You can be the best cracked programmer but if you are not consistent then even a vibe coder would put you to shame.
Anthropic has filed for an IPO on Nasdaq under the ticker ANT.
The S-1 shows very fast revenue growth, but also huge compute and infrastructure commitments that run into the hundreds of billions. Most of the reported losses are accounting charges from rising valuations on convertible notes, not pure cash burn, and the company has a large cash balance. Still, the scale of locked-in spending means it needs significant ongoing capital to keep expanding capacity.
The filing leaves price and share count blank for now. The listing is expected sometime in November.
I think a robust simulation would lead to better robotics. Gaming and entertainment would definitely benefit from world models. But it will expand the horizon of simulation and make robotics training faster. Imagine running simulations and figuring out what actions fail and succeed without actually offloading the training routine physically onto a robot.
Is there a any independently verified evidence that 2.5 lakh shops were closed? Also, If 20-25 markets in a part of Delhi alone are doing business worth ₹10-12K crores in ONE day, then can you estimate the total revenue of all such businesses all over the country in one year?
Once that is done, analyse and tell me if those numbers look practical and align with the data on country's gdp available in public domain.
I'm keeping an eye on JEPA, Cosmos, Genie, World Labs. There are interesting things happening on this front. LLMs are just one tiny piece of the puzzle.
Strong desire narrows attention. Curiosity expands it. Many people follow desire for years and slowly lose the habit of being curious.
Absolutely! The action representation becomes more critical for proper planning and accurate long horizon behaviour.
Cody retweeted
Agree. The shift from pure reaction to internal simulation is real.
One detail still feels under-discussed: once the world model has to condition on the actions themselves, the action representation (esp orientation) start mattering more than it usually does in modelfree setups.
If you’re working on AI agents or robotics, consider pivoting toward world models.
Most current systems react to the present. World models try to predict what happens next. That shift from pure reaction to internal simulation is what enables better planning, fewer real-world trials, and more reliable long-horizon behavior.
The field is still early, but the direction is clear: systems that can model the world will outperform those that only pattern-match the latest observation.
Statistically, more number of aircrafts with male pilots have crashed in the past compared to aircrafts with female pilots.
NVIDIA about to cook
NVIDIA is reportedly eyeing Teflon, the non-stick cookware material, to fix AI server signal loss in next-gen NVLink switch boards. 🔗 wccf.tech/1lgv5
AI guardrails are the constraints that keep a model from saying or doing things it technically can, but shouldn’t.
They sit between raw capability and acceptable behavior. Without them, a system optimized only for next-token prediction will eventually produce outputs that are harmful, misleading, or simply out of scope.
The hard part isn’t adding the rules. It’s deciding where the boundary should be, and accepting that every guardrail is also a trade-off between safety and usefulness.