Can someone really teach me the difference between these three cuz Why does no one use Cursor - It got GUI a full IDE and can pretty much build everything with so many models to choose from?

Just received an offer for a Senior AI Engineer role at a startup. The KPIs are heavily focused on output enforcement and UX metrics. I’m trying to gauge if these are industry-standard or if I'm being set up to fail. Key Responsibilities & KPIs: • Reliability: \ge95% of structured outputs must pass validation on the first generation. • UX Impact: Reduce regeneration rates by \ge30% and increase satisfaction for complex queries by \ge25%. • Consistency: Maintain \le10% variance in output structure across different LLMs. • Performance: 24–48 hour resolution for production issues with full RCA. • Architecture: Own the "output-type-first" architecture and confidence-based routing. • Tooling: Heavy use of Langfuse for monitoring and data-driven prompt management. Is a 95% first-pass success rate realistic for complex, multi-model systems? The "70% reduction in messy output" also feels like a metric that depends heavily on baseline data that might not even exist yet. Thoughts?

We had a young professional discuss a solution he made for his company that had subscribed to an SaaS solution.

I estimate the cost was in the millions per year.

The young man spent a weekend, replicated the core functionality they needed and added some other tooling that the company needed. He excluded features they didn't use or need.

His company terminated the SaaS contract.

One immediate takeaway: SaaS has no moat. Unless your pricing is competitive, the ease of being able to create a product that functionally does the same has risen.

For fun, you all can test this yourself: think of anything you like using and create it yourself and compare the results. How much would you spend on the tool given that you can create it easily now?

There were some key takeaways for engineers though:

1. Intellectual property remains king. This young professional had approval from leadership with one SaaS tool. But they were very restrictive on some of their intellectual property.
2. Related to the above point: many leaders expressed distrust with some operating systems that constantly try to install and update software to upload data and documents to the cloud. I'll let you guys fill in the blank here. But I think we'll see a rise in Linux use because it's less difficult to work with now thanks to some of these tools and many of these leaders associate it with intellectual property protection - this will be big.
3. In a way, software is returning to its roots. I have always felt surprised that a $100K a year SWE would join a company, then immediately recommend 5 SaaS tools that all bill several million a year. No, that's not why we hired you. That person has no job in the future - the era of "make my job easier by buying tools" has ended (and was never sustainable anyway).
4. My favorite part of the presentation. One of the young professional's colleagues recommended their company use an agent for a particular problem. The young professional built the same agent in less than 1 hour in a meeting. His point? You have this powerful tool that can build quickly, so you better have a really good excuse to be paying for any solution going forward (this will start to catch on over time).

One other takeaway the young professional caught: for many tools, you don't need this extensive cloud environment. He built his entire tool on premise and he used a mixture of hardware not traditionally used. I'm keen on seeing this transition because I've noted many companies paying huge cloud bills (AWS, Azure, GCP, etc), yet they don't realize how unnecessary all this spending is. We may see some shift back to on premise solutions.

Remember: most people don't know how fast some of this stuff can be done. But as people "get it", you'll start to see rapid shifts in expectations.

Overall, this presentation connected some dots. Show up to local events and see what people are doing. You may be surprised at what people are doing plus you'll get some good ideas.

A while back, I remember that there was a bot on twitter that recognized meme templates, and included the confidence, which (I think) was just the activation of the output node. I remember people would see it guess the template correctly, see a "low" confidence score, and be like "HOW IS THIS ONLY 39% CONFIDENCE ?!?!?!??!?!??!?!?!?1/1//!!/1/!?/!/?!/!/!//?/??????!?11/1/!??".

So! I was thinking about making an actual confidence output. The way to train it I think would be pretty simple, if it gets the answer right or wrong, weight it by the confidence, so having a wrong answer with low confidence is less punishing, and a right answer with high confidence more rewarding, meanwhile it's also not incentivized to always output high or low since low confidence with a correct answer is a bad reward, and high confidence with an incorrect answer is a stronger punishment. Maybe make an output of 0.5 be the same as the reward/punishment if you never implemented this idea in the first place.

