It’s crazy to think that the entire modern AI revolution started with researchers just trying to mathematically mimic a single biological neuron. When you zoom out, the timeline of AI architectures looks exactly like an evolutionary tree.

We went from struggling to solve basic logic puzzles (shoutout to the XOR problem) to building early neural networks, realizing they forgot things too quickly, battling the vanishing gradient problem, inventing LSTMs to give models actual memory, and eventually landing on Transformers with attention, the architecture that essentially ate the world. Every major roadblock forced a mutation that led to a massive paradigm shift.

But looking at this evolutionary timeline brings up a massive question: If Transformers are just the current "apex predator" of AI architecture, what exactly is the next evolutionary leap?

Are we going to see a completely new paradigm that makes self attention look as primitive as a basic AND gate? How do we even begin to perceive or predict what lies beyond our current models when the jumps are this exponential?

I went down a massive rabbit hole mapping out this entire evolutionary timeline. I wanted to see the exact breadcrumbs that led us here, so I put together a deep dive covering the journey from the first perceptrons to the modern era, complete with the foundational paper citations and the historical tweets that marked these shifts in real-time.

If you want to look at the complete map of how we got here to help predict where we are going, I compiled the whole history here: https://cloudmash.blog/posts/evolution-of-ai-perceptron-to-text-to-video/

Would love to hear your theories on what does the post Transformer era actually look like ? Like what can we imagine , how this intelligence on our command can do for us , how enterprise and big players can use it , what can happen ?

A quantum processor can perform beautifully when its qubits take turns, then stumble when several work together. That problem threatens efforts to build useful machines, which must coordinate many operations before fragile quantum information fades. A five-qubit silicon experiment now shows how to preserve much of that accuracy during simultaneous control.

Researchers at the RIKEN Center for Emergent Matter Science and collaborating institutions traced a major source of interference to the microwave signals controlling the qubits. They then developed a calibration method that compensates for unwanted changes in quantum phase.

Their results, published in Nature Physics, support a simpler wiring approach for larger silicon quantum processors.

Does anyone know which HSM vendors NVIDIA, Intel, Google, and Apple use to protect private keys for firmware signing and Secure Boot, and which commercial HSMs support FIPS 140-3, non-exportable keys, offline/air-gapped operation, and multi-party signing authorization?

I work as a security guard for this office lease building with a bunch of different companies in it. One of the companies threw out a bunch of computers, keyboards etc so I managed to grab 3 " dell optiplex 7460 all in one" computers, power cable, mouse and keyboard. Ideally I'd like to keep on for myself and sell the other 2 after getting into the computer itself but as you can imagine they're all still password protected from the old employees using them. Top of the screen upon booting says access to this system is for authorized personnel only. Has the company name. Asks for a user name and password. Any ideas on what I can do with these or how to maybe do a factory reset of some kind?

Hello! I’m currently working on a business project for school about an idea I have for creating a more advanced air filter for your computer. I’m trying to collect as much responses as possible by the end of the week . If you have a few minutes, I’d really appreciate it if you could take my survey and answer the questions honestly. Every response counts and It would help a lot on getting this business idea started as well as help my grade. Thanks!

https://docs.google.com/forms/d/e/1FAIpQLSc4wzvd00ikGmJ-nBM6uZFH6kBMV7l_J6gLDQC7dt7EIGrUfw/viewform?usp=dialog

Right now, the AI space feels entirely focused on massive datacenter clusters and renting H100s by the hour. But after spending way too much time looking at the actual footprint of these models, I realized that 90% of use cases are completely over engineered.

You don’t always need a multi GPU setup. The AI ecosystem is actually a massive spectrum.

I recently sat down and mapped out the exact tiers of AI models based on their size, the hardware needed to run them, and the point of diminishing returns.

Here are the two extremes and the sweet spot in the middle:

• The 100KB Extreme (TinyML) (Tensorflow Lite , sensor anamoly detection models): We are talking models that run on microcontrollers drawing single-digit milliwatts. They run on kilohertz processors using ultra-quantized integer math. You can run basic sensor anomaly detection or wake-word detection on a device powered by a coin cell battery.
• The Local Sweet Spot (4GB to 40GB) (Mistral 7B, Gemma 2 9B/27B, Qwen 2.5 14B/32B): This is where the magic happens for most devs right now. You can run highly capable 7B to 35B parameter models (like Llama 3 or Qwen) at 4-bit quantization on a standard Mac or a consumer GPU (like an RTX 3060 or 4090). It’s perfect for local RAG, coding assistance, and uncensored chat. VRAM is your only real bottleneck here.
• The 2.5TB Behemoths (Deepseek, Llama , Kimi k3): State of the art massive Mixture of Experts (MoE) routing. To even load these, you need dedicated power infrastructure and server racks of specialized accelerators drawing thousands of watts.

The missing piece: Figuring out the exact math for your hardware

The hardest part about building right now is looking at a model on Hugging Face and trying to calculate exactly how much VRAM you need, what quantization to use, and whether your CPU/GPU will choke on the context window.

So, I wrote a complete deep dive breaking down the math for all tiers of the AI spectrum.

If you want to see the architectural differences at each scale, and a cheat sheet for matching the right model size to your specific hardware, I put the full breakdown on my blog here:

https://cloudmash.blog/posts/ai-model-size-memory-hardware-guide/

Let me know what you guys think especially if you've found any ultra efficient small models/technique that punch above their weight on consumer hardware. And also I would love to hear whether quantization have resulted in major difference in quality , like if anyone have that kind of experience in that.

