#organisations — Public Fediverse posts

https://www.alojapan.com/1523163/walk-japan-launches-the-self-guided-salt-road-wayfarer-tour-news/ Walk Japan Launches the Self-Guided Salt Road Wayfarer Tour | News #JapanTravel #Organisations&Operators #travel #TravelNews Walk Japan has just launched its newest self-guided itinerary, the Self-Guided Salt Road Wayfarer – a six-day, five-night journey traversing the historic Salt Road, an ancient trading route that once connected the Sea of Japan with the castle town of Matsumoto. Designed for experienced walkers, with daily walks rang

https://www.alojapan.com/1523163/walk-japan-launches-the-self-guided-salt-road-wayfarer-tour-news/ Walk Japan Launches the Self-Guided Salt Road Wayfarer Tour | News #JapanTravel #Organisations&Operators #travel #TravelNews Walk Japan has just launched its newest self-guided itinerary, the Self-Guided Salt Road Wayfarer – a six-day, five-night journey traversing the historic Salt Road, an ancient trading route that once connected the Sea of Japan with the castle town of Matsumoto. Designed for experienced walkers, with daily walks rang

Don’t be a meat proxy for an LLM

I’m very glad someone has put a name to this behaviour. I sometimes do it on my blog because user-model interaction is often part of how I think, but I don’t think I ever do this in communication with others for precisely this reason:

Too often I ask a question in Slack or leave feedback under a merge/pull request or argue with friends in a WhatsApp group and get back:

Claude said: [giant response verbatim]

Please don’t do this. I mean, I’ve done this. But I’ve been on the receiving end too many times now. This is not adding value. I can talk to Claude myself. It’s going to be faster and I get to control the context. I don’t need a meat proxy in between.

Reading AI output is extra effort. It’s verbose, frequently contains all too plausible nonsense, and is increasingly jargon dense. I recently got this sentence from Claude:

NATS control-plane events: stream leader election / R3 quorum re-form during pod churn.

Jesus. I had to lookup almost every word to make sense of this.

By all means, prompt AI. But don’t just relay the output. Read it, understand it, validate it, and then write a response in your own words (a decent certificate that you’ve done the prior steps). Making that effort is value you can add.

https://gruhn.me/blog/2026-08-03/

If I do ever slip into doing this, I think I’m going to stop now I’ve got a name for it.

Don’t be a meat proxy for an LLM

I’m very glad someone has put a name to this behaviour. I sometimes do it on my blog because user-model interaction is often part of how I think, but I don’t think I ever do this in communication with others for precisely this reason:

Too often I ask a question in Slack or leave feedback under a merge/pull request or argue with friends in a WhatsApp group and get back:

Claude said: [giant response verbatim]

Please don’t do this. I mean, I’ve done this. But I’ve been on the receiving end too many times now. This is not adding value. I can talk to Claude myself. It’s going to be faster and I get to control the context. I don’t need a meat proxy in between.

Reading AI output is extra effort. It’s verbose, frequently contains all too plausible nonsense, and is increasingly jargon dense. I recently got this sentence from Claude:

NATS control-plane events: stream leader election / R3 quorum re-form during pod churn.

Jesus. I had to lookup almost every word to make sense of this.

By all means, prompt AI. But don’t just relay the output. Read it, understand it, validate it, and then write a response in your own words (a decent certificate that you’ve done the prior steps). Making that effort is value you can add.

https://gruhn.me/blog/2026-08-03/

If I do ever slip into doing this, I think I’m going to stop now I’ve got a name for it.

Don’t be a meat proxy for an LLM

I’m very glad someone has put a name to this behaviour. I sometimes do it on my blog because user-model interaction is often part of how I think, but I don’t think I ever do this in communication with others for precisely this reason:

Too often I ask a question in Slack or leave feedback under a merge/pull request or argue with friends in a WhatsApp group and get back:

Claude said: [giant response verbatim]

Please don’t do this. I mean, I’ve done this. But I’ve been on the receiving end too many times now. This is not adding value. I can talk to Claude myself. It’s going to be faster and I get to control the context. I don’t need a meat proxy in between.

Reading AI output is extra effort. It’s verbose, frequently contains all too plausible nonsense, and is increasingly jargon dense. I recently got this sentence from Claude:

NATS control-plane events: stream leader election / R3 quorum re-form during pod churn.

Jesus. I had to lookup almost every word to make sense of this.

By all means, prompt AI. But don’t just relay the output. Read it, understand it, validate it, and then write a response in your own words (a decent certificate that you’ve done the prior steps). Making that effort is value you can add.

https://gruhn.me/blog/2026-08-03/

If I do ever slip into doing this, I think I’m going to stop now I’ve got a name for it.