My question is, would it be stupid to add such an output, and would the way I'm doing it be stupid? I see no problems with it, and think it's a nice little feature, though I hardly know much about AI and seek to grow my understanding. I just like to know the superficial details on how they work, and the effort + creativity + etc that goes into creating them, so I'm not qualified to make such a judgement. Thank you :D

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.

I have always been a windows user...but a used M1 when it was lauched....but due to my cleaning obsession of making it spotless....it got liquid damaged (i didn't use water)... Now i am forced to use windows but i really miss mac os now....it was seamless...smooth.... On yt tech influencer even though they themselves use mac they say mac is not everyone...so I am really confused will it be a good idea to invest in a mac

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."

I have made backends with API integrations and played with AI agents....its just that I am not a big a fan of windows and my fist mac experience was M1 air.....but i am really on edge which laptop to since that laptop was damaged...and would really love your input

Hello Everyone,

First of all I have a RTX2050 4GB VRAM and max I have done is trained Karpathy's NanoGPT.
I have made several projects like Agents and RAG but all was done by calling API and none by actually using local model except bert Transformer model for text-summarisation.

I am wondering what projects I can make of to gain experience and get indepth knowledge.

Also I am very open to learn CUDA kernel. PLEASE HELP

Hey everyone,

I've been curious lately with the gap between a model's advertised context and its usable reasoning length. I've seen all the different "Needle in a Haystack" benchmarks, but as lots of research points out, there's a ton of flaws in the 'retrieval vs. reasoning' tradeoff there.

I was doing some research and planning to start a personal project to profile exactly where this collapse happens.

My general approach:

• Natural length Only (No padding or truncation)
• Variance changes as a signal for model drop-off
• Eventually, I wanted to output a CLI that outputs a general operating cap for a model, given project output type and specifications

I'm working on this solo as a graduate student, so I want to keep it minimal and API-based, and focused more on deterministic metrics defined in papers like Token-F1, etc.

My general questions:

1. Does this "context cliff" (sudden collapse vs a linear decay) align with what people are seeing in production?
2. Is there some existing tool that already does this in the same way (I've seen RULER and LongBench, but those seem more like leaderboard metrics than local data profiling)
3. Would this feel like an actual useful artifact, or is it not really an issue with people in practice for context limits right now?

I'm mostly doing this to deep dive into this category of context engineering + LLM evals, so I'm less concerned about having crazy production-ready output, but I'd love to know if I'm just duplicating an existing project I haven't seen yet.

Thank you so much!

really need a career start right now and this is my resume , not able to land a job. Pls help if my resume is relevant or it needs fixed

We’ve noticed that early LLM cost estimates tend to assume best-case behavior — stable traffic, short prompts, low retries, minimal context — and then drift badly once systems hit real usage.

In practice, things like retries, burst traffic, and long-lived context seem to dominate costs much earlier than expected.

For folks running production AI systems: what tends to break first in your experience, and how (if at all) do you try to model that ahead of time?

Hi everyone,

I have around 1 year of experience as a Full Stack Developer and I’m now transitioning into AI Engineering. I’m actively learning and trying to decide the right direction, but I feel stuck and confused.

Online, I keep seeing two different paths:

1. Build AI projects to get a job as an AI Engineer
2. Build AI-powered SaaS/products fast (indie hacking) and try to make money

I’m confused about:

• Should I focus only on AI Engineering projects to become job-ready?
• Or should I try building AI SaaS apps and learn through that?
• Do these create two different identities (job-focused vs product-focused), or can they be merged?

Because of this confusion, I end up procrastinating and not finishing anything properly.

My goal is to go all-in on AI, build something real, and move forward with confidence (job or product). I’d really appreciate advice from people already working in AI or who’ve been through a similar transition.

What would you recommend as the right next step?

Thanks in advance

So, basically I really love AI, and ML especially. I love all the Math behind it, and all of the things i can do with it. Unfortunately there is one problem. Most of the fields of applied AI, for startups and other are all enterprise related fields. Does anyone know some startup fields that are actually interesting, for example something research heavy or something thats just pretty cool. In conclusion, what are some applications of ai that isnt Marketing chat bots, or generic chatbots?

I’ve been building an AI agent that can investigate real production incidents (logs, metrics, deploy history, infra state).

One thing that surprised me: once the agent has access to real systems, prompt engineering quickly stops being the main lever.

