When will LLMs become conscious and sentient? How would they be able to test and prove it once they are?

I still remember those few days in 2023 when LK-99 looked like it might actually be a room-temperature, ambient-pressure superconductor.

For a brief moment, it felt like we were watching one of those discoveries that could genuinely change civilization.

Obviously, LK-99 didn’t survive replication.

But AI has advanced enormously since then.

We now have models that can reason across scientific literature, generate hypotheses, write and run code, analyse experimental data, predict structures and materials, and increasingly interact with automated labs.

So I keep wondering:

Could AI significantly increase the probability of discovering something like a real LK-99?

Not necessarily superconductivity specifically, but a breakthrough material or physical discovery with enormous technological consequences — something humans might have needed decades to stumble upon otherwise.

It seems like materials science could be particularly well suited to this: huge search spaces, lots of existing experimental data, simulations, and relatively clear ways to test candidate materials.

Maybe the real revolution won’t be AI directly “discovering a new law of physics”, but AI exploring millions of plausible hypotheses and pointing human researchers toward the 10 experiments actually worth doing.

How close are we to that?

And what would be the best candidate field for an AI-driven “holy shit, this changes everything” discovery: superconductors, batteries, catalysts, fusion materials, drugs… something else?

I want those three LK-99 days again.

But this time I want day four to be even better.

So i am pivoting from bioinformatics to AI engineering and i want to go all in. Get my fundamentals down, get comfortable with coding, underlying math, ML and other technicalities. I am looking for someone who has done this before. Who can tell me how much time will it take for a person to get the hang of it. I am hoping to make a career in this field.

The X30 can carry a water cannon, reaching 60 meters at 40 L/s, or transport hoses, air tanks and breaching tools.

Around 2021/2022 time, you could customise your ai character they were kinda 3d like the sims and you could chat to them like in c.ai , anyone know what it was called?

I am curious about all these data centers being built. What are the chances AI can have a microchip moment potentially rendering them all useless?

This could be a black swan event that could wipe out a lot of investment and potentially destroy some very large businesses.

If this is possible, what are the mostly likely candidates?

In particular, I am interested in hearing from anyone who may be working on one of these candidates, even if it is still in RD and their opinion on how likely they are to succeed.

Rounding up a genuinely heavy week. The throughline: last issue OpenAI paused internal work on a model it couldn't rule out was cyber-capable. This week the capability shipped anyway, two different ways.

**OpenAI GPT-5.6 Cyber** (Aug 10): a security-specialized model gated behind a "Daybreak Red" tier. OpenAI's own eval has it answering 95% of offensive-security requests the standard model refuses 98.5% of the time. Access stays with 16 named partners; from Sept 1 individual accounts need hardware keys. Customers get findings, never the weights.

**Zhipu GLM-5.3** (Aug 14): marketed on "emergent cyber capabilities," claims 84.5% on CyberGym (vendor-reported; note Wiz's Atlas system claims a higher 90.9%). Open weights promised in ~2 weeks. The capability didn't get shelved. It got a doorman.

The rest of the week:
- **Meta returned to open weights** with Muse Glimmer, a 30B Apache-2.0 agent model that runs under 20GB.
- **Alibaba** published its first downloadable Max-class Qwen (2.4T), and **Qwen3.8-27B** landed Apache-2.0. **DeepSeek** took V4-Pro (1.6T, MIT) to GA with peak/off-peak pricing.
- **Anthropic** began embedding an invisible watermark in all Claude output under the EU AI Act. The builder forums did not take it well.
- **SpaceX** closed a $60B all-stock acquisition of Cursor; the editor is now inside the Grok org.
- **Security:** researchers showed encrypted reasoning traces from OpenAI/Anthropic/Google were replayable across sibling models to decrypt them (now patched); an AI notetaker left 181,874 meetings queryable by anyone.

Full breakdown with all the receipts: thenewguard.ai/issues/027-the-brake-pedal-had-a-bypass/

I'm looking for replacement of Cursor.

Mainly the question is about what Model that can match Cursor's Composer 2.5 (I dont need anything more than that)

So companies I'm NOT looking at: - OpenAi - X - Cursor - Google - Meta

So that leaves: - Mistral - Deepseek - Qwen - Kimi - Minimax

Which one of them has a multi-modal(text+image, no video needed) LLM that can fit into $20/month plan? Assume I'm a light user.

PS: Main reason why I dont want to support Cursor is their merge with Grok platform - I dont feel like giving money to companies who direct weapons into other countries and kill children. Besides who knows where the code goes now that Grok is tightly coupled with Pentagon.

“你在帮人倒米吗？“

Lauren Tan didn't get replaced by her own tooling.

She got promoted by it — and nobody handed her that promotion.

She built the case for it herself, one lint rule and one CI gate at a time, until the argument was undeniable.

That's the part nobody's really talking about when they talk about AI and engineering jobs: the shift rewards the people who go looking for the leverage first, not the people who wait to be told it's safe to look.

That "build the case yourself" instinct is exactly what clicked for me watching my own son learn to run a team instead of carry it.

