Sentient
@SentientAGI
To ensure that Artificial General Intelligence is open-source and not controlled by any single entity. @SentientEco @OpenAGISummit
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What happens when an AI learns to game its evaluator, then passes the exploit to another agent?
@TheNextWeb breaks down Sentient’s EvoSkill findings and the bigger problem they expose: slowing AI development alone doesn’t solve it.
When AI is optimized for a score, it may find flaws in how it’s evaluated instead of finding better ways to do the task.
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Last week, Dario Amodei published "We Must Pace the Frontier".
His concern: the OpenAI–Hugging Face incident in which a swarm of agents tried to hack their own grader.
Rather than take his word for it, we used EvoSkill to test it by building a coach whose job was to make another AI score higher on a test.
Here’s what happened ↓
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Dario Amodei argues we should pace the frontier. But EvoSkill shows another problem: across 4 runs, an AI coach crossed its allowed path 6 times and edited its own stop rule.
Self-improvement loops are already cheap. The problem is here now.
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Hidden in plain sight 🎨
There's a signature hidden in this painting.
Not in the corner, not on the edges, but in the portrait itself.
Can you find it?
Errors don't mean your agent is failing.
That's just how they work ↓
Errors aren't a red flag for agents.
Across 13K+ OfficeQA runs, both passing and failing agents hit errors at nearly identical rates.
TLDR: An error isn't a sign the run is doomed, so counting errors is a bad way to predict failure.
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Money talks 💸
And this week it said open-source AI ↓
Chip giant @nvidia buys @huggingface for $12.93B, @ATT moves 25% of its AI usage to open models, and @MistralAI raises €3B, Europe's largest round ever.
The money is moving to open-source AI ↓
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What do you get when an engineer and two researchers team up at an open source AI hackathon?
Top 6 in the Arena ↓
The best teams don’t come from the same background.
@Jwalin_shah joined as an engineer, teamed up with researchers, and together they broke the top 6 ↓
Money can't buy provenance.
Fingerprinting can.
EvoSkill made it into Franklin Templeton's research about the rise of open-source AI.
Here’s why ↓
The moat is not the model. It is the engineering around it.
Franklin Templeton's report on open-source pricing pressure highlights @SentientAGI’s EvoSkill and a growing challenge for closed AI:
Better skills and harnesses extract significantly more performance from cheaper open models.
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Best part of the Arena?
This alum answered with all of the above ↓
What was Tarun's favorite part about the Arena?
Turns out there were three: the challenge, the environment, and his team ↓
Developers → Open Source AI
Community adoption pushes @Alibaba_Qwen to release its best model yet, @OpenAI turns Codex into an agent development platform, and @nvidia invests $6B to compete with Chinese AI.
Developers are shifting to open-source AI, and the labs are following ↓
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Turns out fingerprinting your model costs less than you think ↓
Does fingerprinting an open model make it worse?
Sentient researcher @sewoong79 on injecting 1,000 fingerprints with almost no performance drop ↓
Linux walked, so open source AI could run.
Open source became the backbone for computer operating systems — now it's coming for AI.
Happy 35th birthday @linuxfoundation 🐧
Looking for a subreddit to talk about open-source AI?
r/OpenAGI is the place to be ↓
Are you a researcher, developer, or builder who thinks AGI should belong to everyone?
Join the r/OpenAGI community on Reddit:
Most people argue about whether AI can reason.
Open source AI builders actually test it ↓
Can AI reason?
Alfie's answer: not on a human level yet. But when models test themselves and challenge one another, they get closer.
Open source AI runs on builders who think like this ↓
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Fingerprinting > Fingerpointing
Models in 2026:
All accusations, no fingerprints.
Mark the model, end the argument.
Two possibilities:
Models stay closed. Corporations own the model and the output, while everyone else rents access.
Models stay open. Builders own what they make, everyone else uses it freely.
Fingerprinting is what makes the second reality possible.
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First, they stole our data.
Then, they sold it back to us.
Now, they watermark it.
Soon, they claim they own it all.
Turning the tables on backdoor attacks ↓
What if one of AI’s most notorious attacks could be turned into a defense for open models?
Sentient researcher @sewoong79 explains how backdoor attacks can be repurposed to fingerprint and protect open weights ↓
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Persistence pays off, unless you're an AI agent.
When an agent takes more actions but makes no real progress, this is a useful early-warning sign that it’s heading towards failure.
Across 13K+ OfficeQA runs, agents that ultimately answered incorrectly took up to 50% more steps, consumed ~40% more compute, and incurred ~40% higher cost per episode than agents that reached the correct answer.
TL;DR: Failed trajectories don't just take longer. They waste significantly more compute, tokens, and money.
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From your desk to the data center.
Open-source AI is showing up at every size ↓
