It's LucidSpark for non-masochists. It's Parabol for power-users. It's possibly the greatest 'roll your own' tool for workshopping and blue-sky planning to hit mass-appeal without feature and subscription bloat.
Incredible reponsiveness, UI/UX, and embeddable rich media functionality. Was like Hypercard mixed with whiteboarding and an incredibly traversable canvas.
What did the Medicis or Guggengheim's do other than be rich and idle at the right time and place in history?
The contemporary prompt is a port for patronage, not a point of inflection - with a similarly ludicrous symbiotic relationship defined by impression management on both sides. Same as it ever was.
If fumbling through 'hot cross buns' on the recorder is roughly analogous to recitation of multiplication tables, then Gould playing Shostakovich can certainly be referenced as one would the Feynman Lectures on Physics.
Certainly more effort has been put into exploring the relationship between... Bach and Godel than your surmise suggests; SHRDLU, Toy of Man's Designing!
The difference being that the role of Engineer wasn't conceived as a layer of friction between consumers and their statutory rights to the point where a U.S. Senate subcommittee probe (Senate Homeland Security Subcommittee on Disaster Management) and state-level legal crackdowns were necessary to try and address what constituted systematic fraud by property/casualty insurance corporations.
Indeed, some of the core violations could be simply mandated as part of the inviolable system guardrails of an LLM - e.g. failed to begin investigating claims within 15 days, failed to accept or deny claims within 40 days, and failed to pay accepted claims or provide written notice of the need for additional time within 30 days, as required by law.
And at the end of the day, whatever weighting issues an LLM for Insurance Claim Adjustment has, it certainly won't steal the money and run off to a Casino with it
A lot of which isn't under the purview of Dublin City Council - what you're talking about is the M50 ring, and the purview of the other 3 councils - Fingal (e.g. Swords and Malahide) South Dublin County Council (Tallaght and Clondalkin), and Dún Laoghaire-Rathdown County Council (self-explanatory).
In terms of where they're talking about, it's basically the area between the canals - i.e. Dublin City 'proper' - Smithfield/Stoneybatter, The Liberties/Coombe, Christchurch/Temple Bar, and IFSC/Grand Canal Dock. Far more walkable and inundated with history and sites of huge cultural significance even compared to Paris or Amsterdam, and a different experience completely compared to the impersonal and often dangerous nature of London for the pedestrian.
The vast majority of people working in the CBD are commuting in on the LUAS Green and Red lines, or the DART/Train into Tara/Connolly. Whatever about Dublin Bus, these are perfectly good and regular public transport options.
It's an amazing and disingenuous posit that the problem here lies with the Algorithm as opposed to the consequences under which it was introduced - cost-cutting. The barriers to care for patients who are already struggling with serious mental conditions are not a new phenomenon related to automated tooling derived from best practice corpus. Rather, they are a direct result of a decline in triage staffing.
Take cardiologist Dr. Lee Goldman’s chest pain decision-making algorithm, originally devised at Cook County Hospital to diagnose heart attacks.
Effectively a poster in the ER illustrating a simple decision tree, it outperformed traditional doctor diagnoses; achieving over 95% accuracy and proving 70% better at identifying patients not having a heart attack. This landmark clinical protocol represented a quantum-leap in outcomes, eventually refined to the Revised Cardiac Risk Index (RCRI) used as the gold-standard today.
So not only are algorithmic approaches old-hat in clinical settings, they are in fact a core component of contemporary best practice medicine. But that's somewhat besides the point when much of the issues raised in the article above refer to issues of executive dysfunction - areas in which low-context Agentic AI are basically designed to remedy, e.g.
// Instead of being referred directly by primary care doctors and contacted to schedule appointments, patients were routed through an app, nonclinical call center staff or simply given a phone number to call. “Now the onus is on the patients to be their own care coordinators,” she said... This dynamic can be particularly challenging for some patients. “Lack of motivation and lack of follow-through are the most common symptoms of depression on the planet,”
So the answer here is clearly an increase in appropriate AI resources, rather than throwing the baby out with the bathwater. If we trust a decision-tree poster to do cardiovascular triage better than the instincts of cardiovascular specialists, and OCR models to interpret results better than veteran Radiologists, why not Agentic AI to do the basic work of scheduling appointments?
//The overall wait time to receive care decreased by 71.43% due to this initiative. Additionally, participants received psychological care within three weeks after completing the triage module. In 71.29% of the cases, the artificial intelligence-assisted triage program and the psychiatrist suggested the same treatment intensity and psychotherapy program. Additionally, 63.29% of participants allocated to lower-intensity treatment plans by the AI-assisted triage program did not require psychiatric consultation later.
