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pankajdoharey
Joined 331 karma
Don't fix something that isn't broken!
- pankajdohareyGoogle is far from dead we need grounding of truth, and from what i hear they already have perplexity like answer engine in testing internally.
- In a world where Crabs are trying to rewrite everything in their favourite Crab Speak its nice to see the Reverse. I wonder if it coulkd be used to translate Rust compiler itself to C :-D
- You are trying to make emacs?
- Sure but trying to close source what has been opensource for a decade or trying to reduce features is very strange. I thought those people had higher standards.
- Sad to see these same people were behind GlusterFS.
- This is not a world model, this ise at best the reimplementation of the the NVIDIA prior art around NeRF / 3D Gaussian Splatting and monocular depth, wrapped in a nice product and workflow. What they’re actually shipping is an offline asset generator: you feed it text, images, or video, it runs depth/structure estimation and neural 3D reconstruction, and you get a static splat/mesh world you can then render or simulate in a real engine. That’s useful and impressive engineering, but it’s very different from a proper “world model” in the RL/embodied‑AI sense. Here there’s no online dynamics, no agent loop, and no interactive rollouts; it’s closer to a high‑end NeRF/GS pipeline plus tooling than to something like Google’s Genie/2/3, which actually couples generative rendering with action‑conditioned temporal evolution. Calling this a “world model” feels more like marketing language than a meaningful technical distinction. Infact my definition of a world model is more closer to what Demis has hinted in his discussions, that video gen models like veo are able to intuit they physics from just video trainingdata suggest that there is an underlying manifold in reality that is essentially computable and thus is being simulated by these models. Building such a model would essentially mean building a physics engine of some kind that predicts this manifold.
- Exactly. It sure is around the corner, because they are talking about AGI (Actually Getting Investments).
- People are specialists not generalists, creating a AI that is generalist and claiming it to have cognitive abilities the same as an "well-educated" adult is an oxymoron. And if such system could ever be made My guess is it wont be more than a few (under 5) Billion Parameter model that is very good at looking up stuff online, forgetting stuff when not in use , planning and creating or expanding the knowledge in its nodes. Much like a human adult would. It will be highly sa mple efficient, It wont know 30 languages (although it has been seen that models generalize better with more languages), it wont know entire wikipedia by heart , it even wont remember minor details of programming languages and stuff. Now that is my definition of an AGI.
- Yup this is the way.
- So you are arguing that renting Mainframe is cheaper than datacenter hardware?
- You're absolutely right that switching languages alone doesn't solve the problem. The real issue isn't COBOL itself but the deep entanglement of business logic with the mainframe ecosystem, things like CICS, IMS, and even the way data is stored and processed. But I still think there's a path forward, and I’ll share a thought experiment based on my experience working alongside colleagues who’ve spent years maintaining these systems.
I’ve seen firsthand how much frustration COBOL can cause. Many of my colleagues didn’t enjoy writing it, they stuck with it because it paid well, not because they loved the work. The language itself isn’t the hard part; it’s the decades of accumulated technical debt and the sheer complexity of the environment. Over time, these systems become so intertwined with business processes that untangling them feels impossible. But what if we approached it incrementally?
Imagine taking an existing COBOL codebase, say, for a large insurance system and identifying the core business logic buried within it. These are the rules and conditions that power critical operations, like calculating premiums or processing claims. Now, instead of trying to rewrite everything at once, you build a parallel backend in a modern language like Java or Go. You don’t aim for a literal translation of the COBOL code, you focus on replicating the functionality in a way that makes sense in a modern context. For example, replace hardcoded file operations with database calls, or screen based interactions with REST APIs.
Most mainframe customers already use middleware like MuleSoft or IBM Z/OS Connect to route requests to both systems simultaneously. For every write operation, you update both the mainframe’s DB2 database and a modern relational database like Postgres. For every read operation, you compare the results from both systems. If there’s a discrepancy, you flag it for investigation. Over time, as you handle more and more business scenarios, you’d start covering all the edge cases. This dual system approach lets you validate the new backend without risking critical operations.
Of course, this process isn’t without its struggles. Testing is a huge challenge because mainframe systems often rely on implicit behaviors that aren’t documented anywhere. My colleagues used to joke that the only way to understand some parts of the system was to run it and see what happened. That’s why rigorous testing and monitoring are essential you need to catch discrepancies early before they cause problems. There’s also the cultural side of things. People get attached to their mainframes, especially when they’ve been running reliably for decades. Convincing stakeholders to invest in a multi year migration effort requires strong leadership and a clear case for ROI.
But I think the effort is worth it. Moving off the mainframe isn’t just about saving money though that’s a big part of it. It’s about future proofing your organization. Mainframes are great at what they do, but they’re also a bottleneck when it comes to innovation. Want to integrate with a third party service? Good luck. Need to hire new developers? Most of them have never touched COBOL. By transitioning to a modern platform, you open up opportunities to innovate faster, integrate with other systems more easily, and attract talent who can actually work on your codebase.
In the end, this isn’t a quick fix it’s a long term strategy. But I believe it’s achievable if you take it step by step. Start small, validate constantly, and gradually build up to a full replacement. What do others think? Are there better ways to tackle this problem, or am I missing something obvious?
- Resilience == redundancy, it has been successfully replicated by almost every organisation without mainframes. M-MANGA (Meta, Microsoft, Apple, Netflix, Google, Amazon) Infrastructure is quite resilient.
- Which is why i think cross compiling to other dependencies and porting to other languages is a better solution. Many of these dependencies could be hardware specific. As long as core business solutions could be ported would be a win for everyone stuck in decades of vendor lockin.
- LLM as translators for Cobol code to Java or Go should be attempted. And Shut down the IBM mainframe rent seek business for good permanently.
- My guess is LLVM. Crystal Uses LLVM for code generation while Go has their own compiler backend.
- All I see is Lisp:
#Operator: - Operand 1 - Operand 2
- I used it in production for a client last yr and i compared that with a go implementation. The concurrency support of crystal on a 40 Core machine beast the socks off golang. Crystal both maxes out all the cores to a high degree, on file io and overall speed of execution. The time taken to do the same task Crystal; beats go by a significant margin while being so easy to write. And go is a easy language to write but Crystal is so much more easy to read because of Ruby syntax inheritance.
- Based on what i read it seems a lot of algorithmic work is required to even make them useful. New algorithms have to be discovered and still they will only solve only a special class of problems. They cant do classical computing so your NVIDIA GPU probably may never be replaced by a Quantum GPU.