Maria Diaconu

Entrepreneur loving Tech & Education. Strategy Advisor. Start-ups & Innovative Teams Mentor. Communities Founder. CEO & Founder

[Paul Graham @paulg]

I've added a new question to the list I consider during office hours with YC startups. As well as "Can we induce network effects?" and "Would it make sense to go full-stack?" I now ask "Can we make this AI-proof?" Can we ensure this company still exists if AIs do most work?

[Paul Graham @paulg]

In design, this will be remembered as the colorless era: cars, buildings, movies, the chroma is being sucked out of everything. But of course the pendulum will eventually swing back, and then colorlessness will seem dated.

[Paul Graham @paulg]

A startup idea that only works if there are already a significant number of people using it is not a valid startup idea. There has to be some subset of users who need what you're making so desperately that they'll use it even if no one else is.

[Maria Diaconu @fireladyM]

@jordan_ross_8F Hermes

[Paul Graham @paulg]

This is one of several reasons you shouldn't drop out of (or skip) college to start a startup at 18. Founders who do that tend to match the second paragraph rather than the first, because they haven't had time to have the "earned insight" he describes.

[Garry Tan @garrytan]

The companies I love working with in office hours are the ones where the founder has a specific, weird, earned insight that nobody else has. Not "AI for X." A genuine edge that came from living inside a problem.

The ones that are dying almost always have the same pattern: technically competent founders building something nobody asked for, moving metrics that don't matter, avoiding the conversation with the one user who'd tell them the truth.

The lucky thing is that 2nd type of founder can become the 1st kind if they don't stand still, they are willing to talk to people, try things, and always seek high rate of learning.

[Maria Diaconu @fireladyM]

@AlfieJCarter Vault

[Alfie Carter @AlfieJCarter]

I built 60+ Claude prompts that replace a full B2B marketing and GTM stack.

And I'm giving it away for free.

Most people use Claude to write a post or draft an email.

That's 10% of the game.

This vault replaces the entire stack:

Positioning strategist → homepage audit, ICP research, brand voice bible, competitor ad deconstruction
Content engine → LinkedIn post framework, carousel builder, content repurposing across 4 formats in 15 minutes
Email and PR → human-sounding email, subject line battle test, 5-angle ad brief, full 8-step PR data study chain
Dashboard builder → 7 self-refreshing live dashboards pulling from CRM, Gmail, Search Console, Stripe, and YouTube
Sales and prospecting → client intelligence brief, AI audit questionnaire, AI implementation roadmap, lead list builder

2 more categories.

Each prompt does one job.

Together they run positioning, content, outreach, dashboards, sales, skills, and annual planning inside one free vault.

Just the prompts GTM engineers and B2B founders actually use to stop briefing agencies on work Claude handles in minutes.

If you want it:

1. Connect with me
2. Comment "VAULT"
3. Repost if you want priority access

And I'll send you the full pack.

[Max Junestrand @MaxJunestrand]

Today we're announcing the Legora aOS™.

It's something we've been building toward for three years, and I think it's the most important thing we've ever shipped.

The legal industry has had AI that assists with individual tasks. What it hasn't had is AI that drives entire work products from start to finish.

The Legora aOS changes that. It's a single connected system – matter intake, research, drafting, review, service delivery – orchestrated by the new Legora Agent, running continuously, grounded in your organization’s own knowledge.

The legal teams who use it won't just be faster. They'll operate at a scale that simply wasn't possible before.

We've spent three years being told the legal industry moves too slowly to change. We've also spent three years watching it change faster than almost anyone predicted.

The best time in history to be a lawyer starts today. @WeAreLegora is built to be the partner that makes it possible.

Read the full announcement: https://t.co/UISBeJ1Eru

[Maria Diaconu @fireladyM]

@reachvaldo Fun :)

[Maria Diaconu @fireladyM]

@romanbuildsaas CLAUDE

[Paul Graham @paulg]

This is true of startups too. You launch to learn what you should have built.

[Paul Graham @paulg]

How can you empathize with founders if you don't build stuff yourself?

This was one advantage of running HN while I was running YC. It was an enormous schlep, but it meant I was dealing daily with the same problems as the founders.

[Maria Diaconu @fireladyM]

@timdanilovhi PDF 👍

[Tim Danilov @timdanilovhi]

I built 5 Claude Code protocols that are now running my faceless YouTube business:

• They find ideas
• Write scripts
• Even post videos

And I just described all of them in this 10-page PDF.

I could charge $20 for it...

But because it's Sunday, it's yours for FREE.

RT & Comment "PDF" and I'll DM it to you.

(Must be following)

[Paul Graham @paulg]

This interview with Jessica goes the deepest of any interview she's done into her life story, and how it gave her the unique combination of qualities that made Y Combinator work.

[Alex Kontorovich @AlexKontorovich]

A preview of my talk tomorrow at the Newton Insitute @NewtonInstitute (comments welcome)

My primary interest is research math: solving problems, proving theorems.

