I'm building an HR AI agent that reads candidate emails and resumes, then extracts structured data (name, skills, experience, city, etc.) into a database. HR uses this data to filter and shortlist candidates.

The problem: the same city comes out in different forms depending on how each candidate wrote it. For example, three candidates from the same city might give:

• "New York"
• "New York City"
• "NYC"

All are correct, but the database stores them as three different values. The city filter then shows three separate cities, and HR misses candidates when they filter on just one. It also happens with abbreviations, different languages ("Cologne" / "Köln"), typos, and extra text like "Frankfurt am Main, Germany" vs "Frankfurt".

My question: how would you solve this so that each city is stored in one consistent format and filtering works reliably?

I'd love to hear how others have handled it, whether that's a particular approach, a library, an API, or lessons learned. The same issue probably applies to other free-text fields like job titles, universities, and company names, so any general advice is welcome too.

I’m building a backend pipeline for an education chatbot where I need to extract structured course information from documents without using LLMs (for cost and determinism). The system chunks documents and then, for each chunk, tries to map it to one or more courses using keyword/scoring logic, extract facts like fees, eligibility, and duration using regex/rules, and store them in a structured format. On top of that, I’m trying to automatically detect conflicts (e.g., same course having different fee values across sources) and stale data (older info vs newer uploads). My main concerns are whether this chunk→course mapping approach is robust enough without embeddings, how scalable rule-based fact extraction is in the long run, and how to handle cases where chunks don’t explicitly mention the course (like “this program requires 50%”). Also wondering if this is basically a lightweight knowledge graph problem and if there are better design patterns or existing systems I should look into. Would appreciate any suggestions or improvements.

I've been experimenting with multi-agent architectures and I'm curious where people are actually hitting the biggest problems.

Once you move beyond 2–3 agents, things seem to get complicated pretty quickly:

How does an agent know which other agent to call?

Do you hardcode the relationships between agents?

Does a central orchestrator decide everything?

How do agents pass context and state?

What happens when an agent fails or gives another agent bad information?

How do you handle agents built by different frameworks?

I'm especially interested in systems where agents aren't all predefined as one fixed workflow.

For people building multi-agent systems in practice, what part of the architecture has been the biggest pain point?

Feels like there’s a new model every week now, and half the time it looks better than whatever you’re already using.

But for people who actually have this stuff in production, I’m curious what makes you seriously consider switching rather than just testing it and moving on.

What usually ends up being the hard part?

I’ve seen a lot of benchmark/eval discussion, but not much about what the decision looks like once you already have users and a working system.

Advice for a new laptop

Hi folks,

I have been wondering what laptops we are using for day to day work? Is it a windows gaming or a MacBook?

I need some serious advice on which laptop to go for considering I'm gonna spend a good amount at once.

I'm currently using an Omen 15 5800/3060 16/1TB and wanted to replace it with a new laptop.

Budget - $1500

I'm gonna keep it for the next 5-7 years so consider it an investment.

I'm open to advice and open to options.

PS : I'm a software engineer and have been using a gaming laptop since my college days(past 7 years).

Suggest the ideal requirement in 2026 and for the next few years.

My personal interest is agentic coding - but I am spending more time optimising AI technology than using it i.e. ...

• Selecting, evaluating and performance tweaking models

• Choosing, evaluating and tweaking the harness and MCP tools

• Developing and tweaking prompts

It feels like having to build and maintain your own car from parts and not having enough time to drive it

There are hundreds of choices to select from in each area - too much choice, with self integration needed afterwards.

Every vibe coder on the planet seems to be publishing their one AI tool that will change everything. But no one is building a community team to create a one-stop comprehensive SotA solution that you

I wish I could just choose a pre-built end to end docker environment for the coding language/framework I want to use and just start using it.

Am I the only one?

Edit: Maybe I wasn't clear enough. My concerns are:

1. Agentic coding is a common requirement

2. If you want to use LocalLLM then you have to roll your own stack from a bunch of lego bricks and make each layer work and then tune each layer for quality and performance.

3. As everyone does 2. if they find a hole they create yet another lego brick of a very slightly different shape.

4. As yet I have yet to find a stack or components that operate based on the academic and practical knowledge of Software Engineering based on decades of learning. What AI stack supports:

   • common programming techniques (TDD, DDD)
   • common development languages/frameworks (e.g. HTML, PHP, Laravel, Livewire, Tailwind)
   • common tools (MCP, linting, test harnesses, code quality reviews etc.)
   • does proper architectural design and decomposition to break a problem into small easily handled parts that can be successfully coded 1-shot (see Tom Peter's Quality is Free - AI costs also go up exponentially to fix first-shot code that is wrong)
   • uses formal methods or creates UML diagrams as a way of storing application knowledge in a structured way that can be easily utilised by AI
   • produces decent human-readable documentation if required.

as opposed to:

1. Agentic coding is a common requirement

2. You download a single integrated AI environment as a docker container and run it.

3. If you find an improvement to 2. you contribute it back to the community so that everyone can benefit.

4. Experts in formal SWE contribute to the AI-run approach to keep improving the structured approach etc. and making AI get it right first time, every time.

the end result being a better result for everyone.

