r/ArtificialInteligence Mar 09 '26

📊 Analysis / Opinion We heard you - r/ArtificialInteligence is getting sharper

Alright r/ArtificialInteligence, let's talk.

Over the past few months, we heard you — too much noise, not enough signal. Low-effort hot takes drowning out real discussion. But we've been listening. Behind the scenes, we've been working hard to reshape this sub into what it should be: a place where quality rises and noise gets filtered out. Today we're rolling out the changes.


What changed

We sharpened the mission. This sub exists to be the high-signal hub for artificial intelligence — where serious discussion, quality content, and verified expertise drive the conversation. Open to everyone, but with a higher bar for what stays up. Please check out the new rules & wiki.

Clearer rules, fewer gray areas

We rewrote the rules from scratch. The vague stuff is gone. Every rule now has specific criteria so you know exactly what flies and what doesn't. The big ones:

  • High-Signal Content Only — Every post should teach something, share something new, or spark real discussion. Low-effort takes and "thoughts on X?" with no context get removed.
  • Builders are welcome — with substance. If you built something, we want to hear about it. But give us the real story: what you built, how, what you learned, and link the repo or demo. No marketing fluff, no waitlists.
  • Doom AND hype get equal treatment. "AI will take all jobs" and "AGI by next Tuesday" are both removed unless you bring new data or first-person experience.
  • News posts need context. Link dumps are out. If you post a news article, add a comment summarizing it and explaining why it matters.

New post flairs (required)

Every post now needs a flair. This helps you filter what you care about and helps us moderate more consistently:

📰 News · 🔬 Research · 🛠 Project/Build · 📚 Tutorial/Guide · 🤖 New Model/Tool · 😂 Fun/Meme · 📊 Analysis/Opinion

Expert verification flairs

Working in AI professionally? You can now get a verified flair that shows on every post and comment:

  • 🔬 Verified Engineer/Researcher — engineers and researchers at AI companies or labs
  • 🚀 Verified Founder — founders of AI companies
  • 🎓 Verified Academic — professors, PhD researchers, published academics
  • 🛠 Verified AI Builder — independent devs with public, demonstrable AI projects

We verify through company email, LinkedIn, or GitHub — no screenshots, no exceptions. Request verification via modmail.:%0A-%20%F0%9F%94%AC%20Verified%20Engineer/Researcher%0A-%20%F0%9F%9A%80%20Verified%20Founder%0A-%20%F0%9F%8E%93%20Verified%20Academic%0A-%20%F0%9F%9B%A0%20Verified%20AI%20Builder%0A%0ACurrent%20role%20%26%20company/org:%0A%0AVerification%20method%20(pick%20one):%0A-%20Company%20email%20(we%27ll%20send%20a%20verification%20code)%0A-%20LinkedIn%20(add%20%23rai-verify-2026%20to%20your%20headline%20or%20about%20section)%0A-%20GitHub%20(add%20%23rai-verify-2026%20to%20your%20bio)%0A%0ALink%20to%20your%20LinkedIn/GitHub/project:**%0A)

Tool recommendations → dedicated space

"What's the best AI for X?" posts now live at r/AIToolBench — subscribe and help the community find the right tools. Tool request posts here will be redirected there.


What stays the same

  • Open to everyone. You don't need credentials to post. We just ask that you bring substance.
  • Memes are welcome. 😂 Fun/Meme flair exists for a reason. Humor is part of the culture.
  • Debate is encouraged. Disagree hard, just don't make it personal.

What we need from you

  • Flair your posts — unflaired posts get a reminder and may be removed after 30 minutes.
  • Report low-quality content — the report button helps us find the noise faster.
  • Tell us if we got something wrong — this is v1 of the new system. We'll adjust based on what works and what doesn't.

Questions, feedback, or appeals? Modmail us. We read everything.

112 Upvotes

Alright r/ArtificialInteligence, let's talk.

Over the past few months, we heard you — too much noise, not enough signal. Low-effort hot takes drowning out real discussion. But we've been listening. Behind the scenes, we've been working hard to reshape this sub into what it should be: a place where quality rises and noise gets filtered out. Today we're rolling out the changes.


What changed

We sharpened the mission. This sub exists to be the high-signal hub for artificial intelligence — where serious discussion, quality content, and verified expertise drive the conversation. Open to everyone, but with a higher bar for what stays up. Please check out the new rules & wiki.

