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San Francisco ⇆ Chennai
Joined January 2012
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7 signs you are ready to be a #manager
1. Invest in other people’s success
2. Comfortable delegating
3. Good with feedback and managing conflict
4. Love problem solving
5. Showcase Emotional Intelligence
6. Express vulnerability and own mistakes
7. Act calmly when under pressure
Vasa Krishnamoorthy retweeted
I ended my time at @Meta as a director.
But I started as an engineer on FB Chat.
Everything about it was broken — we had to rewrite it.
And while the effort to fix it is one the projects that led to @reactjs, the most important fix was far simpler...
Here’s the full story:
—
I worked on Facebook Chat for several years, both on the front end and the infrastructure.
Before the major effort to redo the UI, FB Chat was super broken and we had no idea why.
We got tons of bug reports about Chat being broken every day, but we noticed an odd pattern in the data: the volume of reports didn’t match the volume of usage. It was time-shifted from the peaks we’d see in the US.
We didn’t know what was wrong, but we knew the code was a mess.
We set about rewriting both the front-end and the back-end in an effort to fix it.
The front-end rewrite pulled in a whole team of amazing engineers and became one of the big threads that led to @reactjs
In the public eye, we portrayed this project as the one that ultimately fixed Chat.
And the way I’ve usually told it, fixing Facebook Chat and the birth of React are the same story.
But no framework was going to fix the worst problem with Chat.
—
During the time we were working on the Chat rewrite, we were also replacing the original Erlang backend with one written in C++.
This was probably a good move, but the problem wasn’t with Erlang either.
Our initial spec for the new backend didn’t say much about observability, but it was an important feature, and the rewrite forced us to rebuild it.
Little did we know this would lead us to the root cause of our problems…
When we finally gained insight into our deliverability data, we were able to cut it by region.
We noticed Chat was really popular in India. This was before WhatsApp, at a time when SMS wasn’t reliable.
Eventually we pinpointed a region in India where one specific DNS provider was giving out the wrong IP addresses for our Chat servers.
So when people went to use Chat, they would sometimes get a notification that they had a message, and then it would disappear.
Or they’d send a message and it would get lost. All because they were connecting to the wrong IP address.
That was it!
None of the sexy new tech we were working on was going to solve that problem.
Ever.
—
Instead, the solution was to build observability that allowed us to track end-to-end message delivery.
In the end, we could start with a broad cut of our data by country or web browser, and then zoom all the way in to look at what happened to a specific message for a specific user.
Once we pinpointed that the problem was with a DNS server, the matter was resolved with a quick phone call. I don’t know what they did, but I imagine it was something like turning it off and turning it on again.
We sometimes talk about observability as if it’s enough to buy a product like Datadog and just look at the pretty graphs.
Sure, that’s a start.
But true observability is a feature that needs to be built— painstakingly, iteratively, by-definition starting with a shot in the dark.
—
These days, it has become fashionable to poo-poo the idea of being data-driven.
People point out that measurement can distort the phenomenon that is being observed.
They want to make processes “data-informed.”
But this seems like silly backlash against the only rigorous standard in all of software engineering:
That we hold ourselves to an objective standard.
We measure how long things take, how many errors we encounter, how often a process successfully runs to completion.
So here’s what this experience taught me about observability:
When an issue happens in production, time-box the investigation.
Sure, take a few hours to try and figure it out by looking in the logs and inspecting the code.
But if you’re coming to the end of the day and you still don’t have a fix, then push a PR that adds logging.
The first one may be just a guess, but it will begin a process that leads to the truth.
And that is what we should all ultimately be striving for.
—
For more engineering tips and stories, follow me @dmwlff
Vasa Krishnamoorthy retweeted
James Clear: Your goal in life should be to reduce the amount of time it takes you to get out of a bad state.
Vasa Krishnamoorthy retweeted
we will need to rebuild and rewrite much of software to adapt to a new, faster pace.
code used to arrive one drop at a time. soon, if not already, code will be written as if it were a continuous stream, like a violent river twisting and turning as it pleases.
this is not a new thing: big tech cos have had to deal with this with the enormous scale of their engineering teams. but now ai has brought an incredibly elastic and capable workforce wielded by every engineer. this is your problem now, and the only way to adapt is to evolve your systems so agents - even and especially the ones with little context about the right way of doing things - can thrive. make the easy thing the right thing.
your codebase is becoming organic architecture. agent contributions grow around the constraints and guardrails you’ve designed. good software engineering matters even more now! and no, the continuous stream of code is not something you have to watch helplessly. be more ambitious and design systems, frameworks, and languages to control the chaos.
the time for mourning coding is long over
Vasa Krishnamoorthy retweeted
its true, Anthropic really vibecoded their entire backend to Rust and none of you noticed
Anthropic rewrote its Messages API in Rust. Zero Rust devs on the team.
