Earlier when you wanted to try a programming language, you needed to install compiler and IDE software to your computer. Now there are many on-line tools that allow you to test programming with many programming languages.
Many languages on one service:
https://onecompiler.com/
https://tio.run/#
https://www.codechef.com/ide
https://ideone.com/
https://www.codingninjas.com/studio/online-compiler
JavaScript
https://jsfiddle.net/
C and C++
https://www.programiz.com/c-programming/online-compiler/
https://godbolt.org/
https://cppinsights.io/
Python
https://www.programiz.com/python-programming/online-compiler/
https://www.online-python.com
https://www.online-python.com/?utm_content=cmp-true
https://www.w3schools.com/python/python_compiler.asp
https://www.onlinegdb.com/online_python_compiler
https://onecompiler.com/python
https://www.tutorialspoint.com/online_python_compiler.php
https://pythontutor.com/python-compiler.html#mode=edit
https://www.mycompiler.io/online-python-compiler
https://pythontutor.com/visualize.html#mode=edit
https://ide.geeksforgeeks.org/online-python-compiler
https://www.scaler.com/topics/python/online-python-compiler/
https://python.microbit.org/v/3
https://trinket.io/embed/python3/a5bd54189b
Scratch
https://scratch.mit.edu/projects/editor/?tutorial=getStarted
Links to coding tutorials:
https://www.hostinger.com/tutorials/learn-coding-online-for-free
Other tools:
https://coding.tools/
https://webcode.tools/
76 Comments
Tomi Engdahl says:
https://www.epochconverter.com/
Tomi Engdahl says:
https://developer.mozilla.org/en-US/plus/ai-help
Tomi Engdahl says:
Typically, things that software developers do and are known for:
0. Break read only Friday rule
1. Test in prod
2. Spaghetti code
4. No comments or documentation for your code
5. Git commit messages are short or nil
6. Cursing at others for their poor code
7. Have strong imposter syndrome
8. Superiority complex, primarily when you use Vim or Linux as the primary developer machine
Tomi Engdahl says:
Kyle Wiggers / TechCrunch:
Quora’s Poe launches Previews, letting users create web apps like data visualizations, games, and animations using more than one LLM like Llama 3 and GPT-4o
Quora’s Poe now lets users create and share web apps
https://techcrunch.com/2024/07/08/quoras-poe-now-lets-users-create-and-share-web-apps/
Poe, Quora’s subscription-based, cross-platform aggregator for AI-powered chatbots like Anthropic’s Claude and OpenAI’s GPT-4o, has launched a feature called Previews that lets users create interactive apps directly in chats with chatbots.
Previews allows Poe users to build data visualizations, games and even drum machines by typing things like “Analyze the information in this report and turn it into a digestible and interactive presentation to help me understand it.” The apps can be created using more than one chatbot (say, Meta’s Llama 3 and GPT-4o) and draw on info from uploaded files including videos, and can be shared with anyone via a link.
Previews are a lot like Anthropic’s recently introduced Artifacts, dedicated workspaces where users can edit and add to AI-generated content like code and documents. But Artifacts is limited to Anthropic’s models, whereas Previews supports HTML output — with CSS and Javascript functionality at the moment (and more to come in the future, Quora’s pledging) — from any chatbot.
Tomi Engdahl says:
5 New Open Source Programming Languages That You Might Have Missed!
You never know, one of these programming languages can be the next big thing!
https://news.itsfoss.com/new-open-source-programming-languages/
Tomi Engdahl says:
Goodbye Manual Prompting, Hello Programming With DSPy
The DSPy framework aims to resolve consistency and reliability issues by prioritizing declarative, systematic programming over manual prompt writing.
https://thenewstack.io/goodbye-manual-prompting-hello-programming-with-dspy/
The development of scalable and optimized AI applications using large language models (LLMs) is still in its growing stages. Building applications based on LLMs is complex and time-consuming due to the extensive manual work involved, such as writing prompts.
Prompt writing is the most important part of any LLM application as it helps us to extract the best possible results from the model. However, crafting an optimized prompt requires developers to rely heavily on hit-and-trial methods, wasting significant time until the desired result is achieved.
