DeepSeek R1’s rise may be fueled by CCP-backed cyberespionage, illicit AI data theft, and a potential cover-up involving the death of former OpenAI researcher Suchir Balaji.
super cool to see an open initiative like this—love the idea of replicating DeepSeek-R1 in a transparent way.
I do like the idea of making these reasoning techniques accessible to everyone. If they really manage to replicate the results of DeepSeek-R1, especially on a smaller budget, that’s a huge win for open-source AI.
I’m all for projects that push innovation and share the process with others, even if it’s messy.
But yeah—lots of hurdles. They might hit a wall because they don’t have DeepSeek’s original datasets.
The material is definitely practical—Kafka, Docker, Kubernetes, and Jenkins are all industry-standard tools, and the focus on MLOps is refreshing. It’s great to see a course bridge the gap between ML and actual production systems, not just stop at building models. Love that they're also tackling explainability, fairness, and monitoring. These are the things that often get overlooked in practice.
Is it too entry-level? Looking at the labs, a lot of this seems like stuff a mid-level software engineer (or even a motivated beginner) could pick up on their own with tutorials. Git, Flask, container orchestration... all useful, but pretty basic for anyone who's already worked in production environments. The deeper challenges—like optimizing networking for distributed training or managing inference at scale—don’t seem to get as much attention. Maybe it comes up in the group projects?
Also wondering about the long-term relevance of some of the tools they’re using. Jenkins? Sure, it’s everywhere, but wouldn’t it make sense to introduce something more modern like GitHub Actions or ArgoCD for CI/CD? Same with Kubernetes—obviously a must-know, but what about alternatives or supplementary tools for edge deployments or serverless systems? Feels like an opportunity to push into the future a bit more.
Too entry level? Even if every tool is entry level, tying them all together and actually making it work is hard. I'd say it's mid-to-late B.Sc. material.
Relevance? Is there really a huge conceptual difference between Jenkins and the other CI/CD frameworks? If not, if I were them I would just choose a random popular one, and it seems to me that's just what they did.
It’s kind of funny all those supposedly complicated technologies are actually pretty simple when you understand why you are using them. Docker is the best example, it’s hard to understand what is happening unless to understand the problem it’s solving.
I think what you're missing here is that this is now _the_ entry point for year 1 CS students. People come in wanting to do ML. 20 years ago people came in and learned to write databases with Java and used similarly "will probably be deprecated tools". This is just the new starting point.
> Also wondering about the long-term relevance of some of the tools they’re using.
That's what I was wondering about too. It seems to me that eventually someone will build a tool that runs any neural network on any hardware, whether local on one machine, or distributed in the cloud.
Tried Htmx a while back... mixed feelings. Love how easy it is to get basic interactivity—honestly, adding a filter or an upvote button in a couple of lines of HTML feels like magic. No messing with a frontend framework, no bundlers - just works.
But I hit walls when I needed more complex stuff. Like, if I want to keep state on the client (e.g., a live calculator or sliders updating a table), Htmx feels clunky. Sending a request to the server every time a user adjusts a slider—yeah, no. React or Svelte is a better fit there. And if you're already using those tools. Htmx starts to feel redundant. Why add more when you've got everything in one place?
Also, not sure about the recent patch release - Changing default behavior in a minor update? Feels risky, even if it's "fixing a bug." Imagine waking up to find your body tag wiped because you updated without reading the changelog, yikes. Makes me think twice about trusting it in production.
Butfor MPAs or projects that lean heavily on server-side rendering, it’s a game-changer. You’re not rebuilding the wheel, just enhancing it. Htmx has a sweet spot—it’s just not always the right tool for every job. Depends what you're building, I guess...
Wait, why do I need React for a live calculator or sliders updating a table? Does plain JavaScript and the Web APIs no longer exist? Don't Web Components/custom elements exist? I need to immediately jump to React/Vue/etc. at the slightest bit of client-side interactivity?
Also, do people just update and yeet their apps into prod without testing it? What happened to test environments? Test suites? Manual QA? Letting it soak? Where did quality control go?
let’s not ignore the practical side. Algorithms for studying primes drive advances in computing, machine learning, and data science. Cryptography literally depends on them. Plus, big unsolved problems like the Riemann hypothesis could completely reshape number theory.
Green and Sawhney’s work is especially exciting because it shows how tools from one field—Gowers norms—can unlock progress in another. That kind of cross-disciplinary insight is where breakthroughs happen. And yeah, it’s fair to question funding priorities, but basic research has given us antibiotics, GPS, and even computers. Without it, we’d still be in caves.
Cryptanalysis relies on deep conjectural heuristics in analytic number theory. These conjectures becoming theorems wouldn't affect cryptanalysis at all, because their validity is already baked in. If, however, any of these conjectures turn out to be false, there would be ramifications.
