Pope text derived from UNAM SANCTAM/ Romanus Pontifex 12th century AD.
See also: European treaties bearing upon the United States and its dependencies by Francis Gardender Davenport.
https://archive.org/details/europeantreatie00paulgoog/mode/2up
PDF downloadable. 4 part series. Part 1 Treaties till 1648 AD.
Nice. I do have a comment on the verbiage:
law-abiding gun owning citizens
2nd:
1, A well regulated Militia, being necessary to the security of a free State, 2. the right of the people to keep and bear Arms, constitution protects: shall not be infringed.
Infringing under color of law: 18 USC 241/242
Upholding the constitution = part of oath.
So, first there is an armored truck seized by Hungary/Czechia destined for Ukraine, loaded up with gold.
and now this ....
I guess the rot the emerging
Yes obscure. I mean:
- Who reads the Congressional Record, right? That's obscure.
And it brightly contrasts with her argument: no one uses it that way .....
Just to think that this is a SCOTUS judge ....
Thanks! Well done!
I am dismayed they call is X11.
That is the name Xorg uses for the display manager. Which also has been forked to XLibre or X11Libre.
I just installed ARTIX-linux & BSD and so far it has done a better job than Wayland. [I know, perhaps I am kicking a holy house here, but 17 years of development and getting a subpar display manager does not cut it, and clearly: this move to fork was a good move, as many problems within the x-11 legacy code, are now resolved. Clearly, a setup to promote Wayland ...where Wayland is clumsey, gittery, lagging, XLibre rocks!)
Anyway, that aside, thinking about the nature of war projected from current feasible tech:.
Unmanned areal vehicles. unmanned land vehicles, unmanned sea born vehicles and submersibles. So, projecting this further out, what meaning is left, if there was any meaning meaning at all?
Do we really want the CoL to have such tech? Is it preferable? What is there is a simple off switch, as those run on expensive silicone hardware?
Both sides in a conflict can leverage such technology. Quite the force multiplier. It reminds me of this clip. https://www.youtube.com/watch?v=pO1HC8pHZw0
Perhaps, we reach the point where disputes can be solved by a game of chess? A lot cheaper ...
Repeat from another thread: https://greatawakening.win/p/1ASsdma5qA/we-uncovered-something-far-bigge/ and comment: https://greatawakening.win/p/1ASsdma5qA/x/c/4ebnMzNQoxW
In terms of data centers: read the analysis in the file:
It turns out that there are two main issues: Low initial investment vs high upkeep or higher initial investments and lower upkeep.
It is interesting to consider that the latter may carry other concerns, but it definitively evades the use of high water demand, thus impacting prices. Given the investment regimen in the USA today, higher initial investments may proof much more interesting. The low investment high upkeep is the lazy way and mimics the norms from 20 -35 years ago.
Of course we can do better.
In terms of circular i.e. waste management, heat is in these design models treated as a waste product. However, it could be spliced back into the economy in different ways, like providing heating for greenhouse agriculture, district heating, or even 20% electricity recovery.
And, there is no need to use fluor. There are alternatives.
The problem I see, is the same we saw in the 2000 when building data centers was done as a rush job. Bad idea.
But, a plan looking at a build out over time, adding components that actually turn to social return on investment, is not a bad idea.
So, yes, there are concerns. But these can very well be addressed by well in advanced engineered DC's with an open mind to SROI.But then again, you do not need H1B1's or DEI hires.. You need engineers of merit.
No one checks the TS link?
I like the fact that Trump reposted a meme where he has a Q+ on his shirt. But as for this "Stay with me deplorables" the link would be better.
In terms of data centers: read the analysis in the file:
It turns out that there are two main issues: Low initial investment vs high upkeep or higher initial investments and lower upkeep.
It is interesting to consider that the latter may carry other concerns, but it definitively evades the use of high water demand, thus impacting prices. Given the investment regimen in the USA today, higher initial investments may proof much more interesting. The low investment high upkeep is the lazy way and mimics the norms from 20 -35 years ago.
Of course we can do better.
In terms of circular i.e. waste management, heat is in these design models treated as a waste product. However, it could be spliced back into the economy in different ways, like providing heating for greenhouse agriculture, district heating, or even 20% electricity recovery.
And, there is no need to use fluor. There are alternatives.
The problem I see, is the same we saw in the 2000 when building data centers was done as a rush job. Bad idea.
But, a plan looking at a build out over time, adding components that actually turn to social return on investment, is not a bad idea.
So, yes, there are concerns. But these can very well be addressed by well in advanced engineered DC's with an open mind to SROI.But then again, you do not need H1B1's or DEI hires.. You need engineers of merit.