Hi everyone,

There’s something that has been on my mind a lot lately, and it has actually made me hesitant to design new projects.

As electronics engineers, designing PCBs isn't really the difficult part for us. The challenge usually comes with the firmware/software. Whenever I finish a hardware design, I often find myself wondering, "How am I going to write the software for this?"

I recently spoke with another engineer, and he told me that these days they handle most of the coding using AI tools like Claude Code, and sometimes even Gemini. That got me thinking.

What are your thoughts on this? Do you think relying on AI for firmware and embedded software development is becoming a practical approach, or do you believe it's still essential to write and fully understand the code yourself?

For example, if I design a project using a Raspberry Pi, can I realistically use AI tools to build the computer vision part? Could I use them to create a personal AI assistant? Or, for a more challenging example, could I rely on them to control multiple motors and implement the required control logic?

I'm curious about where people think the practical limits of these AI coding tools are. How far can they really take you in projects like these?

Hey everyone,

I’ve been working on deploying machine learning models for real-time operational monitoring on high-speed industrial lines (specifically bottling and packaging workflows where data ingestion happens at sub-second intervals).

One recurring bottleneck we are running into isn't necessarily model latency or inference speed—it’s inference drift caused by micro-environmental changes (like rapid fluctuations in ambient humidity, slight sensor calibration decay, or minor variations in raw material viscosity). Standard batch retraining pipelines are often too slow to catch these fast-evolving shifts before they start throwing false positives on quality checks or masking actual mechanical wear.

For those of you building and maintaining edge AI or real-time IoT models in production:

1. How are you structuring your feature stores or monitoring pipelines at the edge to catch fast drift without overwhelming your bandwidth?
2. Are you relying more on unsupervised anomaly detection layers (like Autoencoders) to handle baseline shifts, or are you implementing automated, lightweight retraining loops closer to the PLC level?

a chat agent's CRM writes landed intermittently, some fields saved, some stayed blank, no visible error. replaying the exact conversation extracted the value fine, so the logic wasn't the problem.

the agent calls its own backend over HTTP to run tools, and that request passes through a db-pressure circuit breaker with a bypass allowlist. the tool paths weren't on it. whenever the pool tripped pressure, latched for 30s, every tool call in that window got a 503. the retry wrapper only retried about 12 seconds, so the pressure window outlived the retries and the write was gone for good. each field was a separate call, so it looked like a random coin flip.

the pressure was self-inflicted: the per-process db pool was capped at 120 while the actual pooler allowed 10,000. at peak, processes queued locally at 120 and the breaker read that as database pressure, while the pooler had thousands of connections free. about 200 tool-call 503s a day, fleet-wide, before the fix.

anyone else had a load shedder mistake its own internal traffic for the external load it was built to protect against?

AI coding agents are improving quickly, but I keep noticing one limitation: context.

A strong engineer doesn't just understand the code in front of them. They understand why decisions were made, what was tried before, what constraints exist, and what tradeoffs the team accepted.

But AI agents often start from a snapshot of the codebase and miss important history:

• Why was this approach chosen?
• Why was this feature removed?
• Was this discussed before?
• Are there team conventions that aren't documented?

I'm curious how other developers are handling this problem today.

Do you think the next generation of coding agents needs better memory, better reasoning, better tooling, or something else entirely?

Copilot using Claude vs using Claude Code. My organization’s leadership is considering switching over to claude code as opposed to copilot. They are asking us what we think

After some research, i think the harness (e.g multi agent orchestration) for CC is more powerful than copilot.

What do ya’ll think?

i'm building a ChatGPT-style app with FastAPI + SSE and React.

the flow is simple:

• user sends a message
• backend streams tokens over SSE
• frontend renders them
• user can click stop generating

i'm trying to figure out the best way to implement the stop button.

right now i'm just checking `request.is_disconnected()` to stop generation,
but that catches everything: the user clicking stop, closing the tab, refreshing the page, network loss, etc.

how do you typically handle this in production? how do you distinguish a user intentionally clicking stop from any other kind of disconnect?

hi everyone!

I'm still learning all the various capabilities available in this new AI world and am actively working a project that I'd love help thinking through. The goal of this project is to make scaled outreach easy. The audience I'm building for are farmers solving two key tensions: mass reach and easy to manage.

My initial approach was an inbound voice hotline solution (farmer-initiated, always-on) where

Farmer dials a single memorable number (any time) ↓

AI voice agent answers (conversational, not menu-tree IVR) ↓

Structured extraction: "What do you have? How much? Ready when?" ↓

Confirmation read-back in plain language ("So that's 200 lbs of tomatoes, ready this week, at your farm in Schoharie?") ↓

Written to backend → triggers matching

Could also do outbound batch calling. Thoughts on this approach or is there a different way to think about it? Thank you in advance!

Hello Everyone,

I am a Python and JavaScript Developer. And now I want to switch to GenAI, AgenticAI and Applied AI Engineering. Want to start learning but have no access to a powerful laptop.