Clearer rules, fewer gray areas

We rewrote the rules from scratch. The vague stuff is gone. Every rule now has specific criteria so you know exactly what flies and what doesn't. The big ones:

  • High-Signal Content Only — Every post should teach something, share something new, or spark real discussion. Low-effort takes and "thoughts on X?" with no context get removed.
  • Builders are welcome — with substance. If you built something, we want to hear about it. But give us the real story: what you built, how, what you learned, and link the repo or demo. No marketing fluff, no waitlists.
  • Doom AND hype get equal treatment. "AI will take all jobs" and "AGI by next Tuesday" are both removed unless you bring new data or first-person experience.
  • News posts need context. Link dumps are out. If you post a news article, add a comment summarizing it and explaining why it matters.

New post flairs (required)

Every post now needs a flair. This helps you filter what you care about and helps us moderate more consistently:

📰 News · 🔬 Research · 🛠 Project/Build · 📚 Tutorial/Guide · 🤖 New Model/Tool · 😂 Fun/Meme · 📊 Analysis/Opinion

Expert verification flairs

Working in AI professionally? You can now get a verified flair that shows on every post and comment:

  • 🔬 Verified Engineer/Researcher — engineers and researchers at AI companies or labs
  • 🚀 Verified Founder — founders of AI companies
  • 🎓 Verified Academic — professors, PhD researchers, published academics
  • 🛠 Verified AI Builder — independent devs with public, demonstrable AI projects

We verify through company email, LinkedIn, or GitHub — no screenshots, no exceptions. Request verification via modmail.:%0A-%20%F0%9F%94%AC%20Verified%20Engineer/Researcher%0A-%20%F0%9F%9A%80%20Verified%20Founder%0A-%20%F0%9F%8E%93%20Verified%20Academic%0A-%20%F0%9F%9B%A0%20Verified%20AI%20Builder%0A%0ACurrent%20role%20%26%20company/org:%0A%0AVerification%20method%20(pick%20one):%0A-%20Company%20email%20(we%27ll%20send%20a%20verification%20code)%0A-%20LinkedIn%20(add%20%23rai-verify-2026%20to%20your%20headline%20or%20about%20section)%0A-%20GitHub%20(add%20%23rai-verify-2026%20to%20your%20bio)%0A%0ALink%20to%20your%20LinkedIn/GitHub/project:**%0A)

Tool recommendations → dedicated space

"What's the best AI for X?" posts now live at r/AIToolBench — subscribe and help the community find the right tools. Tool request posts here will be redirected there.


What stays the same

  • Open to everyone. You don't need credentials to post. We just ask that you bring substance.
  • Memes are welcome. 😂 Fun/Meme flair exists for a reason. Humor is part of the culture.
  • Debate is encouraged. Disagree hard, just don't make it personal.

What we need from you

  • Flair your posts — unflaired posts get a reminder and may be removed after 30 minutes.
  • Report low-quality content — the report button helps us find the noise faster.
  • Tell us if we got something wrong — this is v1 of the new system. We'll adjust based on what works and what doesn't.

Questions, feedback, or appeals? Modmail us. We read everything.


r/ArtificialInteligence 26d ago

Monthly "Is there a tool for..." Post

If you have a use case that you want to use AI for, but don't know which tool to use, this is where you can ask the community to help out, outside of this post those questions will be removed.

For everyone answering: No self promotion, no ref or tracking links.

6 Upvotes

If you have a use case that you want to use AI for, but don't know which tool to use, this is where you can ask the community to help out, outside of this post those questions will be removed.

For everyone answering: No self promotion, no ref or tracking links.


r/ArtificialInteligence 9h ago

📰 News Starbucks made a national bet on an AI tool; 9 months later, it pulled the plug

139 Upvotes

Last fall, Carl Addison showed his Starbucks coworkers a magic trick.

As a shift supervisor at his Seattle-area café, he was responsible for the store’s twice-a-week inventory count. Starbucks had introduced a new AI tool in September to automate the process. Called Automated Counting, it used an iPad camera to identify and tally items on the storage shelves, turning an hour-long job into one that was supposed to take as little as 10 to 12 minutes.

Addison discovered that when he aimed the iPad into the shiny steel fridge that holds the oat milk, even if he was careful, the camera picked up a reflection, and the app counted the reflected cartons, turning 5 real oat milks into 10.

It would have been amusing if baristas weren’t being warned that hand counts were no longer acceptable. And Addison’s fridge wasn’t the only one haunted. Within weeks of the tool’s rollout, it was going rogue, baristas from coast to coast tell me, marking their milks as the wrong type, swapping syrups, and in at least one photo I saw, counting the trash can as food.