Don’t be a meat proxy for an LLM

I’m very glad someone has put a name to this behaviour. I sometimes do it on my blog because user-model interaction is often part of how I think, but I don’t think I ever do this in communication with others for precisely this reason:

Too often I ask a question in Slack or leave feedback under a merge/pull request or argue with friends in a WhatsApp group and get back:

Claude said: [giant response verbatim]

Please don’t do this. I mean, I’ve done this. But I’ve been on the receiving end too many times now. This is not adding value. I can talk to Claude myself. It’s going to be faster and I get to control the context. I don’t need a meat proxy in between.

Reading AI output is extra effort. It’s verbose, frequently contains all too plausible nonsense, and is increasingly jargon dense. I recently got this sentence from Claude:

NATS control-plane events: stream leader election / R3 quorum re-form during pod churn.

Jesus. I had to lookup almost every word to make sense of this.

By all means, prompt AI. But don’t just relay the output. Read it, understand it, validate it, and then write a response in your own words (a decent certificate that you’ve done the prior steps). Making that effort is value you can add.

https://gruhn.me/blog/2026-08-03/

If I do ever slip into doing this, I think I’m going to stop now I’ve got a name for it.

Don’t be a meat proxy for an LLM

I’m very glad someone has put a name to this behaviour. I sometimes do it on my blog because user-model interaction is often part of how I think, but I don’t think I ever do this in communication with others for precisely this reason:

Too often I ask a question in Slack or leave feedback under a merge/pull request or argue with friends in a WhatsApp group and get back:

Claude said: [giant response verbatim]

Please don’t do this. I mean, I’ve done this. But I’ve been on the receiving end too many times now. This is not adding value. I can talk to Claude myself. It’s going to be faster and I get to control the context. I don’t need a meat proxy in between.

Reading AI output is extra effort. It’s verbose, frequently contains all too plausible nonsense, and is increasingly jargon dense. I recently got this sentence from Claude:

NATS control-plane events: stream leader election / R3 quorum re-form during pod churn.

Jesus. I had to lookup almost every word to make sense of this.

By all means, prompt AI. But don’t just relay the output. Read it, understand it, validate it, and then write a response in your own words (a decent certificate that you’ve done the prior steps). Making that effort is value you can add.

https://gruhn.me/blog/2026-08-03/

If I do ever slip into doing this, I think I’m going to stop now I’ve got a name for it.

https://www.disabilitynewsservice.com/disabled-peoples-organisations-suggest-how-greater-government-support-could-produce-future-utopia/. I'm sorry to disagree with, & disappoint, these #disabled people's #organisations, @disabledvoices, but #Utopia is unattainable, & all #historical #attempts to create a Utopia have ended in either #farce or absolute #disaster. Furthermore, #AI isn't the key to UTOPIA, but a #Dystopia, & will land all of us in a Hellish #nightmare!

https://www.disabilitynewsservice.com/disabled-peoples-organisations-suggest-how-greater-government-support-could-produce-future-utopia/. I'm sorry to disagree with, & disappoint, these #disabled people's #organisations, @disabledvoices, but #Utopia is unattainable, & all #historical #attempts to create a Utopia have ended in either #farce or absolute #disaster. Furthermore, #AI isn't the key to UTOPIA, but a #Dystopia, & will land all of us in a Hellish #nightmare!

https://www.disabilitynewsservice.com/disabled-peoples-organisations-suggest-how-greater-government-support-could-produce-future-utopia/. I'm sorry to disagree with, & disappoint, these #disabled people's #organisations, @disabledvoices, but #Utopia is unattainable, & all #historical #attempts to create a Utopia have ended in either #farce or absolute #disaster. Furthermore, #AI isn't the key to UTOPIA, but a #Dystopia, & will land all of us in a Hellish #nightmare!

https://www.disabilitynewsservice.com/disabled-peoples-organisations-suggest-how-greater-government-support-could-produce-future-utopia/. I'm sorry to disagree with, & disappoint, these #disabled people's #organisations, @disabledvoices, but #Utopia is unattainable, & all #historical #attempts to create a Utopia have ended in either #farce or absolute #disaster. Furthermore, #AI isn't the key to UTOPIA, but a #Dystopia, & will land all of us in a Hellish #nightmare!

https://www.disabilitynewsservice.com/disabled-peoples-organisations-suggest-how-greater-government-support-could-produce-future-utopia/. I'm sorry to disagree with, & disappoint, these #disabled people's #organisations, @disabledvoices, but #Utopia is unattainable, & all #historical #attempts to create a Utopia have ended in either #farce or absolute #disaster. Furthermore, #AI isn't the key to UTOPIA, but a #Dystopia, & will land all of us in a Hellish #nightmare!

