Write insights on AI, Tech tools & Prompts✍️| Ghostwriter | DM for Collaboration [email protected] 📥
Washington, DC
Joined October 2024
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🚨 BREAKING: Google Gemini can now analyze any stock like a Wall Street analyst (for free).
Here are 09 insane Gemini prompts that replace $4,000/month Bloomberg terminals:
[ Save for later 🔖 ]
Metix made Fast Search free. Reads like a signal that finding candidates is becoming table stakes, and the real competition in recruiting software moves to who actually gets people into interviews.
Today we're launching @metix_ai for recruiters.
Type your role in one sentence, then pick how far it goes:
👉 Run Fast Search for an immediate list to explore candidate pools and size up the market.
👉 Run Deep Search for the best fits. It starts by clarifying what your role needs and what matters most, then digs into every candidate.
👉 Run the Agent to hand off sourcing, emails and follow-ups. It puts interested candidates on your calendar for interviews.
We send interviews, not resumes.
metix.ai Try Fast Search on a role you're hiring for.
Arpita Trisha retweeted
Today we're launching @metix_ai for recruiters.
Type your role in one sentence, then pick how far it goes:
👉 Run Fast Search for an immediate list to explore candidate pools and size up the market.
👉 Run Deep Search for the best fits. It starts by clarifying what your role needs and what matters most, then digs into every candidate.
👉 Run the Agent to hand off sourcing, emails and follow-ups. It puts interested candidates on your calendar for interviews.
We send interviews, not resumes.
metix.ai Try Fast Search on a role you're hiring for.
Arpita Trisha retweeted
First scene i'm making in Viggle's JST-2 is my old school hallway from a photo. curious how far down it builds
Today we are releasing JST-2, the world's first full-stack physics-native world foundation model.
Many world models learn in pixel/video space. We went back to first principles and built the entire model stack in 3D.
Generate a 3D scene in seconds and expand it in real time. Create characters with motion built in, all through one general architecture.
First scene i'm making in Viggle's JST-2 is my old school hallway from a photo. curious how far down it builds
Today we are releasing JST-2, the world's first full-stack physics-native world foundation model.
Many world models learn in pixel/video space. We went back to first principles and built the entire model stack in 3D.
Generate a 3D scene in seconds and expand it in real time. Create characters with motion built in, all through one general architecture.
Arpita Trisha retweeted
Today we are releasing JST-2, the world's first full-stack physics-native world foundation model.
Many world models learn in pixel/video space. We went back to first principles and built the entire model stack in 3D.
Generate a 3D scene in seconds and expand it in real time. Create characters with motion built in, all through one general architecture.
Arpita Trisha retweeted
Synthesia's impossible interview keeps ramping up the higher you go, so whoever's sitting on the top score really earned it.
Try The Impossible Job interview for a chance to win $10K.
Brain teasers from interview processes at Google, Tesla etc, hosted by an AI avatar.
Do bad, get roasted. Do good, chance to win $$$.
Powered by our Interactive Avatar API.
Try here: synthesia.io/interview ✌️
Synthesia's impossible interview keeps ramping up the higher you go, so whoever's sitting on the top score really earned it.
Try The Impossible Job interview for a chance to win $10K.
Brain teasers from interview processes at Google, Tesla etc, hosted by an AI avatar.
Do bad, get roasted. Do good, chance to win $$$.
Powered by our Interactive Avatar API.
Try here: synthesia.io/interview ✌️
Arpita Trisha retweeted
Try The Impossible Job interview for a chance to win $10K.
Brain teasers from interview processes at Google, Tesla etc, hosted by an AI avatar.
Do bad, get roasted. Do good, chance to win $$$.
Powered by our Interactive Avatar API.
Try here: synthesia.io/interview ✌️
Arpita Trisha retweeted
AI has been given a mind. Now it’s getting an interface.
OpenUI makes AI responses feel less like text and more like actual products — cards, charts, maps, forms, and interactive UIs generated around what you need.
This is where AI UX gets really interesting. 🚀
🤝 Paid partnership
For 30 years, technology has moved in one direction: turning information into intuitive interfaces
Somehow, the smartest software we’ve ever built took us back to a textbox
We’ve spent the last 2 years at @openuidev trying to fix that. 6 months ago we open-sourced it: 10K+ GitHub stars, 6M+ downloads
ChatGPT shipped Intelligent UI and showed everyone what it looks like
Today we’re releasing OpenUI 1.0 bringing it to every other agent
AI has been given a mind. Now it’s getting an interface.
OpenUI makes AI responses feel less like text and more like actual products — cards, charts, maps, forms, and interactive UIs generated around what you need.
This is where AI UX gets really interesting. 🚀
🤝 Paid partnership
For 30 years, technology has moved in one direction: turning information into intuitive interfaces
Somehow, the smartest software we’ve ever built took us back to a textbox
We’ve spent the last 2 years at @openuidev trying to fix that. 6 months ago we open-sourced it: 10K+ GitHub stars, 6M+ downloads
ChatGPT shipped Intelligent UI and showed everyone what it looks like
Today we’re releasing OpenUI 1.0 bringing it to every other agent
Arpita Trisha retweeted
For 30 years, technology has moved in one direction: turning information into intuitive interfaces
Somehow, the smartest software we’ve ever built took us back to a textbox
We’ve spent the last 2 years at @openuidev trying to fix that. 6 months ago we open-sourced it: 10K+ GitHub stars, 6M+ downloads
ChatGPT shipped Intelligent UI and showed everyone what it looks like
Today we’re releasing OpenUI 1.0 bringing it to every other agent
Recruiters already rank candidates behind the scenes — now you can actually see where you stand.