Tomi Engdahl says:
https://hackaday.com/2024/08/05/serve-your-next-website-with-quickbasic/
Tomi Engdahl says:
https://hackaday.com/2024/08/05/embedded-python-micropython-toolkits/
Tomi Engdahl says:
https://www.make.com/en
Tomi Engdahl says:
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Tomi Engdahl says:
HP kehitti uuden turvajärjestelmän tietokoneen käynnistykseen
8.8.202408:01
HP:n Endpoint Security Controller -piiri valvoo, ettei tietokoneeseen ole vaihdettu haitallisia laiteohjelmistoja.
https://www.mikrobitti.fi/uutiset/hp-kehitti-uuden-turvajarjestelman-tietokoneen-kaynnistykseen/70a33d91-5221-4a81-b81c-5bf5467b0ba2
Tomi Engdahl says:
Why Developers Are Ditching GitHub Copilot “I don’t need autocomplete. I need to tell Claude what I want, and it will give me the code.”
Read more at: https://analyticsindiamag.com/developers-corner/why-developers-are-ditching-github-copilot/
Tomi Engdahl says:
https://www.make.com/en/product
Make
One automation platform. Unlimited possibilities.
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Tomi Engdahl says:
https://www.theregister.com/2024/09/03/gnu_screen_5/
Tomi Engdahl says:
https://hackaday.com/2024/09/18/from-high-level-language-to-assembly/
Tomi Engdahl says:
Assessing Developer Productivity When Using AI Coding Assistants
https://hackaday.com/2024/10/15/assessing-developer-productivity-when-using-ai-coding-assistants/
We have all seen the advertisements and glossy flyers for coding assistants like GitHub Copilot, which promised to use ‘AI’ to make you write code and complete programming tasks faster than ever, yet how much of that has worked out since Copilot’s introduction in 2021? According to a recent report by code analysis firm Uplevel there are no significant benefits, while GitHub Copilot also introduced 41% more bugs. Commentary from development teams suggests that while the coding assistant makes for faster writing of code, debugging or maintaining the code is often not realistic.
None of this should be a surprise, of course, as this mirrors what we already found when covering this topic back in 2021. With GitHub Copilot and kin being effectively Large Language Models (LLMs) that are trained on codebases, they are best considered to be massive autocomplete systems targeting code. Much like with autocomplete on e.g. a smartphone, the experience is often jarring and full of errors. Perhaps the most fair assessment of GitHub Copilot is that it can be helpful when writing repetitive, braindead code that requires very little understanding of the code to get right, while it’s bound to helpfully carry in a bundle of sticks and a dead rodent like an overly enthusiastic dog when all you wanted was for it to grab that spanner.
Until Copilot and kin develop actual intelligence, it would seem that software developer jobs are still perfectly safe from being taken over by our robotic overlords.
Devs gaining little (if anything) from AI coding assistants
https://www.cio.com/article/3540579/devs-gaining-little-if-anything-from-ai-coding-assistants.html
Code analysis firm sees no major benefits from AI dev tool when measuring key programming metrics, though others report incremental gains from coding copilots with emphasis on code review.
Coding assistants have been an obvious early use case in the generative AI gold rush, but promised productivity improvements are falling short of the mark — if they exist at all.
Many developers say AI coding assistants make them more productive, but a recent study set forth to measure their output and found no significant gains. Use of GitHub Copilot also introduced 41% more bugs, according to the study from Uplevel, a company providing insights from coding and collaboration data.
The study measured pull request (PR) cycle time, or the time to merge code into a repository, and PR throughput, the number of pull requests merged. It found no significant improvements for developers using Copilot.
In addition to measuring productivity, the Uplevel study looked at factors in developer burnout, and it found that GitHub Copilot hasn’t helped there, either. The amount of working time spent outside of standard hours decreased for both the control group and the test group using the coding tool, but it decreased more when the developers weren’t using Copilot.
Uplevel’s study was driven by curiosity over claims of major productivity gains as AI coding assistants become ubiquitous, says Matt Hoffman, product manager and data analyst at the company. A GitHub survey published in August found that 97% of software engineers, developers, and programmers reported using AI coding assistants.
“We’ve seen different studies of people saying, ‘This is really helpful for our productivity,’” he says. “We’ve also seen some people saying, ‘You know what? I’m kind of having to be more of a [code] reviewer.’”
A representative of GitHub Copilot didn’t have a comment on the study, but pointed to a recent study saying developers were able to write code 55% faster using the coding assistant.
“Our team’s hypothesis was that we thought that PR cycle time would decrease,” Hoffman says. “We thought that they would be able to write more code, and we actually thought that defect rate might go down because you’re using these gen AI tools to help you review your code before you even get it out there.”