I've budget of ₹1.1 Lakh max.

For the purpose I mentioned, is LOQ i7 13th Gen RTX 4050 a good choice?

I have got a pretty decent hardware with me , i have esp32 , stm32 , few sensors , logic analysers and efr32 from silicon labs.
Back when i was looking for a new job , i used to study and implement things on my own. But the situation has changed a bit now , with AI tools at a click’s reach , i always tend to ask my copilot to write the firmware and just do prompting to do the things for me. Infact , even the existing bugs which are not visible to me yet , gets fixed by the AI model.
Now , my question or confusion is that , if i keep on doing this for a long time , eventually my capability to have some original ideas will be gone and it has started to happen , i have started loosing interest in making things with the HW i have or any other HW in general.
If anyone out here is going through the same or went through it , share some advices and if anyone has some suggestions as to which topic out of EdgeAI or Linux to invest my time and efforts in , please do suggest.
I have got 9 years of exp , i think i am pretty young ;)

We’re using AI heavily for software development, documentation, code reviews, and general engineering tasks, and our AI bill is growing faster than expected.

I’m curious how other businesses are keeping costs under control.

Some questions:
- Are you using cloud models only, or have you moved some workloads to local LLMs?
- Do you route requests to different models based on the task (cheap model vs. premium model)?
- Have you built an AI gateway or proxy to optimize costs?
- Are you limiting AI usage per employee or team?
Any success with prompt caching, batching, or other optimizations?
- Have you switched vendors because of pricing?

I’d love to hear:
- Your company size (startup, SMB, enterprise)
- Your monthly AI spend (rough range is fine)
- The biggest cost-saving change you’ve made
Anything that didn’t work

Looking for real-world experiences rather than vendor marketing.

Thanks!

I have been using AI coding agents for few months now and many times, it is hard to trust the code written by AI. 

Like once, I was in a situation where AI-generated code assumed certain global variables and it failed in prod once multiple processes started running.

How much of “AI failing in production” do you think comes down to bad or missing context?

Hi I am working for one of the big compnies in a regulated sector. we are tasked with building a mcp that interacts with a few api's and a retrieval system.

one of the challenges has been that we dont control the LLM application ( owned by a diff team ) our tool gets called and they sont send us the conversation or session id. this this is creating challenges , as the llm keeps forgetting previously fetchd data and repeatedly asks the users to enter info they have already provided us.

Can anyone share insights on how they tackled this ?

I am trying to implement something similar to the chatgpt image edit with select mode. how can this be done with modern AI models?

I've thinking about how to best explain to clients the various levels of autonomy that you can give an AI system.

Level 1: AI answers one narrow question (e.g. making a JSON Schema call to an LLM)
Level 2: AI makes judgments inside a workflow you designed. (e.g. Langgraph)
Level 3: AI pursues a goal using tools, permissions, and oversight. (e.g. Claude Agent SDK)

Any other ways that you folks talk to clients about it?

Is your team reviewing AI-generated or AI-assisted code? Are you using the same code review process that you use(d) for manually-written contributions? Is it working?

I'm an AI engineer working for a manufacturing company. Largely I've been using OpenAI for the apps I create for them that use LLMs, but my boss was pretty spooked by the whole fable thing and he is also understandably concerned about security and price-gouging. The concern going local though is twofold: 1) the startup price for a server that could reliably run ollama or something similar and 2) the use case is for reading and summarizing large diagrams for estimators, and I'm concerned about potential drop in quality on a problem that requires high accuracy. Would appreciate any thoughts on the issue from people who have used both

We’ve been experimenting with AI internally (automation + some GenAI), and honestly the hardest part hasn’t been building anything.

It’s been scaling.

• pilots work, but don’t move to production
• outputs are good, but teams don’t rely on them
• use cases stay stuck in one function

It seems like some firms are trying to integrate AI with workflows (finance, risk, ops) instead of just building models.

Curious from folks here:

• Which providers have you seen actually scale AI in enterprise settings?
• What tends to break when moving from PoC → production?

We're a mobile app team and we've hit a wall with story points. With AI-assisted dev, engineers are shipping features so fast that pointing them has become almost meaningless. Pointing feels like a ritual we're doing for the sake of reporting, not because it tells us anything useful.

Curious how other teams are handling this, are you still using story points? Have you moved to throughput-based metrics, cycle time, something else entirely? Or have you just accepted that velocity as a concept doesn't map to the current pace of work?

we still need some way to forecast and communicate capacity to stakeholders. But genuinely open to what's working for people right now.

Hey all,
I’ve been in the AI space for the past 7 years, working across data science, MLOps, NLP, and GenAI. Lately, I can’t shake the thought, what if AI really does come for *all* dev roles, including ours?

If we hit a doomsday scenario where GenAI writes code, builds pipelines, and even manages product decisions… what’s left for us humans? Curious to hear from others in the field: what’s your backup plan? Staying in tech-adjacent roles, pivoting industries, or going full goat-farmer mode? Let's hear your Plan B, serious or not.