Why We Built NeoSignal

NeoSignal is on a mission to accelerate frontier technology diffusion. We democratize tools and techniques used by frontier technology labs. Several frontier technologies are converging—AI, space, robotics, and autonomy—reshaping scientific domains from drug discovery to materials science to neuroscience. Behind them all sits a shared but complexly intertwined infrastructure: accelerators, models, frameworks, agents, and cloud. NeoSignal publishes signals across frontier technologies and builds high velocity decision making tools.

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Why You Want to Use NeoSignal

In the frontier technology era the returns from speed of adoption are disproportionate. Four factors compound as tailwinds for AI-native teams over laggards:

The productivity gap between AI-native and lagging teams runs 1.2× to 1.7× on well-scoped tasks. Inference costs fall 10× to 100× per year depending on the capability. Hardware price-performance improves roughly 30% annually with continued efficiency gains. Training costs at the frontier rise 2–3× per year, raising the barrier for late adopters.

The longer you wait, the harder it gets to catch up. NeoSignal exists to compress the time between "I should understand this" and "I'm building with it."

The Problem: Frontier Tech Moves Too Fast

Several frontier technologies are converging like AI, space, robotics, and autonomy, reshaping scientific domains from drug discovery to materials science to neuroscience. Behind them all sits a shared infrastructure: accelerators (GPU, TPU, LPU), models (LLMs, SLMs, multimodal), frameworks (orchestration, ML, agentic), agents (autonomous systems, copilots), and cloud (hyperscalers, neoclouds).

Keeping up with any one of these categories is a full-time job. Keeping up with how they interact is nearly impossible. Which models run efficiently on which accelerators? Which frameworks support which agents? Which cloud providers offer what hardware? The permutation space is vast and constantly shifting.

The Five Primitives

NeoSignal tracks five fundamental categories of frontier tech stack components. Each represents a critical layer in the AI infrastructure stack.

Models are the reasoning engines—LLMs, SLMs, multimodal models, and specialized architectures. We track performance metrics like ELO ratings from LMArena, context window sizes, output token throughput, and provider ecosystem. Claude, GPT-5, Llama, Gemini, Qwen—each gets scored across dimensions that matter for production deployment.

Accelerators are the silicon that makes inference and training possible. NVIDIA dominates with H100 and H200, but the landscape includes AMD's MI300X, Google's TPUs, and emerging players like Cerebras and Groq. We track memory capacity, TFLOPS, architecture generation, and power efficiency. When a new Blackwell chip ships, NeoSignal captures its specs and compatibility profile.

Cloud providers offer the infrastructure runway. AWS, GCP, Azure are the hyperscalers, but neoclouds like CoreWeave, Lambda Labs, and RunPod are reshaping GPU access economics. We track GPU availability, region coverage, and pricing tiers. The difference between "available" and "3-month waitlist" matters when you're shipping.

Frameworks are the software layer that orchestrates everything. vLLM for inference, DeepSpeed for distributed training, LangChain for agentic workflows, PyTorch for the foundational AI stack. We track GitHub stars, weekly downloads, community size, and release velocity. Framework choice determines your deployment flexibility.

Agents are the newest primitive—autonomous AI systems capable of multi-step reasoning, tool use, and task execution. Claude Code, Cursor, Aider for coding. CrewAI and AutoGen for multi-agent orchestration. GPT Researcher for autonomous research. Browser-use for web automation. We score agents across planning ability, tool use proficiency, memory management, self-reflection capability, and adoption metrics. This category is evolving fastest, and NeoSignal captures the signal in the noise.

Agent Stack Cards

The Agents category page shows Stack Cards for each tracked agent. Here you see coding agents dominating the top spots—Claude Code at 92, Cursor at 90, Cline at 88—alongside research tools like Perplexity and NotebookLM. Each card displays the agent's score, provider, use case type, and a trend indicator. Hovering over Cursor reveals its model compatibility: it works with Claude Opus, Claude Sonnet, and GPT-5.1. The grid layout enables quick visual comparison across the entire agent landscape, letting you identify which tools are gaining traction and which models power them.

