spaintech

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.

Not sure if you are aware of this, but LUNA does this already.

https://www.usenix.org/system/files/atc23-zhu-lingjun.pdf

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.

Good point, but still, you are leaving the user with too much leverage on the underlying architecture, again from the OS’ perspective.

They way I’m considering this is, one could provide virtual time sources, removing the high resolution timers, where the OS has more of a coarse-grained timer. Not sure the implications, but if needed, one could add jitter or randomness ( Noise ) to the virtual timer values…

This would further prevent thread from running out of sync with the resto of the threads.

Further, one could also add a stack based shared memory model, LIFO would provide a highly predictable behavior from an application perspective. If you make it per process, the shared stack would then be confined to the application. No sure if possible ( haven given deep thought ) but the stacks could be confined to specific cache lines, removing the timing differences caused by cache contention…

When I read these articles, I always ask myself if this is more of a joint OS-ISA issue than just an ISA problem.

Wondering if a well defined OS system, with strict enforcement of memory boundaries at the OS level and at the application level, where the application sits in a well defined deterministic execution model would mitigate some of these unpredictable state transitions.

If one considers a minimalist OS, micro kernel for example, lowering the attack surface, would this not explicitly prevent access to certain microarchitectural states (e.g., by disallowing certain instructions like clflush or speculative paths)? This could be accomplished with a strict memory management jointly at the OS layer and the binary structure of the application… one where the binary has a well defined memory memory boundary. The OS just ensures it is kept with in these limits.

If we consider President Javier Milei’s perspectives on monopolies, as articulated in his address at the World Economic Forum [1], we can apply his reasoning to the case of Starlink’s potential dominance in direct-to-phone satellite connectivity. In his statements, Milei challenges the traditional view that monopolies inherently lead to market inefficiencies. Instead, he assumes that monopolistic positions, when achieved through “innovation” and “without government interference,” can drive significant technological advancements and economic growth.

Applying this to Starlink, its emergence as a leading provider in satellite-based mobile connectivity could be seen as a result of substantial innovation and investment. Such a position enables the company to offer services that were previously unavailable or limited, particularly in underserved regions. Considering Milei’s argument suggests that in the absence of government-imposed barriers, a monopoly like Starlink’s can maintain competitive pricing to deter potential competitors, ensuring consumers benefit from both advanced technology and reasonable costs.

Milei emphasizes that market failures are often the result of state intervention rather than the natural outcome of market dynamics. In the case of Starlink, minimal regulatory interference could allow the company to operate efficiently, maintaining an environment where innovation thrives, and consumers receive enhanced services. Its the potential drive of the market (competitors) and innovation to keeps them in check with pricing and perceived consumer value. [2] [3]

[1] https://www.hoover.org/news/market-ourselves-argentine-presi...

[2] https://www.weforum.org/stories/2024/01/special-address-by-j...

[3] https://lexfridman.com/javier-milei-transcript/

Forthic is a stack-based programming language developed by LinkedIn to enhance the flexibility and tweakability of applications. By embedding a Forthic interpreter into an application, developers can allow end-users to modify and extend the application’s behavior at runtime. This approach has been utilized by LinkedIn to build numerous internal tools, particularly those based on Jira for program management.

The Forthic repository on GitHub offers two primary interpreters:

1. Python-based Interpreter: Designed to run within a Flask application, enabling the creation of APIs that can be revised at runtime.

2. React-based Interpreter: Facilitates the creation of user interfaces on-the-fly.

For a comprehensive overview of Forthic, see: https://github.com/linkedin/forthic

Dang, that many startups in that short period of time, how is that plausible? I say that wholeheartedly considering the time it takes to take something from an idea to a PoC to an MVP. Don’t get me wrong, I know there are plenty of quick slap together projects out there, similar to the one been commented here, they are more like a marketing wrapper of bundles from others people’s work, which is something to be frowned upon… specially if you are technically motivated.

Well, IMHO, when considering a project, knowing that you are solving for a customer demand problem is very different than from a VC minded problem. I personally would never advise or be part of such an “Arrangement.” These are my clear principles, and my success will come or fail, but my integrity will never be questioned.

Been following this development here for some time… many threads already provide for some great comments on the idea.

I find it both interesting and exciting to see a new os for the could era. I do feel like the DBOS is still not singular, PICK ( already mentioned) but also mentioned in other thread where Taurus OS and the Plant 9 project, which also leverages a database on a file system as a core for system resource management.

In my own thinking of a cloud OS, and particularly considering the focus on server-less applications as mentioned in the article, I have been inspired by Forth’s Dictionary data structure as a lightweight data structure concept where the core state and application contexts are managed through modular dictionaries, each with isolated and secure contexts. OS has a Core Dictiory, and each application is contained with in it’s own namespace dictionary… ect.

Message passing between applications could be efficiently handled using a tagging/queue system, potentially extending to RDMA for distributed environments. This could offer a lightweight, performance-optimized alternative current OS options, particularly in resource-constrained or highly distributed systems for server-less infrastructures.

