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23 days ago
Post
2949
Claude Code Self & Continual Learning
Hey everyone! š
30 GitHub Stars in 4 Days - Thank You!
I'm really grateful for the positive response to the Claude Reflect System. In just 4 days, 30 developers have shown interest by starring the project. Thank you so much!
What Is Claude Reflect?
Correct once, never again. Claude Reflect helps Claude Code remember your corrections and preferences across sessions. Instead of repeating the same feedback, the system learns and applies it automatically.
Main Features:
š§ Learning System
- Detects corrections and preferences from conversations
- Stores them permanently in skill files
- Applies learnings in future sessions
š Safety First
- Automatic backups before changes
- YAML validation
- Git version control
ā” Two Modes
- Manual: Run /reflect when you want
- Auto: Reflects automatically at session end
How It Works
If you correct Claude to use pytest instead of unittest, this preference gets saved. Next time, Claude will remember and use pytest automatically. It's that simple.
Getting Started
1. Clone the repository
2. Install dependencies
3. Activate the skill
4. Try it out!
The python-project-creator example shows how the system learns from your feedback.
Give It a Try
https://github.com/haddock-development/claude-reflect-system
Feel free to check it out, give feedback, or contribute. Every bit of input helps improve the project!
Thank you so much for your support!
---
#ClaudeCode #AI #MachineLearning #ContinualLearning #OpenSource #Developer #Coding #Python #Productivity #DevTools #GitHub #SoftwareDevelopment #Programming #AIAssistant #DeveloperTools #CodeQuality #Tech
Feel free to give it a try by yourself.
https://github.com/haddock-development/claude-reflect-system
Hey everyone! š
30 GitHub Stars in 4 Days - Thank You!
I'm really grateful for the positive response to the Claude Reflect System. In just 4 days, 30 developers have shown interest by starring the project. Thank you so much!
What Is Claude Reflect?
Correct once, never again. Claude Reflect helps Claude Code remember your corrections and preferences across sessions. Instead of repeating the same feedback, the system learns and applies it automatically.
Main Features:
š§ Learning System
- Detects corrections and preferences from conversations
- Stores them permanently in skill files
- Applies learnings in future sessions
š Safety First
- Automatic backups before changes
- YAML validation
- Git version control
ā” Two Modes
- Manual: Run /reflect when you want
- Auto: Reflects automatically at session end
How It Works
If you correct Claude to use pytest instead of unittest, this preference gets saved. Next time, Claude will remember and use pytest automatically. It's that simple.
Getting Started
1. Clone the repository
2. Install dependencies
3. Activate the skill
4. Try it out!
The python-project-creator example shows how the system learns from your feedback.
Give It a Try
https://github.com/haddock-development/claude-reflect-system
Feel free to check it out, give feedback, or contribute. Every bit of input helps improve the project!
Thank you so much for your support!
---
#ClaudeCode #AI #MachineLearning #ContinualLearning #OpenSource #Developer #Coding #Python #Productivity #DevTools #GitHub #SoftwareDevelopment #Programming #AIAssistant #DeveloperTools #CodeQuality #Tech
Feel free to give it a try by yourself.
https://github.com/haddock-development/claude-reflect-system
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- 2 replies
Post
1759
Scaling Physical AI: SAM 3D, NVIDIA Cosmos, and Unreal Engine!
The "Sim-to-Real" gap is officially history. In early 2026, we are no longer just rendering data; we are simulating reality. By bridging Metaās SAM 3D, Unreal Engine, and the NVIDIA Cosmos suite, weāve built an autonomous pipeline for Physical AI that evolves itself.
The 2026 Tech Stack:
SAM 3D: Generates high-fidelity digital twins from 2D photos in seconds.
Unreal Engine + MCP: The AI "Director" orchestrates environments via the Model Context Protocol, providing perfect Ground Truth.
NeMo Data Designer: The orchestration hub on GitHub. Following NVIDIAās acquisition of Gretel in early 2025, its leading generative privacy and tabular tech are now fully integrated here.