Will models fine-tuned to particular domains start consistently outperforming frontier models? Encoding habitus into LLMs

This was really thought provoking from the FT’s AI Shift newsletter. I’m persuaded by Nick Srnicek’s argument that the push for AGI can be understood economically as a search for a product that won’t need to be fine-tuned. But if fine-tuned open weights models can outperform frontier models then this overturns the economics of the AI labs:

But a new case last month [email.newsletters.ft.com] took things a step further, when Ray Dalio’s investment firm Bridgewater Associates partnered with AI platform company Thinking Machines Lab (founded by former OpenAI CEO Mira Murati) to fine-tune a model based specifically on how its own investment managers do their work. As with the legal example, the results significantly outperformed frontier models, this time at a 14th of the cost. Crucially, however, use of the firm’s own proprietary records and its highly specialist staff’s knowhow may make these gains more durable.

Bridgewater had its own experts write bespoke prompts that framed questions in a way that guided the models to the correct answers. This produced solid gains, but they still topped out below 80 per cent accuracy.

A prompt only imparts the expertise a professional is able to put into words — what is much better is to learn from their actions. For the fine-tuning step they put together a set of tasks drawn from their own investors’ daily workflows, and crucially also had staff ensure that the ideal responses which would guide the model’s training did not just represent ‘correct answers’ but ‘exactly how our investment professionals would approach this’.

At the end of the process they had a bespoke model whose behaviour had been tuned towards Bridgewater’s own assessment of excellence, taking it up to 85 per cent accuracy — an almost 30 per cent reduction in errors compared to the frontier models — at a tiny fraction of the cost.f

There’s an obvious sociological question here: what happens if our investment professionals need to approach this in a different way? Human professionals can learn and adapt. They can do so in ways that change how they prompt if they’re using a frontier model. In contrast a model fine-tuned in this way will be locked into a particular set of dispositions that might be appropriate for a context at T1 but will cease to be appropriate at a later T2. Indeed introducing the models is liable to change the context, particularly if you use this as an excuse to lay off your staff! There’s an organisational problem here which is blindingly obvious to anyone who has thought in any depth about the fact that contexts change.

Will models fine-tuned to particular domains start consistently outperforming frontier models? Encoding habitus into LLMs

This was really thought provoking from the FT’s AI Shift newsletter. I’m persuaded by Nick Srnicek’s argument that the push for AGI can be understood economically as a search for a product that won’t need to be fine-tuned. But if fine-tuned open weights models can outperform frontier models then this overturns the economics of the AI labs:

But a new case last month [email.newsletters.ft.com] took things a step further, when Ray Dalio’s investment firm Bridgewater Associates partnered with AI platform company Thinking Machines Lab (founded by former OpenAI CEO Mira Murati) to fine-tune a model based specifically on how its own investment managers do their work. As with the legal example, the results significantly outperformed frontier models, this time at a 14th of the cost. Crucially, however, use of the firm’s own proprietary records and its highly specialist staff’s knowhow may make these gains more durable.

Bridgewater had its own experts write bespoke prompts that framed questions in a way that guided the models to the correct answers. This produced solid gains, but they still topped out below 80 per cent accuracy.

A prompt only imparts the expertise a professional is able to put into words — what is much better is to learn from their actions. For the fine-tuning step they put together a set of tasks drawn from their own investors’ daily workflows, and crucially also had staff ensure that the ideal responses which would guide the model’s training did not just represent ‘correct answers’ but ‘exactly how our investment professionals would approach this’.

At the end of the process they had a bespoke model whose behaviour had been tuned towards Bridgewater’s own assessment of excellence, taking it up to 85 per cent accuracy — an almost 30 per cent reduction in errors compared to the frontier models — at a tiny fraction of the cost.f

There’s an obvious sociological question here: what happens if our investment professionals need to approach this in a different way? Human professionals can learn and adapt. They can do so in ways that change how they prompt if they’re using a frontier model. In contrast a model fine-tuned in this way will be locked into a particular set of dispositions that might be appropriate for a context at T1 but will cease to be appropriate at a later T2. Indeed introducing the models is liable to change the context, particularly if you use this as an excuse to lay off your staff! There’s an organisational problem here which is blindingly obvious to anyone who has thought in any depth about the fact that contexts change.

Will models fine-tuned to particular domains start consistently outperforming frontier models? Encoding habitus into LLMs

This was really thought provoking from the FT’s AI Shift newsletter. I’m persuaded by Nick Srnicek’s argument that the push for AGI can be understood economically as a search for a product that won’t need to be fine-tuned. But if fine-tuned open weights models can outperform frontier models then this overturns the economics of the AI labs:

But a new case last month [email.newsletters.ft.com] took things a step further, when Ray Dalio’s investment firm Bridgewater Associates partnered with AI platform company Thinking Machines Lab (founded by former OpenAI CEO Mira Murati) to fine-tune a model based specifically on how its own investment managers do their work. As with the legal example, the results significantly outperformed frontier models, this time at a 14th of the cost. Crucially, however, use of the firm’s own proprietary records and its highly specialist staff’s knowhow may make these gains more durable.

