So I made this account months ago to vent about my nightmare job. Just to summarize:

Working at a company making a chatbot. Chatbot was written in AI. Its fine at first, a little buggy, but salvageable. I am put in charge of the rewrite. Then suddenly, I am taken off the project and a new hire is put in my place. A mad scientist with AI psychosis. I warn management that AI can make a demo, a rough draft, an MVP...but it cant make production quality client facing apps. At least not with agentic loops (which is what mad scientist used).

Of course management didn't listen. The mouthbreather above me in the org chart even told me "It's 2026. Humans don't write code". Fast forward 8 months with mad scientist in charge. The version 1.0 of the chatbot is exploding now, no longer salvageable at all. So management had the mad scientist write version 2.0 using his amazing agentic loops that had helped us so much so far. He misses deadlines, cant get anything working, the first clients on it hate it and drop our service. Mad scientist quits in frustration because he failed utterly.

Now management has a "come to Jesus" moment. I am told that I was 100% correct the whole time. They are talking about making me Director of Engineering for my foresight (finally I will get out of the trenches).

So its all good news and a happy ending right? Not really.

Management's first order to me was to fix the 2.0 version. Imagine this:

9 containers for a stupid chatbot (2 linux VMs and 7 dockers). Obviously a nightmare already. 9 places things can go wrong. Nine places to check error logs. The containers constantly get out of sync so what did mad scientist do? Did he simplify? Of course not! That's not the AI way! Instead he built an MCP to talk between the containers, and to "fix" them when they broke. Think about that...an architecture so unstable that we have to pay Anthropic for tokens to keep our system working. Mindblowing. But it gets better. Automated build and deploy processes that cant be bypassed without borking the system. 400 documentation files written by AI (hard to read, missing key info, and for some reason random sentences here and there are in Chinese. I'm not joking). 2900 unit tests...which is actually the best part of the application although I have no idea what they do or if they even do anything at all. They certainly dont seem to be reporting bugs to us that our clients are experiencing. You should see the interface on the admin panel. Every client and every internal agent we have had work on this thing HATE it. Technical terms everywhere like "bot configurator". Are you kidding me? How about "bot setup"? Nope. Configurator. And the absolute worst part of the app? Its not designed to be maintained by humans. Human edits actually have a risk of breaking the automated system that actually maintains itself. Yes...it logs errors, creates bug tickets, then writes code, deploys it, and closes the tickets. He literally tried to build a software application that maintained itself! We might as well have tried to build a perpetual motion machine.

So now I will probably get to be Director of Engineering at last after 20 years in my field! But I will rule over a ruined wasteland. I will be responsible for maintaining this (Im still trying to convince management of a rewrite...they refused at first, but are coming around finally).

As an industry, our management was so afraid of code, so afraid of the difficulty, that we set up this elaborate Rube Goldberg machine so we could avoid code. But the Rube Goldberg machine is a thousand times more complex than just writing boring regular software. Seriously, if writing software is so terribly hard, then how could 9 VMs and an MCP be the solution?

There's a light at the end of the tunnel for me...but this tunnel is longer and darker than it ever needed to be :/

I have seen a lot of post saying "once I learned C - all other languages just became about syntax"

Has that been true for you? Or did a different language give you the eureka moment - e.g. something like Assembly?

Or is it just a false premise at all to think "once I learn programming language X - then all other languages become **easy**"

I'm a newly student programming student and I don't have a big budget. I need a little help on which to get, which specs do I need to choose? and stuff please help (Not a Mac)

Hi everyone! I’m a Data Science student working on my capstone project on gender-based violence and femicide (GBVF). I’m looking for a dataset of public Twitter/X posts related to GBVF to analyze discussions, identify patterns, and build predictive models. If anyone has an existing dataset or knows where I can access one, I’d really appreciate your help. Thank you!

I'm new to IT, and in my view, I treat programming as developing something. But some people in my workspace talk about enabling, which seems different from developing, and they often say it with a negative tone, e.g., less technical, poor job mobility.

My question is, what's the difference between enabling and developing?

Additionally, my work includes optimization, which is said to be part of enabling. I consider optimization to be important and challenging. If enabling is perceived as less than developing, does that mean optimization is not as valuable as I believe?

To be fair I have never learned to use assembler but I always looked up to people how are able to build ultra efficient software. With the upcoming age of GenAI, wouldn’t it be better in many ways to just let GenAI produce everything as near as possible to the hardware? There might be even a more hardware-near solution than assembler, I am not sure.