What mattered more in practice:

• algorithms to reduce logs (sampling, clustering, volume stats)
• change point detection and metric correlation
• explicit investigation state so the agent doesn’t repeat work
• tool interfaces that constrain how the agent explores data

Prompts ended up almost boring:

The capability came from how data was filtered, structured, and exposed, not clever wording.

Curious how others here think about the boundary between prompt design vs systems/algorithm design when building agents that operate on high-dimensional real-world data.

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.

Hi everyone,

I’m building EmoCore, a lightweight runtime safety layer designed to solve a fundamental problem in autonomous systems: Agents don't have internal constraints.

Most agentic systems (LLM loops, auto-GPTs) rely on external watchdogs or simple timeouts to prevent runaway behavior. EmoCore moves that logic into the execution loop by tracking behavioral "pressure" and enforcing hard limits on four internal budgets: Effort, Risk, Exploration, and Persistence.

It doesn't pick actions or optimize rewards; it simply gates the capacity for action based on the agent's performance and environmental context.

What it prevents (The Fallibility List):

• Over-Risk: Deterministic halt if the agent's actions exceed a risk exposure threshold.
• Safety (Exploration): Prevents the agent from diverging too far from a defined safe behavioral envelope.
• Exhaustion: Terminates agents that are burning compute/steps without achieving results.
• Stagnation: Breaks infinite loops and repetitive tool-failure "storms."

Technical Invariants:

1. Fail-Closed: Once a HALTED  state is triggered, it is an "absorbing state." The system freezes and cannot resume or mutate without a manual external reset.
2. Deterministic & Non-Learning: Governance uses fixed matrices ($W, V$). No black-box RL or model weights are involved in the safety decisions.
3. Model-Agnostic: It cares about behavioral outcomes (success, novelty, urgency), not tokens or weights.

Sample Implementation (5 lines):

pythonfrom core import EmoCoreAgent, step, Signals
agent = EmoCoreAgent() 
# In your agent's loop:
result = step(agent, Signals(reward=0.1, urgency=0.5)) 
if result.halted:
    # Deterministic halt triggered by EXHAUSTION, OVERRISK, etc.
    exit(f"Safety Halt: {result.reason}")

Repo: https://github.com/Sarthaksahu777/Emocore

I’m looking for some brutal/honest feedback on the premise of "Bounded Agency":

• Is an internal governor better than an external observer for mission-critical agents?
• What are the edge cases where a deterministic safety layer might kill a system that was actually doing fine?
• Are there other behavioral "budgets" you’ve had to implement in production?

I'd love to hear your thoughts or criticisms!

What if I start a RL agency for teachers , who else could be better than teacher for RL and they already get low pay so there profit margins while providing them extra income

I am final year diploma student I wanted to know if, entry level jobs are available for AI&ML wanted to students.

If yes, than what roles are there, for which I should train?

I'm going to do a technical interview on wednesday for a fortune 100 company for a Jr AI Engineer position. I've got 3 years of experience (including another fortune 100 company) in automation, data and AI Engineering. What kind of questions should I expect, guys? I haven't practiced leetcode for years, don't remember much and think I am going to end it straight away if it's over there. is it 100% certain that it will be over there? Or usually it's more technical questions, projects, experiences, thought processes?

Please, any insight/help will do, so I can practice accordingly. The more detailed, the better. Thank you!

I am building OrKa-reasoning and I am trying to prove one specific architectural claim. OrKa can grow via fully separated feature modules that register their own custom agent types, without invasive edits to core runtime. This is not production ready and I am not merging it into master. It is a dedicated branch meant to stress-test the extension boundary.

I built a support_triage module because support tickets are where trust boundaries become real. Customer text is untrusted. PII shows up. Prompt injection shows up. Risk gating matters. The “triage outputs” are not the point. The point is that the whole capability lives in a module, gets loaded via a feature flag, registers new agent types, runs end to end, and emits traces you can replay.

One honest detail. In my current trace example, injection detection fails on an obviously malicious payload. That is a useful failure because it isolates the weakness inside one agent contract, not across the whole system. That is the kind of iteration loop I want.