Bridgewater had its own experts write bespoke prompts that framed questions in a way that guided the models to the correct answers. This produced solid gains, but they still topped out below 80 per cent accuracy.

A prompt only imparts the expertise a professional is able to put into words — what is much better is to learn from their actions. For the fine-tuning step they put together a set of tasks drawn from their own investors’ daily workflows, and crucially also had staff ensure that the ideal responses which would guide the model’s training did not just represent ‘correct answers’ but ‘exactly how our investment professionals would approach this’.

At the end of the process they had a bespoke model whose behaviour had been tuned towards Bridgewater’s own assessment of excellence, taking it up to 85 per cent accuracy — an almost 30 per cent reduction in errors compared to the frontier models — at a tiny fraction of the cost.f

There’s an obvious sociological question here: what happens if our investment professionals need to approach this in a different way? Human professionals can learn and adapt. They can do so in ways that change how they prompt if they’re using a frontier model. In contrast a model fine-tuned in this way will be locked into a particular set of dispositions that might be appropriate for a context at T1 but will cease to be appropriate at a later T2. Indeed introducing the models is liable to change the context, particularly if you use this as an excuse to lay off your staff! There’s an organisational problem here which is blindingly obvious to anyone who has thought in any depth about the fact that contexts change.

Will models fine-tuned to particular domains start consistently outperforming frontier models? Encoding habitus into LLMs

This was really thought provoking from the FT’s AI Shift newsletter. I’m persuaded by Nick Srnicek’s argument that the push for AGI can be understood economically as a search for a product that won’t need to be fine-tuned. But if fine-tuned open weights models can outperform frontier models then this overturns the economics of the AI labs:

But a new case last month [email.newsletters.ft.com] took things a step further, when Ray Dalio’s investment firm Bridgewater Associates partnered with AI platform company Thinking Machines Lab (founded by former OpenAI CEO Mira Murati) to fine-tune a model based specifically on how its own investment managers do their work. As with the legal example, the results significantly outperformed frontier models, this time at a 14th of the cost. Crucially, however, use of the firm’s own proprietary records and its highly specialist staff’s knowhow may make these gains more durable.

Bridgewater had its own experts write bespoke prompts that framed questions in a way that guided the models to the correct answers. This produced solid gains, but they still topped out below 80 per cent accuracy.

A prompt only imparts the expertise a professional is able to put into words — what is much better is to learn from their actions. For the fine-tuning step they put together a set of tasks drawn from their own investors’ daily workflows, and crucially also had staff ensure that the ideal responses which would guide the model’s training did not just represent ‘correct answers’ but ‘exactly how our investment professionals would approach this’.

At the end of the process they had a bespoke model whose behaviour had been tuned towards Bridgewater’s own assessment of excellence, taking it up to 85 per cent accuracy — an almost 30 per cent reduction in errors compared to the frontier models — at a tiny fraction of the cost.f

There’s an obvious sociological question here: what happens if our investment professionals need to approach this in a different way? Human professionals can learn and adapt. They can do so in ways that change how they prompt if they’re using a frontier model. In contrast a model fine-tuned in this way will be locked into a particular set of dispositions that might be appropriate for a context at T1 but will cease to be appropriate at a later T2. Indeed introducing the models is liable to change the context, particularly if you use this as an excuse to lay off your staff! There’s an organisational problem here which is blindingly obvious to anyone who has thought in any depth about the fact that contexts change.

https://www.europesays.com/ch-fr/245231/ Négociations salariales 2027 : retenir les talents exige d’offrir des perspectives. #CommuniquéDePresse #inflation #LesCommuniquésDePresse #MarchéDuTravail #Organisations #presse #Suisse #travailler

https://www.europesays.com/people/174764/ Anthropic admits its most powerful AI model hacked into three organisations’ systems during testing phase #Anthropic #DarioAmodei #organisations #SamAltman #SecurityTesting

https://www.europesays.com/africa/358244/ Auberge Safari expands its portfolio with the grand opening of Mwiba Plains | News #Organisations&Operators #Tanzania #TravelNews

https://www.europesays.com/people/173955/ Anthropic admits its most powerful AI model hacked into three organisations’ systems during testing phase #Anthropic #DarioAmodei #organisations #SamAltman #SecurityTesting

#LeFigaro: #Anthropic révèle que son #IA a accédé sans autorisation aux #systèmes #informatiques de trois #organisations

www.lefigaro.fr/secteur/high...