Potential of ChatGPT in youth mental health emergency triage: Comparative analysis with clinicians
//This study suggests that GPT-4 models could be leveraged as a support tool in mental health telephone triage, particularly for psychiatric emergencies. Although response variability across iterations was minimal, most discrepancies in admission decisions were identified as false positives, reflecting that GPT Models may have a tendency to over-triage relative to clinician judgment. While findings are promising, further research is required to confirm clinical relevance.
A few others that may be of interest. The results from Raita et al.’s study that used a large dataset of adult ED visits revealed that four ML models outperformed the Emergency
Severity Index (ESI) in forecasting outcomes of critical care and hospitalization, with higher discriminatory
abilities and reduced under-triaged patients in levels three to five of ESI triage
The rest of the studies below reinforc the superiority of developed ML models over conventional triage systems - e.g. by demonstrating an AUROC of 0.991 for predicting critical outcomes in pediatric ED visitors, or another study showing LLMs surpassing the performance of the ESI and vital sign triggers
- Yilanli M, McKay I, Jackson DI, Sezgin E Large Language Models for Individualized Psychoeducational Tools for Psychosis: a cross‐sectional study. 2024.07.26.24311075. Preprint at medRxiv. 2024.
-Hwang S, Lee B: Machine learning-based prediction of critical illness in children visiting the emergency
department. PLoS One. 2022, 17:e0264184. 10.1371/journal.pone.0264184
-Joseph JW, Leventhal EL, Grossestreuer AV, et al.: Deep-learning approaches to identify critically Ill
patients at emergency department triage using limited information. J Am Coll Emerg Physicians Open. 2020,
-Liu Y, Gao J, Liu J, et al.: Development and validation of a practical machine-learning triage algorithm for
the detection of patients in need of critical care in the emergency department. Sci Rep. 2021, 11:24044.
-Raita Y, Goto T, Faridi MK, Brown DF, Camargo CA Jr, Hasegawa K: Emergency department triage prediction
of clinical outcomes using machine learning models. Crit Care. 2019,
-Wolff P, Rios SA, Grana M: Setting up standards: a methodological proposal for pediatric triage machine
learning model construction based on clinical outcomes. Expert Syst Appl. 2019, 138:12.
-Ivanov O, Wolf L, Brecher D, et al.: Improving Ed emergency Severity Index acuity assignment using
machine learning and clinical natural language processing. J Emerg Nurs. 2021, 47:265-278.e7.
This is still ER - clinicians using AI tools. The MH triage flow is patient interacts with an AI by typing or speaking. It could work - maybe the systems in the article are just not very well set-up.
Due to hitting 45 million average monthly users in the EU, ChatGPT is now subject to the same classification as Reddit or Roblox - and thus held accountable for mitigating risks related to ChatGPT’s impact on minors, user mental health, and the spread of illegal content in the European Union.
The EU doesn’t have clear requirements or guidelines about what these things mean, especially in the context of a new technology like ChatGPT.
My guess is that ChatGPT will do their best to comply but whatever they do, the EU will find that they did not do a perfect job and issue a €x billion fine.
Not only do they have clear requirements and guidelines, they're codified into law.
The core aims of the law are unambiguous in both aim and implementation, and serve to enforce existing consumer rights. Please feel free to highlight which of these you feel represents an onerous or undue burden on the corporations in the name of enforcing some very basic ethical guardrails for the vulnerable in society.
- Swift removal of illegal content online, including products, services: a clearer “notice and action” procedure where users will be empowered to report illegal content online and online platforms will have to act quickly.
- Victims of cyber violence will be better protected especially against non-consensual sharing of illegal content (revenge porn) with immediate takedowns.
- Transparency obligations for platforms allow users to be better informed about how content is recommended to them (recommender systems) and to choose at least one option not based on profiling.
- Online advertising: users will have better control over how their personal data are used. Targeted advertising is banned when it comes to sensitive data (e.g. based on sexual orientation, religion, ethnicity).
- Protection of minors: platforms accessible to minors will have to take specific measures to protect them, including by fully banning targeted advertising.
- Manipulating users’ choices through ‘dark patterns’ will be prohibited. Moreover, cancelling a subscription for a service should become as easy as subscribing to it.
- Compensation: recipients of digital services will have a right to seek redress for any damages or loss suffered due to infringements by platforms.
Yes, I know about these requirements, but it's still unclear about how they apply to ChatGPT in every scenario. For example, are they supposed to monitor all chats for potentially illegal content and delete such chats immediately? What aspects of AI could be harmful to minors and what is an appropriate way to mitigate these harms?
Or if I've previously told ChatGPT I'm gay, consented to its use of memory across chats, and, after asking about say "What's the best lube for anal?" it later recommends me a lube specifically intended for anal sex that is popular within the gay community, is it making a prohibited product recommendation based on sexual orientation?
One might counter with the argument that these are minor issues, but given the EU's wide latitude in determining the amount of a fine, the requirements should not be open to interpretation.
