Plutonic Rainbows

I managed to create a file for consistently using permissions from the single root-level. Hopefully, Claude will now use these permissions across all projects.

I’ve been thoroughly impressed with this new model from Anthropic — its performance, responsiveness, and overall capabilities have exceeded my expectations. I can imagine that relying solely on API calls to access it would be prohibitively expensive for most users. Thankfully, the Max subscription significantly reduces that financial strain, making it much more accessible for regular use. The only drawback, however, is that I still need to budget around $100 each month to maintain access, which can be a challenge at times. Even so, the value it provides makes the cost feel more justifiable.

Anthropic has introduced Claude Opus 4 and Claude Sonnet 4, its latest generation of hybrid-reasoning AI models optimized for coding tasks and solving complex problems.

Photographed by Bruce Weber on May 22nd 1991, at the Fahey/Klein Gallery in Los Angeles, California.

Today, I implemented a neural network model for Solana price prediction that significantly enhances the existing logistic regression approach. The new model leverages sequence-based feature engineering to capture temporal patterns across 10-day windows, allowing it to better recognize trends and market dynamics that unfold over time. By using scikit-learn's MLPClassifier with a carefully designed architecture of two hidden layers and dropout regularization, the model achieves approximately 59% prediction accuracy — a meaningful improvement over the baseline. I also incorporated visualization tools that provide insights into both the model's training progress and its predictions, making the system more transparent and interpretable.

Beyond the core model implementation, I established a comprehensive project infrastructure with proper documentation and organization. This includes detailed model comparison documentation, an enhanced README with clear usage instructions, a structured CHANGELOG to track version history, and improved visualization storage. The codebase now offers two complementary approaches to price prediction, giving users flexibility to choose between a simpler, faster logistic regression model for immediate signals or the more sophisticated neural network for capturing complex temporal dependencies. All implementations maintain the integration of technical indicators and sentiment analysis while adding new capabilities for sequence processing and confidence scoring.

I have developed an application that predicts the price movements of Solana (SOL) against the US Dollar (USD) using a combination of technical analysis and sentiment analysis. The model uses logistic regression to classify whether prices are likely to rise or fall.

Key Features:

• Relative Strength Index (RSI)
• Moving Average Convergence Divergence (MACD)
• Bollinger Bands
• Extracts sentiment scores from news headlines to assess market mood

Application Workflow:

• Generates synthetic price data
• Calculates technical indicators from the data
• Incorporates sentiment scores derived from headlines
• Trains a logistic regression model to classify price movement
• Evaluates model accuracy
• Visualizes buy/sell signals with corresponding sentiment data

This evening, I worked on Syntax Trees. I also updated CLAUDE.md files for each individual model folder.

Spent literally hours trying to create mask adjustments in Photoshop.

I’ve streamlined my approach, realizing that I don’t need an editor like Windsurf or Cursor. Much of what agentic coding does is so complex that I can’t follow it — and the beauty is, I don’t need to. A command-line interface suits me perfectly. I recently purchased the new Max Plan, as it now integrates with Claude Code. API calls to Anthropic were becoming prohibitively expensive, so hopefully this monthly subscription will meet my needs.