Olá 👋

Estou neste momento com um desenvolvimento de software para empresas de contabilidade e consultoria. Temos o software que está a ser usado pelos clientes através de licença

**O software utiliza**
\- Base de dados mySQL
\- Laravel
\- Php
\- Scripts Python automáticos
\- Next.js

Está alojado num servidor **VPS** e na nossa **NAS ASUSTOR**

**Quando fazemos implementações novas, fazemos manutenção automática para a implementar**. Para **evitar interrupções** constantes no software real **qual a melhor opção de criar uma “cópia” do Software oficial e poder ter uma versão de testes** até estar tudo em condições para implementar a atualização definitiva?

**O que recomendam?**
**Obrigado a quem ajudar**

Bro, I’m serious. Whenever I focus on DSA, I end up neglecting development, and whenever I focus on development, I end up neglecting DSA. Help me, guys! I need guidance 😭

Hi, my name is Manish, and I’m based in Delhi. I come from a technical background and have prior experience building websites. I’ve also run Google Ads campaigns and have a good understanding of how to use paid advertising to generate leads and acquire clients.

I’ve saved up some capital that I’m looking to invest in growing a web design agency. I’m specifically looking for a business partner who is already running a web design agency but is struggling to acquire clients or scale their business due to limited marketing resources. Also I want the bank account on the website to be mine.

My plan is to invest in Google Ads and lead generation to help grow the business, while my partner focuses on delivering quality web design services and managing the operational side of the agency.

Ideally, I’d like to build a long-term partnership where we can combine our strengths, share responsibilities, and grow together. I’m also interested in building a strong brand with its own website and business identity, rather than simply working on a short-term project.

I’m looking for someone who is serious about growing their business, open to collaboration, and interested in building something sustainable together.

If you already run a web design agency and are looking for someone to help you scale through paid advertising and client acquisition, I’d love to connect and explore how we could work together.

I'm a junior software developer saving up for a MacBook that I hope to use for the next 5–7 years.

I'm choosing between a used 14-inch MacBook Pro M2 Pro (16GB RAM, 512GB SSD, advertised as 99% condition) and a brand-new 13-inch MacBook Air M5 (16GB RAM, 512GB SSD).

My main workloads are C#/.NET, Node.js, web/app development, Docker, databases, and AI coding assistants.

Portability isn't a priority. I care more about long-term reliability, sustained performance, battery life, and whether 16GB RAM will remain sufficient as my projects grow.

For developers who use Macs daily:

• Which would you choose for the next 5–7 years?
• Does the Pro's active cooling and better display outweigh the Air's newer chip and brand-new battery?
• Would you spend extra on a newer machine, or buy the older Pro if the price were similar?

I'd appreciate real-world experiences rather than benchmark comparisons. Thanks!

Something I keep noticing while building in the agent space for the past year.

Companies roll out AI agents to automate work. Then they hire people to watch those agents. Someone approves every refund. Someone reviews every PR the agent opens. Someone double-checks every email before it goes out.

So the job didn't get automated. It just changed from "doing the work" to "watching the work." Ten agents, ten babysitters.

It's like self-checkout at the supermarket. They removed the cashiers, then put a staff member next to the machines to watch everyone scan. The store still pays a person. The person just stands somewhere else now.

I think this is a big part of why AI feels like a bubble to a lot of people. It's not that the models are bad. They're honestly good enough for a lot of real work. The problem is that the human cost never left. If every agent still needs a human next to it, the savings don't show up, and the promise looks fake.

The fix isn't smarter models. It's trust. An agent should be able to earn the right to act alone, the way a new employee does. Small stuff first, checked against what actually happened (not what the agent says happened), and a human only for the weird cases.

Until that exists, we're not automating work. We're just renaming it.

Curious if others see this:

- If you run agents in production, how much human checking is still behind them?

- Has anyone actually reduced headcount on supervision, or has it only grown?

this is a serious problem if we do not fix that we might see a ai crises in the next few years .

Something's been bugging me.

You give an agent some limit. max_amount: 100, or whatever. You picked it on day one, half guess. Everything above it goes to a human.

That number is never going to change.

Not because the agent is bad. Because nobody's checking. The reviewer approves the same stuff every day, and after two weeks they stop reading and just click. Nobody's asking whether those reviews are still catching anything.

So you've got an intern whose work gets checked forever, and nobody noticed you haven't found a mistake in four months.

The only fix I've found: when something gets escalated, have the agent log what it would have done. Doesn't run it. Just records its answer. Then compare against what the human actually did.

Do that for a while and you've got real data. Agent agrees with the human 98% of the time in this amount band? Move the limit. It drops? Move it back.

Write the rule down before you look at the numbers, or you'll talk yourself into whatever you wanted anyway.

Anyone doing this already? Or is everyone just leaving the number where it is?