Stack Cards: Baseball Cards for AI Components

Every component in NeoSignal gets a "Stack Card"—a standardized view inspired by sports cards and prediction markets. Each card displays:

A 0-100 overall score computed from category-specific rubrics. A score breakdown showing how performance, adoption, ecosystem, and other dimensions contribute. Category-specific metrics: ELO and context window for models, memory GB and TFLOPS for accelerators, GitHub stars and downloads for frameworks. A trend indicator showing whether the component is rising, stable, or declining. Compatibility ratings showing how well it works with components in other categories.

The card format makes complex technology approachable. You can compare Claude 3.5 Sonnet against GPT-4o in seconds. You can see that H100-80GB has higher memory than A100-40GB at a glance. The scores aren't opinion—they're computed from authoritative data sources with transparent methodology.

Cursor Agent Metrics

Click any Stack Card to see the full component detail. This view of Cursor shows everything that drives its 90 score. The Metrics panel displays license type, provider (Anysphere), agent type (coding), base models (claude-3.5-sonnet, gpt-4o, cursor-small), and tool integration count (15). The Score Breakdown reveals dimensional contributions: Adoption leads at 92, Tool Use at 94, Memory Context at 92, Self Reflection at 85, Planning Reasoning at 86. The Compatibility section shows which models work best with this agent—Claude Opus (96), Claude Sonnet (95), GPT-5.1 (92), LangChain (75). Sources link to official documentation. The Related Signals section surfaces recent market intelligence mentioning this component, connecting static component data to dynamic market movements.

Signal Intelligence: 11 Types of Market Movement

NeoSignal surfaces market signals across eleven categories. Each signal type represents a specific kind of actionable information.

Leader Change captures when rankings shift. When Claude overtakes GPT-4 on LMArena, that's a leader change signal. When a new framework hits #1 in weekly downloads, that's captured.

Trend Shift identifies directional changes in adoption or performance. Rising interest in local LLMs. Declining usage of a deprecated framework. These aren't one-time events—they're patterns emerging from data.

Emerging Player spots newcomers achieving significant traction. A new model enters the top 10. A startup's inference engine gains adoption. Early detection of emerging players is competitive intelligence.

Compatibility Alert warns about integration issues. Framework X dropped support for model Y. This GPU doesn't support that quantization method. Compatibility changes can break production systems.

Price Change tracks cost movements across the stack. API pricing updates from OpenAI and Anthropic. Cloud provider rate adjustments. GPU rental cost fluctuations. Cost changes directly impact build-vs-buy decisions.

Benchmark Update captures new performance data. MMLU scores. HumanEval results. MLPerf numbers. Fresh benchmark data updates component rankings.

Adoption Milestone marks significant usage thresholds. Framework crosses 1 million weekly downloads. Model hits 10 billion tokens processed. Adoption velocity indicates ecosystem momentum.

Availability Change signals access changes. GPU now available without waitlist. Model released to general availability. API endpoints added to new regions.

Deprecation Warning provides advance notice of sunsetting. Old model version end-of-life dates. Framework dropping Python version support. Deprecation awareness prevents production surprises.

Security Advisory highlights vulnerabilities and patches. CVEs affecting inference servers. Security fixes in model serving frameworks. Security signals demand immediate attention.

Partnership Announcement captures ecosystem deals. Cloud provider securing exclusive GPU supply. Framework reaching official integration status. Partnerships reshape competitive dynamics.

Each signal carries a confidence score computed across five dimensions: source authority (30%), data quality (25%), recency (20%), corroboration (15%), and specificity (10%). A signal from SemiAnalysis with specific numbers, recent data, and multiple confirming sources scores higher than a vague rumor from social media.

Signals and Chat

The Signals page combines real-time market intelligence with AI-powered research. The main feed displays signals filtered by type—Leader, Trend, Compatibility, Emerging, Price, Benchmark, Adoption, Availability, Deprecation, Security, Partnership—with confidence scores and time stamps. Each signal card shows the signal type badge, affected category, and a summary of the market movement. The chat panel on the right demonstrates contextual research: asking "What are the latest AI market signals?" returns a synthesis of recent developments—frontier model competition intensifying with DeepSeek and Gemini 3 Pro challenging established players, GPT-o1s showing momentum at 1268 ELO, accelerator evolution continuing with NVIDIA B200 and specialized inference chips. The response includes clickable citations to source signals and components.

