LLM Training Dashboard

Open Web App

The LLM Training Dashboard is a fully integrated, AI‑augmented software‑engineering environment designed to make the inner workings of modern language models visible, explorable, and understandable, powering AI across industries with 3 Training Modes: Molecular AI, Scientific Graph & LLM Training — bridging symbolic reasoning and scientific discovery. At its foundation is the 12‑Level Transformer Interpretability Stack — a structured, multi‑layer analytical framework that reveals how models embed information, route attention, form concepts, and evolve reasoning across training. This stack is woven throughout the dashboard’s visual modules, telemetry systems, and training workflows, turning the traditionally opaque behaviour of transformers into something observable and intuitive. The dashboard transforms the entire lifecycle of LLM development — dataset preparation, training configuration, fine‑tuning execution, model conversion, deployment to Ollama, and interactive inference — into a guided, interactive, and highly visual workflow. Instead of requiring users to navigate complex command‑line tools or deep ML frameworks, the dashboard abstracts these processes into intuitive controls and dynamic visualisations that expose what the model is doing internally. Specialised modules such as 3D Graphs, Attention Explorer, and Neural Brain Journey map directly onto the interpretability stack, each illuminating different layers of transformer behaviour. Users can inspect attention patterns, token relationships, embedding structures, activation flows, and reasoning dynamics — gaining insight into how the model interprets input, distributes focus, and evolves during training. These tools make it possible to understand not just the outputs of the model, but the mechanisms that produce them. The dashboard integrates deeply with the Ollama ecosystem, providing one‑click workflows for converting fine‑tuned Hugging Face models into GGUF format, importing them into Ollama, and testing them locally. This end‑to‑end pipeline allows users to go from raw text data to a fully deployable local LLM without leaving the dashboard environment. Data‑quality analysis tools evaluate token counts, vocabulary coverage, sequence distribution, and training feasibility — ensuring that users understand the strengths and limitations of their datasets before fine‑tuning. Detailed telemetry and execution traces reveal how training behaves across epochs, how attention shifts, how concepts form, and how reasoning stabilises — all aligned with the interpretability stack. Beyond training and deployment, the dashboard serves as a research and educational platform. It provides structured guidance, transparent execution traces, and experiment‑comparison tools that help students, engineers, and researchers explore how LLMs behave under different conditions. The interface supports iterative experimentation, allowing users to adjust hyperparameters, compare runs, inspect model artefacts, and refine their training strategies with immediate feedback. As part of the broader LLMTraining.dev ecosystem, the dashboard acts as the central hub for model creation, experimentation, and deployment — bridging the gap between theoretical understanding and hands‑on engineering. It demonstrates the design and implementation of a complete LLM training and interpretability environment, revealing how models think, learn, and make decisions — and empowering users to shape those behaviours through guided, transparent workflows.

Machine & Deep Learning Console

Open Web App

The Machine & Deep Learning Console is a unified, browser‑native experimentation environment designed to make modern ML and DL workflows transparent, structured, and accessible. It consolidates dataset ingestion, model configuration, training execution, telemetry monitoring, interpretability tooling, and deployment‑ready export formats into a single cohesive interface. The console is built to support both classical machine‑learning pipelines and advanced deep‑learning architectures, including transformer‑based models and vision networks, enabling users to move fluidly between traditional statistical methods and state‑of‑the‑art neural systems. The platform organises training into three distinct phases — Core ML/DL, Classical ML, and Transformers + Vision — each providing specialised tooling and visualisation layers. Users can upload tabular datasets, text corpora, or labelled image folders, with the system automatically detecting the dataset type, validating structure, and routing it to compatible model families. This automated dataset intelligence allows seamless transitions between linear regression, random forests, neural networks, Vision Transformers (ViT), MicroLLaMA, MiniLLaMA, and other transformer‑based architectures. During training, the console provides real‑time analytical feedback through loss curves, accuracy trends, throughput metrics, GPU/CPU utilisation, VRAM/RAM consumption, inference latency, and epoch‑level telemetry. These insights allow users to observe how models learn, how performance evolves, and how computational resources are consumed across different architectures. The system also records complete run metadata, enabling reproducibility, cross‑phase comparison, and structured experiment tracking. For transformer‑based models, the console includes advanced interpretability tools such as ViT attention‑weight grids, token‑activation visualisations for MicroLLaMA and MiniLLaMA, and contextual weight inspection. These features expose the internal behaviour of transformer models, allowing users to explore how attention patterns form, how tokens influence predictions, and how model reasoning evolves across epochs. The environment supports deployment workflows through ONNX export, inference runners, and optional GGUF conversion for local execution via Ollama. Users can generate deployment scaffolds, run inference directly from the console, and inspect model outputs with full telemetry. This makes the system suitable not only for experimentation and learning, but also for practical integration into downstream applications.

