Wttr.in Alternatives

wttr.in reviews and mentions

• Building a CLI Tool with Node.js: From Zero to npm
  1 project | dev.to | 15 May 2026

  // api.js const CACHE_TTL = 30 * 60 * 1000; // 30 minutes const cache = new Map(); export async function getWeather(location) { const cacheKey = location.toLowerCase(); // Check cache first if (cache.has(cacheKey)) { const { data, timestamp } = cache.get(cacheKey); if (Date.now() - timestamp < CACHE_TTL) { return data; } } // Fetch from free API (wttr.in) const url = `https://wttr.in/${encodeURIComponent(location)}?format=j1`; const response = await fetch(url); if (!response.ok) { throw new Error(`Location "${location}" not found`); } const data = await response.json(); cache.set(cacheKey, { data, timestamp: Date.now() }); return { location: data.nearest_area[0].areaName[0].value, country: data.nearest_area[0].country[0].value, current: data.current_condition[0], forecast: data.weather, }; }

• AMO Review Process: What Happens After You Submit a Firefox Extension
  1 project | dev.to | 3 May 2026

  { "permissions": ["storage"], "host_permissions": ["https://wttr.in/*"] }

• Building a Weather Widget for Firefox New Tab: API-Free Approach with wttr.in
  1 project | dev.to | 3 May 2026

  async function fetchWeather(location = '') { const url = `https://wttr.in/${encodeURIComponent(location)}?format=j1`; const response = await fetch(url); if (!response.ok) throw new Error(`Weather fetch failed: ${response.status}`); const data = await response.json(); return parseWeatherData(data); } function parseWeatherData(data) { const current = data.current_condition[0]; const today = data.weather[0]; const tomorrow = data.weather[1]; const dayAfter = data.weather[2]; return { temperature: { c: parseInt(current.temp_C), f: parseInt(current.temp_F), }, feelsLike: { c: parseInt(current.FeelsLikeC), f: parseInt(current.FeelsLikeF), }, humidity: parseInt(current.humidity), description: current.weatherDesc[0].value, weatherCode: parseInt(current.weatherCode), forecast: [ parseForecastDay(today), parseForecastDay(tomorrow), parseForecastDay(dayAfter), ], location: data.nearest_area[0], }; } function parseForecastDay(day) { return { date: day.date, maxC: parseInt(day.maxtempC), minC: parseInt(day.mintempC), maxF: parseInt(day.maxtempF), minF: parseInt(day.mintempF), description: day.hourly[4]?.weatherDesc[0]?.value || '', weatherCode: parseInt(day.hourly[4]?.weatherCode || 0), sunrise: day.astronomy[0]?.sunrise || '', sunset: day.astronomy[0]?.sunset || '', }; }

• Making a Firefox Extension Work Offline — Service Workers vs. Cache API
  1 project | dev.to | 3 May 2026

  async function getWeather(city) { const CACHE_KEY = 'weather_cache'; const CACHE_TTL = 10 * 60 * 1000; // 10 minutes // Check cache first const { weather_cache } = await browser.storage.local.get(CACHE_KEY); if (weather_cache && Date.now() - weather_cache.timestamp < CACHE_TTL) { return weather_cache.data; } try { const resp = await fetch(`https://wttr.in/${city}?format=j1`); const data = await resp.json(); // Store in cache await browser.storage.local.set({ [CACHE_KEY]: { data, timestamp: Date.now() } }); return data; } catch (err) { // Return stale cache if available return weather_cache?.data || null; } }

• Why I Used wttr.in Instead of OpenWeatherMap for My Firefox Extension
  2 projects | dev.to | 3 May 2026

  const CACHE_DURATION = 30 * 60 * 1000; // 30 minutes async function fetchWeather(city) { const cacheKey = `weather_${city}`; const cached = await browser.storage.local.get(cacheKey); if (cached[cacheKey]) { const { data, timestamp } = cached[cacheKey]; if (Date.now() - timestamp < CACHE_DURATION) { return data; // Return cached data } } // Fetch fresh data const resp = await fetch(`https://wttr.in/${encodeURIComponent(city)}?format=j1`); const data = await resp.json(); // Cache it await browser.storage.local.set({ [cacheKey]: { data, timestamp: Date.now() } }); return data; }

• How I Built a Firefox New Tab Extension with Pure HTML/CSS/JS (No Frameworks)
  1 project | dev.to | 3 May 2026

  async function fetchWeather(city) { const resp = await fetch(`https://wttr.in/${encodeURIComponent(city)}?format=j1`); const data = await resp.json(); return { temp: data.current_condition[0].temp_C, desc: data.current_condition[0].weatherDesc[0].value, forecast: data.weather.slice(0, 3) }; }