Before 2019, I was accustomed to using Mathematica to check tedious, error-prone algebra in my papers. Do it once, and never waste time checking it again.

But algebra was only part of the issue. If I had a lemma, and in a 60-page paper I might have 20 of them, with a dozen parameters all moving around in different ranges and needing to line up perfectly at the end, then even a single stray minus sign could kill the entire paper. The whole enterprise was extremely complex and fragile. (What I'm describing is very common in loads of fields in modern research math.)

In 2019, I watched a lecture of Kevin Buzzard's, and realized the answer: I should use an interactive theorem prover like Lean to check my lemmas the same way Mathematica checks my algebra. (Of course, as I've since learned, there are many benefits to working formally beyond correctness, and these have been extensively enumerated elsewhere, so I won't repeat them here.)

But my original motivation for getting involved in formalization was simple: I hoped it would speed up my workflow.

It did not.

In fact, formalization is brutally tedious, requiring painstakingly spelling out facts that to a human expert are blatantly obvious.

Fast forward to 2025, and AI was getting genuinely good at helping with formalization. I was already using Claude rather extensively when we crossed the finish line on the "Medium" PNT in July 2025. By September 2025, Math Inc's Gauss system autoformalized the Strong PNT, writing over 20K lines of compiling Lean autonomously. Earlier this month, they outdid themselves again, writing 200K lines autonomously and formalizing Viazovska's theorems on optimal sphere packing in dimensions 8 and 24.

So isn't that the dream? AI can now, in some instances, autoformalize very significant theorems. Can we mathematicians just get back to thinking, sketching, and letting AI do the formalization for us?

Not so fast.

Autoformalization only works because it is built on top of a big, comprehensive, efficient, coherent monorepo of high-quality formalized mathematics, namely Mathlib. And even in the PNT+ and Viazovska examples, the autoformalizations still depended on substantial earlier human work: setting up the right definitions, the right API, the right abstractions, and so on.

So maybe we now get a nice positive feedback loop:

Research
->
formal math (thanks to AI)
->
grows Mathlib
->
enables more research.

Still no.

AI formalization, and frankly the first-pass human formalization too, is usually local, ad hoc, single-purpose work. It is not necessarily general, abstract, efficient, or reusable. So it does not in and of itself help grow Mathlib. The second arrow is broken.

Actually, this is not some temporary annoyance, it is inevitable! The goals of doing research and building libraries are misaligned, like scrambling up a cliff versus building an elevator to the top. Both are trying to go up, but for completely different reasons and in completely different ways.

In fact, it is even worse than that: the second arrow may make the feedback loop negative.

Let us give that second arrow a name: "canonization".

By canonization, I mean the process of taking a local, one-off formalization and turning it into library mathematics: general, reusable, coherent, efficient, and compatible with the rest of the monorepo. This is an extremely difficult and time-consuming task. It requires a large amount of prior knowledge and skill, often in several quite different areas at once. And here's why the feedback loop may be negative: while a rough formalization can certainly be a technical head start, socially it often strands the problem in the worst possible state: too solved to feel pressing, too idiosyncratic to be reusable. If a formalization already exists in some ad hoc form, then people are much less incentivized to do this work! They get less credit for succeeding, there is less urgency, and less motivation.

Does this sound familiar? It's the same structural problem we had back in 2019, going from proved results to formalized results! So the answer should be obvious.

In June 2025, I claimed that (quasi)autoformalization, meaning not entirely autonomous but allowing human intervention and steering, was the greatest short-term challenge in realizing the dream of speeding up research [K2025]. The corresponding claim today is:

(Quasi)auto-canonization is the greatest short-term challenge for AI systems.

I personally know of only one AI company so far that seems to be taking this challenge seriously, namely Harmonic with its Aristotle agent. Imagine if we get this right. Definitions will still be difficult to automate, but there are orders of magnitude fewer definitions than theorems. Once those foundations are laid (which will still be a ton of human time and effort!), everything else can scale on top.

Right now, the vast majority of research mathematicians working in formalization are, very commendably, working toward growing Mathlib. But they comprise maybe 1% of all professional mathematicians. This is not necessarily because people do not want to work formally. It is because the current system does not match how most mathematicians want to work.

People are diverse. They have different strengths and weaknesses, different interests, different workflows. If we embrace an ecosystem where people are encouraged to formalize freely, with heavy AI assistance, and where the right pieces later get (quasi)auto-canonized into the central monorepo, then I think we could potentially be in position, given the right incentives, training, and culture-shifts, to move from a handful to the majority of mathematicians doing math formally.

[Paul Graham @paulg]

Trying to make founders describe their products in simple everyday language is like trying to push together the north poles of two magnets.

[Austen Allred @Austen]

If you’re not using AI you’re dramatically falling behind of what is possible.

If you think AI is performing everything perfectly the first time you’re going to drive yourself into a ditch.

[Paul Graham @paulg]

It's a surprisingly useful heuristic to ask startups what would happen in a sci fi novel about them. It doesn't just work for product ideas but even for names: today we found a new name for a company by asking what it would be called in a science fiction story.