DeepSpec MLX Dashboard

Open Web App

The DeepSpec MLX Dashboard is a modern, Apple‑Silicon‑optimised environment that reimagines speculative decoding through an interactive and visual interface. Built entirely on MLX, it transforms the foundational ideas introduced in DeepSeek’s original DeepSpec repository into a developer‑friendly experience that runs seamlessly on Mac hardware. Instead of requiring multi‑GPU setups or deep CUDA expertise, the dashboard makes speculative decoding accessible to researchers, developers, and students through a clean browser interface. At its core, the dashboard provides a complete ecosystem for exploring, benchmarking, and visualising speculative decoding. The Regeneration Console allows users to compare draft and target model outputs interactively, revealing latency, acceptance, and verification patterns that explain how acceleration emerges. The Micro‑Benchmark Runner offers fine‑grained performance analytics, displaying latency trends, acceptance timelines, and draft‑versus‑target deltas through advanced charts. The Draft Model Playground enables live speculative decoding runs with adjustable parameters such as temperature and token limits, while the Token Flow Visualiser animates the decoding process in real time, showing how tokens are accepted, rejected, or regenerated step by step. Complementing these interactive modules, the Target Cache Explorer provides a detailed view of cached samples, sequence lengths, and input IDs, helping users understand model caching behaviour. The Model Inspector lets users load and examine MLX or GGUF models—such as Qwen3‑0.6B—and view architecture details, runtime configuration, and device mode. Integrated GGUF Conversion Tools make it easy to convert MLX, PyTorch, or Hugging Face models into GGUF format for deployment, with quantization presets and direct download options. A built‑in Export JSON Utility allows users to extract runtime configurations, benchmark results, and regeneration data for external analysis. Throughout the environment, a live System Status panel refreshes every two seconds, displaying CPU, memory, and GPU metrics alongside device information such as platform, architecture, and Python environment. This transparency ensures that users can monitor host performance as decoding runs unfold. Every component of the DeepSpec MLX Dashboard is designed to bridge the gap between research and practical application. It turns speculative decoding from a low‑level algorithm into a hands‑on, visual experience—one that makes acceleration dynamics intuitive, measurable, and deeply informative. By combining the inspiration of DeepSeek’s original research with the flexibility of MLX and the efficiency of Apple Silicon, the dashboard stands as both a tribute to the pioneering work of DeepSpec and a forward‑looking tool for modern AI experimentation.