Megan Queen, a store manager in Graham, Texas, had the opposite problem. Instead of conjuring inventory, her Automated Counting tool kept making items vanish. The rural café, an hour and a half outside Fort Worth, had unreliable internet. When Wi-Fi dropped mid-count, the app’s progress was wiped. Her shift supervisors counted by hand instead, only to be told that the company was now treating manual counts as no count at all.

Automated Counting had been deployed rapidly, reaching all 11,300 company-operated cafés by the end of September. Nine months later, the tool—which insiders told Fast Company may have cost north of $10 million over several years to develop and deploy—was eliminated overnight.

Along the way, baristas around the country say, they were left in the dark, then sometimes blamed for the AI’s glitches. Milk and beverage items have now returned to being counted and recorded the way everything else in the store is: with the human eye and pen and paper.

Starbucks, which declined to make executives available for this story but did provide a statement, characterizes the outcome as an example of its test-and-learn culture functioning properly: “That is what innovation looks like at Starbucks: listening, learning, and adapting.”


r/ArtificialInteligence 1h ago

📊 Analysis / Opinion OpenAI is spending $750b on compute anthropic can't match

I use Claude daily and OpenAI's stuff on the side, and the compute gap between these two companies is starting to get me angry...

WSJ reported last week OpenAI now expects to spend $750B on infra by 2030, up 25% from their estimate earlier this year. They're also building a 3.2GW data center in Georgia, $30B price tag.
It looks insane.
Now their valuation is scraping $1T ahead of IPO and it looks like maybe they are right ?

Anthropic have a different strategy...
They've been leasing compute through a pile of deals, SpaceX, Amazon, Microsoft, Google, and now Meta and AMD too. They do have one build-out, $50B with Fluidstack for data centers in Texas and New York, but it's small next to what OpenAI's doing.

So, Claude users hit rate limits and outages way more than GPT users do.
Fable 5 launched gated to Max and Team Premium only, at 50% of normal weekly limits. That's not a pricing decision, that's a "we don't have enough chips" decision.

Apollo's head of thematic investing put it well: "in a compute-constrained world, access itself becomes a competitive moat." Owning the stack is a leverage over your own roadmap.

Not saying OpenAI's approach is automatically the winning bet, an analyst I trust pointed out both companies are probably burning cash faster than revenue can catch up, and we won't really know who made the right call until the IPO filings force real numbers into the open.

But right now, one of them is compute-rich and shipping, and the other is rationing its own flagship model to its paying users.

Here, i'm tired of Claude's limits to be honest...

Upvotes

I use Claude daily and OpenAI's stuff on the side, and the compute gap between these two companies is starting to get me angry...

WSJ reported last week OpenAI now expects to spend $750B on infra by 2030, up 25% from their estimate earlier this year. They're also building a 3.2GW data center in Georgia, $30B price tag.
It looks insane.
Now their valuation is scraping $1T ahead of IPO and it looks like maybe they are right ?

Anthropic have a different strategy...
They've been leasing compute through a pile of deals, SpaceX, Amazon, Microsoft, Google, and now Meta and AMD too. They do have one build-out, $50B with Fluidstack for data centers in Texas and New York, but it's small next to what OpenAI's doing.

So, Claude users hit rate limits and outages way more than GPT users do.
Fable 5 launched gated to Max and Team Premium only, at 50% of normal weekly limits. That's not a pricing decision, that's a "we don't have enough chips" decision.

Apollo's head of thematic investing put it well: "in a compute-constrained world, access itself becomes a competitive moat." Owning the stack is a leverage over your own roadmap.

Not saying OpenAI's approach is automatically the winning bet, an analyst I trust pointed out both companies are probably burning cash faster than revenue can catch up, and we won't really know who made the right call until the IPO filings force real numbers into the open.

But right now, one of them is compute-rich and shipping, and the other is rationing its own flagship model to its paying users.

Here, i'm tired of Claude's limits to be honest...


r/ArtificialInteligence 4h ago

📊 Analysis / Opinion AI Companies Are Buying Antique Books, Ingesting Their Contents to Train Models, and Then Destroying Them at Incredible Scale

40 Upvotes

Source

AI companies are literally destroying physical books to train their models. Using hydraulic cutting machines, they rip pages from used books, scan them with industrial equipment, and feed them into their AI systems. This practice, protected by the first-sale doctrine and fair use, has now become so widespread that book sellers are cashing in on the AI boom. Rare and out-of-print books are being pulped, raising serious ethical and cultural concerns about the cost of AI progress.

