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stephenb1334 
in lora-library/inme about 1 month ago

Add application file
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mindchain 
posted an update 6 months ago
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3160
Claude Code Self & Continual Learning

Hey everyone! 👋

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:

🧠 Learning System
- Detects corrections and preferences from conversations
- Stores them permanently in skill files
- Applies learnings in future sessions

🔒 Safety First
- Automatic backups before changes
- YAML validation
- Git version control

⚡ 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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mindchain 
posted an update 6 months ago
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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 Meta’s SAM 3D, Unreal Engine, and the NVIDIA Cosmos suite, we’ve 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 NVIDIA’s 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 Gretel’s 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
mindchain 
posted an update 6 months ago
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Skill Reflect: A Concept for Automated AI Skill Mastery

Let’s 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. That’s not a workflow; that’s just a digital chore. I wanted to see if I could push Claude further—to see if I could build a system that actually learns and refines itself. That’s how the Claude-Reflect-System (Skill Reflect) was born.

But here’s the thing: this isn’t some polished, final product. It’s a concept. It’s a blueprint. I’ve 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"—the gap between "okay" and "mastery"—and 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.

I’m a big believer in the law of causality. Action, reaction. Cause and effect. If you control the cause—the way the AI thinks about its mistakes—you 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. Let’s 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
mindchain 
posted an update 6 months ago
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Neural Traffic Control: Orchestrating Multi-Path Reasoning 🚥
The future of AI isn't just about "better" models—it’s 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.

1️⃣ The Backbone: Stabilized High-Dimensional Routing (arXiv:2512.24880) Using DeepSeek’s 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.

2️⃣ 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.

3️⃣ 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.

4️⃣ 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. 🧠
mindchain 
posted an update 6 months ago
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The Architecture of 2026: Beyond the Token Trap 🚀

We are witnessing a tectonic shift in Transformer architecture. It’s no longer just about "predicting the next token"—it’s about executing latent plans on a high-speed data highway.

What happens when we combine DeepSeek’s stability with Google’s strategic intelligence?

1️⃣ The Infrastructure: DeepSeek’s 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.

2️⃣ The Intelligence: Google’s 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. 🧠

#MachineLearning #DeepSeek #GoogleAI #Transformer #AIArchitecture
hesamation 
posted an update 7 months ago
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this is big... 50 AI researchers from Bytedance, Alibaba, Tencent, and other labs/universities just published a 300-page paper with surprising lessons about coding models and agents (data, pre and post-training, etc).

key highlights:

> small LLMs can beat proprietary giants
RL (RLVR specifically) gives small open-source models an edge over big models in reasoning. a 14B model trained with RLVR on high-quality verified problems can match the performance of OpenAI's o3.

> models have a hard time learning Python.
mixing language models during pre-training is good, but Python behaves different from statically typed languages. languages with similar syntax (Java and C#, or JavaScript and TypeScript) creates high positive synergy. mixing Python heavily into the training of statically typed languages can actually hurt because of Python's dynamic typing.

> not all languages are equal (coding scaling laws)
the amount of data required to specialize a model on a language drastically depends on the language. paper argues like C# and Java are easier to learn (less training data required). languages like Python and Javascript are actually more tricky to learn, ironically (you see AI most used for these languages :)

> MoE vs Dense (ability vs stability)
MoE models offer higher capacity, but are much more fragile during SFT than dense models. hyperparams in training have a more drastic effect in MoE models, while dense models are more stable. MoE models also require constant learning rate schedules to avoid routing instability.

> code models are "insecure" by default (duh)
training on public repos makes models learn years of accumulated insecure coding patterns. safety fine-tuning often fails to work much on code. a model might refuse to write a hate speech email but will happily generate a SQL-injection vulnerable function because it "works."

read the full paper:
From Code Foundation Models to Agents and Applications: A Practical Guide to Code Intelligence (2511.18538)
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SelmaNajih001 
posted an update 8 months ago
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How Financial News Can Be Used to Train Good Financial Models 📰
Numbers tell you what happened, but news tells you why.
I’ve written an article explaining how news can be used to train AI models for sentiment analysis and better forecasting. Hope you find it interesting!

Read it here: https://huggingface.co/blog/SelmaNajih001/llms-applied-to-finance

I would love to read your opinions! I’m open to suggestions on how to improve the methodology and the training
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SelmaNajih001 
posted an update 8 months ago
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Which is the best model to use as a signal for investment?
Here who is gaining the most:
https://huggingface.co/spaces/SelmaNajih001/InvestmentStrategyBasedOnSentiment

The Space uses titles from this dataset:
📊 SelmaNajih001/Cnbc_MultiCompany

Given a news title, it calculates a sentiment score : if the score crosses a certain threshold, the strategy decides to buy or sell.
Each trade lasts one day, and the strategy then computes the daily return.
For Tesla the best model seems to be the regression 👀
Just a quick note: the model uses the closing price as the buy price, meaning it already reflects the impact of the news.
SelmaNajih001 
posted an update 9 months ago
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Which is the best model to use as a signal for investment? 🤔
I’ve created a Space where you can compare three models:
-Two available on my profile
- ProsusAI/finbert
You can try it here:
👉 https://huggingface.co/spaces/SelmaNajih001/InvestmentStrategyBasedOnSentiment
The Space uses titles from this dataset:
📊 SelmaNajih001/Cnbc_MultiCompany

Given a news title, it calculates a sentiment score : if the score crosses a certain threshold, the strategy decides to buy or sell.
Each trade lasts one day, and the strategy then computes the daily return.

