Arya Chandra

Founding Growth Marketer @blinkdotnew (YC W22) | ex - Emergent (YC S24)

[Arya Chandra @AryacChandra]

Claude design this, claude design that.

This video was made with Remotion, Kling 3.0, ElevenLabs and Nano Banana Pro.

Under $50 in a week. 643k views on X, 200k on LinkedIn.

https://t.co/D8kz4ddieK

[Claude @claudeai]

Introducing Claude Design by Anthropic Labs: make prototypes, slides, and one-pagers by talking to Claude.

Powered by Claude Opus 4.7, our most capable vision model. Available in research preview on the Pro, Max, Team, and Enterprise plans, rolling out throughout the day.

[Arya Chandra @AryacChandra]

Another banger with Opus 5.5 and Hyperframes

[Arya Chandra @AryacChandra]

Sonnet 5.5 (Medium reasoning) + Hyperframes btw.

[Arya Chandra @AryacChandra]

@trymirage For motion graphics i would still use hyperframes. Ive been a remotion user for a long time but with opus 5.5 hyperframes is just too good. And ive also tried tesseract. Hyperframes is ahead

[Arya Chandra @AryacChandra]

@liutauras_liu @grok what is he talking about

[Arya Chandra @AryacChandra]

You'll actually be very surprised with how far you can get with just Opus 5.5 (High) by prompting "You are a product designer and this is your best work - this goes at the top of your portfolio. The personas for this website are [X, Y and Z]. Get really creative and create a modernized version of this website. I want to be very surprised when you deliver the final work."

[Arya Chandra @AryacChandra]

@stephanlivera Did you use hyperframes or remotion for this

[Arya Chandra @AryacChandra]

@ShubhAgrawal26 Lol i know who youre talking about

[Arya Chandra @AryacChandra]

@robin_faraj Here you go, these are my notes:

https://t.co/SPXqGY1Bdn

[Arya Chandra @AryacChandra]

My X feed is full of posts on Jev by @typesafeai and as a Growth Marketer i've been really confused about what it really is.

So I decided to study up on it - here are my notes:

> Jev is not an LLM. It is a decision classifier. For eg: if you point it to a Basketball and give it 3 options:
1. Orange
2. Yellow
3. Green

and ask it "Hey Jev what is the colour of this basketball?" It will return a confidence score for each colour like so:

Orange: 90%
Yellow: 5%
Green 5%

now, the options that you just sent it is called 'schema'.

> Jev can answer 3 question types:
1. Choice - picks from a set of options. Returns a probability for each option and an overall confidence score.

2. Score - rates an input against ordered levels, such as low, medium, and high. Returns a continuous score, the underlying distribution, and a confidence value.

3. Noul - answers a yes or no question. Returns the probability that a statement is true.

> traditional autoregressive models generate tokens sequentially but Jev can sample parallely. Now what does that mean:

Lets say you ask ChatGPT 4 questions regarding a support ticket :
1. Is it urgent? 2. Should we escalate? 3. Which department? 4. Is a refund needed?

ChatGPT will respond to you one by one:

Is it Urgent -> Yes -> Should we escalate -> Maybe -> Which department -> Billing -> Is refund need? -> Maybe

Jev will answer all 4 questions parallely because of the scoring system:

Customer message
│
├── Urgent? → 94%
├── Escalate? → 81%
├── Department? → Billing (97%)
└── Refund needed? → 89%

> What are the Use Cases of Jev: Typesafe says it is most appropriate for cases where decisions are required instead of open ended generation.

Imagine the support ticket use case:

It can classify a customer message into these categories and provide further direction for a traditional LLM like GPT to take over. Like if Jev decides an immediate reply is required (99.8%) it gets passed to GPT to generate the reply in text form to be sent to the customer.

Another use case I can think of is lead classification: You get many inbound leads through your form -> Jev decides which leads are high intent leads and passes it to GPT -> GPT can then send an email to the high intent lead.

[Arya Chandra @AryacChandra]

@adamc0dez get on their waitlist, mine took less than 24hrs to get approved

[Arya Chandra @AryacChandra]

@ilavanyajain Wrote about it here:

https://t.co/SPXqGY1Bdn

[Arya Chandra @AryacChandra]

My X feed is full of posts on Jev by @typesafeai and as a Growth Marketer i've been really confused about what it really is.

So I decided to study up on it - here are my notes:

> Jev is not an LLM. It is a decision classifier. For eg: if you point it to a Basketball and give it 3 options:
1. Orange
2. Yellow
3. Green

and ask it "Hey Jev what is the colour of this basketball?" It will return a confidence score for each colour like so:

Orange: 90%
Yellow: 5%
Green 5%

now, the options that you just sent it is called 'schema'.

> Jev can answer 3 question types:
1. Choice - picks from a set of options. Returns a probability for each option and an overall confidence score.

2. Score - rates an input against ordered levels, such as low, medium, and high. Returns a continuous score, the underlying distribution, and a confidence value.