NVIDIA Cosmos Transfer: Neural rendering that adds hyper-realism to Unreal Engine outputs.
NVIDIA Cosmos Predict: Predicts physically accurate motion (falling, sliding) without manual animation.
NVIDIA Cosmos Reason: The automated supervisor checking every frame for logical and physical consistency.
The Workflow:
Asset Capture: SAM 3D turns real-world photos into Nanite meshes for Unreal Engine.
Orchestration: NeMo Data Designer (with Gretel-powered integrity) defines the data schema, while AI builds the world in Unreal Engine.
Completion: NVIDIA Cosmos (Transfer & Predict) adds photorealism and physics, while NVIDIA Cosmos Reason guarantees quality.
By combining Gretelās data heritage with the visual power of Unreal Engine, we generate 100,000 perfect frames per hour. Weights and tools are on Hugging Face. Stop labeling. Start simulating.
#PhysicalAI #SAM3D #NVIDIACosmos #UnrealEngine #NeMo #Gretel #SyntheticData #HuggingFace #Robotics #AI #ComputerVision
The "Sim-to-Real" gap is officially history. In early 2026, we are no longer just rendering data; we are simulating reality. By bridging Metaās SAM 3D, Unreal Engine, and the NVIDIA Cosmos suite, weāve built an autonomous pipeline for Physical AI that evolves itself.
The 2026 Tech Stack:
SAM 3D: Generates high-fidelity digital twins from 2D photos in seconds.
Unreal Engine + MCP: The AI "Director" orchestrates environments via the Model Context Protocol, providing perfect Ground Truth.
NeMo Data Designer: The orchestration hub on GitHub. Following NVIDIAās acquisition of Gretel in early 2025, its leading generative privacy and tabular tech are now fully integrated here.
NVIDIA Cosmos Transfer: Neural rendering that adds hyper-realism to Unreal Engine outputs.
NVIDIA Cosmos Predict: Predicts physically accurate motion (falling, sliding) without manual animation.
NVIDIA Cosmos Reason: The automated supervisor checking every frame for logical and physical consistency.
The Workflow:
Asset Capture: SAM 3D turns real-world photos into Nanite meshes for Unreal Engine.
Orchestration: NeMo Data Designer (with Gretel-powered integrity) defines the data schema, while AI builds the world in Unreal Engine.
Completion: NVIDIA Cosmos (Transfer & Predict) adds photorealism and physics, while NVIDIA Cosmos Reason guarantees quality.
By combining Gretelās data heritage with the visual power of Unreal Engine, we generate 100,000 perfect frames per hour. Weights and tools are on Hugging Face. Stop labeling. Start simulating.
#PhysicalAI #SAM3D #NVIDIACosmos #UnrealEngine #NeMo #Gretel #SyntheticData #HuggingFace #Robotics #AI #ComputerVision
Post
2054
Skill Reflect: A Concept for Automated AI Skill Mastery
Letās be real for a second: most of us are using AI all wrong. We send a prompt, get a "meh" answer, and then spend twenty minutes fixing it ourselves. Thatās not a workflow; thatās just a digital chore. I wanted to see if I could push Claude furtherāto see if I could build a system that actually learns and refines itself. Thatās how the Claude-Reflect-System (Skill Reflect) was born.
But hereās the thing: this isnāt some polished, final product. Itās a concept. Itās a blueprint. Iāve built the foundation of a recursive reflection loop that forces the AI to step back, look at its work, and act as its own harshest critic. It identifies the "skill delta"āthe gap between "okay" and "mastery"āand closes it. This logic isn't just for Claude; you can grab this architecture and drop it right into codex-cli, terminal agents, or whatever stack you're building.
