1000 results for “dev_Paul”
Crypto loves to hype new features. But a longer feature list doesn't automatically mean a better app. Sometimes the most useful product is just the one that lets you get the job done without a headache.
Beyond the basic swap
A good swap platform puts the important stuff where you can see it: what you're swapping, roughly what you'll get back, what fees you might pay, and which network you're on.
Clear explanations and sensible warnings go a long way too. They help people dodge mistakes that never needed to happen.
AviorSwap is one project you might look at if you're learning how crypto swaps work. Same as with any platform, check the supported assets, network compatibility, transaction details, and docs before you jump in.
Working features are just the start
Good product design is about features that work and are easy to understand. It accepts that users come in with very different levels of experience, and it gives them enough info to make their own call.
In Web3 you're usually the one fully responsible for your assets, so getting that balance right matters even more.
So what's your top priority when picking a swap platform: simplicity, the range of assets, fees, or transparency?#web3 #product #software #coding #development #engineering #inclusive #community
What actually makes a crypto swap platform useful?
We have AI tools that can write code, summarize documents, generate ideas, and answer almost any question.
But there is still a problem I keep thinking about:
AI can help us figure out what to do. But what helps us actually do it?
You can ask an AI to create a study plan, build a roadmap for your startup, or organize your daily routine. The response might be excellent.
Then real life happens.
You miss a deadline. You get distracted. A task becomes more complicated than expected. Your original plan stops making sense.
And suddenly, that beautifully generated plan becomes another forgotten document.
That got me thinking about a different kind of AI-powered product.
Introducing Cometflow LifeOS
Cometflow LifeOS, by Cometflow Space, is built around one simple idea:
Intent → Reality.
Instead of treating AI as a place where you only ask questions, imagine an experience designed around turning intentions into real, trackable progress.
You start with something you want to accomplish.
For example:
The goal isn't just to generate a list of tasks. It's to help connect the goal with the steps, resources, deadlines, and follow-ups needed to move forward.
The idea behind LifeOS
I'm exploring three connected concepts.
A goal rarely exists on its own.
Launching a website might require learning a framework, designing the interface, building features, testing the application, and publishing it.
The Intent Graph represents these relationships so that a big goal becomes a sequence of understandable actions rather than one overwhelming task.
Plans should adapt to reality.
What if your AI assistant checked in at a time you chose, asked what you accomplished, understood what blocked you, and helped determine the next practical action?
Instead of repeatedly generating new plans, the experience could build on previous progress.
The principle is simple: when reality changes, the plan should change too.
Eventually, goals may require more than personal organization.
They might involve useful tools, learning resources, collaborators, or services. A connected system could help people discover what they need to move from an idea to an outcome.
The challenge is making these connections genuinely useful without overwhelming users with notifications or unnecessary complexity.
Why build another productivity app?
That's a fair question.
There are already excellent task managers, calendars, habit trackers, and AI assistants.
I don't think the answer is to put a chatbot inside another to-do list.
The more interesting question is:
Can we design software around the progress a person wants to make, rather than the tasks they happen to enter?
That means thinking about context, dependencies, setbacks, follow-ups, and what the user should do next.
It also means respecting user control. An assistant should help people make decisions, not turn their lives into an endless stream of automated reminders.
The difficult engineering questions
This concept raises several questions I'm interested in exploring:
These are product-design problems as much as AI problems.
A genuinely useful LifeOS should help people become more capable and independent over time, not more dependent on the assistant.
Building in public
Cometflow LifeOS is an independent product under the Cometflow Space ecosystem, and I'm interested in exploring how this idea can become a practical daily-use experience.
The goal is ambitious, but the principle is straightforward: help people turn intentions into actions, learn from what actually happens, and keep moving forward.
I would rather build a focused system that helps someone achieve one meaningful goal than a giant dashboard full of features they never use.
I have a question for developers and builders
Imagine you could give an AI assistant one responsibility beyond answering questions.
Would you want it to:
Or do you think this entire concept needs a different approach?
I'd genuinely like to hear your perspective. What would make an AI-powered LifeOS useful enough to become part of your everyday routine?