Imagine a founder asking a simple question.

How many active customers do we have?

The sales dashboard says 8,200.

The product analytics tool says 7,600.

The finance report says 7,950.

Each team has a reasonable explanation for its number.

Now the problem isn't really the dashboard. It's that nobody agrees on what an active customer means or which system should be trusted.

And adding AI on top of that data probably won't make the disagreement disappear.

I'm curious how other teams approach this.

Do you start by fixing the data pipelines, agreeing on metric definitions, or assigning ownership of the data?

What's actually worked for you?

Stanford HAI's 12 takeaways from the 2026 AI Index include an "entry-level squeeze": employment among software developers aged 22 to 25 fell nearly 20% since 2024.

A figure like this doesn't show a cause. Hiring cycles, interest rates and AI could all play a part. If you're early in your career, or you hire juniors: what are you actually seeing in applications, interviews and offers? What's working for people who are getting in?

Just fyi to check your providers. eg Railway is what we use for our hosting and our API keys got leaked and our tokens did definitely got stolen and used. So I would probably rotate any keys you have anywhere, and check with your providers!

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'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

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?

HIRING SOFTWARE DEVELOPERS

📢 WE’RE HIRING: SENIOR SOFTWARE DEVELOPERS!💻🚀

Our software consulting firm is looking for highly experienced Senior Software Developers with at least 10 years of professional development experience to join our growing team!

🔍 Required Skills & Qualifications

✅ Minimum of 10 years of professional software development experience
✅ Strong proficiency in one or more of the following programming languages and technologies:

* Python

* Java

* JavaScript

* Go (Golang)

* C# / .NET

✅ Fluent in English, both written and spoken
✅ Strong problem-solving, analytical, and communication skills
✅ Ability to work independently and collaborate with clients and development teams
✅ Experience designing, developing, debugging, and maintaining software applications
✅ Professionalism and the ability to communicate technical concepts clearly to clients

SALARY RANGE IS.
$1500-$2000 BASED ON PROJECT & EXPERIENCE

LOCATION: UNITED STATES, INDIA, IRAN
💼 About the Role

As part of our software consulting firm, you may work on diverse client projects, develop software solutions, collaborate with technical teams, and help businesses solve complex technology challenges.

📩 Interested in applying?

Send your updated resume, portfolio, and a brief introduction highlighting your experience and technical expertise via DM or email.

Please include your preferred programming language and years of experience.

We’re looking for experienced professionals who are passionate about technology and delivering excellent solutions!

📌 Know someone who might be a great fit? Tag them or share this post!

#Hiring #SeniorSoftwareDeveloper #SoftwareEngineer #PythonDeveloper #JavaDeveloper #JavaScriptDeveloper #GolangDeveloper #DotNetDeveloper #TechJobs #SoftwareConsulting

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?

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?

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 was laid off back in May, and since then I've been applying without success for developer positions. I know the market's been rough, but I have a nagging feeling that this may no longer be a viable career.

My Professional Experience

• I have used Java and MySQL throughout my career.
• My previous position had an outdated, highly niche tech stack (health insurance). Testing was performed, but test suites were absent (I begged the leadership to get that set up the entire time I was there).
• About 4-5 years experience total, so I'm in the awkward middle ground between junior and mid.
• Backend work primarily, but I've dabbled with full-stack before.

What I'm Good At/Comfortable With

• Java & OOP in general
• SQL: Worked with both MySQL and PostgreSQL. One gap to fill is stored procedures.
• REST APIs: Not a lot of professional experience, but I am very familiar with CRUD operations, and know how to set them up in

What I've Been Working On Building

• Spring Boot experience: Feel like I've made a lot of headway over the past five months. Hoping to complete a project and post it by the end of the month.
• AWS: Slow progress here.

Gaps I'm Aware I Have

• Test Suites: Hoping to fix this with a project I'm building. I've worked with JUnit before, just not in a professional setting. Still trying to figure out how to make it with a database connection
• Docker/Kubernetes: Worked with it, still don't think it makes a lick of sense.
• Kafka: Played with it, but no real opportunity to apply it or a project where I could actually use it.
• Cloud: No professional experience. I am currently studying AWS, and plan to take the AWS Certified Developer Associate exam next month. Admittedly, not very confident, but I'll see it through.
• NoSQL: Is MongoDB a good one to start with?
• CI/CD Tools

Attitude Towards AI

• I recognize AI's utility in generating prototypes, dummy data for testing, and basic debugging.
• I try to avoid using AI to write code unless I feel I have the foundation necessary to understand the output. I refuse to lower myself to vibe coding.
• As for AI as a coding tool in general, I am...wary, to say the least: a lot of websites seem to be getting worse by the day.

For anyone who's been laid off and found work, or people who've kept their jobs, what has worked for you? What things do I need to adjust in my skill development, and what things am I doing right?