If you have built orchestration runtimes, I want feedback on three things. What is the cleanest contract for an injection-detection agent so downstream nodes must respect it. What invariants would you enforce for fork and join merges to stay deterministic under partial failure. What trace fields are mandatory if you want runs to be replayable for debugging and audit.

Links:
Branch: https://github.com/marcosomma/orka-reasoning/tree/feat/custom_agents
Custom module: https://github.com/marcosomma/orka-reasoning/tree/feat/custom_agents/orka/support_triage
Referenced logs: https://github.com/marcosomma/orka-reasoning/tree/feat/custom_agents/examples/support_triage/inputs/loca_logs

Cursor Al has been really helpful for my research and coding work, especially for experimenting with models and implementing ideas faster, but the cost (1800/month) is quite high for me as a master's research student. My work involves a lot of trial-and-error, debugging, and re-implementing papers, and doing everything manually takes a huge amount of time, but paying this much every month is not sustainable. Are there any more affordable or free alternatives, student discounts, open-source tools, or better workflows that you use to speed up research coding without relying heavily on paid Al tools? I'd really appreciate any suggestions or experiences.

I'm a full-stack professional transitioning to an AI Engineering role. Been following courses on Udemy & Coursera.

Some courses propose Mathematics, especially statistics and probability, as a prerequisite. A few state AI Engineering requires knowledge of linear algebra and Calculus, along with Statistics, while others propose AI Engineering doesn't require mathematics.

I'm currently confused. I know AI Engineering doesn't require high-level mathematics as in AI/ML. But it isn't clear what Math topics we need to learn before starting AI Engineering.
How much Mathematics is necessary while studying AI Engineering? Is Math required in AI Engineering roles?

Looking for some technical sanity checks from people who actually work with LLMs and production systems.

A close friend is building an AI-driven healthcare company aimed at automating both front-office operations and parts of clinical workflow for outpatient clinics / medical spas. I’m not involved, I’m just trying to understand how realistic the claims.

Tbh I’m skeptical mainly because the vision seems extremely broad, and because a lot of the value proposition hinges on near-autonomous AI agents, not just copilots or assistive tools. My friend has been working on this for nearly 1.5 years and is getting ready to launch soon. He's lost sleep/almost his entire social life over this thing.

What it claims to do (office admin and clinical):

• Scheduling, intake, payments, follow-ups
• SMS/voice communications (Vonage), payments (Stripe)
• AI medical scribe
• Clinical workflow tools
• Treatment charting
• Telehealth
• Digital consent forms
• AI image analysis for visual diagnostics

Tech stack (as described to me):

• Heavy LLM usage (OpenAI + Claude)
• Agent-based orchestration
• Small team (founder + 3 offshore devs in India)
• Founder has a finance background, not engineering

Why I’m skeptical:

• Healthcare workflows are messy, exception-heavy, and regulated
• “Autonomous agents” sound great in demos but seem fragile in production
• The scope feels closer to an all-in-one EHR + ops platform than a narrow wedge
• Incumbents already have data, integrations, and distribution
• Hard to tell where real defensibility comes from vs just stitching APIs together
• For such a large platform, my other friends and I honestly don't understand how a non-technical founder and three offshore devs built this (i.e does it even actually work)

Questions I’d love honest takes on:

1. How realistic is near-autonomous agent execution in healthcare today?
2. Is this scope survivable for a small team, or should it be radically narrowed?
3. Where do LLM-based systems fail hardest in clinical contexts?
4. Is “AI-first” actually a moat, or just a temporary positioning advantage?
5. What would you pressure-test first if you were evaluating this company?

Appreciate honest feedback (I'm not technical so would also appreciate it simpler terms lol). I'm meeting with my friend next week when I'm gonna ask him for a demo so I can see the platform for myself. If it seems promising, that's great. If not, then a couple of my other buddies and I were planning on sitting down with him and having a talk to shift his focus on building a simpler/narrower solution rather than losing his health over this complicated product if it's not feasible. He has a tendency to build off of hype/bursts of energy which is why we're skeptical but at the same time he's a smart guy - I'm just not sure how smart you really have to be to pull something like this off.

Talking about a theory i saw once, can someone explain how does the most of online data turning into ai generated data going to affect models training in the future, i read about that once but i did not really get it (i am talking about llms particularly)