Plutonic Rainbows

I have successfully addressed all critical issues identified in the redundancy analysis for the blog project. The most significant improvement was cleaning up the massive log files that were consuming 164MB of storage – the build.log (28MB) and deploy.log (136MB) have been cleared down to minimal placeholder files of 44 bytes each.

To prevent this issue from recurring, I updated the .gitignore file to use a comprehensive *.log pattern instead of the previous specific deploy.log entry, ensuring all future log files will be excluded from version control.

Upon investigation, the reported orphaned pagination files (index2.html through index103.html) were found to not actually exist – the build system is correctly generating index.html for the first page and page2.html, page3.html, etc., for subsequent pages as intended.

These changes provide immediate benefits including substantial storage savings, cleaner repository structure, improved deployment efficiency, and prevention of future log file bloat in the codebase.

My Claude Max bill is due in two days. Based on ccusage, I would have spent $174 over the past five days using API calls alone. So, for now at least, it seems to offer pretty good value.

After spending countless hours trying to get Claude to respect my permission settings, I was beginning to feel frustrated and stuck. Every time I tried to make progress, I was interrupted by the same repetitive and intrusive menu prompts asking for permission yet again. It felt like a never-ending loop. Eventually, while browsing Reddit in search of answers, I stumbled upon a post that appeared to offer a solution. According to the post, the key was to use a specific command-line flag: claude --dangerously-skip-permissions. I gave it a try, and to my relief, it worked. This single command effectively bypassed the constant permission requests, allowing Claude to run smoothly without the interruptions that had been slowing everything down.

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