I was lucky to get a few gigs a while back, but online everything has dried up. Kinda feeling hopeless tbh. Things not going up to standard these days so any tips would be helpful. Anyway best of luck to everyone looking for gigs these days.

got an offer from a small US startup. CTC is good, fully remote, employment in india would be through an EOR.

when I asked about benefits they said they'll provide a "standard India benefits package".

which sounded fine until I realized I have no idea what I should actually be checking😭

PF etc is one thing.

I'm more confused about the stuff that actually varies between companies:

health insurance only for me or spouse/kids too?
can parents be added?
term life?
how much leave?
WFH allowance?
anything else people genuinely use?

one EOR I was reading about, Asanify, at least explicitly talks about group health/ family cover, term life and other benefits administration instead of just writing "competitive benefits" and moving on.

but I'm 24 and single so I can also see myself choosing ₹1L more salary over half these things today and regretting it later lol.

for people who've worked at startups/MNCs, what are the 2-3 benefits you'd actually check before joining?

not free snacks/gym-discount type stuff. actual useful ones.

Hey everyone!

I'm a CSE student graduating in 2027, currently preparing for software engineering and full-stack developer roles.

Recently, I received feedback in a Nokia interview that my projects could be more creative and stand out more. So, I'm looking for ideas for full-stack projects that go beyond the usual task managers, e-commerce apps, and basic CRUD applications.

I'd love to know:

\\- What full-stack projects have you built that helped you stand out in interviews?

\\- What are some unique, practical project ideas that recruiters and interviewers find interesting?

\\- What features or technical aspects make a full-stack project impressive? For example, authentication, real-time updates, caching, system design, or handling complex workflows.

\\- For freshers, is it better to build one polished, production-ready project or multiple smaller projects?

\\- If you're preparing for full-stack roles in 2026, what tech stack are you using, and what would you recommend?

I'm looking for projects that solve real problems, have meaningful features, and give me something interesting to discuss during interviews — not just another tutorial-based project.

I'd really appreciate suggestions from people who have built full-stack projects or recently interviewed for full-stack developer roles.

Thanks!

Hey everyone!

I'm 17 years old and getting close to finishing high school. I've already gained a good amount of experience through software development internships, freelance projects, and programming tutoring.

I've been programming for a while now and have worked with quite a few different technologies.

Here's a quick overview of what I know:

• Languages: Python, JavaScript, SQL, C#, HTML, CSS
• Frontend: React, Tailwind CSS
• Backend: Django, Python
• Other: Unity, Git, GitHub

I'm pretty comfortable building projects and learning new technologies, but I'm trying to figure out what I should focus on to make myself a stronger candidate for my first entry-level software developer job.

I know the job market is competitive, especially for junior developers, so I want to make sure I'm spending my time learning skills that employers actually look for. I also understand AI is very invasive but I am using many AI tools (codex, claude, gpt, curosr, copilot, grok, groq, etc) to speed up development. I mainly use it for debugging, learning new concepts, or boiler plate but i do understand 100% of my code and I love learning more about tech.

For those of you already working in the industry, what technologies or frameworks would you recommend someone in my position focus on? Are there any major gaps in my current stack? Should I go deeper into what I already know or branch out into things like TypeScript, Node.js, Java, .NET, or cloud technologies?

I'd really appreciate any advice, especially from people who have recently landed their first developer job or have experience hiring junior developers.

Thanks!

I’m looking to kick off my freelancing journey as a DevOps Engineer and wanted to ask for advice from those of you who’ve been in this space.

My skill set includes working with AWS (certified Solutions Architect Associate), Terraform, Docker, Kubernetes, CI/CD pipelines, Jenkins/GitHub Actions, and automation with Bash/Python. I’d like to use these to find freelance opportunities, whether short-term gigs or long-term clients.

What platforms would you recommend as the best starting point for DevOps freelancers?

I have tried Upwork and I spent around 100 connects on proposals and heard nothing back even if the gigs they want are fairly easy.

Any niche platforms focused on DevOps or cloud engineering?

Recently I had convo with the medium scale buisness owner about automating their works with multiple departments so they need some ERP application. Usually these kind of buisness depend on small scale or medium scale IT companies to build softwares and manage their data. But after AI era they concern about the data is handled by single vendor is like threat. So that person paid claude and developed an application with his domain knowledge. Literally he don't need an support from any It company he chose tech stack and build the application. For fine tuning little bit he came to me for help. Then I was wondered about the software industry change after AI. I never get panic before about the AI in the day to day life. But it is biggest down fall for the small scale IT companies who develop solutions for the regional customers. There is a question in my mind what will be the future of small companies who sell softwares for regional buisness. How It companies stands out with this faster development with AI (claude, codex, gemini) how to propsose a customer that AI alone not solves your problem but also how software engineers provide value.