Tools: Calculators and Advisors for AI Infrastructure

NeoSignal includes a suite of seven tools for AI infrastructure planning. These aren't generic calculators—they're informed by NeoSignal's component data and designed for real deployment scenarios.

Memory Calculator estimates GPU memory requirements for training transformer models. Input your model parameters, batch size, sequence length, precision, and parallelism strategy. The calculator computes memory breakdown across parameters, gradients, optimizer states, and activations. It applies ZeRO stages, activation checkpointing, and offloading configurations to show effective memory per GPU. Output includes whether your config fits on target hardware and specific GPU recommendations.

TCO Calculator compares total cost of ownership between API providers and self-hosted infrastructure. Input your monthly request volume, average token counts, and model size. The calculator computes monthly costs for Anthropic, OpenAI, and self-hosted options. It generates break-even analysis showing when self-hosting becomes cost-effective. The tool draws on NeoSignal's cloud provider pricing data for accurate estimates.

Parallelism Advisor recommends optimal tensor, pipeline, and data parallelism configurations. Input your model architecture, GPU type, and available GPU count. The advisor calculates memory distribution across parallelism dimensions and generates ready-to-use DeepSpeed ZeRO and FSDP configuration snippets. It estimates communication overhead and efficiency metrics for each strategy option.

Quantization Advisor recommends quantization methods based on your deployment target. Input model, hardware (GPU/CPU/edge), quality priority, and serving engine. The advisor scores quantization methods—INT8, FP8, GPTQ, AWQ, GGUF—against your requirements. Output includes memory savings estimates, quality impact predictions, and serving engine configuration snippets.

Serving Engine Advisor recommends inference engines based on latency and throughput requirements. Input your model, latency target, throughput target, and GPU configuration. The advisor compares vLLM, TensorRT-LLM, SGLang, and llama.cpp against your requirements. Output includes predicted performance, batching configuration, deployment manifests, and autoscaling guidance.

Spot Instance Advisor recommends spot instance strategies with savings estimates. Input workload type, interruption tolerance, cloud providers, and GPU requirements. The advisor analyzes spot pricing across regions, estimates savings versus on-demand, and recommends checkpointing strategies. Output includes expected interruption rates, recommended fallback mechanisms, and risk assessment.

Component Browser provides advanced filtering and side-by-side comparison of NeoSignal components. Multi-select filters by category, score range, and compatibility. Side-by-side comparison of up to four components with metrics matrix. Export comparisons as artifacts for team sharing.

All tools run client-side for instant feedback. Calculations complete in under 500ms. Model and GPU configurations are pre-populated from NeoSignal's component database, ensuring consistency between tools and component data.

Stack Builder: Compose Compatible Stacks

The Stack Builder lets you compose a full AI stack by selecting components across categories. Pick a model, accelerator, cloud provider, framework, and agent. As you build, the system computes real-time compatibility scores.

Select Claude 3.5 Sonnet and the builder shows which accelerators it runs efficiently on, which frameworks support it, and which cloud providers offer the right infrastructure. Select H100 and see which models are optimized for Hopper architecture. The compatibility matrix visualizes pairwise relationships across your entire stack.

High compatibility (above 80) displays green. Medium compatibility (50-80) shows yellow. Low compatibility (below 50) flags red with explanation. Conflicts surface before you commit to infrastructure decisions.

The builder isn't just for planning—it's for validation. Before signing a cloud contract or committing to a framework, verify your stack components work together. Catch incompatibilities in the builder instead of discovering them in production.

Chat: Questions with Knowledge Grounding

NeoSignal's chat isn't a generic LLM wrapper. It's grounded in NeoSignal's curated knowledge base—research papers, benchmarks, official documentation, industry reports, and component data.

When you ask "What's the best model for code generation?", the response draws on current LMArena rankings, HumanEval benchmarks, and NeoSignal's component scoring. When you ask about GPU memory for Llama 70B training, the chat references the same calculations as the Memory Calculator tool.

The chat understands context. On a component detail page, it knows which component you're viewing. On a tool page, it has access to your current configuration and results. Ask "Why did I get this recommendation?" and the chat explains based on your inputs.

Responses include citations linking to source knowledge files, external URLs, or component cards. Citations are rendered as clickable pills that jump to the source. No hallucinated URLs—every citation is traceable to NeoSignal's knowledge graph.