Claude wrote the code. Humans wrote the plan and checked old vs new matched on real traffic. CPU dropped 40-50%.
Pretty sure "never do the big rewrite" is dead advice.
@katelyn_lesse on @latentspacepod
Vasa Krishnamoorthy retweeted
"Hunger beats tenure."
Great write-up by Felix on why his team at Anthropic dropped coding interviews: newer engineers are flying past stronger, more tenured coders.
Execution isn't expensive anymore. Inside a company, the signal is picking an idea that moves the business and prototyping it. Outside, it's a portfolio of small prototypes. Both should show off your taste and judgment.
Claude Code Desktop started as a weekend prototype by two newer engineers.
The coding interview is dead, at least for my team at Anthropic. I've seen enough to know that it no longer provides signal. I removed it from loops this month.
I have a spicy, optimistic take - I think there's an opportunity for junior engineers: felixrieseberg.com/hunger-be…
Vasa Krishnamoorthy retweeted
Meta's been owning this since June and has open research on it! arxiv.org/pdf/2605.30208
Automating Low-Risk Code Review at Meta: RADAR, Risk Calibration, and Review Efficiency:
Vasa Krishnamoorthy retweeted
Give your agent a way to verify its work. This can dramatically improve the quality of output.
With this process in place, Claude Code was able to write 90%+ of its code itself. When generating software at a faster rate than you can review it, the bottleneck shifts to proving that it works. @bcherny and team invested enough in verification, we've been able to move fast while maintaining stability.
Code reviews aren't going away, but you need to think of it differently. One human reading through line-by-line is no longer going to work as a gate, it needs to evolve into a multi-layered verification system, built with fast deterministic feedback loops for your agents.
This is where e2e tests, feature maps (as @poteto has been advocating), property tests and multi-agent adversarial reviews come in. These all provide opportunities to catch errors early and fix them before things get out of hand.
You don't have enough eyeballs for the amount of code being written so your focus needs to be on product intent, risk assessment and evaluating evidence rather than reviewing code. Delegate the code checking work to your automated tests, scale your approvals based on risk, and turn your test failures into living evaluations. This approach helps maintain speed without losing reliability.
Let the agents do what they do best and let you focus on what makes you irreplaceable: judgement, direction, and ownership. We tried to capture how we handle code review and verification in this writeup. Hopefully it's helpful.
Claude writes about 90% of Claude Code's code.
From June to September, merged PRs more than doubled while the share reverted fell by more than 40%.
We wrote about how we changed code review to get there, and how to adopt it:
claude.dev/blog/solving-code…
Vasa Krishnamoorthy retweeted
I spent 48 hours in SF hanging out with billionaires, data labs and AI founders and execs running $1B to $100B companies.
My observations:
1. Sooo MANY stories of data brokers making like $2M a WEEK selling company data to labs. I can't tell if it's the beginning or a bubble, and I kept wondering which niches are left. Digging into it actively.
2. At dinner, someone asked the table, "If a new model drops next month, does your company get better or worse?" HALF the table went quiet! I think that's the best question to ask about any AI startup right now.
3. The bar people talk about now is hitting $1B in revenue in 12 months (I guess that's the new unicorn?). What I didn't hear much about is retention. AI revenue can come in fast and leave fast, and I think we're going to see a lot of stories of AI companies with huge revenue and terrible retention.
4. It's pretty clear we're living through the reinvention of the computer. The model is the CPU, the harness is the operating system, and skills and plugins are the apps. Thinking about it that way makes it easier to see where the whitespace is.
5. We're maybe 12 months from an agent that can watch you work for a week and then do your job without any instructions. The screen recording plus agent memory plus local model combination makes this possible right now.
6. Agent harnesses feel like the new GPT wrappers, in a good way, especially vertical ones. The best teams I talked to treat the model as swappable. One founder walked me through routing different models to different jobs: one for taste and visuals, a cheaper one for the everyday stuff, another for text.
