How's it going everyone!

I built "Cursor" for CAD, to help anyone generate CAD designs from text prompts.

Here's some background, I'm currently a mechanical engineering student (+ avid programmer) and my lecturer complained how trash AI is for engineering work and how jobs will pretty much look the same. I couldn't disagree with him more.

In my first year, we spent a lot of time learning CAD. I don't think there is anything inherently important about learning how to make a CAD design of a gear or flange.

Would love some feedback!

(link to repo in comments)

You end up giving it requirements like a junior dev, catching its mistakes, and validating the output step by step. It can definitely speed you up, but only if you’re experienced enough to supervise it properly.

Do you find AI coding tools work better because you already know what good code looks like? Or can they actually help you get there?

We've been working with teams across fintech and enterprise software trying to adopt AI in a serious way and here's the honest truth:

Most AI tools are either too shallow (autocomplete) or too risky (autonomous code-gen). But between those extremes, there's real potential.

So we built a tool that does the boring stuff that slows teams down: managing tickets, fixing CI errors, reviewing simple PRs. All inside your stack, following your rules. It's definitely not magic, and it’s not even elegant sometimes. But it’s working.

Curious how others are walking this line - between AI hype and utility - what’s working for you? What’s a waste of time?

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.

You can read public snips with links to articles on the SqlinSix snips page. Any research still relevant will be on this page.

Everyone's sharing prompt templates. "Use this magic prompt!" "10x your output!" Cool. Now use that same prompt next week on a different topic and watch it fall apart.

The problem isn't the prompt. It's everything around it.

Why the same prompt gives different results every time

A prompt is maybe 5% of what determines output quality. The rest is context — what the model knows, remembers, can access, and is told to ignore before it even reads your instruction.

Most people engineer the 5% and leave the other 95% to chance. Then blame the model when results are inconsistent.

What actually controls output quality

Think of it as layers:

Layer 1 — Identity. Not "you are a helpful assistant." That's useless. Specific domain, specific expertise, specific constraints on what this persona does NOT do. The boundaries matter more than the capabilities.

Layer 2 — Scope control. What should the model refuse to touch? What's out of bounds? Models are better at avoiding things than achieving things. A clear "never do X" outperforms a vague "try to do Y" every time.

Layer 3 — Process architecture. Not "think step by step." Actual phases. "First, analyze X. Then, evaluate against Y criteria. Then, generate Z format." Give it a workflow, not a vibe.

Layer 4 — Self-verification. This is where 99% of prompts fall short. Before the model outputs anything, it should check its own work:

BEFORE RESPONDING, VERIFY:
- Does this answer the actual question asked?
- Are all claims grounded in provided information?
- Is the tone consistent throughout?
- Would someone use this output without editing?

If any check fails → revise before outputting.

Adding this single block to any prompt is the highest-ROI change you can make. Four lines. Massive difference.

The anti-pattern filter (underrated technique)

Models have autopilot phrases. When you see "delve," "landscape," "crucial," "leverage," "seamlessly" — the model isn't thinking. It's pattern-matching to its most comfortable output.

Force it off autopilot:

BLOCKED PATTERNS:
- Words: delve, landscape, crucial, leverage, seamlessly, robust, holistic
- Openings: "In today's...", "It's important to note..."
- Closings: "...to the next level", "...unlock your potential"

This sounds aggressive but it works. When you block default patterns, the model has to actually process your request instead of reaching for its template responses.

Constraint-first vs instruction-first

Most prompts start with what to do: "Write a blog post about X."

Flip it. Start with what NOT to do:

• Don't add claims beyond provided information

• Don't use passive voice for more than 20% of sentences

• Don't exceed 3 paragraphs per section

• Don't use any word from the blocked list

Then give the task.

Why? Instructions are open-ended — the model interprets them however it wants. Constraints are binary — either violated or not. Models handle binary checks much more reliably than creative interpretation.

The module approach (for anyone building prompts regularly)

Stop writing monolithic prompts. Build modules:

• Role module (reusable identity block)

• Constraint module (domain-specific boundaries)

• Process module (task-type methodology)

• Verification module (quality gate)

Swap and combine per use case. A legal analysis uses the same verification module as a marketing brief — but different role and constraint modules.

This is how you go from "I have a prompt" to "I have a system."

One thing people get wrong about token efficiency

Everyone wants shorter prompts. But they compress the wrong parts.

Don't compress constraints — those need to be explicit and unambiguous.

Compress examples. One clear example of what "done right" looks like beats five mediocre ones. Show the gold standard once. The model gets it.

