Development is getting more toxic then ever. 2-2.5 years ago we were using claude and ai while laughing at its capabilities, now i think karma is hitting us, the working environment is getting worse over time, companies want shipment and stressfully shipment these days, while there is no time for reviewing code, refactoring code because it is not scalable for the future, using the brain muscle only for a critical issues. Bro no one is even understanding how he wrote the code and it is getting worse with every model anthropic or openAi drops. Everything is seen as the tokens you use.There is no room for critical thinking and real problem solving, bussiness people are getting crazy with ai and then expect it finished in 2 days, remembering intern days we were meant to think to debug to take days for a small ticket, now a 100+ prs are getting merged in one project in a couple of days.And the thing that started to be really alerting is that agentic ai's are getting better and most times with right prompts they are not leaving a real concerning technical debt. We all know that ai will not take our place a 100% but it is effecting the market, environment everything by 200%. If anyone loves programming for solving problems and critical thinking now is facing a hard time being comfortable and happy with his job.
What is your opinion guys?

​

I've been wanting to extend my phone's second screen via USB for a while now, but I can't find any easy or fast apps, and AI makes it very complicated.

I'm using Linux Mint xfce.

Hardware:

6gb of ram

Intel i5 2nd gen

No dedicated GPU

Our finance lead got a voicemail that sounded exactly like our CFO, asking her to push a vendor payment before end of day. She called him back and he never left it. Now IT is asking me how to detect AI generated voice on voicemails and inbound calls and all I have is "listen for weird pauses", which clearly isn't going to hold up. Is anyone running something on the audio itself, or is it all callback procedures?

I’ve been thinking about something lately.
AI assistants are getting incredibly capable, but I feel like there’s still a fundamental problem with how they remember us.
Sometimes I want AI to remember things about me — my preferences, past experiences, important decisions, or ongoing projects — so I don’t have to explain everything again.
But at the same time, the idea of an AI building an increasingly detailed profile of my life is a little unsettling.
**What if AI memory worked differently?**
Imagine having a personal memory system where:
You decide what AI is allowed to remember.
You can see, edit, or delete individual memories.
Different AI assistants can access only the information you explicitly allow.
Sensitive memories stay private unless you choose otherwise.
Essentially, AI that gets to know you over time, without giving up control of your personal information.
I know some AI assistants already have memory features, but I’m curious whether people actually trust them.
**A few genuine questions:**
Do you actually want AI to remember more about you, or does that feel invasive?
Have you ever been frustrated because an AI forgot something important from a previous conversation?
Would having full control over AI memory change how comfortable you feel using it?
Or is this solving a problem that most people don’t really care about?
I’m exploring this as a potential side project, but I’m genuinely unsure whether there’s a real need.
I’d especially love to hear skeptical opinions.

Good morning everyone,

Having finished my internship, I was offered a position to stay at the company as a Junior AI Engineer. I’m making this post because I’ve been with the company for 2-3 months now, and my actual workload is very light. So far, I’ve been assigned tasks like building a simple PDF-based RAG/AI assistant and doing some basic QA testing, but that’s pretty much it.

Over the last few weeks, I’ve been spending most of my working hours without any assigned tasks. I’m using this time for self-study, but I feel like I’m not really growing or developing within the company.

Has anyone else experienced something like this at the start of their career? How did you handle it, and what’s the best way to approach my Senior / Lead to ask for more responsibility or tasks?

I’m building a repository intelligence tool and want to understand where it could be genuinely useful before developing it further.

Currently, it analyses public Python repositories on GitHub, creates snapshots at specific commits, compares code structure and relationships, highlights changes such as removed APIs, and generates an evidence-linked report.

It doesn’t automatically fix bugs or certify that a release is safe.

I’m interested in situations where understanding a repository becomes difficult or time-consuming—whether you’re maintaining it, reviewing changes, taking over someone else’s code, or investigating a failure.

A few questions for developers and engineering leads:

• What’s a recent task where you spent too long figuring out how a codebase worked or what changed?
• How did you investigate it, and where did your existing tools fall short?
• What specific output would have helped you make a decision or finish faster?
• Is this a recurring problem or something you face only during migrations, incidents, or handovers?

I’m considering use cases such as release comparisons, onboarding, change-impact analysis, and regression investigation, but I don’t want to assume those are the right priorities.

Concrete examples would be especially helpful. And if Git, your IDE, or existing tools already handle this well enough, I’d like to hear that too.

I'm a software developer working at a bank, and I feel like we're way behind when it comes to AI adoption.

Every time AI comes up, the conversation is mostly around security, compliance, data privacy, etc. I understand these are important, especially in banking, but it feels like we're moving extremely slowly.

For example, our bank deployed a GPT model through Azure AI Foundry and built an internal ChatGPT-like interface for employees. That's pretty much the extent of AI adoption I've seen so far.

Meanwhile, I keep seeing people talk about using Claude Code, Cursor, AI agents, and building AI-powered features using LLM APIs.

I'm curious how things work at other companies.

• Are you actually building AI-powered features using APIs from OpenAI, Anthropic, etc.?

• How does your company approach new AI initiatives? Do developers get to experiment and propose ideas, or does everything have to go through multiple approval processes?

• How are you handling security and compliance concerns when integrating AI into existing applications?

• Are companies actually using AI agents and automation in production, or is most of it still experimentation?

I'm especially interested in hearing from developers working at banks, fintech companies, or other large enterprises.

I want to understand what real-world AI adoption looks like beyond just building an internal ChatGPT wrapper.

How is your company approaching this? Are you seeing meaningful AI adoption, or is it mostly hype and internal chatbots?

I’m asking because the API marketplace now describes two very different models, and most comparison posts ignore the distinction.

Model 1: the marketplace - third‑party providers list their APIs; you browse and subscribe. RapidAPI is the classic example, and APILayer and Zyla Labs also operate here. The selection is huge, but each API is its own product with separate pricing, quotas, and quality standards.

Model 2: the catalog, a smaller set of services accessed under a single subscription and a single key. You get fewer choices, but only one bill and one integration pattern.

Where each model shines

• Need something niche, such as a specific sports‑data feed? Marketplace.
• Need a boring utility layer that every product ends up requiring (file conversion, validation, OCR, geolocation)? Catalog saves you from managing dozens of separate plans.
• Shipping agents? Fewer keys and a single billing ceiling matter more than you might think.

I’m a co‑founder at ApyHub, which uses the catalog model, so take my framing with that in mind.

Genuinely curious

• Which marketplaces are you using right now?
• Has anyone left a marketplace, and why?
• Do you care more about selection or about having a single bill?

So, I am working on a project for which I am currently using a free api key.
And now I am thinking of deploying it as an extension on chrome, but the major problem is, do I keep my key and let the users use it, or ask them to BYOK. But doing so will raise concern when a new user see a newly added extension asking them to enter their own API key.
This reduces the trust instantly.
Can anyone suggest what can I do in this situation?

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