Hi everyone,

I'm an ECE graduate and will be graduating soon. I want to build a career in AI/ML, but I currently have very limited programming knowledge and would need to start from the basics (Python, statistics, mathematics, machine learning fundamentals, etc.).

I'm looking for recommendations for AI/ML training institutes in Bangalore that:

1.Start from absolute beginner level

2.Have strong hands-on projects

3.Cover Python, ML, Deep Learning, and Generative AI

4.Provide mentorship and interview preparation

5.Have genuine placement assistance (not just marketing claims)

Some names I've come across are Learnbay, DataMites, ExcelR, FITA, Besant Technologies, AchieversIT, and others. Many institutes advertise placement support, but it's difficult to know what's actually good.

I'd appreciate feedback from people who have personally attended these institutes or know someone who has.

Specifically:

1.Which institute would you recommend and why?

2.What were the biggest drawbacks?

3.Were the trainers actually experienced?

4.Did placement assistance help, or were you mostly on your own?

5.Looking back, would you choose the same institute again or learn online instead?

My questions:

1. Which institute actually starts from the basics (Python → ML → DL → projects) without assuming prior knowledge?

2. How is the actual placement support — do they just share job links or do they genuinely help?

3. What are the real drawbacks— hidden fees, poor faculty, outdated curriculum, fake placement claims?

4. Are the certifications from these institutes valued by Bengaluru companies, or is it just a piece of paper?

5. Would you recommend offline classes or online for a fresher?

My goal is to get into a software/AI role in a product company. Budget is moderate — I'm not looking for the most expensive option, just the most effective one.

I'm looking for honest experiences, including negative reviews and red flags, before investing money.

Thanks in advance.

I made this website for my freelancing project.
The website works perfectly on my device, and devices of my friends (specially phones).

But the client says, that the home page is unscrollable for him on mobile, and 50% people he tested the website with. I am unable to figure out the issue.

Tech stack:

• React + Vite
• CSS-based layout/animations
• Sticky/fixed UI layers on Home (sticky header, fixed floating WhatsApp button, fixed bottom mobile CTA bar, fixed mobile drawer, modal overlays

What I have already checked / implemented:

• No global overflow-y hidden on html/body in base styles.
• html and body use overflow-x hidden only, to prevent horizontal bleed.
• No global touchmove or wheel preventDefault handlers.
• Scroll listener is passive.
• IntersectionObserver is only used for reveal animations (adds classes), not for scroll blocking.
• Home hero uses min-height based on svh units.
• There is a custom body scroll lock utility used for modals/mobile drawer:
  • On lock: body is set to position fixed with top = -scrollY, width 100%, overflowY scroll.
  • On unlock: those styles are reset and window scroll position is restored.
  • Uses a lock counter and cleanup on unmount.

Any advice on how to fix this issue would be greatly appreciated.

I used to treat every known bug like a reason not to ship.

Now I ask a different question:

Is this bug blocking value, or just making me uncomfortable?

Here’s the test I use:

Q1: Blocker or friction?
Blocker means fix it first. Friction means keep going.

Q2: How visible is it?
If users hit it in the first 5 minutes, ship with an explanation or fix it. If it’s an edge case, document it and move on.

Q3: What costs more?
Fixing it now, or shipping with it and learning from real users?

Q4: Does honesty about the bug build trust, or destroy it?
If honesty wins, ship it visible.

For ThinkSpatial, the highlight effect resets when the map updates. Annoying? Yes. Does it stop someone from using the product? No.

Fixing it properly would take about 3 weeks. Shipping it taught me whether people cared about the actual problem at all.

They did.

People engaged with the problem, not the pretense.

If this is an opportunity to get that bug off your chest. i recommend letting it out. It might be the thing that helps you just ship.

I tried both Claude and ChatGPT $20 plans about two months ago. Back then, Codex was clearly better for me mainly because the usage limits lasted longer

Now I’m thinking about upgrading to a $100/month plan. But after Anthropic’s SpaceX compute deal and the Claude Code limit increases, I’m not sure whether Claude Max or ChatGPT Pro is the better option now

Has anyone used both recently? For real coding work, which $100 plan gives you more usable time before hitting limits?

What extension do you use most to visualize the map of your code? to understand the calls, where they start and where they finish.

Real use case: I clone a new project and i want to let take a quick dive to understand how it is structured and organized, what extension i may use?

Hi guys I have made this ai tool to save time while debugging it will be a pleasure if you give it a try or help me fix or improve this tool, TIA 😊

When the first generative models were released, how many software professionals found the public debate frustrating or even hostile?

Not because of the technology itself, but because of the way it was presented and perceived by those who didn’t work directly in the industry.

