Cómo crear un bot de Telegram en Python que solo responda a su propietario

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Un bot de Telegram puede servir de mando a distancia para un script o un agente de IA en tu propia máquina, sin app propia ni servidor público. Como cualquiera puede escribirle, el artículo propone un diseño de emparejamiento: un código aleatorio de un solo uso que caduca a los 10 minutos y se vincula al ID numérico del remitente. Para recibir mensajes sin exponer puertos ni certificados se usa long polling con getUpdates, y el autor explica que el timeout HTTP debe superar la espera del long poll. Según sus pruebas locales, este enfoque evita miles de peticiones inútiles frente a un sondeo corto, aunque el error de una línea que disparó las peticiones se presenta como advertencia. El texto incluye una implementación de unas 70 líneas de Python con la biblioteca estándar.

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morluto/rea Automates Binary and API Reverse Engineering With Agent-Driven Analysis

The morluto/rea project is an agent-driven orchestration tool meant to speed up reverse engineering of closed-source binaries and undocumented APIs, which typically demands manual static analysis and trial-and-error. Its three harnesses cover binary analysis via a Ghidra wrapper, live API fuzzing that rebuilds OpenAPI 3.1 specs from network traffic, and an LLM-augmented pattern matcher for contextual reasoning. The author reports practical issues, including Ghidra running out of memory on large stripped Rust binaries, which was worked around by chunking analysis and caching p-code in Redis. Browser process zombies from unbounded parallelism were fixed by capping concurrency at three using p-limit. Because agent output is unstructured, the author also added Zod schemas to validate reconstructed API specs at runtime.

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ProgrammingDEV Community ·

How to Organize AI Prompts in Text Files and Refine Them Over Time

Rather than leaving useful AI prompts buried in chat history, the article recommends saving a dedicated text file for each recurring task. History is sorted only by time, mixes working and failed versions, and loses refinements, so it works as a record rather than a reusable asset. Each prompt file is suggested to cover its purpose, context, instructions, things to avoid, and an example of good output. The author advises separating context and don'ts from the instructions so fixes are easier to target when results fall short. The piece also compares storage options such as plain files, note-taking apps, AI tool custom instructions and text expanders, suggesting text files as the master copy.

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ProgrammingDEV Community ·

Retail shop owner teaches himself Python to replace his spreadsheet with small tools

Eric, who ran a small retail shop for eleven years and does not code professionally, introduces himself to the DEV Community. He began by managing cash flow, stock, and pricing in a large spreadsheet that only he understood. Last year he started learning basic Python to organize that workbook and became interested in building small tools that each answer a specific business question. He describes himself as a beginner who relies heavily on lookups, but says the community's supportive tone drew him in. He plans to mostly read and ask questions, and invites others who built tools for small businesses or switched into coding from other trades to share their experiences.

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ProgrammingDEV Community ·

Building a Tested Docs Site with Thally and Lessons on Modern Developer Experience

The author built the JSON Server Field Guide, a companion site for json-server@1.0.0-beta.15, with a rule that every behavioral claim must be backed by recorded HTTP fixtures rather than assumptions. They chose Thally, a documentation framework built on Next.js 16 with Turbopack, because it is a full App Router application that avoids the limits of proprietary static generators. The write-up covers scaffolding, content modeling, interactive MDX consoles, CI quality checks, and production deployment, along with the engineering obstacles met along the way. Thally's features highlighted include fast hot reloading, built-in AI-readable endpoints, a CLI that audits links and frontmatter, and a file ownership system that protects custom content from upstream overwrites.

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