3. Noul - answers a yes or no question. Returns the probability that a statement is true.

> traditional autoregressive models generate tokens sequentially but Jev can sample parallely. Now what does that mean:

Lets say you ask ChatGPT 4 questions regarding a support ticket :
1. Is it urgent? 2. Should we escalate? 3. Which department? 4. Is a refund needed?

ChatGPT will respond to you one by one:

Is it Urgent -> Yes -> Should we escalate -> Maybe -> Which department -> Billing -> Is refund need? -> Maybe

Jev will answer all 4 questions parallely because of the scoring system:

Customer message
│
├── Urgent? → 94%
├── Escalate? → 81%
├── Department? → Billing (97%)
└── Refund needed? → 89%

> What are the Use Cases of Jev: Typesafe says it is most appropriate for cases where decisions are required instead of open ended generation.

Imagine the support ticket use case:

It can classify a customer message into these categories and provide further direction for a traditional LLM like GPT to take over. Like if Jev decides an immediate reply is required (99.8%) it gets passed to GPT to generate the reply in text form to be sent to the customer.

Another use case I can think of is lead classification: You get many inbound leads through your form -> Jev decides which leads are high intent leads and passes it to GPT -> GPT can then send an email to the high intent lead.

[Arya Chandra @AryacChandra]

@irabukht How will you use Jev to produce pages? Its a decision classifier

[Arya Chandra @AryacChandra]

@typesafeai Some materials that I went through:
1. https://t.co/CNhTdKyQF9
2. https://t.co/Cn0gdgV0vc
3. https://t.co/SwgTlwHZu6

Hope this helps!

[Arya Chandra @AryacChandra]

My X feed is full of posts on Jev by @typesafeai and as a Growth Marketer i've been really confused about what it really is.

So I decided to study up on it - here are my notes:

> Jev is not an LLM. It is a decision classifier. For eg: if you point it to a Basketball and give it 3 options:
1. Orange
2. Yellow
3. Green

and ask it "Hey Jev what is the colour of this basketball?" It will return a confidence score for each colour like so:

Orange: 90%
Yellow: 5%
Green 5%

now, the options that you just sent it is called 'schema'.

> Jev can answer 3 question types:
1. Choice - picks from a set of options. Returns a probability for each option and an overall confidence score.

2. Score - rates an input against ordered levels, such as low, medium, and high. Returns a continuous score, the underlying distribution, and a confidence value.

3. Noul - answers a yes or no question. Returns the probability that a statement is true.

> traditional autoregressive models generate tokens sequentially but Jev can sample parallely. Now what does that mean:

Lets say you ask ChatGPT 4 questions regarding a support ticket :
1. Is it urgent? 2. Should we escalate? 3. Which department? 4. Is a refund needed?

ChatGPT will respond to you one by one:

Is it Urgent -> Yes -> Should we escalate -> Maybe -> Which department -> Billing -> Is refund need? -> Maybe

Jev will answer all 4 questions parallely because of the scoring system:

Customer message
│
├── Urgent? → 94%
├── Escalate? → 81%
├── Department? → Billing (97%)
└── Refund needed? → 89%

> What are the Use Cases of Jev: Typesafe says it is most appropriate for cases where decisions are required instead of open ended generation.

Imagine the support ticket use case:

It can classify a customer message into these categories and provide further direction for a traditional LLM like GPT to take over. Like if Jev decides an immediate reply is required (99.8%) it gets passed to GPT to generate the reply in text form to be sent to the customer.

Another use case I can think of is lead classification: You get many inbound leads through your form -> Jev decides which leads are high intent leads and passes it to GPT -> GPT can then send an email to the high intent lead.

[Arya Chandra @AryacChandra]

@commas The biggest problem today is to find affiliate marketers. Does your platform solve that?

[Arya Chandra @AryacChandra]

@gouthamjay8 How are you growing famewall today? Whats the #1 channel for you? Is it still at 6k mrr or has it increased/decreased.

[Arya Chandra @AryacChandra]

This ad cost $50 and 4 days to make - cost of human taste.

[Arya Chandra @AryacChandra]

Imagine Instinct but on your alexa device. I would definitely pay for it.

I too have been thinking why neither amazon or google are building something like this - keep it on a paid plan like google one or something and everyone will pay for it.

[Julian Lehr @julianlehr]

Amazon and Google sold ~600M+ Alexa and Nest devices that could barely do anything except playing music and setting timers (and even that worked reliably maybe 30% of the time). Now we have effectively AGI-level intelligence, but nobody seems to be building smart speakers around it. Why?

[Arya Chandra @AryacChandra]

@Remotion thanks!

[Arya Chandra @AryacChandra]

I wish @Remotion had a Figma like option where I could customize the positioning of elements manually and then ask agent to apply it in code.

Currently have to design in Figma and then I have specify each and every detail to my agent. Is there a workaround to this? Can Paper MCP help?

[Arya Chandra @AryacChandra]

@Remotion Do you have any documentation for this?