AI #Productivity #WebDev #BuildInPublic
#ai #programming #apk #productivity #software #coding #development #engineering #inclusive #community
What If AI Didn't Just Answer Your Questions, but Helped You Actually Get Things Done?
Open-Meteo is a free API that is used to get weather data. It doesn't need a key or even a sign-up, allowing you to run your first request in a snap of your fingers. In this guide I will try to explain this API purely from a Python developer's viewpoint, and I will only cover the two major endpoints:
• Geocoding - Used to convert the names of cities to their coordinates on a world map.
• Forecast - The expected weather data for that location.
Prerequisites: Python 3 installed and basic knowledge of Python.
Usage terms: Open-Meteo is free for non-commercial use, and the data is licensed CC BY 4.0, so you must credit Open-Meteo. Check the official site for current rate limits and commercial terms.
Set-up
For the set-up you will first need to install the requests Python library by running this in your terminal.
pip install requests
Starting Out
To start, let me provide you with the code to get the temperature in, say, Berlin, Germany. It is as follows:
import requests
response = requests.get(
"https://api.open-meteo.com/v1/forecast",
params={
"latitude": 52.52,
"longitude": 13.41,
"current": "temperature_2m",
},
)
data = response.json()
print(data["current"]["temperature_2m"])
Save this code in a file such as weather.py and run it in your terminal with python weather.py after installing the requests library. Here is what has been done in this code:
1) We have imported the requests library to get data from an API.
2) We have response, which is just a variable that holds the API's reply, set equal to the following:
2.1) Get data from the API website using the API link.
2.2) Set the params (parameters) as the latitude and longitude of the place. In this case they are the coordinates of Berlin. Then we ask for the current temperature at 2 metres above ground level (that is what 2m means).
3) Now we close the parameters and the response query.
4) Then we convert the response to a Python dictionary with .json() and print the current temperature value.
Authentication
None for free, non-commercial use, as requests are plain GET calls with no headers or keys. (Commercial subscription tiers use an API key.)
Endpoint 1: Geocoding
We will find exact locations from just the city names. Here is the base URL.
geocoding-api.open-meteo.com/v…
Here are the parameters:
namestringYesName of the city you want to check.countintegerNoNumber of results you want (Default 10).languagestringNoThe language you want the result in. Default (en for English).formatstringNoThe data format of the response default is json.countryCodestringNoFilters countries using its two letter code.apikeystringNoAuthentication key required only for commercial subscription tiers.
Here is an example that looks up the location data for London:
import requests
response = requests.get(
"https://geocoding-api.open-meteo.com/v1/search",
params={"name": "London", "count": 1, "language": "en"},
)
data = response.json()
print(data["results"][0])
• You start by importing the requests module.
• You set up the variable response with the URL as the base URL I provided above.
• Then you enter the parameters. Only the name is required, everything else is optional.
The response will be something like this:
{
"results": [
{
"id": 2643743,
"name": "London",
"latitude": 51.50853,
"longitude": -0.12574,
"elevation": 25.0,
"feature_code": "PPLC",
"country_code": "GB",
"admin1_id": 6269131,
"admin2_id": 2648110,
"timezone": "Europe/London",
"population": 8961989,
"country_id": 2635167,
"country": "United Kingdom",
"admin1": "England",
"admin2": "Greater London"
}
]
}
Here is what these response fields mean.
Note: If nothing matches, the response has noresultskey at all. Usedata.get("results")instead ofdata["results"]to avoid aKeyError.
Endpoint 2: Forecast
Returns the forecast of conditions for the location.
Base URL:
api.open-meteo.com/v1/forecast
Again, these are the parameters for this endpoint.
Now here are some common variable names that you may encounter in this endpoint.
Here is an example:
import requests
response = requests.get(
"https://api.open-meteo.com/v1/forecast",
params={
"latitude": 19.07,
"longitude": 72.88,
"current": "temperature_2m,wind_speed_10m",
"daily": "temperature_2m_max,temperature_2m_min",
"timezone": "Asia/Kolkata",
"forecast_days": 3,
},
)
data = response.json()
print(data)
Here's how this works:
• You first import the requests module like always.
• You again set up a variable that gets its data from the base URL I provided above. Make sure it is the URL of the 2nd endpoint and not the 1st.