“We heard that people are ending up being more reviewers for this code than in the past, and you might have some false faith that the code is doing what you expect it to,” Hoffman adds. “You just have to keep a close eye on what is being generated; does it do the thing that you’re expecting it to do?”
In the trenches, development teams are reporting mixed results.
Developers at Gehtsoft USA, a custom software development firm, haven’t seen major productivity gains with coding assistants based on large language model (LLM) AIs, says Ivan Gekht, CEO of the company. Gehtsoft has been testing coding assistants in sandbox environments but has not used them with customer projects yet.
“It becomes increasingly more challenging to understand and debug the AI-generated code, and troubleshooting becomes so resource-intensive that it is easier to rewrite the code from scratch than fix it.”
—Ivan Gekht, CEO, Gehtsoft
“Using LLMs to improve your productivity requires both the LLM to be competitive with an actual human in its abilities and the actual user to know how to use the LLM most efficiently,” he says. “The LLM does not possess critical thinking, self-awareness, or the ability to think.”
There’s a difference between writing a few lines of code and full-fledged software development, Gekht adds. Coding is like writing a sentence, while development is like writing a novel, he suggests.
“Software development is 90% brain function — understanding the requirements, designing the system, and considering limitations and restrictions,” he adds. “Converting all this knowledge and understanding into actual code is a simpler part of the job.”
Like the Uplevel study, Gekht also sees AI assistants introducing errors in code. Each new iteration of the AI-generated code ends up being less consistent when different parts of the code are developed using different prompts.
“It becomes increasingly more challenging to understand and debug the AI-generated code, and troubleshooting becomes so resource-intensive that it is easier to rewrite the code from scratch than fix it,” he says.
Seeing gains
The coding assistant experience at Innovative Solutions, a cloud services provider, is much different. The company is seeing significant productivity gains using coding assistants like Claude Dev and GitHub Copilot, says Travis Rehl, the CTO there. The company also uses a homegrown Anthropic integration to monitor pull requests and validate code quality.
Rehl has seen developer productivity increase by two to three times, based on the speed of developer tickets completed, the turnaround time on customer deliverables, and the quality of tickets, measured by the number of bugs in code.
Rehl’s team recently completed a customer project in 24 hours by using coding assistants, when the same project would have taken them about 30 days in the past, he says.
Still, some of the hype about coding assistants — such as suggestions they will replace entire dev teams rather than simply supplement or reshape them — is unrealistic, Rehl says. Coding assistants can be used to quickly sub out code or optimize code paths by reworking segments of code, he adds.
“Expectations around coding assistants should be tempered because they won’t write all the code or even all the correct code on the first attempt,” he says. “It is an iterative process that, when used correctly, enables a developer to increase the speed of their coding by two or three times.”
Tomi Engdahl says:
https://bandysc.github.io/AvaloniaVisualBasic6/
Tomi Engdahl says:
Top 6 Most Popular API Architecture Styles
https://www.youtube.com/watch?v=4vLxWqE94l4
Tomi Engdahl says:
https://www.securityweek.com/how-to-improve-the-security-of-ai-assisted-software-development/
Tomi Engdahl says:
The Definitive Make.com HTTP & API Masterclass
https://www.youtube.com/watch?v=JdKC8TOPfJE
In this video, I cover everything & anything about the HTTP request module. This is a top 1% skill that’ll let you connect any platform to Make.com—even if it’s not natively supported. Incredibly important if you want to upskill and take your Make.com talents to the next level.
lol beans says:
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Tomi Engdahl says:
How to Connect Any API To Make.com
https://www.youtube.com/watch?v=TMrVVO6r-y0
Make.com HTTP (Integromat) Setup for cURL API GET Request
https://www.youtube.com/watch?v=MKq_P92NNf8
Tomi Engdahl says:
Missing the Make.com Converger Module? – Set Variable Workaround
https://www.youtube.com/watch?v=7-fE0W1bzoo
Tomi Engdahl says:
Top 7 Ways to 10x Your API Performance
https://www.youtube.com/watch?v=zvWKqUiovAM
Tomi Engdahl says:
https://codemirror.net/
Extensible Code Editor
CodeMirror is a code editor component for the web. It can be used in websites to implement a text input field with support for many editing features, and has a rich programming interface to allow further extension.
Tomi Engdahl says:
https://www.hackster.io/news/a-shiny-new-programming-language-e41357506c46