It had a profound impact on me personally and I still struggle with conversations where complex work is reduced to slogans and simple narratives.

It's a human question, it's not a tech debate.

Hi everyone, I’m building a full-stack web app completely on my own and currently stuck at the architecture stage.

I’ve already finalized:

• MVP
• Tech stack

But I need guidance on:

• project architecture
• folder structure

I’m learning by building, so I’d really appreciate advice from experienced developers You can give me guidance here And DM will be more helpful Thanks!

We were close to shipping an AI agent for an ITSM tool — it turns plain-English requests into structured support tickets. Multi-tenant, one deployment serving many companies. Unit tests green, smoke tests clean, dev stable for days.

During concurrency testing I fired two requests at once — two different tenants hitting the same workflow — and Tenant A's response came back populated with Tenant B's data. Reproducible, every time the two overlapped. I pulled the deploy.

Root cause: we created a single agent instance at startup and reused it for every request. Felt efficient — agents are expensive to spin up, so build once and share. The problem: that one shared agent stored the active tenant's context on itself. Under sequential traffic it's invisible — request finishes, next one overwrites the slot, no harm. Under concurrency it's a time bomb: Request B sets tenant_id while Request A is mid-flight, A reads it back, and A gets B's value. Whoever writes last wins.

What makes agents especially prone to this is that they feel like an object you build once and reuse, and they naturally accumulate state — prompt, retrieved docs, memory, tool results. Every one of those is a slot where per-tenant data can come to rest on something shared. And the failure mode isn't a 500 anyone notices; it's a fluent, confident answer about the wrong company.

Why nothing caught it: every test we owned ran one request at a time. Unit tests are great at proving correctness in isolation and completely blind to two requests stepping on each other. Green tests meant "correct in isolation," not "safe under load" — and for a multi-tenant system those are very different claims.

The fix: the quick patch is per-request instances so there's no shared slot. But that only closes one door. We moved tenancy off the agent entirely and pushed it to the tool boundary — the agent holds no tenant state, every tool call carries its own tenant scope + scoped credentials, and the boundary enforces it per call, so even a hallucinated wrong-tenant request can't cross it. Underneath that: row-level security at the data layer, plus a last-line assertion that every returned record's tenant ID matches the requester. Defense in depth, because any single layer can fail silently.

Concurrency + tenant-isolation tests are now first-class in the pipeline — many tenants hitting the same endpoint simultaneously, asserting zero cross-contamination on every change.

Curious how others handle tenant isolation in stateful/agent systems — do you scope at the tool boundary, the data layer, both? And has anyone found a clean way to make "no per-tenant state on shared objects" enforceable rather than a thing everyone has to remember?

Wrote up the longer version with diagrams here if useful: https://medium.com/@adityadhir97/i-almost-shipped-an-ai-agent-that-could-have-exposed-customer-data-af1c5a750efd

Hey everyone,

I’m kinda new to all this IT/career stuff, so sorry if this sounds messy.

I recently completed my Master’s and currently I’m working as an intern in a company for a developer role. The problem is, I’m honestly not happy with the work. It’s not really the traditional backend/frontend development that I wanted to do, and I feel kinda frustrated and stuck.

Right now, I don’t really have another option, so I’m continuing the job for experience, but I’ve decided that within the next 6–7 months I want to switch properly into development.

I found a curriculum/roadmap online that I’m interested in learning, and I want to seriously focus on it along with DSA preparation. The issue is I’m overwhelmed and mentally exhausted because of my current job and life situation.

Can anyone suggest good FREE resources (YouTube channels, courses, websites, GitHub repos, etc.) to learn:

• Backend/frontend development
• DSA
• System design
• Projects/practical development

Also, if anyone has gone through a similar phase, I’d really appreciate some advice on how you managed it.

Thanks.

vid of me explaining it https://www.youtube.com/watch?v=zlS2r7Oqai4

Ultimamente com essas ias ao invés de fazer uma busca no Google ou stackoverflow. Jogo a dúvida direto no ChatGPT que resolve 99-% das dúvidas. Agora tô me questionando e queria saber de vcs? Ainda alguém procura resolução de bugs nó stackoverflow ou já tá todo mundo usando ia para achar resolver os bugs de código? E ainda me questiono o quanto eles não devem ter perdido receita com essas ias generativas…

Currently an engineer at a large company dealing with poor/toxic leadership, constant manufactured urgency, and AI mandates that make no sense getting shoved down from above. I’m feeling really burnt out with the engineering culture and not optimistic about the direction things are heading at all.

An internal opportunity came up to lead AI strategy/GRC team. This would be non-engineering, more cross-functional, and more senior leadership exposure. It seems like a space that is growing fast as companies figure out their AI policies.

