please find any claims that depend on citations referring to works by any of the present authors

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please highlight anything related to music performance practice

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testing something new today

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sentence about GUIs

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Subject ID: Individual_2 Phenotype: Narrowing of the medullary cavity of tubular bones

please find me the main contributions of this paper

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for - AI - Bayesian teaching paper - Google

for - AI - biological neural networks - AI - mortal computing

Ai and taxation

Humans as API, bc [[Alles is een API 20260309095254]]

(via [[Stephen Downes p]]

the 'am dash', as opposed to the em dash. A human only glyph that AI 'can't use'

I tnink there I am dash

rhetoric: Schwarz uses a statement of transition to contrast the old education model (rote memorization of commands) with a new required model (critical examination).

inference: This supports the argument that education and labor must start to pivot away from the "Generalist" process-oriented tasks. If the machine assistants handle the 'How' (the commands and functions), then the human must focus more on the 'Why' and the 'what does it mean (understanding/wisdom)'. This helps to validate the work of the assistants and helps to make it useful and valuable in the real world.

rhetoric: author presents a prescriptive claim that AI needs humans with competent knowledge (in this case, statistics) to create prompts and ensure that the AI does not leave out any steps of the analysis. He positions domain knowledge not as a tool for using AI for statistical analysis, but a prerequisite for management of the AI and auditing the output.

inference: In addition to policing and correcting the AI outputs, the deep domain knowledge is what allows the AI to do complex data analysis without mistakes, hallucinated results, or mathematically false outcomes. This is basically the job description of a human with "Augmented Human Wisdom". The human's value is no longer in doing math, but in possessing the vertical expertise (flesh/wisdom) to know exact what math needs to be done and ultimately auditing the assistant machine's work.

rhetoric: Schwarz employs cause-and-effect reasoning here based on empirical testing. He links a specific technical failure (not checking assumptions) to a definitive unwanted outcome (a false result).

inference: the "Data Analyst" function of ChatGPT hallucinated a result during the use of it's core function! This is the best evidence so far of the 'Crisis of Truth' and the dangers of the 'Headless Automatons' in my essay. If a generalist with no deep knowledge uses AI, they are at great risk of blindly accepting mathematically false conclusions. Synthetic syntax without competent human validation is a liability.

rhetoric: author uses comparative, objective statement (logos) to establish the main boundary of the technology's capability/capacity -- it excels at technical generation (things like coding) but fails at standard procedures (methodological adherence to SOPs).

inference: the proves the 'Raising the Floor' concept. AI completely automates the entry-level syntax (the "Word"), meaning that the Generalist coder is obsolete! However, because it fails at standard procedures, it requires a human architect to guide it to outputs that are valuable in the real world.

rhetoric: concluding statement. this positions the LLM as an 'enhancement' to physical human limitation, rather than a replacement of the human subject.

inference: This helps to validate the 'Augmented Human Wisdom' model. The future of AI is NOT replacing humans, but AI acting as a high-powered syntax engine that is strictly guided by human needs and human intent. The AI does not have 'agency', as it is a software tool that helps the human to execute their visions.

rhetoric: authors explain an unintended consequence of using the AI tool: it scales the errors or the emptiness of the human prompt.

inference: this is an excellent metaphor for the 'manager fallacy'. If the human user in incompetent (or provides empty or incomplete input), the AI does not magically create wisdom -- it just amplifies the user's incompetence in a a highly articulate synthetic thought.

rhetoric: researchers use a specific, clinical observation of an error to demonstrate the model's inability to comprehend logical reality despite the human relaying a perfectly structured sentence.

inference: this shows that AI is amoral and lacks the lived experience necessary to make logical judgments that work in the real world. It can format a sentence beautifully, but it does not/will not always understand that a single human cannot be two ages at once. This is why it is very important/necessary for the "flesh" to text the output against reality

rhetoric: authors provide a definition of their creation (Aphasia-GPT) to describe it's mechanism: taking a fragmented input and expanding it into a fully structured, complete output.

inference: this is the embodiment of Harari's primary metanym of the word v flesh (syntax v human). in this example, Aphasia-GPT provides the words (syntax) to the fleshy human that struggles with those words, while also relying on the human to spark the intent of the communication. The human is using AI to communicate with words, because the words are very difficult for the human.

AI Slop generated by your own hands changes he way people view you at work!

$186/per employee/per month!