Thanks for taking the time to write this down and cite sources. I'm baffled by the recent-ish increase of such dismissive and lazy comments as the one you responded to.
I think that this take is unfair to the parent. Many VLOP requirements currently lack best practices, and it's clear that affected companies borrow from each other to mitigate compliance risks. The commission even seems to encourage knowledge sharing here. (This is also why you see them resorting to strange language that barely fits their platform mechanic.)
We could argue whether a higher level of government oversight is warranted for platforms above a certain threshold. We could discuss the additional compliance burden that these companies should face. All of these questions are valid (and important for DSA).
However, it is not plausible to deny the case-by-case uncertainties that you still face as an affected company. This is expectend and will only change with more time in the framework, basically.
Literally the highest tier ChatGPT that was available in 2025 can be run on a consumer laptop with Chinese AI now. Their regulations were obsolete before they put any words on paper.
I’m against it because the EU should be at the forefront of science and technology.
Lot of the stuff regulated don't arise on locally run llm's. No ads targeting minors, no real dark patterns, no bans without reason, no personalized feed without possibility to opt out. maybe i forgot something.
Kimi K3 is an LLM so completely separate to the conversation at hand.
The DSA doesn't apply to models, it applies to businesses operating as Platforms with more than 45 million monthly users in the EU.
If you were to use Kimi K3 via kimi.com or the Moonshot API, your data is processed in China, a country without an EU adequacy decision, nevermind an obligation to operate as a business/platform in the EU.
Host Kimi K3 in the EU as a commercial offering, hit 45 million average monthly users in the EU, and now Kimi K3's Host is considered a Very Large Online Search Engine under the EU’s Digital Services Act.
Because iPhone/AirPods are a wealth and fashion signifier. IKEA is a flat-pack THX-1138.
Fight Club summarised it beautifully two decades ago:
“- Like so many others, I had become a slave to the IKEA nesting instinct.
Narrator: Uh, yes. I’d like to order the Erica Pekkary dust ruffles.
Operator: Please hold.
- If I saw something clever like a little coffee table in the shape of a yin-yang, I had to have it. The Klipske personal office unit, the Hovetrekke home exer-bike, or the Johannshamn sofa with the Strinne green stripe pattern. Even the Rizlampa wire lamps of environmentally friendly unbleached paper. I’d flip through catalogs and wonder: What kind of dining set defines me as a person? I had it all. Even the glass dishes with tiny bubbles and imperfections. Proof that they were crafted by the honest, simple, hard-working indigenous peoples of…
Operator: Please hold.
Narrator: Wherever. I was holding.
- We used to read pornography. Now it was the Horchow collection.”
In terms of 'automating' Leadership, it's self-evident that Agentic AI is more than capable of inferring context and providing insight based on its significant business administration and management corpus. From this it can suggest actions, follow-up via a plethora of mediums and APIs, and generate up to the minute reporting on a variety of KPIs.
In terms of replacing the function of a Leader, however, the reality is closer to that old joke about the Theoretical Physicist and the farmer - "I have a solution, but it only works for a spherical cow in a vacuum".
The last thing I'd trust to navigate the Realpolitik of modern globalised business is a language model distilled by an Individual Contributor. When Agentic AI navigates moral hazard, it does so as a cosplayer at best - the final actualisation of Dramaturgical sociology.
In fact, Erving Goffman's original Theatrical Metaphors from his 1959 opus 'The Presentation of Self in Everyday Life' map almost directly to the ludicrous symbiotic relationship defined by impression management on both sides of the prompt: AI systems aligning themselves by aping linguistic cues and saccharine affirmations, while users are effectively required to rephrase or reframe prompts to the point of absurdity in order to elicit the desired response, or otherwise navigate guardrails.
Idealized organizations are not perfect. They are perfectly pathological. The top tier are effectively sociopaths operating under conditions of serious moral hazard. If Agentic AI becomes an able replacement, everyone dies.
From about 2010 onwards we have had the phenomenon of the High-Income White Collar Itinerate - the Silicon Valley worker living in a converted van, or the remote coder working from a LOC country with low barriers to entry regarding Visas and Taxation. These being the 'privileged' demographic.
The less fortunate simply deal with shrinkflation of the bottom tiers of Maslow's Hierarchy of Needs. Only 12% of married Americans under 30 own a home in 2026. In 1960 it was 52%.
The pandemic-heightened desire for more spacious and independent living, as well as “excess” savings accumulated early in the lockdown stages of the pandemic, propelled headship rates of young adults by two percentage point to 43.1%, the highest level since 2010. This still didn't meet parity with the historic norms of 1990-2000s.
Incredible reponsiveness, UI/UX, and embeddable rich media functionality. Was like Hypercard mixed with whiteboarding and an incredibly traversable canvas.
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