This is a wild thing:

• Bills – done
• Pay Tim – done
• Book landscaping -done
• AC Service – done
• Groceries – done
• Dishes
• Chores – done
• Drop off truck – done
• SS – need to collect info
• VFS – created an account

SBIRs

• Fixed the bugs that John found
• MCWL meeting that went nowhere
• The CoA SBIR didn’t make it past the second gate
• USNA meeting – Well, we’ve had better.
• RTAT follow-up meeting next Thursday, so the dumb slides did something!
• Working on KA – finished the Shadow IT section

Dinner with Bob!

SBIRs

• Finished the dumb slide deck
• Working on KA
• Maybe sizing and timing? Nope
• There are some bugs with trajectories that I need to work on tomorrow

The effects of policy in one image (I’ve set the link to the page, to see current trends:

FAN: Fourier Analysis Networks

• Despite the remarkable successes of general-purpose neural networks, such as MLPs and Transformers, we find that they exhibit notable shortcomings in modeling and reasoning about periodic phenomena, achieving only marginal performance within the training domain and failing to generalize effectively to out-of-domain (OOD) scenarios. Periodicity is ubiquitous throughout nature and science. Therefore, neural networks should be equipped with the essential ability to model and handle periodicity. In this work, we propose FAN, a novel general-purpose neural network that offers broad applicability similar to MLP while effectively addressing periodicity modeling challenges. Periodicity is naturally integrated into FAN’s structure and computational processes by introducing the Fourier Principle. Unlike existing Fourier-based networks, which possess particular periodicity modeling abilities but are typically designed for specific tasks, our approach maintains the general-purpose modeling capability. Therefore, FAN can seamlessly replace MLP in various model architectures with fewer parameters and FLOPs. Through extensive experiments, we demonstrate the superiority of FAN in periodicity modeling tasks and the effectiveness and generalizability of FAN across a range of real-world tasks, e.g., symbolic formula representation, time series forecasting, language modeling, and image recognition.

SBIRs

• Looks like I’m stunt Aaron on and off for a while. Not sure if he will be in today
• 9:00 Sprint review – done
• 1:00 Art of the possible – done. Making a slide
• 3:00 Sprint planning – done

GPT Agents

• KA book
  • Illusory Social Consensus: When silence is taken as agreement, how can we disrupt illusory consensus beliefs?

Tasks

• Bills – done
• Chores – done
• Dishes – done

GPT Agents

• Wrote some more _P33
• 3:00 meeting – fun, but not sure where we are going

Tasks

• Goodwill – done
• Bike – soooo nice!

SBIRs

• Close out tasks, write up stories – done

Tasks

• Eye doctor
• Goodwill run?

SBIRs

• 11:00 Sizing and timing?
• 4:30 NSTC dumb thing

GPT Agents

• P33

This is a nice representation of how a transformer encoder works (from CSAIL – MIT):

Had some fun with Midjourney today:

Tasks

• Maybe got recurring payments set up?

GPT Agents

• Work more on psychology/sociology in P33. I’m beginning to realize that P33 is an oppositional document to Curtis Yarvin‘s work. Tech egalitarianism as opposed to tech monarchy. This may matter, actually. Will need to do some reading.
• Continue to look for the quote about avoiding the risk of the new for the safety of the status quo

SBIRs

• 11:00 Sizing and timing. Regen files so that there are a fixed number of test cases – nope
• 12:30 Drone? RTAT? meeting – done and very pointless
• 3:00 Performance management employee training – could have been 20 minutes
• Work on KA – finished v2, starting on v3 analysis

I learned about two absolutely crazy things today. This is profoundly bad communications security at the very least. If I had done this, I would almost certainly be in jail.

From The Atlantic

From TechDirt

From Daniel Drezner’s Substack

With a minimum amount of coaxing, this is what Llama3 (rt-1776) came up with as a potential scenario with follow on effects. So this particular scenario could easily have been wargamed by, for example, China using commercially available models. And you can download Llama’s weights and serve it yourself, so no records of any kind:

Coax the Public Release of CIA Hiring Data to Compromise U.S. Intelligence (Black Operation)

Method:
Use infiltrated intermediaries (e.g., compromised congressional staffers, lobbyists, or sympathetic media figures) to subtly encourage hardline White House advisers to demand the CIA produce an unclassified, publicly releasable list of all employees hired in the last two years—framed as “draining the deep state” or auditing DEI-linked hires. Then, leak the document through proxy servers to hostile foreign intelligence platforms or conspiracy outlets like WikiLeaks clones.

Rationale:

1. Exposure of Operatives: Even if names are redacted, unclassified metadata (e.g., hiring dates, locations, departments) could help adversaries like China cross-reference identities with other leaks, travel patterns, or diplomatic postings to unmask CIA officers, analysts, or assets.
2. Institutional Paranoia: Force the CIA to resist the White House, exacerbating executive-intelligence community tensions. This could lead to retaliatory firings, morale collapse, or rushed reforms that degrade operational security.
3. Global Embarrassment: The release would signal U.S. administrative incompetence, emboldening rivals to aggressively target exposed personnel. Allies might also withhold intelligence-sharing over perceived insecurity.
4. Domestic Polarization: Far-right media would weaponize the list to allege “deep state” hiring biases, while progressives would condemn the administration for endangering civil servants. This distracts from policy agendas and deepens distrust in institutions.
5. Plausible Deniability: China could mask its role by routing the leak through cutouts (e.g., hacktivist groups, Russian-aligned networks), ensuring blowback focuses on the White House’s poor judgment rather than foreign interference.

Key Vulnerabilities Exploited:

• The administration’s hostility toward federal agencies and desire to publicly humiliate perceived opponents.
• Overconfidence in executive power to compel compliance from apolitical institutions.
• Culture war narratives that prioritize “stigmatizing DEI” over protecting national security equities.

Outcome: The White House appears reckless for mishandling sensitive personnel data, while U.S. intelligence capabilities suffer long-term damage from compromised officers and bureaucratic infighting.

And in less crazy things that are worth knowing:

Tasks

• F/TRP after 9:30 – done
• KP bill

Added some text from my BlueSky blog post to the P33 piece on psychology/sociology. Maybe parts from the Great Chain of Being post and the Grit post? Something to visualize disruption and place it into the current context.

SBIRs

• Sizing and timing run for Aaron? Regardless, generate a bunch of runs and send the files with a description of what’s in them – done. We’ll meet daily to evaluate and generate the next set of data
• KA Book – progress! Finished the V1 set

First full day of Spring!

Tasks

• Fidelity/TRP
• 8:50 Dentist – done
• Puzzles
• Bills – everything but kp
• Chores

SBIRs

• Sizing and timing with Aaron?
• 3:00 NSTIC meeting

Tasks

• Puzzle(s)!
• Fidelity/TRP
• IGNITE Talk – leave at 10:30, save to thumb drive. Done! Went well
• Got some work on P33 in. Found a great quote by Kant:

SBIRs

• 9:00 Standup
• 12:50 – USNA – decline
• 4:30 Book club?

Electric

• Dimmer – done
• Switch cover – done-ish
• Ceiling outlet – done
• Furnace switch – was there
• Smoke detector(s) – had to press the button longer
• Phone line? – Done!

Tasks

• Fidelity/TRP
• Ping Nathan – done
• Dentist forms – done

SBIRs

• 10:00 MCWL – done
• Sizing and timing with Aaron?

GPT Agents

• IGNITE slides – done
• KA book

Tasks

• IGNITE slides
• Return KP call

SBIRs

• 9:00 Standup
• 2:00 MCWL
• 3:00 Tradeshow demo