Nikhil Ramesh

I enjoy building software. Applied AI research. Bollywood music. Jazz.
Father. Husband. Son.
Founder Fellow @southpkcommons

[Aditya Agarwal @adityaag]

Today we’re announcing SPC Fund IV: $575M to help founders find their life’s work.

A decade of guiding founders through -1 to 0 brings @SPC to $2B in AUM and, more importantly, a 1200 member community of the world’s most talented technologists. We strongly believe that:

→ The person comes before the idea. At -1, generational founders are often busy reading, prototyping, discarding, and earning the right to their own conviction.
→ Ambition is social. The people you surround yourself with in the early days play a big part in inflecting your ambition.
→ Patience compounds. It is better to work on something you have deep conviction in rather than simply sprint at the first idea.

The quiet months we protect at the beginning have produced three funds in the top 10% of their vintages, and companies like @baseten, @GammaApp, @render, @GoodfireAI, @LumaLabsAI, and @tryprofound. We are proud to have worked with all these founders in their -1 phase and beyond.

While we defined -1 to 0, we're no longer bound by it. Fund IV allows us to partner with companies well beyond launch.

We're doing this now because we've never seen a wider gap between what's possible and what's being attempted. Intelligence is abundant. The cost of trying ideas keeps falling, and problems that seemed insurmountable a few years ago are quickly becoming solvable.

To our members, to the founders who trusted us before anyone else did, and to the LPs who had conviction in our own -1 journey: thank you. This milestone belongs to you as much as to us.

To the builders looking to find your life’s work: let’s get started.

[Nikhil Ramesh @nikutopian]

Absolutely fascinating project from @zan2434 and @eddiejiao_obj

[Zain Shah @zan2434]

Imagine every pixel on your screen, streamed live directly from a model. No HTML, no layout engine, no code. Just exactly what you want to see.

@eddiejiao_obj, @drewocarr and I built a prototype to see how this could actually work, and set out to make it real. We're calling it Flipbook. (1/5)

[Nikhil Ramesh @nikutopian]

Very interesting blog post. https://t.co/LcY4AaD6WL
I think it is a peek into how software engineering is going to evolve, for most of us in the near future.

[Aditya Agarwal @adityaag]

SPC turns 10 this year.

A decade ago, Ruchi and I just wanted a space for people who weren't ready to start a company but knew they wanted to build something.

We wanted to create a startup that would help other startups and founders.

The best founders aren't just creative. They're ferociously competitive. They want to win. Not because they care about status — because they're greedy.

Greedy to bring about the change they know must happen.

And so I am feeling greedy. We have had a great 10 years. But I want more.

I want more startups to exist and solve big problems. I want to work with more insane and wacky builders.

The problems get bigger. The stakes get higher. Your ambition expands to fill the space.

If you're in the -1 to 0 phase and feeling both terrified and alive: that's the signal. Don't ignore it.

Be more ambitious.

We at SPC certainly are going to be maximally aggressive and ambitious on our end.

It's Day Minus One. Let's build.

[Aditya Agarwal @adityaag]

They say attention is all you need.

So get more attention @spc.

Founder Fellowship applications due Feb 1.

[Nikhil Ramesh @nikutopian]

After years of overthinking it, I finally started writing publicly.
My first post is a reflection on simplicity—in philosophy, software engineering, and AI-driven system design.

If you’re curious, here’s the piece:
In Pursuit of Simplicity
https://t.co/BPsRjtDBZt

[Aditya Agarwal @adityaag]

What do we mean when we talk about talent density @spc?

This is the company you'll keep in the member residency. Why it's the best place to figure out what you want to build.

[Augustine Mavor-Parker @MavorParker]

At Vmax, we are automating the construction of RL environments and the post-training of agents. We are hiring members of technical staff and research fellows. Come join us in SF! (link to apply in comments).

[Nikhil Ramesh @nikutopian]

@henrythe9ths FUNDRAISE

[Nikhil Ramesh @nikutopian]

Evals are what turn prototypes into production agents. Test data, quality rubrics, metrics and looking at the data - are all critical to drive the "quality hill-climb" — I just prefer calling it opportunity analysis, not error analysis. :)

[Andrew Ng @AndrewYNg]

Readers responded with both surprise and agreement last week when I wrote that the single biggest predictor of how rapidly a team makes progress building an AI agent lay in their ability to drive a disciplined process for evals (measuring the system’s performance) and error analysis (identifying the causes of errors). It’s tempting to shortcut these processes and to quickly attempt fixes to mistakes rather than slowing down to identify the root causes. But evals and error analysis can lead to much faster progress. In this first of a two-part letter, I’ll share some best practices for finding and addressing issues in agentic systems.

Even though error analysis has long been an important part of building supervised learning systems, it is still underappreciated compared to, say, using the latest and buzziest tools. Identifying the root causes of particular kinds of errors might seem “boring,” but it pays off! If you are not yet persuaded that error analysis is important, permit me to point out:
- To master a composition on a musical instrument, you don’t only play the same piece from start to end. Instead, you identify where you’re stumbling and practice those parts more.
- To be healthy, you don’t just build your diet around the latest nutrition fads. You also ask your doctor about your bloodwork to see if anything is amiss. (I did this last month and am happy to report I’m in good health! 😃)
- To improve your sports team’s performance, you don’t just practice trick shots. Instead, you review game films to spot gaps and then address them.