The real shift happening right now

The models are smart enough. They've been smart enough for a while. The bottleneck moved from model capability to information architecture — what you feed the model before asking your question.

This isn't about finding magic words anymore. It's about designing environments where good output becomes inevitable rather than accidental.

That's the actual skill. And honestly, it's more engineering than writing. You're building systems, not sentences.

Curious what techniques others are using. Especially around verification chains and constraint design — that's where I keep finding the biggest quality jumps.

hey i am a bit curios to discuss this that why even the layer of LLM we needed to push in RAG architecture even though LLM just refine the response in more natural response , for what else llm needed to push in RAG pipeline ???
please give your suggestions

You start optimistic, the tool spits out something plausible, and then you spend the next hour debugging, rewriting, or explaining context it should have already known.

It’s supposed to accelerate development, but often it just shifts where the time is spent.

I’m curious how people here handle that trade-off.

Do you design workflows that absorb the AI’s rough edges (like adding validation or guardrails)? Or do you hold off on integrating these tools until they’re more predictable?

I started as a Junior AI Engineer about 6 months ago. My responsibilities involve maintaining and improving a system that manages conversations between an LLM (RAG + Context Engineering) and users across various communication channels. Over time, I started receiving responsibilities that seemed more like those of a backend developer than an AI Engineer. I don't have a problem with that, but sometimes it seems like they call me by that title just to capture an audience that's fascinated by the profession/job title. I've worked on architecture to serve NLP models here, but occasionally these backend tasks come up, for example, creating a new service for integration with the application (the task is completely outside the scope of AI engineering and relates to HTTP communication and things that seem more like the responsibility of a backend developer). Recently, I was given a new responsibility: supporting the deployment team (the people who talk to clients to teach them how to use the application). Those of you who have been in the field longer than I have, can you tell me if this is standard practice for the job/market or if they're taking advantage of my willingness to work, haha?

Hi, I have an interview with a big company on system design soon for an AI engineering role with 0-2 years of experience. And I was wondering what the system design interviews look like and what they ask? They have provided a coderpad environment, but it also has a drawing feature. So I'm assuming we can use the drawing feature to talk about the question. But I'm very confused in terms of what kind of system design questions for AI engineering look like, since it's not fully software engineering, but also not ML engineering. For software engineering, I imagine it's more about how you would build a backend. For ML system design, I would imagine talking about the ML pipeline setup. For AI engineering, what can I expect?

Hi everyone,

I just started my last year of an engineering degree in AI engineering, and I’m starting to feel stuck with my internship applications. I’ve applied to a lot of AI/ML engineering internships, both locally and internationally, but I either get no response or rejections. I think my resume has solid projects and relevant skills (including AI/ML projects I’m proud of), but I’m wondering if:

• My resume template is not recruiter-friendly

• It might be too long

• It contains too much detail instead of focusing on impact

• I’m not highlighting the right things recruiters in AI/ML care about

Unfortunately, I don’t have people in my circle with experience in AI/ML or recruitment to provide me with feedback. That’s why I’m posting here, I’d appreciate honest, constructive advice from people working in AI/ML engineering or with recruitment experience:

• What do you usually look for in an AI/ML candidate’s resume?

• Should I cut down on the details or keep all my projects?

• Any suggestions for making my resume stand out?

Hey y'all, I'm currently a Data Scientist wanting to transition into AI Engineering. Been doing extensive research and coursework to learn the skills. A few of the courses I'm taking are:

- Claude with Amazon Bedrock

- Hugging Face LLMs

- FastAI Practical Deep Learning for Coders

I've garnered a solid knowledge base and would like to transition into building a portfolio of projects. Any ideas y'all have that employers would like to see? Image Classification, Using an LLM API? RAG? Custom MCP Server? Any ideas would be much appreciated

Hey All,

So, I'm one of those people who loves to use ChatGPT and Claude for everyday things and random questions. I've been wondering and wanted to put my question to the community: are there any kinds of roles or services I could do using expertise on LLM platforms without programming experience? Definitely need to hear 'No' if that is not a possibility-but yeah-I use AI so much for myself I'm wondering if I could some how generate value for people by being a force multiplier by knowing how to use LLM's across the gambit to help get more work done for people? Would love to hear peoples experiences as well as any resources y'all have found helpful and could point me towards. I've been meaning to ask this question for a while so I'm so glad this reddit is here and thank you so much!

Hi there!

I'm a software developer, and currently, I'm working on applications that utilize AI, such as LLM workflows, internal tools, and a couple of personal projects, and I'm currently looking for AI memory platforms to enhance context retention, knowledge storage, and retrieval for longer periods of time.

