You're comparing inference frameworks, and you have a question: "Does vLLM support speculative decoding?" You could search the documentation, parse through GitHub issues, or ask a general-purpose AI that might hallucinate the answer. None of these options understand what you're actually trying to accomplish—choosing infrastructure for your specific deployment.
NeoSignal AI Chat with signals context and citations
NeoSignal AI Chat knows what you're looking at. In this screenshot, the chat panel shows a conversation about "Frontier Model Competition Intensifying"—a direct response to the signals visible in the feed. The AI synthesizes DeepSeek's rise, Gemini 3 Pro's strong positioning, and GPT-o1s's momentum into actionable analysis. When you're on a component page, it knows that component. When you're using the Memory Calculator, it knows your configuration. Responses cite NeoSignal's curated knowledge sources—authoritative data like LMArena, SemiAnalysis, and official documentation—not generic web results.
The benefit: answers grounded in your context and verified sources. You get expert-level guidance that understands your current task, with citations you can verify.
Detailed Walkthrough
The Context Problem
Traditional AI assistants operate in a vacuum. They don't know what you're looking at, what you've already tried, or what tools you're using. You spend half your conversation providing context that should be obvious from your environment.
NeoSignal AI Chat solves this with deep integration into the platform. The chat automatically receives context about your current page, active tool configurations, and recent interactions. When you ask a question, the AI already knows what you're trying to accomplish.
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Context Types
NeoSignal Chat recognizes four context types that shape its responses:
Component Context: When viewing a component detail page (like /component/claude-3-5-sonnet), the chat knows you're focused on that specific model, accelerator, framework, or agent. Ask "How does this compare to GPT-4o?" and it understands "this" refers to Claude 3.5 Sonnet without you specifying.
Tool Context: When using NeoSignal tools like the Memory Calculator or Serving Engine Advisor, the chat receives your current configuration and results. Ask "How can I reduce memory usage?" while viewing a Memory Calculator result, and the response considers your specific model size, parallelism settings, and current GPU selection.
Signal Context: When browsing the Signals feed, the chat can synthesize market intelligence into narrative analysis. Ask "What's driving model competition right now?" and it draws from the signals you're viewing to provide current market perspective.
Blog Context: When reading NeoSignal blog posts, the chat understands the article content. Ask follow-up questions about concepts mentioned in the post, and responses build on that foundation.
Smart Prompts
NeoSignal AI Chat suggests context-aware prompts based on your current view and conversation state:
Empty State Prompts: When you haven't started a conversation, the chat suggests relevant starting points. On a model page: "How does this model's reasoning capability compare to alternatives?" On the Memory Calculator: "What optimizations would reduce my memory footprint?"
Follow-Up Prompts: After each assistant response, contextual follow-up suggestions appear. Asked about model comparison? Follow-ups might include "What about inference costs?" or "Which framework works best with this model?" The system analyzes the last response to generate relevant next questions.
Category-Colored Prompts: Prompts are color-coded by category—purple for Models, cyan for Accelerators, emerald for Cloud, amber for Frameworks—matching NeoSignal's visual language so you can quickly identify the domain.
Citation System
NeoSignal AI Chat grounds every response in verifiable sources through a structured citation system:
Component Citations: References to NeoSignal components render as clickable pills: [[component:claude-3-5-sonnet:Claude 3.5 Sonnet]] becomes a link to the component page. Click to verify the claims about that component.
External Citations: Links to authoritative sources appear as clickable references: [[external:https://lmarena.ai:LMArena]] links to the source directly. Every claim traces back to evidence.
Tool Citations: References to NeoSignal tools link to the relevant tool page: [[tool:memory-calculator:Memory Calculator]] takes you to the calculator where you can run your own analysis.
Blog Citations: References to NeoSignal blog content link to specific sections: [[blog:why-we-built-neosignal:detailed-walkthrough:detailed walkthrough]] deep-links to that heading.
The citation system ensures you can verify any claim the AI makes. No hallucinations—just traceable analysis.
Knowledge Sources
NeoSignal AI draws from curated, authoritative knowledge sources:
Component Data: The complete NeoSignal component database with scores, metrics, compatibility mappings, and score breakdowns. When the AI discusses a model's reasoning capability, it references actual benchmark scores.
Signals Intelligence: Current and historical market signals with confidence scores and data points. The AI can synthesize market movements into narrative analysis.
Curated Knowledge Base: LLM-optimized markdown files from authoritative sources including LMArena leaderboards, SemiAnalysis infrastructure analysis, HuggingFace documentation, MLPerf benchmarks, ThoughtWorks Technology Radar, and official provider documentation.
Tool Calculations: When you have active tool results, the AI can reference specific numbers from your configuration. "Your current configuration requires 1,151.7 GB peak memory" cites your actual Memory Calculator output.
Conversation Management
NeoSignal AI Chat provides full conversation lifecycle management:
Message History: Conversations persist across sessions. Return to NeoSignal and your previous chat remains available. The scrollable message area shows the full conversation with user messages on the right and assistant responses on the left.
Save Conversations: Click the bookmark icon to save the current conversation to your history. Saved conversations can be resumed later, shared with teammates, or exported.