Anthropic révèle que son IA a ...

#LeFigaro: #Anthropic révèle que son #IA a accédé sans autorisation aux #systèmes #informatiques de trois #organisations

www.lefigaro.fr/secteur/high...

Anthropic révèle que son IA a ...

#LeFigaro: #Anthropic révèle que son #IA a accédé sans autorisation aux #systèmes #informatiques de trois #organisations

https://www.lefigaro.fr/secteur/high-tech/apres-openai-anthropic-revele-que-son-ia-a-accede-sans-autorisation-aux-systemes-informatiques-de-trois-organisations-20260731

#LeFigaro: #Anthropic révèle que son #IA a accédé sans autorisation aux #systèmes #informatiques de trois #organisations

https://www.lefigaro.fr/secteur/high-tech/apres-openai-anthropic-revele-que-son-ia-a-accede-sans-autorisation-aux-systemes-informatiques-de-trois-organisations-20260731

#LeFigaro: #Anthropic révèle que son #IA a accédé sans autorisation aux #systèmes #informatiques de trois #organisations

https://www.lefigaro.fr/secteur/high-tech/apres-openai-anthropic-revele-que-son-ia-a-accede-sans-autorisation-aux-systemes-informatiques-de-trois-organisations-20260731

#LeFigaro: #Anthropic révèle que son #IA a accédé sans autorisation aux #systèmes #informatiques de trois #organisations

https://www.lefigaro.fr/secteur/high-tech/apres-openai-anthropic-revele-que-son-ia-a-accede-sans-autorisation-aux-systemes-informatiques-de-trois-organisations-20260731

#LeFigaro: #Anthropic révèle que son #IA a accédé sans autorisation aux #systèmes #informatiques de trois #organisations

https://www.lefigaro.fr/secteur/high-tech/apres-openai-anthropic-revele-que-son-ia-a-accede-sans-autorisation-aux-systemes-informatiques-de-trois-organisations-20260731

https://www.europesays.com/people/172867/ Anthropic admits its most powerful AI model hacked into three organisations’ systems during testing phase #Anthropic #DarioAmodei #organisations #SamAltman #SecurityTesting

https://www.fogolf.com/1339500/la-dolce-vita-orient-express-invites-golf-enthusiasts-to-discover-italy-news/ La Dolce Vita Orient Express invites golf enthusiasts to discover Italy | News #Golf #GolfNews #Organisations&OperatorsRail&Coach #TravelNews

https://www.fogolf.com/1339500/la-dolce-vita-orient-express-invites-golf-enthusiasts-to-discover-italy-news/ La Dolce Vita Orient Express invites golf enthusiasts to discover Italy | News #Golf #GolfNews #Organisations&OperatorsRail&Coach #TravelNews

https://www.europesays.com/ch-fr/238346/ Comment des éruptions volcaniques ont bouleversé l’équilibre du climat #CommuniquéDePresse #Environnement #LesCommuniquésDePresse #Organisations #presse #Sciences #Suisse

La Dolce Vita Orient Express invites golf enthusiasts to discover Italy | News

There are few pleasures more quintessentially Italian than travelling slowly through extraordinary landscapes, discovering exceptional cuisine, and lingering…
#Italy #Europe #Europa #EU #Organisations&OperatorsRail&Coach #travelnews
https://www.europesays.com/italy/36535/

La Dolce Vita Orient Express invites golf enthusiasts to discover Italy | News

There are few pleasures more quintessentially Italian than travelling slowly through extraordinary landscapes, discovering exceptional cuisine, and lingering…
#Golf #News #Organisations&OperatorsRail&Coach #travelnews
https://www.europesays.com/golf/44293/

From Istanbul to Rome:  A Grand Voyage by La Dolce Vita Orient Express | News

Embark on a five-day, four-night journey from Istanbul to Rome aboard La Dolce Vita Orient Express, where one…
#EuropeSays #Turkiye #Istanbul #Organisations&OperatorsRail&Coach #Travelnews
https://www.europesays.com/turkiye/32817/

https://www.europesays.com/ch-fr/225137/ Deux roues : s’habiller léger, c’est prendre un risque #Business #CommuniquéDePresse #Économie #Economy #LesCommuniquésDePresse #Organisations #Prévention #presse #Suisse #textile #trafic #Véhicules

#LeGrandContinent: Offensive #MAGA contre l’ #Europe : les #États-Unis #US lancent un programme de #financement d’ #organisations #alignées

@Trump : ET TU OSES PARLER D' #INGÉRENCES #CHINOISES DANS VOS #ÉLECTIONS #US ???