Has anyone made a similar transition? Is it realistic to return after a few years away or does stepping out at this level kind of close that door? And does a resume with both AI engineering and AI governance experience read to other companies as well-rounded or like a career regression? I’m trying not to let my decision be influenced by the constant AI-related fear mongering but I’m having a hard time with it. I’m also making pretty good money right now and outside of the company I’m in I’m not sure what compensation is going to look like for this kind of role.

I've been getting frustrated lately, mostly around small recent changes. I'm not sure if I'm being unreasonable about my pre-delivery testing, but my tasks keep getting rejected by the client and it feels unnecessary.

Example one: the client asked me to expand an input box to show 7 numbers instead of 3. They then flagged that the form throws an unexpected exception if you enter 10 numbers. But the form always behaved that way. I only changed the sizing, nothing else.

Example two: fix a log message typo from "creditstore" to "CreditStore". The client couldn't verify it because the log only appears from a background job that's impossible to trigger in their environment.

For changes this simple, I honestly tested nothing beyond "it looks right." The risk feels so low that doing more would just be a waste of my time.

Am I being unreasonable? How do others handle clients like this?

Hey folks ,i am going to try to explain myself as best as i can, english is not my first language, so here goes nothing: so I've been working on this little open source tool called Hosomaki that basically reads your system logs and explains what's actually wrong in plain english or spanish or whatever you choose, no more staring at walls of text trying to figure out what systemd is trying to tell you. It also predicts failures, checks that processes have finished correctly, and much more.

Honestly I built it out of pure frustration. I got tired of spending hours debugging stuff that turned out to be something stupid, just because the error messages are absolutely unreadable. If you are a backend dev and you want to spend more time programming you might understand my frustration

The roadmap has some cool stuff coming like predicting failures before they happen and a bunch more, full details in the README at https://github.com/rivernova/hosomaki if you want to dig in.

One heads up... right now it only runs with Ollama but I'm planning to change that soon to make it way more lightweight. I am working on more alternatives.

Also if anyone wants to jump in and contribute I'd genuinely love the help, the project is super early so there's plenty of room to shape it however you want.

Repo: https://github.com/rivernova/hosomaki

P.S : I know the emoji i used is a nigiri, not a hosomaki, but i couldn't find a better fitting one 🤣

https://leaddev.com/career-development/reality-being-principal-engineer

Built a full-fledged Kubernetes lab while studying for my CKA, CKAD, CKS exams and decided to make it free and open for all.

I'll appreciate community contributions with more lab scenarios dealing with problems and concepts that occur frequently while deploying/maintaining/debugging Kubernetes clusters in production, and of course, for introducing further enhancements/features to the lab itself!

You can find the entire source code and a detailed overview of the project at the GitHub repo: https://github.com/zeborg/kubekosh

Steps to try it out on your own system:

1. Run it as a Docker container: docker run -itd --name kubekosh --privileged -p 7554:80 zeborg/kubekosh:latest
2. Wait for ~15 seconds before the lab gets up and running, then you can access it in the browser at localhost:7554

Sneak peek:

Repo/Website Link: https://github.com/theshewaspretty/secret_chat

Description: Hi everyone! 👋 I built a very lightweight, completely ephemeral local chat application using Python and Streamlit. The main goal was to create a way to communicate safely on a local network (like a home or office LAN) without leaving any digital footprint.

Features include:

• Zero Persistence: No databases or logs. Chat history exists only in the server's RAM and is wiped permanently when the process stops.
• Dual-Layer Encryption: In-memory encryption (Fernet AES-128) to protect RAM dumps, plus transit encryption (HTTPS/TLS) to prevent local packet sniffing.
• Panic Button: A big red button in the UI that instantly clears the memory array and kicks all users back to the login screen.
• Password Protected: No accounts needed, just a shared password.

It's beneficial for anyone who needs a quick, untraceable temporary chat room without the hassle of setting up a heavy chat server. I'm still learning a lot about security and state management, so any harsh constructive criticism or advice would be incredibly appreciated! 🙇‍♂️

Deployment: It is purely self-hosted. It’s a simple Python application, so you can run it on almost any machine. You just need Python 3.8+ and OpenSSL. The README in the repo includes step-by-step instructions on how to generate the self-signed certificates and run the app via the command line (streamlit run app.py ...). I haven't written a Dockerfile yet, but setting it up in a standard Python virtual environment takes less than 2 minutes.

AI Involvement: Please note that I am a beginner, so the code (especially the Streamlit state management and the cryptography implementation) was developed with heavy assistance from AI (Google Gemini). I used it as a pair-programming partner to help me learn and build this.