Prolog Lab

Open Web App

Prolog Lab is a browser‑resident Prolog development environment designed as an academically rigorous platform for the study and practice of logic programming. It embeds a full SWI‑Prolog engine directly within the client, enabling a self‑contained computational logic workspace that requires no installation, external servers, or network‑dependent execution. The system supports the complete expressive range of modern Prolog, including recursive definitions, DCGs, modules, attributed variables, and CLP(FD) finite‑domain constraint solving, thereby providing an authentic environment for both foundational teaching and advanced experimentation. A central aim of Prolog Lab is to render the operational semantics of logic programming empirically observable. Program execution can be examined at the level of unification, backtracking, choice‑point management, constraint propagation, and variable‑binding evolution. This makes the environment particularly well suited for academic contexts in which the behaviour of the Prolog resolution mechanism is itself an object of study. Constraint‑based programs benefit from explicit domain‑state visualisation and propagation traces, allowing students and researchers to analyse solver behaviour with a degree of transparency rarely available in conventional IDEs. The environment integrates a fully functional terminal that reproduces the behaviour of the standard SWI‑Prolog REPL. Users can interact with the engine through the canonical ?- interface, consult and reload program files, inspect predicate definitions, and employ Prolog’s native debugging facilities. This terminal operates in concert with the visual execution tools, enabling a hybrid workflow that combines traditional REPL‑based reasoning with instrumented semantic inspection. Prolog Lab also provides persistent workspaces in which program files, editor tabs, and query histories are automatically preserved across page refreshes and browser restarts. This persistence transforms the system from a transient teaching tool into a stable research environment capable of supporting extended experimentation, iterative model development, and long‑running coursework. Taken together, these features position Prolog Lab as a compact yet powerful laboratory for computational logic. It is suitable for undergraduate and postgraduate teaching, for research in symbolic AI and constraint reasoning, and for exploratory work in knowledge representation, search strategies, and declarative problem‑solving. By combining a modern web interface with a fully embedded Prolog engine and deep execution introspection, Prolog Lab provides a technically robust and pedagogically rich environment for studying the principles and practice of logic programming.

Logic Lab

Open Web App

Logic Lab is an interactive, browser‑based environment that provides a complete workspace for constructing, evaluating, and analysing formal logical expressions. The system supports both propositional and predicate logic, allowing users to enter formulas, define domains, specify predicates, and observe the resulting truth‑functional or quantifier‑based behaviour in real time. Logical expressions can be typed directly into the interface, where the system parses them, checks their structure, and evaluates them under all relevant assignments or interpretations. For propositional logic, Logic Lab generates full truth tables that display the behaviour of formulas across every possible valuation. Users can examine how connectives interact, how compound expressions reduce, and how logical consequence emerges from the relationships between propositions. The environment makes the evaluation process explicit by showing each intermediate step, enabling users to understand how formulas behave under different truth conditions. For predicate logic, the system allows users to define a domain and specify a predicate function, then evaluates universal and existential quantifiers over that domain. This makes it possible to test the validity of quantified statements, explore the effect of changing domains, and observe how predicate definitions influence logical outcomes. The interface provides immediate feedback, showing whether a quantified expression holds, fails, or depends on specific elements of the domain. Logic Lab also includes an integrated reference section that provides access to foundational topics such as quantifiers, satisfiability, models, Boolean algebra, execution order, recursion, and the relationship between propositional logic, predicate logic, and Prolog. Users can explore examples, revise core concepts, and connect theoretical material with practical evaluation tools. The environment supports iterative experimentation, allowing users to adjust formulas, modify domains, and test alternative interpretations without leaving the workspace. By combining expression parsing, truth‑table generation, quantifier evaluation, domain‑predicate testing, and integrated reference materials, Logic Lab offers a functional and transparent environment for learning, exploring, and applying formal logic. It provides a responsive and academically rigorous space in which logical structures become observable, manipulable, and open to systematic analysis.