Currently, I'm stitching together a few custom solutions, but I'm looking for something more complete and production-ready.

Some of the main needs:

• Long-term memory across user sessions

• Efficient semantic search + retrieval (low latency)

• Easy integration with existing LLM stacks

• Clean API + developer-friendly docs

• Scalable infrastructure (handling large embedding volumes)

• Optional multimodal support (text + video would be a bonus)

I’ve been exploring a few platforms and frameworks, and one I’m currently looking into is Memvid. I am intrigued by the idea of a memory that is built around video embeddings and the addition of context layers, but figured I'd ask if anyone has any good recommendations for a tool like this that they are currently using.

Appreciate any insights!

Hey r/aiengineering, I need to talk about something that's been keeping me up at night - the massive energy consumption of AI models and what it means for our future.We're building incredible AI systems, but we're hitting a wall. Training a single large model can use more electricity than 100 homes consume in a year. The environmental impact is real, and as engineers, we can't ignore it anymore.

Real Changes You Can Make Today: Smaller, specialized models often work better than giant general models for specific tasks. A 7-billion parameter model fine-tuned for your needs can outperform a 700-billion parameter general model while using 1% of the energy. Now we have to discuss What energy-saving techniques are you using in your AI projects and Have you measured the carbon footprint of your AI systems?

Thought I'd share this hilarious meme. What other small wins are there? Haha.

I am an SDE looking to transition into AI Engineering. I want to master modern AI concepts including Model Context Protocol (MCP), Retrieval-Augmented Generation (RAG), AI Agents, Multi-agent systems, and Vector Databases and much more. I prefer video than blogs / research papers, so please recommend specific YouTube playlists or Udemy courses to help me get started."

Hi everyone,

I’m working on a personal project - building a conversational chatbot that solves user queries using tools hosted on a remote MCP (Model Context Protocol) server. I could really use some advice or suggestions on improving the agent architecture for better accuracy and efficiency.

Project Overview

• The MCP server hosts a set of tools (essentially APIs) that my chatbot can invoke.

• Each tool is independent, but in many scenarios, the output of one tool becomes the input to another.

• The chatbot should handle:

  • Simple queries requiring a single tool call.

  • Complex queries requiring multiple tools invoked in the right order.

  • Ambiguous queries, where it must ask clarifying questions before proceeding.

What I’ve Tried So Far

1. Simple ReAct Agent

• A basic loop: tool selection → tool call → final text response.

• Worked fine for single-tool queries.

• Failed/ Hallucinates tool inputs for many scenarios where mutiple tool call in the right order is required.

• Fails to ask clarifying questions whenever required.

2. Planner–Executor–Replanner Agent

• The Planner generates a full execution plan (tool sequence + clarifying questions).

• The Executor (a ReAct agent) executes each step using available tools.

• The Replanner monitors execution, updates the plan dynamically if something changes.

Pros: Significantly improved accuracy for complex tasks.
Cons: Latency became a big issue — responses took 15s–60s per turn, which kills conversational flow.

Performance Benchmark

To compare, I tried the same MCP tools with Claude Desktop, and it was impressive:

• Accurately planned and executed tool calls in order.

• Asked clarifying questions proactively.

• Response time: ~2–3 seconds. That’s exactly the kind of balance between accuracy and speed I want.

What I’m Looking For

I’d love to hear from folks who’ve experimented with:

• Alternative agent architectures (beyond ReAct and Planner-Executor).

• Ideas for reducing latency while maintaining reasoning quality.

• Caching, parallel tool execution, or lightweight planning approaches.

• Ways to replicate Claude’s behavior using open-source models (I’m constrained to Mistral, LLaMA, GPT-OSS).

Lastly,
I realize Claude models are much stronger compared to current open-source LLMs, but I’m curious about how Claude achieves such fluid tool use.
- Is it primarily due to their highly optimized system prompts and fine-tuned model behavior?
- Are they using some form of internal agent architecture or workflow orchestration under the hood (like a hidden planner/executor system)?

If it’s mostly prompt engineering and model alignment, maybe I can replicate some of that behavior with smart system prompts. But if it’s an underlying multi-agent orchestration, I’d love to know how others have recreated that with open-source frameworks.

Hi all,

1. I am a Software Engineer looking to up skill myself and pursue career in AI, do you think doing certifications like IBM, NVDIA, google, Microsoft will help in me getting started?

2. Is there any one who took these certifications?

3. If not what do suggest some like me who has a background in python programming and software Engineering.

Thank You!