• How I Built a Production AI Agent in Python for $5/month Using Open Source
  1 project | dev.to | 16 Apr 2026

  from langchain.llms import Ollama from langchain.agents import initialize_agent, Tool from langchain.agents import AgentType from langchain.memory import ConversationBufferMemory import requests # Initialize the local model llm = Ollama(model="mistral", base_url="http://localhost:11434") # Define tools your agent can use def get_weather(location: str) -> str: """Fetch weather for a location""" try: response = requests.get( f"https://wttr.in/{location}?format=j1" ) data = response.json() current = data['current_condition'][0] return f"Weather in {location}: {current['temp_C']}°C, {current['weatherDesc'][0]['value']}" except Exception as e: return f"Error fetching weather: {str(e)}" def calculate_something(expression: str) -> str: """Simple calculator tool""" try: result = eval(expression) return f"Result: {result}" except: return "Invalid expression" def search_knowledge_base(query: str) -> str: """Search your own knowledge base""" # This would connect to your vector DB return f"Found information about {query}" # Create tool objects tools = [ Tool( name="Weather", func=get_weather, description="Get current weather for a location" ), Tool( name="Calculator", func=calculate_something, description="Perform mathematical calculations" ), Tool( name="Knowledge", func=search_knowledge_base, description="Search the knowledge base" ), ] # Set up memory memory = ConversationBufferMemory(memory_key="chat_history") # Initialize the agent agent = initialize_agent( tools, llm, agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION, memory=memory, verbose=True ) # Use the agent result = agent.run("What's the weather in London and what's 25 * 4?") print(result)

• AI Agents in Rails — Tool Use, Function Calling, and Multi-Step Workflows
  1 project | dev.to | 7 Apr 2026

  # app/services/tool_executor.rb class ToolExecutor def self.execute(tool_name, arguments) case tool_name when "search_documents" search_documents(arguments["query"]) when "get_weather" get_weather(arguments["city"]) when "create_task" create_task(arguments["title"], arguments["priority"] || "medium") else { error: "Unknown tool: #{tool_name}" } end end private def self.search_documents(query) chunks = Retriever.new(query).call results = chunks.map { |c| c.content.truncate(200) } { results: results } end def self.get_weather(city) # In production, call a real weather API response = Net::HTTP.get( URI("https://wttr.in/#{URI.encode_www_form_component(city)}?format=j1") ) data = JSON.parse(response) current = data.dig("current_condition", 0) { city: city, temp_c: current["temp_C"], description: current["weatherDesc"].first["value"] } end def self.create_task(title, priority) task = Task.create!(title: title, priority: priority, status: "pending") { id: task.id, title: task.title, priority: task.priority } end end

• The Ultimate Guide to Building AI-Powered Web Apps with the Vercel AI SDK in 2026
  1 project | dev.to | 2 Apr 2026

  import { streamText, tool } from 'ai'; import { openai } from '@ai-sdk/openai'; import { z } from 'zod'; export async function POST(req: Request) { const { messages } = await req.json(); const result = streamText({ model: openai('gpt-4o'), messages, tools: { getWeather: tool({ description: 'Get current weather for a location', parameters: z.object({ city: z.string().describe('The city name'), country: z.string().optional().describe('ISO country code'), }), execute: async ({ city, country }) => { // In production: call a real weather API const response = await fetch( `https://wttr.in/${city},${country}?format=j1` ); const data = await response.json(); return { temperature: data.current_condition[0].temp_C, description: data.current_condition[0].weatherDesc[0].value, humidity: data.current_condition[0].humidity, }; }, }), searchDocs: tool({ description: 'Search the internal knowledge base', parameters: z.object({ query: z.string().describe('The search query'), limit: z.number().default(5), }), execute: async ({ query, limit }) => { // Connect to your vector store (Pinecone, pgvector, etc.) const results = await vectorStore.similaritySearch(query, limit); return results.map(r => ({ content: r.pageContent, score: r.score })); }, }), }, maxSteps: 5, // Allow multi-step tool use }); return result.toDataStreamResponse(); }

• 150+ Free APIs You Can Use Without an API Key (2026 Edition)
  2 projects | dev.to | 25 Mar 2026

  import requests # Random dog image dog = requests.get('https://dog.ceo/api/breeds/image/random').json() print(dog['message']) # URL to a random dog photo # Weather (no key!) weather = requests.get('https://wttr.in/London?format=j1').json() print(f"London: {weather['current_condition'][0]['temp_C']}C") # Random user profile user = requests.get('https://randomuser.me/api/').json() print(user['results'][0]['name']) # IP geolocation location = requests.get('https://ip-api.com/json/').json() print(f"You're in {location['city']}, {location['country']}")

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  www.saashub.com | 15 Jun 2026

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