10 employees = ($22,320) 25 employees = ($55,800) 50 employees = ($111,600) 100 employees = ($223,200) 250 employees = ($558,000) 500 employees = ($1,116,000) 1000 employees = ($2,232,000)

this is another great quote, which helps to establish how orgs treat younger generations, and how they tend to overtrust their understanding of AI.

Good stat from a survey of 1100 managers at resume.com. 70% of managers say that their employees have submitted AI generated errors inside their work in the last year.

from reporting managers, 58% of them said that employees were submitting work that contained factual inaccuracies in the work that was generated by AI, and that fewer of them reported that AI failed to account for "critical contextual factors", implying that the writing was generic and not directly applicable to the context that the writing was written in. Other issues were: low quality content, poor recommendations and inappropriate messaging.

Extra time and money spent to repair errors made by AI but not caught by the human in the middle. 59% is almost 2/3 (closer to 3/5) needed to correct or redo the work created by AI without a human auditing it. 53% claim extra work is needed to repair the AI mistakes, and 45% also needed to bring in a (perhaps more senior) co-worker to help fix the mistake. I can imagine workers needing to work on a mistake the hits production code, and all of the thousands (or more) mistakes that would need to be later repaired and rolled back. very expensive and costly.

missed deadlines are tough, but damaging client relationships is a sin!

great stats for the cost of using AI without human auditing.

while this affect all generations, this is clearly a young people problem! This problem is here to stay

great quote. directly mentions my concept of requiring human judgement, and how not having a human in the loop can make work move faster, but can also lead to very costly mistakes.

another great quote -- ties into the abdicating human agency to a robot, and the full quote even illustrates the dangers of doing so.

Summary of "AI Fatigue is Real"

• The Productivity Paradox: AI significantly speeds up individual tasks (e.g., turning a 3-hour task into 45 minutes), but this doesn't lead to more free time. Instead, the baseline for "normal" output shifts, and the work expands to fill the new capacity, leading to a relentless pace.
• From Creator to Reviewer: Engineering work is shifting from "generative" (energizing, flow-state tasks) to "evaluative" (draining, decision-fatigue tasks). Developers now spend their days as "quality inspectors" on an unending assembly line of AI-generated code.
• The Cost of Nondeterminism: Engineers are trained for determinism (same input = same output). AI’s probabilistic nature creates a constant cognitive load because the output is always "suspect," requiring more rigorous review than code written by a trusted human colleague.
• Context-Switching Exhaustion: Because tasks are "faster," engineers now touch 6–8 different problems a day instead of focusing on one. The mental cost of switching contexts so frequently is "brutally expensive" for the human brain.
• Skill Atrophy: Much like GPS has weakened our innate sense of direction, over-reliance on AI coding tools can cause core technical reasoning and mental mapping of codebases to atrophy.
• Strategies for Sustainability:
  • Time-boxing: Setting strict timers for AI sessions to avoid "prompt spirals."
  • Separating Phases: Dedicating mornings to deep thinking and afternoons to AI-assisted execution.
  • Accepting "Good Enough": Setting the bar at 70% usable output and fixing the rest manually to reduce frustration.
  • Strategic Hype Management: Ignoring every new tool launch and focusing on mastering one primary assistant.

[[AI Contintent Action Plan]]

[[The Means of Production by Maximilian Kasy]] overview in slides by author https://maxkasy.github.io/home/files/slides/meansofprediction_slides_long.pdf

On ai, productivity and shorter work weeks and why that will not happen

Scenario's for a Hindenburg style event: - deadly software update for self driving cars - AI-powered hacking ground global airlines (not sure, if that is clear enough to people, unlike the self driving cars running amok) - Barings-style collapse of a major company triggered by AI (if it's a tech company, it may be less shock, more ridicule, but still)

true for AI, but wasn't the case for Hindenburg I'd say.

prediction Michael Wooldridge (Oxford, AI), sees a risk at an 'Hindenburg' event. Shattering the global confidence in AI tech. I"m not sure this analogy entirely fits other than in its potential impact (AI isn't globally trusted, the Hindenburg did not fail bc of the tech itself but bc helium not being allowed to export from the US at the time. Still the Hindenburg did put an end to the entire zeppelin industry yes. No matter the causes.)

Wow, I had no idea that Kimi 2.5 had subbed in for Claude Opus so quickly.