They use prompts they can move across models over fine-tuning, and A/B test the output on what users actually do with it: do they edit it, export it, share it? A vertical harness is basically four things: the connectors for that industry's data, evals written by people who know the work, rules for what the agent can and can't touch, and a clean screen for a human to review. The model is maybe 20% of it.
7. There's a lot of reverse engineering going on/cloning going on more than I've ever seen. People are decompiling software and rebuilding it with agents, and someone showed a suite of like 10 apps that are worth $5B+ each all that he cloned open source. Me and everyone are still thinking through what it means when software gets this easy to copy.
8. My guess is the value moves to data. If the software can be rebuilt quickly, what's worth paying for in a legacy SaaS company is the years of customer data and workflows inside it. Some SaaS companies are secretly data companies.
Gets me thinking about how do you truly value data for this saas companies? Like if we were buying these dying saas companies at 2x ARR before (as example), maybe there is a better metric. Idk, just thinking out loud here.
9. It feels like a good chunk of legacy software won't make it to the agentic era, especially anything built around people clicking through a lot of screens.
10. Every company I talked to wants to pipe its internal data into one place and expose it to agents through MCP. Then they look at the data and realize it's a mess: five versions of the same customer, old Slack channels, PDFs nobody can search. Cleaning that up feels like a big consulting opportunity!
11. A lot of founders are building the basic building blocks for agents: APIs, data layers and tools. Some of the companies I saw barely have a UI because the main user is an agent. I really believe the startups that build primitives to this agentic era will do extremely well. Building blocks is the best way to think about it.
12. Labs can get plenty of training data, but it's harder to get real examples of agents failing inside companies. Whoever has those logs has something valuable.
13. Someone running a big consumer company told me the next fight is over memory. The model that remembers the most about you wins, and the smartest model matters less than people think.
14. Physical AI came up a lot. Astra I think was the first model that got people into AI can actually do CAD, and I think a lot of people are thinking what does vibe manufacturing really look like. The line I keep thinking about came from a robotics founder: the hardest problems in AI are deployment problems now, not research problems. Their robots work on their own, call a human when they get stuck, and every one of those calls becomes training data.
15. Cybersecurity companies are having a moment and are actually cool companies to build now, especially the ones that clean up after a breach. Every agent with access to your systems is a new door into your company, and a lot of those doors were left open.
16. Some startups are worth more dead than alive now. A shutdown company with 5 years of Slack, email and code can get paid for licensing all of it. I wouldn't be surprised if founders start factoring that into when they shut down.
17. People are starting to talk about monetizing the agent web. It feels really early, but when the most popular user of the internet is an agent, what startups should you be building?
18. I think agent insurance is coming. Once an agent makes an expensive mistake, insurers will want audits before they cover anyone.
19. Several people said the hire they want most is someone with taste who can write evals. The models are getting good at generating ideas, so the hard part is judging which ones are good.
20. New trend I observed: startup raises VC, uses venture dollars to become a creator, startup fails, founder falls back on being a full-time creator and incubates other products bootstrapped.
21. Getting SIGNIFICANT distribution is HARD right now. Founders are sponsoring buses, buying billboards like crazy and throwing parties with famous DJs to get noticed. Kinda feels dotcom era in that sense.
Attention is what's scarce! The growth guy going viral sharing his UGC strategy from Wispr Flow is a good example. It's the sauce everyone wants, and I truly believe there are probably only 50 people on the planet who know how to scale these types of businesses efficiently in this era.
Always good to get that dose of SF. I felt sooo lucky to have such an incredible trip during such an incredible time in building businesses. Surreal in so many ways.
Happy to be home and locked in.
Until next time!
Vasa Krishnamoorthy retweeted
Alexandr Wang on what matters in an age of abundant intelligence:
“The scarce resource isn’t going to be intelligence or agency. I really think it’s going to be vision and ambition.”
“Do you have a clear view of what you want the world to look like in the future? What is the one way in which you wanna put your finger on the scale for how the future of the world will develop?”
“AI will make that easier... But the flip side of that is then all of a sudden you can dream bigger.”
Vasa Krishnamoorthy retweeted
Moats! Does anyone have one anymore?
Yes. Yes, they do.
But only a small subset of what people call moats create real long-term value and defensibility.
Not actually a moat:
1. Distribution: This does matter more than ever. Building products has gotten easier, but getting them to customers has not. Distribution, however, does not create durable defensibility. It helps create the raw ingredients for one, but it is not itself a moat.
