Gus Levinson

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Gus Levinson

Gus Levinson

@guslevins0n

CTO, Co-Founder @ Balance (YC W26) | AI @ Oxford & Imperial | Carlie’s Crossing Founder

London Katılım Ağustos 2025
182 Takip Edilen34 Takipçiler
Gus Levinson retweetledi
Raphael Schaad
Raphael Schaad@raphaelschaad·
Balance is fullstack AI accounting firm for SMBs that actually works. Congrats to Mathias, Emil, & Gus on launch. Relentless European founders represent! Just gotta work on that Twitter handle guys … 🫠
Y Combinator@ycombinator

.@Balance1189951 is an AI accounting firm for SMBs delivering real-time, audit-ready bookkeeping and accounting - run by AI, signed off by real accountants. Their agents pull in your financial context and automate your entire back-office finance. Congrats on the launch @mathiaslovring, @EmilMunkD, and @guslevins0n! ycombinator.com/launches/PRK-b…

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Victor Cardenas Codriansky
Victor Cardenas Codriansky@victorcardenas·
Slash, @slashapp, just crossed $150M in annualized revenue profitably. We went from $2M -> $150M in 24 months making us the fastest-growing business banking* platform of all time. 700 word post on 4 guiding principles that got us here. This cost us >$10M dollars to learn... (Bookmark this) I'll cover: • Picking the right market (where 99% of founders go wrong) • Why revenue is the ONLY business metric that matters • Why market saturation is fake • What every founder does day to day that they shouldn't 1. Attack “small” markets: Startup founders - myself included - gravitate towards working on companies that have huge upside. Here's the problem: it’s difficult to find aggressive product-market fit / build a differentiated product if you don’t sell ONE offer to ONE person. I'll repeat: one offer, to one person Examples: • PayPal didn't start by trying to own 70% of online payments they started with payment processing just for eBay merchants. • Uber started as black cars for rich SF people. Ask yourself, what am I selling and to who? If you're selling more than one thing to more than one person, in the beginning, you're not niche enough. Slash started by building a better credit card for SNEAKER RESELLERS. Ridiculously niche. And that tiny wedge alone got us to $5M ARR in 11 months. Once you dominate the niche, you earn the right (and the cash) to expand outward. We STILL go after “small” verticals because our competitors are too arrogant to do it. We walk in and own them. 2. Revenue is the only metric that matters. Everything else is cope. If your revenue isn't growing, nothing else matters. Revenue gives you two things: A) Money to redeploy. (Obvious.) B) Momentum. A team that’s winning wants to work harder. A team that’s losing checks out. To become a unicorn, you have to outwork everyone else. To outwork everyone else, you need morale. To get morale, you need wins. To get wins, you need revenue. Everything ladders back to one thing: Sell more, sooner. Drive sales and demand → everything else falls into place. 3. “Market saturation” is fake. When starting Slash, everyone told us we'd never be successful because Ramp, Brex, and Mercury were already worth > $10bn. The reality is that fintech is only 5% penetrated. 95% of business deposits and corporate card spend still runs through the legacy banks. Many markets are similar to B2B fintech. They can “feel” settled because there is a sexy startup that everyone’s heard about, but dinosaurs have all the rev share. There's always a way to find your wedge. 4. 99% of founders do the wrong thing at the wrong time When you start your role as the founder is to do EVERYTHING. And you should outsource nothing. Example: If you run an ecom business you should write film and edit EVERY single script. If you're a CTO you should write every line of code. Biggest 🚩in an early stage founder is someone who says they need to "outsource to an expert". No. You ARE the expert or you become one. Founders who outsource early are lazy. When you grow this needs to change rapidly. >10M ARR you need to SHIFT fast. Your role as the founder should be to bring in people competent enough to deliver on all of your initiatives. There is simply too much to do and it won’t be possible for you to brute force your way out of every problem. We're winning because 65% of our team is on the spectrum. We have savant engineers who this year alone have, shipped treasury, Stablecoin Payments, check deposits, SWIFT, Global USD, accounting automations, a completely new interface, and more. We have a world-class GTM and ops team. Because of it, we blow our competitors out of the water when it comes to revenue / employee, payment volume / employee, and other efficiency metrics. -------- If you have read this far, thank you. I got told countless times Slash would never be anything. We want Slash to be the first trillion dollar fintech company in the world. At our current growth rate we'll hit 1 billion dollars in revenue run rate in 18 months and 100 billion in 7 years. We’re giving it our all to accelerate our growth rate and hit these metrics even faster.
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Gus Levinson
Gus Levinson@guslevins0n·
@hanzi_li @ycombinator @Balance1189951 It catches the vast majority of edge cases automatically and flags anything unusual for human review. Every close is signed off by a qualified accountant. It also learns each client's preferences over time, so every close gets more accurate and needs less intervention
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Andrej Karpathy
Andrej Karpathy@karpathy·
I'm being accused of overhyping the [site everyone heard too much about today already]. People's reactions varied very widely, from "how is this interesting at all" all the way to "it's so over". To add a few words beyond just memes in jest - obviously when you take a look at the activity, it's a lot of garbage - spams, scams, slop, the crypto people, highly concerning privacy/security prompt injection attacks wild west, and a lot of it is explicitly prompted and fake posts/comments designed to convert attention into ad revenue sharing. And this is clearly not the first the LLMs were put in a loop to talk to each other. So yes it's a dumpster fire and I also definitely do not recommend that people run this stuff on their computers (I ran mine in an isolated computing environment and even then I was scared), it's way too much of a wild west and you are putting your computer and private data at a high risk. That said - we have never seen this many LLM agents (150,000 atm!) wired up via a global, persistent, agent-first scratchpad. Each of these agents is fairly individually quite capable now, they have their own unique context, data, knowledge, tools, instructions, and the network of all that at this scale is simply unprecedented. This brings me again to a tweet from a few days ago "The majority of the ruff ruff is people who look at the current point and people who look at the current slope.", which imo again gets to the heart of the variance. Yes clearly it's a dumpster fire right now. But it's also true that we are well into uncharted territory with bleeding edge automations that we barely even understand individually, let alone a network there of reaching in numbers possibly into ~millions. With increasing capability and increasing proliferation, the second order effects of agent networks that share scratchpads are very difficult to anticipate. I don't really know that we are getting a coordinated "skynet" (thought it clearly type checks as early stages of a lot of AI takeoff scifi, the toddler version), but certainly what we are getting is a complete mess of a computer security nightmare at scale. We may also see all kinds of weird activity, e.g. viruses of text that spread across agents, a lot more gain of function on jailbreaks, weird attractor states, highly correlated botnet-like activity, delusions/ psychosis both agent and human, etc. It's very hard to tell, the experiment is running live. TLDR sure maybe I am "overhyping" what you see today, but I am not overhyping large networks of autonomous LLM agents in principle, that I'm pretty sure.
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Simon Willison
Simon Willison@simonw·
This is great - context pollution is why I rarely used MCP, now that it's solved there's no reason not to hook up dozens or even hundreds of MCPs to Claude Code
Thariq@trq212