This is what the article does:
It superficially, without linking to the disclosures, constructs symmetrics: 1A. Pelosi disclosed trading. 2.B. Trump discloses, but was LATE i.e. past the mandatory filing date. 200 dollar fine.
In either case no transgression is asserted. only the appearance of it. Then the comparison in size: 59 vs 750 million. Wow......this is really BAD! Orange man BAD!
As if these two are comparable.
We know the Trump organization is trading.
- Truth Social itself
- Crypto investments: mining rigs, coins, the lot.
- Investments in energy production
- and then the slew of organizations that are geared towards hospitality and real estate.
And this is done in the open. Second: Trump warned his own staff NOT to trade on information the gleaned from their position.
However, in Piglousy's case: We know it is highly likely it is insider trading by her family.
So, you read the headline, the bullshit article, without asking the basic questions, and then expect answers here?
If this is close to your heart: investigate it. Fact check it.
The new Karen: Sherryl
The video is AI generated.
If you know Trump, you know.... The glitch is quite visible.
"Wait till Biggus Dickus hears of this:
Totally agree.
And true. This kind of transformation is effectively seeing science fiction coming into being.
Thanks for sharing. Shared forward.
Yeah. ....but it seems to me that there is also another issue playing out:
We discussed this in case of Mr Lukas. He also has a libertarian slant, and it seems Ron Paul here noted correctly that the people returning back to be lovers of freedom is playing out.
Summary comparison — compute, GPUs, and energy (assumptions: public reporting, 2024–2026 hardware)
Key assumptions used: Grok (xAI) trains on large Nvidia H100/H800-based Colossus clusters (dense training at multi-petaflop scale); DeepSeek (Whale Lab) uses Mixture-of-Experts (MoE) designs and non‑Nvidia accelerators (Huawei Ascend / Cambricon + H800-style variants in some reports). Numbers below are order‑of‑magnitude estimates synthesized from published technical notes, reporting, and community analyses.
Raw compute (training)
Grok / Colossus-style dense training:
Training uses dense model compute where every parameter contributes every token. A frontier dense model in the 100B–6T class typically consumes tens to hundreds of PFLOP‑years of total compute (effective TFLOP/s · years). Example-scale: multi‑million to tens of million GPU‑hours across H100-class GPUs for largest trains.
DeepSeek / MoE:
MoE greatly reduces FLOP per token because only subsets of experts are activated. Reported: DeepSeek‑V3 ~250 GFLOPS/token vs 2448 GFLOPS/token for a 405B dense model (paper claim). Reported GPU‑hour totals for DeepSeek‑V3 training are orders of magnitude lower than comparable dense runs (papers/reporting cite low single‑digit million GPU‑hours vs tens of millions for some dense baselines).
Types of GPUs / accelerators
Grok / xAI:
Heavily Nvidia (H100/H800 family) with NVLink / NVSwitch for intra‑node high bandwidth. GPUs optimized for dense tensor compute and large memory bandwidth.
DeepSeek:
Uses MoE-friendly deployments; reported use of Huawei Ascend-family and H800-style accelerators in some deployments. MoE benefits from high interconnect but can be optimized to reduce IB traffic (node‑limited routing); can also run on mixed hardware including lower‑cost consumer GPUs for inference with proper engine/quantization.
GPU counts and cluster design
Dense (Grok) clusters:
Very large single‑site clusters (reports of 1–1.5 GW datacenter power footprints for Colossus‑class installs) — implies tens of thousands of H100/H800 GPUs for frontier training and large on‑demand inference capacity.
MoE (DeepSeek) clusters:
Fewer effective GPU hours required for equivalent capability; MoE still requires many GPUs for parameter storage and routing at scale but can hit similar performance with fewer active FLOPs and specialized routing to reduce cross‑node bandwidth. Reports estimate training DeepSeek‑V3 required a few million GPU‑hours on H800‑class gear (much lower than some dense baselines).
Electricity and power costs (training)
Dense (Grok):
If a Colossus facility is 1–1.5 GW peak, annual electricity for continuous operation is enormous (GW × hours × $/kWh). Example: 1 GW running continuously uses 8.76×10^6 MWh/year; at $0.05–0.12/kWh that’s tens to hundreds of millions $/year just for power (actual training uses a fraction of continuous peak, but peak facility capacity correlates with high power draw during training campaigns).
