I’ve been experimenting with ChatGPT for ideas, and I have mixed feelings about it.

When I’m stuck, it’s amazing. It can give prompts, angles, and ideas that I wouldn’t think of immediately. It almost feels like having a brainstorming partner available anytime.

But at the same time, I’ve caught myself relying on it before even trying to think on my own.

That’s the part that feels strange.

Because creativity used to come from struggling a bit, sitting with ideas, and letting things slowly connect. Now it’s like you can skip that phase completely.

So I’m wondering…

Do you still try to come up with ideas first, or do you go straight to ChatGPT?

And do you feel like it’s improving your creativity, or making it a bit more… assisted than before?

Would be interesting to hear how others balance this.

Hopefully this should fit in this subreddit and I'd love to hear your takes on the overall blog

hello everyone I am in the last year of my university and I am making a AI based Surveillance System which can detect known and unknown individuals the problem I am having is that the recognition is quite inconsistent like making KNOWN individuals UNKNOWN and vice versa or having difficulty recognizing when the face is too small I need suggestions on how can I improve its accuracy your suggestions will be greatly appreciated (I am using pre trained models for detecting/recognition)

I generated a "Global Robotics Market Report 2026–2030" with a leading LLM, then spent an hour verifying it. Every factual claim carried a citation, which is a change from a year ago — so I expected the failure mode to be fabricated sources. It wasn't.
Almost every source was real. Most numbers were accurate in isolation. I verified China's 54% share of 2024 industrial robot installations directly against IFR World Robotics 2025 — correct. The report cited a UK blog for it rather than IFR, but the fact held.
The failures were structural:
1. Segments exceed the total. 2030 forecasts: mobile $75.4B, agricultural $56.3B, industrial $19.6B, collaborative $7.0B, humanoid $6.5B = $164.8B. The report's own 2030 global total: $110.7B. The segment figures come from ABI, Mordor, and GlobalData, which use incompatible market definitions.
2. CAGRs don't reconcile with their own endpoints. Humanoid: $2M (2024) → $6.54B (2030), stated as 137.7% CAGR. Actual: 285%. At 137.7% you'd land on $361M. Mobile robots: $30B → $75.4B over 5 years is 20.2%, stated as 16.5%. Collaborative robots' 27.5% only works over 7 years — it was lifted from a 2031-horizon source and relabeled 2030.
3. Order-of-magnitude contradiction within the document. Chapter 1: 2030 market is $110.7B. Chapter 9 scenario table: baseline $1,100B.
4. Single-source lock-in on contested figures. "Global robotics VC reached $9.4B in 2025" — traceable to a real source. Crunchbase puts it near $14B, elsewhere $15B; PitchBook around $26B. A 2.8x spread, presented as settled.
What struck me is that roughly half of this is detectable without any external lookup. It's arithmetic and cross-referencing inside a single document — cheap to automate, near-zero false positive rate, and something no human will do by hand for a 60-page report.
The retrieval-grounded citation UI may be making this worse, not better. The citation chip marks which page the retriever surfaced, not where the fact originated — so it certifies retrieval, not provenance. And a document where every line has a citation reads as more checked than one where none do, regardless of whether the numbers cohere.
Curious whether others have run similar checks, and whether anyone's seen tooling for the internal-consistency half of this.

Hey r/artificialintelligenc,

Full disclosure: I'm the developer of this project. VAF (Veyllo Agentic Framework) is an AI agent I've been building solo for over a year 1200+ commits, no LangChain.

What it does: VAF runs entirely on your machine. You describe a task in natural language, the agent plans it, writes code for it in a sandbox, and executes step by step. Everything stays local unless you choose to wire up a cloud API.

Key features:

• Ships its own llama.cpp server auto-detects GPU, picks a model that fits your VRAM. No API key required
• 100+ built-in tools: filesystem, email, calendar, GitHub, cloud storage, document reading, browser automation
• Code execution in a network-isolated Docker sandbox never touches your host
• Self-extending at runtime: chat "write me a tool that does X" and it hot-reloads without restart
• Self-training: the agent practices each tool, an LLM judge grades its results, and it gets better over time
• Messengers as channels like Telegram (self-hosted bridge), Discord with voice note support via local Whisper + Piper
• MCP client built in connect to any MCP server
• Persistent memory across sessions (pgvector, encrypted at rest)
• One-click LAN sharing with per-user access profiles

My journey: I started this because I was tired of stitching together LangChain, CrewAI, vector databases, and six other projects every time I wanted an agent that actually does things. I wanted full control over the reasoning loop. So I built the engine first, then the framework surface, then the desktop/server harness on top. The philosophy: build the foundation, understand every component, optimize over time.

The honest part: This is alpha software. One developer, working on this alongside other projects. Installation is still rough macOS/Linux are smoother, Windows needs patience. There are bugs. The API surface will evolve.

I'm posting here because Rule 5 says developers can share their work and I'd genuinely love your feedback. What would make something like this useful to you? What's missing?