EDIT: Hello everyone, taking advice from your comments, I have added new model examples and kept old ones as they fit in the already assigned tiers, for reference , year of launch is added alongside the model.

Can storing games on an HDD for a long time without plugging it in cause damage?

I has want to make mechanical computer run base 3 coded software. My solution, see saws.

See saws connected to shafts in conjunction with other see saw and shafts in specific ways to enable logic gates running on 1,0, and -1

(-1 pictured above)

-1=far left side down

0= perfect equilibrium

1=right side down

What would the math behind converting binary logic gates to trinary like so be?

How would I calculate that?

I’ve started building a browser-based sandbox for experimenting with balanced ternary computing.

The reason is fairly simple: I want a place where I, and hopefully other people, can try different ternary designs without first deciding what the “right” architecture is.

The simulator uses:

-1, 0, +1

and lets you build small primitives, combine them into reusable components, nest those components several levels deep, test them independently and inspect how signals propagate.

I’m deliberately trying not to just recreate binary logic with three values. Instead, I want to explore whether things like three-way selectors, -1 / 0 / +1 comparison results, ternary control signals, MIN/MAX, different normalize/carry designs and different primitive sets lead to something more natural.

One of the goals is also to make it possible to compare multiple implementations of the same thing and eventually share designs with other people.

It’s still quite low-level, so it’s probably easiest to get into if you already have some familiarity with digital logic or computer architecture.

Everything runs locally in the browser. There is no backend, no account and no cloud storage. Your projects stay in your browser unless you export them.

The project is completely free and MIT licensed.

Live:
https://grunna.github.io/ternary/

Source:
https://github.com/grunna/ternary

It’s still early and experimental, which is also why feedback and alternative design ideas are very welcome.

Full disclosure, this is my podcast. I think this community would enjoy the stories, though.

Ted started out in tech support at Delphi Internet in the early '90s, back when teenagers were opening accounts with fake credit card numbers. He tracked one of them to a house in Pennsylvania and called it. The kid's father, a reverend, answered and promised to take him out to the woodshed.

Ted went on to run security for Boston investment firms. His favorite hire was Mr. Mojo, a guy he paid to physically break into his own offices. Mojo got into one building wearing a visitor sticker he'd pulled out of the smokers' trash. In Dublin he got through a locked door by blowing into a plastic bag.

Ted was also at DEF CON in 1999, where someone shut off the air conditioning during the opening ceremony. His theory is that the best hackers come from music or philosophy, not computer science.

It's onefjef episode 62, it's audio only, and it's on all the platforms. Here's the Spotify and Apple Podcasts links.

Hi, I wanted to get my daughter a new computer for her to do studies and exams at home. I am a long-term iOS user (because of my work) so I suggested that a Mac Mini M4 might fit the bill, partially because I know zero about Windows machines, and partly because, at the time they were £500, and partly because there are literally a bazillion choices. Then the prices exploded and the new M5 and M6 came out and they are even more expensive 😰

Can anyone recommend a machine in this budget? She doesn't play games on the computer, but may do. Her studies are chemistry and maths mainly, and the remote lab kit software is compatible with both OS's. I can't imagine she needs much storage.

Her current machine is actually my old university machine from many years ago - Intel Core i7 860 2.8GHz, 16GB RAM, NVidia GeForce 210. I imagine that any new machine will be vastly superior!?

We're in design phase, and we'd rather get picked apart here than after concrete is poured.

Site and cooling. Nalaikh, a district of Ulaanbaatar. Winters around -25°C give us roughly 7 months of free-air cooling. Design targets: PUE ~1.06 in winter, ~1.18 in summer on mechanical, ~1.10 blended. Uptime Institute Tier III, N+1.

Power. 0.078$/kWh. The grid is coal-heavy, which we know. Solar + BESS are in the mix from day one, and we're working on renewable PPAs.

Connectivity. New cross-border dark fiber on rail right-of-way, dual north/south transit.

Sovereign by design. Zero-trust network architecture plus confidential computing on the GPUs and CPUs (TEEs with remote attestation). Customer data and model weights stay encrypted in use, and you can cryptographically verify that we, the operator, can't access them, whichever route the traffic takes. Keys are customer-held.

Scale and timeline. Phase 1 is 2,048 Blackwell-class nodes (~16k GPUs, ~40 MW IT), scaling to ~6,100 nodes by Phase 3. Ground breaks spring 2027.

Offering GPUaaS, an inference API, sovereign/air-gapped zones, and wholesale colocation and capacity offtake.

What would you poke at first: electrical topology, generator/fuel contracts, fiber routing, free-air intake through winter smog and spring dust, or whether our confidential computing setup actually holds up?

If you're planning training or inference capacity for 2028 and want to talk, DMs are open.

Mau sedikit bercerita, gua otodidak selama 7 tahun mengenai programing dan music developing, programing (khususnya buat web development dan game development) adalah hobi gua dari kecil hingga saat ini begitu juga dengan bikin music, semua kode dulu yg gua tulis line by line sekarang bisa diselesaikan sama AI dengan 1x prompt.. dan bisa dibilang kayaknya gua ga lama lagi akan beralih pakai AI Dibanding ngescript line by line Karena ngejar waktu.. karena menurut gua orang yang ga ngikutin perkembangan zaman akan stuck disitu situ aja. Is it correct?

Apa sudut pandang kalian mengenai serba AI dizaman digitalisasi sekarang?