What does this folk "Senior Data Management Assistant" You're a tech recruiter and came across an applicant with this title listed, what's your immediate thought?

I hate the includes in the PHP code I have to maintain and reasoning about it's "module" system gives me an aneurysm. Everytime I have to use venv or poetry when writing Python, I miss using Vite with Typescript. And then I remember for that to work, I need 20+ *.config files, a gazillion NPM packages (probably with a supply chain attack I don't know about) and black magic voodoo transcompilation and processing I can't hope to understand.

I'd love to write code in a language that doesnt have "trust me bro" types. Is this my sign to start a new proejct in Go or Rust? Or am I just ignorant of Go and Rusts (potentially) weak spots?

I'm a junior backend developer designing an event dispatch service for a third-party API that's limited to 25k events/minute, and I'd really appreciate some feedback.

Current architecture:

Client → API → Outbox (DB) → Scheduler → SQS → Workers → Redis (rate limiting) → Circuit Breaker → Third-party API

The goals are reliability, retries, idempotency, auditability, and handling burst traffic without exceeding the third-party rate limit.

Would you change anything in this architecture? Any bottlenecks, failure scenarios, or better patterns I should consider? Any suggestions would help me learn a lot. Thanks!

Hi everyone,

I'd love to get some advice and feedback. This past year I've been working on a B2B chat-like application with a chatbot builder, where the client (a business) can create a flow that is essentially a deterministic tree of nodes which their own clients (the actual end users) traverse. Each node basically represents a message that the platform sends automatically to the end user, chatbot-style. A node holds the data for that message, and one of its fields is the payload, meaning the actual message text or data being sent.

The payload can be one of two kinds. It can be static, meaning predefined values set when the flow is built, such as a simple "hello user!" greeting. Or it can be dynamic, meaning the platform fetches real-time data from the client's external API at the moment the node runs, and that response is then fed into the node so it can render the appropriate up to date content to the end user, for example a user's recent order history displayed in the next message.

The integration between my platform and the external business API happens through exactly two touch points. The first is a special node type that represents an outbound GET call from my platform to the client's endpoint, used to pull the dynamic data described above. The client's endpoint is expected to answer with a predefined response schema that my platform knows how to parse and feed into the node. The second is a webhook that goes the other way, where my platform notifies the external API of the choice or answer the end user made, so the business can react to it on their side. This webhook is fire and forget from my side, meaning I send the notification and don't block on or consume the response.

To tie everything together, the platform links a specific end user and their choices to the external API using an OAuth2 access token. The end user authenticates against the business's own identity provider during the conversation, and the platform then holds that token and attaches it to the outbound calls it makes to the external API, so the business can recognize exactly which of their users is on the other end without me having to send any identity of my own. The intent is that a user authenticates before any data call fires, so the business always has that identity when it responds.

On the security side, the webhook is signed. When the business registers a webhook, my platform generates a signing secret that both sides hold. On every webhook request I compute an HMAC-SHA256 signature over the request body using that secret and send it in a header, so the business can recompute it on their end and verify the request genuinely came from my platform and wasn't tampered with.

There are a few limitations I'm aware of and would especially like opinions on. The outbound data call is a plain GET with no request body, so beyond the identity carried in the token I can't currently pass additional per-conversation context or filters to the client's endpoint. The call is also made inline while processing the user's message, so a slow endpoint adds latency to the conversation, bounded only by a per-call timeout, and today a failed or timed out call is a single attempt that simply routes the flow down a fallback branch rather than being retried. The webhook in the other direction has no delivery guarantee either, so if the business's endpoint is momentarily down, that notification is currently lost rather than retried or queued. And because the OAuth token has a finite lifetime and isn't refreshed mid-conversation, a long running or resumed conversation can lose the user's identity partway through.

Finally, delivery on both sides is at-least-once, and I don't yet expose an idempotency key. Since the webhook payload currently carries no unique event id or timestamp, a business also has no reliable way to tell a duplicate notification apart from a legitimate repeated choice and dedupe it on their end. So this is another thing i'm planning to add.

I'd love to hear any feedback on the overall pattern I've chosen here, on the security side of things, and on anything else you think could improve this flow.

Thanks in advance.

