Saurabh Bora

Engineering, Tech Enthusiast who loves to solve problems using technology!

[Justin Skycak @justinskycak]

You cannot optimize what you haven’t mastered manually. You haven’t earned the right to automate until you’ve felt the friction of the manual labor.

[Satish Bora @skbtwitt]

@SakalMediaNews is highlighting citizen issues like traffic, potholes, signal discipline and road safety.
Faced 1hr of traffic for just 3- 4 km on Nashik-Pune road.
Maharashtra can build a real-time civic command system. Happy to contribute my IT, AI and product experience.

[taoki @justalexoki]

can't believe i spent my whole life becoming Good At Computer only for Computer to become Better At Computer

[Sanjog bora @SanjogBora]

Got into Round 2 of VibeCon. Here’s my pitch!
Builder. Designer. Vibecoder.
@emergentlabs @mukundjha #emergent #yc #vibecon

[Stitch by Google @stitchbygoogle]

Meet the new Stitch, your vibe design partner.

Here are 5 major upgrades to help you create, iterate and collaborate:
🎨 AI-Native Canvas
🧠 Smarter Design Agent
🎙️ Voice
⚡️ Instant Prototypes
📐 Design Systems and DESIGN.md

Rolling out now. Details and product walkthrough video in 🧵

[Andrew Ng @AndrewYNg]

I’d like to share a tip for getting more practice building with AI — that is, either using AI building blocks to build applications or using AI coding assistance to create powerful applications quickly: If you find yourself with only limited time to build, reduce the scope of your project until you can build something in whatever time you do have.

If you have only an hour, find a small component of an idea that you're excited about that you can build in an hour. With modern coding assistants like Anthropic’s Claude Code (my favorite dev tool right now), you might be surprised at how much you can do even in short periods of time! This gets you going, and you can always continue the project later.

To become good at building with AI, most people must (i) learn relevant techniques, for example by taking online AI courses, and (ii) practice building. I know developers who noodle on ideas for months without actually building anything — I’ve done this too! — because we feel we don’t have time to get started. If you find yourself in this position, I encourage you to keep cutting the initial project scope until you identify a small component you can build right away.

Let me illustrate with an example — one of my many small, fun weekend projects that might never go anywhere, but that I’m glad I did.

Here’s the idea: Many people fear public speaking. And public speaking is challenging to practice, because it's hard to organize an audience. So I thought it would be interesting to build an audience simulator to provide a digital audience of dozens to hundreds of virtual people on a computer monitor and let a user practice by speaking to them.

One Saturday afternoon, I found myself in a coffee shop with a couple of hours to spare and decided to give the audience simulator a shot. My familiarity with graphics coding is limited, so instead of building a complex simulator of a large audience and writing AI software to simulate appropriate audience responses, I decided to cut scope significantly to (a) simulating an audience of one person (which I could replicate later to simulate N persons), (b) omitting AI and letting a human operator manually select the reaction of the simulated audience (similar to Wizard of Oz prototyping), and (c) implementing the graphics using a simple 2D avatar.

Using a mix of several coding assistants, I built a basic version in the time I had. The avatar could move subtly and blink, but otherwise it used basic graphics. Even though it fell far short of a sophisticated audience simulator, I am glad I built this. In addition to moving the project forward and letting me explore different designs, it advanced my knowledge of basic graphics. Further, having this crude prototype to show friends helped me get user feedback that shaped my views on the product idea.

I have on my laptop a list of ideas of things that I think would be interesting to build. Most of them would take much longer than the handful of hours I might have to try something on a given day, but by cutting their scope, I can get going, and the initial progress on a project helps me decide if it’s worth further investment. As a bonus, hacking on a wide variety of applications helps me practice a wide range of skills. But most importantly, this gets an idea out of my head and potentially in front of prospective users for feedback that lets the project move faster.

[Original text: https://t.co/UP6arTWAdV ]

[Saurabh Bora @sausbora]

I always had multiple ideas tinkering in my brain but since I would never dig deeper into the idea it was very difficult to convince my mind to work towards that. But once you have the overall flow chalked out and some sort of concreteness it becomes easy to navigate & execute.

[Andrew Ng @AndrewYNg]

AI’s usefulness in a wide variety of applications creates many opportunities for entrepreneurship. Here, I’d like to share what might be a counter-intuitive best practice that I’ve learned from leading AI Fund, a venture studio that has built dozens of startups with extraordinary entrepreneurs. When it comes to building AI applications, we strongly prefer to work on a concrete idea, meaning a specific product envisioned in enough detail that we can build it for a specific target user.

Some design philosophies say you shouldn’t envision a specific product from the start. Instead, they recommend starting with a problem to be solved and then carefully studying the market before you devise a concrete solution. There’s a reason for this: The more concrete or precise your product specification, the more likely it is to be off-target. However, I find that having something specific to execute toward lets you go much faster and discover and fix problems more rapidly along the way. If the idea turns out to be flawed, rapid execution will let you discover the flaws sooner, and this knowledge and experience will help you switch to a different concrete idea.

