spaintech

While both TLA+ and ADA/Spark might be necessary, I haven’t noticed much mention of ADA/SPARK here, which kind of surprised me that they are not used in conjunction as frequent as I might have thought.

For some critical software we developed, we used TLA+ for high-level formal specification. As mentioned earlier, transitioning the actual implementation to another language can be challenging, especially if partial hardware bootstrapping is required. We ended up implementing the high-level specification created in TLA+ using ADA/Spark, which minimized our exposure to buffer and assertion failures. However, optimizing the code to meet performance thresholds was at times frustrating and time-consuming, like any low-level implementation with a new tool/language for us.

I’m curious about any new tools or workflows for leveraging TLA+ high-level specification implementation into other languages like C and Rust. What approaches are people taking once the formalized specification is verified in TLA+ to complete the implementation?

I might be out of the loop, but using TLA+ spec and translate then to other languages hasn’t been a successful use case for LLMs. While they can be helpful, a significant effort is still required to ensure that implementations accurately adhere to the specifications.

I enjoy this article showing up here once in a while. It makes me think about the stack of abstractions we actually live in… CPU -> microcode -> ISA -> firmware/BIOS -> OS + drivers -> C abstract machine -> your app. ( I left our virtualization purposely thinking of a bare metal stack )

Current ISAs have so much machinery underneath that it’s hard to tell when you’re talking to the iron and when you’re talking to the microcode

You can still argue that Forth on a Forth CPU is a genuinely low-level language. :)

You can like the character or not, but there is a trend I’m following ( heavily vested in NVIDIA, so tongue in cheek when I say this ) that might be highly align with Zitron. Looking at the moves from NVIDIA ( Groq )and AMD ( Taalas ) which are pure inference plays. I believe this shows that the impetus to train a better-bigger model might be coming to a level of maturity that might merit a serious threat to the frontier labs.

For frontier labs, their fund-train-new model play might not be as effective, and a shift of spent of compute cost moving away from training to inference might be a tell-tell sign of the LLM as we know it plateauing out as scale is just not as effective. Open models might also be placing a major pressure on meeting then revenue targets need to sustain the model, lots of customer hosting their own inference to mitigate costs.

If you only move the needle just slightly in the direction of inference, frontier labs will soon loose their alphas. Becoming just another SaaS for inference might not be as attractive unless you are Google/MSF ( IMHO ).

Should this pan out, it could be a scenario where the NeoClouds could soon loose their biggest customers, so I tend to agree with that aspect of Zitron’s view.

Thoughts?

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

Correct, I updated the title of the original paper. Thank you your bringing up.

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?

This is a fascinating study, it reveals that ant groups significantly improve their problem-solving abilities through effective cooperation, whereas human groups do not show similar enhancements and can even perform worse when communication is limited. This difference is attributed to ants’ simple cognitive structures, which facilitate seamless collaboration, while humans’ complex cognition leads to variations that hinder efficient group performance. It seems that the advantages of collective cognition depend on the underlying cognitive and cooperative mechanisms of the species… found it a great read.

Dito on the upvote, esthetics are pleasing and I love the transition of the board. Not sure if that was sand or not… but now in hunting for the files… would make a nice gift for a friend that has it all, rated at 2000+ in chest.