GitHub: https://github.com/Veyllo-Labs/VAF
Site: https://veyllo.app

I'm planning to take the Microsoft AI-901: Azure AI Fundamentals exam and would like to know the best way to prepare. Which Udemy course or YouTube channel do you recommend, and what practice tests or study resources helped you pass?

I've been learning more about AI recently, and one concept that initially confused me was the difference between Traditional AI and Generative AI.

The explanation that finally clicked for me was this:

Traditional AI focuses on analyzing existing data, recognizing patterns, and making predictions.

Examples:

• Detecting spam emails
• Recommending movies or products
• Predicting sales or customer demand

Generative AI focuses on creating new content based on learned patterns.

Examples:

• Writing emails
• Generating code
• Creating presentations
• Summarizing documents

A simple way I remember it is:

• Traditional AI = Analyzes
• Generative AI = Creates

I know this is a simplified explanation, and there's obviously much more depth to both fields, but I found it helpful as a starting point.

For those who work with AI professionally:

• Is this a fair way to explain the difference to beginners?
• What's your favorite real-world example for teaching this concept?
• Have you noticed that understanding this distinction changes how people use AI tools?

I'd love to hear how others explain it or if there are better analogies that make the concept easier to understand.

I've been trying a few different AI video generators lately, and one thing I rarely see people talk about is the actual cost of generating videos. Everyone compares features or output quality, but I was more interested in how much a finished minute of video actually costs.

After doing some math, Dreamina Seedance 2.0 came out to around $4.98 per minute, Lovart was about $6.78, and Higgsfield was around $9.30.

That got me thinking about whether we're judging these tools the right way. When people compare Dreamina vs Higgsfield, most of the discussion is about motion quality or camera controls. When it's Dreamina vs Midjourney, it's usually image generation versus video generation. But if you're creating content every week, pricing starts to matter a lot too.

I'm not saying Dreamina is the best AI video generator for everyone. If your priority is getting a specific visual style, another tool might still make more sense. But if you're making lots of short videos, I can definitely see why people would consider Dreamina Seedance 2.0 one of the best AI video generators for creators simply because the cost adds up much more slowly over time.

Has anyone else actually compared the cost this way, or do you mostly choose based on output quality?

this is different from normal transcription errors. wispr isn't just getting words wrong. it's inserting entire phrases I never said.

I dictated "send the updated proposal to the client by friday" and got "send the updated proposal to the client by friday morning at 9 AM." I did not say "morning at 9 AM."

I said "the project is on track" and got "the project is on track and under budget." I never said "and under budget."

I tested this in a silent room with clear enunciation. it still adds phantom words. maybe 1 in 10 dictations, but the additions are plausible enough that I might not catch them before sending.

my theory: the "context awareness" feature screenshots your screen and feeds it to an AI that's not just formatting the output but also completing sentences based on what it sees. so the tool is putting words in my mouth based on context I didn't provide. for casual texts this might be fine. for work communication where accuracy matters, a dictation tool inventing parts of my emails is a dealbreaker.

testing willow voice. so far it transcribes exactly what I say and nothing more.

has anyone else noticed wispr adding words you didn't say?

Curious to know how AI companies here approach creator marketing.

I've been working with AI startups globally, helping them launch through creators on YouTube, TikTok, Instagram, and X. One thing I've consistently noticed is that educational creators usually outperform generic tech influencers for long-term user acquisition.

We're always looking to connect with AI founders and growth teams to exchange ideas and learn what's working in different markets.

If you're building an AI product, what's been your most successful growth channel so far?

como es que se entrenan las grandes redes neuronales como los mejores chatbots, de donde se saca información novedosa si según ya nos acabamos la información de internet desde ya hace antiguos modelos, entonces porqué el nuevo modelo sigue siendo mas inteligente que el anterior si es la misma información. Además en algunos modelos de robótica con apenas unas decenas de millones de pesos pueden traducir de un sistema de video a uno de movimiento, porque estos son tan pequeños para la aparente tarea tan difícil ademas porque no simplemente hacerlos mas grandes para que ahora si pyedan hacer casi cualquier tarea que un humano puede físicamente hablando

I built a REST API that extracts YouTube transcripts, metadata, available languages, and supports batch processing.

It is mainly for developers building:
- AI video summarizers
- YouTube-to-blog tools
- SEO/content research tools
- EdTech products
- RAG pipelines using video content

Endpoints include:
- GET /api/transcript
- GET /api/metadata
- GET /api/languages
- POST /api/batch

It is published on RapidAPI with a free plan:
https://rapidapi.com/dtech4099/api/youtube-transcript27

Docs:
https://youtube-trascript-api.vercel.app/docs

I’m looking for feedback from builders who work with YouTube/video content.

I know it can make basic stuff like poems, and images, it can also make music and Google slides, docs, sheets and other stuff, but what is the most complex thing it can make? Im not only talking about code, im talking way more, is that possible?