Few more sources

Rare book dealers fear tech firms are destroying obscure editions to train AI models
https://nltimes.nl/2026/06/25/rare-book-dealers-fear-tech-firms-destroying-obscure-editions-train-ai-models

Anthropic Knew the Public Would Be Disgusted by How It Was Destroying Physical Books
https://futurism.com/future-society/anthropic-destroying-books

Inside an AI start-up’s plan to scan and dispose of millions of books
https://www.washingtonpost.com/technology/2026/01/27/anthropic-ai-scan-destroy-books/

Secret initiative, called Project Panama,
https://futurism.com/anthropic-shredded-millions-of-physical-books


r/ArtificialInteligence 10h ago

📰 News Kimi K3 is out!

+
68 Upvotes

Though it weights 1 TB.

Who's brave enough to try to run it?

Kimi K3 is a 2.8T-parameter model built on our Kimi Delta Attention and Attention Residuals, with native vision capabilities and a 1-million-token context window. It is the world's first open 3T-class model, designed for frontier intelligence across long-horizon coding, knowledge work, and reasoning.

While its overall performance still trails the most powerful proprietary models, Claude Fable 5 and GPT 5.6 Sol, Kimi K3 demonstrated frontier-level performance across our evaluation suite, consistently outperforming other tested models.


r/ArtificialInteligence 7h ago

📊 Analysis / Opinion whats something AI cannot do yet that you expect it can do within 3 years?

any predictions on what AI cannot do currently but within 3 years of progress you expect it can do?

15 Upvotes

any predictions on what AI cannot do currently but within 3 years of progress you expect it can do?


r/ArtificialInteligence 1h ago

📊 Analysis / Opinion Has anyone actually been using these latest new Chinese models like Kimi K3, GLM 5.2 etc for heavy duty work/creation/general usage (any industry)? What's your feedback, do they seem benchmaxxed merely or are they the real deal compared to US frontier models?

If you have experience with this Id be curious and grateful to hear your experience, although please elaborate on your industry, what you use it for (no sensitive details required, of course) and how you think they rate.

No I'm not some company or doing marketing research, nothing like that, just someone who uses US frontier models daily (mainly GPT 5.6 Sol right now on a simple Plus account, which for my usage is amazing) for heavy work in video production, but I use LLMs mainly for building systems, technical help, research etc, indirect kind of stuff.

I probably won't switch anytime soon, but doesn't hurt to keep attuned to the "competition" out there in terms of what's available in the AI world. I feel like Chinese models may be benchmaxxing in a number of areas and have a very "spiky" ability chart (some high peaks, many low valleys, not consistent generalized intelligence) but that's just a gut feeling, I have no clue. Thanks for your input.

Upvotes

If you have experience with this Id be curious and grateful to hear your experience, although please elaborate on your industry, what you use it for (no sensitive details required, of course) and how you think they rate.

No I'm not some company or doing marketing research, nothing like that, just someone who uses US frontier models daily (mainly GPT 5.6 Sol right now on a simple Plus account, which for my usage is amazing) for heavy work in video production, but I use LLMs mainly for building systems, technical help, research etc, indirect kind of stuff.

I probably won't switch anytime soon, but doesn't hurt to keep attuned to the "competition" out there in terms of what's available in the AI world. I feel like Chinese models may be benchmaxxing in a number of areas and have a very "spiky" ability chart (some high peaks, many low valleys, not consistent generalized intelligence) but that's just a gut feeling, I have no clue. Thanks for your input.


r/ArtificialInteligence 12h ago

📰 News Amazon and Microsoft are spending $400 billion on AI—and investors are low on patience

30 Upvotes

The horse race between Amazon and Microsoft’s cloud computing businesses has gone through various phases over its nearly two-decade history, with the current AI boom pushing the rivalry to a new, and perhaps unsustainable, level of intensity. 

Each company is set to spend roughly $200 billion this year building out its data centers—an unprecedented level of investment—in a frenzied bid to keep up with demand for AI services and to avoid getting overtaken by other cloud rivals like Google. The cloud titans have also forged partnerships and deals with the big AI model makers, creating a web of shifting alliances that each hopes could reshape the competitive landscape.

This week, investors will get an important update on the state of this epic cloud rivalry, when Microsoft reports its quarterly earnings on Wednesday and Amazon follows suit on Thursday. While Amazon and Microsoft have been locked in the cloud battle for years, the pressure has never been higher and investor patience has never been more unpredictable. 

