How to Build a Python AI Assistant on Android Using Termux

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Developers can create a functional AI assistant on an Android device using Termux and Python, without needing a computer or complex development environment. The setup requires installing Python and the OpenAI package inside Termux, along with a valid API key from an AI provider. A short Python script handles user input, sends queries to the AI model via the OpenAI API, and prints responses directly in the terminal. Credentials are stored securely in a separate .env file using the python-dotenv package to prevent accidental exposure. The basic assistant runs in a continuous loop and can be extended with features like conversation history or custom commands.

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Dropbox Joins Google Gemini as Connected App for File Search and Automation

Dropbox has launched as an official connected app for Google Gemini, allowing eligible users to find, summarize, share, and manage Dropbox files directly within Gemini Chat. The integration also extends to Gemini Spark, Google's multi-app workflow automation environment, where Dropbox can serve as the file layer alongside other third-party services like Canva and Instacart. The rollout is currently limited to US-based users who are 18 or older, use a personal Google account, work in English, and access Gemini via the web app at gemini.google.com. Access to Gemini Spark is further tied to Google AI Pro and AI Ultra subscription plans, meaning neither a Dropbox business account nor a work Google account alone qualifies a user. The integration does not replace Dropbox as a storage platform but aims to reduce context-switching by making Dropbox content accessible within AI-driven conversations and workflows.

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ProgrammingDEV Community ·

Why Debugging Your Trading Backtest Matters More Than Upgrading Your Model

Algorithmic trading backtests can produce misleadingly strong results when they inadvertently use information that would not have been available at the time of a simulated trade decision. A common cause is joining datasets on date columns alone, which can allow post-decision data — such as earnings released minutes after market close — to influence earlier trade signals. Developers are advised to track multiple timestamps per data point, including when information was published, received, and processed, to accurately determine what a strategy could have known. Even filtering retrieved documents cannot fully eliminate look-ahead bias if an underlying model was trained on data that includes future outcomes. Rigorous validation of data eligibility, execution assumptions, and model complexity is essential before trusting any backtest result.

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ProgrammingDEV Community ·

Combine Webhooks and Scheduled Polling to Minimize API Key Revocation Gaps

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ProgrammingDEV Community ·

LLM Basics Explained: Weights, Parameters, Tokens, and Sampling Controls

A Large Language Model (LLM) is fundamentally a collection of numerical weights derived from training data using architectures like Transformers, with model size measured in parameters — for example, a 120B parameter model contains 120 billion such weights. To run a model locally, sufficient RAM and CPU or GPU resources are required, with smaller models like 7B parameters being feasible on consumer laptops using tools such as Ollama or LM Studio. At its core, an LLM works as a next-word predictor, assigning probability scores to possible continuations and selecting outputs based on those scores. The randomness of output can be controlled through parameters like Temperature, Top-k, and Top-p, where lower temperature favors factual responses and higher temperature encourages creative or varied outputs. Text is broken into smaller units called tokens during a process called tokenization, and a model's context window defines the maximum number of tokens it can process at one time.

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