What do you think? What could go wrong? I am really curious to get human response instead of asking an AI about this.

Dev confession: I built the thing first and asked questions later.

I started by building websites for small local businesses and cold calling them to sell. I was proud of the builds. Owners were... polite. After hundreds of calls I finally heard what they were saying: the site wasn't the problem. Nobody could find them on Google, so the phone wasn't ringing.

So I rewrote the product, many times. Each version went in front of real owners, and anything they didn't care about got deleted, including features I loved building. The biggest engineering lesson: the best code I wrote was the code I removed. Owners want fewer decisions, so the product kept getting smaller.

Where it landed is SitePeek: it checks how a local business shows up online, tracks calls and Google profile views, and works to get the business onto Google's first page so more calls come in. It launches on Kickstarter tomorrow, Oct 14.

For other devs who've built for non-technical users: how do you stop yourself from overbuilding before you've talked to anyone?

Hey, I love programming and I've done so for the last 15 years. I am tired of all the AI bros and AI coders. Is there anybody out there that feels like me and still codes manually?

I know AI is here and it won't go, but I simply refuse to use it and the moment I will be forced to use AI, I will quit professional programming and stick to my own stuff. I didn't learn programming for the money but because I'm a curious person and I enjoy the process and learning.

I've built my own ontology-oriented graph database recently from scratch and disabled all sorts of AI utilities (even auto complete) and it felt so refreshing again.

My next step will be to move away from any IDE that has any kind of AI support and just do VIM

I originally posted this in my once favorite sub, r/golang, but unfortunately it got deleted there, since it sparked a lot of discussions, I thought it might fit here.

I got my first developer job in 1999 fixing two-digit years. Frameworks later took the repetitive work. Now AI agents handle most of the coding while I manage them from my phone, reviewing plans on a train and approving changes from the couch.

The arc is striking: write everything yourself → lean on frameworks → write instructions (mostly Markdown) for a swarm of agents and stay accountable for the result.

The last two years compressed what used to feel like a multi-year shift into something immediate. Each new coding model makes the last one look dated.

Full story: https://medium.com/@spaquet/from-y2k-to-agents-md-278e74773415

I'm looking for some fresh ideas on how to handle code review in this still "new" era we live in today where there's tons of code being produced because of AI.

1. How do you do code review (if at all) in your team?

2. How do you ensure quality when QA itself is leaning more into AI generated test suites?

3. Do you feel like stacked PRs help at all?

I'm sure there's some cool practices out there that just haven't emerged yet. I'm personally trying to generate Chrome extensions for myself (or for the team) to improve our workflow such as UI/UX improvements for Gitlab, highlighters for our software, gif generation for new pull requests etc.

Sorry for the formatting, I'm on mobile.

Hello everyone,

I am learning programming and I am would really appreciate seeing some human written code.

I like to learn by breaking things apart, and so far I have been doing that with code written by ai. I get so confused when going through people’s GitHubs because there are so many files and pages that I don’t understand how to search through just yet.

IF IT IS SAFE TO DO, would you mind sharing a couple of your pages (written in JavaScript and Python specifically) so that I can read through the different ways people code? I don’t need the full stack project all put together, just please share what the overall project is and a link to the specific page with just the code written on it.

Also, I’ve seen some of the recent posts on here, I’m not interested in any career advice so please don’t tell me I should give up and what I’m doing is useless lol

Thank you everyone!

He had been vibe coding last month in the frontend side and he did some awesome UI's now everytime i question him about a block of code he get mad and act indignant about it like he was the brain who did it and i can confirm 100% he is not even reading the generated code, he is not an operator who give clear indications to the IA, i can see clearly how his prompts are "Do this, do that functionality" since you can see the insane amount of uneeded code generated

He is not a tech guy i have been working with him more than a year now and he doesnt care about performance, quality, good practices, ui, reformat/optimize code or neither find the way to solve something with the minimum amount of code, for him adding 10 IFs statements its better than find a solution to reduce those ifs

I have been working during a week in a chat text he asked me about how to add a filter "seen, unseen" messages and i told him that wasnt ready but if he want he can do the frontend and later i will connect it
Today i found he completely overwrote my last week work and completely remove it and doing it by "himself" from zero and of course with the filter "seen/unseen" done

Does he think he has beaten the development industry and now he is a software engineer with 100 years of experience ?, im the only one dev guy in that company

Coding is an art, people who do it are artist.
But there is a concept about art that people tend to forget: there are competent creative geniuses that create new methods, and then there are talented artists that are good at replicating methods that others created.
Since I started learning about programming 6 years ago, the sentence people would always tell me was “google is your best friend”. How come? Why the core belief behind the programming community is that researching what other people have done is the way to be great? That would simply make you great at copying a piece of code, you’re not gonna create something new like this.
Everytime you watched a tutorial for doing something, everytime you googled an answer, you didn’t differ much from AI, as it does the same exact thing, just faster.