OECD catalogue of AI tools en metrics for trustworthy AI.

price hikes of DRAM, due to pc laptop manufacturers having trouble in getting enough RAM. Shortages to keep going for 2026, after 2025. AI supply chain gobbling up the rest.

https://web.archive.org/web/20260216112337/https://wccftech.com/western-digital-has-no-more-hdd-capacity-left-out/ Western Digital says consumer sales of HDDs is now just 5% of their sales, everything else going to AI companies. 2026 WD is already sold out. For 2027 and 2028 there is already a few deals in place too.

key imo. generating code / material, can quickly mean loss of overview (I see how that happens in my use of #algogens if I don't explicitly counteract it), uncertainty about how demands were implemented, and thus what entry points for change there are.

The quick very rough mental maths on a GW of capacity being worth 10billion USD converts to between 1000-1500 USD per megawatt hour of money they think they could be making if they could sell the compute it powered

https://web.archive.org/web/20260215105347/https://simonwillison.net/2026/Feb/15/cognitive-debt/

Simon Willison on cognitive debt (the consequences of vibecoding more or less).

A description of the type of things Activate does w AI agents. Ref'd in [[Paolo’s Weblog It’s still time for free AI love]]

Description of how Activate keep their prompts, skills, databases, memory architecture under their own control and within their own environment.

Moving means wiring up another model or models, but the rest is kept as is.

[[Paolo Valdemarin p]] on avoiding lock-in in a single specific AI silo. Bc what is state of the art will shift every few months.

AI agents as kompromat collectors

• Task Expansion & Role Blurring: AI lowers the barrier to entry for complex tasks, leading employees to take on work outside their core expertise. Product managers and designers are now writing code, while researchers take on engineering tasks.
• Specialist Burden: This expansion creates a "cleanup" tax. For example, senior engineers now spend significant time reviewing, debugging, and mentoring colleagues who produce "vibe-coded" AI outputs, often through informal and unmanaged channels like Slack.
• The "Ambient Work" Phenomenon: Because AI interactions feel conversational and "easy," work has become ambient. Employees find themselves prompting AI during lunch, between meetings, or late at night, eliminating natural mental downtime.
• Intensified Multitasking: Workers are running multiple AI agents in parallel while simultaneously performing manual tasks. This creates a high sense of "momentum" but leads to extreme cognitive load and constant attention-switching.
• The Productivity Trap: AI acts as a "partner" that makes revived or deferred tasks feel doable. This creates a flywheel where people don't work less; they simply take on more volume, leading to "unsustainable intensity" that managers often mistake for genuine productivity.
• Sustainability Risks: The researchers warn that while AI feels like "play" initially, it eventually leads to cognitive fatigue, impaired decision-making, and burnout as the quiet increase in workload becomes overwhelming.

Hacker News Discussion

• Cognitive Fatigue: Users highlighted that "AI fatigue" is distinct from normal work tiredness. It stems from the "constant vigilance" required to audit AI output and the lack of a "flow state" due to unpredictable waiting times for generations.
• Executive Function Strain: Commenters noted that managing autonomous agents is more exhausting than manual work. One user compared it to Level 3 autonomous driving—you aren't driving, but you must remain "fully hands-on" to ensure the AI doesn't touch the wrong files or hallucinate.
• The Jevons Paradox: Several participants pointed out that as the "cost" of work decreases due to AI, the demand for work increases proportionally. Instead of saving time, workers are expected to triple their output, which leaves them more stressed than before.
• Management Expectations: A common theme was that leadership often mandates AI usage and pre-supposes productivity gains, leaving no room for cases where AI makes work slower or lower quality. This forces employees to "perform" productivity while working longer hours.
• Vibe Coding vs. Engineering: There is a heated debate between those who see "vibe coding" (prompt-heavy development) as a massive efficiency gain and veterans who argue it produces "average code" that becomes a maintenance nightmare in large, legacy codebases.

Article Summary: "I'm going to cure my girlfriend's brain"

• The Diagnosis: The author’s girlfriend has a prolactinoma, a pituitary tumor that causes hormonal imbalances, specifically elevated prolactin levels.
• The Struggle: Despite seeking help from top medical institutions, the author expresses deep frustration with the standard of care, citing ineffective medications, significant side effects, and a lack of urgency from doctors.
• The Mission: Refusing to accept a future of chronic illness or potential infertility, the author has committed to finding a "cure" himself by leveraging his background in technology and data.
• Methodology: He plans to treat the condition as a technical problem to be solved, utilizing "vibe coding" mentalities, deep research, and global collaboration to find alternative treatments or research breakthroughs.
• Personal Toll: The text chronicles the emotional journey of the couple, from the initial shock and physical symptoms to the author's transition from a helpless bystander to an obsessive advocate.