Probabilistic Minesweeper

Open Web App

Probabilistic Minesweeper is an interactive demonstration of Bayesian reasoning applied to a grid‑based search problem. The app models an autonomous agent exploring a hidden‑mine environment and continuously updating its beliefs about the mine’s location based on sensory feedback. Each move produces one of three outcomes—boom, adjacent, or nothing—and the system immediately recalculates a posterior probability distribution over all grid cells. The interface visualises these updates as a dynamic probability heatmap that evolves in real time as the agent explores the board. Posterior Evolution provides a chronological record of every probability distribution generated during the run. Each frame captures the agent’s belief state at a specific step, allowing users to observe how the posterior sharpens, shifts, or collapses as new evidence arrives. This creates a complete inference timeline that can be replayed or exported for analysis. Probability History (Top Cells) highlights the most likely mine positions at each step. Instead of showing the full distribution, this view focuses on the top‑ranked cells and how their probabilities rise or fall over time. It offers a concise way to understand the agent’s decision pressure and how competing hypotheses evolve as the agent gathers more information. Tried Positions Array (x, y) records every location the agent has visited. Each entry shows the exact grid coordinates of the agent’s movement history, forming a trace of its exploration strategy. This array makes it easy to reconstruct the agent’s path, understand why certain cells were chosen, and analyse how the agent balances exploration with probabilistic reasoning. The Status Log provides a step‑by‑step narrative of the agent’s experience. It reports the sensed outcome at each position, the inferred directional likelihoods when an adjacent signal is detected, and the agent’s internal state transitions. The log acts as a readable explanation of the inference process, making the underlying reasoning transparent and accessible. The demo includes full playback controls. Users can pause the autonomous agent at any moment, step backwards to review earlier inference states, or step forwards to examine the next update in detail. This makes the system ideal for teaching, debugging, and exploring probabilistic reasoning at a granular level. Together, these features illustrate how Bayesian inference enables autonomous agents to navigate uncertainty, refine hypotheses, and progressively narrow down hidden variables in environments inspired by Minesweeper and Wumpus World. The app is designed for clarity, reproducibility, and educational value, offering a complete visual and analytical view of probabilistic decision‑making in action.

Visual Time Perception & Symbolic Reasoning

Open Web App

SymbolicReasoning.llmtraining.dev is an interactive research environment that transforms visual time perception into structured symbolic logic through continuous computation. It forms part of the LLMTraining.dev ecosystem and demonstrates how perceptual reasoning can be expressed as symbolic structure within an AI‑augmented development workflow. At the centre of the environment is the Symbolic Canvas Clock, a dynamic visual interface where each frame of an analog clock is captured, analysed, and converted into symbolic data. The system performs real‑time frame interpretation, detecting the hour, minute, and second hands through a colour‑mask segmentation pipeline supported by a Hough‑transform fallback. Every frame produces a detailed symbolic snapshot containing the interpreted time, geometric centre, hand angles, and a complete diagnostic trace of the reasoning process, including candidate angles, pixel counts, mask strength, fallback usage, and detection confidence.\n\nThe Live Processing Histogram visualises candidate angle distributions and mask diagnostics as they evolve over time, while the Symbolic Output Console presents the structured JSON produced for each frame. The Realtime Capture & Conversion Engine drives continuous symbolic reasoning, allowing the system to interpret geometric input without external metadata. Users can flip the clock orientation, capture snapshots, export symbolic logs, and inspect the diagnostic metrics that explain how each symbolic decision is made. Every conversion includes a pipeline summary, offering transparency into how the system interprets shape, angle, and spatial relationships. A major enhancement to the environment is the integrated Build Dataset feature, which transforms the continuous symbolic stream into a supervised training dataset suitable for LLM fine‑tuning. When activated, the system automatically generates multiple training samples for each frame, including natural‑language interpretations, next‑state predictions, anomaly analyses, debug‑metric explanations, and concise human‑readable descriptions. These samples appear in a dedicated scrollable dataset panel and can be exported as JSONL for direct use in the LLMTraining.dev fine‑tuning workflow. This addition turns SymbolicReasoning.llmtraining.dev into a complete end‑to‑end pipeline where perceptual input becomes symbolic structure and symbolic structure becomes training data for large language models. By bridging perception, logic, and model training, Symbolic Reasoning illustrates how meaning can emerge purely from geometry and computation. It stands as both a visual experiment and a cognitive model, showing how an AI system can learn to reason symbolically from the world it perceives.

