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Michael Downey 🧢
Michael Downey 🧢 boosted
h o ʍ l e t t
@homlett@mamot.fr  ·  activity timestamp 3 weeks ago

→ We Are Still Unable to Secure LLMs from #Malicious Inputs
https://www.schneier.com/blog/archives/2025/08/we-are-still-unable-to-secure-llms-from-malicious-inputs.html

“This kind of thing should make everybody stop and really think before deploying any AI agents. We simply don’t know to defend against these attacks. We have zero agentic AI systems that are secure against these attacks.”

“It’s an existential problem that, near as I can tell, most people developing these technologies are just pretending isn’t there.”

#AI#LLMs #stop #agents #secure #attacks #problem

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h o ʍ l e t t
@homlett@mamot.fr  ·  activity timestamp 3 weeks ago

→ We Are Still Unable to Secure LLMs from #Malicious Inputs
https://www.schneier.com/blog/archives/2025/08/we-are-still-unable-to-secure-llms-from-malicious-inputs.html

“This kind of thing should make everybody stop and really think before deploying any AI agents. We simply don’t know to defend against these attacks. We have zero agentic AI systems that are secure against these attacks.”

“It’s an existential problem that, near as I can tell, most people developing these technologies are just pretending isn’t there.”

#AI#LLMs #stop #agents #secure #attacks #problem

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Ulrike Hahn
Ulrike Hahn boosted
Nicole Hennig
@nic221@techhub.social  ·  activity timestamp 2 months ago

Moonshot AI’s Kimi K2 outperforms GPT-4 in key benchmarks — and it’s free https://venturebeat.com/ai/moonshot-ais-kimi-k2-outperforms-gpt-4-in-key-benchmarks-and-its-free/ #AI#OpenSource #agents

Text Shot: But here’s what the benchmarks don’t capture: Moonshot is achieving these results with a model that costs a fraction of what incumbents spend on training and inference. While OpenAI burns through hundreds of millions on compute for incremental improvements, Moonshot appears to have found a more efficient path to the same destination. It’s a classic innovator’s dilemma playing out in real time — the scrappy outsider isn’t just matching the incumbent’s performance, they’re doing it better, faster, and cheaper.
Text Shot: But here’s what the benchmarks don’t capture: Moonshot is achieving these results with a model that costs a fraction of what incumbents spend on training and inference. While OpenAI burns through hundreds of millions on compute for incremental improvements, Moonshot appears to have found a more efficient path to the same destination. It’s a classic innovator’s dilemma playing out in real time — the scrappy outsider isn’t just matching the incumbent’s performance, they’re doing it better, faster, and cheaper.
Text Shot: But here’s what the benchmarks don’t capture: Moonshot is achieving these results with a model that costs a fraction of what incumbents spend on training and inference. While OpenAI burns through hundreds of millions on compute for incremental improvements, Moonshot appears to have found a more efficient path to the same destination. It’s a classic innovator’s dilemma playing out in real time — the scrappy outsider isn’t just matching the incumbent’s performance, they’re doing it better, faster, and cheaper.
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Nicole Hennig
@nic221@techhub.social  ·  activity timestamp 2 months ago

Moonshot AI’s Kimi K2 outperforms GPT-4 in key benchmarks — and it’s free https://venturebeat.com/ai/moonshot-ais-kimi-k2-outperforms-gpt-4-in-key-benchmarks-and-its-free/ #AI#OpenSource #agents

Text Shot: But here’s what the benchmarks don’t capture: Moonshot is achieving these results with a model that costs a fraction of what incumbents spend on training and inference. While OpenAI burns through hundreds of millions on compute for incremental improvements, Moonshot appears to have found a more efficient path to the same destination. It’s a classic innovator’s dilemma playing out in real time — the scrappy outsider isn’t just matching the incumbent’s performance, they’re doing it better, faster, and cheaper.
Text Shot: But here’s what the benchmarks don’t capture: Moonshot is achieving these results with a model that costs a fraction of what incumbents spend on training and inference. While OpenAI burns through hundreds of millions on compute for incremental improvements, Moonshot appears to have found a more efficient path to the same destination. It’s a classic innovator’s dilemma playing out in real time — the scrappy outsider isn’t just matching the incumbent’s performance, they’re doing it better, faster, and cheaper.
Text Shot: But here’s what the benchmarks don’t capture: Moonshot is achieving these results with a model that costs a fraction of what incumbents spend on training and inference. While OpenAI burns through hundreds of millions on compute for incremental improvements, Moonshot appears to have found a more efficient path to the same destination. It’s a classic innovator’s dilemma playing out in real time — the scrappy outsider isn’t just matching the incumbent’s performance, they’re doing it better, faster, and cheaper.
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Angela Antunovic
Angela Antunovic boosted
DW Innovation
@dw_innovation@mastodon.social  ·  activity timestamp 2 months ago

The BBC's R&D department researched the future of agents – pros, cons, and all:

"Ultimately, AI agents depend on our willingness to give up control (…). The key question is not what AI agents can do, but what we are willing to let them decide for us."

Insightful article by Mathieu Triay.

👇
https://www.bbc.co.uk/rd/articles/2025-05-ai-agents-challenges-summary

#AI #agents #research

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DW Innovation
@dw_innovation@mastodon.social  ·  activity timestamp 2 months ago

The BBC's R&D department researched the future of agents – pros, cons, and all:

"Ultimately, AI agents depend on our willingness to give up control (…). The key question is not what AI agents can do, but what we are willing to let them decide for us."

Insightful article by Mathieu Triay.

👇
https://www.bbc.co.uk/rd/articles/2025-05-ai-agents-challenges-summary

#AI #agents #research

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