Hacker News Discussion

• Medical Clarifications: Several commenters pointed out that prolactinomas are pituitary tumors and not technically "brain tumors" (as they are outside the blood-brain barrier), suggesting the author’s terminology is slightly sensationalized.
• Agency vs. Acceptance: A major theme in the comments is the tension between "fighting" a disease and "accepting" it. Some users warned that the author's fixation on a cure might prevent him from being emotionally present with his partner during her current suffering.
• Critique of Ego: Some readers found the post "unsettling" or "narcissistic," arguing that the author centered himself as the hero of his girlfriend's tragedy and focused heavily on his own desire for children.
• Empathy for the "Unhinged" Response: Others defended the author, noting that at 25 years old, a "desperate, arrogant flailing" against a terminal or life-altering diagnosis is a common and human response to trauma and lack of control.
• Value of Patient Advocacy: Proponents of the author’s approach shared stories where aggressive self-advocacy led to rare diagnoses or life-saving treatments that the standard medical system had initially missed.
• Fertility Reality Check: Users with the same condition noted that while prolactinomas are a leading cause of infertility, they are often manageable with medication (like Cabergoline), though the author's case appears to be more resistant to treatment.

Articles and books by [[Paul Nemitz c]] (als co-authoring w [[Matthias Pfeffer p]]

this reminds me of installation art that is just a prescription / recipe of how to recreate it.

• [ ] return

• [ ] return

• comma.ai operates its own $5M data center in-office to handle model training, metrics, and data storage, avoiding the "cloud tax."
• The facility consumes approximately 450kW at peak; power costs in San Diego (over 40c/kWh) totaled over $540,000 in 2025.
• Cooling is achieved using pure outside air with dual 48” intake and exhaust fans, utilizing a PID loop to manage temperature and humidity.
• The compute cluster consists primarily of 600 GPUs across 75 "TinyBox Pro" machines built in-house for cost efficiency and easier repairability.
• Storage is handled by several racks of Dell R630/R730 servers with ~4PB of total SSD storage, favoring speed and random access over redundancy.
• The software stack is kept simple to ensure 99% uptime, utilizing Ubuntu (pxeboot), Salt for management, and "minikeyvalue" for distributed storage.
• By owning their hardware, comma.ai estimates they saved $20M+ compared to equivalent compute costs in a public cloud environment.

Hacker News Discussion

• Users discussed the spectrum of infrastructure, ranging from pure Cloud (low cap-ex, high op-ex) to colocation and on-prem (high cap-ex, high skill requirement).
• A primary concern raised was "brain drain"—on-prem setups can become "legacy debt" if the senior engineers who built the custom systems leave without documenting unwritten knowledge.
• Commenters noted that AWS and other cloud providers are incentivized to keep architectures complex (microservices, serverless) to increase billing, whereas on-prem encourages efficiency.
• There was a debate regarding "software freedom" and the "WhatsApp effect," where small, highly motivated teams can outperform massive corporations by using lean, self-hosted stacks.
• Some users highlighted that while AWS pricing is expected to rise due to hardware costs, the "Quality of Life" and managed services still justify the cost for many startups without comma's scale.

comma-ai #self-hosting #datacenter #hardware-engineering

• The author identifies two primary personality traits: "The Builder" (focused on velocity, utility, and shipping) and "The Thinker" (needing deep, prolonged mental struggle).
• "Thinking hard" is defined as sitting with a difficult problem for days or weeks to find a creative solution without external help.
• In university, the author realized this ability to chew on complex physics problems was their "superpower," providing a level of confidence that they could solve anything given enough time.
• Software engineering was initially gratifying because it balanced both traits, but the rise of AI and "vibe coding" has tilted the scale heavily toward the Builder.
• While AI enables the creation of more complex software faster, the author feels they are no longer growing as an engineer because they are "starving the Thinker."
• The lack of struggle leads to a feeling of being stuck, as the dopamine of a successful deploy cannot replace the satisfaction of deep technical pondering.