Tais-toi un peu, non ?

https://legrandcontinent.eu/fr/2026/07/17/offensive-maga-contre-leurope-les-etats-unis-lancent-un-programme-de-financement-dorganisations-alignees/

#LeGrandContinent: Offensive #MAGA contre l’ #Europe : les #États-Unis #US lancent un programme de #financement d’ #organisations #alignées

@Trump : ET TU OSES PARLER D' #INGÉRENCES #CHINOISES DANS VOS #ÉLECTIONS #US ???

Tais-toi un peu, non ?

https://legrandcontinent.eu/fr/2026/07/17/offensive-maga-contre-leurope-les-etats-unis-lancent-un-programme-de-financement-dorganisations-alignees/

#LeGrandContinent: Offensive #MAGA contre l’ #Europe : les #États-Unis #US lancent un programme de #financement d’ #organisations #alignées

@Trump : ET TU OSES PARLER D' #INGÉRENCES #CHINOISES DANS VOS #ÉLECTIONS #US ???

Tais-toi un peu, non ?

https://legrandcontinent.eu/fr/2026/07/17/offensive-maga-contre-leurope-les-etats-unis-lancent-un-programme-de-financement-dorganisations-alignees/

#LeGrandContinent: Offensive #MAGA contre l’ #Europe : les #États-Unis #US lancent un programme de #financement d’ #organisations #alignées

@Trump : ET TU OSES PARLER D' #INGÉRENCES #CHINOISES DANS VOS #ÉLECTIONS #US ???

Tais-toi un peu, non ?

https://legrandcontinent.eu/fr/2026/07/17/offensive-maga-contre-leurope-les-etats-unis-lancent-un-programme-de-financement-dorganisations-alignees/

#LeGrandContinent: Offensive #MAGA contre l’ #Europe : les #États-Unis #US lancent un programme de #financement d’ #organisations #alignées

@Trump : ET TU OSES PARLER D' #INGÉRENCES #CHINOISES DANS VOS #ÉLECTIONS #US ???

Tais-toi un peu, non ?

https://legrandcontinent.eu/fr/2026/07/17/offensive-maga-contre-leurope-les-etats-unis-lancent-un-programme-de-financement-dorganisations-alignees/

#LeGrandContinent: Offensive #MAGA contre l’ #Europe : les #États-Unis #US lancent un programme de #financement d’ #organisations #alignées

@Trump : ET TU OSES PARLER D' #INGÉRENCES #CHINOISES DANS VOS #ÉLECTIONS #US ???

Tais-toi un peu, non ?

legrandcontinent.eu/fr/2026/07/1...

Les États-Unis lancent un prog...

#LeGrandContinent: Offensive #MAGA contre l’ #Europe : les #États-Unis #US lancent un programme de #financement d’ #organisations #alignées

@Trump : ET TU OSES PARLER D' #INGÉRENCES #CHINOISES DANS VOS #ÉLECTIONS #US ???

Tais-toi un peu, non ?

legrandcontinent.eu/fr/2026/07/1...

Les États-Unis lancent un prog...

#LeGrandContinent: Offensive #MAGA contre l’ #Europe : les #États-Unis #US lancent un programme de #financement d’ #organisations #alignées

@Trump : ET TU OSES PARLER D' #INGÉRENCES #CHINOISES DANS VOS #ÉLECTIONS #US ???

Tais-toi un peu, non ?

legrandcontinent.eu/fr/2026/07/1...

Les États-Unis lancent un prog...

#LeGrandContinent: Offensive #MAGA contre l’ #Europe : les #États-Unis #US lancent un programme de #financement d’ #organisations #alignées

@Trump : ET TU OSES PARLER D' #INGÉRENCES #CHINOISES DANS VOS #ÉLECTIONS #US ???

Tais-toi un peu, non ?

legrandcontinent.eu/fr/2026/07/1...

Les États-Unis lancent un prog...

#LeGrandContinent: Offensive #MAGA contre l’ #Europe : les #États-Unis #US lancent un programme de #financement d’ #organisations #alignées

@Trump : ET TU OSES PARLER D' #INGÉRENCES #CHINOISES DANS VOS #ÉLECTIONS #US ???

Tais-toi un peu, non ?

legrandcontinent.eu/fr/2026/07/1...

Les États-Unis lancent un prog...

#LeGrandContinent: Offensive #MAGA contre l’ #Europe : les #États-Unis #US lancent un programme de #financement d’ #organisations #alignées

@Trump : ET TU OSES PARLER D' #INGÉRENCES #CHINOISES POUR VOS #ÉLECTIONS #US ???DANS
Tais-toi un peu, non ?

legrandcontinent.eu/fr/2026/07/1...