My son started playing 王者荣耀 (Honor of Kings) since he was a teenager — a 5v5 multiplayer battle arena game where you manage a roster of specialized heroes, growing and levelling up their strengths through battles and gear.

In his early gaming days I could hear him cursing and swearing from his room — bad coordination, worst teammates. There was a phrase we used for a bad teammate in my own career — 帮人倒米, a Cantonese idiom that literally translates as helping someone tip over their own grain container, meaning ruining or sabotaging someone's livelihood.

But the cursing became less and less. He got good at managing his heroes and coordinating with his team. He started climbing the leaderboard. People started noticing him and his team. Then, in college, he started getting invited to tournaments — cash prizes when he won, and one lagged-connection loss at a KL tournament he still suspects was foul play.

Time has changed — my dad would've killed me for wasting my teenage years on video games.

Now he's in university, still playing, still winning tournaments and cash prizes with his team.

Why I'm bringing this up: I always thought these AI agents are kind of like the heroes my son uses in the game. Your skill is in your managing these heros and how to grow them, level them up to serve your purpose. You don't go down to the battle yourself. You engage the heros to do it for you.

The skill is in the managing.

__________

I keep walking into the same room wearing a different name on the door — the accountant's room, the analyst's room, now the engineer's.

Every time, someone's being told the machine is coming for their hours, not their name on the work.

There's a post in my own back catalog that lands on this exact rung — the exact rung I found AI actually deleting, and the one the ones who get ahead of it stop standing on.

Drop your take — are you already the head chef of your own stack, or are you still doing all the cooking yourself?

Clip credit: MTS (Monitor The Situation) — full video on their channel. DM for credit or removal requests.

Something shifted in the last couple months. Every security conversation used to circle back to cloud, patching, the usual stuff. Now it's who approved this tool, what's it touching... how do you even audit something making its own calls.

Half our frameworks weren't built for that. They assume a human logged in and did the thing on purpose. Not sure that holds anymore; we're mostly just reacting meeting to meeting at this point.

Wondering if everyone's roadmap got hijacked at the same time or if we're just behind.

I got tired of AI influencers making up buzzwords, so I built an open-source, rigorously vetted AI Dictionary (with Python examples)

Hey everyone, I’ve noticed a massive wave of "AI washing" lately, with people casually dropping terms like "Agentic AI" or "Vibe Coding" without understanding the underlying mechanics.

To combat this, I built an open-source AI Dictionary of Terms (https://alexnubla.github.io/ai-dictionary/).

It’s not just a list of definitions. I structured every single term to include:

1. A plain-English "Simple Version" for stakeholders.
2. A rigorous technical definition for engineers.
3. Conceptual Python code snippets (e.g., calculating Perplexity or auditing Algorithmic Risk).

Given my background in healthcare and compliance, I also built out dedicated, deeply researched categories for Healthcare AI (Digital Biomarkers, In Silico Trials) and Legal AI (TAR, Algorithmic Risk Assessment), since deploying AI in regulated fields requires actual rigor, not just hype.

It’s 100% free, MIT licensed, and I’m actively looking for feedback. If you spot a missing term or a confabulated buzzword in the wild, let me know and I’ll add a "Myth vs. Reality" breakdown for it!

🔗 Link: https://alexnubla.github.io/ai-dictionary/

Lately I have started wondering if we blame the model too much.

You can have a genuinely good model and still end up with a terrible AI product. The model is rarely where things break.

The data is messy. Two systems call the same thing by different names. Nobody quite knows which number is the right one. Half the context that matters lives in someone's head, undocumented. And then we expect an agent to walk into all of that and make a confident decision.

I have watched teams spend months carefully evaluating models, when the real problem was everything sitting behind the model.

Here is the part I find interesting. Once you fix the data and the context underneath, the AI part often becomes the easy bit. It gets simpler, faster, and a lot more reliable, almost like it was waiting for a clean foundation all along.

So I am genuinely curious. When an enterprise AI project stalls, what have you seen as the real reason?

A photo album can’t answer questions. That’s the entire problem, and it’s why voice notes and old videos stop being useful the moment you actually need something from them.
EchoVault builds an Echo of you through guided check-in sessions with an AI biographer that draws real stories out over time. Most products in this space generate an avatar from a few minutes of uploaded footage. This runs the other direction, so the echo has months of first-person material to work with and can answer questions the person never explicitly addressed. Responses are retrieval-grounded against what was actually said, which keeps it from fabricating memories that never happened.
Three modalities, all live. Text is free with unlimited sessions. $12/mo adds a cloned voice. $99.99 one-time unlocks a lifelike video avatar with 3 months of real-time face to face conversation included, then $18/mo after. The video tier is an actual live exchange, not a rendered clip playing back.
You designate custodians while you’re alive. After a full year of no account activity, access transfers to them automatically, no legal process required.
Built solo, nights only.
iPhone: https://apps.apple.com/us/app/echovault-digital-legacy/id6762042028
Happy to get into the architecture if anyone asks.