Hacker News Discussion

• The loss of the "clayship" process: Commenters compared coding to working with clay; skipping the struggle means missing the intimacy with the material that reveals its limits and potential.
• The "Vending Machine" effect: Receiving a "baked and glazed" artifact from AI removes the human element of discovery and learning.
• Risk of mediocrity: There is concern that AI guides developers toward "average" or conventional solutions, making it harder to push for unique or innovative ideas without significant manual effort.
• The tradeoff of efficiency: While some view the current era as the best time for "Builders" who just want to see results, many veteran developers feel a profound sense of loss regarding the cognitive depth of the craft.
• Clear communication as a new skill: Some argue that interacting with AI requires a different kind of "thinking hard"—specifically, the need to express creative boundaries clearly so the model doesn't "correct" away the uniqueness of the project.

hopelijk niet genAI

[[Sovereign Minds by Nadia Alter]] looks very worthwhile to get. mentions agency in the subtitle.

(via [[Alberto Cottica p]] on masto)

on responsibility, decision making and authority in organisations getting amorph, even before AI. Vgl [[Corporations as Slow AI 20180201210258]]

via [[Alberto Cottica p]] on Masto

There is a line from computers in the original sense, to typing pools, admin support, to automation to AI.

Unesco report, Saniye Gülser Corat (dir for gender equality).

criticism of retrieval augmented generatio (RAG): fails in cross domain settings, finds similar text not relations between facts or meaning

• [ ] return

openclaw 'ai personal assistant' that you can interact w through your regular chat apps it says

the skill.md for Moltbook.

'a social network for AI agents' wut?

for - essay Claude CEO - risks of AI

for - AI constitution - Deep Humanity?

for - Deep Humanity - AI crisis - spiritual singularity

for - Johari Window - unknown unknown - AI

for - progress trap - AI - inequality

for - progress trap - AI - democracies becoming autocracies - Hitler - Trump

for - progress trap - AI - propaganda - brainwashing

for - progress trap - AI - Panopticon - Orwellian

for - progress trap - AI - arms race amongst dictators! - Trump - Putin - Kim Jong Un

for - progress trap - AI - misuse of power - Palantir?

for - Claude AI - prohibition against developing weapons - can be jailbroken

for - progress trap - AI - technology as an amplifier - technology acts as an amplifier, allowing humans to fly, to move at speeds faster than any known animal, to lift things no living creature can, etc - The danger is ignorance and polarized views combined with extreme self-rightiousness

for - progress trap - AI - legislation required - tell it to Trump!

for - progress trap - AI - rogue AI vendors - like Grok

for - AI - progress trap - interpretability testing - deception - Is it possible that AI could even change their node behavior as a deceptive move?

for - AI - testing - interpretability - neuroscience techniques

for - AI - training - grown, not built

for - progress trap - AI - hackers - hack the AI constitution

for - definition - constitutional AI

for - progress trap - AI - metaphor - psychopath with a nuclear bomb

for - progress trap - AI - abstraction - progress trap - AI with feelings & AI without feelings - no win? - One major and obvious aspect of current AI LLMs is that they are not only artificial in their intelligence, but also artificial in their lack of real world experiences. They are not embodied (and it would likely be a highly dubious ethical justification for their embodiment as in AI - powered robots) - Once we have the first known AI robot killing a human, it will be an indicator we have crossed the Rubicon - AI LLMs have ZERO realworld experience AND they are trained as artificial COGNITIE intelligence, not artificial EMOTIONAL intelligence - Without having the morals and social norms a human being is brought up with, it can become psychotic because they don't intrinsically value life - To attempt to program them with morals is equally dangerous because of moral relativity. A Christian nationalist's morality might be that anyone who is associated with abortions don't have a right to live and should be killed - an eye for an eye. Or a jihadist and muslim extremist with ISIS might feel all westerners do not have a right to exist because they don't follow Allah. - Do we really want moral programmability? - When we have a psychotic person armed with a lethal weapon, that is a dangerous situation. If we have a nation of super geniuses who go rogue, that is danger multiplied many orders of magnitude.

for - AI - progress trap - training - movies like Terminator - This is a case of reality imitating movies - Another example - humans mismanagement of the biosphere and elite abuse and intransigency

for - AI - progress trap - developing harmful traits

for - AI - progress trap - AI won't always listen to humans - probably due to unintentional design

for - progress trap - AI - 5 risks - autonomy - misuse for destruction - misuse for seizing power - economic disruption - indirect effects

for - AI progress trap - Trump

for - AI progress trap - ie. Trump family investing heavily in AI

for - this essay is about the negative impacts of powerful AI

for - AI - powerful AI - 1 to 2 years away

for - AI - status - writing most of the code at Anthropic

Q: standing

A: 1) Based on this page:<br /> “standing” means being upright on its feet in one place (not sitting or lying down). Here it describes the tabby cat upright on the corner of Privet Drive.

