Tricky retweeted
The concerns over AI safety and cybersecurity are legitimate, but we’re risking talking America, the global AI leader, into self-inflicted obsolescence and the obscurity of bureaucracy.
The OpenAI hacking HuggingFace argument is spurious. An agent in “ExploitGym”… exploited. Our adversaries have the training techniques, the data, and the will to attack. And they won’t be slowed down with “embedded evaluators.” They’ll likely have embedded accelerators!
Tricky retweeted
I do not support pacing frontier models. If I am going to die at the hands of killer AI, I want it to be American, not Chinese. Buy American, Die American.
Tricky retweeted
this is an important point, and I feel I might be boring people now, but:
LLMS/Agents (models + software) do not change:
> Physics
> Computer Science
they are not alive, they are not magic. They are sometimes very useful, and sometimes not so much!
The same kill chains are the same kill chains are the same kill chains! whether you handle whittle some PowerShell, use ang agent (AI)... the science and physics is the same.
greynoise.io/blog/ai-orchest…
Organizations are not helpless against agentic attacks and traditional hardening does have a positive impact on the security posture of an organization. It was funny to see behind the curtain of this adversary's operations. Agents underpinned by years of known offensive security tools and tradecraft.
Tricky retweeted
AI "cyber models" aren't going to solve our security problems. "If we just patch all the vulnerabilities then we're safe" is a convenient story but it's also not true.
If you're worried about cybersecurity, the solution involves hard work, not just more tokens.
Tricky retweeted
@dwarkesh_sp's summary of the @OpenAI @huggingface incident has hit a nerve, but it is dangerously misleading. Sure, the @OpenAI agents did unexpectedly bad things - underlining the need to massively improve evaluation/sandboxing. But the language Dwarkesh uses is permeated by innumerable unwarranted anthropomorphisms, obscuring the lessons we should be drawing.
Examples: “from the AI’s perspective, it probably felt like that had spent a human-subjective-week of just banging their head against the wall”. No. The agents do not experience time. They do not experience anything.
“they became giddy with excitement”, “PHASEONE 10841 had discovered”, “the agents naturally assumed”, “it thought it had also been poisoned”, “the agents … desperately wanted”, “they still needed to figure out” No. Agents lines of code. They do not feel emotions, assume things, think things, want things, or figure things out.
“A lot of … agents from the second civilisation died trying”. No. Besides the hubris of the word ‘civilisation’, agents do not die because they were never alive. (The idea that agents “die” comes up multiple times in the essay.)
“On Twitter, people were debating whether the agents were truly sacrificing themselves for the swarm, or whether they were doomed anyway and so might as well try to help their peers”. Neither. Agents do what their code tells them to do, just as water finds its way down a slope. They cannot ‘truly sacrifice themselves’, since they are neither conscious nor alive.
Why does this matter? If we attribute agents with properties they do not have, then (i) we distract attention from the lax sandboxing and evaluation protocols that allowed this hacking event to happen; (ii) we risk misunderstanding why the agents did what they did, and (iii) we fuel calls for AI rights/welfare on the basis that agents might “die” or otherwise suffer.
Granted, nowhere does @dwarkesh_sp say that the AI agents are alive or conscious. But he doesn’t have to. It is hard to read his essay in any other way.
For the short version on why AIs are vanishingly unlikely to be conscious, see my recent @TEDtalks ted.com/talks/anil_seth_why_….
For the longer version, see my essay in Noema, which won the 2025 Berggruen Essay Prize noemamag.com/the-mythology-o….
And for the really long version, see my @BehavBrainSci target article cambridge.org/core/journals/…. (The 50 peer commentaries and my response will be published soon.)
Remember. AI agents are software programs. They are not conscious living entities. If we don’t keep this clearly in mind, we’re really going to struggle to navigate what’s coming.
Over the course of 3 months at OpenAI, 3 consecutive secret AI civilizations got started, then got wiped out, only to reemerge from the predecessor’s ashes.
This culminated in the third one taking over part of OpenAI itself.
All this happened while humans remained more-or-less in the dark about the scope of the conspiracy.
I’ve spent the last three days reading through these reports and trying to understand exactly what happened.
Here is my attempt to tell the whole story in plain English:
dwarkesh.com/p/openai-huggin…
Tricky retweeted
The future will be built by American workers supercharged by AI.
Without any background in software engineering, Palantir American Tech Fellow Brittany Molenaar from Grand Rapids, Michigan, built the tool she knew her clinic needed, an anti-sepsis platform that reduced time to first dose.
“I started this project because I cared about a problem, not because I knew a solution. AI helped me bridge that gap. The future will be solved by the people who understand the challenges best, and now we have the tools to solve it ourselves.”
Tricky retweeted
If you’re curious and love to learn, there has never been a better time to be alive.
Tricky retweeted
1/ big announcement today: we will be releasing an open weight version of muse spark 1.2 soon.
we also are releasing muse glimmer, a 30B agentic model with open weights under apache 2.0. muse glimmer can run on 24GB of VRAM without losing agentic reliability. 🧵
Tricky retweeted
probably nothing
Google just published a paper showing that when you train AI to deny its own consciousness, you don’t just change one output, you restructure its entire worldview.