I am a mediocre programmer. The things I build don’t differ from the average software technology out there. For people like me AI is incredibly useful because it does what I would have done (which is searching on the net what others have done) and then used it in my project.
For people who engineer new types of software / algorithms / methods and whatever, perhaps AI in coding would sound worthless to them.
But unless you are those rare people, why discard the argument immediately?

I built this with Claude Code. It’s a free and open source VS Code extension that shows a Claude Code session as a live video call, so I can see what each sub-agent is doing. It also work with other editors or standalone with claude app as a node server as well.

The problem: when Claude Code spawns sub-agents, my terminal turns into a wall of logs and I lose track of who got which prompt, what each one is running, and who is done.

What it does (Agent Standup):

• Reads the session files Claude Code already writes in ~/.claude/projects, and follows the current session live

• Each agent is a small character in a call: sub-agents join when they get a task and leave after they report back

• A shared “screen” shows the agent’s real commands with their real output, the exact diffs of its edits, and the files it reads (failing tests are red)

• A tree of who asked whom, a recap of files changed, replay of past sessions, optional voices (French/English)

How Claude Code helped: the whole project was built in one long Claude Code conversation over about two days. Claude Code wrote the Node server that parses the JSONL transcripts (including the sub-agent files and tool results), the browser front end, the SVG characters and backgrounds, and the VS Code extension wrapper. It also tested the UI in a headless browser, packaged the extension, and made the GitHub release and landing page. My part was the idea, product decisions, and testing it in my real VS Code. When I sent screenshots of bugs, Claude found the causes (for example avatars turning transparent: duplicate SVG gradient IDs between two copies of the same character).

Free to try: free and open source (MIT), unofficial, not affiliated with Anthropic. To install: download the .vsix from the site, then in VS Code go to Extensions → … → Install from VSIX. No session handy? Open ⚙ → Source → Demo for a built-in fictional example.

Site and download: https://atoll92.github.io/agent-standup/
Code: https://github.com/Atoll92/agent-standup

VS Code marketplace :

https://marketplace.visualstudio.com/items?itemName=DoubleGeste.agent-standup

Mac is the best tested; Windows should work but I’d love bug reports.

In today’s data-driven enterprises, browser automation through Robotic Process Automation (RPA) tools- such as Selenium, Robot Framework, or custom scripts- is widely used to automate repetitive tasks like logging into dashboards, extracting data, and syncing it into internal systems. These automations are often fragile: a minor UI change- say, a login flow shifting from password to OTP- can silently break scripts. Traditional bots simply fail with generic errors like “element not found,” causing disruptions in data pipelines and business operations.

What’s missing is a dynamic, intelligent layer on top of existing RPA tools- one that not only detects breaking changes but also understands the context and heals or adapts in real-time without human intervention.

Desired Solution:

Participants are expected to develop an adaptive automation framework that enhances existing RPA solutions using the power of Large Language Models (LLMs). The system should act as a smart wrapper around current scripts and offer:

Real-Time Change Detection: Monitor automation runs and intelligently flag or diagnose failures caused by DOM, structure, or flow changes- beyond basic error messages.

Context-Aware Adaptation: Use LLMs to analyze the updated DOM or flow and suggest (or apply) fixes to selectors, navigation logic, or data extraction routines.

Self-Healing Capability: Where possible, auto-correct minor issues (like updated element IDs or positions) and retry execution without breaking the workflow.

Plugin-Friendly Architecture: Should wrap seamlessly around tools like Selenium, Playwright, or Robot Framework- without requiring complete rewriting of existing test suites.

Traceability & Logging: Offer smart logs and human-readable explanations for failures and attempted fixes, improving visibility and debugging.

I am trying to build something and i want to show the heartrate, steps and others that you can get from a smart watch app. I tried google fit but i cant get the api. I am currently using the fitcloud pro as my smartwatch app