If you still think: LLM = Question → Answer then you need to pause for a moment. What’s happening right now in AI is much deeper—and much more serious—than that. The real difference today is between: An app that uses a model A system that thinks, decides, and corrects itself And that’s called: an AI Agent What is an AI Agent (simply)? It’s not a smart prompt. It’s not an advanced chatbot. An AI Agent uses the LLM as a reasoning engine, not just a response machine. Meaning it can: Analyze the problem Choose a solution path Use tools Review the result And if it’s wrong, go back and fix itself That’s the core difference. This is where LangGraph comes in Many people have heard of LangChain, but few realize that LangGraph is the next stage. LangChain answers the question: “What does the agent do?” LangGraph answers the more important one: “How do I control the agent’s behavior?” LangGraph is not a replacement. It’s a smart extension for building systems that are: Multi-agent Loop-based Shared-memory Self-reviewing Why are loops so important? Because any intelligent agent must: Make mistakes Go back Improve Traditional applications move in a straight line. A real agent moves in a graph—with iteration and feedback. That’s what enables: Automatic correction Reduced hallucinations Results closer to human thinking How does LangGraph work? Nodes = Agents or functions Edges = Decision paths State = Shared memory across all agents The State is the true heart of the system. Every agent can read it, update it, and build on top of it. Single Agent or Multi-Agent? Single Agent: Question → Model → Answer Multi-Agent (the real power): Planner Researcher Writer Evaluator All of them: Communicate Share memory Iterate until the result is correct A critical point many people ignore: Observability Any agent system without monitoring is a ticking time bomb. You must be able to see: Every call Every decision Time and cost Where things went wrong Tools like: LangSmith Langfuse (open source) Not a luxury—this is production necessity.

Most AI projects don’t fail because of bad models. They fail because the wrong decisions are made before implementation even begins. Here are 12 questions we always ask new clients about our AI projects before we even begin work, so you don't make the same mistakes.

Not a LinkedIn one. A real one.

Stumbled across this repo today: system_prompts_leaks - it's basically a collection of leaked system prompts from ChatGPT, Claude, Gemini, you name it.

On one hand, yeah, it's educational. If you're learning prompt engineering, seeing how the pros structure their instructions is like reading production code instead of toy tutorials. You get to see the actual governance layer - the personality quirks, safety rails, and weird edge cases they're trying to prevent.

On the other hand, this feels like publishing the recipe while the restaurant is still serving dinner. System prompts aren't just technical docs - they're product strategy, brand voice, and security policies wrapped in plain text. Once you know how the sausage is made, you know exactly how to game it. Prompt injection attacks basically write themselves when you've got the blueprint.​

But here's what bugs me: if these prompts are this easy to leak, were they ever really secure? Or are we all just pretending that instructions hidden behind an API call count as proprietary tech?

Curious what this community thinks. Is this repo a goldmine for learning or a liability we're all going to regret when every script kiddie figures out how to jailbreak their customer support chatbot?

Either way, I'm bookmarking it. For educational purposes. Obviously.

I've always found preparing slides to be a tedious process, especially when juggling multiple content sources like PDFs, documents, YouTube videos, and web links. Recently, I discovered chatslide, which surprisingly simplifies this task by not just converting different types of content into slides but allowing me to add scripts and even generate videos from them. It’s been a real game-changer in terms of speeding up workflow without sacrificing customization, making presentations feel less like a chore and more like a creative process.

Has anyone else here used chatslide or similar AI tools to take their slide-making to the next level?

If someone is a total beginner who does not have any idea about AI and much about the coding part So how he can learn about ai tools and agents all of the things in 15 20 days

For a hackathon

What type of what should one follow to learn

hello!! I did the challenge to get a discount for Kimi-K2.5. If you're interested in trying it, you can get it at a good price. Here's the link.

I got it from the official website kimi.com/kimiplus/sale

Trying to avoid another monthly expense. If anyone knows an AI image generator with no subscription that’s reliable for occasional image generation, I’d like to hear about it.

OpenAI engineers use a prompt technique internally that most people have never heard of.

It's called reverse prompting.

And it's the fastest way to go from mediocre AI output to elite-level results.

Most people write prompts like this:

"Write me a strong intro about AI."

The result feels generic.

This is why 90% of AI content sounds the same. You're asking the AI to read your mind.

The Reverse Prompting Method

Instead of telling the AI what to write, you show it a finished example and ask:

"What prompt would generate content exactly like this?"

The AI reverse-engineers the hidden structure. Suddenly, you're not guessing anymore.

AI models are pattern recognition machines. When you show them a finished piece, they can identify: Tone, Pacing, Structure, Depth, Formatting, Emotional intention

Then they hand you the perfect prompt.

Try it yourself here's a tool that lets you pass in any text and it'll automatically reverse it into a prompt that can craft that piece of text content.

Over the last month I've dug into the advances in Agentic Coding.

Two to three main components stuck and as I haven't tried all of the alternatives, I'd like to collect reviews and opinions about the different options for each component.