One test of concreteness is whether you’ve specified the idea in enough detail that a product/engineering team could build an initial prototype. For example, “AI for livestock farming” is not concrete; it’s vague. If you were to ask an engineer to build this, they would have a hard time knowing what to build. Similarly, “AI for livestock tracking in farming” is still vague. There are so many approaches to this that most reasonable engineers wouldn’t know what to build. But “Apply face recognition to cows so as to recognize individual cows and monitor their movement on a farm” is specific enough that a good engineer could quickly choose from the available options (for example, what algorithm to try first, what camera resolution to use, and so on) to let us relatively efficiently assess:
- Technical feasibility: For example, do face recognition algorithms developed for human faces work for cows? (It turns out that they do!)
- Business feasibility: Does the idea add enough value to be worth building? (Talking to farmers might quickly reveal that solutions like RFID are easier and cheaper.)

Articulating a concrete idea — which is more likely than a vague idea to be wrong — takes more courage. The more specific an idea, the more likely it is to be a bit off, especially in the details. The general area of AI for livestock farming seems promising, and surely there will be good ways to apply AI for livestock. In contrast, specifying a concrete idea, which is much easier to invalidate, is scary.

The benefit is that the clarity of a specific product vision lets a team execute much faster. One strong predictor of how likely a startup is to succeed is the speed with which it can get stuff done. This is why founders with clarity of vision tend to be desired; clarity helps drive a team in a specific direction. Of course, the vision has to be a good one, and there’s always a risk of efficiently building something that no one wants to buy! But a startup is unlikely to succeed if it meanders for too long without forming a clear, concrete vision.

Building toward something concrete — if you can do so in a responsible way that doesn’t harm others — lets you get critical feedback more efficiently and, if necessary, switch directions sooner. (An earlier letter in The Batch also discussed when it’s better to go with a “Ready, Fire, Aim” approach to projects.) One factor that favors this approach is the low cost of experimenting and iterating. This is increasingly the case for many AI applications, but perhaps less so for deep-tech AI projects.

I realize that this advice runs counter to common practice in design thinking, which warns against leaping to a solution too quickly, and instead advocates spending time understanding end-users, deeply understanding their problems, and brainstorming a wide range of solutions. If you’re starting without any ideas, then such an extended process can be a good way to develop good ideas. Further, keeping ideas open-ended can be good for curiosity-driven research, where investing to pursue deep tech with only a vague direction in mind can pay huge dividends over the long term.

If you are thinking about starting a new AI project, consider whether you can come up with a concrete vision to execute toward. Even if the initial vision turns out not to be quite right, rapid iteration will let you discover this sooner, and the learnings will let you switch to a different concrete idea.

[Original text: https://t.co/8CkePKyBJ4 ]

[Saurabh Bora @sausbora]

🤯🤯

[Boston Dynamics @BostonDynamics]

We promise this is not a person in a bodysuit. https://t.co/S9FgfpqvrW

[Rowan Cheung @rowancheung]

Historic day in AI.

First, OpenAI changed the AI video world and Google made enormous progress in LLM capabilities.

Then we saw huge developments from Meta, Slack, CodeSignal, X/Grok, Microsoft, University of Michigan, LangChain, and Magic.

Here's EVERYTHING you need to know:

[Saurabh Bora @sausbora]

@TigranSloyan This looks amazing, eagerly waiting to try out and learn stuff through this!

[Saurabh Bora @sausbora]

History is created, India becomes the first country to land on the south pole of the moon🚀🌚
Congratulations to the team @isro , really a proud moment!

[Saurabh Bora @sausbora]

Couldn't agree more!! Absolutely important to have your basics & fundamentals in place.

[Arpit Bhayani @arpit_bhayani]

Harsh reality: It will take you 10 years to realize how great our college curriculum is and how critical the core Computer Science subjects are.

Do not get misguided by these "EdTech cum SalesTech" startups and influencers. Everything at scale just boils down to

👇

[Saurabh Bora @sausbora]

My Flipkart axis bank credit card has arrived but still the status in app is not updated and also I haven't received the 1000 Rs voucher @Flipkart @flipkartsupport

[Saurabh Bora @sausbora]

@pratik_ratadiya Congratulations bro 👏🎉

[Romeen Sheth @RomeenSheth]

Early career years are painful.

You feel like an idiot 98% of the time - lost, confused and insecure.

I wish I had a cheat sheet of principles for my first job.

So I put one together.

Here are 20 things about building a career I wish I knew sooner:

[Saurabh Bora @sausbora]

Some great insights to ponder on before entering the new year!

[Chris Hladczuk @chrishlad]

The 10 most powerful insights I discovered this year:

[Saurabh Bora @sausbora]

The Clean Coder – Reading Experience https://t.co/iIoGJfnXHy via @sausbora

[Saurabh Bora @sausbora]

@tanaypratap Hooks will really get you hooked!

[Saurabh Bora @sausbora]

Great article for beginners who have just started to learn in depth about how should a system be designed as compared to directly moving towards the so called "microservices architecture"
https://t.co/3phtxDeBuk

[Saurabh Bora @sausbora]

@SimonHoiberg Spread operator as it avoids the need of reassignment.