Just a quick note: the model uses the closing price as the buy price, meaning it already reflects the impact of the news.
If I had chosen the opening price, the results would have been less biased but less realistic given the data available.
SelmaNajih001 
posted an update 9 months ago
SelmaNajih001 
posted an update 9 months ago
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Finally, I uploaded the model I developed for my master’s thesis! Given a financial event, it provides explained predictions based on a dataset of past news and central bank speeches.
Try it out here:
SelmaNajih001/StockPredictionExplanation
(Just restart the space and wait a minute)

The dataset used for RAG can be found here:
SelmaNajih001/FinancialNewsAndCentralBanksSpeeches-Summary-Rag
While the dataset used for the training is:
SelmaNajih001/FinancialClassification

I also wrote an article to explain how I've done the training. You can find it here https://huggingface.co/blog/SelmaNajih001/explainable-financial-predictions

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SelmaNajih001 
posted an update 9 months ago
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Introducing a Hugging Face Tutorial on Regression

While Hugging Face offers extensive tutorials on classification and NLP tasks, there is very little guidance on performing regression tasks with Transformers.
In my latest article, I provide a step-by-step guide to running regression using Hugging Face, applying it to financial news data to predict stock returns.
In this tutorial, you will learn how to:
-Prepare and preprocess textual and numerical data for regression
-Configure a Transformer model for regression tasks
-Apply the model to real-world financial datasets with fully reproducible code

Read the full article here: https://huggingface.co/blog/SelmaNajih001/how-to-run-a-regression-using-hugging-face
The dataset used: SelmaNajih001/FinancialClassification
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SelmaNajih001 
posted an update 9 months ago
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Introducing SelmaNajih001/StockPredictionExplanation, built with GRPO and RAG:
-GRPO trains the model to predict and explain stock direction.
-RAG grounds explanations in historical financial news and central bank speeches.
Together, they create a system that forecasts stock movements and shows the reasoning behind them.
Full article: Explainable Financial Predictions — https://huggingface.co/blog/SelmaNajih001/explainable-financial-predictions
Try it here: StockPredictionExplanation Space — SelmaNajih001/StockPredictionExplanation
SelmaNajih001 
posted an update 9 months ago
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Predicting Stock Price Movements from News 📰📈
I trained a model to predict stock price movements (Up, Down, Neutral) from company news.
Dataset: Articles linked to next-day price changes, covering Apple, Tesla, and more.
Approach: Fine-tuned allenai/longformer-base-4096 for classification.
Outcome: The model captures the link between news and stock movements, handling long articles and producing probability scores for each label.
Comparison: Shows promising alignment with stock trends, sometimes outperforming FinBERT.
Feel free to try the model and explore how news can influence stock predictions SelmaNajih001/SentimentAnalysis
hesamation 
posted an update 10 months ago
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a senior engineer at google just dropped a 400-page free book on docs for review: agentic design patterns.

the table of contents looks like everything you need to know about agents + code:
> advanced prompt techniques
> multi-agent patterns
> tool use and MCP
> you name it

read it here: https://docs.google.com/document/d/1rsaK53T3Lg5KoGwvf8ukOUvbELRtH-V0LnOIFDxBryE/edit?tab=t.0#heading=h.pxcur8v2qagu

you can also pre-order on Amazon (published by Springer) and the royalties goes to Save the Children: https://www.amazon.com/Agentic-Design-Patterns-Hands-Intelligent/dp/3032014018/
hesamation 
posted an update 11 months ago
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longer context doesn't generate better responses. it can even hurt your llm/agent. 1M context window doesn't automatically make models smarter as it's not about the size; it's how you use it.

here are 4 types of context failure and why each one happens:

1. context poisoning: if hallucination finds its way into your context, the agent will rely on that false information to make its future moves. for example if the agent hallucinates about the "task description", all of its planning to solve the task would also be corrupt.

2. context distraction: when the context becomes too bloated, the model focuses too much on it rather than come up with novel ideas or to follow what it has learned during training. as Gemini 2.5 Pro technical report points out, as context grows significantly from 100K tokens, "the agent showed a tendency toward favoring repeating actions from its vast history rather than synthesizing novel plans".

3. context confusion: everyone lost it when MCPs became popular, it seemed like AGI was achieved. I suspected there is something wrong and there was: it's not just about providing tools, bloating the context with tool use derails the model from selecting the right one! even if you can fit all your tool metadata in the context, as their number grows, the model gets confused over which one to pick.

4. Context Clash: if you exchange conversation with a model step by step and provide information as you go along, chances are you get worse performance rather than providing all the useful information at once. one the model's context fills with wrong information, it's more difficult to guide it to embrace the right info. agents pull information from tools, documents, user queries, etc. and there is a chance that some of these information contradict each other, and it's not good new for agentic applications.

check this article by Drew Breunig for deeper read: https://www.dbreunig.com/2025/06/26/how-to-fix-your-context.html?ref=blog.langchain.com
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hesamation 
posted an update 12 months ago
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in case you didn’t know, Claude now has a developer training course with certificates,

this is better than anything you can find on Coursera.

covers Claude Code, MCP and its advanced topics and even more:

https://www.anthropic.com/learn/build-with-claude
hesamation 
posted an update about 1 year ago
hesamation 
posted an update about 1 year ago
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I really like how this seven-stage pipeline was laid out in the Ultimate Guide to Fine-Tuning book.

It gives an overview, then goes into detail for each stage, even providing best practices.

It’s 115 pages on arxiv, definitely worth a read.

Check it out: https://arxiv.org/abs/2408.13296