Is LF only for technical users?
No. Commercial LF is planned to include guided onboarding, contextual assistance, and training for users with different levels of AI experience.
LF • Coming Soon