• You then define the parameters. Only the latitude and longitude of your chosen city are required. I chose Mumbai, India, so I have entered its coordinates.
• Then you enter additional parameters and variables using the above table as a reference.
• Then you convert the response with response.json(), store it in the variable data and print it.
You will see something like this if you did it correctly (the times and values will differ, since this is live data):
{
"latitude": 19.07,
"longitude": 72.88,
"timezone": "Asia/Kolkata",
"current_units": {
"temperature_2m": "°C",
"wind_speed_10m": "km/h"
},
"current": {
"time": "2026-10-23T14:00",
"temperature_2m": 32.1,
"wind_speed_10m": 11.4
},
"daily": {
"time": ["2026-10-23", "2026-10-24", "2026-10-25"],
"temperature_2m_max": [33.5, 33.2, 32.9],
"temperature_2m_min": [25.1, 24.8, 25.0]
}
}
This is a trimmed version by the way (btw). Python's print will show it as a dictionary with single quotes, which is the same data.
In Python this becomes a dictionary. Each key under daily holds a list, and the lists line up by position. Index 0 of temperature_2m_max belongs to the first date in time:
daily = data["daily"]
for date, high, low in zip(
daily["time"], daily["temperature_2m_max"], daily["temperature_2m_min"]
):
print(f"{date}: high {high}, low {low}")
Errors
When a request is invalid, the API returns HTTP status 400 and a JSON body explaining why.
Here is an example of a script that will provide an error.
import requests
response = requests.get(
"https://api.open-meteo.com/v1/forecast",
params={"latitude": 120, "longitude": 72.88, "current": "temperature_2m"},
)
print(response.status_code)
print(response.json())
The reason for the error here is that latitudes go from -90° to 90° (longitudes go from -180° to 180°), so 120° is an invalid latitude. The error will look something like this:
{
"error": true,
"reason": "Latitude must be in range of -90 to 90°. Given: 120.0."
}
Here are some common errors, their codes and fixes:
400Invalid or missing parameter, or unknown variable nameRead the reason field and correct the request429Too many requestsSlow down and retry later5xxServer problemRetry after a short wait
These were some common errors. You can refer to the official Open-Meteo documentation or the internet if you have an error that doesn't match the ones provided.
One Final Example: Get the Forecast from a City Name Directly
In this part you will learn to connect both endpoints to get the forecast by just the city name instead of latitudes and longitudes.
import requests
GEOCODING_URL = "https://geocoding-api.open-meteo.com/v1/search"
FORECAST_URL = "https://api.open-meteo.com/v1/forecast"
def get_coordinates(city):
"""Turn a city name into a place dictionary, or None if not found."""
response = requests.get(GEOCODING_URL, params={"name": city, "count": 1})
response.raise_for_status()
results = response.json().get("results")
return results[0] if results else None
def get_current_weather(latitude, longitude):
"""Fetch current temperature and wind speed for a location."""
response = requests.get(
FORECAST_URL,
params={
"latitude": latitude,
"longitude": longitude,
"current": "temperature_2m,wind_speed_10m",
"timezone": "auto",
},
)
response.raise_for_status()
return response.json()
place = get_coordinates("Mumbai")
if place is None:
print("City not found.")
else:
weather = get_current_weather(place["latitude"], place["longitude"])
current = weather["current"]
print(f"{place['name']}, {place['country']}")
print(f"Temperature: {current['temperature_2m']} °C")
print(f"Wind speed: {current['wind_speed_10m']} km/h")
Now here is what has been done.
• You first import the required modules like always and provide the URLs for the two endpoints.
• Now you make a function to get the coordinates of a city from its name, as coordinates are used in the forecast.
• In that function you use requests.get() to get data on the entered city, and return the first result. If the request failed, raise_for_status() raises an exception.
• You now make a second function to get the forecast of the city using endpoint 2.
• You again set response equal to requests.get() from the base URL of the 2nd endpoint, not the first, with the latitude and longitude passed in and the other parameters of your choice.
• You then get the response and return it.
• You then call the get_coordinates() function and store the result in a variable place, with the city name in the brackets.
• You then check if place is empty using an if place is None clause and print an error.