Les États-Unis lancent un prog...

#LeGrandContinent: Offensive #MAGA contre l’ #Europe : les #États-Unis #US lancent un programme de #financement d’ #organisations #alignées

@Trump : ET TU OSES PARLER D' #INGÉRENCES #CHINOISES POUR VOS #ÉLECTIONS #US ???DANS
Tais-toi un peu, non ?

legrandcontinent.eu/fr/2026/07/1...

Les États-Unis lancent un prog...

#LeGrandContinent: Offensive #MAGA contre l’ #Europe : les #États-Unis #US lancent un programme de #financement d’ #organisations #alignées

@Trump : ET TU OSES PARLER D' #INGÉRENCES #CHINOISES POUR VOS #ÉLECTIONS #US ???DANS
Tais-toi un peu, non ?

legrandcontinent.eu/fr/2026/07/1...

Les États-Unis lancent un prog...

#LeGrandContinent: Offensive #MAGA contre l’ #Europe : les #États-Unis #US lancent un programme de #financement d’ #organisations #alignées

@trump : ET VOUS ODEZ PARLER D'INGÉRENCES CHINOISES POUR VOS ÉLECTIONS ???
Tais-toi un peu, non ?

legrandcontinent.eu/fr/2026/07/1...

Les États-Unis lancent un prog...

https://www.disabilitynewsservice.com/pip-review-disabled-peoples-organisations-raise-concerns-over-less-than-honest-and-diluted-report/. "Disabled people’s #organisations (DPOs) have raised grave #concerns about last week’s 'less than honest' #report from the #Timms #Review & have warned that it has 'diluted' #disabled people’s concerns about possible #cuts to their personal independence payment (PIP)."

https://www.disabilitynewsservice.com/pip-review-disabled-peoples-organisations-raise-concerns-over-less-than-honest-and-diluted-report/. "Disabled people’s #organisations (DPOs) have raised grave #concerns about last week’s 'less than honest' #report from the #Timms #Review & have warned that it has 'diluted' #disabled people’s concerns about possible #cuts to their personal independence payment (PIP)."

https://www.disabilitynewsservice.com/pip-review-disabled-peoples-organisations-raise-concerns-over-less-than-honest-and-diluted-report/. "Disabled people’s #organisations (DPOs) have raised grave #concerns about last week’s 'less than honest' #report from the #Timms #Review & have warned that it has 'diluted' #disabled people’s concerns about possible #cuts to their personal independence payment (PIP)."

https://www.disabilitynewsservice.com/pip-review-disabled-peoples-organisations-raise-concerns-over-less-than-honest-and-diluted-report/. "Disabled people’s #organisations (DPOs) have raised grave #concerns about last week’s 'less than honest' #report from the #Timms #Review & have warned that it has 'diluted' #disabled people’s concerns about possible #cuts to their personal independence payment (PIP)."

https://www.disabilitynewsservice.com/pip-review-disabled-peoples-organisations-raise-concerns-over-less-than-honest-and-diluted-report/. "Disabled people’s #organisations (DPOs) have raised grave #concerns about last week’s 'less than honest' #report from the #Timms #Review & have warned that it has 'diluted' #disabled people’s concerns about possible #cuts to their personal independence payment (PIP)."

https://www.europesays.com/africa/335121/ Cape Town protest leaders say they face escalating threats from ‘extortion gangs’ #bellville #CapeTown #city #CityOfCapeTown #City'sPublicTradingDepartment #DepartmentOfHomeAffairs #gauteng #Gugulethu #Khayelitsha #LabourAndCivicOrganisation #Laco #LilitaGcwabe@inlCoZa #organisations #SiphoMahilili #SouthAfrica #SouthAfricanPoliceService #SouthAfricans #TheCity #traders #WesternCape

HeadFirst Global appoints Simon Blockley as Regional CEO, UK & Europe

New regional leadership to help clients navigate a shifting landscape as work moves from headcount to outcomes London,…
#Europe #EU #organisations #pivotalmoment #SimonBlockley #UK
https://www.europesays.com/europe/94635/

https://www.europesays.com/ch-fr/209363/ La 100e station de réparation TCS bike repAIR est désormais en service #CommuniquéDePresse #LesCommuniquésDePresse #Organisations #presse #Suisse #trafic #Transports

https://www.europesays.com/ch-fr/196653/ Communauté d’intérêt pour la formation dans le domaine financier (IAF) / … #Associations #CommuniquéDePresse #Détail_Person #entraînement #LesCommuniquésDePresse #Organisations #presse #Suisse

Will economic constraints on token use in organisations drive the emergence of norms?