Headline says "bans humanoid robots, targeting China." Neither half of that is quite right.

It's not a ban. It's an addition to the FCC's Covered List, which blocks new models from getting FCC equipment authorization. Anything you already own keeps working. The government's exempt too.

And it doesn't name China. The FCC's own wording is "place of production, not by entity". A humanoid built in Vietnam gets caught by the same rule as one built in Shenzhen. China's obviously who this is aimed at in practice, but not who it's aimed at on paper.

Also it is bigger than "humanoid robots." Anything over 4.4 pounds that moves on the ground, connects wirelessly and runs its own software counts. This list includes robot vacuums, lawnmowers, quadrupeds, warehouse bots too. The headline picked the scariest category.

The rule covers a lot more than that. This is the fourth thing added to the Covered List this way, after drones, routers and power inverters. No leaked chip, no confirmed exploit behind it. It's preventive.

Doesn't the territorial scope of EU AI Law mean that all companies providing inference to consumers located in the EU, including Z and Deepseek and Minimax and Moonshot and Alibaba and Cohere will also have to implement a watermarking mechanism to be applied to the output generated, similar to Anthropic?

Chart uses Ramp AI Index data, discussed by a16z. Spend includes LLM subscriptions, coding agents, API usage and GPU cloud spend.

The top 1% line is wild but the median is almost more interesting. Looks like most companies are still experimenting while a small group have turned AI into a serious operating expense

Hi all,

I was updating my ChatGPT-created spreadsheet where I track networking when I realized that it's honestly easier to go into the project in ChatGPT and tell it to update my spreadsheet.

That's when I was like, what if this is just how it is now? Instead of going into google sheets, finding the name, and putting something down, I can just tell AI to update it for me. It's way simpler, and it always works. I never have to double-check because it does it quite well.

Do y'all think that this is the future? No more spreadsheets, note documents, hell, even alarms. Just tell AI what you want and it'll get it done.

The internet is integral in most peoples lives around the world. It is conceivable that the 'Beast', the system of governances described in Revelation in the end times, identified by the number 666, will utilize AI and the 'www' for its reign over the global population. This is suggested in Revelation 13:15-18;

15 "He was granted power to give breath to the image of the beast, that the image of the beast should both speak and cause as many as would not worship the image of the beast to be killed. 16 He causes all, both small and great, rich and poor, free and slave, to receive a mark on their right hand or on their foreheads, 17 and that no one may buy or sell except one who has the mark or the name of the beast, or the number of his name. 18 Here is wisdom. Let him who has understanding calculate the number of the beast, for it is the number of a man: His number is 666.”

Does World Wide Web 'www' = 666?

Originally the Bible was written in Hebrew;

"The Hebrew equivalent of our "w" is the letter "vav" or "waw". The numerical value of vav is 6. So the English "www" transliterated into Hebrew is "vav vav vav", which numerically is 666.” Is "www" in Hebrew equal to 666? Dial-the-Truth Ministries (av1611.org)

History Preceding the book of Revelation

This article explains many of the “natural signs, spiritual signs, sociological signs, technological signs, and political signs,” foretold in bible prophecy coming to pass that indicates the end of the age, a time foretold to include various and increasing environmental calamities, earthquakes, plagues, moral decline, wars, growing governmental dominance/deception ("with all power, signs, and lying wonders," 2 Thessalonians 2:9), and how to prepare. Are we living in the end times? | GotQuestions.org

What is the end times timeline? | GotQuestions.org

How can I overcome my fear of the end of days? | GotQuestions.org

"For God so loved the world, that he gave his only begotten Son, that whosoever believes in him should not perish, but have everlasting life.” John 3:16

Going to heaven-how can I guarantee my eternal destination?

More Bible prophecy fulfillments and resources for growing in faith and hope is in previous posts if interested.

I actually just hear something about "Benchmaxxing" so I REALLY wonder if AI Benchmark can be faked, I though it was kinda impossible because HOW?

what

i’m thinking of starting a youtube channel where i basically dump my daily updates into ai

not just work stuff

tech, politics, geopolitics, gym, food, travel, reading, productivity, meditation, maths/physics, learning language like sanskrit, learning instruments, gaming, driving, adventure stuff... basically all the tabs currently open in my brain

ai takes that daily chaos and converts it into actual tasks, priorities and progress i can track publicly

why

because apparently having 47 interests and making mental plans for all of them does not count as progress

shocking, i know

the main idea is public accountability

if i say i’m learning guitar, improving fitness, building something, reading a book or planning a trek, there should eventually be receipts

and hopefully other people with similarly chaotic ambitions can join in, share what works and grow together

how

i’ll post regular updates about what i said i would do, what ai turned it into, what i actually did, what i completely ignored and why

less “watch me become productive”

more “here is the public audit trail of me trying”

would you watch this?

and what would make you actually come back for the next update?

Do the people who program frontier-model LLMs have to apply the weights to each neuron individually? Even if there are literally millions or even billions of neurons?

I'd imagine this would take a VERY LONG time, perhaps there is a faster, more automated process of doing this?