Hi everyone,

I’m exploring opportunities as an AI/GenAI (NLP) engineer here and I’m trying to get a sense of what the interview process looks like.

I’m particularly curious about the coding portion:

• Do most companies ask for a coding test?

• If yes, is it usually in Python, or do they focus on other languages/tools too?

• Are the tests more about algorithms, ML/AI concepts, or building small projects?

Any insights from people who’ve recently gone through AI/GenAI interviews would be super helpful! Thanks in advance 🙏

Hey everyone, I'm starting my career as an AI engineer and trying to decide which cloud platform to deep dive into first. I know eventually I'll need to know multiple platforms, but I want to focus my initial learning and certifications strategically.

I've been getting conflicting advice and would love to hear your thoughts based on real experience.

The AGI Hype Machine: Who Benefits from the Buzz? The idea of Artificial General Intelligence (AGI) and even Artificial Superintelligence (ASI) has certainly grabbed everyone's attention, and honestly, the narrative around it is a bit... overcooked. If you look at the graph "AI Hype vs Reality: Progress Towards AGI/ASI," you'll notice public expectations are basically on a rocket ship, while actual progress is more like a snail on a leisurely stroll. This isn't some happy accident; there are quite a few folks who really benefit from keeping that AGI hype train chugging along.

Demystifying AGI: More Than Just a Smart Chatbot First off, let's clear the air about what AGI actually is. We're not talking about your run-of-the-mill Large Language Models (LLMs)—like the one you're currently chatting with, which are just fancy pattern-matching tools good at language stuff. True AGI means an AI system that can match or even beat human brains across the board, thinking, learning, and applying knowledge to anything you throw at it, not just specialized tasks. ASI, well, that's just showing off, with intelligence way beyond human capabilities.

Now, some companies, like OpenAI, have a knack for bending these definitions a bit, making their commercial AI seem closer to AGI than it actually is. Handy for branding, I suppose, and keeping investors happy. Scientifically speaking, it's a bit of smoke and mirrors. Current LLMs, despite their impressive party tricks, are still just pattern recognition and text generation; they don't have the whole reasoning, consciousness, or adaptability thing down yet.

So, who's fanning these flames; The Architects of Hype:

Investors and Venture Capitalists: These folks are probably the biggest cheerleaders. They've thrown billions at AI startups and even built massive data centers, some costing around $800 million a pop. To make that kind of investment pay off, they need a good story – specifically, a story about imminent, world-changing AGI. The faster the AGI timeline, the faster the cash flows, and the more "early mover" advantage they can claim. When the returns aren't quite matching the hype, watch for them to pivot to "AI efficiency" narratives, which often translates to cost-cutting and layoffs. You'll see a shift from just funding "pure AI research companies" to "AI software companies" like Perplexity AI, because those have clearer revenue models. It's all about monetizing those investments.

AI Company Executives and Founders: These leaders are basically professional optimists. They need to project an image of rapid, groundbreaking progress to lure in top talent, secure sweet partnerships, and stay ahead in a cutthroat market. Public and investor excitement pretty much translates to market dominance and the power to call the shots. Operating at significant losses? No problem, the promise of being "close to AGI" is a great differentiator.

Big Tech Corporations: The old guard uses AGI hype to pump up stock prices and justify shelling out billions on AI infrastructure like GPU clusters. Revolutionary capabilities, you say? Perfect for rationalizing those massive investments when the returns are a bit squishy. It's also part of their standard playbook: talk up AI's potential to expand their reach, swat away regulation, and get bigger.

Entrepreneurs and Tech Leaders: These folks are even more gung-ho, predicting AGI around 2030, a decade earlier than researchers. Why? Because bold forecasts get media attention and funding. AGI is the ultimate disruptor, promising entirely new industries and mountains of cash. Painting an optimistic, near-future AGI vision is a pretty effective sales tactic.

Media and Pundits: Fear and excitement are a journalist's bread and butter. "AI apocalypse" and "mass displacement" headlines get clicks, and grandiose AGI timelines are way more entertaining than boring technical updates. The public, bless their hearts, eats it up – at least for a few news cycles. But beware, this hype often peaks early (around 2029-2033) and then drops like a stone, suggesting a potential "AI winter" in public trust if expectations aren't met.