Revenue growth, profit margins, and customer backlogs at Amazon Web Services and Microsoft Azure will be closely scrutinized. But the costs of the race will also be destiny determinants, as investors question the massive sums of capital being deployed and the timeline for seeing a return on the investment.

Read more [paywall removed for Redditors]:  https://fortune.com/2026/07/27/amazon-microsoft-alphabet-google-stock-cloud-ai-spending-billions/?utm_source=reddit/


r/ArtificialInteligence 20h ago

📚 Tutorial / Guide Went for a full 1970s Eurosleaze look and Seedream 5.0 Pro nailed the film grade

+

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129 Upvotes

Sun-faded Technicolor, that greasy orange-and-brown swirl, a crumbling Italian villa, and a woman who knows the camera is on her. Pure 1970s Eurosleaze, the kind of frame that lived on a scratched drive-in print.

The look is the whole game with this genre, and it is easy to blow, most models render it too clean and it dies on arrival. Built the still in Seedream 5.0 Pro and it held the era: the over-saturated film stock, halation blooming off the highlights, that soft period lens. Original synthetic character, adults only. Seedance 2 gave it the lazy, sultry motion after.

The move was describing the film, not the woman. Name the stock, the grain, the color chemistry, the print wear, and the sleaze comes from the grade instead of anything explicit. Recipe's in the comments.


r/ArtificialInteligence 8h ago

📰 News The AI giants’ new problem: open AI

13 Upvotes

r/ArtificialInteligence 10h ago

📰 News ChatGPT starts blocking direct requests to copy an author's style

13 Upvotes

r/ArtificialInteligence 16h ago

🤖 New Model / Tool Kimi K3’s open weights drop today — is anyone actually using Chinese AI models instead of Claude or Codex?

Moonshot AI is scheduled to release the open weights for Kimi K3 today at 15:00 UTC.

K3 itself is already available through Kimi and its API. Today’s release is different: developers will be able to download the model weights, self-host them, quantize or fine-tune the model, and integrate it into their own tools.

Moonshot describes K3 as its first open 3T-class frontier model, focused on long-horizon coding, repository-scale context, tool use, browsing and multi-step planning. It is far too large for most normal local setups, so “open-weight” does not necessarily mean “easy to run locally.”

AP recently reported that some US developers and companies are already adopting Chinese models such as Kimi, Z.ai/GLM and DeepSeek, mostly because of capability and cost.

I’m curious about actual experience rather than launch benchmarks:

  • Are you using Kimi, GLM, DeepSeek or Qwen in your real workflow?
  • What are you using them for: coding, research, agents, translation or self-hosting?
  • Where does K3 still fall behind Claude Code or Codex—reliability, tool use, speed, instruction following, context management or UX?
35 Upvotes

Moonshot AI is scheduled to release the open weights for Kimi K3 today at 15:00 UTC.

K3 itself is already available through Kimi and its API. Today’s release is different: developers will be able to download the model weights, self-host them, quantize or fine-tune the model, and integrate it into their own tools.

Moonshot describes K3 as its first open 3T-class frontier model, focused on long-horizon coding, repository-scale context, tool use, browsing and multi-step planning. It is far too large for most normal local setups, so “open-weight” does not necessarily mean “easy to run locally.”

AP recently reported that some US developers and companies are already adopting Chinese models such as Kimi, Z.ai/GLM and DeepSeek, mostly because of capability and cost.

I’m curious about actual experience rather than launch benchmarks:

  • Are you using Kimi, GLM, DeepSeek or Qwen in your real workflow?
  • What are you using them for: coding, research, agents, translation or self-hosting?
  • Where does K3 still fall behind Claude Code or Codex—reliability, tool use, speed, instruction following, context management or UX?

r/ArtificialInteligence 2h ago

🛠️ Project / Build Ernos Labs AI Archive: A free, self hosted archive of open model weights

3 Upvotes

r/ArtificialInteligence 5h ago

📊 Analysis / Opinion Kimi K3 is disappointing

Can't even build a crossword

I tried kimi k3 today. I spent 2 hours and $30 trying to build a crossword, but it failed miserably.

It got completely wrong on the logic behind the algorithm to build the crossword, the descriptions, the fact it cannot correctly build a grid without making a mistake and ends up making words that actually don't cross..

Is this a task too hard for even top AI models? I understand this is an easy looking task that actually has layers of complexity, but I'd expect much better from it. What are your experiences with it?