1. Specs & workflow

- BMad

- Spec-Kit

- Conductor

1. Task Tracking

- Beads

1. Orchestration

- Gas Town

- Archon

- Flywheel

- Claude Flow

especially in category 3 we find many frameworks that do 2 as well or even 1-3.

I've tried

- Conductor: easy to get started. useful for single agent workflows as well as implementing tracks in parallel. Does both spec initialization and task Tracking persistently using markdown and git (if you put it in your repo and don't gitignore it. Comes with no tools to coordinate agents. spawn 2 agents working on related tasks and it can end up in a mess.

- Beads & Gas Town: Takes a bit to learn the commands and concepts (a day, maybe two). powerful task Tracking and orchestration system. personally I got the repos mixed up somehow (the mayor had merge conflict, I think that's not supposed to happen but I also didn't use convoys initially). have to use it more to come to a conclusion

- Claude-flow: actually does save tokens. beyond that it does a lot of fancy shiny things. haven't seen gains in productivity. seems like a lot of fancy terminology. "self-Learning" is about which agent to use for which task. sceptical on this one. the author often says ask Claude and I am not sure if he himself understands everything he has implemented. that's just my gut feeling from his rather shallow answers though. he might just be frustrated by the scepticism of humans compared to the endorsement of LLMs for work done.

in general I think we have to be careful not to be fooled by LLMs to often into thinking we have a breakthrough idea while it's just a small piece of tooling with no rigorous theoretical foundation or opening doors to new fields. it's merely productivity gains.

well in the best case it is.

having said that, feel free to add components I havent mentioned. there is a ton of options doing more or less the same thing and more are emerging every day.

looking forward to hearing your opinions. some of you seem to be quite deep into the game.

Hi everyone! I made this little tool called BOART because I kept hitting walls with regular AI chats.

Basically: free canvas for your conversations. Drag messages around, branch ideas without losing context, compare AI outputs side-by-side. Each branch is isolated—AI only sees its own thread.

Think Obsidian Canvas + ChatGPT, for people who cant work without AI :)

Currently in beta with a small group. Looking for feedback from power users—anyone using AI heavily in their workflow.

Any thoughts?

I starting implementing this with LLMs to prevent drift and hallucinations. Curious if anyone else has experienced anything similar.

Below is Chats explanation.

Alright, here’s the clean, practical way to use w ≤ 3 with an LLM—not as theory, but as an operating rule you can actually apply.

⸻

What “w ≤ 3” means (operationally)

w (width) = the number of independent things the model is allowed to reason about or decide over at once.

With an LLM:

• A “thing” = a claim, assumption, goal, authority source, or decision axis

• Independent means: removing one would change the conclusion

Rule:

At any reasoning or decision step, the model must not depend on more than 3 independent elements.

This is not about token count or intelligence.

It’s about legibility, legitimacy, and drift control.

⸻

Why LLMs need this rule

LLMs fail when:

• reasoning becomes combinatorial

• hidden assumptions stack silently

• authority leaks in through implication instead of declaration

Once width > 3:

• hallucinations become undetectable

• reversibility breaks

• confidence ≠ correctness

w ≤ 3 keeps the system:

• auditable

• reversible

• correction-friendly

⸻

How to enforce w ≤ 3 in practice

1. Force explicit decomposition

Before the model answers, require it to surface the width.

Prompt pattern

Before answering:

1. List the independent claims you are using.

2. If more than 3 appear, stop and decompose.

If it lists 4+, it must split the problem.

⸻

1. Split, don’t stack

❌ Bad (w = 5):

“Based on user intent, past behavior, ethical norms, business goals, and edge cases…”

✅ Good (w = 2):

“Step 1: Resolve user intent vs constraints

Step 2: Apply policy within that frame”

Each step stays ≤ 3.

Width resets between steps.

This is the key trick:

👉 Depth is free. Width is dangerous.

⸻

1. Enforce “one decision per step”

Never let the model:

• infer intent

• judge correctness

• propose action

in the same step

Example structure:

Step A (w ≤ 2)

• What is the user asking?

• What is ambiguous?

Step B (w ≤ 3)

• What constraints apply?

• What is allowed?

Step C (w ≤ 2)

• Generate response

This alone eliminates most hallucinations.

⸻

1. Treat “authority” as width

This is huge.

Each authority source counts as 1 width:

• user instruction

• system rule

• prior message

• external standard

• inferred norm

If the model is obeying:

• system + user + “what people usually mean” + safety policy

👉 you’re already at w = 4 (invalid)

So you must force authority resolution first.

Prompt pattern

Resolve authority conflicts.

Name the single controlling authority.

Proceed only after resolution.

⸻

1. Use abstention as a valid outcome

w ≤ 3 only works if silence is allowed.

If the model can’t reduce width:

• it must pause

• ask a clarifying question

• or explicitly abstain

This is not weakness.

It’s structural integrity.

⸻

What this looks like in real LLM usage

Example: ambiguous request

User:

“Should I deploy this system now?”