Iām a big believer in the law of causality. Action, reaction. Cause and effect. If you control the causeāthe way the AI thinks about its mistakesāyou dictate the effect: a perfected skill. This is a playground for builders who are tired of stochastic guessing. I want you to take this. Fork it. Break it. Make it better. This is an open invitation to the community to take this reflection loop and see how far we can push the boundaries of agentic reasoning. Whether you're building Claude Code plugins or just want to automate your self-learning, the code is there for you to smash. Stop accepting the first draft. Letās build something that actually thinks.
https://github.com/haddock-development/claude-reflect-system
#Skills #ClaudeCode #ClaudeCodeSkills #ClaudeCodePlugins #ClaudeCodeMarketplace #CodexCLI #AI #SelfLearning #Automation #OpenSource #LLM #Reasoning #Causality #Matrix #Concept
Letās be real for a second: most of us are using AI all wrong. We send a prompt, get a "meh" answer, and then spend twenty minutes fixing it ourselves. Thatās not a workflow; thatās just a digital chore. I wanted to see if I could push Claude furtherāto see if I could build a system that actually learns and refines itself. Thatās how the Claude-Reflect-System (Skill Reflect) was born.
But hereās the thing: this isnāt some polished, final product. Itās a concept. Itās a blueprint. Iāve built the foundation of a recursive reflection loop that forces the AI to step back, look at its work, and act as its own harshest critic. It identifies the "skill delta"āthe gap between "okay" and "mastery"āand closes it. This logic isn't just for Claude; you can grab this architecture and drop it right into codex-cli, terminal agents, or whatever stack you're building.
Iām a big believer in the law of causality. Action, reaction. Cause and effect. If you control the causeāthe way the AI thinks about its mistakesāyou dictate the effect: a perfected skill. This is a playground for builders who are tired of stochastic guessing. I want you to take this. Fork it. Break it. Make it better. This is an open invitation to the community to take this reflection loop and see how far we can push the boundaries of agentic reasoning. Whether you're building Claude Code plugins or just want to automate your self-learning, the code is there for you to smash. Stop accepting the first draft. Letās build something that actually thinks.
https://github.com/haddock-development/claude-reflect-system
#Skills #ClaudeCode #ClaudeCodeSkills #ClaudeCodePlugins #ClaudeCodeMarketplace #CodexCLI #AI #SelfLearning #Automation #OpenSource #LLM #Reasoning #Causality #Matrix #Concept
Post
1836
Neural Traffic Control: Orchestrating Multi-Path Reasoning š„
The future of AI isn't just about "better" modelsāitās about high-precision orchestration. We are moving from linear processing to Parallel MTP-Reasoning, where we manage neural traffic across stabilized, transparent, and recursive highways.
1ļøā£ The Backbone: Stabilized High-Dimensional Routing (arXiv:2512.24880) Using DeepSeekās mHC (Manifold-Constrained Hyper-Connections), we solve the instability of deep MoE architectures. By projecting weight updates onto the Birkhoff Polytope, we ensure that our "Simpsons-style" expert lanes maintain mathematical identity. This is the hardware-level stability needed to run multiple reasoning paths without collapse.
2ļøā£ The Vision: Gemma Scope 2 & Feature Steering You can't steer what you can't see. Gemma Scope 2 provides the "X-ray" for our highways. By using Sparse Autoencoders (SAEs), our Meta-Controller identifies the active features in each expert lane. We don't just route data; we route intent by monitoring feature-drift in real-time.
3ļøā£ The Logic: Recursive Open Meta-Agents (arXiv:2512.24601) We integrate the ROMA (Recursive Open Meta-Agent) framework. Instead of a flat response, the model operates in a recursive loop, refining its internal state before any output occurs. This is the "brain" of our [Meta-Controller GitHub Repo], enabling the model to simulate and discard weak logic internally.
4ļøā£ The Simulation: Parallel MTP-Reasoning This is where it comes together: Multi-Token Prediction (MTP) meets Parallel Simulation. Our Python-driven controller runs three parallel Gemma 3 instances.
The Process: 3 paths generated simultaneously.
The Filter: A 500-token lookahead window.
The Decision: The Meta-Controller uses SAE-data from Gemma Scope to select the path with the highest logical fidelity.