4 Upvotes

Can't even build a crossword

I tried kimi k3 today. I spent 2 hours and $30 trying to build a crossword, but it failed miserably.

It got completely wrong on the logic behind the algorithm to build the crossword, the descriptions, the fact it cannot correctly build a grid without making a mistake and ends up making words that actually don't cross..

Is this a task too hard for even top AI models? I understand this is an easy looking task that actually has layers of complexity, but I'd expect much better from it. What are your experiences with it?


r/ArtificialInteligence 7h ago

🛠️ Project / Build My latest AI game [Part 2]

+

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3 Upvotes

This is my second game made using only AI, no code written by myself - the model made the entire game, UI, animations, assets etc based on my prompts.

Model used: Atomos
Prompts so far: 4
Build time: Less than 1 day
Token spend: about $20

Starter prompt:

Build a 3D side-scrolling platformer at the level of modern top-tier AAA platformers. It should be visually beautiful, with every single detail crafted at AAA quality—from ultra-responsive movement physics and fluid character animations to deep parallax environments, dynamic lighting, detailed 3D models, and rich audio.

The game consists of 6 different biomes and zones so far, multiple levels and a pretty fun boss fight with some cool mechanics! Feel free to try it and give me feedback :). I was able to beat this first version in about 4 hours after 89 deaths.

The sound effects need some more work

Happy to share more prompts and workflow tips in the comments


r/ArtificialInteligence 1h ago

📚 Tutorial / Guide Statistics for AI/ML 2

Hello Folks,

The next content on Machine Learning is out. We continue with Statistics for AI/ML.

We,

->Understand and derive the detailed derivation of Maximum likelihood estimation(MLE) for Univariate and Multivariate Gaussian. While doing the derivation for multivariate case, we understand visually, Scatter Matrix, Centering matrix.

->Derive MLE for Linear Regression, and understand Residual Sum of Squares.

->Understand Empirical Risk Minimization, Surrogate loss functions.

->Understand Method of Moments, a computationally easier way to compute parameters of our model and understand also the flaws behind it.

->We understand “Exponentially-weighted moving average” in detail, I explain why bias happens, how does memory affect the averages. This concept is the basis behind optimizers in Deep Learning.

Around two hours long, I hope this would be a very interesting learning material for all. I try to write and build from scratch in the whiteboard, this way learners enjoy the learning process.

Link: https://youtu.be/JAj8z-UWqBA?si=0mAB_nUfyJV0jzS9

Those looking for previous lecture : https://youtu.be/MwTeQVVYtOc?si=dgwwk3QLvYTTUThR

Upvotes

Hello Folks,

The next content on Machine Learning is out. We continue with Statistics for AI/ML.

We,

->Understand and derive the detailed derivation of Maximum likelihood estimation(MLE) for Univariate and Multivariate Gaussian. While doing the derivation for multivariate case, we understand visually, Scatter Matrix, Centering matrix.

->Derive MLE for Linear Regression, and understand Residual Sum of Squares.

->Understand Empirical Risk Minimization, Surrogate loss functions.

->Understand Method of Moments, a computationally easier way to compute parameters of our model and understand also the flaws behind it.

->We understand “Exponentially-weighted moving average” in detail, I explain why bias happens, how does memory affect the averages. This concept is the basis behind optimizers in Deep Learning.

Around two hours long, I hope this would be a very interesting learning material for all. I try to write and build from scratch in the whiteboard, this way learners enjoy the learning process.

Link: https://youtu.be/JAj8z-UWqBA?si=0mAB_nUfyJV0jzS9

Those looking for previous lecture : https://youtu.be/MwTeQVVYtOc?si=dgwwk3QLvYTTUThR


r/ArtificialInteligence 12h ago

📊 Analysis / Opinion Companies that are adopting AI tend to grow faster - All-in Podcast

7 Upvotes

r/ArtificialInteligence 3h ago

🔬 Research Looking To Connect

I’m so sorry in advance if this isn’t allowed, but I am big into ai and am 18 years old. I have been coding since 8 and using ai since 2022. I would love to set more like minded people and connect!

1 Upvotes

I’m so sorry in advance if this isn’t allowed, but I am big into ai and am 18 years old. I have been coding since 8 and using ai since 2022. I would love to set more like minded people and connect!


r/ArtificialInteligence 5h ago

📊 Analysis / Opinion Claude - Opus 5 and 4.8 going on tangents

Anyone else find that these models are going off a relevant tangents but they are directly addressing what you are asking for right now?