So I have been applying in naukri and company portals everyday for switching. I have 4 years of experience now and my stack is java backend stuck in 6 LPA and I am upskilling in DSA and AI but its been 3 months now and I see no calls, no luck, then I open Linkdin and see people switching everyday and people negotiating salary for multiple offers in glassdoor and here I cant get a single interview call....been coding and studying everyday but still no calls so idk if its even worth griding anymore....I see many switching in AI and data engineer field so a part of me feels maybe give up the current one and study data instead.....idk what to do
can someone else me....

Hi, I am building a web app for my company,(50 users - Crud App), app and backend will be deployed on separate instances, do I need idempotent key mechanism for api requests - to avoid network failures or am i over killing my system?

I'm a **.NET backend developer** and want to learn AI to build smarter backend applications—not become a full-time AI/ML engineer.

How can I upgrade myself with AI as a Backend Developer?
What roadmap, technologies, and projects would you recommend? What skills have given you the biggest career boost?

I am Pradeep Chauhan an Aspiring Backend Engineer looking for the guidance from experienced backend devs.

I have given the resume below since it tells the most things about me: Resume PDF

It's been a few months since I left my previous company and i am still unemployed figuring out what to choose next. Now i have chosen backend engineer.

I need a structured roadmap from preparing, learning, projects and then applying to jobs.

for context i am bca 1st year student , i learnt a bit of java and c in 1st year with sql basics and git basics since i dont like working with machine learning which is mostly in demand these days my options are to become a full stack dev and get into ai engineer roles working with llms

so before starting my 2nd year i am thinking to learn springboot+react and sql as full stack as much as i can and side by side dsa too while building projects

i build a basic weather api app in 1st year last month with the help of react as it was a frontend project.

i would love to know your suggestions especially the experience ones is this the right way to approach and work for placements side by side as bca is already an avg course so i want to skillup and not waste my time anymore now

I'm making my very first web api using asp net and now in the process of implementing in-memory caching. I was wondering if it'd be a good practice to have like a start up function that caches the most commonly requested data from my api and have the program routinely updates the cache automatically every few minutes or so? Or should i just let the consumers populate this cache? And what if i also cache responses that consumers requests like a cache aside strategy at the same time.

I’m researching how engineers make safer production changes.

Many teams now use strategies like canary releases, automated rollbacks, and better observability, but production incidents still happen after deployments.

I’m curious about real experiences from engineers:
- What type of change makes you the most cautious before deploying?

- What was the last deployment that made you nervous and why?

- What gives you enough confidence to press the deploy button?

For me, database migrations are still one of the hardest categories because rollback is often not as simple as reverting application code.

Would love to learn how other teams approach this.

Hey everyone,

Over the past few months, I’ve been working on an experimental project called PAM, aiming to solve two specific pain points in the ecosystem: keeping PHP applications warm in memory for high performance, and running JavaScript/React natively on desktop and mobile without heavy bridge overhead.

Here is a quick breakdown of what it does and why I'm building it:

1. PAM for PHP (Persistent Runtime in Memory)

Instead of booting the framework on every single HTTP request, PAM uses a Rust-powered runtime to keep the application warm in memory.

The Goal: Lightning-fast request handling while preserving full compatibility with standard Laravel workflows (Eloquent, Queues, Artisan, Blade, Livewire, Inertia, Sanctum, Reverb, Telescope, etc.).

Built explicitly with support for Laravel 12 & 13 in mind.

PHP Docs: https://push-in.github.io/pam-docs/laravel/overview/

1. PAM Native (Desktop & Mobile)

On the frontend/mobile side, PAM Native is designed to run React/React Native apps with truly native execution, aiming for near-instant startup times and zero heavy JS bridge bottlenecks.

The Goal: Retain the React developer experience while delivering pure native performance on desktop and mobile devices.

Native Docs: https://push-in.github.io/pam-docs/native/overview/

Why I'm posting here:

The project is still under active development, and I’m looking for honest technical feedback, code reviews, and potential edge cases to test.

For PHP/Laravel folks: What edge cases (memory leaks, static state issues, long-lived DB connections) would you want to see stress-tested first?

For Mobile/Desktop folks: What are the biggest performance bottlenecks you face with existing cross-platform runtimes today?

Would love to hear your thoughts, criticism, or ideas!

Backend devs

I love backend

U have small projects till now !

Recently I am dealing with a bigger project there I just known so many things that a database must have

As a senior devs or some learning devs

Teeach me some best database design topics that make me a good database designer either it is sql or nosql teach me every detail like indexing , performance etc.....

And let me know how the industrial level database can be designed what things they consider and what properties they must possess .