Conversation History Tab: Switch to the History tab to browse previous conversations. Each entry shows the conversation title (auto-generated from the first user message), message count, and relative timestamp. Click to load and continue any conversation.
New Conversation: Click the plus icon to start fresh. The current conversation saves automatically if it has unsaved changes.
Export to Markdown: Download any conversation as a markdown file. The export strips citation syntax for clean reading while preserving the full conversation structure.
Artifacts System
Beyond conversations, NeoSignal AI can create and manage artifacts—saved configurations and results from tools:
Artifact Types: Stack compositions, memory calculations, TCO analyses, parallelism strategies, quantization recommendations, serving configurations, spot strategies, and component comparisons all save as artifacts.
Artifacts Tab: Switch to the Artifacts tab to browse saved items. Each artifact shows its type, title, and creation date. Click to load the artifact into its corresponding tool.
Cross-Tool Navigation: Artifacts link to their source tools. A saved memory calculation opens in the Memory Calculator with all inputs restored. A saved stack opens in the Stack Builder ready for further refinement.
Streaming Responses
NeoSignal AI Chat uses streaming responses for immediate feedback:
Real-Time Display: Responses appear token-by-token as they're generated. You see the AI's thinking develop rather than waiting for a complete response.
Typing Indicator: During generation, a subtle animation indicates the response is still streaming. The interface remains responsive—you can scroll through previous messages while waiting.
Error Handling: If generation fails, a clear error message appears with retry guidance. Credit exhaustion shows a specific prompt to add credits rather than a generic error.
Credit System
NeoSignal AI Chat operates on a credit system:
Credit-Based Access: Each message consumes credits based on response length and complexity. The system tracks usage and displays remaining credits.
Insufficient Credits: When credits run out, a friendly message appears explaining the situation with a direct link to add more credits. No cryptic errors—just clear guidance.
Free Tier: New users receive starter credits to explore the platform. Upgrade for additional capacity.
Mobile Experience
NeoSignal AI Chat adapts to mobile devices:
Overlay Panel: On mobile, the chat opens as a full-width overlay that slides in from the right. A backdrop dims the main content for focus.
Touch Targets: All interactive elements meet 44px minimum touch target size for comfortable mobile interaction. Buttons, tabs, and links are easily tappable.
Escape to Close: Press Escape (or tap the backdrop) to close the chat on mobile. On desktop, Escape returns from History/Artifacts tabs to Chat.
Integrated on Desktop: On large screens (lg breakpoint and above), the chat appears as an integrated side panel that doesn't overlay the main content. You can view signals and chat simultaneously.
Real-World Usage Patterns
Component Research: You're evaluating Claude 3.5 Sonnet for a new project. Navigate to its component page, open chat, and ask: "What are this model's strengths for code generation compared to alternatives?" The response references actual HumanEval scores, cites the component data, and suggests related models to consider.
Tool Guidance: You're using the Memory Calculator and the result shows your configuration exceeds GPU capacity. Ask the chat: "How can I fit this model on my available hardware?" The response considers your specific configuration, suggests enabling ZeRO-3 or activation checkpointing, and explains the tradeoffs with citations to relevant documentation.
Signal Analysis: The Signals feed shows several leader change events. Ask: "What do these benchmark updates mean for my current stack?" The chat synthesizes the signals into implications for your use case, citing specific signals and their confidence scores.
Stack Planning: You're building an inference stack and have questions about compatibility. Ask: "Will vLLM work well with this model on H100s?" The response checks NeoSignal's compatibility data, cites the relevant engine characteristics, and provides concrete deployment guidance.
Technical Architecture
NeoSignal AI Chat is built on a robust technical foundation:
React Query Integration: Chat state syncs through React Query for consistent caching and optimistic updates. Messages appear instantly while background saves complete.
Context Provider: A ChatContext provider manages pending prompts (for blog-triggered questions), blog context, and panel state across the application.
Tool Context Provider: A separate ToolContextProvider tracks active tool configurations. Tools register their state when results are computed, and the chat automatically includes this context.
Authentication Integration: Chat requires authentication. Unauthenticated users see a friendly login prompt explaining the chat's capabilities rather than a blank panel.
Framer Motion: Message animations use Framer Motion for smooth entry/exit transitions. New messages fade in with subtle upward motion.
The Grounded AI Approach
NeoSignal AI Chat embodies a philosophy: AI assistance should be grounded, contextual, and verifiable.
Grounded means every claim traces to a source. The AI doesn't speculate—it references NeoSignal's curated knowledge base and component data. When it says "Claude 3.5 Sonnet scores 95 on NeoSignal's rubric," that's a fact you can verify on the component page.
Contextual means the AI understands your environment. It knows what page you're on, what tool you're using, what configuration you've built. You don't waste time explaining context that's already visible in your browser.
Verifiable means you can check the work. Citations link to sources. Component references link to component pages. Tool references link to tool calculations. The AI shows its reasoning through traceable references.
This approach delivers expert-level guidance without the trust problems of unconstrained AI. You get the speed of AI assistance with the reliability of cited sources. Ask questions, get answers, verify claims, make decisions. NeoSignal AI Chat compresses the research cycle from hours to minutes.