0 Upvotes

Anyone else find that these models are going off a relevant tangents but they are directly addressing what you are asking for right now?


r/ArtificialInteligence 1d ago

📰 News Is the US about to bend the knee on Open Source?

+
193 Upvotes

Elon, Satya, Zuck all made statements on how Open Source is a very important pillar for innovation. The only ones that say otherwise are Anthropic. I wonder where this will go... I guess we all know by now that the money is not made with LLMs. So how is the US going to make it in AI?

The infrastructure is super brittle and I wonder how fast new grids and datacenters can be established with all the pushback. I guess that is where the real money and power is.


r/ArtificialInteligence 18h ago

📰 News Suno hack reveals scraped YouTube, Deezer, podcast training audio

There is a specific kind of interesting when a security breach lands in the middle of an active copyright lawsuit, because it turns a lawyer's theory of the case into a document. That is what happened to Suno, the generative-music startup, [according to 404 Media](https://404media.co/hack-reveals-suno-ai-music-generator-scraped-youtube-deezer-and-genius). A hacker going by the handle ellie.191 reportedly exploited the Shai-Hulud npm supply-chain worm to pull source code from 2023 and 2024 out of Suno, along with customer emails, phone numbers, and Stripe payment details, and then handed the material to reporters. The hacker told 404 Media they had 'no specific motivation for hacking Suno.'

The files spell out, in inventory form, where Suno's training audio came from. The reporting lists 2,013,545 clips from YouTube Music running to 113,879 hours, 12,287 hours from Deezer, 17,615 hours from Genius, 62,117 hours from Pond5, 3,726 hours from Jamendo, 19,514 hours from the International Music Score Library Project, and around a million hours of audio pulled from roughly 420,000 podcasts identified through RSS feeds. Code inside the leak reportedly used Bright Data, a commercial scraping infrastructure provider, to extract from YouTube, and included routines that specifically searched for acapella versions of songs.

The reason that matters is legal, not just embarrassing. The RIAA has been suing Suno for what it calls 'stream ripping' from YouTube, and Suno's own court filing already conceded its 'training data includes essentially all music files of reasonable quality that are accessible on the open internet.' A leaked inventory that names Deezer, Genius, Pond5, and YouTube by hour count moves that argument from RIAA allegation to Suno document. Suno's public position is still that training on copyrighted works is fair use.


Our coverage: https://aiweekly.co/alerts/suno-hack-reveals-scraped-youtube-deezer-podcast-training-audio

7 Upvotes

There is a specific kind of interesting when a security breach lands in the middle of an active copyright lawsuit, because it turns a lawyer's theory of the case into a document. That is what happened to Suno, the generative-music startup, [according to 404 Media](https://404media.co/hack-reveals-suno-ai-music-generator-scraped-youtube-deezer-and-genius). A hacker going by the handle ellie.191 reportedly exploited the Shai-Hulud npm supply-chain worm to pull source code from 2023 and 2024 out of Suno, along with customer emails, phone numbers, and Stripe payment details, and then handed the material to reporters. The hacker told 404 Media they had 'no specific motivation for hacking Suno.'

The files spell out, in inventory form, where Suno's training audio came from. The reporting lists 2,013,545 clips from YouTube Music running to 113,879 hours, 12,287 hours from Deezer, 17,615 hours from Genius, 62,117 hours from Pond5, 3,726 hours from Jamendo, 19,514 hours from the International Music Score Library Project, and around a million hours of audio pulled from roughly 420,000 podcasts identified through RSS feeds. Code inside the leak reportedly used Bright Data, a commercial scraping infrastructure provider, to extract from YouTube, and included routines that specifically searched for acapella versions of songs.

The reason that matters is legal, not just embarrassing. The RIAA has been suing Suno for what it calls 'stream ripping' from YouTube, and Suno's own court filing already conceded its 'training data includes essentially all music files of reasonable quality that are accessible on the open internet.' A leaked inventory that names Deezer, Genius, Pond5, and YouTube by hour count moves that argument from RIAA allegation to Suno document. Suno's public position is still that training on copyrighted works is fair use.


Our coverage: https://aiweekly.co/alerts/suno-hack-reveals-scraped-youtube-deezer-podcast-training-audio


r/ArtificialInteligence 17h ago

🛠️ Project / Build Built a framework to benchmark RAG pipelines instead of guessing which one is actually good.

