Bias for Action | AI/ML | UT Austin | IIT Delhi
Austin, TX
Joined November 2025
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Prashant Chandel retweeted
Most people treat a zero-failure record as a flex. In computer science, there is a sharper perspective famously known as "Umeshisms."
During his PhD at UC Berkeley under quantum computing pioneer Umesh Vazirani, computer scientist Scott Aaronson noted an aphorism his advisor loved to repeat:
"If you have never missed a flight, you are spending too much time in airports."
The math logic is simple: a 0% miss rate often means you are overpaying in friction, prep time, and missed opportunity. Occasional failure is often the price of a better expected-value tradeoff.
Aaronson named these "Umeshisms" in a 2005 blog post and penned a few sequels in the same spirit:
- "If you have never been rejected, you are not asking enough."
- "If you never cut yourself while shaving, you are not shaving close enough."
The lesson is recognizing when extreme risk aversion costs you more than the risk itself.
Prashant Chandel retweeted
Many people expect that current AI is ready to cure cancer and do breakthrough new science. ARC-AGI-3 envs are like a microcosm of the scientific method: you must observe a tiny world, form a theory of how it works, test it, iterate until correct. Over the course of a few minutes.
If AI can't do it in an ultra-simple, ultra-small scale setting that is explicitly designed to be as accessible as possible, I expect there are a few steps missing until AI can crack the nature of reality.
"2+ people can do it out of an unfiltered pool of 10 people that might well be a below-average sample" is not the sign of a insurmountable challenge. It's not certainly where I would set the bar for "super intelligence".
ASI is when AI is better than *every single human* -- for instance we have ASI for chess and Go today.
Prashant Chandel retweeted
I interviewed Sundar Pichai, and he made an interesting prediction for where we'll be in 3 years:
"Any content can go from any modality to any modality. It doesn't matter how the content was originally created -- people can consume it in many different forms."
"I'm very confident three years from now you will look back at what we have today and realize it was pretty primitive. It's like looking at a flip phone."
For example, YouTube clips become blogs, newsletters become podcasts without recording.
In this world, consumers would choose the format, and creators just supply the ideas, brand, and personality.
Net-net, I think this is a good thing for content.
It kills the "more content" strategy because when anyone can produce everything, volume stops being an advantage. So, the creators who win will focus on quality.
People confuse slop with AI, but they're not the same thing. Plenty of creators already use AI in their workflows and get zero hate -- people just don't realize it. Also, YouTube's new policies target slop, not AI.
If the content is good, people don't care how it was made.
Prashant Chandel retweeted
Life really does get better when you follow your obsession and block out every other goal.
Prashant Chandel retweeted
Soon, one teenager in a bedroom will have access to more intellectual labor than the entire Manhattan Project.
Prashant Chandel retweeted
Study hard what interests you the most in the most undisciplined, irreverent and original manner possible.
Prashant Chandel retweeted
INSTEAD OF WATCHING NETFLIX TONIGHT.
Spend 1 hour with this.
Claude AI FULL COURSE that teaches you how to BUILD and AUTOMATE anything.
The people who watch this tonight will wake up tomorrow with a skill that most people will not have in 2 years.
The people who skip it will still be watching Netflix next year wondering why nothing in their life has changed.
Your call.
Prashant Chandel retweeted
The way you use Google Maps will never be the same.
Introducing Ask Maps, a new way for you to ask anything about anywhere, with help from the latest Gemini models. Rolling out now on Android and iOS in the US and India.
Prashant Chandel retweeted
The most impressive people I know spent their time with their head down getting shit done for a long, long time.
Prashant Chandel retweeted
GOODBYE CANVA.
This AI agent builds Gamma investor decks automatically with live market research, competitive analysis and financial projections.
Here's the problem:
Building investor decks is brutal. You're Googling market sizes, stalking competitors, guessing at financials and dragging boxes around in Canva.
Takes 2-3 weeks. Costs $5K-$15K if you hire an agency. And it's outdated by the time you pitch.
This AI agent solves it:
→ Submit product details via form
→ Perplexity runs 3 parallel research calls (TAM/SAM/SOM, competitors, GTM)
→ Claude structures the pitch narrative across 18 slides
→ Gamma generates a branded, professional deck
→ Delivered to your inbox with PDF + edit link
No manual research. No design skills. No waiting on agencies.
What you get:
→ Problem/solution framing that hooks investors
→ Market size with real data and sources
→ Competitive landscape with differentiation matrix
→ Business model with unit economics
→ Go-to-market strategy with milestones
→ Team section and funding ask
All data current. All slides branded. All in ~4 minutes.
Want the complete n8n AI Agent workflow?
RT + Comment "LAUNCH" + Like
(Must be following so I can DM)
Prashant Chandel retweeted
Multi-agent AI systems are eating single-agent architectures.
But most teams have no idea how to build them.
As agent systems get more complex, we're seeing a clear shift from single-agent architectures (one agent handles all tasks) to multi-agent architectures that distribute work across specialized components.
The trade-off is real though: multi-agent systems can handle way more complex workflows, but they introduce serious coordination challenges.
So what does a well-structured multi-agent system actually look like? There are four key components:
𝗦𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗼𝗿𝘀
The orchestration layer handles planning, routes queries to the right specialized agents, and refines queries as needed. Think of this as the air traffic controller of your agent system.
𝗦𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘇𝗲𝗱 𝗔𝗴𝗲𝗻𝘁𝘀
Task-specific agents that each do one thing well:
• Query rewriters that reformulate user input
• Data collection selectors that choose the right knowledge sources
• Retrievers that fetch relevant information
• Tool routers that pick the appropriate tools
• Answer synthesizers that generate final responses
𝗠𝗲𝗺𝗼𝗿𝘆
Both short-term (working memory, compression) and long-term (vector databases for episodic and factual storage). This is critical because of the context window challenge - agents need to constantly decide what information stays active, what gets compressed, and what's stored externally for later retrieval.
𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 𝗮𝗻𝗱 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗦𝗼𝘂𝗿𝗰𝗲𝘀
The actual tools, APIs, vector databases, and external knowledge sources that agents can access and interact with.
What makes this architecture powerful is how these components work together. The supervisor coordinates specialized agents, which use memory to maintain state and context, while accessing various capabilities to actually get things done.
But here's the thing - this added complexity means you need robust orchestration. Each specialized agent needs to know its role, communicate effectively with other agents, and handle failures gracefully. The coordination overhead is non-trivial.
Want to make multi-agent systems actually work?
Get your copy of this free Context Engineering Guide and see how to structure queries, memory, agents, and tools for real-world LLMs 🧡
weaviate.io/ebooks/the-conte…
Prashant Chandel retweeted
What is the best documentary or docuseries you've recently watched on @netflix? Or in the last year?