To improve your agentic AI system, don’t just stack up the latest buzzy techniques that just went viral on social media (though I find it fun to experiment with buzzy AI techniques as much as the next person!). Instead, use error analysis to figure out where it’s falling short, and focus on that.

Before analyzing errors, we first have to decide what is an error. So the first step is to put in evals. I’ll focus on that for the remainder of this letter and discuss error analysis next week.

If you are using supervised learning to train a binary classifier, the number of ways the algorithm could make a mistake is limited. It could output 0 instead of 1, or vice versa. There is also a handful of standard metrics like accuracy, precision, recall, F1, ROC, etc. that apply to many problems. So as long as you know the test distribution, evals are relatively straightforward, and much of the work of error analysis lies in identifying what types of input an algorithm fails on, which also leads to data-centric AI techniques for acquiring more data to augment the algorithm in areas where it’s weak.

With generative AI, a lot of intuitions from evals and error analysis of supervised learning carry over — history doesn’t repeat itself, but it rhymes — and developers who are already familiar with machine learning and deep learning often adapt to generative AI faster than people who are starting from scratch. But one new challenge is that the space of outputs is much richer, so there are many more ways an algorithm’s output might be wrong.

Take the example of automated processing of financial invoices where we use an agentic workflow to populate a financial database with information from received invoices. Will the algorithm incorrectly extract the invoice due date? Or the final amount? Or mistake the payer address for the biller address? Or get the financial currency wrong? Or make the wrong API call so the verification process fails? Because the output space is much larger, the number of failure modes is also much larger.

Rather than defining an error metric ahead of time, it is therefore typically more effective to first quickly build a prototype, then manually examine a handful of agent outputs to see where it performs well and where it stumbles. This allows you to focus on building datasets and error metrics — sometimes objective metrics implemented in code, and sometimes subjective metrics using LLM-as-judge — to check the system’s performance in the dimensions you are most concerned about. In supervised learning, we sometimes tune the error metric to better reflect what humans care about. With agentic workflows, I find tuning evals to be even more iterative, with more frequent tweaks to the evals to capture the wider range of things that can go wrong.

I discuss this and other best practices in detail in Module 4 of the Agentic AI course on https://t.co/zGHUh1loPO that we announced last week. After building evals, you now have a measurement of your system’s performance, which provides a foundation for trying different modifications to your agent, as you can now measure what makes a difference. The next step is then to perform error analysis to pinpoint what changes to focus your development efforts on. I’ll discuss this further next week.

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

[Nikhil Ramesh @nikutopian]

If you are a parent looking for a device to help nurture your kids' curiosity, without them getting addicted to screens, check out Bunny!!

[Naman Jain @theBhulawat]

Introducing Bunny - world's first curiosity device for kids

It’s screenfree..it’s portable..

We raised $1M from @spc to reimagine how kids thrive in the age of AI, safely.

Comment 'Bunny'. Our nephew will pick 50 families that get it for free this holiday season…

[Nikhil Ramesh @nikutopian]

@theBhulawat @spc Awesome launch guys !! Go Bunny !!

[South Park Commons @spc]

1/ Uttara Labs
AGI, but make it: Accounting General Intelligence. @shastry & @anilkatti showed us Cadel AI—a virtual CFO—that validates contracts, checks budgets, & more https://t.co/2V5vxN9NU1

[Aditya Agarwal @adityaag]

The interesting opportunities now are the illegible ones. Asymmetric risk and reward. Crazy, heretical ideas.

Let us help you be as ambitious as the moment demands

Applications for the $1M @spc Founder Fellowship close August 3
https://t.co/6X8z6eh9QZ

[Aditya Agarwal @adityaag]

1/ We're on the precipice of the most fundamental computing revolution I've seen in my life. You should have FOMO. Don't waste it in local maxima.

The $1M @spc Founder Fellowship application is open. Here’s what it takes to build at venture-scale in the age of AI:

[Nikhil Ramesh @nikutopian]

@thegautam This approach seems fascinating @thegautam . Congrats on the great results.

[Aditya Agarwal @adityaag]

1/ We’re humbled to announce @spc Fund III: $275M to support exceptional founders from day -1.

Since 2016, we’ve had a simple thesis: greatness is more likely to emerge when high talent density meets high curiosity. That's why we focus on -1 to 0.

[Nikhil Ramesh @nikutopian]

@deedydas If you want to learn more about the legal aspects of this executive order, here is a post on https://t.co/JmZIPgRCji

https://t.co/eybcJCdQDZ

You can ask more law related questions on that link.

[Nikhil Ramesh @nikutopian]

Really good post by @chipro on Agents. A deep dive into planning, tool integration and the challenges.

https://t.co/stDcdUjeKt

[Nikhil Ramesh @nikutopian]

Researchers are predicting near-term AGI with transformative potential. But how soon will society catch up? A great exploration of cutting-edge AI, benchmarks, and agents by @emollick in this blog.

[Ethan Mollick @emollick]

There has been a definite shift in recent weeks where insiders in the various AI labs are suggesting that very intelligent AIs are coming very soon.

I wrote a bit about why this might be happening and what we can take away from their apparent confidence. https://t.co/6hVincKQ0o