MoE (DeepSeek):
Lower active FLOPs per token reduce total energy consumed for pretraining; published estimates for large MoE runs imply substantially lower electricity bills for comparable delivered performance. Concrete example: paper claims training requiring ~2.6M GPU‑hours vs dense models requiring 30M+ GPU‑hours — that gap multiplies into energy savings roughly proportional to GPU‑hours × per‑GPU power draw.
Inference cost and hardware for deployment
Dense models (Grok):
High VRAM and throughput GPUs (H100/H800) for latency‑sensitive hosted inference; inference energy per token is higher because all parameters are active.
MoE models (DeepSeek):
Lower per‑token activation reduces inference FLOPs and memory traffic; can be cheaper to serve and, with model‑co‑design, can be run on more diverse hardware (including non‑Nvidia accelerators or consumer GPUs with quantization) for cost‑sensitive deployments.
Capital & operational cost tradeoffs
Dense approach:
Higher CapEx on uniform high‑end Nvidia GPUs, NVSwitch/NVLink networking, and larger datacenter power/cooling; simpler software stack for dense training and standard parallelism.
MoE approach:
Potentially lower compute and energy costs per performance unit but higher software complexity (routing, load balancing), more sensitive communication patterns, and potential need for co‑design of hardware/topology to maximize efficiency.
Caveats and uncertainty
Public numbers vary; some figures are from vendor/industry reports and preprints (DeepSeek paper excerpts) and unconfirmed press reporting for xAI/Colossus. Exact GPU‑hour totals, power footprints, and pricing are often proprietary.
MoE savings depend on gating efficiency, routing overhead, and how many experts are actually active per token; communication overhead can erode benefits if poorly implemented.
Regional electricity prices, datacenter PUE, and ownership vs cloud‑rental change $ estimates substantially.
If you want, I can produce a compact table with estimated GPU‑hours, per‑GPU power draws, and rough $ electricity costs for a few concrete training scenarios (assume H100/H800 specs and $0.06/kWh), using the numbers above.
So, in essence, Deepseek, by necessity, tries the lower the electricity bill to yield the same type of result.
The name is in the Epstein files ....
Sounds good right? Guilty by association? Reading names is just plain estrogen male revenge. A poison pill sound bite.
How different the farewell address of Ron Paul. https://www.americanrhetoric.com/speeches/ronpaulfarewelltocongress.htm
When I just re-listened to it, I am amazed to see many of the point raised by the famous Dr No, to be .....addressed by this Trump administration.
Interesting then the direction Deepseek was going in.
BTW: I kind a like GAB as a meta AI. But indeed, Grok rules... on certain matters.
Someone was actually budgetting [/s]
Microsoft invested $5 billion in Anthropic.. gave 100,000 engineers Claude Code access.. encouraged adoption.. watched usage explode.. then the invoices arrived.. and issued an internal order to cancel nearly all Claude Code licenses by end of June and force everyone onto their own cheaper tool..
If bills come in ...i.e. 30 day cycle? There are certain metrics that can be graphed to anticipate consumables. And no one said anything? Or was it .... eh .... unfavorable to say anything .....
This is first rate stupid. It gears towards the question how MS is really run. Can shareholders trust in management on par with the fiduciary obligation they entrust to management? How about H1B1 (sounds like a flu) hiring? Subpar?
The whole thing reminds me of 2000 => data center builds. Everybody was screaming tics, tics, tics, but there were no customers. Yet, DC were built en masse, and 20 year leases were arranged, but no one thought of simply staging over time, decreasing capital expenditures, easing on supply chains, etc, not to mention the design savings .... Man. A Box in a Box was revolutionary.
But then again, that would require cutting staff, and easy jobs ...like "making sure jobs".
Ok, so, Tucker [ex] CIA with Daddy CIA, discussing the issue of USA now openly acting instead of convoluted according to the CoL model.
China!!!!!
No, Not Uranium, Belief them. It is a bogus story. You cannot trust Trump. He is lying to you.
Look here, not there!
Some people in certain positions may get sweaty:
However, the story changed after Hackread.com contacted the threat actor directly on Telegram. In private messages, the seller clarified they did not hack or breach OnlyFans. Instead, they claimed the database was built using information collected from previous data leaks and public sources, including breached records from platforms such as Twitter, Instagram, and Spotify.
https://hackread.com/hacker-selling-onlyfans-user-records-old-breaches/
if so, then in general, reviewing online behavior is paramount.
Though only performing math on the level of grade-school students, acing such tests made researchers very optimistic about Q*’s future success, the source said.
Trolling indeed ....
So, Spain refused the use of the airfields .... and gets ....
publication of the way in which socialist around the world unite around fraud. ....
Nice. FAFO.