DefiLlama Reviews

Scikit-learn Reviews

DefiLlama mentions (142)

• How to Build a Crypto Arbitrage Bot Across EVM Chains

  Building a crypto arbitrage bot across EVM chains comes down to three things: quotes from every chain in parallel, a fast comparison loop that spots price gaps larger than fees, and a slippage-tolerant executor that actually lands the trade before the gap closes. Arbitrage windows on EVM chains typically last under 30 seconds on majors and 2-10 seconds on L2s, which means your bot's latency budget is tight — every... - Source: dev.to / 3 months ago

• Best DCA Bot Strategies with a Swap API in 2026

  The best DCA bot strategy in 2026 is one that runs as a scheduled job without human intervention — which means the swap API it calls must require no API key and no account. Dollar-cost averaging (DCA) is the most-executed automated strategy in crypto: cron-triggered swaps that buy a fixed dollar amount on a fixed cadence, regardless of price. On-chain DCA vaults collectively hold over $1.2 billion in TVL across... - Source: dev.to / 3 months ago

• How to Build an AI Agent for Memecoin Trading

  Building an AI agent for memecoin trading comes down to four pieces: an LLM that picks what to trade, a swap API that returns executable calldata, a wallet that signs transactions, and — most importantly — a slippage retry loop that handles the inevitable reverts. Memecoin volume on Ethereum and Base routinely exceeds $2 billion per day, and the share handled by autonomous agents is growing fast. But memecoins... - Source: dev.to / 3 months ago

• 5 Best Swap APIs for Automated Yield Farming

  DeFi yield farming now accounts for 36.5% of all DeFi activity, with total value locked peaking at $171.9 billion in late 2025 before stabilizing around $130-140 billion in early 2026 (DeFiLlama). Auto-compounding platforms like Beefy Finance run across 30+ chains, delivering APYs from 8% to 40% by automatically reinvesting rewards -- sometimes multiple times per day (CoinCodex). - Source: dev.to / 4 months ago

• 5 Ways to Monitor Token Prices Across 46 EVM Chains

  DeFiLlama provides a free, keyless API specifically designed for on-chain token pricing. Unlike CoinGecko's exchange-aggregated approach, DeFiLlama derives prices from on-chain DEX pools across 500+ chains tracking 7,000+ protocols. This makes it particularly useful for DeFi-native tokens that may not be listed on centralized exchanges. - Source: dev.to / 4 months ago

Scikit-learn mentions (40)

• Detecting Ingress Tool Transfer (T1105) with Python

  Certutil.exe or notepad.exe opening an external connection lands in rare because, fleet-wide, those processes almost never egress. Tune the <= 3 threshold to your environment size. For a more principled version, score each (process, destination) pair by frequency and treat the long tail as the hunt queue, which is the same idea behind scikit-learn's rarity-based anomaly methods without the model overhead. - Source: dev.to / about 2 months ago

• Best AI Cybersecurity Training for Security Teams: How to Pick

  Pre-configured environment. A working VM or container with Jupyter, pandas, scikit-learn, and transformers already installed. Realistic security datasets loaded. GTK Cyber students work in the Centaur VM, a free Apache 2.0 portable lab. If the first hour of training is fighting CUDA installs, the course is not ready. - Source: dev.to / 2 months ago

• Where to Get Hands-On AI Training for Cybersecurity Professionals

  Pre-configured environment. A good course ships a VM or container with Jupyter, pandas, scikit-learn, PyTorch or transformers, and realistic security datasets loaded. GTK Cyber students work in the Centaur VM, a free Apache 2.0 portable lab. No setup tax. - Source: dev.to / 3 months ago

• How Anomaly Detection Actually Works in Security Operations

  Isolation-based models: Build random decision trees that split features. Points that are isolated quickly (short average path length across trees) are anomalies. IsolationForest in scikit-learn implements this. Handles high-dimensional feature spaces without assuming a distribution. - Source: dev.to / 3 months ago

• Building a Personalized Meal Recommendation System

  In practice, you’ll want to use libraries (like scikit-learn or TensorFlow.js for more advanced modeling), but the principle remains: find what similar users enjoy, and use that as a basis for recommendations. - Source: dev.to / 5 months ago