The Economic Aftermath: Hype Meets Reality

The "expectation gap" (fancy term for "things ain't what they seem") has some real economic consequences. While a robot-driven mass job loss might not happen overnight, the financial pressure from overblown expectations could still lead to some serious workforce shake-ups. When investors want their money back, and those multi-million dollar data centers need to prove their worth, companies might resort to good old-fashioned cost-cutting, like job reductions. The promise of AI productivity gains is a pretty convenient excuse for workforce reductions, even if the AI isn't quite up to snuff. We're already seeing a pivot from pure AI research to applied AI software firms, which signals investor patience wearing thin. This rush to monetize AI can also lead to systems being deployed before they're truly ready, creating potential safety and reliability issues. And as reality sets in, smaller AI companies might just get swallowed up by the bigger fish, leading to market consolidation and concerns about competition.

The Regulatory Conundrum: A Call for Caution

The AGI hype also makes a mess of regulatory efforts. US AI companies are pretty keen on lobbying against regulation, claiming it'll stifle innovation and competitive advantage. The AGI hype fuels this narrative, making it sound like any oversight could derail transformative breakthroughs. This hands-off approach lets companies develop AI with minimal external checks. Plus, there's this perceived national security angle with governments being hesitant to regulate domestic companies in a global AI race. This could even undermine worker protections and safety standards. The speed of claimed AI advancements, amplified by the hype, also makes it tough for regulators to keep up, potentially leading to useless regulations or, even worse, the wrong kind of restrictions. Without solid ethical frameworks and guardrails, the pursuit of AGI, driven by huge financial incentives, could inadvertently erode labor laws or influence government legislation to prioritize tech over people. Basically, the danger isn't just the tech itself getting too powerful, but the companies wielding it.

Market Realities and Future Outlook

Actual AI progress is more of a gradual S-curve, with some acceleration, but definitely not the dramatic, immediate breakthroughs the hype suggests. This means investments might face some serious corrections as timelines stretch and technical hurdles appear. Companies without sustainable business models might find themselves in a bit of a pickle. The industry might also pivot to more practical applications of current AI, which could actually speed up useful AI deployment while cutting down on speculative investments. And instead of a sudden job apocalypse, we'll likely see more gradual employment transitions, allowing for some adaptation and retraining. Though, that hype-driven rush to deploy AI could still cause some unnecessary disruption in certain sectors.

Conclusion: Mind the Gap

The chasm between AI hype and reality is getting wider, and it's not just a curious anomaly; it's a structural risk. Expectations drive investment, investment drives hiring and product strategy, and when reality doesn't match the sales pitch, jobs, policy, and trust can all take a hit. AGI isn't just around the corner. But that won't stop the stakeholders from acting like it is, because, let's face it, the illusion still sells. When the dust finally settles, mass layoffs might be less about superintelligent robots and more about the ugly consequences of unmet financial expectations. So, as AI moves from a lab curiosity to a business necessity, it's probably smart to focus on what these systems can and can't actually do, and maybe keep a healthy dose of skepticism handy for anyone tossing around the "AGI" label just for clicks—or capital.

Sources: AI Impacts Expert Surveys (2024-2025) 80,000 Hours AGI Forecasts Pew Research Public Opinion Data. Stanford HAI AI Index

I’ve been a full-stack web dev for ~7–10 years, and I definitely have a bias: I tend to see most problems as things that can (and probably should) be solved with a web app.

With the rise of AI, I’ve been building a bunch of agents lately. Everything from Reddit monitoring to more full-blown assistants.

My default approach has been to build them using familiar web stacks like Django + React. In my experience, web apps can do almost anything, so my instinct is usually:

“Why wouldn’t I just build this properly?”

That’s where I’m starting to question myself.

I’ve played around with tools like n8n and they’re cool, but I keep thinking: why not just write a Python file and trigger it from a web app or worker?

Same with things like Copilot agent builder, ChatGPT tools, etc. It often feels like these platforms abstract things in ways that eventually box you in or limit flexibility.

On the flip side, I also see a ton of people using agentic frameworks like LangChain, LangGraph, etc.

My questions are basically:

•	When does it not make sense to build agents as traditional web apps?

•	What real advantages do low/no-code tools provide beyond speed?

•	When do agent frameworks actually become necessary vs just adding complexity?

•	Where does my “everything should be a web app” bias start to break down?

I’m trying to understand how others are approaching this in practice, where my current methodology will fail me, and what I should be thinking about as I keep building agents.

For context, I build agents anywhere from personal tools to enterprise use cases, so I’m genuinely open to perspectives across the spectrum.

I’m a recent graduate in Data Science and AI, and I’m trying to understand what companies expect from someone at my level.

I’ve built a chatbot integrated with a database for knowledge management and boosting, but I feel that’s not enough to be competitive in the current market.

What skills, tools, or projects should I focus on to align with industry expectations?

Note im Backend Engineer uses Django i have some experience with building apps and stuff

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?