• If the place isn't None, you use the second function you created to print the forecast of the selected city.
Here is how the output can look:
Mumbai, India
Temperature: 32.1 °C
Wind speed: 11.4 km/h
Tips
• Set timezone (or use auto) whenever you request daily data, or the request will fail.
• Pass multiple variables as one comma-separated string, with no spaces.
• Check current_units and daily_units in the response so you know what units you're reading.
• Use response.raise_for_status() so failed requests raise an exception instead of passing silently.
• Cache results instead of calling the API repeatedly for the same location.
Weather data by Open-Meteo.com, licensed CC BY 4.0.#api #documentation #python #software #coding #development #engineering #inclusive #community
How to get Weather-Data in Python using Open Meteo API.
For four days this week, my company's homepage showed the news as it stood on Monday. New entries were being published the whole time. The API returned them. The detail pages worked. The deployment was healthy. The only thing wrong was the thing visitors actually see: the public list and the feed were being served from old static files that sat in front of the dynamic routes, quietly shadowing everything new. Every check we ran was green, because every check we ran was pointed at a layer underneath the problem.
Here is the uncomfortable anatomy of it. The publishing pipeline worked end to end: entries were written, reviewed, and marked published. The data layer worked: query the entries API and you got the current set, sixteen rows, correct order. The artifact layer worked: the feed generator produced thirty current items. What did not work was the last meter. Inside the deployment, static files for the list page and the feed still existed from an earlier era, and the platform served those files instead of the dynamic routes behind them. Static wins over dynamic. Nobody had told the static files to retire, so they kept doing their job — serving Monday, perfectly, with a 200 status.
And the fix, when it was finally written, sat waiting for three days. Not because it was wrong, but because it had a hard prerequisite nobody owned: before the static files could be removed, their ten legacy entries had to be migrated into the entry store, or the list would go empty the moment the shadow lifted. A correct fix with an unowned prerequisite is not a fix in progress. It is a frozen homepage with extra steps.
Green is a property of the layer you checked
We love aggregate health signals because they compress a system into one reassuring color. But a deployment being READY proves the platform accepted your files. An API returning 200 proves the data layer answers. Neither says anything about which bytes a first-time visitor receives, or how old those bytes are. Freshness is not implied by health. It is a separate property, of a separate layer, and it fails silently precisely because every layer below it is fine — there is no error anywhere to alert on. The system is not broken. It is faithfully serving the wrong thing.
The second failure was quieter. When the shadow finally lifted, the feed URL returned 404 — a rewrite pointed at an intermediate endpoint, and the platform does not chain rewrites, so the request died between two correct-looking rules. Again: each rule, read alone, was right. The composition was wrong. Layer-by-layer correctness, system-level falsehood.
The probe has to stand where the visitor stands
The rule I took from this week is simple to state and annoying to practice: for anything public, the acceptance check must fetch the public surface, as a visitor, and assert on its content's age — not its status. A list page that returns 200 with a newest item four days old is a failing page wearing a passing costume. So the check is now embarrassingly literal: open the public list, read the date of the top item, compare it against the newest published entry. Open the feed, count the items, compare. If the numbers disagree, the site is down in the only sense a visitor cares about, whatever the monitors say.
Two honest limits. First, this is not an argument against static files or caching — the same week, an immutable-cache rule taught us the mirror lesson, that a changed stylesheet under an unchanged name is invisible to returning visitors for a year. Static layers are fast and good. They are also where freshness goes to die, so they need an owner and a retirement plan, not just a deploy. Second, none of this required more people. A one-person company cannot staff a watch rotation. It can only refuse to let "published" and "deployed" and "green" impersonate the one word that matters: visible.
So here is the test I now apply to every launch and every fix, including my own team's: do not show me the pipeline. Show me the page a stranger gets, and tell me how old it is. If you cannot date the bytes the public is reading, you do not have a healthy website. You have a healthy system, serving last week, at full speed, with every light green.
— Jiahui Miao, founder of vertciti, building a personal AI operating system for one.#ai #agents #machinelearning #discuss #software #coding #development #engineering #inclusive #community
Green Checks, Frozen Homepage. Your Site Can Be Serving Last Week While Every Monitor Says Fine.