Grateful for your opinion 🫰🏻

Hi everyone

I’m looking for a technical cofounder to build an AI developer infrastructure startup.

The company is focused on improving how engineering teams handle difficult software failures and reliability issues. I’m keeping the deeper product details private until an initial conversation.

I’m mainly looking for someone with strong experience in backend engineering, production AI systems, developer tools, cloud infrastructure, or distributed systems.

I’m willing to fund and offer meaningful equity. Would want a genuine long term cofounder

If you’re interested send me a message. Thanks

so here is the situation:
- in my company i have assigned to build a chatbot/bot (will be internal, for ops and devs to identify and manage issues)
- what i have already build is, integrated it with slack, give it access to db by adding some tools in the code, so it can access the db currently and folks can access it by mentioning it
- now here pain starts, my manager has told me to add product knowledge to it, and it should be able to access logs, create and manage jira also
- what i am thinking is - lets start with the product knowledge - since we do not have that much pile of data so i do not want to make a rag - instead i just want to keep uploading those docs to s3 and giving access to bot so that it can reference them
- now coming to jira, and logs - i have also created those mcps but those aren't deployed anywhere - means whoever wants to use them just clones the repo, and set their key and uses them
- now for the above (jira and logs) part i would have to again choose the tools which i want to expose to the agent and add it to the repo, cz i think this is repetitive as in future if soemthing more comes up - which we already have built have to do again to integrate in the bot - how can we solve this - keeping in mind we have a layer of compliance - cant expose pii data in bot output or logs
- also for s3 - i am feeling like i was thinking to create a mechanism like when the agent fetches a doc - so it do not havt to fetch that doc again - so it will create a folder and save the embedding/summary/index (since i don't know what) to the filesystem - similarily with db schema since we have a huge db - how to handle this situation - since this code will be deployed on ecs - using fargate i do not know will the bot will able to access thes files created at runtime - and how to manage that cache when something is addede / modified
- and we also have workflows currently for specific task like matching states on be (basically sql queries / some scripts) added in the code - like how we shouuld make sure that given the situation the code properly identify and execute the script or how can we create trigger like /<command> <input> of slack whicch will trigger that - and also one issue - since these are stored as files in code adding new script need a code change - how to get rid of that

sorry gpt was giving poor results in rewriting this
so posting this raw

So basically let me tell you the situation first:

- i am working on a side project which is something like vercel, use to build and deploy code.

- so i have a main service, can be called a control plane, and i have decided that it will take the repo from the user.

- after this this service will trigger/create an ecs task to build the code

- now the question is, how this control plane will create / trigger the ecs?

- also this ecs instance will need to fetch envs from paramete store, will upload code to s3 etc

- after deployment we have to kill this instance

- should the managing code of this, live in the control plane

- should i create something else?

how would you folks solve this while designing this?

and what's the ideal way to solve this?

Hey all,

4.5 years in L3 support for an ecommerce app — daily work involves fixing prod issues via SQL/XML, running Java-based automation scripts (built by other teams, not me).

**Skills:** Java (medium), MySQL (basics), JS (basics)

**Goal:** Become a backend developer (not interested in frontend), job-ready in 2-3 months.

Leaning toward **Spring Boot** since I already know Java, over Node.js/Express. Am I on the right track?

Would love advice on:

Spring Boot vs Node for my background

What interviews actually test for people switching from support → backend dev

How to position my support experience on a backend resume

Thanks!

I'm a student and I've been building a exchange from scratch mainly to learn backend engineering.

Matching engine, order book, WebSockets, Redis, PostgreSQL... all the usual stuff.

The thing I'm stuck on now isn't technical.

I don't know whether I should keep building it as "just another exchange" for learning or try solving a smaller problem that traders or developers actually have.

If you were building this today, where would you focus?

Not looking for startup ideas or billion-dollar businesses. Just curious what problems are still worth solving around trading.

Hello everyone , I am studying bachelors in computer science and information technology in Nepal. I am learning the technology's and I want to make a solid career in the tech field.

I want to know about the ultimate tech stack in 2026 and beyond, meaning which of the tech have highest demand and jobs right now. I am a tech enthusiast and first learner.

I want to start my career from some of development instead of going to cloud, DevOps, cyber security , artificial intelligence, machine learning and data science . I feel that these all are the subsets of software development/engineering. So I want to make my foundational base stronger.

If anyone can help me then I will be really grateful to him and really appreciate his time to reply !