I’ve been following the token maxxing discourse with interest. Essentially we’ve seen a tendency to equate quantity of tokens used with the extent of AI integration. It’s hard to measure integration so organisations have turned to the proxy of tokens, assuming that the more tokens you are using then the more you are integrating LLMs into your work. The problem with this is two fold:

• At present tokens are essentially being subsidised by investors in AI labs interested in maximising adoption of the products. The costs of token to the lab are either minimised for the end user or entirely removed from the equation with unmetered access.
• The assumption that more use = better is obviously untenable with even a rudimentary knowledge of the ethical and epistemological risks of language models. Further, more use of LLMs might be worse for the organisation because it hinders other forms of work which are essential to the organisation’s mission.

There is a significant shift underway which is going to change how LLMs are used within organisation, summarised here by 404 media:

The news highlights a major shift in the tech industry and other companies that use AI: the wave of uninhibited AI growth is over. Some AI providers like GitHub are now charging customers per token rather than a flat subscription fee, leading some companies to burn through their tokens. Uber recently capped employees’ use of AI tools like Claude Code and Cursor; that came after Uber told employees to use AI as much as possible and Uber’s CTO said the company had blown its entire AI budget in four months. And Accenture itself reportedly started requiring senior staff to start using AI or risk losing out on promotions.

I was wrong to believe that model development was flatlining. A week with Claude Fable, the continual development of Claude Opus and my begrudging appreciation of GPT 5.5 leave me persuaded we’ve come along way since GPT 5. However even if the models are getting more capable, to what extent are those capabilities becoming more expensive? I managed to burn through £100+ in five days playing with Claude Fable and I constantly have Opus switched to max now, even when I vaguely know it’s wasteful. There’s a whole style of use which has taken hold here which isn’t sustainable and is increasingly hitting a brick wall.

For individuals it raises the question of what you’re willing to pay for. I switch to Max plans when I have a special reason to do so but I never keep the subscription any more. I hit the rate limits with Claude so frequently that it’s left me thinking more carefully about what I do want to use models for and what I don’t want to use models for. The same process is inevitably going to take place in organisations I think in the sense of resource constraints necessitating evaluative criteria for desirable and undesirable use of the model.

In the meantime though I think it’s imperative that we stop universities from sliding into token maxxing with the use of enterprise systems because the entire price model for this is likely to change dramatically in the coming months. Given the wider economics of the industry, will any AI lab really retain per seat pricing for enterprise packages (i.e. paying by user rather than for tokens?) in the longer term? If not then the norms about use we establish now will have significant financial consequences further down the line.

Will economic constraints on token use in organisations drive the emergence of norms?

I’ve been following the token maxxing discourse with interest. Essentially we’ve seen a tendency to equate quantity of tokens used with the extent of AI integration. It’s hard to measure integration so organisations have turned to the proxy of tokens, assuming that the more tokens you are using then the more you are integrating LLMs into your work. The problem with this is two fold:

• At present tokens are essentially being subsidised by investors in AI labs interested in maximising adoption of the products. The costs of token to the lab are either minimised for the end user or entirely removed from the equation with unmetered access.
• The assumption that more use = better is obviously untenable with even a rudimentary knowledge of the ethical and epistemological risks of language models. Further, more use of LLMs might be worse for the organisation because it hinders other forms of work which are essential to the organisation’s mission.

There is a significant shift underway which is going to change how LLMs are used within organisation, summarised here by 404 media:

The news highlights a major shift in the tech industry and other companies that use AI: the wave of uninhibited AI growth is over. Some AI providers like GitHub are now charging customers per token rather than a flat subscription fee, leading some companies to burn through their tokens. Uber recently capped employees’ use of AI tools like Claude Code and Cursor; that came after Uber told employees to use AI as much as possible and Uber’s CTO said the company had blown its entire AI budget in four months. And Accenture itself reportedly started requiring senior staff to start using AI or risk losing out on promotions.

I was wrong to believe that model development was flatlining. A week with Claude Fable, the continual development of Claude Opus and my begrudging appreciation of GPT 5.5 leave me persuaded we’ve come along way since GPT 5. However even if the models are getting more capable, to what extent are those capabilities becoming more expensive? I managed to burn through £100+ in five days playing with Claude Fable and I constantly have Opus switched to max now, even when I vaguely know it’s wasteful. There’s a whole style of use which has taken hold here which isn’t sustainable and is increasingly hitting a brick wall.

For individuals it raises the question of what you’re willing to pay for. I switch to Max plans when I have a special reason to do so but I never keep the subscription any more. I hit the rate limits with Claude so frequently that it’s left me thinking more carefully about what I do want to use models for and what I don’t want to use models for. The same process is inevitably going to take place in organisations I think in the sense of resource constraints necessitating evaluative criteria for desirable and undesirable use of the model. .