Conversations persist. Save a conversation and return to it later. Export to markdown for documentation. The chat history becomes a searchable knowledge artifact.

Knowledge Graph: Curated Intelligence

Behind NeoSignal's signals, chat, and recommendations sits a curated knowledge graph. We don't just aggregate data—we meticulously process it into structured intelligence.

The process starts with source curation. We deep research industry analyst reports from SemiAnalysis, ThoughtWorks Technology Radar, Menlo Ventures AI Index. We track popular leaderboards like LMArena, OpenRouter rankings, and HuggingFace metrics. We monitor AI registries and ecosystem trackers.

Each source undergoes quality assessment. We score source authority—tier 1 sources like primary benchmark data rank higher than tier 5 speculation. We assess data quality—quantitative benchmarks over qualitative impressions. We factor recency—data from the last 7 days scores higher than 6-month-old reports.

From processed sources, we extract relevant AI signals and stack components. We score these individually and relative to existing database entries. We identify inter-relationships—which signals relate to which components, which components are compatible with each other.

The knowledge graph grounds NeoSignal's AI chat, ensuring responses are current, accurate, and traceable. It powers signal generation, automatically detecting market movements from knowledge updates. It informs component scoring, keeping rankings fresh as new benchmark data arrives.

All knowledge files follow a consistent format: markdown with YAML frontmatter containing source metadata, topic classification, and version tracking. The format is LLM-optimized—easy to embed and retrieve, easy for language models to consume and cite.

Access: Credits-Based Model

NeoSignal runs on a credits-based access model. Browsing components, signals, and using tools is free. Chat interactions consume credits.

Free tier provides 10 credits monthly. Enough to explore the platform and verify value. Credits reset each month—no rollover, no complexity.

Premium tier at $19/month provides 300 credits. That's $0.063 per interaction—37% savings versus pay-as-you-go. Premium suits individual practitioners with regular research needs.

Pro tier at $199/month provides 4,000 credits. That's $0.05 per interaction—50% savings. Pro suits teams and power users with intensive workflow integration.

Pay-as-you-go at $0.10 per credit offers flexibility without subscription commitment. Minimum purchase is 50 credits ($5).

One credit equals one chat message. The pricing reflects real AI inference costs—NeoSignal uses frontier models for response quality. Subscription credits reset monthly; purchased credits expire after 12 months.

How It's Built

NeoSignal's architecture prioritizes speed and reliability. Next.js 15 with React 19 provides the frontend foundation—Server Components for initial load performance, client-side React Query for interaction responsiveness.

Supabase handles data persistence with PostgreSQL and Row Level Security. The component, signal, and rubric data models are designed for efficient querying across filtering, search, and compatibility lookups.

OpenRouter powers the chat backend, providing access to frontier models with fallback options. The RAG pipeline embeds knowledge chunks and retrieves relevant context for each conversation.

Stripe handles payment processing. Webhooks synchronize subscription state. Database transactions ensure credit operations are atomic—no race conditions, no negative balances.

The codebase follows strict principles: avoid overengineering, reuse existing abstractions, minimize complexity. A bug fix doesn't need surrounding code cleaned up. A simple feature doesn't need extra configurability. The right amount of complexity is the minimum needed for the current task.

What's Next

The frontier keeps moving. NeoSignal will too.

Expanded agent tracking as autonomous systems mature. The agents category is the newest and fastest-evolving primitive. We're adding coding agents, research agents, browser automation agents, and multi-agent orchestration frameworks as they emerge and prove traction.

Deeper benchmark integration with sources like LMArena, OpenRouter, and HuggingFace. Automated data pipelines will surface benchmark updates as signals within hours of publication.

Community features for sharing stack configurations and tool results. Save your Memory Calculator config and share with your team. Export Stack Builder compositions for architecture documentation.

Additional tool categories covering training optimization, deployment automation, and cost monitoring. The tools suite grows based on practitioner needs.

But the core mission stays fixed: compress the time between signal and action. In an era where the productivity gap between fast and slow adopters keeps widening, that time compression is worth more every day.

NeoSignal exists because frontier technology diffusion shouldn't require a full-time dedicated research function. Because stack decisions shouldn't be based on outdated blog posts. Because compatibility issues shouldn't surface in production.

The infrastructure landscape moves at AI speed now. NeoSignal moves with it.