2. Execution: Yes, speed is critical for startups. But a company that has to sprint forever is not a good business. Ultimately, it will misstep, slow down, and competition will catch up. Speed gives a company more shots on goal as it tries to build a great product and a great business, but it must eventually find something more durable.
3. Brand: The idea is that when everyone is building the same thing, the most resonant brand will win. But for startups, brand works more like customer acquisition spend: Put money in, get growth out. It’s not something that will effectively keep others from competing long-term. Brand can become a true moat over time (as with Coke or Nike or Apple), but it just takes too long to really matter for most startups.
What you're looking for is compounding advantages: a self-reinforcing flywheel that gets stronger and stronger over time, ultimately making a company very hard to compete with long-term.
There are only four compounding advantages that really matter. Every major tech company has at least one:
1. Switching costs: The more deeply embedded a product is in the way a customer works, the harder it is for that customer to switch. This increases retention and revenue per customer, which can be reinvested into the product.
2. Network effects: The more users a product has, the better it gets for everyone. This increases engagement and attracts more users.
3. Scale economies: The more volume a company has, the larger and more efficient its operations can be. This lowers unit costs, funding lower prices or a better product at the same price, further driving up volume.
4. Data effects: The more usage a product has, the more data it generates. This data can be used to improve the product and increase usage.
If you can’t see the potential for any in the startup you’re considering joining, that’s a red flag.
In today’s incredible post, @danhockenmaier shares a simple framework you can run in about an hour after getting an offer:
1. Identify a startup’s compounding advantage
2. Gauge how strong it could get
3. Measure whether it’s working
Inside: dozens of real company examples, the specific metrics to track, and benchmarks for what good and great look like.
Read it here: lennysnewsletter.com/p/how-t…
Vasa Krishnamoorthy retweeted
.@joshwoodward: "Every company right now needs a set of people who's job is to be at the frontier.
You want people plugged into the hive mind.
They're the people who have eight $200/month subscriptions on everything.
You have to architect your organization where this can exist."
Every company needs to think and operate like a labs team now.
What’s possible changes every few months, and to stay ahead, you need to be experimenting at the frontier.
Here's my conversation with @JoshWoodward, Head of @GoogleLabs, the @GeminiApp, and @GoogleAIStudio.
We discuss:
🔸 What skills are rising fastest in value right now
🔸 Where the best ideas actually come from
🔸 How to recognize PMF early
🔸 Why you should keep a list of what’s ‘almost possible’
🔸 Three rules for standing up an internal labs team
Listen now 👇
youtu.be/3I6QYkccd3o
Vasa Krishnamoorthy retweeted
on a related note, there's been some chatter recently about software engineering skills (eg the ruby/rust/elixir drama). i'm a bit torn because while i do feel like they're software engineering principles and patterns are still important, at the same time i wonder if there will come a point in time when they just won't matter anymore. not that they're no longer useful, but that your own intuition can fill in the gaps as you use agents in an intuitive sense rather than knowing explicitly what they're doing
if that doesn't make sense, the analogy i have is the first iphone. 20 years ago you had to be a professional with expensive equipment, now just with a phone and some creativity you can film content that's compelling to a new audience. i don't know if there's a term for this but i want to call it "skill compression". where what used to be gatekept and prohibitive to experts becomes accessible to a new audience of creators with a whole new meta. the details have sort of faded away and now the craft is not in knowing the equipment and how to use it, but how you use it creatively
we're going to see a whole new generation of builders who've never seen or written a line of code build incredible things
Vasa Krishnamoorthy retweeted
Good read
🗞️ if your team is too busy doing their 'normal job' to experiment with AI, you're preparing them to be replaced
ghuntley.com/replaced/
In this post, I guess this's the last time I'll say it. AI use is no longer optional if you wish to be employed. What follows is advice for current employees and hiring managers on how to hire in 2026 to source AI-first candidates.
Vasa Krishnamoorthy retweeted
You need both taste and judgement. You need taste to know what is good, and you need judgement to understand the price of quality.
If you ask a model for a component or migration plan, it will create a lot of options, very quickly. Most of those drafts seem OK. So once you have a collection of work that meets the basic criteria, you can no longer use taste to select between them. You must choose.
I like to think of this as two filters. There is the taste, and there is the judgement. And I think a lot of people mix those up.