The Result: A self-correcting, transparent, and multi-threaded reasoning engine. We aren't just scaling parameters; we are scaling architectural precision. š§
The future of AI isn't just about "better" modelsāitās about high-precision orchestration. We are moving from linear processing to Parallel MTP-Reasoning, where we manage neural traffic across stabilized, transparent, and recursive highways.
1ļøā£ The Backbone: Stabilized High-Dimensional Routing (arXiv:2512.24880) Using DeepSeekās mHC (Manifold-Constrained Hyper-Connections), we solve the instability of deep MoE architectures. By projecting weight updates onto the Birkhoff Polytope, we ensure that our "Simpsons-style" expert lanes maintain mathematical identity. This is the hardware-level stability needed to run multiple reasoning paths without collapse.
2ļøā£ The Vision: Gemma Scope 2 & Feature Steering You can't steer what you can't see. Gemma Scope 2 provides the "X-ray" for our highways. By using Sparse Autoencoders (SAEs), our Meta-Controller identifies the active features in each expert lane. We don't just route data; we route intent by monitoring feature-drift in real-time.
3ļøā£ The Logic: Recursive Open Meta-Agents (arXiv:2512.24601) We integrate the ROMA (Recursive Open Meta-Agent) framework. Instead of a flat response, the model operates in a recursive loop, refining its internal state before any output occurs. This is the "brain" of our [Meta-Controller GitHub Repo], enabling the model to simulate and discard weak logic internally.
4ļøā£ The Simulation: Parallel MTP-Reasoning This is where it comes together: Multi-Token Prediction (MTP) meets Parallel Simulation. Our Python-driven controller runs three parallel Gemma 3 instances.
The Process: 3 paths generated simultaneously.
The Filter: A 500-token lookahead window.
The Decision: The Meta-Controller uses SAE-data from Gemma Scope to select the path with the highest logical fidelity.
The Result: A self-correcting, transparent, and multi-threaded reasoning engine. We aren't just scaling parameters; we are scaling architectural precision. š§
Post
3660
The Architecture of 2026: Beyond the Token Trap š
We are witnessing a tectonic shift in Transformer architecture. Itās no longer just about "predicting the next token"āitās about executing latent plans on a high-speed data highway.
What happens when we combine DeepSeekās stability with Googleās strategic intelligence?
1ļøā£ The Infrastructure: DeepSeekās mHC Moving from a single-lane residual stream to a multi-lane highway. Using the Birkhoff Polytope, mHC ensures mathematical stability (Identity Mapping) while routing specialized data through dedicated lanes.
2ļøā£ The Intelligence: Googleās Meta-Controller An internal AI unit that lives inside the Transformer. It escapes the "Token Trap" by extracting data to create a latent plan, steering the model via Temporal Abstraction.
The Synergy: In a Topological Transformer, the Meta-Controller finally has the "dedicated lanes" it needs to steer complex reasoning without causing gradient explosions.
We aren't just making models bigger; we are making them architecturally smarter. š§
#MachineLearning #DeepSeek #GoogleAI #Transformer #AIArchitecture
We are witnessing a tectonic shift in Transformer architecture. Itās no longer just about "predicting the next token"āitās about executing latent plans on a high-speed data highway.
What happens when we combine DeepSeekās stability with Googleās strategic intelligence?
1ļøā£ The Infrastructure: DeepSeekās mHC Moving from a single-lane residual stream to a multi-lane highway. Using the Birkhoff Polytope, mHC ensures mathematical stability (Identity Mapping) while routing specialized data through dedicated lanes.
2ļøā£ The Intelligence: Googleās Meta-Controller An internal AI unit that lives inside the Transformer. It escapes the "Token Trap" by extracting data to create a latent plan, steering the model via Temporal Abstraction.
The Synergy: In a Topological Transformer, the Meta-Controller finally has the "dedicated lanes" it needs to steer complex reasoning without causing gradient explosions.
We aren't just making models bigger; we are making them architecturally smarter. š§
#MachineLearning #DeepSeek #GoogleAI #Transformer #AIArchitecture