ClawForgeAi

Open Web App

ClawForgeAI is an integrated, browser‑based orchestration environment designed to support the execution, coordination, and analysis of multi‑agent AI workflows. Developed as a robust and extensible platform, it provides a structured interface through which users can initiate autonomous tasks, monitor agent behaviour, and examine the internal reasoning processes that underpin complex computational activities. The system incorporates a modular runtime capable of managing concurrent agents, background jobs, scheduled tasks, and tool‑augmented reasoning, enabling users to observe how different components interact within a controlled and analytically transparent environment. At its core, ClawForgeAI offers a unified dashboard that exposes real‑time telemetry, including task progression, agent outputs, system logs, and performance metrics. This allows users to trace the lifecycle of an agent‑driven workflow from initial prompt construction through intermediate reasoning steps to final output generation. The platform supports the integration of custom tools and plugins, enabling the extension of agent capabilities and the incorporation of domain‑specific functionality. Its design makes the internal dynamics of agent‑based systems visible, providing insight into planning behaviour, tool invocation patterns, and the structural properties of multi‑step reasoning. ClawForgeAI is suited to both pedagogical and research‑oriented contexts, supporting experimentation with autonomous agents, evaluation of tool‑augmented LLM behaviour, and the study of orchestration strategies in AI‑assisted software engineering. Its architecture enables iterative refinement of workflows, allowing users to test hypotheses about agent coordination, compare execution traces, and analyse the reliability and interpretability of agent‑driven processes. By operating as a locally hosted, self‑contained system, ClawForgeAI ensures data privacy, computational independence, and consistent performance while providing an academically rigorous environment for exploring the methodological foundations of agent‑based AI systems. In doing so, it serves as a central analytical and operational component within the broader LLMTraining.dev ecosystem, bridging conceptual research with practical, observable execution.

Token Calc

Open Web App

Token Calc is a browser‑based analysis tool designed to measure how datasets behave when processed through model‑aligned tokenisation. It provides an environment where users can upload text, JSON, or JSONL files and immediately see how the content is segmented into tokens using a deterministic, locally executed tokeniser. JSON files are compacted automatically so that indentation and formatting do not inflate token counts, ensuring that the results reflect the actual semantic content of the dataset rather than its structure. Once a file is uploaded, Token Calc evaluates total token volume, average and maximum sequence lengths, and the distribution of tokens across all samples. These measurements reveal how large the dataset truly is, how variable its entries are, and whether its structure is appropriate for fine‑tuning or multi‑epoch training. Users can adjust the number of epochs and observe how token requirements scale, allowing them to estimate training workloads before committing computational resources. The system includes a local pricing catalog that maps token counts to estimated costs for providers such as OpenAI and Anthropic. Users can filter by provider, compare pricing structures, and understand how dataset size translates into financial cost for both training and inference. This makes Token Calc useful not only for technical evaluation but also for planning and budgeting. Token Calc supports iterative refinement by allowing users to upload multiple versions of a dataset and compare how preprocessing, cleaning, or restructuring affects tokenisation outcomes. This enables a methodical approach to dataset engineering, where each modification can be evaluated quantitatively before training begins. By combining deterministic tokenisation, cost modelling, dataset diagnostics, and a fully local execution model, Token Calc offers a precise and transparent environment for understanding the quantitative foundations of LLM training. It serves as a practical and academically rigorous component of the LLMTraining.dev ecosystem, connecting raw dataset structure with the computational and financial realities of modern language‑model development.

GLB Explorer

Open Web App

GLB Explorer is a browser‑based environment for loading, inspecting, and analysing GLB and GLTF models with no installation or setup. The system opens 3D assets directly in the browser and renders them in real time using WebGL and Three.js, providing a smooth viewing experience with HDR lighting, PBR materials, and intuitive orbit controls that make navigation natural and responsive. Once a model is loaded, the viewer exposes its internal structure, allowing users to examine meshes, nodes, transforms, materials, and animations exactly as they exist in the source file. The interface is designed to make the anatomy of a 3D asset visible. Users can isolate individual meshes, toggle their visibility, inspect geometry and metadata, and explore the hierarchy of nodes that define how the model is constructed. Animation playback is fully interactive, with timeline control that allows users to pause, scrub, and analyse motion sequences frame by frame. This makes the tool suitable not only for viewing but also for debugging and understanding how assets behave inside larger pipelines. GLB Explorer supports rapid iteration by providing immediate feedback as models are loaded or adjusted. It is capable of handling complex assets from game engines, procedural generators, and AI‑driven creation tools, making it useful across a wide range of workflows. The viewer emphasises clarity and precision, presenting the internal components of a model in a way that helps users diagnose issues, verify structure, and confirm that materials and animations have been exported correctly. The application is built through an AI‑augmented development workflow that combines human design with machine‑assisted refinement, resulting in a lightweight, responsive, and minimal codebase. It forms part of the broader LLMTraining.dev ecosystem, where each tool focuses on providing a specialised, high‑quality environment for technical exploration. Within this ecosystem, GLB Explorer serves as a dedicated space for understanding and validating 3D assets, turning the browser into a capable and informative inspection tool for artists, developers, and researchers working with modern 3D pipelines.

