Hi there 👋🏼, I’m Yani, but you can call me Yans! This account has one and only purpose: to teach you about Software Engineering/Computer Science 💻 and Tech! As a Student in SI, I just want to share my passion for those that might be interested in it
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ps5-linux has been released! You can now turn your PS5 Phat console on 3.xx and 4.xx FWs into a fully functional Linux PC gaming device!
https://t.co/WiiQiPCoIa
TCL's first OLED monitor features a sleek 6.4mm design. It is reminiscent of what we were promised 15-20 years ago OLED would enable in the future. The monitor launches ahead of TCL's bigger push into OLED. Read more👇🏼
I build jobx
A job board that gathers tech opportunities posted here.
I was tired of searching for good freelance gigs, so I built it to get notified every time a new opportunity is discovered !
Link below 👇
Do people understand this model can run on your MacBook Pro or Mac Mini?
Claude Opus level intelligence, for FREE
If it’s not clear not clear:
A free, open-weight model (24gb) you can download right now and run on your laptop is competing with models that cost $200/month
AGI is not coming.
We are nowhere near AGI. What we have today is inference, not learning.
Models get trained once on huge fixed datasets, then frozen. You ask questions, they remix patterns they already saw. Nothing updates. Nothing sticks. Talking to the model does not make it smarter. It does not learn from you. Ever.
Learning is still slow, expensive - and offline.
Look at self driving. You drive around a pothole, make a U turn, and come back. The car’s AI does not learn that you just solved that exact problem. It reacts the same way every time using sensors and rules. Do this 20 times a day and it still has zero memory that the pothole exists. It just re sees it. That is why edge cases never die. There is no local learning. No accumulation.
No 'oh yeah, I’ve seen this before'
LLMs work the same way. Tell it your name and it does not remember. The only reason it looks like memory is because scaffolding keeps shoving your name back into the prompt every time and sanitizing the output.
The model itself has no idea who you are and cannot learn from interaction. It is structurally incapable.
And the scaffolding is the worst part. It is pure duct tape. Just prompts on prompts on prompts around a frozen model. When something breaks, nobody fixes learning. They add another layer. Another rule. Another retry. Another evaluator model judging the first model.
So you end up with systems that are insanely complex but mentally shallow. Debugging is hell because behavior comes from hack interactions, not a learnable core. Tiny prompt tweaks cause wild behavior shifts. Latency goes up. Costs go up. Reliability goes down. None of this compounds into intelligence. It just hides the cracks.
Until we have real persistent learning and real memory inside the system, there is no AGI.
LLMs are not built for this. You cannot prompt your way out of it. You need a totally different architecture. Yann LeCun is right.
And even then, what architecture can actually learn online, store memory, and stay stable on today’s hardware?
Best case, maybe 5-10 yrs.
Right now it is all inference. It looks magical, but the emperor has no clothes. A lot of people see it. Almost nobody says it out loud.
Do this:
1. Open AWS and create an account.
2. Go to EC2, spin up an instance, generate a key pair, and SSH into it from your local system. Just play around install Nginx, deploy a Node app, break things, fix them.
3. Decide to launch something? Go to Security Groups open ports for HTTP (80), HTTPS (443), and SSH (22).
4. Grab your instance’s public IP, open it in your browser, and you’ll see your app running on the cloud.
5. Start scaling ? create another EC2 instance and set up an Application Load Balancer to route traffic.
6. Tired of adding instances manually? Use Auto Scaling Groups (ASG) define min/max instances, attach a launch template/AMI of your app, and AWS will scale automatically based on CPU or request load.
7. Getting millions of requests and your DB is choking? Use Kafka or SQS for buffering high-throughput workloads. Learn more about async
8. Store images, videos, or backups? Use S3 connect it from your app via the SDK, and make a file uploader.
9. Use RDS for managed databases (MySQL/Postgres). AWS handles backups, scaling, and maintenance.
10 Use CloudWatch to monitor your instances, logs, and metrics. Set up alerts when CPU spikes or memory usage is high.
11. For networking, explore VPCs subnets, route tables, gateways. This is where you learn how AWS isolates traffic securely. You will learn really well about networking here. Just create a vpc and play around
12. Once you’re comfortable, try Terraform to automate infra because that's 100X easier to manage state.
Stage 13: Say Fuck AWS for stealing money and buy your own VPS, set up Docker, or use Coolify for one-click PaaS setups.
@wangbin579 I know my way around MySQL and OracleSQL quite well but i’m still in the beginning of my journey with PostgreSQL. You’re saying i should still be okay ?
Ubuntu’s plan to replace the GNU Core Utils with Rust-based reimplementations is going exactly as poorly as predicted.
Some Rust versions being 17 times slower than the battle tested GNU C / C++ version. And other Rust-based versions simply failing to work on large files.