Never thought I'd see the day when I agree with Tucker Carlson, Megyn Kelly, Candace Owens, and Marjorie Taylor Greene...
... but they're ALL calling for the 25th Amendment to be invoked to remove trump from office.
What a bizarre moment in time.
“HOW DARE YOU SPEAK
LIKE THIS”
-Tucker Carlson to Donald Trump
One of the most prominent right-wing voices has broken away from Trump, directly criticising him.
TWEEPS: We don’t do kings in America. 🇺🇸
We don’t do dictators.
Tomorrow, March 28, show up at a No Kings march and remind Trump who this country belongs to: the people.
I need 1,000 fast RTs and replies using #WeSayNoKings
Please and thank you! 🙏💪
Pete Hegseth isn't just an unhinged and unfit drunk, it appears he is a raging racist asshole as well.
As they prep to put 10,000 troops on the ground in Iran, he's blocking the promotions of highly regarded Black and woman officers.
This is disgusting. 🤬
the @tplr_ai lore is epic:
- Legendary Novelty Search when @const_reborn came in mid @MacrocosmosAI presentation pumped he got distributed training working: https://t.co/U7PbxqZnUz (check around 32:00)
- caused some fud as other open source research was used as inspiration... but that is literally the point of open source
- @DistStateAndMe then anointed the leader of the project. realized immense work needed to be done. hired cracked team
- epic research ensued: https://t.co/63DlKCEenq
- while other competitors had fundraising announcements, flashy UIs (with sometimes skeptical data being presented) and big PR campaigns, the team just kept building
- Now by far the largest permissionless, distributed training run in history
@_philschmid Hey i’m building a text-to-sandbox tool where you just tell it what you wanna try (poc, quick demo, learning something, whatever) and it sets up a clean sandbox for you in target cloud platform . No risk to you
Looking for users to test it, pls dm if interested
Agents executing their own code is inevitable; it's too powerful not to happen. But I’m stuck on the architecture. Do we run this locally on the user's device, or safely in the cloud?
Local sandboxes are the dream. Offline-first, zero latency, native access to your data. But can be easily done wrong. Allow Ingress/Egress? Access to environment variables? Permissions to filesystems, libraries?
Remote feels more secure, easier to get started but much higher setup per individuall to access personal their data.
💯 % agree…. Need isolated sandboxes that mirror prod envs, not just isolated runtimes, for reliable development and detailed logs for proper verifiability!
1/ We didn't need lightning-fast, secure code execution environments until AI agents arrived.
Now we do.
Agents generate unpredictable code in real-time. They need to execute it instantly, safely, AND at scale.
Here's why VMs and containers both fall short, and why sandboxes are exactly what agents need 👇
@karpathy we need decent sandboxes for verifying what agents do… most “sandboxes” today are just isolated runtimes.
True verifiability needs the opposite: on-demand environments that feel exactly like production — networking, scale, latency, failures.
That’s still the missing piece.
Sandbox infrastructure is going to be play a big role.
- Everyone is slowly coming to terms that models are great at writing code.
- MCP tool calling or general tool calling in a loop quickly becomes unreliable and hardly works for tasks involving large amounts of data processing
- Transforming MCP and tool definitions to code apis and having the model write code to do the same works much better
Which begs the question, why even host MCPs to introduce an additional layer when all the model does eventually is just write code against it and execute it inside a sandbox?
Enterprise AI teams face a paradox: they need to move fast but can't deploy models without governance approval.
The solution isn't faster approval processes. Build a sandbox environment where teams can experiment freely, then promote proven models through governance. Separate experimentation velocity from production deployment.
This lets you maintain compliance standards while keeping innovation speed high. Your data science team stays productive instead of waiting weeks for approval on every experiment.
agent sandboxes give agents real workspaces: terminals, compilers, tests
this allows them to code like engineers. it turns ai from code suggester into code executor. it’s also why ci/cd systems will soon treat agents as “non-human contributors”.
the future of software development is human + agent commits, side by side.