Venn Diagram Generator

Open Web App

The Venn Diagram Generator is an interactive, browser‑based environment designed to support the construction, analysis, and visual exploration of set‑theoretic relationships. Developed as a lightweight yet methodologically robust tool, it provides a structured interface in which users can define sets, specify their elements, and observe the resulting intersections, unions, and differences through dynamically rendered diagrams. The system integrates a real‑time computation engine that processes set operations as the user edits inputs, ensuring that the visual representation remains consistent with the underlying mathematical structure. At the core of the environment is a responsive diagramming module that generates two‑set and three‑set Venn diagrams with precise geometric alignment, enabling users to examine the spatial and logical relationships between sets in a clear and analytically meaningful manner. The platform supports the import and modification of set data, the visualisation of exclusive and shared regions, and the automatic computation of intersections and complements. Its rendering engine is designed to make abstract set‑theoretic concepts observable, allowing users to explore the behaviour of overlapping sets, cardinalities, and region‑specific membership with immediate visual feedback. The Venn Diagram Generator is suitable for both introductory and advanced engagement with set theory, supporting pedagogical use, exploratory analysis, and the development of formal reasoning skills. Its design encourages iterative experimentation, enabling users to refine set definitions, test hypotheses about relationships between data groups, and analyse the structural properties of sets in a controlled and transparent environment. By operating as a locally hosted, self‑contained system, the tool provides a responsive and privacy‑preserving workspace that integrates mathematical precision with the accessibility of a browser‑native interface, making it a valuable resource for teaching, research, and practical work in foundational mathematics, logic, and data classification.

Franbo Cube

Open Web App

Franbo Cube is a precision‑engineered WebGL environment where motion, geometry, and data coexist in real time. It transforms video and audio into a responsive 3D object whose every rotation, shader update, and frame render is tracked through a live telemetry engine. The cube’s internal mathematics — rotation matrices, spin vectors, and quaternion interpolation — are continuously exposed, allowing users to observe the computational rhythm behind its motion. At its core, Franbo Cube operates as a self‑diagnosing system. The telemetry layer captures frame timing, render latency, and GPU throughput, translating them into visual feedback that mirrors the cube’s physical state. Adjustments to material, brightness, or playback mode trigger immediate recalculations across the cube’s geometry pipeline. Each change propagates through the transformation stack — from vertex rotation to camera projection — updating angular velocity and quaternion state in real time. The result is a transparent feedback loop where motion and mathematics evolve together. The cube’s mathematical foundation is built on quaternion‑based rotation, ensuring smooth interpolation and avoiding gimbal lock during continuous spin. Camera matrices are recalculated dynamically to maintain spatial coherence, while telemetry panels display live data streams of frame rate, delta time, and GPU load. This architecture turns the cube into both a visual sculpture and a diagnostic instrument — a system that reveals its own physics as it performs. Franbo Cube demonstrates how WebGL can serve as both an artistic and analytical medium. Beneath its cinematic surface lies a telemetry engine that measures, visualises, and explains its own behaviour. It invites users to explore the relationship between adjustment and computation, aesthetics and analytics — proving that beauty and data can share the same frame, rendered with mathematical precision and real‑time transparency.