Front-end Date Pickers

HugoTea:

The Gov.uk design team published research around data entry for dates https://designnotes.blog.gov.uk/2013/12/05/asking-for-a-date… Ultimately they found the best experience was three text boxes, day, month and year. They also have this “pattern” for helping with implementation https://design-system.service.gov.uk/patterns/dates/

joelanman:

(I worked on this pattern) Our guidance is to accept both names and numbers in the back end so people can enter either. In our testing, text inputs (with a numeric keyboard on mobile) is better than selects. Some people struggle with using selects, we don’t see those issues in text inputs. That guidance maybe be about people not knowing the order of the inputs (DD MM YY, MM DD YY) but our pattern is clearly labeled for each input so we don’t get that issue.

You Should Write An Agent

Code

The full code from the article:

import json
import subprocess
from openai import OpenAI

client = OpenAI()

tools = [{
    "type": "function",
    "name": "ping",
    "description": "ping some host on the Internet",
    "parameters": {
        "type": "object",
        "properties": {
            "host": {
                "type": "string",
                "description": "hostname or IP",
            },
        },
        "required": ["host"],
    },
}]

context = []

def ping(host=""):
    try:
        result = subprocess.run(
                ["ping", "-c", "5", host],
                text=True,
                stderr=subprocess.STDOUT,
                stdout=subprocess.PIPE)
        return result.stdout
    except Exception as e:
        return f"error: {e}"

def call(tools):
    return client.responses.create(model="gpt-5", tools=tools, input=context)

def tool_call(item):
    result = ping(**json.loads(item.arguments))
    return [item, {
        "type": "function_call_output",
        "call_id": item.call_id,
        "output": result
    }]

def handle_tools(tools, response):
    if response.output[0].type == "reasoning":
        context.append(response.output[0])
    osz = len(context)
    for item in response.output:
        if item.type == "function_call":
            context.extend(tool_call(item))
    return len(context) != osz

def process(line):
    context.append({"role": "user", "content": line})
    response = call(tools)

    while handle_tools(tools, response):
        response = call(tools)
    context.append({"role": "assistant", "content": response.output_text})
    return response.output_text

def main():
    while True:
        line = input("> ")
        result = process(line)
        print(f">>> {result}\n")

if __name__ == "__main__":
    main()

https://transitions.substack.com/p/what-burning-26-billion-prompt-tokens

https://github.com/vinhnx/vtcode

https://ampcode.com/how-to-build-an-agent

https://github.com/gustofied/P2Engine

https://x.com/rerundotio/status/1968806896959402144

https://simonwillison.net/2025/Aug/9/

https://news.ycombinator.com/item?id=45840088

https://news.ycombinator.com/item?id=45891968

MC_squaredJL via Reddit:

I think an AI interface could help with the problem. Specific issues:

• New patients: Not all adult NP need to be scheduled 1 hour with Dr and then 1 hour in hygiene which is normal protocol. We gave them a flowchart and list of questions to determine if they should be scheduled as above or for 90 minutes with the hygienist. Literally 3 questions. I have a 17yo new patient scheduled as an adult. She could have been scheduled 1 hour in hygiene.
• We have 1 intraoral scanner for 2 docs. We have color coded the appointments that need the scanner and told them how to offset the appointment so assistants do not need the scanner at the same time. My partner and I have crown preps scheduled at the exact same time today. Twice.
• Last week it was root canals at the same time. Also equipment needed at the same time.
• They do not get the concept of scheduling for production. I’ve literally had days that my first 3 hours were $0 because they scheduled crown seats, denture appointments, surgical follow-ups.

Reading the comments, it looks like the underlying issue is lack of SOP training. However, despite a lot of training and meetings, it’s understandable that it’s difficult to apply protocols in the moment. Especially because receptionists will lack the understanding of why it’s important to separate appointments so that equipment doesn’t have to be shared.

AI’s Dial-Up Era. The essay argues that AI is in its dial-up era for two reasons:

1. AI is still in its infancy stage, where the benefits brought by the technology have increased productivity and jobs, rather than decrease/eliminate it. Looking at other industries, it’s because there is still demand and its growth hasn’t been saturated.
2. AI is showing parallels to the dot-com crash, where a lot of companies jumped on the WWW hype. As the author points out, a lot of companies are becoming overvalued and do not show any Product Market Fit.

The article shows that Automation hasn’t reduced jobs (apart from the tech sector, but that’s a different story) for several reasons:

• real-world complexity
• regulatory/insurance hurdles
• Jevons Paradox: “economic principle that a technological improvement in resource efficiency leads to an increase in the total consumption of that resource, rather than a decrease”.

ByteCoder:

I feel like we’re back in the mainframe era. A lot of software can’t operate without an internet connection. Even if in practice they execute some of the code on your device, a lot of the data and the heavyweight processing is already happening on the server. Even basic services designed from the ground up to be distributed and local first - like email (“downloading”) - are used in this fashion - like gmail. Maps apps added offline support years after they launched and still cripple the search. Even git has GitHub sitting in the middle and most people don’t or can’t use git any other way. SaaS, Electron, …etc. have brought us back to the mainframe era.

bccdee:

I find the argument for the bubble to be extremely straightforward.

Currently, investment into AI exceeds the dot-com bubble by a factor of 17. Even in the dot-com era, the early internet was already changing media and commerce in fundamental ways. November is the three-year anniversary of ChatGPT. How much economic value are they actually creating? How many people are purchasing AI-generated goods? How much are people paying for AI-provided services? The value created here would have to exceed what the internet was generating in 2000 by a factor of 17 (which seems excessive to me) to even reach parity with the dot-com bubble.