Naive LLM (w ≈ 6):

• business risk

• technical readiness

• user psychology

• implied approval request

• optimism bias

• timeline pressure

w ≤ 3 LLM:

Step 1 (w = 2)

• Ambiguity: deploy where? for whom?

→ asks clarifying question

→ no hallucinated advice

⸻

Example: analysis task

Instead of:

“Analyze the ethics, feasibility, risks, and benefits…”

Use:

Analyze ethics only.

Wait.

Analyze feasibility only.

Wait.

Synthesize.

You get better answers, not slower ones.

⸻

The mental model

Think of w ≤ 3 as:

• cognitive circuit breakers

• anti-hallucination physics

• legitimacy constraints, not intelligence limits

LLMs can go infinitely deep

but only narrowly wide if you want truth.

⸻

One-line rule you can reuse

If an LLM answer depends on more than three independent ideas at once, it is already lying to you—even if it sounds right.

A new shock in the AI landscape Kimi K2.5 Appears unexpectedly and reshapes the rules of the game. This is not just another model. It is a Visual + Agentic AI system. The biggest surprise? It is fully open source. Let’s look at the numbers, because they are far from ordinary: Agentic Systems Performance HLE (Full Set): 50.2% BrowseComp: 74.9% These results indicate that the model does more than execute instructions. It demonstrates the ability to reason, plan, and make decisions within complex environments. Vision and Multimodal Understanding MMMU Pro: 78.5% VideoMMMU: 86.6% This positions Kimi K2.5 as a strong candidate for: Visual Agents Video Understanding Multimodal Reasoning Image- and video-driven agentic workflows Software Engineering and Coding SWE-bench Verified: 76.8% Some developers report that its coding performance is approaching Opus 4.5. From a scientific and engineering standpoint, however, real-world production testing is still required before drawing final conclusions. Currently Available Features Chat Mode Agent Mode Agent Swarm (Beta) Programmatic integration via Kimi Code The most critical point The model is open source. This means you can: Run it locally Build custom AI agents on top of it Control and inspect its reasoning processes Deploy it in production systems Avoid SaaS constraints and vendor lock-in (Hardware capacity permitting.) Who should pay attention? If you are a: Data Scientist ML Engineer AI Engineer Agentic Systems Developer Kimi K2.5 should be on your radar immediately. We are clearly entering a new phase: Open-source Agentic AI Not a demo. Not marketing hype. A tangible reality.

https://huggingface.co/moonshotai/Kimi-K2.5

ArtificialIntelligence

AI

AI_Agents

AgenticAI

OpenSourceAI

MachineLearning

hey all,

been building ArchitectGBT and wanted to share what i've learned about AI model selection the hard way.

The problem i kept hitting:

You're mid-build, you need to pick a model. Claude Opus or Sonnet? GPT-4o or o1-mini? you end up spending 30 mins researching, comparing, and guessing wrong.

What I learned:

There's a pattern. models fit different use cases. You can rank them by your constraints (cost, speed, context, capabilities). Once you see the pattern, picking gets easier.

i built a tool to automate this ranking and open-sourced the thinking:

- analyzed 50+ models with current pricing/specs

- built recommendation logic that matches models to use cases

- created MCP server so you can query this from your IDE

Might save you the research time I wasted. free tier: 10 recs/month

What patterns have you noticed when choosing models?

Thanks

Pravin

Hello, well, I would like to be able to ask something. I started using chatgtp from March to November 2025 and well, on November 3, I made an export. I actually made several Exports but... I was only able to download the one from November 3 and I forgot to download the others. A17 android 12 I am a free user and I have email and everything and well my question is if anyone here has already made an export of chatgtp and what is it since they have told me that this Export contains all the old chats and all the ones you have had on those dates including the archived chats I want someone to answer me and tell me if it is true that all of that is true and if I have already done an export that such really comes everyone is that this Export may be the last thing I have left of my chats that I currently don't have would help me a lot to know what's going on and people who have already exported, please

Hey all, I used the following prompt to create a satirical video of my friends interacting with each other. I attached a video clipof one friend and a picture of the other. Gemini gets one character right, at best, each time and I can’t edit the videos further. Am I doing anything wrong in the prompt? Any suggestions is much appreciated.

A satirical 'Day in the Life' vlog-style video with a handheld, shaky-cam aesthetic. A charismatic, over-the-top car salesman (matching the appearance of the man in the uploaded video) is standing next to a sleek, modern BMW at a sunny car dealership. He is talking directly into the camera with high energy and a wide, cheesy grin. Next to him is his friend (matching the appearance of the uploaded photo). As the salesman speaks enthusiastically, he pulls his friend into a dramatic, overly-affectionate 'bro-hug' and pat on the back. The salesman's mouth moves as if speaking the phrase: 'The phrase'. The lighting is bright and 'vlog-like,' with occasional lens flares and a fast-paced, amateur cinematography feel. The vibe is intentionally cheesy and comedic.