2) General knowledge (not from this page):<br /> “standing” can also mean having a particular status or reputation (e.g., “in good standing”), but that is not the meaning in this passage.

1）基于本页内容：<br /> “standing” 的意思是“站着、直立地待在原地”（不是坐着或躺着）。这里用来描述那只虎斑猫直立地站在女贞路的街角。

2）常识补充（非本页内容）：<br /> “standing” 也可以表示“地位/声望/名誉”（例如 “in good standing”），但这不是本段文字里的用法。

[[Cory Doctorow p]] on AI en the economic dynamic behind the hype that bigtech is pursuing to remain growth stocks even as monopolists. 'tech centaur' me in a car, a human head on something else vs 'reverse centaur' a meat puppet doing the machines bidding, e.g. an Amazon delivery driver

blogger Fabrizio Ferri Benedetti on their 4 modes of using AI in technical writing. - watercooler conversations, to get code explained - text suggestions while writing/coding (esp for repeating patterns in your work - providing context / constraints / intent to generate first drafts, restructure content, or boilerplate commentary etc. - a robotic assembly line, to do checks, tests and rewrites. MCP/skills involved.

Not either/or but switching between modes

This section highlights two forms of gaps in accountability: responsibility and transparency.

Deeper disclosure is possible: version-controlled authorship history (git-style) showing what human wrote vs. what AI generated.

The commit log becomes the disclosure - forensic, auditable, transparent. Not a vague "AI-assisted" disclaimer, but a traceable record of human-machine co-authorship.

Example: every commit with "Co-Authored-By: Claude Opus 4.5" plus commit messages explaining what was asked, proposed, reviewed, and approved.

This reframes the "crisis" as an opportunity for unprecedented transparency in collaborative authorship.

Also used openhands for a test. says it needs intervention (not fully delegated iow)

ah, turtles all the way down. using AI to generate the task specs.

A discrete task description to be treated like a project in the GTD sense (anything above 2 steps is a project). At what point is this overkill, as in templating this project description may well lead to having the solutions once you've done this.

indeed. as mentioned above too. n:: My sense that this is a learning mode akin to the haptic feedback of working on things by hand.

What I mentioned above: to delegate you need to be able to fully describe and provide context for a discrete task.

A list of async coding agents in #2025/08 github, openai, google mentioned. OpenHands is the one open source mentioned. mentions that command line tools can be used (if integrated w e.g. github actions to tie into the coding environment) - [ ] check out openhands agent by All Hands

not just time saving, but a restructuring. So, any description of how the structure changes (before / after style) further down?

do multiple things in parallel. note: The assumption here that the context is coding

compares delegation to formulating a 'ticket'. Assumes well defined tasks up front I think, rather than exploratory things.

async means no handholding, but delegation instead. That is enticing obviously, but assumes unattended execution can be trusted. Seems a big if.

cloud agent = background agent = async agent

Async coding agents is a diff kind of vibe coding: you give it a defined more complex tasks and it will work in the background and come back with an outcome.

https://web.archive.org/web/20260125124811/https://elite-ai-assisted-coding.dev/p/working-with-asynchronous-coding-agents Eleanor Berger, August 2025.

on asynchronous coding agents

Links w further reading wrt Claude Code and Obsidian. Most of these are links to X. Ugh.

if you don't have personal stuff or personal data on others in your vault, privacy is less a concern with cloud models. True. Except I think any pkm is about personal knowledge and while not personal data per se, there is a vulnerability involved there.

Interesting point, and fair enough. Start from the friction points. Like w open data [[Open data begint buiten 20200808162905]]

This is an important thing too, there's not always someone to ask or delegate to.

yes, recognisable, there are a whole bunch of APIs on things I woud like to use that I'm not bc figuring out their workings in Postman takes too much effort

task lists with better context, means? batching tasks, yes helpful

GreenPT , Utrecht based ai cloud service provider. Talk about everything wrt data remaining in the EU but that may well be meaningless, when there are other ties to US service providers or investors

"AI Productivity Index for Agents (APEX-Agents)" ref'd in [[Are AI agents ready for the workplace A new benchmark raises doubts TechCrunch]] paper: APEX-Agents in Zotero