wiesbadener
> RLMs are not agents, nor are they just summarization. The idea of multiple LM calls in a single system is not new — in a broad sense, this is what most agentic scaffolds do. The closest idea we’ve seen in the wild is the ROMA agent that decomposes a problem and runs multiple sub-agents to solve each problem. Another common example is code assistants like Cursor and Claude Code that either summarize or prune context histories as they get longer and longer. These approaches generally view multiple LM calls as decomposition from the perspective of a task or problem. We retain the view that LM calls can be decomposed by the context, and the choice of decomposition should purely be the choice of an LM.
It also made me wonder if this reflects conditions for individuals / SMEs rather than large corporate accounts. And I didn’t expect the story to come out of Durban - I would’ve guessed the US.
I haven't been to Durban lately, but my understanding is that the broader SA economy has been under pressure (high unemployment, etc.), and that can hit smaller/local businesses first. So it could simply be a rough patch for your market right now.
That said, if you’re seeing campaigns picking up on other platforms, you might be onto something, at least for your niche.
Looking at your website/content: you’re selling an experience, and this seems like a product that really benefits from strong visual marketing. Make it easy for someone new to "get it" in the first 10 seconds.
Three ideas: 1) Ride local trends: build demos around what’s currently hot in SA/Durban and showcase that with the Magicpods in short, punchy videos. 2) Consider adjacent use cases: beyond magic shows, this could be compelling for advertising, especially at conventions (e.g., ICC). That might be a natural expansion path if event bookings are slowing. 3) Try Airbnb Experiences, or local platforms, like daddysdeals.co.za :-)
To my understanding, they market their ML stack as four layers [1]:
- Platform Intelligence: ready-made OS features (e.g., Writing Tools, Genmoji, Image Playground) that apps can adopt with minimal customization.
- ML-powered APIs: higher-level frameworks for common tasks—on-device Foundation Models (LLM), plus Vision, Natural Language, Translation, Sound Analysis, and Speech; with optional customization via Create ML.
- ML Models (Core ML): ship your own models on-device in Core ML format; convert/optimize from PyTorch/TF via coremltools, and run efficiently across CPU/GPU/Neural Engine (optionally paired with Metal/Accelerate for more control).
- Exploration/Training: Metal-backed PyTorch/JAX for experimentation, plus Apple’s MLX for training/fine-tuning on Apple Silicon using unified memory, with multi-language bindings and models commonly sourced from Hugging Face.
I share your worries on this topic.
I saw you experiment a lot with python. Do you have a python-focused sandboxed devcontainer setup for Claude Code / Codex you want to share? Or even a full stack setup?
Claude's devcontainer setup (https://github.com/anthropics/claude-code/tree/main/.devcont...) is focused on JS with npm.
> No VM, No C, No JIT. Just PyXL.
Is the main goal to achive C-like performance with the ease of writing python? Do you have a perfomance comparision against C? Is the main challenge the memory management?
> PyXL runs on a Zynq-7000 FPGA (Arty-Z7-20 dev board). The PyXL core runs at 100MHz. The ARM CPU on the board handles setup and memory, but the Python code itself is executed entirely in hardware. The toolchain is written in Python and runs on a standard development machine using unmodified CPython.
> PyXL skips all of that. The Python bytecode is executed directly in hardware, and GPIO access is physically wired to the processor — no interpreter, no function call, just native hardware execution.
Did you write some sort of emulation to enable testing it without the physical Arty board?
They provide insights into his language, themes and help with the text analysis. A general learning goal is to connect Kafka's life and works to broader historical and social issues.
So maybe translating the respective Reclam booklets will help non-native speakers, too. They for sure helped me.