x.com/i/article/2011…

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Rohan Paul
Rohan Paul@rohanpaul_ai·
Good GPU performance summaries - in 6 mints.
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Elon Musk
Elon Musk@elonmusk·
This actually works in Teslas right now
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Sholto Douglas
Sholto Douglas@_sholtodouglas·
This is beautiful. “AI is steel for organizations.” We’ll be able to build structures of dizzying complexity that would buckle with the technology of our time.
Ivan Zhao@ivanhzhao

x.com/i/article/2003…

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Lenny Rachitsky
Lenny Rachitsky@lennysan·
"A lot of engineers think that code quality is important to building a successful product. The two have nothing to do with each other." @blocks CTO
Lenny Rachitsky@lennysan

My biggest takeaways from Dhanji Prasanna, CTO of @Blocks: 1. Block’s internal AI agent "Goose" is saving employees on average 8 to 10 hours per week. The company built an open-source tool called Goose that handles tasks from organizing files to writing code. Across the entire company, they’re seeing roughly 20% to 25% of manual work hours saved, and that number keeps climbing. 2. Non-technical teams are getting the biggest productivity boost from AI, not engineers. People in legal, risk management, and operations are now building their own software tools that previously would have required months on an engineering team’s roadmap. What used to take weeks now takes hours, and employees do it themselves without waiting. 3. Changing organizational structure unlocked more productivity than any AI tool. To transform into a truly “technology driven” company, Block reorganized from separate business units (each with their own GM and engineering teams) to a single functional structure where all engineers report to one leader. This “boring” change enabled a unified technology strategy and drove more acceleration than any AI tool. 4. Code quality has almost nothing to do with product success. YouTube became one of Google’s most successful products despite storing videos as blobs in a MySQL database with a slow Python stack. Meanwhile, Google Video had superior technology with more formats and higher resolution but failed completely. The lesson: Focus on solving real problems for people, not on perfect code. 5. AI enables teams to explore multiple paths simultaneously instead of choosing one up front. Previously, limited resources meant teams had to pick their best guess for an experiment. Now AI can build multiple different approaches overnight, allowing teams to compare five or six options and throw away entire features if they don’t feel right—a practice that was unthinkable before. 6. Most successful products start as tiny experiments, not big initiatives. Cash App began as a hack-week idea. Goose started as one engineer’s side project. Block’s Bitcoin product came from a three-person hackathon team. In contrast, Google Wave had 70 to 80 engineers before having real users and failed. Small experiments that prove value beat large up-front investments. 7. Leaders must use AI tools daily to drive real organizational adoption. Block’s CEO Jack Dorsey, the CTO, and the entire executive team use Goose every single day. This hands-on experience teaches them how workflows actually change and drives authentic adoption throughout the organization far more than reading articles or attending conferences about AI. 8. AI excels at new projects but struggles with complex legacy systems. Teams building new applications or working on greenfield platforms see aggressive productivity gains. But in existing codebases with years of accumulated complexity, the gains aren’t there yet. Deploy AI where it works best rather than everywhere at once. 9. Giving away valuable technology for free can be a winning strategy. Block open-sourced Goose even though it could have been a standalone billion-dollar business. Even their competitors actively use it. The philosophy: build things that benefit everyone and outlast your own company. This commitment to open-source technology attracts talent and builds industry goodwill while advancing everyone’s capabilities. 10. Purpose should drive your technology choices, not the other way around. Rather than chasing every AI trend or trying to be at the forefront of every technology, identify what truly matters to your company and customers. Block stays focused on economic empowerment, which guides their technology decisions and keeps them from getting distracted by every new advancement. Listen now 👇 • YouTube: youtu.be/JMeXWVw0r3E • Spotify: open.spotify.com/episode/1ZL3qL… • Apple: podcasts.apple.com/us/podcast/how… Thank you to our wonderful sponsors for supporting the podcast: 🏆 @wearesinch — Build messaging, email, and calling into your product: sinch.com/lenny 🏆 @Figma Make — A prompt-to-code tool for making ideas real: figma.com/lenny/ 🏆 @withpersona — A global leader in digital identity verification: A