The first filter is taste. Taste is the feeling for what makes a thing good.
Taste is that feeling you get first, before you're able to put into words what is good or bad about something. You've had that experience where something is bad but you're not sure why, until you've worked it through and can describe it. "This user experience was really clumsy" or "that error message was really aggravating." Taste is built up by experience and practice.
It comes from looking at a lot of things, and making a lot of things, and remembering the good bits. Kant's great insight into aesthetic taste was that you could appreciate the form, without having a stake in the outcome. Engineers care about the outcomes much more than the forms, but the feeling is the same.
Taste can disqualify a lot. It can disqualify 90% of drafts very quickly. But it's not enough to make a decision between the few remaining options.
The second filter is judgement. Judgement is understanding the price of quality. The most perfect solution may not be finished by the time that you need it. The sanest architecture may require the users to migrate while you're in the middle of the final upgrade.
Judgement is the understanding of what you give up in time and risk to get good things, for the sake of the users, for the sake of the team. Judgement is made from domain knowledge and experience from previous decisions.
People with great taste but poor judgement make beautiful work too late. They make the right thing at the wrong time, or the wrong thing at the right time. People with judgement but no taste make on time but don't make things people love.
Both taste and judgement are essential. Filter to a few options with taste, and filter to one option for delivery with judgement.
Vasa Krishnamoorthy retweeted
You don't have to commit your entire life to optimism, just try it on for a week. Start thinking "what if it all worked out?" and "what a time to be alive!". Then compare your state of mind to the week before filled with pessimism and doomer nonsense.
Vasa Krishnamoorthy retweeted
We'll be spending a lot more time trying to understand the outputs of language models. A few thoughts, tips & tricks:
Writing. Something I've had success with: Ask your LLM to explain something in ASD-STE100, it's a controlled language specification originally developed for aerospace maintenance documentation. LLMs well-versed in this language and it comes with heavy constraints on clean writing style that I often find a lot more readable. Sometimes I've tried to soften it a bit e.g. ask for "80% of the way to ASD-STE100" because the spec is quite stringent. But even better:
Diagrams / images. Instead of writing, ask your LLM to create a diagram. These can be a lot easier to process, parse, and understand. But even better:
Web pages. Ask for output "in HTML" to get a beautiful, interactive webpage. LLMs are getting really good at frontend and can create beautiful experiences, animations, etc. But even better:
Explainer videos. The output format I am most bullish on is fully custom / bespoke explainer videos generated on any arbitrary topic. Experiment with things like "Create a 3b1b style video explainer on X. Use my ElevenLabs API key for audio narration". (you'd need an API key for the latter or you can ask your LLM to find you decent free alternatives that use your local compute). This is actually starting to work!
In summary:
- As LLMs get better, they will do more and more of the legwork autonomously, and a lot more of our work will rise up the abstractions into oversight and understanding.
- Luckily, LLMs can help here too because as intelligence and code are increasingly abundant, you can ask for large, custom, discardable software artifacts (e.g. web apps, video explainers) that would have never made sense to create before. Push the boundaries here and you'll be surprised.
Vasa Krishnamoorthy retweeted
Where does money invested into the AI buildout actually go?
For every $100 flowing into the supply chain:
- $50 to chips
- $20 to power
- $15 to networking
- $15 to cooling, buildings, and land
More charts in State of Markets II: a16z.news/p/state-of-markets…
Introducing our State of Markets pt 2, along with a companion podcast where we unpack the data and discuss what comes next.
Tech is the everything cycle.
Supply: putting the buildout in context, just passed railroads as % of GDP. The wisdom of Elon is real: the factory (or the datacenter!) is the product.
Demand: diffusion is so, so early. Median AI vendor spending in the top 1% of companies is 8x that of the top 10%. Only about 30% of S&P 500 companies report a quantified AI impact, which means there’s a substantial opportunity in connecting models to a company's data and workflows. Diffusion into companies is one of the main themes of the next 5 years.
We’re entering the agent work period. Only a few million users today, but applicable to billions of internet users with massive surplus created. META/GOOG monetize US users at $200+ per year today. Agent opportunity is much higher.
Mega-trends the next 5 years: Consumer agents, Robotics, Autonomy, AI x bio, Personal health, Diffusion into enterprise, New era of American Dynamism.
Much more in our SoM report here - a16z.news/p/state-of-markets…
@a16z @sarahdingwang @aleximm @santiago__rdz