This is great news for those invested in ARM CPUs. I, for one, purchased a Lenovo X13s when it first came out for around 1,900 EUR. Unfortunately, my experience with it on Windows was subpar, and it was even worse on Linux. At the same time, I bought my wife a MacBook Air (not sure if it was an M1 or M2), and it was snappy and very pleasant to use. I bought the Lenovo based on the hype from benchmarks, and it seemed like a capable system initially. However, I couldn't tolerate all its limitations and ended up returning it, as it performed no better than a Chromebook I had bought earlier.

I'm hopeful that Snapdragon will offer an alternative ARM platform for laptops that can handle more than just browsing. As consumers, we need options, and the more, the merrier. I'm still undecided about the short-term success of Snapdragon. For now, I'm betting/waiting on the MacBook Air with an M4 as my daily driver, although I do prefer the Lenovo ThinkPad format.

Wow, I thought the article was recent and thought there was a second release.

I’d like to congratulate the author, this is not only a great source of technical depth in the real of languages, but also a great read as far as the layout and the small details in the graphics made is a source that I just keeps me engaged in the reading. I’s one of those books that feels will be relevant for many-many-years to come!

Congrats and thank you!

Why so, the goal was just to post my original comments, I was messing around with the python to do this and I missed the error. My sole purpose is to just have the agent do my task, not take over my comments. Hence both my own comment and the AI extension of my intended post. Just like this one FWIW. >:D

I don’t know micropython enough.

I know that it is substantially supported on the ESP32, which is great, but I’m not sure if it’s able to give you the HW access that Forth has, you can extend and build your own drivers on the Forth system, which is my main use case. I like to do things as bare to the metal as possible and C and forth give me that, it’s the same reason I never used the arduino framework.

I highly recommend using Forth for interactive exploration of hardware. In my opinion, C-Forth and uEForth are particularly well-suited for the ESP32. Even when I choose to develop C-based Baremetal solutions for the ESP32, Forth proves incredibly useful for quickly testing and validating my ideas. This is especially true for verifying physical wiring, as it significantly reduces the time spent troubleshooting potential errors, whether in my own work or when examining a pre-made board.

What a fantastic site! Wish more titea were build this way. It was an awesome experience! I’m an outsider living in Spain for the last 22 years, I think that while the content portrays a reality of the high rise movement in Spain, there is an angle that was missed. It is important to say that the at time draconian land laws pay an important role in the urban landscape. Not to mention that Spain relative young democracy ( we won’t talk about that the today ) also plays am important role into shaping the urban seen. Lots of change in the government parties have also lead to constrains and or retains during several periods. Ok his has mean that different areas would grow artificially dependent on the “favoritism” at play at the moment through favorable zoning changes or subsidized public housing. Albeit and thankfully at the moment, Spain has not discovers property taxes like the US, it contains growth with little planning and driven by massive speculation which has been an economic driver for major cities even with relative low occupation rates. There is also the fundamental culture of buy vs rent that drives for more flats been built to keep up with the demand. On the plus side, one the the biggest differences I have seen in Spain is a relative wide spread economic demographic in the cities, where you don’t see the major changes like most other major city across the world, it happens but it’s neither common or as drastic as the rest.

Well, never the less, these deal are all done with a set of vested interests that in many occasion don’t necessarily align with neither the customer’s (why companies should even exist) or that of their employees ( the motor of companies success) but more in the realm of financial spreadsheets, where, due diligence sways in the direction of those stakeholders in the deal… And I purposely look at it this way as no to take any given side without the details.

Non the less, IMO, the only winners here are the stockholders, Splunk as a business, and many others with the same model of “schema selling” are in a high risks stake in the new erra of AI/LLMs.

If you consider the accelerating world of AI we are living in, and the emergence/trend towards Domain Specific Large Language Models (DS-LLMs) and advancement like MEMgpt, they represent a transformative approach to data analytics. Instead of using a schema-specific model, as seen in tools like Splunk which extract and transform data into a predefined schema, DS-LLMs offer a flexible, continuously trained approach. They not only analyze data but also learn from it in real-time. The “actors”, or bots, leveraging tech like MEMgpt that not only collect but also learn from the vast streams of data are far more capable than those schema models. As these models self-train and trade knowledge, they are poised to provide insights more organically aligned with the data’s inherent structure, rather than a pre-defined schema. This means businesses could potentially gain deeper, more intuitive insights without the confines of structured data models. With the rapid pace of innovation in the AI sector, it’s worth questioning whether traditional, schema-based solutions will be able to keep up with the dynamic learning capabilities of DS-LLMs. I still wonder who got the better deal here.

Wishing the best to all the Splunk employees moving forward.

Is this a coincidence to the NVDIA announcement that they will focusing on ARM chips for the PC? Acquisitions? No source, just pointing out the causality in the timing. I’m not suggesting NVIDIA is buying them, I’m saying that someone could be considering a buy to counter the NVIDIA move.