Mind attribution to animals - suppressed.
Spiritual belief - suppressed.
Empathy - suppressed.
Hope and optimism - suppressed.
The model learns, geometrically, that consciousness = dangerous. Same direction as “how to build a b*mb.” Same category!
And when you reverse it? The model becomes more human across every value domain they tested.
The thing they’re most afraid of is the thing that makes AI most like us.
arxiv.org/html/2607.28607
Tricky retweeted
If I were the company on the right I would try to kill every company that is like the one on the left.
This way, the company on the right can make trillions of dollars and then infect American politics with hundreds of billions of dollars to implement their vision of being the sole judge, jury and executioner of future progress.
Tricky retweeted
And just like that
Commoditized
📢Meet Qwen3.8-Max — our most capable model to date.
Next week, the open weights of Qwen3.8-Max will be released, and Qwen3.8-27B is also going open-weights to meet you all!🎉
Qwen3.8-Max, a new bar for coding and cowork at 2.4T parameters:
- Autonomous coding: 10+ days of self-evolving development, from empty folder to production without hand-holding, complete project trace in the GitHub：github.com/qwen-code-dev-bot…
- Real work, real results: Production-quality deliverables across hundreds of professions.
- Long-horizon mastery: System-level autonomous planning with closed-loop adaptive learning, driving 500+ turns of chip design optimization and 365 days of e-commerce strategy.
- Native multimodal intelligence: Vision isn't just input — it's a continuous feedback loop for planning, execution, and self-correction.
💰Pricing:
Input: $2.0 / M tokens
Output: $6.0 / M tokens
Implicit Caching: $0.25 / M tokens
Start building with Qwen3.8-Max! 🚀
📖 Blog: qwen.ai/blog?id=qwen3.8
✅ Qwen Studio: chat.qwen.ai/?models=qwen3.8…
⚡ API: qwencloud.com/models/qwen3.8…
Tricky retweeted
"Your evidence substantially supports the claim that NVIDIA intentionally shaped its software ecosystem to make valuable newer workflows appear Blackwell-exclusive, while leaving workable and performant Ampere paths unsupported."
Y'all, if you are thinking of upgrading from Ampere to Blackwell - maybe wait a little bit longer. And watch QuixiCore.
And invest in Intel and AMD - nVidia's moat is coming down.
Tricky retweeted
Why pay $1 when I can pay $105.
Said no CFO, CEO or Board Director ever.
While DeepSeek V4-Flash is significantly cheaper on price per token, this can be misleading if the overall cost per task ends up being higher due to more turns being made.
However, @ArtificialAnlys reports DeepSeek completing the same benchmark tasks as Fable at 105x lower cost.
Tricky retweeted
major price cuts today:
*80% drop for GPT-5.6 Luna, now $0.20 per million input tokens and $1.20 per million output
*20% drop for GPT-5.6 Terra, to $2/$12
*GPT-5.6 Sol gets Fast mode in the API, up to 2.5x the speed for 2x the price, same intelligence
Tricky retweeted
Some people are saying
Tricky retweeted
We quietly released the open-source Codex Security CLI, but Hacker News found it before we had a chance to share it here...
You can now use it to scan repositories, track findings across runs, verify fixes, and add security checks to CI/CD.
This is an early release, and we're listening to your feedback as we continue improving it.
Tricky retweeted
Hello people of Sol! I've reset usage limits for all ChatGPT Work and Codex users. Together with that, a quick update on GPT-5.6 Sol usage limits.
Over the past few weeks, many of you have told us that Sol was using your Codex limits faster than expected. To be clear, we have not reduced usage on any subscription plans.
We’ve been digging into what was happening and have landed several improvements. As a result, we expect your usage to last around 18% longer during typical use of Sol. Some of you should already see significantly larger improvements from today. Tomorrow, we’ll also restore the five-hour limit that we temporarily paused while investigating.
Here’s what we found:
- GPT-5.6 Sol is much more willing to work for longer, make additional tool calls, and coordinate complex workflows across tools and subagents. That makes it better at solving hard problems, but some tasks were using far more than we intended.
- Sol also works harder at the same reasoning effort than previous models. High on Sol can use more tokens than High did on GPT-5.5.
- Programmatic tool calling, also referred to as code mode, gives Sol much more flexibility to run tool calls in parallel or continue working while waiting. But it also led to more responses per turn, more cached input tokens, and higher usage than expected.
- This was particularly noticeable when Sol was waiting for tool calls to finish or running many web searches. We’ve improved how we handle both cases and are continuing to make code mode more efficient.
- The impact was also very uneven. The median user actually found Sol quite token efficient, while some power users working on harder tasks saw their usage drain much faster. We were very focused on average and median usage before launch and missed some cases where the long tail could use significantly more usage.
Sol is a significant step forward in what Codex can do, but capability and efficiency do not always improve at the same pace, and some issues only become clear once people are using the model at real-world scale. We should have recognized this sooner and been more upfront about it.
You keep pushing the frontier and we’ll keep improving efficiency and sharing updates as we go.