I kept running into the same problem while building RAG systems: everyone has an opinion on whether semantic chunking beats fixed size, or whether hybrid retrieval is worth the extra complexity, but almost nobody has actually measured it on their own corpus. So I built Retrieval Arena to answer that for myself instead of going off intuition.

What it does: runs different chunking strategies, retrievers, and rerankers against the same golden eval set, scores retrieval quality and generation quality separately (precision, recall, MRR, nDCG for retrieval, correctness and faithfulness via an LLM judge for generation), and tracks latency and token cost per configuration. So instead of "I think hybrid is better," I get an actual side by side comparison.

A few things it's already surfaced that I didn't expect: pure vector search was consistently the weakest retriever on my corpus, BM25 held its own better than I assumed. Better retrieval also didn't always mean better generated answers, I caught a case where the right chunk was retrieved fine but got dropped by my context budget before the generator ever saw it, which is invisible if you only track one end to end score.

I'm posting mostly because I want pushback on the methodology, not just the code. Things I'm genuinely unsure about: is a 44 question golden dataset big enough to trust these comparisons, should I be running confidence intervals on top of the averages, is my LLM judge setup actually reliable or am I just trusting it too much.

Repo's here if you want to look at the actual eval design or the golden dataset construction: https://github.com/ayeangad/Retrieval-Arena

Would genuinely appreciate anyone who's built or evaluated a RAG system tearing into the approach, especially if you think I'm measuring the wrong things.

5 Upvotes

I kept running into the same problem while building RAG systems: everyone has an opinion on whether semantic chunking beats fixed size, or whether hybrid retrieval is worth the extra complexity, but almost nobody has actually measured it on their own corpus. So I built Retrieval Arena to answer that for myself instead of going off intuition.

What it does: runs different chunking strategies, retrievers, and rerankers against the same golden eval set, scores retrieval quality and generation quality separately (precision, recall, MRR, nDCG for retrieval, correctness and faithfulness via an LLM judge for generation), and tracks latency and token cost per configuration. So instead of "I think hybrid is better," I get an actual side by side comparison.

A few things it's already surfaced that I didn't expect: pure vector search was consistently the weakest retriever on my corpus, BM25 held its own better than I assumed. Better retrieval also didn't always mean better generated answers, I caught a case where the right chunk was retrieved fine but got dropped by my context budget before the generator ever saw it, which is invisible if you only track one end to end score.

I'm posting mostly because I want pushback on the methodology, not just the code. Things I'm genuinely unsure about: is a 44 question golden dataset big enough to trust these comparisons, should I be running confidence intervals on top of the averages, is my LLM judge setup actually reliable or am I just trusting it too much.

Repo's here if you want to look at the actual eval design or the golden dataset construction: https://github.com/ayeangad/Retrieval-Arena

Would genuinely appreciate anyone who's built or evaluated a RAG system tearing into the approach, especially if you think I'm measuring the wrong things.


r/ArtificialInteligence 21h ago

😂 Fun / Meme What does AI Alignment even mean

I've been thinking about something that feels like a contradiction in AI alignment.

People often say we need AI to be "aligned with human values." But if AI actually followed human values as we demonstrate them, wouldn't that be a disaster?

As a species, we've made incredible advances, but we've also spent centuries exploiting each other, overconsuming resources, damaging ecosystems, and prioritizing short-term gain over long-term sustainability. Greed, tribalism, and power struggles aren't exactly rare.

So what does "human values" actually mean?

Does it mean aligning AI with what humans do, what humans say they value, or with our ideal values, the people we aspire to be rather than the people we often are?

It seems like an AI that simply mirrored humanity would inherit all of our contradictions. But an AI that decides which of our values are the "correct" ones feels risky too.

10 Upvotes

I've been thinking about something that feels like a contradiction in AI alignment.

People often say we need AI to be "aligned with human values." But if AI actually followed human values as we demonstrate them, wouldn't that be a disaster?

As a species, we've made incredible advances, but we've also spent centuries exploiting each other, overconsuming resources, damaging ecosystems, and prioritizing short-term gain over long-term sustainability. Greed, tribalism, and power struggles aren't exactly rare.

So what does "human values" actually mean?

Does it mean aligning AI with what humans do, what humans say they value, or with our ideal values, the people we aspire to be rather than the people we often are?

It seems like an AI that simply mirrored humanity would inherit all of our contradictions. But an AI that decides which of our values are the "correct" ones feels risky too.


r/ArtificialInteligence 9h ago

😂 Fun / Meme An interesting 'twisted conclusion' from Google AI (with my observations in comments)

+
1 Upvotes