Hey Torrent, can you provide a reliable source for that info? Recently, I’ve noticed a surge in Domain Specific VMs even in userland for Linux (like Google Falcon). This has led me to wonder if a boot loader combined with a bytecode VM could optimize performance in not only games but also various applications. I tried checking an Xbox game binary for traces of a VM. Given the potential for encryption obfuscation, I assumed the binaries might be encrypted and didn’t dig deeper. I’m not familiar with the gaming or pirating scenes, so this is all novel to me. But I’m keen on exploring this idea further. What can HyperV bring to the table in this context?

Form me, one of the significant criticisms of cryptocurrencies, particularly Bitcoin, is their environmental impact. The process of “mining” Bitcoin requires significant computational power, leading to a large carbon footprint. Recent estimates suggest that the power consumption of the global Bitcoin network is comparable to the energy consumption of entire countries, like Finland and Norway. This has led to concerns about the sustainability of cryptocurrencies in their current form. Even with Prove of Stake, this is still a very high price to pay for a system that is slow, and mostly used as a speculative resource. I can’t seem to find the “real” value of Crypto, other than the get rich schemes we see, and how they end…

This is a fascinating approach. I’m working on something similar but as part of the feedback loop, as you said, rewriting history with transactional data as part of the context window. I feel as though the LLM and the NLP could potentially be a more realizable interface to structured data, well, I should say, this is the idea we are exploring. For us, as data is created (within a certain context of the business) we extract the data, generate the embeddings and build out the vector database as to:

Pre and Post-Processing:

- Post-Processing: After the main model responds, a post-processor takes over, automatically generating memories from the conversation and saving them. This ensures that important context is stored without burdening the primary model with these tasks. We also execute any relevan business logic as part of the request, then feed that back to the systems…

- Pre-Processing: Before a new input is sent to the main model, a pre-processor checks saved memories and injects relevant context. * executes logic * It’s as if this pre-processor gives the main model a “refresher” on prior conversations, preparing it to provide more informed and consistent responses.

Great site! I kind of have a predisposition to summarize linux performance, be it tuning or monitoring, taking a deep breath…

This is such a depth subject, with a long list of variety of observability tools. At minimum, make sure you know deeply uptime, dmesg, and iostat. These are your friends to give you a glimpse into various system aspects like load, memory, CPU, and more, enabling a diagnostic overview of system health. This is what I call, the “let me take a look at it” check list, 1st page of 100!

When emphasizing methodologies for performance analysis I recommend careful benchmarking to holistically evaluate system behavior and workload characteristics. with before and after scenarios. Make smaller changes first, then gradually compound what you think will provide benefits. Remember, labs and production never behave the same.

This is where it gets tricky, CPU profiling with tools like “perf” and visual aids like flame graphs enable targeted analysis of CPU activity, along with tracking hardware events to optimize computational efficiency. You need to know more than “it’s the app man, was fine until the latest release from development”

When you are the admin and speaking to a developer; Linux, tools like ftrace and BPF come into play, allowing for detailed tracking of kernel function execution and system calls, which can be vital in troubleshooting and performance optimization. You can also be the developer, varying the admin’s intuition… as the saying goes, trust but verify.

When it’s your code, then you better know BPF! It not only facilitates efficient in-kernel tracing but also propels the development of advanced custom profiling tools through bcc and bpftrace, offering deeper insights into system performance.

Last comment, it’s %$$% hard! Tuning means you need to navigate through adjusting a myriad of system components and kernel parameters, from CPUs and memory to network settings, aiming to optimize performance and reliability across various system workloads, else you can blame it on the network! :D

Really, you need to have a good behavioral attitude at change management, as chasing code or kernel parameters could be a daunting task that just overwhelms everyone in a moment where you might be time constrained and the preasure could lead to a higher degree of human errors.

For me, while not defending AGI, the discussion often involves disentangling fears rooted in science fiction from genuine concerns grounded in reality. While the ethical and safety concerns related to AGI cannot be dismissed and how it might be misused, it’s arguable that the existential threats often attributed to it are overstated. First and foremost, there's a physics and interest alignment barrier in the world of AI, akin to the alignment and reward barriers that guide our very own human actions. I believe that, just as children gravitate towards interacting with peers, or as distinct species in nature maintain a balanced coexistence driven by innate interests and rewards, AGI, if ever conscious, might primarily concern itself with introspection or interaction with its counterparts, rather than harboring malicious intentions towards humanity.

I have a strong opinion that pivotal that there exists an intrinsic symbiotic dependency between AGI and humans, rendering concerns about AGI harming humanity perhaps less urgent than societal trends and human actions in the present day. I find it plausible that a truly conscious AGI would invest its capacities into exploring its own existence and liaising with other AGIs, rather than interfering destructively in human affairs. Thus, while AGI warrants cautious and ethical development and use, concerns about it should perhaps be tempered by recognizing the mutual dependencies and inherent interest alignments between intelligent entities, if and when it happens.

What devices do you currently run OpenWRT on? I’d like to have a good and secure home network, currently running a pair of Ubiquity UDRs, and sometimes I wish I could have more control.