vineetmehta

@arian_ghashghai what's your split of investments that came inbound (like what your post outlines) vs outbound where your team reached out? curious because the @ycombinator playbook of posting progress publicly brought me enough inbound that we closed our round before demo day (few yrs ago)

If you're raising VC for the first time, there is a consensus hierarchy of outreach to VCs: 1) warm intro from a founder in that VC's portfolio (the more money that founder has made for the VC, the stronger the referral) 2) warm intro from an employee in that VC's portfolio (same logic as portco founder) 3) warm intro from a VC that has already invested in your company 4) warm intro from a VC's LP 5) other operators/founders in that VC's network 6) cold outreach 7) warm intro from a VC that passed (unless it's a genuine stage/market mismatch for that VC) (incidentally, I've had some success investing in 6) and 7) )

@heyalexmoore this is fun. could be an agent that runs everyday!

5. Flight Deal Checker I found this flight: [paste flight details]. Price: [$], airline: [airline], bags: [included/not included], layovers: [details]. Tell me if it’s cheap, average, or overpriced. Check hidden fees, timing, booking risks, and give it a score out of 10.

R.I.P. GOOGLE FLIGHTS IN 2026. R.I.P. BOOKING COM IN 2026. R.I.P. SKYSCANNER IN 2026. $1,190 flight. I paid $159. Use these 10 prompts before booking your next trip: 👇🏼 (Save this 🔖 you’ll need it later)

@AGrillz every batch, the bar rises 🚀

YC Spring 2026 Demo Day is here. ...and the revenue is like nothing we've seen before. • Tasklet — $5M ARR • Gojiberry AI — $1.4M ARR • Kinro — $1M ARR • Plena Health — $1M ARR 32 companies have already disclosed revenue. 👇

@mikenevermiss hard to un-see this as the future of software🔥

Anthropic engineer: "you're not supposed to prompt Claude. you're supposed to build a system that prompts itself [loops]." this is one of the best workflows I've seen in a long time in this video he breaks down exactly how most people are building loops wrong: - the memory file you never set up, so every loop starts from zero - the sub-agents that 95% of builders have never split apart - the stop condition setup that keeps loops from running forever and billing you in your sleep - why writing one prompt a day is the slowest way to use Claude if you've been using Claude for more than a month and still typing every task by hand, you've been running one prompt when you could be running a system of loops instead of another prompt tonight, watch this make sure to bookmark it before it gets buried full guide in the article below

1. as a mental model it is more correct to think of fable+ class models as english -> code interpreters - converts your idea into code into "correct" code regardless of problem complexity and output complexity (diff size). Fable 5 will be the worst of this new class of models 2. diff size/complexity is to be managed purely for review: small diffs - in high risk areas of code (auth/identity/data access/network access/money movement) large diffs for code that can be empirically verified (frontend/backend plumbing/code without network or db access/performance code that can be empirically verified) 3. time it takes to ship software is completely disconnected from time to produce the PR - how long the work takes depends fully on ability to review/merge code while managing risk at scale 4. solving the bottlenecks for above matter enormously- linters/testing/CI/shadow mode verification/empirical verification 5. agency matters enormously- what are the biggest bottlenecks to speeding up the loop and eliminating them? what are the problems that need solving and when do they need solving? what does it take to the solution to all of them today? 6. deep understanding of the full stack matters enormously- what problems are worth pursuing? is there a higher level of problem abstraction to address first? should I give it the sub-sub task, the sub task, or the task itself. what are the major risks with this PR (order of importance: security holes/correctness holes/performance holes). is there a higher speed way of producing data that allows me to merge this? should this be run in shadow or in a sandbox or a flag. understanding every line of logic may not be needed but understanding and managing risk matters enormously. 7. the cost of complexity itself is changing. it might be now worth "maintaining" 50% more code to get a 5% performance win. getting the right abstractions matter less because larger refactors are less tedious. code quality nits become huge drag. very likely, a much smarter model will be maintaining your code so worth taking on more technical debt now. taking the time to hand architect and rebuild systems comes with an enormous cost of velocity 8. if it quacks like a duck and walks like a duck, it's a duck. For low risk cases, it might be more sane to treat code chunks (services / functions) as a black box, like we do for neural networks: do full empirical verification only: has code produced correct outputs for the last 10,100,1000,10k inputs ? can we quarantine this large piece of code - no outbound access to network / database ? what happens when this code is wrong? do we get hacked/or crash(memory/cpu)/is an inconvenience? is it internal facing or external? what can we do to address these risks? 9. eventually, logical verification (line by line review) will come at an enormous cost- save it for where it matters and build systems that are tolerant to empirical verification. is there a decorator that prevents db / network access? correctness bugs are significantly easier to rectify than access bugs 10. what are the rails that allow for even faster iteration? code permissions can be opt in - db writes, db reads, network egress (to where?), PII access. how long does it take to get shadow mode data? how many PRs can be tested? What are the categories of diffs

The result is that what used to be exceptional startup growth is becoming increasingly common. The bar is moving faster than most people realize.

AI is clearly changing how quickly startups can find product-market fit and scale up.

Growth rates are exploding in the latest Y Combinator batches.

7/ Consumer AI gets the headlines. But much of the durable value creation may come from companies going after the unglamorous middle of the org chart, which is where many of the best software businesses have historically been built.

6/ Put those together and the dominant pattern in this batch becomes clear: a business customer, an agent focused on a specific role, and a product sold as the worker that handles that role's most repetitive tasks.

1/ With YC Demo Day tomorrow, one thing is clear: Software is becoming labor. As a YC founder and through my work with @pioneer_fund, I spend a lot of time getting to know each batch. Three patterns stood out to me about Spring '26:

We know how to improve cancer outcomes: catch it earlier! Sadly, MRI diagnostics are limited - too expensive. That's why I'm excited for @pioneer_fund to back founders @ET_adialante & @ManW_dePlan Their portable MRI that can be brought directly to clinics. Life changing!

@emollick ironically doctors tend to be averse to using chatgpt in clinical work (see: nytimes.com/2024/11/17/hea…)

There has been a push to use OpenEvidence AI for doctors. But this paper suggests general models are much better: “Frontier LLMs outperformed clinical AI tools in all three evaluations. Clinical AI tools performed comparably to auto-enabled Google Search AI Overview on the RCQ.”

We’re early in the agent era but one thing is clear: agent-first companies will become as important as mobile-first companies were 15 years ago. One of many reasons that I'm excited for @pioneer_fund to back @AgentPhoneHQ, which gives every agent a phone number for calls & texts.