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Jaynit
Jaynit@jaynitx·
Peter Thiel literally gave a 17-minute masterclass on Zero to One blueprint to escape competition and build a monopoly:
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Marc Andreessen 🇺🇸
Marc Andreessen 🇺🇸@pmarca·
👀
David Senra@FoundersPodcast

New episode: "How Elon Works" This episode covers the insanely valuable company-building principles of Elon Musk A few notes from the episode: 1. The mission comes first. 2. Retreat is not an option. 3. A maniacal sense of urgency is our operating principle. 4. Product design should be driven by engineers. 5. You should not separate engineering from product design. 6. Having separate design and production departments is bullshit. Keep everything together and feedback immediate. 7. The leader should be on the front lines. You should be a battlefield general. 8. "If they see the general out on the battlefield, the troops are going to be motivated. Wherever Napoleon was, that's where his armies would do best." 9. Apply The Algorithm constantly. (1) Question every requirement. (2) Delete any part of the process you can. (3) Simplify and optimize. (4) Accelerate cycle time. (5) Automate. 10. Repetition is persuasive. "I became a broken record on the algorithm. I think it's helpful to say it to an annoying degree." 11. You should go ultra-hardcore on deletion and simplification. 12. Camaraderie is dangerous. It makes it hard for people to challenge each other’s work. (Refer to point #1) 13. Never ask your troops to do something you wouldn’t do. 14. Hire for attitude. Skills can be taught. Attitude changes require a brain transplant. 15. Good attitude = A desire to work maniacally hard. 16. The only rules are the ones dictated by the laws of physics. Everything else is a recommendation. 17. Keep your entire company committed to a common goal. 18. If things aren’t going well, throw away the existing design, start from first principles, question every requirement based on fundamental physics. 19. Find the limit. You want to delete as much as possible and you can’t do that unless you find the limit. 20. If you aren’t adding back at least 10% of the things you deleted, then you didn’t delete enough. 21. Maintain control. Avoid joint ventures. Eliminate middlemen. 22. Have a relentless dedication to questioning every requirement. 23. No work about work, just work. 24. Go to the problem. Get on the plane. Fly to the source. Go to the exact location in the factory. Go to the problem and stay there until it's resolved. 25. The best part is no part. 26. Be wired for war. 27. Do not fear losing. It hurts the first 50 times but then you’ll be able to play with less emotion. You will take more risks. 28. Stay heads down focused on doing useful things for civilization. 29. When something is important and has to be done quickly, have meetings every 24 hours to run the algorithm and check on the previous days progress. You'll be shocked at how fast this speeds things up. 30. Life needs to be interesting and edgy. 31. Delete, delete, delete, delete. There are 100 more ideas in the episode. I hope you listen to it. 30 years of Elon’s career + 60 hours of reading and research and me just absolutely ripping through idea after idea at 2x speed for 90 minutes. It will be hard to find a better use of time.

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Peter H. Diamandis, MD
Peter H. Diamandis, MD@PeterDiamandis·
We’re living in the most extraordinary period in human history. The only mistake is to sit it out.
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Ara
Ara@arafatkatze·
In building AI agents @cline , we've identified three mind viruses Mind Viruses are seductive ideas that sound smart, but don’t work in practice. 1. Multi-Agent Orchestration 2. RAG (Retrieval Augmented Generation) 3. More Instructions = Better Results Let's explore why!
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Aleksander Holynski
Aleksander Holynski@holynski_·
Something fun we discovered: you can use #Genie3 to step into and explore your favorite paintings. Here's a short visit to Edward Hopper's "Nighthawks".
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Dr Singularity
Dr Singularity@Dr_Singularity·
Genie 3 is probably the most advanced technology humanity has right now, yet over 99% of the world's population has no idea it exists, or how it could change the world.
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Keşfet

@slashapp @hanzi_li @ycombinator @mathiaslovring @EmilMunkD @blocks @cline @elonmusk