It personally feel that Forth if often overlooked as a solution. It’s great for lowlevel embedded work… even on complicated x86 hardware. I also think that people shy away because the tooling is thin and often DIY, but a Forth exokernel plus a single-purpose app can squeeze more from the hardware.
Tethered Forth programming on small devices is an underrated opportunity, IMO. There is also the opportunity to revise the OpenBoot project, I remember the days of automation for deployments at the Bios, it was an amazing tool for massive deployments of Sun T systems in telcos.
My take on why Google bought Wiz is pretty straightforward. First off, Wiz brings a rock-solid CRM loaded with all those juicy contracts from the top cloud players. Add to that a proven enterprise team that knows exactly how to sell the product, and whom to sell to. And you’ve got a recipe for success. Every Wiz win is just a possible upsell for GCP; especially when GCP isn’t even the market leader in cloud. IMO, it opens the door to a whole lot of sales opportunities and deep-rooted relationships with top-tier cloud customers. To me, that all points to a pretty hefty price tag on the table
I agree, hence my direct comment of malicious firmware… For me, the open question is, can one still write a malicious firmware on the ESP32 without the non documented opcodes?
First and foremost, I have no affiliation with any of the authors previously mentioned. However, I would like to pose a question to the community:
Is it feasible to exploit these undocumented HCI commands to develop malicious firmware for the ESP32? Such firmware could potentially be designed to respond to over-the-air (OTA) signals, activating these hidden commands to perform unauthorized actions like memory manipulation or device impersonation.
However, considering that deploying malicious firmware already implies a significant level of system compromise, how does this scenario differ from traditional malware attacks targeting x86 architectures to gain low-level access to servers?
If an LLM’s logic is derived primarily from its training phase… essentially, by following patterns it has previously seen; doesn’t that underscore the critical role of training? We invest significantly in reinforcement learning and subsequent processes, so if the paper’s claim is accurate, perhaps we need to explore innovative approaches during the training phase
When a language model is trained for chain-of-thought reasoning, particularly on datasets with a limited number of sequence variations, it may end up memorizing predetermined step patterns that seem effective but don’t reflect true logical understanding. Rather than deriving each step logically from the previous ones and the given premises, the model might simply follow a “recipe” it learned from the training data. As a result, this adherence to learned patterns can overshadow genuine logical relationships, causing the model to rely on familiar sequences instead of understanding why one step logically follows from another.
In other words, language models are advanced pattern recognizers that mimic logical reasoning without genuinely understanding the underlying logic.
We might need to shift our focus on the training phase for better performance?
Tethered Forth programming on small devices is an underrated opportunity, IMO. There is also the opportunity to revise the OpenBoot project, I remember the days of automation for deployments at the Bios, it was an amazing tool for massive deployments of Sun T systems in telcos.