Curious to see how I’d rank based on my title, experience, and location. 👀
Try it with @metix_ai
Every recruiter search creates a ranking. Most people have never seen one they're in.
Today you can see yours @metix_ai. Paste your LinkedIn and see where you rank against people with your title, in your area, at your experience level.
You don't need to sign up. go.metix.ai/recruiters-view/
Arpita Trisha retweeted
Every recruiter search creates a ranking. Most people have never seen one they're in.
Today you can see yours @metix_ai. Paste your LinkedIn and see where you rank against people with your title, in your area, at your experience level.
You don't need to sign up. go.metix.ai/recruiters-view/
Arpita Trisha retweeted
Getting traffic and users shouldn’t require juggling a dozen tools.
Dots turns your website into a team of smart agents that work to help you grow faster. 👀
Worth trying: @okara_ai
Introducing Dots for marketing
Drop in your website and it deploys a team of faster and smarter agents to help you get traffic, and users.
Try it now at okara.ai
Arpita Trisha retweeted
I enjoyed the founder’s perspective.
What stayed with me was the question of how machines can reason about actions and their consequences.
I have spent more than a decade studying causality. For me, causality is about understanding the rules that govern how actions change the world, and reasoning about their consequences before acting.
Scaling has made today's models remarkably capable at spotting patterns and predicting what comes next. A system that acts in the physical world needs more. It has to ask what will happen if it acts, check the outcome, and adapt when reality doesn't match the plan. In the physical world, trial and error can be costly or unsafe, and an early mistake can carry through the rest of a task.
When we started Aether AI, robotics was the obvious place to begin.
CRIS-0 is our first integrated system built on these ideas. It tracks the conditions that matter for a task, predicts how each action will change them, and replans when something unexpected happens. In our demo, that means taking a metal can off a plate before heating it, and telling an empty can from an unfinished drink while tidying a room.
Physical AI is just our first proving ground. We're building towards causal intelligence that also works for forecasting, scientific discovery, and more.
The technical write-up is linked in the comments.
Arpita Trisha retweeted
What if an AI learning app helped a child do more of the thinking?
That’s what interests me about @gakkuai. In its writing workshop, a learner prefers movies for the excitement. Raku asks them to weigh that against the pleasure of imagining scenes while reading, as their reasons take shape in a visual map.
Having the reasons in view gives the learner something to reconsider and explain. The useful outcome is a point of view they understand well enough to put into words.
See how Gakku makes room for that thinking: gakku.ai
What if an AI learning app helped a child do more of the thinking?
That’s what interests me about @gakkuai. In its writing workshop, a learner prefers movies for the excitement. Raku asks them to weigh that against the pleasure of imagining scenes while reading, as their reasons take shape in a visual map.
Having the reasons in view gives the learner something to reconsider and explain. The useful outcome is a point of view they understand well enough to put into words.
See how Gakku makes room for that thinking: gakku.ai
Arpita Trisha retweeted
I have spent more than a decade studying causality. For me, causality is about understanding the rules that govern how actions change the world, and reasoning about their consequences before acting.
Scaling has made today's models remarkably capable at spotting patterns and predicting what comes next. A system that acts in the physical world needs more. It has to ask what will happen if it acts, check the outcome, and adapt when reality doesn't match the plan. In the physical world, trial and error can be costly or unsafe, and an early mistake can carry through the rest of a task.
When we started Aether AI, robotics was the obvious place to begin.
CRIS-0 is our first integrated system built on these ideas. It tracks the conditions that matter for a task, predicts how each action will change them, and replans when something unexpected happens. In our demo, that means taking a metal can off a plate before heating it, and telling an empty can from an unfinished drink while tidying a room.
Physical AI is just our first proving ground. We're building towards causal intelligence that also works for forecasting, scientific discovery, and more.
The technical write-up is linked in the comments.
I enjoyed the founder’s perspective.
What stayed with me was the question of how machines can reason about actions and their consequences.
I have spent more than a decade studying causality. For me, causality is about understanding the rules that govern how actions change the world, and reasoning about their consequences before acting.
Scaling has made today's models remarkably capable at spotting patterns and predicting what comes next. A system that acts in the physical world needs more. It has to ask what will happen if it acts, check the outcome, and adapt when reality doesn't match the plan. In the physical world, trial and error can be costly or unsafe, and an early mistake can carry through the rest of a task.
When we started Aether AI, robotics was the obvious place to begin.
CRIS-0 is our first integrated system built on these ideas. It tracks the conditions that matter for a task, predicts how each action will change them, and replans when something unexpected happens. In our demo, that means taking a metal can off a plate before heating it, and telling an empty can from an unfinished drink while tidying a room.
Physical AI is just our first proving ground. We're building towards causal intelligence that also works for forecasting, scientific discovery, and more.
The technical write-up is linked in the comments.
